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Record for a UF thesis. Title & abstract won't display until thesis is accessible after 2010-08-31.

Permanent Link: http://ufdc.ufl.edu/UFE0016063/00001

Material Information

Title: Record for a UF thesis. Title & abstract won't display until thesis is accessible after 2010-08-31.
Physical Description: Book
Language: english
Creator: Mccaskill, George
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Forest Resources and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Statement of Responsibility: by George Mccaskill.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Jose, Shibu.
Electronic Access: INACCESSIBLE UNTIL 2010-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0016063:00001

Permanent Link: http://ufdc.ufl.edu/UFE0016063/00001

Material Information

Title: Record for a UF thesis. Title & abstract won't display until thesis is accessible after 2010-08-31.
Physical Description: Book
Language: english
Creator: Mccaskill, George
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: Forest Resources and Conservation -- Dissertations, Academic -- UF
Genre: Forest Resources and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Statement of Responsibility: by George Mccaskill.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Jose, Shibu.
Electronic Access: INACCESSIBLE UNTIL 2010-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0016063:00001


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LONGLEAF PINE (Pinus palustris Mill) ECOSYSTEM RESTORATION ON COASTAL
WET PINE FLATS: DEVELOPING A MONITORING PROGRAM USINTG VEGETATION
AND SOIL CHARACTERISTICS


















By

GEORGE L. McCASKILL


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2008































O 2008 George L. McCaskill


































To my beloved wife Susana, my wonderful son Pedro, my mother, brothers and sisters









ACKNOWLEDGMENTS

I would like to thank my supervisory committee members, Drs. Shibu Jose (Chair), Andy

Ogram, Alan Long, Wendell Cropper, Eric Jokela, and Jack Putz for their advice, time and

support. In particular, I want to thank Dr. Shibu Jose for providing me the opportunity to attend

graduate school and providing me with funds to complete the project. I would also like to thank

Dr. Andy Ogram for his extensive advice and laboratory assistance concerning the soil microbial

analysis procedures. His knowledge and help on soil microbial ecology helped make this study

possible. Wendell Cropper with his specialized knowledge on ecosystem modeling and

ecosystems in general provided invaluable improvements to my research. Thanks to Drs. Alan

Long, Jack Putz and Eric Jokela for providing me with the scientific /philosophical continuity

necessary for evaluating pine plantations and natural forests. I would also like to thank Dr. Craig

Ramsey and Dr. Ashvini Chauhan for their assistance from field work to laboratorial analysis of

the proj ect. The efforts of Tim Baxley in data collection work and Chris Dervinis with making

the HLPC operational are truly appreciated. I would also like to thank my fellow graduate

students, Drs. Susan Bambo, Pedram Daneshgar, Diomides Zamora, and Dawn Henderson for

their friendship and support during my studies.

In addition, I would also like to thank the Florida Division of Forestry for funding the

restoration site, and the personnel of Topsail Hill State Park, St. Marks National Wildlife Refuge,

and the Chassahowitzka Wildlife Management Area of Florida' s Fish & Wildlife Commission

for providing permission and assistance in establishing the reference sites.

I could not give a full acknowledgement without giving special thanks to my wife Susana

and my son Pedro for their support and patience throughout our time at the UF. I would also like

to thank my mother, brothers and sisters for always supporting me throughout my extended

period of education.












TABLE OF CONTENTS


page

ACKNOWLEDGMENTS .............. ...............4.....


LIST OF TABLES ................. ...............8.._.._ .....


LIST OF FIGURES .............. ...............9.....


AB S TRAC T ............._. .......... ..............._ 12...


CHAPTER


1 MONITORING LONGLEAF PINE RESTORATION INT COASTAL WET PINE
FLAT COMMUNITIES ............_...... ._ ...............14....


Longleaf Pine Ecosystems ............ .......__ ...............14..
Monitoring Restoration Success ............ .......__ ...............15..
Monitoring Soil Characteristics ............ .......__ ...............16..
Developing a Monitoring Program ............ .......__ ...............17.

2 FOREST STRUCTURE AND PLANT SPECIES DIVERSITY IN WET LONGLEAF
PINE FLAT S ACRO SS A CHRONO SEQUENCE ......____ ..... ... .__ .. ......._......2


Introducti on ............ ..... .._ ...............21...
Materials and Methods .............. ...............25....

Study Sites ............ ..... ._ ...............25....
Forest Age Classes .............. ...............27....
Field M easurements............... ..............2
Data Analysis............... ...............29
Re sults............... .. .... ..__ _.... .............3
Overstory Stand Structure .............. ...............30....
Understory .............. ...............30....
Discussion ................. ...............3.. 1..............
Over story ................. .... ....... ...............3.. 1....
Plant Species Diversity ................. ...............32................
Conclusions............... ..............3


3 PATTERNS OF SOIL CHEMICAL AND MICROBIAL PROPERTIES ALONG A
CHRONO SEQUENCE INT WET LONGLEAF PINE FLAT S OF FLORIDA ................... ...45

Introducti on ............ ..... ._ ...............45....
Materials and Methods .............. ...............50....

Study Areas .............. ..... ...............50.
Soil Sampling and Preparation .............. ...............50....
Soil Chemical Analysis .............. ...............51....
Net Nitrogen Mineralization ............_ ..... ..__ ...............51...












Microbial Biomass............... ...............52
Data Analysis............... ...............54
Re sults................... ..... ....... ........ ...... ...... ..... .......5
Soil Types, Soil Organic Matter, and Soil pH............... ...............55...
Net Nitrogen Mineralization............... .............5
M icrobial Properties .............. ...............56....
Discussion ................. ...............57.................
Conclusions............... ..............5


4 RELATIONSHIP BETWEEN VEGETATION AND SOIL CHARACTERISTICS IN
WET LONGLEAF PINE FLATS ALONG FLORIDA' S GULF COAST ..........................67


Introducti on ............. ..... .._ ...............67....
Materials and Methods .............. ...............69....

Study Areas .............. ...............69....
Field M easurements............... ..............6
Soil Sampling and Preparation .............. ...............70....
Soil Chemical Analysis .............. ...............70....
Mineral Nitrogen Fluxes............... ....... ...........7
Bacterial Abundance and Microbial Dynamics............... ...............71
Experimental Design and Analysis .............. ...............73....
Re sults............... ....__ ..........__ .... .............7
Nitrifying Bacteria and Nitrogen Mineralization .............. ...............74....
Overstory .............. ...............75....
Understory .............. ...............75....
Discussion ........._ ....... ...............76....
Conclusions............... ..............7


5 MONITORING RESTORATION SUCCESS USING VEGETATION AND SOIL AS
KEY INDICATORS: CASE STUDY OF A WET LONGLEAF PINE FLATS
RESTORATION PROJECT ............_...... ...............85....


Introducti on ............. ...... ._ ...............85...
Materials and Methods .............. .... ...............88..
Pt. Washington Restoration Site ............_...... ...............88..
Pine Survival and Growth .............. ...............90....

Vegetation Sampling .............. ...............90....
Reference Sites .............. .... ...............91..
Soil Sampling and Preparation .............. ...............91....
Data Analysis............... .. ... ............9
Pine survival and growth ............. ..... ._ ...............92..
Under story ................. ........... ...............92......
Biogeochemical indicators .............. ...............93....
Re sults ................. ............ .. ...............95......
Ecological Classification .................. ...............95.................
Pine Growth and Vegetation Control .............. ...............96....
Treatment Effects-Biogeochemical Indicators ................ ............. ......... .......96












Discussion ................. ...............97.................
Conclusions............... ..............10


6 SUMMARY AND CONCLUSIONS ................ ...............112...............


Research Implications in Coastal Wet Longleaf Pine Flats Restoration ............... .... ...........1 17


APPENDIX SPECIES CODE LIST ........._.___........... ...............120...


LIST OF REFERENCES ................. ...............122....... ......


BIOGRAPHICAL SKETCH ................. ...............138....... ......















LIST OF TABLES


Table page

2-1 Qualitative classification system for downed and standing deadwood. ............................36

2-2 Forest structural and plant species diversity means among age classes. ...........................36

2-3 Indicator values for plant species in three forest age classes..........._.._.. .....................37

3-1 Soil and stand properties between reference sites............... ...............61.

3-2 Soil chemical and microbial biomass means between age classes. ............. ..................62

3-3 Soil nitrogen mineralization means (Nmin) for dry season 2002 and wet season 2005....62

3-4 Differences in soil biogeochemical relationships based upon Spearman rank
correlations r as stratified by forest age class (n = 48). ............. ........ .............6

4-1 MPN enumerations of nitrifying bacteria in young and old longleaf pine forest soils......80

4-2 Ammonification and nitrification in young and old longleaf pine forest soils. .................80

4-3 Soil biogeochemical relationships with stand attributes based upon Spearman Rank
correlations r as stratified by forest age class (n = 48). .................. ................8

5-1 Correlations and biplot scores for the biogeochemical variables by pine .......................102

5-2 Plant Indicator Values (IndVal) (percent of perfect indication) with associated
biogeochemical variable by pine flat type.. ............ ...............102.....

5-3 Correlations and biplot scores for the biogeochemical variables by forest age class......103

5-4 Plant Indicator Values (IndVal) (percent of perfect indication) with associated
biogeochemical variable by forest age class ................. ...............103..............

5-5 The means for soil biogeochemical variables between reference site locations and the
Pt. W ashington restoration site. ............. ...............104....

5-6 Pt. Washington actual vs. predicted indicator values. ............. ..... ............... 10

A-1 Species list. ............. ...............120....










LIST OF FIGURES


FiMr page

1-1 Florida' s Gulf Coast Flatwoods zone where the wet pine flat sites are located. ...............20

2-1 Locations of the Pt. Washington Longleaf Pine Restoration site ( ) and the reference
sites within Gulf Coast Flatwoods subecoregion of Florida. ................ ......................38

2-2 Nested plot sampling design applied at three different sites (age classes) for each
reference location. ........... ..... .._ ...............39....

2-3 Mean stand density (trees per hectare) along a 1 10-year longleaf pine
chronosequence as measured from 26 differently aged stands. .............. ....................39

2-4 Mean stand DBH, height, BA, and volume along a 1 10-year longleaf pine
chronosequence as measured from 26 differently aged stands. .............. ....................40

2-5 Downed woody debris and standing deadwood (snag) accumulations along a 110-
year longleaf pine chronosequence as measured from 26 differently aged stands. ...........41

2-6 Decomposition levels by forest age class as measured from 26 differently aged
stands............... ...............42.

2-7 Composition of understory vegetation by forest age class. ................ ................ ...._42

2-8 Shannon-Wiener Diversity and Coleman Rarefaction indices along a 110-year
longleaf pine chronosequence as measured from 26 differently aged stands. .................. .43

2-9 Mean stand density versus the Shannon-Wiener Diversity index and mean stand
height versus the Coleman Rarefaction index as measured from the young, mid-age
and mature age longleaf pine stands ........._.._. ....._... .........._. ....._. .......44

3-1 Soil organic matter content versus soil moisture as measured from 26 differently
aged stand s. .............. ...............63....

3-2 Soil pH versus soil organic matter content (percent) as measured from 26 differently
aged stand s. .............. ...............64....

3-3 Total net nitrogen mineralization, ammonification and nitrification rates (mg -1
nitrogen / kg -1 soil / month ) along a 110-year chronosequence as measured from
26 differently aged stand s.. ............ ...............64.....

3-4 Trends for microbial biomass carbon (Cmb) and net nitrogen mineralization rates
(Nmin) along a 1 10-year longleaf pine chronosequence as measured from 26
differently aged stand s. ............. ...............65.....










3-5 Microbial biomass carbon versus net nitrogen mineralization rates as measured from
26 differently aged stands.. ............ ...............65.....

3-6 Fungal biomass carbon ( C ) along a 1 10-year longleaf pine chronosequence as
measured from 26 differently aged stands.. ............. ...............66.....

3-7 The fungal-to-microbial biomass ratio and fungal biomass carbon levels (means)
during the earlier and later portions of chronosequence respectively, as measured
from 26 differently aged stands along the 1 10-year longleaf pine chronosequence. .........66

4-1 Net nitrogen mineralization versus stand volume as measured from 26 differently
aged stand s. .............. ...............8 2....

4-2 The fungal biomass (FB)-to-microbial biomass (MB) ratio versus stand height as
measured from 26 differently aged stands. .............. ...............82....

4-3 Fungal biomass carbon (Cfb) versus stand basal area (BA) as measured from stands
grouped within the mid-aged and mature age classes only ................. .......................83

4-4 Coarse woody debris accumulation versus fungal biomass carbon (Cfb) as measured
from 26 differently aged stands.. ............ ...............83.....

4-5 Coleman Rarefaction index versus the fungal biomass (FB)-to-microbial biomass
(MB) ratio as measured from 26 differently aged stands.. ............ .....................8

4-6 Shannon-Wiener diversity H' index versus the fungal biomass (FB)-to-microbial
biomass (MB) ratio as measured from 26 differently aged stands.. ............ ..................84

5-1 Pine flat type determined by a three-dimensional ordination biplot derived from
Canonical Correspondence Analysis (CCA) of 192 plots using understory plant
species abundance and soil biogeochemical data including the Pt. Washington
restoration site ................. ...............105................

5-2 A threer-dimensional ordination biplot derived from Canonical Correspondence
Analysis (CCA) of 192 plots using understory plant species abundance and soil
biogeochemical data collected within the young, mid-aged, mature age class, and the
Pt. W ashington restoration site. ............. ...............106....

5-3 Monthly variation of total nitrogen mineralization, ammonification and nitrification
rates (mg-l kg-l month- ) obtained from field incubation of soils (untreated) during 14
months before and after the 2002 treatments. ................. ...._.._ ............. .......0

5-4 Net nitrogen mineralization means mg (NH4+ + NO3-) / kg-l soil / month for the
control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometuron methyl-
hexazinone mix and Arsenal: imazapyr. ......__....._.__._ ............. ..........10










5-5 Net ammonification mean monthly rates (mg-l NH4+ / kg-l soil / month) for the
control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometuron methyl-
hexazinone mix and Arsenal: imazapyr. ......__....._.__._ ............. ..........10

5-6 Net nitrification mg -1 NO3- / kg -1 soil / month; for the control, Oust: sulfometuron
methyl, Velpar: hexazinone, sulfometuron methyl- hexazinone mix, and Arsenal:
imazapyr; applied in different growing seasons, frequencies, and time of year. .............108

5-7 Microbial biomass carbon (Cmb) mg ~1C / kg -1 soil; for the control, Oust:
sulfometuron methyl, Velpar: hexazinone, sulfometuron methyl- hexazinone mix,
and Arsenal: imazapyr.. ........._.__........... ...............109 ....

5-8 Microbial biomass carbon (Cmb) mgl carbon / kg-l soil from soils treated only one
year and two consecutive years of applications ................. ...............109........... ..

5-9 Microbial biomass carbon (Cmb) mg carbonn / kg soill levels measured at the
reference sites and the Pt. Washington restoration site ................. .........................110

5-10 Fungal biomass carbon mg -1 carbon / kg -1 soil; for the control, Oust: sulfometuron
methyl, Velpar: hexazinone, sulfometuron methyl- hexazinone mix, and Arsenal:
imazapyr ......_ ................. ...............110......

5-11 Pools and fluxes of nitrogen in the RESDYN restoration model. MP, metabolic pool;
grass&forbs, holocellulose pool; shrubs, lignocellulosic pool; and CWD, woody
pool .......... ................ ...............111......









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

LONGLEAF PINE (Pinus palustris Mill) ECOSYSTEM RESTORATION ON COASTAL
WET PINE FLATS: DEVELOPING A MONITORING PROGRAM USINTG VEGETATION
AND SOIL CHARACTERISTICS

By

George L. McCaskill
August 2008

Chair: Shibu Jose
Maj or: Forest Resources and Conservation

Longleaf pine ecosystem restoration should include more than reforestation or the

application of prescribed fire. It must include the restoration of all the maj or functions and

processes within the forest ecosystem along with restoring overstory and understory species

composition. Despite many longleaf pine restoration proj ects on coastal pine flats, there is no

monitoring protocol in place to evaluate the success of an all-inclusive restoration effort. The

goal of this study was to establish an ecological traj ectory using selected indicators for wet

longleaf pine flats as a monitoring framework for restoration proj ects.

The first specific obj ective was to quantify the vegetational attributes of longleaf pine flat

ecosystems along a chronosequence (2-years after stand replacement to 110-years-old) of stands

from within the Gulf Coast Flatwoods zone in Florida. Overstory structure and understory plant

species diversity were quantified along the chronosequence. Mean diameter at breast height

(dbh), height, and basal area increased until 60-70 years, and then declined. Stand volume

continued to increase. Stand density decreased before reaching a steady state. Coleman

rarefaction and Shannon-Wiener diversity indices for understory plants exhibited opposite trends

during early stand development, but reached "equilibrium" during the mature (> 90 years) phase.









The second obj ective was to examine soil chemical and microbiological properties along

the same chronosequence. Net nitrogen mineralization (Nmin), soil microbial biomass carbon

(Cmb), and fungal biomass carbon (Cfb) increased from the young to the mid-aged age stands and

declined from the mid-aged through the mature age stands. Ammonium production dominated

nitrogen cycling and ammonium enrichment occurred on these wet sites by reduction of nitrate

(the DNRA pathway). The biogeochemical attributes showed that Florida' s Gulf coastal pine

flats reach a self-organizing threshold after 85-90 years.

The third obj ective was to examine the interrelationships between the structural

(vegetative) and functional (soil biogeochemical) attributes. Nmin, Cmb and Cfb increased with

increases in dbh, height, basal area, and volume. Plant species diversity decreased as the FB-to-

MB ratio increased. Nitrate levels and nitrifying bacteria numbers were higher in young forest

soils than old forest soils. Based upon the indicators, coastal longleaf pine flats reach a steady

state threshold with a lower and less variable (tighter) nitrogen cycle at 90 years.

The final obj ective was to determine if observed structural and functional attributes were

useful for evaluating restoration proj ects. An ongoing restoration proj ect at the Pt. Washington

State forest was evaluated for its ecological trajectory following various restoration treatments

involving herbicides. The site was determined to be a wet flatwoods based upon environmental

ordination and plant species indicator analysis. Herbicide use increased soil microbial biomass

carbon and net nitrogen mineralization rates. Imazapyr was the most effective herbicide

treatment for this wet pine flats site based upon the level of shrub control, minimum impacts on

herbaceous species diversity, and desired structural attributes of the overstory.

Key words: Longleaf pine, reference communities, monitoring, ecological indicators, herbicides.









CHAPTER 1
MONITORING LONGLEAF PINE RESTORATION IN COASTAL WET PINE FLAT
COMMUNITIES

Longleaf Pine Ecosystems

The longleaf pine (Pinus palustris Mill) ecosystems that historically dominated the lower

Coastal Plain from Virginia to Texas currently occupies less than 3 % of its original area (37

million ha) (Frost, 2006). This reduction in area has resulted in a great loss of habitat necessary

for many plant and animal species (Wade et al. 2000; Van Lear et al. 2005). Longleaf pine

ecosystems are naturally maintained by frequent fires that reduce vegetative competition during

pine seedling and sapling development (Boyer, 1990). Fires, natural or prescribed, have become

severely restricted, especially by urban expansion because of liability and property damage

concerns (Achtemeier et al. 1998; Haines et al. 2001).

For the last thirty years, forest industries in the South preferred to replace longleaf pine

stands with slash pine (Pinus elliottii Engelm.) on wet sites and with loblolly pine (Pinus taeda

L.) on upland areas (Croker & Landers, 1987). Slash and loblolly pines are considered easier to

regenerate and managers have little need to address the longleaf pine' s unpredictable period of

establishment (grass stage). Furthermore, they also reach commercial size faster than longleaf

pine, which shortens the economic rotation (Outcalt, 2000).

In recent years, there has been a great deal of attention given to the restoration of the

extensive and species-rich longleaf pine ecosystem. There have been attempts to restore 400,000

ha of longleaf pine in the Southeast during the past decade (WMI, 2006). This effort creates a

need for monitoring protocols to be in place for evaluating the success of these restoration

efforts. While established monitoring guidelines and programs are active for many of the other

forest ecosystems in other parts of the U. S. (ERI, 2003), the lack of such established directives









can hinder longleaf pine restoration proj ects in establishing functional and self-sustaining

ecosystems across its originally extensive range (Devries et al. 2003).

Monitoring Restoration Success

Three established strategies for assessing a restoration effort are direct comparison,

attribute analysis, and traj ectory analysis (SER, 2004). Permanent-plot studies have been used to

directly identify changes over time after stand replacing harvests or other impacts. Attribute

analysis uses the measurement of ecological indicators to evaluate ecosystem conditions without

directly considering patterns over time. Traj ectory analysis uses data collected over periodic time

intervals to identify if restoration trends are toward a reference condition (SER, 2004). We used

a combination of attribute analysis and traj ectory analysis to monitor our restoration proj ect.

Since expensive permanent-plot studies are generally limited to less than 30 years, conducting a

traj ectory analysis employing a space-for-time substitution or chronosequence can be an efficient

alternative for describing general trends over time or to test hypotheses based upon forest

succession (Pickett, 1989). Chronosequencial studies make use of a group of sites that have

similar biotic, climatic, soil biogeochemical, and historical characteristics, but differ in age since

a harvest or other stand replacing disturbance (Pickett, 1989). By comparing the different aged

sites, one can identify changes in composition or function between decades, centuries, or even

millenniums (Williamson et al. 2005).

There is a critical requirement for the different aged sites to be subj ected to the same

historical conditions and have the same species available over the chronosequence to give

validity for using the space-for-time substitutions. One must also deal with separating spatial

variability from the variability associated with time (Veldkamp et al. 1999). A recent

chronosequence study examined the relationships between stand development and understory

vegetation on 15 stands ranging from 7 to 427 years in a mixed conifer forest along the









California-Oregon border. Regression analysis showed canopy openness was positively

correlated with total understory cover, species richness, diversity, and composition. Surprisingly,

no correlations were observed between any of the measured stand attributes. Shrub and

graminoid species were negatively correlated, and forbs were positively correlated, with stand

age (Jules et al. 2008). Another study used detailed forest inventory and climatic data from 43

stands along a 250-year chronosequence to assess the effects of disturbance and climate on

biomass accumulation patterns across Russia. Regression analysis indicated as expected the

highest biomass increments in the warmest regions and the lowest in the coldest regions. Spruce

(Picea spp.) and birch (Betula spp.) forests had the highest biomass increments while larch

(Larix spp.) and aspen (Populus spp.) forests had the lowest biomass accumulation. The faster

growing spruce and birch forests had declines in biomass accumulation rates after 150 years

whereas the slower growing larch and aspen never showed declines during the 250-year

chronosequence (Krankina et al. 2005).

Monitoring Soil Characteristics

In addition to vegetative characteristics, mineral pools, and the mineralization of key

elements have been identified as important attributes for evaluating restoration success in recent

years (Muiller et al. 2000; Muiller and Lenz, 2006). During the last decade there has been a maj or

effort at assessing the effects of different forest management practices on the long-term soil

productivity of southern pine forests (Burger and Kelting, 1999), including coastal wet pine flats

(Lockaby and Walbridge, 1998; Lister, 1999; Burger and Xu, 2001; Burdt, 2003). These studies

have assessed treatment effects utilizing a set of soil indicators (Kelting et al. 1999) including

soil pH, soil organic matter content, soil moisture content, and the mineralization levels of

nitrogen, and phosphorus (Reynolds et al. 2000; Redding et al. 2004). For example, a recent

chronosequence study examined the relationship between biomass accumulation and nitrogen









availability over 87 years in Populus grandidentata forests. Overstory biomass increment

increased with stand age while understory biomass levels decreased. Net nitrogen mineralization

rates were found to decrease during the first 18 years after harvest than increase over the next 70

years (White et al. 2004). In an earlier investigation, forest floor microbial biomass was studied

in a chronosequence of northern hardwood forest stands ranging from 3 years after clearcut to

120 years. Microbial biomass increased during the early successional stage, decreased during the

mid-aged stage, and then increased during the late successional stage. Soil organic matter

followed a pattern similar to microbial biomass. There was no trend in the fungal-to-bacterial

ratio along the chronosequence. Soil moisture was strongly and positively correlated with fungal

biomass. Soil pH was negatively correlated with fungal biomass. Finally, ammonium (NH4'

production increased from the early to mid-aged stages and then decreased from the mid-aged to

late successional stages (Taylor et al. 1999).

Developing a Monitoring Program

A good monitoring program should be well focused on just a few key indicators to provide

for statistically sound information (Lindenmayer, 1999). The standards for restoration are

obtained from measuring key environmental indicators at the restoration site and comparing

them to established reference communities (SER, 2004). In ecological restoration, the pathway

from the degraded condition to the restored, self-sustaining condition is called the ecological

traj ectory (Stanturf et al. 2001). Predicting the ecological traj ectory of a longleaf pine forest is

difficult because of the great variety of disturbance regimes associated with southern pine forest

ecosystems along the Gulf Coast (Palik et al. 2002).

To define when a given ecological traj ectory has reached a self-sustaining state it is

important to establish some specific goals for the restoration proj ect (Hobbs & Harris, 2001).

Two notable standards are to restore viable populations of key native species in natural patterns









of abundance and distribution, and to sustain key geomorphologic, hydrological, ecological,

biological, and evolutionary processes within the normal ranges of variation (ecological

integrity; Miiller et al. 2000). Forest structure and plant species composition are two of the

indicators being monitored in this study to capture the successional and developmental forces.

Soil chemical properties, net nitrogen mineralization, and soil microbial dynamics were also

included as indicators to insure that key biogeochemical, ecological, and biological processes are

also being evaluated (Harris, 2003; Miiller and Lenz, 2006).

How does one determine if these goals are being achieved along the successional pattern?

The normal range of variation along a spatial scale can be determined by using a series of

reference communities that are evenly distributed along the distinct ecologically identified range,

to compare with the restoration site (Harris, 1999). To evaluate changes in restoration along the

chronosequence, each reference community had to contain stands representing distinct ages

distributed evenly as possible along the 110-year scale (Miiller, 1998).

In summary, the following steps have been recommended to insure that a monitoring plan

functions properly: a) Set monitoring goals, b) identify the resources to monitor, c) establish

threshold levels, d) develop a sampling design, e) collect and analyze data, and f) evaluate results

(Block et al. 2001). The overall goal of this study was to establish an ecological traj ectory using

selected indicators for wet longleaf pine flats so that it can be used as a monitoring framework

for restoration proj ects. The next four chapters will address the following four specific obj ectives of

this study.

1. Quantify the vegetational attributes of longleaf pine flat ecosystems, along a

chronosequence (2-years after a stand replacing disturbance to 110-years) of stands from

within the Gulf Coast Flatwoods zone of Florida.










2. Examine soil chemical (soil organic matter content, pH, plant-available phosphorus, net

nitrogen mineralization) and microbiological (microbial biomass carbon, fungal biomass

carbon) properties along the same chronosequence.


3. Examine the interrelationships between the structural (vegetative) and functional (soil

biogeochemical) attributes.


4. Determine if the observed structural and functional attributes could be used to evaluate

restoration proj ects.







































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Figure 1-1.Florida' s Gulf Coast Flatwoods zone where the wet pine flat sites are located (Florida
DEP, 2002).


-rB










CHAPTER 2
FOREST STRUCTURE AND PLANT SPECIES DIVERSITY IN WET LONGLEAF PINE
FLATS ACROSS A CHRONOSEQUENCE

Introduction

In recent years, there has been a great deal of interest in restoration of the longleaf pine

ecosystem, one of the most threatened ecosystems in the United States with less than 3% of its

original extent remaining. There have been attempts to restore 400,000 hectares of longleaf pine

in the Southeast during the past decade alone (WMI, 2006). This situation creates a need for

developing monitoring protocols to evaluate the success of these restoration efforts. While

established monitoring programs are in place for many forest ecosystems in other parts of the

U. S. (ERI, 2003), the lack of such established guidelines can hinder the restoration of the

longleaf pine forest as a functional, self-sustaining ecosystem across its former range (Devries et

al. 2003).

Community structure and species composition are two key attributes often evaluated in

restoration proj ects (Brockway et al. 2005). However, reliable information on the ecological

traj ectory of longleaf pine ecosystems have hampered monitoring of restoration proj ects in

Florida and elsewhere in the Southeast. Although past research has examined the structure and

species composition of upland longleaf pine ecosystems, little information exists on the temporal

patterns of forest structure and plant species diversity in wet longleaf pine flat communities

located along the coastal lowlands of Florida' s Gulf Coast (Michener, 1999). Wet pine flats are

pine-dominated, poorly drained, broad plain wetlands (Stout and Marion, 1993; Harms et al.

1998).









In Florida, plant species richness has been found to increase with soil moisture until an

ecotone between wet pine flats and cypress swamps is reached (Huck, 1986; Walker, 1993;

Kirkman et al. 2001; Walker and Silletti, 2006). This ecotone is the zone where one finds wet

flatwoods and wet savanna subtypes of the coastal wet pine flat (Messina and Conner, 1998).

Their overstories are dominated with varying mixtures of longleaf and slash pines, but also might

contain a component of Choctawhatchee sand (Pinus clause var. immuginata) and/or pond

(Pinus serotina) pine (Parker and Hamrick, 1996).

The environment for Florida' s wet pine flats is the 1,240 km-long Gulf Coast, containing

sounds, bays, and offshore islands. This coastal landscape is continuously shaped by active

fluvial deposition and shore zone processes which promote and maintain the formation of

beaches, swamps and wet mineral flats. The local relief ranges from 0 to 20 m in elevation.

Annual precipitation ranges from 1300-1600 mm and average annual temperatures vary between

190-210 C. Growing seasons are long, lasting 270-290 days (McNab and Avers, 1994). Soil

parent material consists of marine deposits containing limestone, marl, sand, and clay. The

dominant soils are Aquults, Aquepts, Aquods, and Aquents. These highly acidic soils have

thermic and hyperthermic temperature regimes and an aquic moisture regime. The maj or forest

type of this region is the longleaf-slash pine flatwoods, while water oak (Quercus nigra), swamp

tupelo (Nyssa sylvatica var. biflora), sweetbay (M~agnolia virginiana), and cypress (Taxodium sp.)

are found along the maj or river drainages and isolated depressions (McNab and Avers, 1994).

Florida' s subecoregional Gulf Coast Flatwoods (Figure 2-1) covers the maj ority of this

geographical area where both pine savannas and coastal flatwoods occur in close association

with cypress ponds (Myers and Ewel, 1990; Griffith et al. 1994). Because of the growing









conditions, wet pine flats are highly productive ecosystems, and represent more than one million

ha in the Southeast (Burger and Xu, 2001).

There are almost 200 rare vascular plant taxa found in the great variety of habitats

classified as longleaf pine ecosystems. In addition to the majority of them being found in Florida

(Collins et al. 2001), the richest sites are found in these wet pine flats and their associated

wetlands (Walker, 1993). The uniqueness of Florida' s wet pine flat communities make them

crucial for plant species diversity and the rarity of plants to be evaluated in any monitoring plan

for restoration (Walker, 1993; Collins et al. 2001; LaSalle, 2002).

One of the ways by which restoration progress can be monitored is by examining the

ecological traj ectory of the restored site and comparing it with the ecological traj ectory of

reference sites. Post-stand replacement secondary forest succession has been well studied in

other ecosystems and is considered to follow four stages of development (Peet and Christensen,

1980; Oliver, 1981). Standlnitiation commences after a stand-replacing disturbance has

occurred. Plants regenerate from sprouts, seed banks or newly dispersed seeds. Any advanced

regeneration (saplings and seedlings) is released by the disturbance and commences accelerated

growth. The Stem Exclusion stage is when the individual trees in the stand come under fierce

competition for light, water, and nutrients. Canopy closure results in a great reduction of stand

density as the residual stocking contains fewer, larger trees. Thiderstory Reinitiation occurs

when some of the dominant overstory trees begin to die forming gaps for new regeneration.

Finally, the Old Gia th1 l or Steady-State stage is reached when gap dynamics dominates the

landscape and the forest is now all-aged. Snags and downed logs are also found throughout the

landscape (Perry, 1994). Gap dynamics is maj or source of regeneration in natural longleaf pine

forests (Brockway and Outcalt, 1998). Steady-state is tied to the ability of a system to be self-










organizing and resilient (Muiller et al. 2000; Muiller and Lenz, 2006). Self-organizing forces

become especially apparent during the understory reinitiation stage of forest succession when the

steady-state mosaic begins to form.

When identifying patterns of succession along a chronosequence, stand changes caused by

disturbance must also be considered (Frelich, 2002; Pickett and Cadenasso, 1995). The longleaf

pine forest is a pyro-climax ecosystem which relies on short fire return intervals to maintain the

"steady-state" stage over other woody plant species (Wade et al. 2000). In coastal wet pine flats,

wind and precipitation are also maj or "shapers" of longleaf pine communities. Hurricanes

directly affect the canopy structure of longleaf pine stands through gale-forced winds, opening

them up to sunlight and changing the composition of the flora and fauna that occupy them.

Hurricanes also affect longleaf pine stands by the extensive flooding that accompanies the wind.

Extended flooding can cause changes in both the above and below ground productivity (Johnston

and Crossley, 2002; Palik et al. 2002).

Anthropogenic effects caused by human activities in forests can also change the forest

structure. Timber harvesting, grazing, and prescribed fires can cause changes in the structural

complexity of forests having negative effects by exposing surface soils and reducing biodiversity

(Redding et al. 2004; Van Lear et al. 2005). In addition, climatic changes such as increased

atmospheric CO2 leVOIS may affect the soil biotic community, adding to the potential negative

feedbacks toward the productivity of aboveground plant communities (Peacock, 2001; Frelich,

2002). Measuring forest structural data along a chronosequence will make it possible to evaluate

change along the life cycle of coastal wet longleaf pine flats.

The obj ective of this study was to examine stand structural attributes and understory plant

species diversity along a 110-year chronosequence. We hypothesized that stand DBH, height,









basal area (BA), and volume would increase while stand density and plant species richness and

diversity would decrease through the mid-age. We expected these parameters to reach a

threshold or steady-state during the mature phase when the understory reinitiation stage of

succession has begun. Quantifieation of this ecological traj ectory would help establish

monitoring thresholds in terms of stand structure and plant species composition for restoration

proj ects.

Materials and Methods

Study Sites

Three study sites were established along a wet pine flats located within three kilometers of

Florida' s Gulf Coast. They were Topsail Hill State Park, St. Marks National Wildlife Refuge,

and Chassahowitzka Wildlife Management Area. They were found between the cities of

Pensacola and Tampa Bay (Figure 2-1), a narrow zone that makes up the maj ority of the Natural

Resource Conservation Service's Eastern Gulf Coast Flatwoods ecoregion (MLRA 152A) and

the National Oceanic and Atmospheric Association' s Panhandle Coast unit of the Louisianan

reserve (National Estuary and River Reserve System). Both of these federal designations make

this zone unique from an ecological as well as hydrological perspective. Within the Eastern Gulf

Coast Flatwoods zone is the Gulf Coast Flatwoods (75I) subecoregion of Florida (Figure 2-1;

Griffith et al. 1994). Any environmental variations between these sites were minimized by

establishing very specifically defined spatial scales. This was accomplished by stratifying the

important segments of the whole system (e.g. Florida' s Gulf coastal flatwoods) down to the

smallest distinct scale as possible (e.g. wet pine flats within 3 kilometers of the coast) in order to

take meaningful measurements (Chertov et al. 1999; Frelich, 2002; Miidler et al. 2000). The first

120 years of longleaf pine succession has been included with wet pine flats situated within 3 km

of the Florida Gulf coast as the temporal and spatial scales in this study. These two scales were









determined from an in-depth preliminary survey of stand conditions found within the Gulf Coast

Flatwoods zone of Florida and at the restoration site.

The herbaceous ground cover of wet longleaf pine flats is very diverse due to the warm

temperatures and high rainfall. Broomsedge (Andropogon virginicus), wiregrass (Aristida strict

var. beyrichiana), witch grass (Dichanthelium spp.), goldenrod (Solidago odora, meadow

beauty (Rhexia alifan2us), fetterbush (Lyonia lucida, and aster (Aster adnatus) are found on both

subtypes (Brewer, 1998). Pine savannas are distinguished from wet flatwoods by a greater

abundance of beak sedge (Cyperus), nut rush (Scleria cilliata), bloodroot (Iahuinhes~lllr~

caroliniana), pitcher plants (Salrracenia), and orchids (Calopogon) or (Platanthera). Coastal

flatwoods have a greater presence of titi (Cliftonia monophylla), swamp tupelo, gallberry (Ilex

glabra), saw palmetto (Serenoa repens), and sweetbay. Where fire is restricted, catbrier (Smilax

pumila) can be a prevalent vine species (LaSalle, 2002).

All three of the selected sites have a moisture gradient as represented by cypress swamps,

wet pine savannas, and wet pine flatwoods. All three sites have active restoration management

programs where fire has been used for more than 20 years on approximately a three-year-return

interval. All of the sites are primarily managed to enhance habitat for threatened species

associated with longleaf pine ecosystems, and are managed by a state or federal agency.

The southern site on the spatial gradient is the Chassahowitzka Wildlife Management

Area in Hernando County, FL. It is approximately 12, 140 ha, and the soils are dominated by

Basinger Eine sands (sandy, siliceous, hyperthermic spodic Psammaquents) and Myakka Eine

sands (sandy, siliceous, hyperthermic aeric alaquods) (Hyde et al. 1977; Spencer, 2004).

The St. Marks National Wildlife Refuge in Wakulla and Jefferson Counties, FL consists

of 25,900 ha and the maj ority of the soils are mapped as the Scranton series (sandy, siliceous,









thermic humaqueptic Psammaquents) and the Leon series (sandy, siliceous, thermic aeric

Alaquods) (Reinman, 1985; Allen, 1991).

Topsail Hill State Park in Santa Rosa County, FL, contains 610 ha of some of the oldest

longleaf pine stands in Florida. The soils are Pickney sand series (sandy, siliceous, thermic

cumulic humaquepts) and the Leon series (Overing and Watts, 1989; White, 2001).

Forest Age Classes

The 110-year chronosequence starts from the point of stand replacement to the oldest

stands measured in our reference sites. The following age classes were used to stratify and

analyze changes in forest structure and plant species composition within the different stands at

each of the reference sites. There are 12 replicates per age class for the stand data and 48

replicates per age class for plant species data. The age classes provide a means to identify the

structure of stands within specific time periods along the chronosequence and to detect changes

from one time period to the next (Midller, 1998; Aravena et al. 2002). In this paper, a tree is

defined as a woody plant with a diameter at breast height (DBH) of greater than 10 cm. A

sapling is a woody plant with a DBH of less than 10 cm but greater than 2.5 cm. Finally, a

seedling is a woody plant that is less than 91.5 cm in height (Wenger, 1984).

The young age class: A young age stand exists when the maj ority of the stocking (> 70%)

can be found as seedlings and saplings. The average stand age should be < 20 years since

replacement.

The mid-aged class: The mid-age stand should have stocking (>70%) dominated by a

mixture of poles and small sawlog size trees (10-30 cm DBH). The average stand age should be

between 20 and 55 years old.









The mature age class: A stand is considered within the mature age class when the maj ority

of stocking (>70%) can be found as dominant sawlog trees (30-45 cm DBH). The stand age

should be > 55 years old.

Field Measurements

Each reference location had a cluster of three one-hectare blocks, containing stands

representing each of the three previously defined age classes. Each one-hectare block was sub-

divided into four randomly placed 400 m2 meaSurement plots. Tree height and DBH were

measured on all trees > 10 cm DBH. At least two of the dominant trees were cored at breast

height to determine stand age. Stand density (trees/ha), basal area (m2/ha) and standing volume

(m3/ha) were calculated from these data. In addition, the volume (m3/ha) of all snags and downed

woody debris (CWD) were also calculated. The equation used for tree and snag estimates was:

V = (0.000078539816*(DBH2))*tree height.

The volumes of downed logs were estimated with Smalian's metric equation:

V = [((D2) + (d2))*0.00003927]* log length (m),

where D = diameter large end (cm) and d = diameter small end (Wenger, 1984).

We adapted the system of five decomposition states for snags and downed woody debris used by

Spetich et. al. (1999). The decomposition descriptions translated to five levels of decomposition

deductions by percent (15, 30, 45, 60, and 75%; see Table 2-1).

Each 400 m2 plOt contained four smaller 1 m2 Subplots randomly placed within the larger

plot for understory sampling (Figure 2-3). Percent cover of each species was assessed using a

modified Daubenmire method incorporating eight different levels (Daubenmire, 1959). Coleman

rarefaction and the Shannon-Weiner diversity indices were calculated for each stand (Koellner

and Hersperger, 2004; Colwell, 2006).









Data Analysis

A three stage balanced nested design was used to integrate the indicators measured at

different scales, and among sites (Figure 2-2). Hypothesis testing for differences between means

was accomplished by using two-sample t-test with an alpha of 0.05 and a two-tailed confidence

interval. The sampling of nine distinct reference locations produced a dataset where the

assumptions for analysis of variance (ANOVA) was not ensured; therefore, non-parametric tests

were used to detect any significant differences among the reference sites and among the distinct

forest age class segments (SAS, 2002).

Trends over time and between variables were obtained from linear regression using the

general linear model (PROC GLM) (Yang et al. 2006; SAS, 2002). Plant species indicator

analysis (IndVal) was used to measure the level of relationship between a given plant species to

categorical units such as pine flat subtypes or forest age classes. It calculates the indicator value

d of species as the product of the relative frequency and relative average abundance in each

categorical cluster. Indicator species analysis is used to attribute species to particular

environmental conditions based on the abundance and occurrence of that species within the

selected group. A species that is a "perfect indicator" is consistent to a particular group without

fail. Indicator values range from 0 to 100, with 100 being a perfect indicator score. Because

indicator species analysis is a statistical inference, a test of significance is applied to determine if

species are significant indicators of the groups with which they are associated (Dufrene and

Legendre, 1997). This is achieved by the Monte Carlo permutation test procedure (1000

iterations), where the significance of a P-value is determined by the number of random runs

greater than or equal to the inferred value (oc=0.10). Accuracy is defined from the binomial 95%

confidence interval (Strauss, 1982).












Overstory Stand Structure

The mean stand DBH, height, BA, and volume varied significantly among the age classes

(Table 2-2). For example, the mean DBH for the young stands was 6.0 cm, 23.4cm for the mid-

age stands, and 30.0cm for the mature age stands. Height, BA, and volume exhibited similar

results. Snags and downed woody debris had similar values for the young and mid-age stands,

but was significantly higher for the mature stands (Table 2-2). Regression analysis revealed

trends over the chronosequence for stand structural variables. Except for stand density, all of the

stand variables increased with forest age class. Stand density was highly variable over time and

did not exhibit any specific patterns (Figure 2-3). As expected, stand DBH increased with age

until 85-90 years then began to reach a steady-state (Figure 2-4). Stand height also increased

over time, but reached an asymptote at 85-90 yrs. Stand basal area and volume followed similar

regression curves as with DBH and height (Figure 2-4). Downed woody debris accumulation

levels were highly variable, but in general increased over the 110-year chronosequence (Table 2-

2; Figure 2-5). The volume of standing deadwood (snag) increased through the mid-age stands

and then decreased thereafter (Figure 2-6). The level of CWD decomposition remained the same

for the young and mid-age stands, but was lower for the mature age stands (Figure 2-7).

Understory

The abundance of grasses and forbs decreased, and the abundance of shrubs increased over

the three forest age classes (p < 0.05; Figure 2-8). The Shannon-Wiener diversity index ranged

from 1.28 2.40 for the dataset. In general, Shannon-Wiener diversity index decreased over time

(Figure 2-9). Shannon-Wiener diversity index also decreased with stand density during the

young age class, but increased with stand density during the mature age class (Figure 2-10). The

Coleman rarefaction index ranged from 7.2-22.0 for the dataset (Table 2-9). The Coleman


Results









rarefaction index increased with stand height during the young age class, but decreased with

stand height during the mid-aged class (Figure 2-10).

Bluestem grass, blueberry (Vaccinium spp.), and witchgrass were the dominant plant

species indicators for the young age stands (p < 0.022). Meadow beauty, wiregrass, and Carolina

redroot were the dominant plant species indicators for the mid-aged stands (p < 0.067).

Gallberry, running oak (Quercus pumila), and dangleberry were the best plant species indicators

for the mature age stands (p < 0.1; Table 2-3).

Discussion

Overstory

The overstory variables of mean stand DBH, stand height, stand BA, and volume exhibited

strong positive relationships with stand age as expected. Even downed logs and snags,

heterogeneous variables among the sites and within age classes, produced a weakly positive

trend with stand age (p < 0.042). Stand density showed no clear pattern along the

chronosequence, owing to the high variability found within density across the age gradient. The

data showed most of the growth variables reaching an asymptote around 85-90 years.

When the chronosequence stand data were compared to growth and yield of natural

longleaf pine stands, our stands were found to have lower basal area (14 m2 VS. 25 m2) at age 30,

but comparable stand volumes (150 m3 VS. 130 m3) at age 60 (Farrar, 1985). The steady-state

phase for these forests is reached around 85-90 years, may be shorter than the steady state of 110

years for longleaf pine ecosystems in Texas, reported by Chapman (1909).

All of the stands over 85 years measured at our reference sites had large gaps containing

saplings and some poles. This structure would indicate that the understory reinitiation stage of

secondary succession was well along and the ecosystem's self-organization capacity was

apparent. Of the aboveground indicators, both stand density and CWD had the greatest









heterogeneous datasets based upon statistical analysis. This great variability in stand density and

CWD reflects the differences that must be evaluated when comparing "natural" versus

intensively managed forest stands.

Stand growth is high during the early phase, but is slowed during the mid-aged class when

the stem exclusion state is reached. Interspecific competition results in mortality of individuals

and the snag accumulation rates increase. This was evident along the chronosequence when snag

accumulation peaked between 60 and 80 years. During the mature phase, stand growth is slowed

as the forest reaches a steady state. This indicates that the mid-aged class was the maj or period

for CWD accumulation brought on by both competition and disturbance (Spetich et al. 1999).

Downed logs and snags continued to accumulate during the mature phase, but with a decreasing

rate of decomposition.

Plant Species Diversity

The species diversity exhibited interesting patterns along the chronosequence. Stand

density had a strong negative relationship with the Shannon-Wiener diversity index within the

young age class, but a positive relationship during the mature age class (Figure 2-10). The

Coleman rarefaction index increased with stand height within the young age class then decreased

during the mid-aged class (Figure 2-10). The change in response between stand height and

Coleman rarefaction, and stand density with the Shannon-Wiener diversity index is due to how

these two distinct indices calculate species diversity.

The Coleman rarefaction function gives more weight to the rarity than commonness of

species. The function looks at the 'P' diversity where the turnover of species between two

distinct species pools is measured. There was an expected number of species E(s) where Monte

Carlo iterations were performed to predict which species would be more likely appear (Hurlbert,

1971). In this case, species diversity became related to habitat diversity or habitat heterogeneity










(Hersperger and Koellner, 2004). As habitats become more complex (layered) with tree growth

during early stand development, rarefaction index increases with species turnover rates, and as

the number of rare species increase. This approach can better assess disturbance changes to the

site than the (information theory measure) Shannon-Wiener diversity index (Gotelli and Colwell,

2001).

The Shannon -Wiener diversity index responded positively to a stand with higher tree

density during the mature age class. These mature higher density stands may have more habitat

homogeneity than a stand with greater openness. Habitat homogeneity influences the Shannon-

Wiener index's evenness function 'J'. Species evenness may be easier to attain where habitat

homogeneity is greater. This is why the Shannon-Wiener index's H' value decreased as habitat

heterogeneity increased during early stand development, or may increase in older stands with

higher stand density (homogeneity). Since the Shannon-Wiener index's evenness function gives

equal weight to rare and common species, it does not measure local patterns of assemblage

where disturbance impacts could be assessed (Pianka, 1966). The Shannon-Wiener diversity

index still should be included with a rarefaction index during assessments because it is an

abundance-based function where the total number of species (richness S') that are found within

an area are measured. In addition, a measurement of the relative abundance (N) and degree of

equality among species (evenness 'J') are also calculated (Poole, 1974).

The young, mid-aged, and mature age classes varied in the abundance of grasses, forbs,

and shrubs. Even with the goal of applying prescribed fire every three years at the reference sites,

shrub species increased and graminoid species declined over the age classes. The mature age

class changed with running oak for mesic sites and gallberry for the wetter sites. The young age

class had blueberry for mesic conditions and bluestem grass for the wetter sites. In this case,









more forest structure brought on by stand maturation could represent a drying effect on soil

conditions as represented by a change in plant species.

Conclusions

Stand DBH, height, and basal area increased until 85-90 years when they began to reach a

steady state. Coarse woody debris accumulation levels were highly variable, but tended to

increase with age. Standing deadwood also increased with age up to 60-80 years and began to

decline thereafter. The decomposition levels of CWD were constant through the mid-aged class,

but declined from the mid-age to the mature stage. The levels of shrub species were significantly

higher in the mature sites than either the young or the mid-aged classes.

Tree growth during early stand development translates to habitat heterogeneity as partial

shading brings in new groups of plant species. At this point, stand height had a strong positive

relationship with Coleman rarefaction index and stand density had a strong negative relationship

with Shannon-Wiener diversity index. The plant species turnover rates as indicated by the

Coleman rarefaction index were high and the evenness of plant species as indicated by the

Shannon-Wiener were very low. The evenness of plant species was not attained until the mature

age class when competition was reduced, and the number of plant species entering the ecosystem

was equal to the number of plant species leaving it. During this time period, Shannon-Wiener

diversity index had a strong positive relationship with stand density and the Coleman rarefaction

index had a negative relationship with stand height.

The results have shown interesting trends along the chronosequence for wet longleaf pine

flat communities along the Gulf coast of Florida. The results indicate that Florida' s Gulf Coastal

longleaf pine flats reach the understory reinitiation condition at approximately 85-90 years. This

would mean the forest is nearing a steady, self-organizing state, perhaps a threshold point for

attaining restoration success in terms of structural attributes.









Three areas of this research warrant further attention. First, investigations concerning

coarse woody debris in southern pine forests is lacking, probably due to a perception that any

accumulation would be limited by prescribed fire. In this research, we found heavy

accumulations at sites in each of the forest age classes. Secondly, experiments in plant

community assemblage should be conducted to take a closer look at the relationships between

the commonly used Shannon-Wiener diversity index and the Coleman rarefaction index.

Coleman rarefaction was a stronger index during early stand development, but showed no

advantage over the Shannon-Wiener index during later stages of stand development. Finally, our

research found that shrub species dominated the mature aged stands even with aggressive fire

management programs. Many of the plant species that were classified as woody do not have

pioneer patterns similar to gallberry, saw-palmetto, or runner oak. They never dominated the site.

There should be studies that focus on the less known woody species and their benefits to longleaf

pine forest ecosystems.











Table 2-1. Qualitative classification system for downed and standing deadwood.
Characteristic Decay Class
I II III IV V
Leaves Present Absent Absent Absent Absent
Twigs Present Present Absent Absent Absent
Bark Present Present Often Present Often Absent Absent
Bole Shape Round Round Round Round to Oval Oval to Flat
Wood Consistency Solid Solid Semi-Solid Partly Soft Soft
Wood Strength Firm Firm Firm Breakable Fragmented
Decomposition Deduction 15% 30% 45% 60% 75%
Spetich et al. 1999; Adapted from Spies et al (1988).




Table 2-2. Forest structural and plant species diversity means among age classes.

Stand
Stand Stand Stand Stand Stand Coleman Shannon-
Age Basal CWD
Age Diameter Height Ara density Volume Rarefaction Wierner
Class Ara(m3/ha)
(years) (cm) (m) (lh (Trees/ha) (m3/ha.) ES(63) Diversity

6 Young 0.3 0.2 0.0 300 0.0 17.5 10.96 2.04
8 Young 0.7 2.8 0.1 200 0.0 13.3 12.93 2.24
9 Young 8.3 4.9 0.3 50 1.3 1.3 15.78 1.91
10 Young 8.5 6.0 0.3 75 1.8 2.6 15.78 1.91
17 Young 16.1 8.9 5.6 250 60.8 11.1 16.97 2.25
*Mean 6.0a 3.3a 1.3a 175a 12.9a 9.5a 14.51a 2.07a
24 Mid-Aged 19.5 10.7 2.3 350 26.0 2.5 16.69 2.29
27 Mid-Aged 25.7 11.7 5.4 125 62.0 0.7 16.69 2.29
29 Mid-Aged 13.6 8.2 7.3 400 84.0 4.3 20.11 1.91
31 Mid-Aged 19.8 10.1 7.1 250 78.0 1.5 17.90 2.32
34 Mid-Aged 23.3 11.5 10.1 225 128.0 76.0 13.28 1.72
36 Mid-Aged 17.2 11.1 11.1 425 125.0 25.3 20.11 1.91
40 Mid-Aged 29.9 19.4 1.8 75 34.0 3.5 12.99 2.14
42 Mid-Aged 21.9 10.7 1.0 25 10.1 10.0 12.99 2.14
46 Mid-Aged 25.8 18.0 20.6 425 395.8 80.7 9.99 1.28
50 Mid-Aged 35.0 20.9 4.9 50 101.2 5.8 9.99 1.28
52 Mid-Aged 26.8 12.3 11.5 275 174.5 1.8 7.99 1.78
*Mean 23.4b 13.0b 7.7b 216a 112.2b 9.3a 14.41a 1.97a
60 Mature 27.7 19.1 6.1 250 115.0 18.1 15.55 1.94
61 Mature 18.4 12.2 14.1 450 192.3 13.7 17.90 2.32
62 Mature 41.7 17.8 24.2 225 433.1 58.3 10.91 1.41
68 Mature 24.9 12.5 7.3 125 122.3 105.8 12.80 1.62
71 Mature 35.1 15.5 10.2 225 156.1 61.5 7.99 1.78
86 Mature 26.0 16.6 10.7 175 206.3 9.6 12.80 1.62
95 Mature 37.5 18.2 11.1 175 202.5 12.7 17.89 1.56
101 Mature 30.2 13.2 13.7 175 234.1 11.4 7.20 1.16
105 Mature 33.4 17.5 11.0 125 194.3 1.0 17.89 1.56
110 Mature 34.0 17.4 15.9 250 278.7 12.3 17.89 1.56
*Mean 30.0e 15.50 11.80 217a 201.70 30.0b 14.25a 1.86a
* Means followed by the same lower case letters are not significantly different (alpha=0.05). CWD represents snags and downed
logs. The sample size for stand data by age class was n=12 and the understory vegetation data by age class was n=48.











Table 2-3. Indicator values for plant species in three forest age classes.
Age Class Plant Species Age Class
Young Mid-Aged Mature SD P-Value Veg Type
Young Andropogan virginicus 34 3 1 3.09 0.001 Grass
Dichanthelium ovale 33 10 2 3.49 0.002 Grass
Vaccinium sp. 25 2 9 3.27 0.019 Shrub
Pteridium aquilinum 12 1 0 2.28 0.021 Forb

Mid-Aged Rhexia alifanus 0 28 0 3.03 0.001 Forb
Cyperus sp. O 25 0 2.20 0.001 Grass
Lachnanthes caroliana 8 25 1 2.98 0.005 Forb
Arisitida var. beyrichiana 1 23 0 2.54 0.001 Grass
Solidago odora 0 9 1 2.09 0.066 Forb

Mature Ilex glabra 20 11 37 3.04 0.007 Shrub
Quercus pumila 17 3 29 3.31 0.014 Shrub
Gaylussacia fr~ondosa 2 3 13 2.81 0.099 Shrub
Licania michauxii 0 0 10 2.17 0.040 Shrub
Kalmia hirsuta 0 0 7 1.96 0.094 Shrub
INDICATOR VALUES (% of perfect indication based on combining the values for relative abundance and relative
frequency). The sample size for the understory vegetation data by age class was n=48.

















































Locations of the Pt. Washington Longleaf Pine Restoration site and the
reference sites within Gulf Coast Flatwoods subecoregion of Florida (Griffith,
1994).


*







Figur


Selected Reference Communities
1. Chassahowitzka Wildlife Management Area
2. St. Marks National Wildlife Refuge
3. Topsail Hill State Park


e 2-1.











400 m2 Forest plot


1 ha Age Class Site


Figure 2-2. Nested plot sampling design applied at three different sites (age classes) for each
reference location.


**


**


*


* *


0 20 40 60 80

Stand Age (Years)


100 120


Figure 2-3. Mean stand density (trees per hectare) along a 1 10-year longleaf pine
chronosequence as measured from 26 differently aged stands.

















'*


* *


ee e *



,'y =-0.0043x2 + 0.7571x + 0.4671
~R2 =0.75
p <0.0013

0 25 50 75 100 125


*


,se
'*
ase


e **


y =-0.0026x2 + 0.4109x + 0.9501
R2 =0.72
p <0.0004

25 50 75 100 125



y =-0.0115x2 +3.6533x 24.902
R2 =0.76
p <0 .0004


25


S20


S15


a10




0 5


350

im 300

1 250

200

S150




~ 0


y =-0.0018x2 + 0.3297x -1.9355
SR = 0.46
p <0.0025


'-


* .


* ,


e


- *


1
.r



25 50 75 100
Mean Stand Age (Years)


0 25 50 75 100 125
Mean Stand Age (Years)


Figure 2-4. Mean stand DBH, height, BA, and volume along a 1 10-year longleaf pine
chronosequence as measured from 26 differently aged stands.











**


y = 0.3717x + 0.5181
R2 = 0.16
p < 0.0414


4L *


2 0 a


0 20 40 60 80 100 120


y = -0.0094x2 + 1.1249x 13.057
R2 = 0.25
p < 0.086


*
*
*


*


20 40 60


80 100


120


Stand Age (Years)


Figure 2-5. Downed woody debris and standing deadwood (snag) accumulations along a 110-
year longleaf pine chronosequence as measured from 26 differently aged stands.


*













O Young a7 Mid-Aged gg Mature


Forest Stage Class


Figure 2-6.


Decomposition levels by forest age class as measured from 26 differently aged
stands. Percent levels represent the amount of decay.


OYoung OMid-Aged


HMatu re


5


4,

03

03


Grasses


Forbs


Shrubs


Vines


Vegetative Type




Figure 2-7. Composition of understory vegetation by forest age class.


b













2.40


y = 5E-07x2 0.0071x + 2.19
a R2 = 0.40
,~p < 0.0006


e *
2.20-


2.00 -a


1.80


1.60


1.40


1.20


1.00


** *

*


0 20 40 60 80 100 120


20.00


' 4
* *


**


~


15.00




10.00



.0 .0


* *





y = 7E-05x3 0.01x2 + 0.3696x + 12.054
R2 = 0.23
p < 0.0373

0 20 40 60 80 100 120


0.00


Stand Age (Years)

Figure 2-8. Shannon-Wiener Diversity and Coleman Rarefaction indices along a 110-year
longleaf pine chronosequence as measured from 26 differently aged stands.

















C *
e~ + Young AgeClass








y =-1E-05x2 +0.004x + 1.7514
R2 =0.62
p <0.0001


e **









y =-0.0609x3 + 0.8965x2 2.5441x + 12.187
~R2 =0.70
p <0.0001


0 3 6 9 12


6 9 12 15 18 21 24



y =0.4086x2 -11.76x + 94.552
R2 =0.37
p< 0.0024








*


2.20
I
S2.00




1.40

~1.20


0 100 200 300 400 500


600


2.40


*
* *


rg a

*Mid-Aged
Class

y =-6E-06x2 + 0.0023x + 1.7807
~R2 =0.08 e


Sy =0.149x2 -5.0778x + 52.855
R2 =0.61
~p <0.0001






r r


~O17


~i14 1


11


a,


2.00


1.80

1.60 -


1.40 -

1.20


0 100 200 300 400 500


2.40

I 2.20

S2.00

S1.80

~1.60

=1.40


y =9E-06x2 -0.0023x + 1.6464
R2 =0.56
p <0.0032


* *


eMature Age Class


**


1.00


0 100 200 300 400 500

Stand Density (Trees I ha)


6 9 12 15 18
Stand Height (Meters)


21 24


Figure 2-9. Mean stand density versus the Shannon-Wiener Diversity index and mean stand
height versus the Coleman Rarefaction index as measured from the young, mid-age
and mature age longleaf pine stands.









CHAPTER 3
PATTERNS OF SOIL CHEMICAL AND MICROBIAL PROPERTIES ALONG A
CHRONOSEQUENCE INT WET LONGLEAF PINE FLATS OF FLORIDA

Introduction

Soil nutrient dynamics and their relationship to forest stand development have been

under investigation for some time (Odum, 1969; Vitousek and Reiners, 1975). Studies describing

soil nutrient status, in particular nitrogen, and its influence on stand productivity and canopy

nutrient dynamics have focused mostly on plantations (Morris and Boemer, 1998; Kirkman et al.

2001; Allen and Schlesinger, 2003), but research conducted in natural stands are also found in

the literature (Zak et al. 1990; Vance and Entry, 2000; Aravena et al. 2002; Chapman et al. 2003;

White et al. 2004). Similar studies are rare in the longleaf pine ecosystem, one of the most

threatened ecosystems in the United States (Wilson et al. 2002). For example, patterns of

nitrogen mineralization, the relationship between nitrogen levels and soil microbes, and how this

relationship changes over time, have not been given much attention (Johnston and Crossley,

2003). Such information could aid the efforts of restoration professionals who are interested in

not only restoring the structural attributes of the longleaf pine ecosystem, but its functional

attributes as well.

One way to study the relationship between forest stand development and soil microbial

dynamics is to use a chronosequence of similar stands having different ages since stand

replacement (Pickett, 1989; Williamson et al. 2005). In an earlier investigation, Taylor et al.

(1999) studied forest floor microbial biomass of northern hardwood forest stands ranging from 3

years after clearcut to 120 years. These authors reported an increasing trend in microbial biomass

with age during the early successional stage. However, microbial biomass decreased with age

during the mid-aged stage, but increased again during the late successional stage. Soil organic

matter followed a pattern similar to microbial biomass. They further reported that fungal biomass









was positively correlated soil moisture and negatively correlated with soil pH. Finally,

ammonium (NH4 ) prOduction increased from the early to mid-aged stages and decreased from

the mid-aged to the late successional stage (Taylor et al. 1999). In another study, investigators

wanted to detect the effects of plant diversity on the levels of fungal microbes by measuring the

populations of fungal-feeding nematodes. As the plant community succeeded toward late

successional conditions, there was little effect on the numbers of fungal-feeding nematodes

(Kardol et al. 2005). The relationship between fungal growth and nematode populations was

more complex than the investigators surmised.

Recently, increased emphasis has been placed on examining soil microbial communities

during soil assessments, especially when monitoring restoration projects (Harris, 2003; Johnston

and Crossley, 2003). Some of the measured soil biotic variables have included microbial biomass

carbon and nitrogen (Vance and Entry, 2000; Wilson et al. 2002), most probable numbers (MPN)

of microbial functional groups (Schmidt and Belser, 1982), fungal biomass estimates

(Montgomery et al. 2000), and complete community profiling (Bailey et al. 2002).

Studies from other parts of the U. S. and the world have also contributed to our

understanding of the soil community relationships. For example, a growing number of studies

have indicated that soil microbial communities with distinct functional groups inhabit different

forest types (Pennanen et al. 1999). A black pine (Pinus nigra) forest in Austria was found to

have higher relative amounts of fungi and actinomycetes in the soil microbial biomass than were

found in a neighboring oak-beech (Quercus petrea Fagus sylvatica) hardwood forest (Hackl et

al. 2005). Researchers conducting a study in England found that soil moisture, pH, and microbial

biomass levels decreased along a successional gradient from moorland to grassland to mature

pine forest (Chapman et al. 2003). Researchers in Finland found great variability within soil









microbial populations when measured beyond a 3-4 m radius (Pennanen et al. 1999). This result

could imply soil microbial measurements farther than 4 meters apart may need to be considered

as independent samples and not replications. A recent study in a longleaf pine ecosystem found

that nitrogen mineralization rates declined significantly along a moisture gradient from xeric

sandhills through the pine scrub to wet pine flatwoods, but the levels of soil carbon, nitrogen,

microbial biomass carbon, and aboveground net primary productivity (ANPP) had reverse trends

(Wilson et al. 2002). These studies illustrate the strong interactions that exist between soil

biogeochemical properties and vegetative changes in the aboveground cover type.

Fires are important agents of natural disturbance in many forest types, and especially in

maintaining southern pine forests (Outcalt, 2000). Fires have variable effects on soil microbial

biomass in forest soils where fire intensity, season, and weather play a role (Wilson et al. 2002).

Hurricanes, another prominent disturbance along the Gulf coast, can have maj or impacts on

forest structure by strong winds (Palik et al. 2002), but the associated flooding may have bigger

impacts on soil productivity, biogeochemistry and soil microbial populations (Lockaby and

Walbridge, 1998).

Anthropogenic disturbance effects from human activities in forests can also have effects on

the functioning of forest soils. In a military installation study focused on the effects of different

levels of vehicle traffic on the soil microbial community within a longleaf pine forest,

investigators found that increasing levels of traffic produced a decrease in the fungal biomass

(Peacock et al. 2001). Fertilization can have negative feedbacks. In a grassland restoration study,

fertilizer additions caused increases in the number of bacteria, but a decrease in the fungal

population. Sites with no fertilizer had larger fungal biomass levels, a greater number of

legumes, and higher plant species richness than the fertilized areas (Smith et al. 2003). Changes









in soil microbial functional groups caused by natural or human-induced disturbances can have

negative impacts on long term soil nutrient cycles.

Ecosystem health has been described in terms of nutrient retention or the ability of an

ecosystem to prevent nutrient loss (Odum, 1969). Ecosystems have been identified as "leaky"

where nitrate (NO3 ) WAS found in higher concentrations, or as nitrogen-limited or having a

"tight" nutrient cycle where the less mobile ammonium (NH4 ) ion was in higher concentrations

(Davidson, 2000). The steady-state development stage of succession (Oliver, 1981) has been

described as the time period of forest succession when the ecosystem's nutrients are held tightly

within (Odum, 1969). Vitousek and Reiners (1975) concluded the "tightest" period of nutrient

retention was during the mid-aged period when nutrients are brought into short supply by heavy

competition. They further concluded that nutrient retention in an ecosystem actually reflects

biomass accumulation patterns. They suggested that differences between net nitrogen input and

output were proportional to the rate nitrogen was incorporated into net biomass increment.

Biogeochemical equilibrium would be signified when the differences between nitrogen inputs

and outputs would be equal to zero, or during the period of late succession when net biomass

accumulation is close to zero (Zak et al. 1990). Given the relationship between biomass

accumulation and nutrient retention, the biogeochemical thresholds should be found when the

ecosystem is self-organizing or during the understory reinitiation stage of succession (Oliver,

1981).

Recent investigations have found an internal mechanism by which excessive nitrate is

conserved in wet forested ecosystems. In upland forested environments, examined within both

the temperate and tropical zones, investigators have discovered that dissimilatory nitrate

reduction to ammonium (DNRA) was a maj or pathway for transforming nitrate to ammonium,









and preventing losses from leaching or by the denitrifieation pathway (Silver et al. 2001;

Huygens et al. 2007). Through 15N tracing, investigators discovered the maj ority of any surplus

nitrate was reduced by DNRA, rather than reduced by denitrification or immobilized by soils.

The common conditions found at the research sites were wet soils, high organic carbon, and

normally nitrogen-limited environments.

We conducted our studies within longleaf pine ecosystems located along the Gulf Coast

Flatwoods zone. This coastal region is found between the panhandle community of Pensacola

and Tampa Bay, Florida. Various ecological studies have investigated the changes in plant

community composition along soil moisture gradients within the Gulf Coast Flatwoods zone, but

none have examined the soil chemical and microbial properties along a chronosequence.

Previous research has concluded that plant species richness increases along a soil moisture

gradient until an ecotone between mesic flatwoods and cypress swamps is reached (Huck, 1986;

Walker, 1993; Kirkman et al. 2001). This ecotone is the interface where one would Eind the wet

flatwoods and wet pine savanna subtypes of the coastal longleaf-slash pine flat (Messina and

Conner, 1998). There are almost 200 rare vascular plant taxa found in the great variety of

habitats classified as longleaf pine ecosystems. In addition to the majority of them being found in

Florida (Collins et al. 2001), the richest sites are found in these wet pine flats and their associated

wetlands (Walker, 1993). Wet pine flats represent more than 1 million ha in the Southeast

(Burger and Xu, 2001). Plant species richness of wet longleaf pine communities has been

positively correlated with soil productivity (Kirkman et al. 2001), and specific soil properties

(Wilson et al. 2002). Soil characteristics need to be included with plant species richness in any

restoration assessment of coastal wet longleaf pine flat ecosystems to couple functional with

structural attributes (Johnston and Crossley, 2002).









Our main obj ective was to measure soil pH, moisture content, organic matter content

(SOM), plant-available phosphorus, soil nitrogen mineralization rates (Nmin), soil microbial

biomass carbon (Cmb) and fungal biomass (Cfb) along a 110-year chronosequence to determine

the ecological traj ectory in terms of soil chemical and microbial characteristics for longleaf pine

in coastal wet pine flat communities. We specifically tested our hypothesis that this group of soil

biogeochemical indicators measured along a 110-year chronosequence would follow a pattern

similar to the biomass accumulation curve of forest succession (Vitousek and Reiners, 1975). In

response to rapid increase in growth during the early years of stand establishment, we predicted a

similar increase in net nitrogen mineralization rates, microbial biomass and fungal biomass

levels. We hypothesized that these variables would decrease at some point during the late mid-

aged phase and reach a threshold some time during the mature phase.

Materials and Methods

Study Areas

Three representative locations along Florida' s Gulf Coast Flatwoods zone (720 km) were

selected for this study. The three locations were Topsail Hill State Park, St. Marks National

Wildlife Refuge, and the Chassahowitzka Wildlife Management Area of the Florida Fish and

Wildlife Conservation Commission. At each location, four 400 m2 plOts representing each of

early, mid, and mature age classes of longleaf pine stands were laid out for vegetation (reported

in Chapter 1) and soil sampling. The different successional ages (age classes) represented a

chronosequence of 110-years.

Soil Sampling and Preparation

Soils samples (> 500 g) were taken from four (1m2) quadrats taken in each of the 400 m2

plots during September of 2005 and 2006 for general analysis. The samples were taken from the

upper 10 cm of the 'A' horizon, not including the organic layers. An additional sampling was









conducted in August, 2005, at which time a paired-soil sample was buried in place for incubation

(Eno, 1960; described later). The incubated samples were taken out during the September, 2005

sampling period. All samples were immediately stored at 4oC until analysis. A sub-sample (20g)

from all of the samples was used for determining moisture content after oven drying at 105oC for

72 hours.

Soil Chemical Analysis

The soil samples were analyzed at the University of Florida soil testing laboratory (UF

Analytical Research Laboratories), Gainesville, Florida. Soil water pH was determined from

prepared slurries using a soil-to-water ratio of 1-to-2 (EPA method 150. 1). Plant-available

phosphorus was determined with the use of Mehlich-1 extractant (H2SO4 & HCL) and measured

on an inductively coupled plasma (ICP) spectrophotometer (EPA method 200.7; Nelson et al.

1953). Soil organic matter content (SOM %) was determined by the Walkley-Black method

(Walkley, 1947).

Net Nitrogen Mineralization

Net nitrogen mineralization was determined by the buried bag technique (Eno, 1960).

Forty eight samples were collected from each reference location and the restoration site for a

total of 144. In general, one bag was buried in situ for incubation during August 2005 (Eno,

1960) and the other bag taken to the soil lab for analysis. The incubated bags were collected and

analyzed after 30 days. Mineral nitrogen was extracted from 20 g of both soil samples with 60 ml

2N KCL and placed in a shaker for one hour. They were then filtered through # 42 Whatcom

filter papers into 20 ml scillination bottles. The samples were analyzed by the University of

Florida' s Analytical Research Laboratory for ammonium (EPA method 350. 1) and nitrate (EPA

method 353.2) with a continuous auto-flow analyzer. Net mineralization was calculated as the









difference between incubated-N and initial-N (corrected for soil moisture) (Keeney & Nelson,

1982).

Microbial Biomass

Soil microbial biomass C was determined by chloroform fumigation-extraction (Vance et.

al., 1987), with the following modifications. Twelve grams of soil were sieved from soil samples

stored at 40 C and then placed in 50 ml centrifuge test tubes. Matching 12 g soil samples were

set aside in additional 50 ml centrifuge tubes as the control. The soil samples were fumigated in a

desiccator with 40 ml of alcohol-free chloroform placed into a center beaker, an additional 0.5

ml of chloroform was placed into each centrifuge tube. The top of the desiccator was pressure

sealed and vacated until the chloroform began to boil. The tubes were then incubated for 24

hours at 250C. The dessicator was then opened, resealed, and after the chloroform was reboiled,

incubated for an additional 24 hours.

The control and fumigated samples were extracted with 36 ml of 0.05 M K2SO4, Shaken

(360 rpm) on an orbital shaker for 1 hour, and centrifuged @ 6000 rpm for 15 minutes. The

supernatant was then filtered through # 42 Whatcom filter papers into 20 ml scintillation vials

and frozen until analysis. Levels of total organic carbon (TOC) were determined on a Shimadzu

TOC-VCSH analyzer (Vance & Entry, 2000). Microbial biomass carbon was calculated as:

[(fumTC ConTC) / 0.51] / (Soil Wt.) = mg C kgl dry wt. soil (Joergensen, 1996). The value of

0.51 is the conversion factor equal to the extractable portion of microbial biomass in a forest soil.

Fumigated and non-fumigated blanks were measured to correct for the chloroform and potassium

persulfate.

Soil fungal biomass levels were determined by a physical disruption method for extraction

of ergosterol from soil samples (Gong et al. 2001) with the following modifications. Six grams

of soil were mixed with 9 ml of 00C methanol and 1.9 g of glass beads into 20 ml scintillation









vials. The vials were vortexed for 30 seconds, shaken (360 rpm) on an orbital shaker for 1 hour,

and refrigerated over night. An aliquot of 1.8 ml was placed into 2 ml micro-centrifuge tubes and

centrifuged @ 11,000 rpm for 20 minutes. After extraction, the samples needed to be filtered

before running through a High Performance Liquid Chromatography (HPLC) computerized

machine. A syringe was used to remove 1.5 ml of the supernatant from the micro-centrifuge and

filtered through a 0.20 Clm fi1ter into amber colored 2 ml glass HPLC vials. The HLPC vials were

covered with aluminum foil and stored in the dark at 00C until ready to inj ect into the HPLC.

Each sample was quantified on a Beckman Coulter HPLC equipped with an UV detector,

a pump, an auto-sampler, and through a C-18 reverse-phased analytic column (4.6 x 250 mm).

The UV detector was set at 282 nm and pure methanol was used as the mobile phase at a flow

rate of 1 ml per minute. Extracts (100 Cl) were inj ected while the column pressure was

maintained at 1000 psi. Pure ergosterol (Sigma) was recrystalized in pure methanol at different

concentrations to establish a set of standards. The standard curve was constructed from a linear

regression relationship between peak area and ergosterol concentration.

Ergosterol recoveries were calculated from the difference between spiked and non-spiked

paired samples divided by the amount of ergosterol added. Under such conditions, an isolated

peak was identified from Hield samples at approximately 13 minutes, based upon the peaks

obtained from the ergosterol standards. Established from results of previous investigations, an

average conversion factor for 3.65 Clg ergosterol per mg of soil is converted to fungal biomass

(mg-l /g -1 soil) when multiplied by 220 (Montgomery et al. 2000). Fungal-to-microbial biomass

ratios were determined using a ratio of the calculated soil fungal biomass carbon and the soil

microbial biomass carbon for each sample.









Coastal wet longleaf pine flats experience long periods of standing water (Harms et al.

1998). This flooding causes changes in the biogeochemical cycling of nutrients. These forested

wetlands also contain highly acidic soils that require modifications to standard soil biochemical

analysis techniques normally used in moderate pH (6.0) wetlands. The following modifications

were necessary in order to produce good laboratory results.

The microbial biomass carbon was extracted from soils using a lower 0.05 M K2SO4

extractant instead of the standard molarity (0.5 M) for improved efficiency in these low pH soils

(Haney et al. 2001). The samples were centrifuged before filtering to reduce the high amount of

woody material found present in the soil samples. A relatively new ergosterol extraction method

by physical disruption was utilized to simplify the process for analyzing fungi in a large number

of soil samples (Gong et al. 2001). A lower conversion factor for fungal biomass was used to

account for the flooded conditions on soil fungal growth (Montgomery et al. 2000).

Data Analysis

A three stage balanced nested design was used to integrate the indicators measured at

different scales and among sites. Hypothesis testing for differences between means was

accomplished by using two-sample t-test with an alpha of .05 and a two-tailed confidence

interval. Since the monitoring of the restoration site with nine distinct reference locations

produced a dataset where the assumptions for analysis of variance (ANOVA) were not ensured,

non-parametric tests were used to detect any significant differences among the reference sites

and among the distinct age class segments (SAS, 2002).

Correlations between soil moisture, soil chemical and microbial abundances were

determined using Spearman's rank (r) correlations (Dumortier et al. 2002; SAS, 2002; Spyreas

and Mathews, 2006). Trends between variables were obtained from linear regression using the

general linear model (PROC GLM) (Yang et al. 2006; SAS, 2002). The chronosequencial trends









were enhanced by incorporating moving average smoothing (MA model) as a data filter to

reduce cyclical and seasonal variations found in the datasets for a number of the indicators

affected by climate (Platt and Denman 1975; Kumar et al. 2001; Ittig, 2004). The trend analysis

was followed by loglo data transformations where necessary to stabilize variances prior to

analysis. Partial Canonical Correspondence Analysis with multivariate regression (proc

CANCORR) was used to determine the relative contributions of the different variables to the

relationship (SAS, 2002; Fortin and Dale, 2005).

Results

Soil Types, Soil Organic Matter, and Soil pH

All three reference sites contained taxonomically equivalent soil types. All of the soils

had similar soil properties (sandy, acidic, thermic, aquic). The soils were also found to be

functionally equivalent even when compared by drainage class (Table 3-1). Soil organic matter

content (SOM) was found to increase from 1% to 4.5% as gravimetric soil moisture increased

from 20% to 60% of soil weight (Figure 3-1). Soil pH decreased from a pH of 5.0 to 4.0 as SOM

increased from 1% to 4.5% (Figure 3-2). The plant-available phosphorus tests produced too

many non-detectable samples for any meaningful results (Table 3-2).

Net Nitrogen Mineralization

Net nitrogen mineralization rates (Nmin) increased during the young age class, peaked

during the mid-age class, and then decreased after 60 years (Figure 3-3). Mean Nmin rates were

12 mg N / kg soil / month for the young age stands, 14 mg N / kg soil / month during the mid-

aged class, and 8 mg N / kg soil / month during the mature age class for the reference sites (table

3-2). The pattern for Nmin rates followed microbial biomass levels (Cmb) over the 110-year

chronosequence (Figure 3-4). Nmin rates increased from 5 mg N / kg soil / month to 20 mg N /

kg soil / month as Cmb inCTreSed from 100 mg-l C / kg soil to 1000 mg-l C / kg soil (Figure 3-5).









Nitrate production was 13.8 mg-1 NO3 / kg soil during April 2002 and 135.7 mg-l NO3 / kg soil

during August 2002. In comparison, Nitrate production was 4.7 mg-l NO3 / kg soil during April

2005 and 1.9 mg-l NO3 / kg soil during August 2005 (Table 3-3; Figure 5-3, Chapter 5).

Ammonium production was 13.4 mg-l NH4 / kg soil during April 2002 and 104.7 mg-l NH4 / kg

soil during August 2002. During 2005, ammonium production was 8.9 mg-l NH4 / kg soil during

April and 9.6 mg-l NH4 / kg soil during August (Table 3-3).

Nmin was positively related to ammonification (NH4 ) (r > 0.810; p < 0.0001) during all

three age classes, but not correlated with nitrification. Nmin became positively correlated with

soil moisture and SOM (r > 0.460 (p < 0.01) during the mid-aged class and remained so through

the mature age class (Table 3-4). Ammonium production was negatively related to nitrate

production (NO3-) during the mid-age and mature (r = 0.470; p < 0.001) age classes (Table 3-3).

Microbial Properties

Mean soil microbial biomass carbon (Cmb) lCVOIS were 275 (mg C / kg soil) for the young

age stands, 416 (mg C / kg soil) during the mid-aged class, and 339 (mg C / kg soil) during the

mature age class for the reference sites (Table 3-2). Mean soil fungal biomass carbon (Cfb) levels

were 102 (mg C / kg soil) for the young age class, 163 (mg C / kg soil) for the mid-aged stands,

and 125 (mg C / kg soil) during the mature age class at the reference sites (Table 3-2). Fungal

biomass carbon increased during the first 60 years (~200 mg C / kg soil), then decreased down to

110 years (~100 mg C / kg soil; Figure 3-6). The fungal-to-microbial biomass ratio (FB-to-MB)

decreased from a mean value of 0.4 to 0.2 during the first fifteen years after establishment, and

then increased to 0.8 at 50 years (Figure 3-7). Microbial biomass (Cmb) had a negative

relationship (r > 0.400 (p < 0.01) with soil pH during the mid-aged and mature age classes

(Table 3-3).









Discussion

The nitrogen mineralization (Nmin) process in high soil moisture conditions was dominated

by ammonium production (NH4 ), with low concentrations of nitrate being measured. The net

nitrification rates represented 50% of the production during 2002 and less than 25% during 2005.

The net nitrogen mineralization rates were 10 magnitudes greater during 2002 compared to 2005

(Table 3-3). Similar results between NO3- and NH4' Were meaSured in a study comparing xeric,

mesic, and wet longleaf pine sites in southern Georgia (Wilson et al. 2002).

When Nmin became positively correlated with soil moisture and SOM during the mid-age

and mature age classes, nitrate levels (NO3-) became negatively correlated to ammonium (NH4 )

production. The dynamics indicates a portion of the NO3~ WAS converting to NH4' during

saturated conditi ons. Thi s conditi on might b e indicating the di ssimilatory nitrate-reducti on-to-

ammonium (DNRA) process is taking place during flooded conditions. Little dinitrogen (N2) gaS

is lost to the atmosphere or NO3- by leaching when the DNRA pathway is dominant.

Flooding causes a lower redox potential (Eh < 0.6), and with a sufficient supply ofNO3

and labile carbon, DNRA became the preferred pathway over denitrification, resulting in the

enriched pool of NH4' (Stevens et al. 1998). Investigators examined the changes in nitrogen and

phosphorus availability in longleaf pine sites from wetlands through an ecotone to upland sites,

and they measured higher levels of nitrate and phosphorus taken from soils in the middle of

wetland sites than found in the ecotone or upland sites. However, the upland sites had higher

amounts of labile nitrate than the wetter sites (Craft and Chiang, 2002).

The anaerobic conditions and a high supply of non-labile nitrate in wet longleaf pine sites

are conducive to DNRA. During anaerobic conditions, the DNRA pathway provides NH4' to

plants and microbes, requiring less energy to assimilate than NO3~ aSSimilation (Silver et al.

2001). The characteristics favoring DNRA over denitrification are high rainfall, a high C:N ratio,









and a forested ecosystem that is naturally N-limited during dry conditions. The DNRA pathway

has been determined to be less sensitive to higher soil temperatures and a lower pH than

denitrification. The DNRA pathway is now considered a maj or nitrogen conservation mechanism

in humid forest ecosystems (Silver et al. 2001; Huygens et al. 2007).

One of the reasons why the nitrogen mineralization patterns closely followed microbial

biomass changes over the chronosequence (Figure 3-4) was because the nitrogen mineralization

data did not contain significantly high levels of nitrate during the 2005 growing season.

Heterotrophic bacteria and fungi that dominate the soil microbial biomass produce ammonium

from organic nitrogen. Only a few chemoautotrophic bacteria produce the maj ority of nitrite and

nitrate (Richards, 1987).

Soil nitrogen mineralization rates increased during stand establishment, but eventually

decreased after canopy closure as longleaf pine stands entered the stem exclusion phase of stand

development (Oliver, 1981). Other studies in hardwood forests have found increases in nitrogen

mineralization rates after stand replacing harvests up to 60 years, then declining to a constant

range (Zak et al. 1990).

Investigators evaluating the affects of ponderosa pine restoration treatments on

mycorrhizal fungi, determined treatments promoting graminoid and herbaceous ground cover

had a positive relationship to levels of arbuscular mycorrhizal (AM) fungi (Korb et al. 2003).

These authors also discovered a positive relationship between stand BA and levels of

ectomycorrhizal (EM) fungi (Korb et al. 2003). In an earlier experiment in a slash pine forest,

Sylvia and Jarstfer (1997) reported a strong competition that exists between AM weeds and EM

pine roots (Sylvia and Jarstfer, 1997). The implication would be that AM fungal levels were high

at harvest, declined during the first 15 years as growing EM trees crowded out the AM










groundcover. Eventually the AM fungi were replaced with EM fungi, and the overall fungal

biomass levels increased after 15 years. This pattern is similar to our results (Figure 3-7).

Phosphorus availability was very limited in these sites as indicated by the poor results.

Similar results have been reported in loblolly pine plantations throughout the South (Martin and

Jokela, 2004). Fertilization can dramatically improve biomass accumulation, but unless it is

maintained, nutrient-deficient soils can result from the fast pine growth (Adegbidi et al. 2005).

Phosphorus levels were found to be higher and P-mineralization rates lower in wet southern pine

forests (Grierson et al. 1999; Craft and Chiang, 2002). In our study, soil organic matter content

(SOM) was found to increase with soil moisture, and increased levels of SOM caused decreases

in soil pH. As soil organic matter increases, it forms complexes with Mg2+ and Ca2+cations in

solution, releasing H+ ions into soil solution from organic acids (Brady and Weil, 2002). This

relationship was confirmed by a negative relationship between SOM and soil pH. A lower soil

pH usually leads to lower nitrogen mineralization rates (Morris and Boerner, 1998). Active

bacterial respiration and microbial biomass levels substantially decline below a soil pH threshold

of 5.0, resulting in lower rates of nitrogen mineralization (Baath and Anderson, 2003). Lower

mineralization rates results in higher organic matter accumulation.

Conclusions

Nitrogen cycling was dominated by ammonium production during the wet 2005 growing

season when compared to a drier 2002. There was ammonium enrichment at the cost of nitrate

levels. This probably indicates that the dissimilatory-nitrate reduction-to-ammonium (DNRA)

pathway was prominent during the flooded 2004-2005 growing seasons. The net nitrogen

mineralization rates, microbial biomass carbon, and fungal biomass carbon increased between

the young and mid-aged classes, then decreased between the mid-aged and mature age classes.

The FB-to-MB ratios increased dramatically up to 60 years, then decreased to 110 years. Finally,









soil organic matter content (SOM), increased with soil moisture. Based upon the results, this

group of soil biogeochemical indicators follows biomass accumulation patterns and will attain

biogeochemical equilibrium after a stand age of approximately 60-70 years. The threshold

would be during the mature age class after the understory reinitiation phase of forest succession

has started.

Soil biogeochemical studies require a great amount of resources and equipment to

conduct an ecosystem-level analysis. The research could have been improved if a series of soil

samples were analyzed over a two-year period, at 3-month intervals instead of annual sampling.

However, the cost of running net nitrogen mineralization, microbial biomass and ergosterol

determinations would be quite high. Our research has shown some interesting results, but

additional research is required to explore the biogeochemistry of wet longleaf pine flats. This

would include exploring the soil organic matter accumulation vs. flooding cycles in facultative

wetland pine sites, the relationship between tree root mass and fungal biomass during longleaf

succession, and the effects of competition between mycorrhizal and saprophytic fungi during

longleaf pine development.











Table 3-1. Soil and stand properties between reference sites.
LOCATION SOIL SOIL MOISTURE TEMPERATURE DRAINAGE
GREAT TEXTURE REGIME REGIME CLASS
GROUPS (Top 10 cm)
Chassahowitzka
Wildlife
Management
Area
Psanunaquent Sandy Aquic Hyperthermic Very poorly
drained
Alaquod Sandy Aquic Hyperthermic Poorly drained
St. Marks
National
Wildlife Refuge
Psanunaquent Sandy Aquic Thermic Very poorly
drained
Alaquod Sandy Aquic Thermic Poorly drained
Topsail Hill
State Preserve
Humaquept Sandy Aquic Thermic Very poorly
drained
Alaquod Sandy Aquic Thermic Poorly drained
STAND BASAL AREA AND SOIL BIOCHEMICAL PROPERTIES (Mean Values*)
DRAINAGE STAND pH NET NITROGEN MICROBIAL FUNGAL
CLASS BASAL [ H+] MINERALIZATION BIOMASS BIOMASS
AREA RATES (mg N / CARBON CARBON
(n? /ha) kg soil / month) (mg C / kg soil) (mg C / kg soil)
Very poorly
drained 6.5a 4.4a 11.6a 374.3a 133.8a
Poorly
drained 8.3a 4.5a 9.9a 356.1a 135.3a
Means followed by the same lower case letters are not significantly different. (alpha=0.05)





Table 3-3. Soil nitrogen mineralization means (Nmin) for dry season 2002 and wet season 2005.
.Nmin
Nmin Nmin
Date (mg N / (mg NO3 Nmk
kg soil) / kg soil)
soil)
Apr-02 27.2b3 13.8b 13.4b
Aug-02 240.4a 135.7a 104.7a
Sep-02 189.1a 128.4a 60.7a
Apr-05 7.6b 4.7b 8.9b
Aug-05 11.5b 1.9b 9.6b
*Means followed by the same lower case letters are not significantly
different (alpha=0.05). The sample size for the 2002 soil data was n=180
and for the 2005 soil data was n=144.


Young
Young
Young
Young
Young
Mean
Mid-Aged
Mid-Aged
Mid-Aged
Mid-Aged
Mid-Aged
Mid-Aged
Mid-Aged
Mid-Aged
Mid-Aged
Mid-Aged
Mid-Aged
Mean
M atu re
Mature
Mature
Mature
Matu re
Mature
M atu re
Mature
Mature
Mature
Mean


2.4
8.3
23.6
22.3
5.3
12.0a
21.8
9.6
40.8
9.2
2.2
17.0
-0.7
3.5
16.4
11.4
19.1
14.0a
0.8
10.7
32.6
10.0
-0.4
6.4
7.7
5.4
6.0
3.7
8.4b


92.2
33.7
253.0
448.6
674.5
275.4a
1169.5
703.6
402.1
762.6
285.1
403.8
30.3
98.6
321.0
131.8
275.8
416.3b
33.4
519.6
753.6
511.5
132.5
241.8
1.0
570.2
83.0
82.7
338.7ab


2.75
1.56
1.89
1.96
1.69
1.97a
0.83
1.29
3.55
2.09
3.02
2.49
1.03
1.03
1.16
1.29
3.95
1.98a
1.36
0.70
3.62
3.55
4.54
0.90
1.63
1.49
1.49
1.63
2.02a


4.10
4.70
4.50
4.60
4.30
4.44a
4.50
4.70
4.40
4.30
4.60
4.30
4.60
4.80
4.00
5.30
4.10
4.51a
4.90
4.50
4.10
4.00
4.00
4.40
4.30
4.30
4.50
4.70
4.44a


0.40
ND
0.04
ND
ND

*
ND

ND

ND
ND
ND
ND

1.28
ND
0.14

0.8
ND
ND
ND
ND
ND
0.0
ND
ND
0.5


29.0
31.8
57.3
72.8
102.2
101.5a
39.8
24.1
34.0
83.2
191.1
83.5
98.5
41.9
117.1
95.7
154.4
162.9b
38.4
92.9
59.9
189.3
36.6
53.9
40.3
88.0
28.5
85.9
125.3ab


7.08
6.76
12.73
15.82
23.77
14.48a
0.03
0.03
0.08
0.11
0.67
0.21
1.00
0.43
0.36
0.73
0.56
14.45a
1.00
0.18
0.08
0.37
0.28
0.22
1.00
0.15
0.34
1.00
14.25a


* Means followed by the same lower case letters are not significantly different (alpha=
by age class was n=48.


-0.05). The sample size for the soil data


Table 3-2. Soil chemical and microbial biomass means between age classes.

Net Nmin Cmb SOM Plant Avail-
Stand Age Soil pH Ofb FB to MB
(years) AgleClass (mg '/kg' (mg / kg- Content [ H+] P (mg/kg mg/kg soil Ratio
Soil /year) / soil) (%) soil)


I


I


I


I
































































0.0 0.1 0.2 0.3 0.4 0.5 0.6


0.7 0.8


Soil Moisture Content (g H20 / g Soil dwt)



Figure 3-1. Soil organic matter content versus soil moisture as measured from 26 differently
aged stands.


Net Nmm NH4 Min NO3 Min MOiSture Soil pH SOM Cmb

Net Nmin 0.885**** 0.333*

NH4~ Min 0.342*
Mid-Aged Class (25-55)
Net Nmm NH4 Min NO3 Min MOiSture Soil pH SOM Cmb

Net Nmin 0.805**** 0.367* 0.468**

NH4~ Min -0.310* 0.513***
NO3 i
Soil pH -0.411**
Mature Age Class(60-1 10)
Net Nmm NH4 Min NO3 Min MOiSture Soil pH SOM Cmb

Net Nmin 0.860**** 0.471*** -0.413** 0.470**

NH4~ Min -0.528*** 0.449** -0.329*
Soil pH -0.412**


Table 3-4. Differences in soil biogeochemical relationships based upon Spearman rank
correlations r as stratified by forest age class (n = 48).
Prob > |r| under HO: Rho=0
Young Age Class (6-20)


Significance of the Spearman rank correlation test: blank: non-significant, *0.05 < p I 0.01,
**0.01 < p I 0.001, ***0.001 < p 5 0.0001, **** p < 0.0001.


6.0
y = 6.5506x 0.2389
5.0 -R2 = 0.49
p < 0.0001

4.0-


* *


**
eg' *













*y = -0.1615x + 4.7683
* R2 = 0.31
* P< 0.0029


** *


* ***
**


3.8
0.00 1.00 2.00 3.00 4.00 5.00

Soil Organic matter content (%/)


Figure 3-2. Soil pH versus soil organic matter content (percent) as measured from 26 differently
aged stands.


y = -0.0016x2 + 0.1273x + 5.7195
R2 = 0.46
p < 0.0258


* **


** s


*6*


0 20 40 60 80 100 120


Stand Age (Years)

Figure 3-3. Total net nitrogen mineralization, ammonification and nitrification rates (mg -1
nitrogen / kg -soil / month ) along a 110-year chronosequence as measured from
26 differently aged stands. The data was filtered with moving average smoothing to
remove seasonal and cyclic effects.












1000




1 0 0


..



.: u,


=


O


C) 0 20 40 60 80 100 120

Stand Age (Years)

*Microbial Biomass Carbon o Net Nitrogen Mineralization


Figure 3-4. Trends for microbial biomass carbon (Cmb) and net nitrogen mineralization rates
(Nmin) along a 1 10-year longleaf pine chronosequence as measured from 26
differently aged stands. The data was filtered with moving average smoothing to
remove seasonal and cyclic effects.


30


'E25 **


z20


iEi

10


S5
z

z 0


*
*


* *


* *

.*


y = 0.0157x + 6.2013
R2 = 0.31
p < 0.004


0 200 400 600 800 1,000

Microbial Biomass Carbon (mg C / kg soil)



Figure 3-5. Microbial biomass carbon versus net nitrogen mineralization rates as measured from
26 differently aged stands. The data was filtered with moving average smoothing to
remove seasonal and cyclic effects.
















y = -0.0328x2 + 3.8711x + 57.154
R2 = 0.31
*p < 0.0054


*

e *
** *


**,
*


0 20 40 60 80 100 120

Stand Age (Years)


Figure 3-6. Fungal biomass carbon ( C ) along a 1 10-year longleaf pine chronosequence as
measured from 26 differently aged stands. The data was filtered with moving average
smoothing to remove seasonal and cyclic effects.


y =0.0252x2- 5.7258x + 424.05
R =0.40
p <0.0321


c
** m300


r E, 250

S200
O
cn150
o
m 100

50


y =0.0011x 0.0483x + 0.7088
R = 0.80
p <0.0003


4


t


\ a


**
**-*-*


*
*


0 6 12 18 24 30 36 42 48
Stand Age (years)


48 54 60 66 72 78 84 90 96 102 108 114
Stand Age (years)


Figure 3-7. The fungal-to-microbial biomass ratio and fungal biomass carbon levels (means)
during the earlier and later portions of chronosequence respectively, as measured
from 26 differently aged stands along the 1 10-year longleaf pine chronosequence.









CHAPTER 4
RELATIONSHIP BETWEEN VEGETATION AND SOIL CHARACTERISTICS IN WET
LONGLEAF PINE FLATS ALONG FLORIDA' S GULF COAST

Introduction

There have been many efforts on assessing the inter-relationships between community

structure, plant species composition and soil biochemical attributes of forested ecosystems

around the globe (Goebel et al. 2001; Peacock et al. 2001; Wilson et al. 2002; Allen and

Schlesinger, 2003). For example, researchers in northwest Spain found that specific herbaceous

species assemblages were indicators for soil pH, soil organic matter levels, C: N ratios, and high

or low levels of soil nitrogen, phosphorus, potassium, calcium, and magnesium (Zas and Alonso,

2002). South American researchers compared mature natural alerce (Fitzroya cupressoides)

forests with mature mixed beech (Nothofagus-Podocarpus) forests in the Chilean Andes and

found that the mixed beech-conifer forests that contained greater tree and plant species

biodiversity, had significantly higher soil nitrogen mineralization rates (Perez et al. 1998). These

and other studies have greatly contributed to our understanding of the soil-vegetation community

relationships for various ecosystems in the U. S. and other parts of the world (Vance and Entry,

2000; Reynolds et al. 2000; Chapman et al. 2003; Korb et al. 2003; Hackl et al. 2005). However,

the reasons why soil nutrient and microbial dynamics influence community structure and

composition of the longleaf pine ecosystem of the southeastern U. S. still remains unexplored.

Longleaf pine ecosystem is one of the most threatened ecosystems in the U. S. Knowledge about

the interrelationships among soil chemical, microbial and vegetational characteristics of the

longleaf pine ecosystem may aid in restoring it to a healthy, functional ecosystem across its

rang~e.

The concept of using soil chemical and microbial properties in combination with

vegetation attributes for monitoring restoration proj ects has gained momentum in the recent past.









For example, researchers monitoring the restoration of ponderosa pine (Pinus ponderosa) forests

in Arizona explored the relationship between mycorrhizal and plant functional groups (Korb et

al. 2003). They discovered that arbuscular mycorrhizal (AM) fungi were highly positively

correlated with increases in grasses and forbs, and negatively correlated with tree cover and pine

litter. Ectomycorrhizal (EM) fungi had no response to the restoration treatments, but had a high

positive correlation to stand basal area (Korb et al. 2003). A companion study found that as

plant species richness increased primarily due to an increase in legumes and stress tolerant

plants, there was a corresponding increase in soil fungi and an abundance of fungi relative to

bacteria (Smith et al. 2003).

A growing number of studies have indicated that soil microbial communities with distinct

functional groups inhabit different forest types (Pennanen et al. 1999). A black pine forest in

northeastern Austria was found to have higher relative amounts of fungi and actinomycetes in the

soil microbial biomass than were found in a neighboring oak-beech hardwood forest (Hackl et al.

2005). Chapman et al. (2003), investigating native woodland expansion in England, found that

soil moisture, pH, and microbial biomass levels decreased along a successional gradient from

moorland to grassland to mature pine forest, but the fungal component increased. In beech

(Fagus sp.) forests of Denmark, researchers found that different fractions of coarse woody debris

supported distinct fungal species. Larger trees parts contained more fungal species, smaller

pieces had higher densities of a few species, and snags were species-poor. They concluded that

coarse woody debris should be left as whole trees compared to smaller or larger pieces to insure

high species richness in the fungal community of the forest floor (Heilmann-Clausen and

Christensen, 2004). These studies illustrate the strong interactions that exist between soil

biogeochemical properties and aboveground cover type.









The obj ective of this study was to examine the relationships between key soil chemical and

microbial properties and the overstory and understory plant characteristics of a wet longleaf pine

flat community in the Gulf Coastal Plain of Florida. We hypothesized stand volume will show a

positive relationship with soil nitrogen mineralization, which, in turn, will be driven by the

microbial community dynamics in the soil. We also hypothesized that the fungal biomass will

increase as coarse woody debris accumulated on the forest floor and the standing stock increased

over time.

Materials and Methods

Study Areas

Three reference site locations along a spatial gradient from within the Coastal Flatwoods

subecoregion of Florida (Chapter 1), sub-divided into three one-hectare blocks, representing

young, mid-aged, and mature age classes, were used in this study. The different successional age

classes represented a chronosequence of 110-years across a moisture gradient containing mesic

flatwoods, wet flatwoods, wet savannas, and bordered by cypress ponds. The three hectares

established at each reference location was scaled to match the three hectares established at the

restoration site (Chapter 5). The three locations are Topsail Hill State park, St. Marks National

Wildlife Refuge, and the Chassahowitzka Wildlife Management Area of the Florida Fish and

Wildlife Commission (Chapter 2).

Field Measurements

Each site had a cluster of three one-hectare blocks, containing stands representing each of

the three previously defined age classes. Each one-hectare block was sub-divided into four

randomly placed 400 m2 measurement plots. To assess the forest structure, tree height and

diameter-at-breast height (DBH), were measured on all trees > 10cm DBH. At least two of the

dominant trees were cored to determine stand age. Stand density (trees/ha), basal area (m2/ha)









and volume (m3/ha) were calculated from this data. In addition, the volume (m3/ha) of all snags

and coarse woody debris (CWD) were also measured (Spetich et al. 1999).

Each 400 m2 plot contained four randomly placed 1 m2 subplots for understory sampling

(Chapter 2; Figure 2-2). Percent cover of each species was assessed using the Daubenmire

method modified to estimate eight levels of percent cover (Daubenmire, 1959). Coleman

rarefaction and the Shannon-Weiner diversity indices were calculated for each stand based upon

four sub-samples (Colwell, 2006; Koellner and Hersperger, 2004).

Soil Sampling and Preparation

Soils were sampled (> 500 grams) from within the vegetation survey (1 m2) quadrats taken

in the top 10cm of the 'A' horizon. The sampling took place during August and September of

2005, and September of 2006, at each of the reference sites and the restoration test site. They

were immediately stored at 4oC until analysis. A sieved and oven dried (105oC) sub-sample (20g)

was used for determining moisture content.

Soil Chemical Analysis

The soil samples were analyzed at the University of Florida Soil Testing Laboratory (UF

ARL), Gainesville, Florida. Soil water pH was determined from prepared slurries using a soil-to-

water ratio of 1-to-2 (EPA method 150. 1). Plant-available phosphorus was determined with the

use of Mehlich-1 extractant (H2SO4 & HCL) and measured on an inductively coupled plasma

(ICP) spectrophotometer (EPA method 200.7). Soil organic matter content (SOM %) was

determined by the Walkley-Black method. The gravimetric soil water content was determined in

2005 and 2006 for all of the samples analyzed.









Mineral Nitrogen Fluxes

Net nitrogen mineralization was determined by comparing collected paired soil samples

contained in plastic bags. Forty eight samples were collected from each reference location for a

total of 144. One bag was buried in situ for incubation during August, 2005 (Eno, 1960) and the

other bag taken to the soil lab for analysis. The incubated bags were collected and analyzed after

30 days. Mineral nitrogen was extracted from 20 g of both soil samples with 60 ml 2N KCL and

placed in shaker for one hour. They were then filtered through # 42 Whatcom filter papers and

analyzed by the UF ARL for ammonium (EPA method 350.1) and nitrate (EPA method 353.2)

with a continuous auto-flow analyzer. Net Mineralization was calculated as the difference

between incubated-N and initial-N (corrected for soil moisture) (Keeney & Nelson, 1982).

Bacterial Abundance and Microbial Dynamics

Enumeration of nitrifying bacteria was determined by the most probable numbers (MPN)

method for densities of ammonium and nitrite oxidizing bacteria using a five tube dilution

(Schmidt and Belser, 1982). The ammonium oxidizing bacteria were incubated in a medium of

di-ammonium sulfate, and the nitrite oxidizing bacteria were incubated in potassium nitrite. The

tubes were incubated for 8 weeks for the first readings and 16 weeks for the final readings. A pH

indicator of bromothymol blue was used to determine pH changes caused by increased

respiration of the ammonium oxidizing bacteria. Positive readings for the nitrite oxidizing

bacteria were determined from a nitrate test reagent of diphenylamine in sulfuric acid solution

(Schmidt and Belser, 1982).

Soil microbial biomass C was determined by chloroform fumigation-extraction (Vance et.

al., 1987), with the following modifications. Sieved 12 grams of soil were taken from soil

samples stored at 40 C and then placed in 50 ml centrifuge test tubes. Matching 12 gram soil

samples were set aside in additional 50 ml centrifuge tubes as the control. The soil samples were









fumigated in 24 tubes per desiccator with 40 ml of alcohol-free chloroform placed into a center

beaker and an additional 0.5 ml of chloroform was placed into each centrifuge tube. The top of

the desiccator was pressure sealed and vacated until the chloroform began to boil. The tubes

were then incubated for 24 hours at 250C. The dessicator was then opened, resealed, and after the

chloroform was reboiled, incubated for an additional 24 hours. The control and fumigated

samples were extracted with 36 ml of 0.05 M K2SO4, Shaken (360 rpm) on an orbital shaker for 1

hour, and centrifuged @ 6000 rpm for 15 minutes. The supernatant was then filtered through #

42 Whatcom filter papers into 20 ml scintillation vials and frozen until analysis. Levels of total

organic carbon (TOC) were determined on a Shimadzu TOC-VCSH analyzer (Vance & Entry,

2000). Microbial biomass carbon was equal to [(fumTC ConTC) / 0.51] / (Soil Wt.) = mg C kg

dry wt. Soil ~'(Joergensen, 1996). The value of 0.51 is the conversion factor equal to the

extractable portion of microbial biomass in a forest soil. Fumigated and non-fumigated blanks

were measured to correct for the chloroform and potassium persulfate.

Soil fungal biomass levels were determined by a physical disruption method for extraction

of ergosterol from soil samples (Gong et al. 2001); with the following modifications. Weighed 6

grams of soil were mixed with 9 ml of 00C methanol and 1.9 grams of glass beads into 20 ml

scintillation vials. The vials were vortexed for 30 seconds, shaken (360 rpm) on an orbital shaker

for 1 hour, and refrigerated over night. An aliquot of 1.8 ml was placed into 2 ml micro-

centrifuge tubes and centrifuged @ 11,000 rpm for 20 minutes. A syringe was used to remove

1.5 ml of the supernatant from the micro-centrifuge tubes and filtered through a 0.20 Clm filter

into amber colored 2 ml glass HPLC vials. The HLPC vials were covered with aluminum foil

and stored in the dark at 0 degrees C until ready to inj ect into the HPLC. Each sample was

quantified on a Beckman Coulter HPLC equipped with an UV detector, a pump, an auto-









sampler, and through a C-18 reverse-phased analytic column (4.6 x 250 mm). The UV detector

was set at 282 nm and pure methanol was used as the mobile phase at a flow rate of 1 ml per

minute. Extracts (100 CIl) were inj ected while the column pressure was maintained at 1000 psi.

Pure ergosterol (Sigma) was recrystalized in pure methanol at different concentrations to

establish a set of standards. The standard curve was constructed from on a linear regression

relationship between peak area and ergosterol concentration. Ergosterol recoveries were

calculated from the difference between spiked and non-spiked paired samples divided by the

amount of ergosterol added. Under such conditions, an isolated peak was identified from field

samples at approximately the 13 minutes, based upon the peaks obtained from the ergosterol

standards. An averaged conversion factor for 3.65 Clg ergosterol per mg of soil translates to a

fungal biomass (mg /g soili) when multiplied by (220) (Montgomery et al. 2000). Fungal:

microbial biomass ratios were represented by a ratio of the calculated soil fungal biomass, and

the soil microbial C biomass levels for each sample.

Experimental Design and Analysis

A three stage balanced nested design was used to integrate the indicators measured at

different scales, and between sites. Since the monitoring of the restoration site with nine distinct

reference locations produced a dataset where the assumptions for analysis of variance (ANOVA)

were not ensured, non-parametric tests were used to detect any significant differences between

the reference sites and between the distinct forest age classes (SAS, 2002).

Inter-relationships between forest structural variables, understory species diversity indices,

and the soil biogeochemical variables were determined by Spearman's rank (r) correlations using

SAS 8.2 (Dumortier et al. 2002; SAS, 2002; Spyreas and Mathews, 2006). Trends between

variables were obtained from linear regression using the general linear model (PROC GLM)









(Yang et al. 2006; SAS, 2002). These trends were enhanced by incorporating moving average

smoothing (MA model) as a data filter to reduce seasonal variations found in the datasets for a

number of the indicators affected by climate (Platt and Denman 1975; Kumar et al. 2001; Ittig,

2004). The trend analysis was followed by loglo data transformations where necessary.

Results

Nitrifying Bacteria and Nitrogen Mineralization

Young forest soils at one of the reference sites, St. Marks, had numbers of ammonium

oxidizing bacteria (AOB) that were 34 times greater (14,690 / g soil) than that found in soils

from the mature sites (427 / g soil) (Table 4-1). The higher AOB numbers in the young forest

soils corresponded to lower ammonium production (0.14 mg NH4+ / kg soil/month) and higher

nitrate production (Table 4-2). Topsail Hill State Preserve also had numbers of ammonium

oxidizing bacteria that were 60 times greater in the young forested soils (240 / g soil) than found

in soils from the mature sites (4 / g soil) (Table 4-1). However, the young wet pine savanna had

very high ammonium production compared to nitrification (Table 4-2). The mesic mature forest

soils at Topsail had lower ammonium levels than the wet young forest soils (Table 4-2). The

numbers of AOB at St. Marks (14,690) were significantly larger compared to the numbers

measured at Topsail Hill (240). The higher AOB numbers in the soil under the young forest at St.

Marks resulted in lower ammonification 0.14 mg NH4+ / kg soil/month compared to the soil from

the young forest at Topsail Hill 17.9 mg NH4' / kg soil/month. St. Marks had larger numbers of

AOB in the old forest soils (427 vs. 4.0), but the level of ammonium production was smaller 2.98

vs. 4.98 mg NH4' / kg soil/month when compared with the soils from the Topsail old forest site

(Table 4-2). The numbers of nitrite oxidizing bacteria (NOB) showed differences between the

age groups (427 / g -1 soil, in young soil vs. 4 / g-l soil, in old soil), but not between the sites









(Table 4-1). The nitrate production levels in young (2.39 vs. 1.74 mg NO3- / kg soil/month) and

old (1.57 vs. 0.9 mg NO3- / kg soil/month) soils were similar between the sites (Table 4-2).

Overstory

Stand volume increased with net nitrogen mineralization (Nmin) until the volume reached

200 m3 / ha, when Nmin decreased substantially (Figure 4-1). All of the overstory stand variables

were positively correlated with microbial biomass carbon (Cmb) during the young age class, but

mean stand DBH and height were negatively correlated with Cmb during the mid-aged and

mature age classes (Table 4-3). Similar to Cmb, all of the forest structural variables were

positively correlated with fungal biomass carbon (Cfb) during the young age class, but remained

positively correlated with Cfb during the mid-aged class (Table 4-3). FB-to-MB ratios increased

with stand height during the mid-aged and mature age classes, when the mean stand height was

greater than 7.5 m (Figure 4-2). Cfb increased by more than 130% as stand BA approached 10 m2

/ ha. However, Cfb declined by 30 % as the stand BA grew from 10 m2 / ha to 20 m2 / ha (Figure

4-3). Coarse woody debris CWD was positively correlated with Cfb during the mid-aged and

mature age class (Table 4-3). Cfb increased by more than 45 % as CWD increased from 1 to 55

m3 / ha (Figure 4-4). Stand density had a positive relationship with soil organic matter content

(SOM) during the young and mid-aged class, but not during the mature age (Table 4-3).

Understory

Coleman rarefaction was positively correlated with Cmb during the young and mid-aged

class, and negatively correlated to Cmb during the mature age class (Table 4-3). The Coleman

rarefaction index decreased by 50% and Shannon-Wierner diversity index by 25% as the FB-to-

MB ratio approached 1.0 (Figure 4-5; Figure 4-6) The Coleman rarefaction index and the

Shannon-Wiener diversity index were also negatively correlated with soil organic matter content

(SOM), during the young age and mature age classes (Table 4-3).









Discussion

Net nitrogen mineralization declined at a stand volume of 200 m3 / ha which corresponds

to a stand age of 90 years (Chapter 2; Figure 2-9). This could be a stand volume threshold where

fungi and actinomycetes have become the maj or decomposers in the microbial community due to

lignin concentrations (Richards, 1987). Even in high soil moisture conditions, the forest soils

from young longleaf pine stands had significantly higher levels of nitrifying bacteria than soils

from mature pine sites. The nitrifying bacteria data confirmed that nitrification rates were higher

during the young age class than measured in the mature aged stands. The AOB numbers were

highly variable between sites, but the NOB numbers were similar. Nitrate levels were lower and

ammonium levels were higher in the soils from the mature forest sites compared to the soils from

the young forests. The higher levels of ammonium and lower levels of nitrate in mature forest

soils could be an indication of a nitrogen conserving (tighter) ecosystem (Davidson, 2000). There

was an exception with the wet young Topsail pine savanna soil that had higher ammonification

levels than the mesic mature Topsail soil. Higher ammonium levels and lower nitrification levels

have been measured in wet longleaf pine sites when compared to more xeric sites (Wilson et al.

2002). Ammonium production was higher and nitrate production was lower in the soils from the

unburned Topsail Hill sites compared to St. Marks. The larger numbers of nitrifying bacteria

measured at St. Marks NWR compared to Topsail Hill State Park were probably due to the

higher frequency of prescribed fire implemented at St. Marks. Higher nitrification rates after

prescribed fire have been measured in a number of studies (Cookson et al. 2007; Hart et al. 2005;

Wilson et al. 2002). In addition, researchers studying disturbance in a Norway spruce (Picea

abies) forest measured large enumerations of ammonium oxidizing bacteria (AOB) in sites

recently harvested, but only detected very small numbers (< 10 / gm) in mature undisturbed sites

(Paavolainen and Smolander, 1998).









The effect of forest growth on the environment represents more than creating a preference

for shade tolerant plant species or the creation of a multi-layered architecture. It also represents

the evolution of soil organic matter (SOM) inputs from an easily decomposed substrate to a

SOM complex having a higher portion of recalcitrant material. As the inputs to the soil change,

there is a corresponding change in the soil microbial community as ectomycorrhizal and

saprophytic fungi play greater roles. This relationship between the aboveground component and

the belowground biological community is important in shaping the ecological traj ectory of

ecosystems (Hackl et al. 2005).

The positive relationship between stand density and soil organic matter (SOM) through

the mid-aged class illustrates the effect of site quality on stand productivity. Stand BA and

volume had strong positive relationships with Cmb up to the mature age class (60 years+).

Correlations also showed strong positive relationships between most of the forest growth

variables (DBH, height, BA) and Cfb levels, again up to the mature age class (60 years+). These

two relationships reinforce how the rate of stand volume growth is interdependent on the rate of

organic matter decomposition and nutrient cycling (Vitousek and Reiners, 1975).

Regression analysis produced a trend showing that Cfb increased dramatically as stand

basal area decreased. A ponderosa pine restoration study produced similar results showing

positive relationships between increases in forest basal area and higher levels of ectomycorrhizal

(EM) fungi (Korb et al. 2003). The relationship between the fine root biomass of trees and EM

fungal levels has also been well established (Hendricks et al. 2006; Wallander et al. 2001; Sylvia

and Jarstfer, 1997). Cfb was also found to increase with higher accumulations of CWD.

Researchers in Demark determined that a combination of larger DBH logs and the greater

surface area of smaller diameter CWD, promoted the highest level of fungal species richness










(Heilmann-Clausen and Christensen, 2004). Whether the increase in Cfb during longleaf pine

succession was due to the size of a tree's root system (mycorrhizal fungi) or in part due to

increases in coarse woody debris accumulation (saprophytic fungi), fungal biomass (Cfb)

increased as the average stand height increased. These results indicate that both Cmb and Cfb are

important soil variables for longleaf pine flat development, but the Cfb portion of the biomass

becomes more important over time as the ecosystem requires the decomposition of larger

amounts of CWD and the improved cycling of nutrients (Hackl et al. 2005; Leckie et al. 2004;

Pennamen et al. 1999).

The relationship between the FB-to-MB ratio and the Coleman rarefaction index was

similar to Shannon-Wiener diversity index, species diversity decreased as the fungal component

increased. Both Coleman Rarefaction and Shannon-Wiener diversity H' indices were also

negatively related to SOM during the young and mature age classes. Through a restoration study

in England, researchers also found a negative relationship between native plant species richness

and soil fertility. Never the less, in contrast to our results, they found a positive relationship with

plant species richness and FB-MB ratios. The investigators attributed this positive relationship to

a greater presence of legumes in the lower fertile soils (Smith et al. 2003).

Conclusions

The maj ority of the soil biogeochemical indicators influenced longleaf pine stand growth,

and as stands developed, changes in aboveground vegetation influenced the soil biogeochemical

indicators. Net nitrogen mineralization increased with stand volume until a threshold of 200 m3

ha (stand age = 90 years). Nitrate was found to be in higher concentrations in the young forest

soils than the mature forest soils. Populations of nitrifying bacteria (AOB + NOB) were also

found to be higher in the young forest soils. At Topsail Hill, ammonium levels were found to be

higher in the wet young pine savanna soils than the mesic mature soil. Higher soil moisture









translates to lower nitrification levels. The relationships between fungi and increases in stand

height or coarse woody debris accumulation indicate a strong continual relationship between the

soil biogeochemical indicators and longleaf pine stand development. The dynamics of this

relationship might be better understood if the measured fungal biomass could have been

identified as arbuscular mycorrhizal (AM) fungi, ectomycorrhizal (EM) fungi, or saprophytic

fungi along the chronosequence. The dominance of fungi negatively affected the Coleman

Rarefaction and Shannon-Wiener diversity indices. This may have indicated a decrease in

species richness, but the functional redundancy component of ecosystem resilience has probably

been strengthened. The strong relationships between forest biomass accumulation and soil

biogeochemistry should be assessed in any monitoring event. Nitrogen cycling appears to

become tighter in mature forests at a threshold of 90 years. This condition is dependent on

mycorrhizal and saprophytic fungi dominating the soil microbial biomass.










Table 4-1. MPN enumerations of nitrifying bacteria in young and old longleaf pine forest soils.

Enumerations ( MPN g )
Site Locations Ammonium Oxidizers Nitrite Oxidizers

St. Marks Seedling site (6 yrs.) 1.4690 X 104 (0.278, 6.318) 0.4273 X 103 (0.103, 1.385)
St. Marks Mature site (100 yrs.) 0.0427 X 104 (0.103, 1.385) 0.0040 X 103 (0.005, 0.123)
Topsail Hill Sapling site (19 yrs. 0.0240 X 104 (0.047, 0.965) 0.4273 X 103 (0.103, 1.385)
Topsail Hill Mature site (100 yrs 0.0004 X 104 (0.005, 0. 123) 0.0036 X 103 (0.005, 0.123)
*All values are expressed in units of MPN per gram (wet weight) of 0 to 10-cm soil and are averages of three
replicates. Lower and upper limits in parentheses reflect 95% confidence intervals.


Table 4-2. Ammonification and nitrification in young and old longleaf pine forest soils.
Ammonification Nitrification
(mg NH, / kg soil (mg NO3 / kg
Site Locations / month) soil / month)
St. Marks Seedling site (6 yrs.) 0. 14 2.39
St. Marks Mature site (100 yrs.) 2.98 1.57
Topsail Hill Sapling site (19 yrs.) 17.9 1.74
Topsail Hill Mature site (100 yrs.) 5.98 0.9
Values expressed as mean monthly rates and based on the dry weight of soil.





Significance of the Speannan rank correlation test: blank: ne
*0.05 < p I 0.01, **0.01 < p 5 0.001, ***0.001 < p 5 0.000


on-significant,
1, **** p < 0.0001.


Stand Height 0.524**** 0.543****

Stand Density 0.394**

Stand DBH 0.513**** 0.510****
Stand BA 0.542**** 0.593****

Stand Volume 0.540**** 0.564****

CWD

Shannon-0.584****
Diversity
Coleman0.464*** -0.363**
Rarefaction
Mid-Aged Time Interval ( 25- 55 )
FB-to-MB
Cmb SOM Cfb
ratio

Stand Height -0.345* 0.296* 0.476***

Stand Density 0.509*** 0.581**** -0.358*

Stand DBH -0.401** -0.365** 0.319* 0.539****

Stand BA 0.465*** 0.585**** 0.348*

Stand Volume 0.360* 0.502*** 0.457**

CWD 0.326* 0.339*

Shannon-0.396** -0.290*
Diversity
Colman 0.351* 0.302* -0.322* -0.517***
Rarefaction
Mature Time Interval ( 60 110 )

FB-to-MB
Cmb SOM C,
ratio

Stand Height -0.646**** -0.289* 0.446**

Stand Density

Stand DBH -0.429** 0.422**
Stand BA

Stand Volume

CWD 0.326* 0.648**** 0.293*

Shannon-0.439**
Diversity
Coleman-0.348* -0.565****
Rarefaction


Table 4-3. Soil biogeochemical relationships with stand attributes based upon Spearman Rank

correlations r as stratified by forest age class (n = 48).
Prob > |r| under HO: Rho=0
Regeneration Time Interval (6 20 )
FB-to-MB
Cmb SOM Cfb
ratio













~y = 1.8739x2 35.731x + 233.45
v, R2 = 0.44
S16 I p < 0.0038


S 12
0 #




*1





0 50 100 150 200 250 300

Stand Volume (m3 / ha)

Figure 4-1. Net nitrogen mineralization versus stand volume as measured from 26 differently
aged stands.







1.2
y = 0.0041X2 0.0621x + 0.4083
1 -I R2 = 0.41 e
p < 0.0017


* ,


*


*i


Stand Height (meters)


Figure 4-2. The fungal biomass (FB)-to-microbial biomass (MB) ratio versus stand height as
measured from 26 differently aged stands.













350-

300-



200-

S150 *

10 ** y = 9.5282x + 93.367
e R2 = 0.35
50 0 r p < 0.0154


0 5 10 15 20 25

Stand BA (m2 / ha)

Figure 4-3.Fungal biomass carbon (Cfb) versus stand basal area (BA) as measured from stands
grouped within the mid-aged and mature age classes only.





300

y = 1.5077x + 105.9
250 -1 R2 = 0.33 *
*p < 0.0029
S200-


o 150 -1 r L

S100 -1


50 -1



-5 5 15 25 35 45 55

Coarse Woody Debris (m3 / ha)

Figure 4-4. Coarse woody debris accumulation versus fungal biomass carbon (Cfb) as measured
from 26 differently aged stands. The data was filtered with moving average
smoothing to remove seasonal and cyclic effects.














































y = -0.9518x + 2.2266
R2 = 0.37
p < 0.0017





* *


0 0.2 0.4 0.6 0.8


y = -10.243x + 18.279
*R2 = 0.37
e ,* t a p < 0.0025




-*
*


0 0.2 0.4


0.6 0.8


FB-to-MB ratio

Figure 4-5.Coleman Rarefaction index versus the fungal biomass (FB)-to-microbial biomass
(MB) ratio as measured from 26 differently aged stands. The data was filtered with
moving average smoothing to remove seasonal and cyclic effects.


2.50




2.00




1.50


1.00


FB-to-MB ratio

Figure 4-6. Shannon-Wiener diversity H' index versus the fungal biomass (FB)-to-microbial
biomass (MB) ratio as measured from 26 differently aged stands. The data was
filtered with moving average smoothing to remove seasonal effects.









CHAPTER 5
MONITORING RESTORATION SUCCESS USING VEGETATION AND SOIL AS KEY
INDICATORS: CASE STUDY OF A WET LONGLEAF PINE FLATS RESTORATION
PROJECT

Introduction

Ecosystems ecology requires the integration of structural and functional characteristics for

developing a holistic understanding of ecological change caused by natural or anthropogenic

disturbance. However, these two characteristics of ecosystems have generally been studied

separately along vegetative and geochemical gradients (Muller, 1998). Soil chemical and biotic

properties need to be included as indicators with forest structural and vegetative compositional

measurements for the integration to take place (Johnston and Crossley, 2002). Soil microbial

community analysis also provides a means to measure how responsive soils are to disturbance

and restoration treatments (Harris, 2003). Additionally, the inter-relationships between

vegetation and soil characteristics have also been identified and used to assess site quality. In

pine plantation research, specific soil properties have been found to be associated with the

growth of specific tree and plant species. Similarly, certain groups of plant species may indicate

specific soil conditions (Burger and Kelting, 1999; Zas and Alonzo, 2002). This combination of

above and below ground data can also be used to ecologically verify if a restoration site falls

within the spatial gradient of the reference sites (Goebel et al. 2001).

The research reported in Chapter 3 determined that net nitrogen mineralization rates

increased until 90 years. It was also determined that the soil fungal-to-microbial biomass ratio

increased with stand growth and total woody debris accumulation. Finally, soil fungal biomass

increased with mean stand height (Chapter 4). These results show strong relationships exist

between stand development and soil biochemical dynamics. This paper examines a case study of

a restoration proj ect, hereafter referred to as the Pt. Washington restoration proj ect in Florida.









The Pt. Washington restoration project was initiated in 2001 to convert a slash pine

plantation to a longleaf pine ecosystem. The effects of low-level herbicide applications on

longleaf pine development and understory species richness were evaluated. The central goal of

this experimental application of herbicides was to determine which herbicide, as a substitute for

fire, would produce the best results for longleaf pine seedling survival and growth, understory

plant species richness and composition, and soil nitrogen mineralization.

Herbicides are currently being used in restoration proj ects throughout the United States

for promoting the establishment of native grasslands, assisting in the control of exotic invasive

species as part of integrated pest management programs, for weed control during early forest

stand development, and to combat eutrophication from unwanted plant growth in aquatic

ecosystems (Sigg, 1999). Yet, many environmentally-sensitive managers and scientists are

hesitant to support the use of herbicides making it imperative that the correct herbicide is used in

the proper environment, with the lowest feasible application rates (Murphy, 1999; Sigg, 1999).

What primary factors make the restoration of coastal longleaf pine flats unique compared

to other pine ecosystems? First, longleaf pine regeneration is dependent on a grass stage when

the pine seedlings are able to survive light surface fires and during fierce vegetative competition

(Boyer and Peterson, 1983; Boyer, 1990). Longleaf pine seedlings have been known to stay in

the grass stage from 5-20 years. This protective state can make the growth rates of longleaf pines

unpredictable (Haywood, 2000). Secondly, although many longleaf pine ecosystems are found

on a variety of upland sites (Peet and Allard, 1993), coastal wet pine flats are unique because

they are located on low, rain-fed coastal terraces where weather patterns maintain high soil

moisture conditions for extended periods during the growing season (Messina and Conner,

1998).









The longleaf pine grass stage becomes the critical factor in the amount of time the pine

remains in the seedling stage when prescribed fire is restricted. One study found that the

application of a hexazinone herbicide helped to release longleaf pine seedlings from fierce

vegetative competition, enhancing conditions for leaving the grass stage (Haywood, 2000). A

recent study of herbicides in an old field restoration proj ect found a higher first-year seedling

survival rate and a higher percentage of seedlings out of the grass stage (2nd year) than with no

herbicide treatment (Ramsey et al. 2003). There have also been studies to control fuel loads by

different fuel reduction techniques, including herbicide applications. An earlier study found that

fire and mechanical removal of fuels had immediate but short-term impacts on reducing fireline

intensity levels, but herbicide applications had longer term affects on reducing fuel levels,

starting in the second year after treatment and lasting up to six years (Brockway et al. 1998).

Herbicides can be the short-term substitute for fire when applied in the correct manner, and

within the correct environment.

In 2004, the Pt. Washington restoration proj ect was expanded to include the development

of a monitoring program, the addition of bio-indicators for evaluation including soil microbial

dynamics, and the use of reference sites for establishing a chronosequence for the restoration site

evaluation. We predict the overstory, understory, and soil biogeochemical indicators will be

useful for ecologically classifying the Pt. Washington restoration site as a mesic flatwoods, wet

flatwoods, or wet savanna. We will use them for trying to detect differences among the four

herbicide treatment effects applied on the restoration site. Finally, we will use them to predict the

development or ecological traj ectory in wet longleaf pine flat restoration. The predicted values

will be presented with pine growth results on the effects of herbicide treatments applied in the

second year after planting compared to first year only, consecutive herbicide treatments (1st &









2nd Year), and whether an early or late spring application changes the effects (McCaskill data,

2006).

Materials and Methods

Pt. Washington Restoration Site

The longleaf pine restoration proj ect is located on the Point Washington State Forest

(30020'l16.04" N, 860 4'l19.22" W) in southern Walton County, Florida. This coastal wet pine

flats site was approximately a 4-ha, 26 year-old slash pine plantation having a basal area of 1.85

m2 / ha and an average dbh of 19.1 cm as measured in 1991. It contained scattered residual

longleaf pine saplings and poles as part of the stand' s stocking. The adj acent area makes up

approximately 15 ha of mixed slash and longleaf pine surrounding a cypress dome, and

contained within the greater 6800 ha Pt. Washington State Forest. The understory plant

community was dominated by broomsedge, a smaller component of wiregrass, and a group of

shrub species highlighted by gallberry, saw palmetto, running oak, and dangleberry (Gayhtssacia

fr~ondosa). The annual precipitation averages 1500 mm with most of it occurring during the late

summer. The soil belongs to the Leon series and classified as sandy, siliceous, thermic aeric

Alaquods. This soil series signifies that they are very poorly drained soils (Jokela and Long,

1999). Since this pine flats forest is found very close to the coast (within 3 kilometers), its soils

were formed on sandy quaternary parent material derived from marine deposits (Stout and

Marion 1993). These soils are described as highly weathered, acidic, infertile substrates

(LaSalle, 2002).

The surrounding area consists of wet pine savannas and wet flatwoods sites that are found

within Florida' s Gulf Coast Flatwoods zone (Chapter 1; Griffith, 1994). Florida' s Gulf coastline

is continuously shaped by active fluvial deposition and shoreline processes which promote and

maintain the formation of beaches, swamps and mineral flats. The local relief is less than 20 m in









elevation. The annual precipitation ranges from 1300-1,600 mm, and the average annual

temperatures vary between 190 to 210 C. The growing season is long, lasting 270-290 days. The

parent material consists of marine deposits containing limestone, marl, sand, and clay. The

dominant soils are Aquults, Aquepts, Aquods, and Aquents. These acidic soils have thermic and

hyperthermic temperature regimes and an aquic moisture regime. The soils are poorly drained,

deep, and moderately textured. The dominant vegetative cover consists of longleaf-slash pine

forests with a smaller component of Choctawhatchee sand and/or pond pine (McNab and Avers,

1994; Parker and Hamrick, 1996).

As the first step towards restoring the Pt. Washington site back to longleaf pine, the

overstory of slash pine was clearcut during August 2001. The site was roller chopped once and

prescribed burned in October 2001. There was no existing bedding or any other hydrological-

modifying practice applied. A randomized complete-block design (RCB) with six blocks was

used to measure the effects of four vegetation-control chemical mixtures on the dynamics of the

understory plant species and pine growth and survival. Five plots were randomly located within

each of the six blocks. All treatment plots were 26.6m x 24.4 m, and included at least a 3-m

buffer strip between plots. The six blocks with buffers make up approximately 3.5 ha within the

4 ha clearcut.

In December 2001, one-year-old containerized longleaf pine seedlings were hand-planted

at 3.1 x 1.8 m spacing. Seedlings were planted in rows to facilitate the application of herbicides.

In March 2002, four herbicide treatments Sulfometuron methyl (methyl 2-[[[[(4,6-dimethyl-2

pyrimi dinyl)amino] carb onyl] amino] sulfonyl ]b enzoate) at 0.26 ai kg ha- Hexazinone (3 -

cyclohexyl-6-(dimethylamino)- 1-methyl-1 ,3,5-triazine-2,4(1H,3H)-dione) at 0.56 ai kg ha- ,

Sulfometuron (0.26 ai kg ha- ) + Hexazinone (0.56 ai kg ha- ) mix, and Imazapyr (4,5-dihydro-









4methyl-4(1 -methylethyl)-5-oxo-1l-H-imidazol2-yl-3 pyridinecarboxylic acid) at 0.21 ai kg ha l,

were applied in a 1.2 m band over the top of seedlings using a knapsack sprayer. In each block,

one treatment plot was kept herbicide-free as a control plot (Ranasinghe, 2003).

Pine Survival and Growth

Pine survival and growth (root collar diameter and height) were monitored at the end of the

growing season every year, through 2006. Seedling height was measured using a ruler, from the

soil surface to the top of the bud. Root collar diameter (RCD) was measured using a digital

caliper. Stem volume index (SVI) was calculated with the measured RCD and height data.

Vegetation Sampling

A preliminary vegetation survey was conducted (June 2001) prior to overstory harvest and

site preparation to assess the initial percent cover of understory species. After study

establishment and herbicide application, four vegetation surveys were conducted. Two randomly

selected Im2 quadrats were sampled within each treatment plot and the same location was

revisited for subsequent surveys. In every survey, all plants found within the quadrat were

identified to species and assigned to shrub, graminoid, forb, or fern vegetation classes. Percent

cover was ocularly estimated by species using the modified Daubenmire scale (Daubenmire,

1959). In addition to percent cover, the number of stems and average stem height were collected

for the woody understory species. These plant surveys were conducted concurrently at the

reference sites (described below) during the 2004 growing season. Coleman rarefaction and the

Shannon-Weiner diversity indices were calculated for each stand (Colwell, 2006; Koellner and

Hersperger, 2004). The assemblage pathway for the plant community was determined from these

measurements over time using Canonical Correspondence Analysis (CCA) ordination (Palmer,

1993).









Reference Sites

Three representative locations along a spatial gradient from Pensacola to Tampa Bay (720

km) sub-divided into three one-hectare blocks, representing young, mid-aged, and mature age

class; were used in this study. The different stages (age classes) represented a chronosequence of

110-years. The three locations are Topsail Hill State Park, St. Marks National Wildlife Refuge,

and the Chassahowitzka Wildlife Management Area of the Florida Fish and Wildlife

Commission.

A three stage balanced nested design was used to integrate the indicators measured at

different scales, and between sites. Each reference site had a cluster of three one-hectare blocks

containing stands that represent young, mid-aged, and 100' year-old age class. Each one-hectare

block was sub-divided into four randomly placed 400 m2 meaSuring plots where forest structure

and coarse woody debris (CWD) were determined. Within each 400 m2 Subplot, vegetation was

inventoried on four randomly placed 1 m2 quadrat using the same modified Daubenmire scale

method utilized at the restoration site.

Soil Sampling and Preparation

Soils were sampled from within the vegetation survey quadrats taken from the top 10cm, at

each of the reference sites and the restoration test site during August of 2005, and September of

2005 and 2006. They were stored at 4o C until analysis. Sub-samples were sent to the University

of Florida soil testing lab for analysis of soil pH by prepared slurries using a soil-to-water ratio

of 1-to-2 (EPA method 150.1i), organic matter content (%) by the Walkley-Black method, and

plant-available phosphorus by the use of Mehlich-1 extractant (H2SO4 & HCL) and measured

on an inductively coupled plasma (ICP) spectrophotometer (EPA method 200.7). Soil microbial

biomass was determined by chloroform fumigation-extraction extraction (Vance et. al., 1987).

Net nitrogen mineralization rates were estimated from in-situ incubation of soil samples (Eno,










1960). Fungal biomass levels were determined by soil ergosterol analysis (Gong et al. 2001).

Fungal-to-microbial biomass ratios were calculated along the gradient (Montgomery et al. 2000).

A sieved and dried (105oC) sub-sample was used to determine moisture content.

Data Analysis

Pine survival and growth

Pine survival, RCD, and height data collected during five growing seasons were analyzed

using analysis of variance (ANOVA) within the framework of a randomized complete block

design (RCBD) using JMP IN version 5 (SAS Institute, Inc.). Height and RCD comparisons

were made separately for seedlings in the grass stage (GS) and out of the grass stage (OOGS)

using a threshold height of 12 cm (Haywood, 2000). The study addressed only the main effects

of herbicide treatment, and tests of these effects were not dependent on the assumption of no

treatment x block interaction. Block effects were therefore treated as random effects in a

univariate ANOVA model with two independent variables: 'treatment' with 'Block&Random' as

a covariate. Data were log-transformed where necessary to meet the assumptions of ANOVA.

Significant differences between treatments were separated with the Tukey-Kramer HSD test.

Following a prescribed fire in February of 2007, post-fire seedling mortality was assessed in

June 2006. Post-fire survival was analyzed with ANCOVA, using pre-fire survival as a covariate

(Freeman, 2008).

Understory

The effects of treatments on stem counts and heights of the maj or shrub species were also

analyzed using ANOVA for a randomized complete block design. The study addressed only the

main effects of herbicide treatment, and tests of these effects were not dependent on the

assumption of no treatment x block interaction. Block effects were therefore treated as random

effects in a univariate ANOVA model with two independent variables: 'treatment' with









'Block&Random' as a covariate. Data were log-transformed where necessary to meet the

assumptions of ANOVA. Significant differences between treatments were separated with

Tukey's HSD or Hsu' s MCB. Post-fire treatment differences were analyzed with ANCOVA,

using pre-fire distributions as a covariate (Freeman, 2008; Ranasinghe, 2003).

Biogeochemical indicators

A three stage balanced nested design was used to integrate the indicators measured at

different scales, and between sites. Significant treatment effects on the biogeochemical indicators

(oc=0.05) were also compared with the control using Dunnett' s t-test for multiple means

comparison. Hypothesis testing for differences between means was accomplished by using two-

sample t-test with an alpha of .05 and a two-tailed confidence interval. Since the monitoring of

the restoration site with nine distinct reference locations produced a dataset where the

assumptions for analysis of variance (ANOVA) was not ensured, non-parametric multiple and

linear regression, and multivariate Canonical Correspondence Analysis (CCA) tests (ter Braak,

1994) were used to analyze for similarities and differences between the reference sites and

between the distinct age class segments using SAS version 8.2 (SAS, 2002). For identifying

which variables contribute the most to a given relationship, partial Canonical Correspondence

Analysis using univariate multiple regression (PROC CANCORR) was used to determine the

relative contributions of each indicator (Fortin and Dale, 2005; SAS, 2002).

Trend analysis was enhanced by incorporating moving average smoothing (MA model) as

a data filter to reduce cyclical and seasonal variations found in the datasets for a number of the

indicators affected by climate (Platt and Denman 1975; Kumar et al. 2001; Ittig, 2004). The trend

analysis was followed by logl0 data transformations where necessary.









CCA multivariate analysis functions by relating a primary matrix of plant species

abundance data with a secondary matrix of environmental or soil data. PC-ORD, a PC-based

program (McCune and Meffrod, 1999) containing an algorithm for Canonical Correspondence

Analysis (CCA), was used to examine the overall spatial structure of the individual reference

sites, the restoration site with the understory plant species along vectors (gradients) for soil

chemical, net nitrogen mineralization, and soil microbial values found among the study sites

(Heady and Lucas, 2004) (Palmer, 1993). Linear combinations of environmental variables are

used to maximum the separation of plant species along four dimensional axes. Site scores are

derived from the weighted averages of the associated species scores. The sites are located in the

biplot where the center of the associated species cluster exists. Community structure is

illustrated by the influence of different environmental variables on its ordination (ter Braak,

1994).

Plant species indicator analysis (IndVal) was used to measure the level of relationship

between a given plant species to categorical units such as pine flat subtypes or age class. It

calculates the indicator value d of species as the product of the relative frequency and relative

average abundance in each categorical cluster. Indicator species analysis was used to attribute

species to particular environmental conditions based on the abundance and occurrence of that

species within the selected group. A species that was a "perfect indicator" was consistent to a

particular group without fail. Indicator values range from 0 to 100 with 100 being a perfect

indicator score. Because indicator species analysis is a statistical inference, a test of significance

was applied to determine if species are significant indicators of the groups to which they are

associated (Dufrene and Legendre, 1997). This was achieved by the Monte Carlo permutation

test procedure (1000 runs) where the significance of a P-value was determined by the number of










random runs greater than or equal to the inferred value (oc=0.1). Accuracy was defined from the

binomial 95% confidence interval: p +/- accuracy (Strauss, 1982).

Growth predictions were determined from linear regression using the general linear model

(PROC GLM) (Yang et al. 2006). The multiple regression model selection procedures R-

squared, Backward Elimination, and Mallow Cp were used to determine the combination of

indicators for prediction of each variable. The results from regression analysis were based on

best model selection criteria of minimizing Mallow Cp and maximizing R2 and included only

those indicators having a biological significance level of p < 0.05 (SAS, 2002).

Results

Ecological Classification

The Pt. Washington restoration site can be classified ecologically as a wet pine flatwoods

subtype of the coastal pine flat based upon the results from Canonical Correspondence Analysis

(CCA) ordination, indicator species analysis (IndVal), and pre-harvest stand data. Canonical

Correspondence Analysis ordination indicated the maj ority of the plots measured at Pt.

Washington fall in between the environmental patterns (moisture05, pH, SOM) (Table 5-1) for

mesic flatwoods and wet flatwoods measured at the reference sites (Figure 5-1). Indicator species

analysis produced results showing that gallberry was the indicator for both wet flatwoods and the

Pt. Washington restoration site (Table 5-2). When the data were analyzed by age class, the

ordination produced the same vectors of moisture05, soil pH, and soil organic matter content

(SOM), but with stronger results.(Table 5-3). CCA ordination did not show any clear separation

along age class (Figure 5-2). Indicator species analysis did produce results showing that the

restoration site had similar plant species as the young age class of the reference sites (Table 5-4).

Witch grass and blue stem grass were species indicators for the young age class, while witch

grass and wiregrass were found to be the species indicators of the Pt. Washington restoration site









(Table 5-4). The means for each of the soil biogeochemical variables measured at the restoration

site were found to be within the range of mean values measured at the reference sites, except for

the significantly higher soil microbial biomass levels (Cmb) (Table 5-5). The seasonal trend for

nitrogen mineralization fluxes over a 14 month period was for the rates to increase from winter

through spring, to peak during the middle of August, and to decline through the fall and winter

(Figure 5-3).

Pine Growth and Vegetation Control

A concurrent study produced results from five years of pine growth and four years of

vegetation surveys showing imazapyr and sulfometuron-hexazinone herbicide treatments

significantly reduced longleaf pine seedling survival after four growing seasons. Imazapyr,

followed by hexazinone treatments produced significantly higher numbers of pine seedlings in

the out-of-the-grass stage when compared to the other treatments. Imazapyr also produced

significantly taller pines in the out-of-the-grass stage compared to the other trees. Imazapyr

treatments also resulted in the best control of the overall cover (%) and stem counts of the maj or

shrub species, while producing the highest level of herbaceous richness (Freeman, 2008). From

the same four years of pine data (2002-2006) both imazapyr and hexazinone produced better pine

growth when applied during the second growing season compared to the first (Table 3-6). They

both had higher survival rates as indicated by higher stand densities. Imazapyr produced the best

pine growth of all the treatments when applied during April instead of March. Hexazinone

produced better pine growth when applied in March (Table 3-6; McCaskill data, 2006).

Treatment Effects-Biogeochemical Indicators

Imazapyr produced the highest monthly mean nitrogen mineralization rates while

sulfometuron methyl treatments produced the lowest monthly rates. Most of the herbicides

increased the nitrogen mineralization rates, but imazapyr was the only treatment to produce









statistically significant higher levels of net nitrogen mineralization when compared to the control

(Figure 5-4). This difference was more pronounced for the ammonifieation data (Figure 5-5).

The sulfometuron methyl mixed with hexazinone treatment produced a higher mean than

imazapyr for the nitrifieation data (Figure 5-6). Only the sulfometuron methyl treatment

produced significantly lower microbial biomass levels when compared to the control (Figure 5-

7). Two years of herbicide applications resulted in a significant increase in the soil microbial

biomass carbon when compared to a single year application (Figure 5-8). The mean microbial

biomass carbon levels were higher at the Pt. Washington restoration site than any of the

reference sites (Table 5-2; Figure 5-9). Sulfometuron methyl also produced the lowest levels of

fungal biomass, although not significantly different (Figure 5-10). Fungal biomass carbon (Cfb)

levels failed to detect significant differences among any of the treatments (Figure 5-10).

The predicted values for mean stand DBH, stand density, and stand basal area, were close

to the actual values (Table 5-7). Predicted values involving stand height were different than the

actual restoration site.

Discussion

The vegetative and soil biogeochemical variables collected from the reference sites were

effective for ecologically classifying the restoration site at Pt. Washington. They were able to

determine the pine subtype and the age class. The environmental gradients as evaluated by the

soil biogeochemical indicators were stronger determinants of ecosystem conditions than was age

(Figure 5-1; Figure 5-2). The power of the soil indicators can be realized by the results of the

CCA ordination of the sites along the environmental axes and the plant species indicator analysis

(Figure 5-1; Table 5-2; Table 5-4).

An analysis of all of the treatment effects indicated that Imazapyr produced the best

improvements in pine seedling development and vegetative control while having the smallest










impact on the natural patterns of understory herbaceous richness. These gains are offset by

Imazapyr producing one of the lowest pine seedling survival rates. The survival rate can be

improved if the herbicide is applied at the beginning of the second growing season during April

instead of March (Table 5-6).

Most herbicides are readily broken down by soil microbes causing an increase in their

numbers and activity (Haney et al. 2002). Some researchers have found certain herbicides cause

a reduction in microbial biomass accompanied by an increase in nitrogen mineralization rates

(Busse et al. 2006). The decrease in microbial biomass was attributed to a corresponding

decrease in organic matter inputs from the vegetative control, and not from direct microbial

mortality. In any case, the general response following the application of herbicides has been an

increase in the soil nitrogen mineralization rates (Li et al. 2003).

If Imazapyr, a leucine, and isoleucine protein inhibitor, was the only treatment to produce

significantly higher net nitrogen mineralization rates when compared to the control, then some

factor must have partially interfered with the affects of the other chemical treatments on nitrogen

cycling. The factor of interference may be tied to Imazapyr being the only chemical amongst

this group of herbicides to be currently registered for use in aquatic systems (Langeland et al.

2006). The Leon soil series found on this restoration site has a moderate soil leaching rating and

a high soil runoff rating for pesticide selection (Obreza and Hurt, 2006). The concerns for

hexazinone, an photosystem II quinone inhibitor, are mobility in soils and persistence in water. It

was also found to inhibit ammonification and promote denitrification, dominant transformations

during flooding events (Vienneau et al. 2004).

Sulfometuron methyl, an acetolactate synthase inhibitor, has been found to quickly move

off-site when applied to sites in contact with wetlands (Michael et al. 2006). The chemical has









also been found to be toxic in low concentrations to many strains of pseudomonas, a maj or

heterotrophic bacteria commonly found in forest soils (Boldt and Jacobsen, 1998). This mortality

was attributed to the acetolactate synthase inhibition (ALS) property of sulfometuron methyl

(Whitcomb, 1999). This finding might explain the reduction in microbial biomass found in our

experiment from applying sulfometuron methyl.

The chemical properties of hexazinone and sulfometuron methyl limited the treatment

effects on this coastal wet pine flat when flooding and the associated high water tables were

present. Nitrification was impacted more than ammonification by excessive water from flooding.

This condition might explain why the sulfometuron methyl-hexazinone treatment had a

significant difference with the control in the nitrification data, but not the net nitrogen

mineralization or ammonification data. The effect of soil moisture content on herbicides was

observed when comparing the ammonification treatment data with the nitrification results

(Figure 5-3; Figure 5-5; Figure 5-6). The results show microbial biomass measurements are able

to detect differences between sites where herbicides have and have not been applied. These Cmb

measurements were also sensitive to the number of herbicide applications used on a given site.

Fungal biomass carbon did not detect any differences among the treatments. Previous studies

have failed to detect any herbicide effects on fungal communities (Busse et al. 2004).

A primary reason for the fungal biomass measurements failing to detect statistically

significant differences between the treatments may be attributed to the time since the treatments

were applied. Most of the individual effects of herbicides on microbial biomass levels are greatly

reduced beyond two years after application (Li et al. 2003). With the exception of the Pt.

Washington nitrogen studies, the soils were collected and analyzed for fungal and microbial

biomass 40 months after the second year treatments. The predictions were generally good except









for height and volume estimates (Table 5-6). Mean stand height values were skewed due to a

group of the 400 m2 forest structure plots measured within the young age class containing

naturally regenerated stands. These naturally regenerated stands are dominated with larger

saplings, poles and some small sawlog-size trees, causing the predicted values for height and

volume in a 6-year old stand to be exaggerated.

Why was Imazapyr more effective than the other herbicide treatments in reducing

vegetation competition without significantly impacting natural patterns of understory succession

within this wet flatwoods site? The answer to this question goes back to achieving the central

goal of this experiment. The Point Washington State Forest restoration site suffers from

extensive seasonal flooding and drought, which adds to pine seedling mortality and complicates

the selection of the proper herbicide for vegetation control. Imazapyr is a broader spectrum

herbicide (less selective) and more effective at controlling perennial woody species. These

properties are critical in mimicking fire effects. Secondly, Imazapyr is more persistent in wet

sandy soils than the other herbicide treatments. This is also a critical factor in wet longleaf pine

flat sites where the water table is constantly near the surface and the effects of herbicide

treatments can be reduced by flooding.

Conclusions

The Pt. Washington restoration site contains elements of mesic flatwoods, wet flatwoods,

and wet savannas. However, based upon CCA environmental ordination, plant species indicator

analysis, and pre-harvest stand data it is a wet flatwoods site. These multivariate techniques were

also useful in determining similarities between the Pt. Washington restoration site and the young

age class data of the reference sites. Imazapyr was the best herbicide treatment for this site based

on its ability to control shrubs and remain effective during flooding events. In general, herbicide

use increased nitrogen mineralization rates, but imazapyr was the only treatment to produce









statistically significant higher levels of net nitrogen mineralization when compared to the

control. Both imazapyr and the sulfometuron methyl-hexazinone treatments had a significant

difference with the control in the nitrification data. The herbicide-treated restoration site had

higher soil microbial biomass carbon levels than the reference sites. Two years of herbicide

applications increased soil microbial biomass carbon over a single application. There was an

indication that sulfometuron methyl treatments caused soil microbial mortality. Higher nitrogen

mineralization rates at Pt. Washington were negatively correlated with both of the species

diversity indices. The net nitrogen mineralization data proved effective at detecting differences

between the herbicide treatments. Soil microbial biomass carbon was sensitive to the amount of

herbicide applied. The predictions were generally good except for height and volume estimates.

Mean stand height values were skewed due to a group of the 400 m2 forest structure plots

measured within the young age class containing naturally regenerated all-aged stands.











Table 5-1. Correlations and biplot scores for the biogeochemical variables by pine flat type.
Correlations* Biplot Scores
Variable Axis 1 Axis 2 Axis 3 Axis 1 Axis 2 Axis 3
Moisture 05 -0.772 0.245 -0.194 -0.351 0.082 -0.058
Soil pH 0.358 -0.875 0.272 0.163 -0.292 0.081
SOM -0.835 0.136 -0.477 -0.380 0.045 -0.142
NetNmin -0.087 -0.297 -0.349 -0.039 -0.099 -0.104
MBc -0.048 0.018 -0. 026 -0.022 0.006 -0.008
FBc -0.291 0.124 0.539 -0.132 0. 042 0.160
FBc:MBc 0.445 0.144 -0.302 0.202 0.048 -0.090
*The Pearson correlations are "intraset correlations of ter Braak (1986).


Table 5-2. Plant Indicator Values (IndVal) (percent of perfect indication) with associated
biogeochemical variable by pine flat type. P-values represent the proportion of
randomized runs (1000) equal to or less than observed values (oc=0.1).
Pine Subtype Plant Species Pine Subtype
Mesic Wet Wet SD P- Veg
Flatwoods Savanna Value Type
Mesic Smilax pumila 25 1 5 4.69 0.038 Vine
Hypericum 17 1 0 3.08 0.024 Forb
hypericoides
Gaylussacia fr~ondosa 16 0 4 3.30 0.057 Shrub
Pteridium aquilinum 12 0 1 3.00 0.066 Fern

Wet Lachnanthes caroliana 0 52 4 3.57 0.001 Forb
Flatwoods
Arisitida beyrichiana 0 36 0 3.51 0.001 Grass
Dichanthelium ovale 6 36 7 4.41 0.007 Grass
Cyperus 1 11 1 2.67 0.088 Grass

Wet Savanna Ilex glabra 19 13 38 3.55 0.009 Shrub
Scleria 17 3 29 3.31 0.014 Grass

Pt. Blocks Blocks Blocks SD P- Veg
Washington 1&2 3&4 5&6 Value Type
Blocks 1 &2 Arisitida beyrichiana 34 10 7 5.97 0.039 Grass
Tragia urens 13 0 0 2.11 0.016 Forb
Blocks 3 & 4 Smilax pumila 0 25 0 5.58 0.001 Vine
Pteridium aquilinum 2 18 0 3.42 0.013 Fern
Blocks 5 & 6 Scleria 0 3 25 6.16 0.078 Grass
Lachnanthes caroliana 0 0 25 4.82 0.024 Forb
INDICATOR VALUES (% of perfect indication based on combining the values for relative abundance and
relative frequency) n=48













CORRELATIONS AND BIPLOT SCORES (7 Biogeochemical Variables)
Correlations* Biplot Scores
Variable Axis 1 Axis 2 Axis 3 Axis 1 Axis 2 Axis 3
Moisture 05 -0.772 0.245 -0.194 -0.521 0.142 -0.106
Soil pH 0.358 -0.875 0.272 0.242 -0.505 0.148
SOM -0.835 0.136 -0.477 -0.563 0.079 -0.260
NetNmin -0.087 -0.297 -0.349 -0.058 -0.171 -0.190
MBc -0.048 0.018 -0.026 -0.032 0.011 -0.014
FBc -0.291 0.124 0.539 -0.196 0.072 0.294
FBc:MBc 0.445 0.144 -0.302 0.300 0.083 -0.164
*The Pearson correlations are "intraset correlations of ter Braak (1986).


Table 5-4. Plant Indicator Values (IndVal) (percent of perfect indication) with associated
biogeochemical variable by forest age class. P-values represent the proportion of
randomized runs (1000) equal to or less than observed values (oc=0.1). Species codes
are found in Appendix A.
Species AgeGroup IndVal p-value
Dich-Anvi Regen 32.9 0.0010
Rhal MidAged 27.8 0.0010
Ilgl Mature 28.4 0.0160
Dich-Arbe Pt Wash 36.4 0.0010


Table 5-3. Correlations and biplot scores for the biogeochemical variables by forest age class.











Table 5-5. The means for soil biogeochemical variables between reference site locations and the
Pt. Washington restoration site.
Net Nmin Plant
Time Stand Soil Soil Cmb SOM .Cfb FB to
Sie ntrvl geMostreMostre(mg l/kg' Soil pH Avall-P
Sit Inervl Ae Mistre oisureSoi / (mg/kg/ Content [H+] ( /kg-1 (mg/kg MB
(years) (years) 2005 2006 soil) (%) mg. /soil) Ratio
year) soil)


Pt Wash Seedling 6 0.27 0.07 2 1198 1.4 4.6 -0.27 126 0.16


Table 5-6. Pt. Washington actual vs. predicted indicator values.
Predicted Values Pt. Washington Actual Values (2006)
Arsenal
Velpar Velpar Velpar Arsenal 2d Arsenal
Age-6 Reference Sites Control 1st Year 2nd Year March 1st Year April
Year
Only Only Application Only Application
Only
DBH (cm) 3.73 2.87 3.03 3.14 3.31 3.26 3.23 3.62
R2 =0.81 p <0.0001
Height (m) 2.09 0.17 0.18 0.25 0.19 0.26 0.29 0.34
R2 =0.82 p <0.0001
Density (trees/ha) 265.57 259 268 298 302 224 244 218
R2 =0.1 p <0.0083
BA (m2/ha) 0.09 0.17 0.19 0.23 0.26 0.19 0.20 0.22
R2 =0.56 p <0.0001
Volume (m3/ha) 17.50 0.03 0.03 0.06 0.05 0.05 0.06 0.07
R2 =0.52 p <0.0001
Predicted DBH = [(-0.00510*Age 2 ) + (0.82861* Age) 1.06278], Predicted Height = [(-0.00288*Age2) + (0.45277*Age) 0.52632],
Predicted Density = [(-0.83301*Age) + 270.56934], Predicted Basal Area = [(-0.00190*Age2) + (0.3323*Age) 1.95528],
Predicted Volume = [(2.46264*Age) + 2.72759]


St. Marks Seedling 6 0.34 0.22
St. Marks Mid-Aged 36 0.26 0.28
St. Marks Mature 110 0.25 0.11
Chassahow Sapling 9 0.44 0.07
Chassahow Mid-Aged 45 0.23 0.08
Chassahow Mature 71 0.57 0.22
Topsail Hill Sapling 19 0.45 0.10
Topsail Hill Mid-Aged 49 0.31 0.10
Topsail Hill Mature 101 0.32 0.09


115 2.8 4.3 0.24 51 0.44
589 1.4 4.6 -0.24 75 0.13
49 1.5 4.6 0.31 87 1.66
186 2.9 4.3 -0.09 105 0.57
145 1.1 4.7 -0.04 161 1.11
369 4.6 4.1 0.23 156 0.42
524 2.9 4.3 -0.36 171 0.33
559 1.9 4.5 -0.50 179 0.32
490 2.0 4.2 -0.40 190 0.39











Axis 2


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OO CI Sol

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restoationsite












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,m, mh o


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Pt.Wahigtn estraio ste
















200

150

100





-00

-50
-10


cnO
ID ~
-e
oo
lulu


-200


Figure 5-3. Monthly variation of total nitrogen mineralization, ammonification and nitriaication
rates (mg-l kg-l month- ) obtained from field incubation of soils (untreated) during 14
months before and after the 2002 treatments.


0 I I I I 1 ********
O Control o Oust a Velpar a Oust-Velpar m Arsenal


Treatments


Figure 5-4. Net nitrogen mineralization means mg (NH4+ + NO3-) / kg-l soil / month for the
control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometuron methyl-
hexazinone mix and Arsenal: imazapyr. Results are from soil samples collected
during 14 months before and after the 2002 treatments.


107


Nitrification


a r
'D"US~ In
~77 o
ooooo, o
) IU IU IU IU N R)


Ammonification














E 60 b


'aa

- 40-
z a



20 -2

0 10-



z O Control 17 Oust a Velpar a Oust-Velpar n Arsenal


Treatments


Figure 5-5. Net ammonification mean monthly rates (mg-l NH4+ / kg-l soil / month) for the
control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometuron methyl-
hexazinone mix and Arsenal: imazapyr. Results are from soil samples collected
during 14 months before and after the 2002 treatments.


90



E 70-

'~60 a

50-

z 40-

30
S20-




0 Control O Oust Velpar Oust-Velpar O Arsenal
Treatments


Figure 5-6. Net nitrification mg -1 NO3- / kg -1 soil / month; for the control, Oust: sulfometuron
methyl, Velpar: hexazinone, sulfometuron methyl- hexazinone mix, and Arsenal:
imazapyr; applied in different growing seasons, frequencies, and time of year. Results
are from soil samples collected during 14 months before and after the 2002 treatments.











1800

1600
1400


'a1200-
Y"1000-

S800-a
600-

O 400-
200-


0 Control O7 Oust Velpar Oust-Velpar m Arsenal
Treatments

Figure 5-7. Microbial biomass carbon (Cmb) mg -1 C / kg -' soil; for the control, Oust:
sulfometuron methyl, Velpar: hexazinone, sulfometuron methyl- hexazinone mix, and
Arsenal: imazapyr. Results are 40 months after second treatment (2006).




1800


1600

1400

1200

1000

800

600

400

200


0
0 One Year a Two Years
N um ber of Appl icati ons

Figure 5-8. Effects of one year and two consecutive years of herbicide applications on microbial
biomass carbon (Cmb) mg-l carbon / kg-l soil from soils. Results are forty months after
second treatment (2006).











1500


1250
0
Y" 1000


Figure 5-9. Soil levels of Microbial biomass carbon (Cmb) mg -1 carbon / kg -1 measured at the
reference sites and the Pt. Washington restoration site. Results are forty months after
second treatment (2006).


O Control O7 Oust a Velpar a Oust-Velpar n Arsenal
Treatments


Figure 5-10. Fungal biomass carbon mg -1 carbon / kg -1 soil; for the control, Oust: sulfometuron
methyl, Velpar: hexazinone, sulfometuron methyl- hexazinone mix, and Arsenal:
imazapyr. Results are four years after second treatment (2007).


St. Marks Chass Topsail Pt. Wash












Undlerstory Species Composition Forest Structure


loorgartic flols
----- Mlnweralsabo~n
---) Immobilisationr


Figure 5-11i. Pools and fluxes of nitrogen in the RESDYN restoration model. MP, metabolic
pool; grass&forbs, holocellulose pool; shrubs, lignocellulosic pool; and CWD, woody
pool. There are distinctive stabilization coefficients for microbial biomass, young
soil organic matter (Y-SOM), and old soil organic matter (SOM) (adapted from
Corbeels et al. 2005).









CHAPTER 6
SUMMARY AND CONCLUSIONS

Ecosystem restoration requires a good monitoring system that allows for the tracking of

success by measuring key ecological indicators at the restoration site and comparing those results

with reference communities. The measured ecological indicators must include monitoring

changes belowground as well as in the aboveground vegetation for the coupling of functional and

structural attributes. The overall objective of this study was to examine stand structure,

understory species composition, and soil chemical and microbial properties along a

chronosequence in longleaf pine wet flats along Florida' s Gulf Coast in an attempt to develop an

ecological traj ectory for this community. Such an ecological traj ectory would serve as the basis

for developing a monitoring framework for restoration proj ects in the southern Gulf Coastal

Plain.

We selected three reference sites within the Gulf Coast Flatwoods subecoregion to

accomplish our objective. Within each reference site we sampled a total of 12 plots, 4 plots each

in the early, mid and mature age classes. This experimental design resulted in 29 different age

groups representing a chronosequence of 2 to 1 10-year-old stands.

The selected reference locations not only represented the highest quality sites that could be

found in Florida, but were also located within the specific range for coastal wet longleaf pine

flats found along Florida' s Gulf coast. Monitoring this very specific biogeographical area (Gulf

Coast Flatwoods subecoregion of Florida) created a spatial gradient pertinent to the restoration

site that we wanted to evaluate. The time scale was limited to the oldest available longleaf pine

stands (110-year old) distributed along the specified spatial range.

The maj or focus in Chapter 2 was to examine overstory stand structure data and understory

plant species composition along the 11l0-year chronosequence. As expected, stand DBH, height,









and basal area increased with age, but reached a steady state plateau around 80-90 years. when

they began to decline. Coarse woody debris accumulation levels were highly variable, but tended

to increase with age. The decomposition levels of CWD were constant through the mid-aged

class, but declined from the mid-age to the mature age class. The level of shrub species was

significantly higher in the mature sites than found in either the young or the mid-aged classes.

Stand growth during early development translates to habitat heterogeneity as partial

shading brings in new groups of plant species. At this point, stand height had a strong positive

relationship with the Coleman rarefaction index and stand density has a strong negative

relationship with the Shannon-Wiener diversity index. The plant species turnover rates as

indicated by Coleman rarefaction values were high and the evenness of plant species as indicated

by Shannon-Wiener was very low. The evenness of plant species was not attained until the

mature stage when the number of plant species entering the ecosystem was equal to the number

of plant species leaving it. At this point, Shannon-Wiener diversity values had a strong positive

relationship with stand density and the Coleman rarefaction index had a negative relationship

with stand height. The equilibrium between Coleman rarefaction and Shannon-Wiener diversity

indices at this stage indicates a steady state in the overstory. Based upon the chronosequencial

trends, Florida' s Gulf Coastal longleaf pine flats reach the understory reinitiation stage at

approximately 90 years. This would mean the forest is self-organizing, a threshold point for

restoration.

In Chapter 3, Our main obj ective was to measure soil pH, moisture content, organic matter

content (SOM), plant-available phosphorus, soil nitrogen mineralization rates (Nmin), soil

microbial biomass carbon (Cmb) and fungal biomass (Cfb) along the same 110-year

chronosequence for determining the ecological traj ectory in terms of soil chemical and microbial









characteristics of longleaf pine in coastal wet pine flat communities. We specifically tested our

hypothesis that this group of soil biogeochemical indicators measured along the chronosequence

would follow a pattern similar to the biomass accumulation curve for forest succession (Vitousek

and Reiners, 1975). In response to rapid increase in growth during the early years of stand

establishment, we predicted a similar increase in net nitrogen mineralization rates, microbial

biomass and fungal biomass levels. We hypothesized that these variables would decrease at some

point during the mid-aged stage and reach a threshold steady-state some time during the early

mature stage when the understory reinitiation process of forest succession has begun.

Nitrogen cycling was dominated by ammonium production during the wet 2005 growing

season when compared to a drier 2002. Nitrification represented 50% of the production during

2002 and less than 25% during 2005. There was ammonium enrichment by nitrate reduction.

This probably indicates that the dissimilatory-nitrate reduction-to-ammonium (DNRA) pathway

was prominent during the flooded 2004-2005 growing seasons. The net nitrogen mineralization

rates, microbial biomass carbon, and fungal biomass carbon increased between the young and

mid-aged classes, then decreased between the mid-aged and mature age classes. The FB-to-MB

ratios increased dramatically up to 60 years, then decreased to 110 years. Finally, soil organic

matter content (SOM), increased with soil moisture. Based upon the results, this group of soil

indicators follows biomass accumulation patterns and will attain biogeochemical equilibrium

after a stand age of approximately 60-70 years. The threshold would be during the mature age

class after the understory reinitiation phase of forest succession has started.

The obj ective of Chapter 4 was to examine the relationships between key soil chemical and

microbial properties and the overstory and understory characteristics of a wet longleaf pine flat

community in the Gulf Coastal Plain of Florida. We hypothesized stand volume will show a










positive relationship with soil nitrogen mineralization, which, in turn, will be driven by the

microbial community dynamics in the soil. We also hypothesized that the fungal biomass will

increase as coarse woody debris accumulated on the forest floor and the standing stock increased

over time.

The maj ority of the soil biogeochemical indicators influenced longleaf pine stand growth,

and as stands developed, changes in aboveground vegetation influenced the soil biogeochemical

indicators. Net nitrogen mineralization increased with stand volume until a threshold of 200 m3

ha (stand age = 90 years). Nitrate was found to be in higher concentrations in the young forest

soils than the mature forest soils. Populations of nitrifying bacteria (AOB + NOB) were also

found to be higher in the young forest soils. At Topsail Hill, ammonium levels were found to be

higher in the wet young pine savanna soils than the mesic mature soil. Higher soil moisture

translates to lower nitrification levels. The relationships between fungi and increases in stand

height or coarse woody debris accumulation indicate a strong continual relationship between the

soil biogeochemical indicators and longleaf pine stand development. The dynamics of this

relationship might be better understood if the measured fungal biomass could have been

identified as arbuscular mycorrhizal (AM) fungi, ectomycorrhizal (EM) fungi, or saprophytic

fungi along the chronosequence. The dominance of fungi negatively affected the Coleman

Rarefaction and Shannon-Wiener diversity indices. This may indicate a decrease in species

richness, but the functional redundancy component of ecosystem resilience is probably being

strengthened. The strong relationships between forest biomass accumulation and soil

biogeochemistry should always be studied in any monitoring event. Nitrogen cycling appears to

become tighter in mature forests at a threshold of 90 years. This condition is dependent on

mycorrhizal and saprophytic fungi dominating the soil microbial biomass.









The obj ectives of Chapter 5 were to use the indicator data to ecological classify the Pt.

Washington restoration site as a mesic flatwoods, wet flatwoods or wet savanna. Secondly, to

use the soil biogeochemical indicators for trying to detect differences among the four herbicide

treatment effects applied on the restoration site. Finally, we will use both the vegetative and soil

biogeochemical data to predict the development or ecological traj ectory in wet longleaf pine flat

restoration. The predicted values will be presented with pine growth results on the effects of

herbicide treatments applied in the second year after planting compared to first year only,

consecutive herbicide treatments (1st & 2nd Year), and whether an early or late spring application

changes the effects (McCaskill data, 2006).

The Pt. Washington restoration site contains elements of mesic flatwoods, wet flatwoods,

and wet savannas. However, based upon CCA environmental ordination, plant species indicator

analysis, and pre-harvest stand data it is a wet flatwoods site. These multivariate techniques were

also useful in determining similarities between the Pt. Washington restoration site and the young

age class data of the reference sites. Imazapyr was the best herbicide treatment for this site based

on its ability to control shrubs and remain effective during flooding events. In general, herbicide

use increased nitrogen mineralization rates, but imazapyr was the only treatment to produce

statistically significant higher levels of net nitrogen mineralization when compared to the

control. Both imazapyr and the sulfometuron methyl-hexazinone treatments had a significant

difference with the control in the nitrification data. The herbicide-treated restoration site had

higher soil microbial biomass carbon levels than the reference sites. Two years of herbicide

applications increased soil microbial biomass carbon over a single application. There was an

indication that sulfometuron methyl treatments caused soil microbial mortality. Higher nitrogen

mineralization rates at Pt. Washington were negatively correlated with both of the species









diversity indices. The net nitrogen mineralization data proved effective at detecting differences

between the herbicide treatments. Soil microbial biomass carbon was sensitive to the amount of

herbicide applied. The predictions were generally good except for height and volume estimates.

Mean stand height values were skewed due to a group of the 400 m2 forest structure plots

measured within the young age class containing naturally regenerated all-aged stands.

Research Implications in Coastal Wet Longleaf Pine Flats Restoration

The monitoring study proved effective at evaluating our restoration site with a set of

indicators that integrated the structural and functional attributes of the wet longleaf pine

ecosystem. The aboveground vegetative variables and the soil biogeochemical measurements

produced similar threshold periods. The selection process for the reference sites also proved

fruitful based upon the sites having similar stand, soil properties, and common understory plant

species among the locations. It was critical to restrict the location of the reference sites to within

the 3 kilometers of the Gulf coast.

Our set of reference sites were selected to evaluate southern coastal pine communities

that are directly affected by tropical storms. The restoration of Gulf coastal wet longleaf pine

flats is distinct from other longleaf pine communities. Flooding caused by active hurricane

seasons can leave these sites inundated for more than two years. This condition causes two maj or

results in the biogeochemistry of these pinelands. First, extended flooding causes the nitrogen

cycle to be dominated by ammonium production. When ammonium becomes scarce, nitrate is

converted to ammonium through the DNRA pathway conserving nitrogen losses. Secondly, long

term flooding results in the accumulation of soil organic matter, causing the pH of the soil

medium to drop. This condition favors fungi and anaerobic bacteria over the aerobes. When the

conditions become dry, there is a great flush of growth in both the overstory and understory

vegetation. The effects of this flooding cycle are greater on younger forests than mature forests










where nitrate is in greater demand because of stand growth requirements. This demand was

expressed by the nitrate levels and numbers of nitrifying bacteria being significantly higher in

soils from the young stands compared to the mature stands. When prescribing fire in these sites,

it is critical not to burn them during a flooding cycle before the flush of growth is completed.

Based upon the conditions at our four sites, that can take 12-14 months after the drying process

has started.

The understory vegetation was also distinct in these wet pine flats. There are higher

densities of facultative wetland grasses and forbs and fewer hardwoods, especially the oaks.

Very few oaks were measured on any of our sites other than the creepers (running oak). Some of

these sites have not been burned in over 5 years. The implication here is the fire return-intervals

can be extended well beyond 2-3 years if flooding conditions exist. The mesic mature sites had a

higher composition of shrub species than the young mesic stands, even under fire return-intervals

of 3 years. Soil moisture in the terms of extended flooding can enrich wet longleaf pine flat soils,

conserve their nitrogen supply, and prevent invasion by shrub species. The flooding cycle can

provide as many benefits to coastal wet pine ecosystems as fire does.

In summary, monitoring needs to include indicators that measure the functions as well as

structural attributes of a given ecosystem. This proved to be extremely important in Gulf coastal

pine communities where soil conditions are distinct from inland ecosystems. It was also

important to restrict the sites to within the Gulf Coast Flatwoods subecoregion of Florida and to

within 3 kilometers of the coast for insuring the same climatic effects that occurred at the

restoration site occurred at each of the reference sites. One result of these stratifications was that

all of the sites had 63 understory plant species in common. This may not have been attainable

had the spatial scale been broader. This set of indicators and the time scale for the









chronosequence can be utilized at other environments where longleaf pine ecosystems are found.

The chronosequence approach is strengthened by having as many replications as economically

feasible at each of the differently aged sites. A difficult and important aspect to monitoring is

selecting the spatial scale for the reference sites. If one is looking to monitor longleaf pine in

mountain terrain found in the northern limit of its range, it would be more effective to restrict the

reference sites to within that environment in order to capture the ecological differences found

within the local climatic and soil conditions. A key direction for future research is to conduct

investigations for improving our understanding of the biogeochemical dynamics that take place

in facultative pine wetlands (i.e., wet pine-dominated mineral flats). This research would need to

include molecular analysis for the identification species that change in soil microbial community

between wet and dry conditions.













Table A-1. Species list.
Scientific name Code Common name


APPENDIX
SPECIES CODE LIST


Shrubs
Asinmina incana
Cyrilla racenmiflora
Gaylussacia dunmosa
Gaylussacia frondosa
Ilex coriacea
Ilex glabra
Ilex vonmitoria
Kalnmia hirsuta
Licania nmichauxii
Lyonia lucida
Magnolia virginiana
M~yrica cerifera
Photinia pyrifolia
Quercus punmila
Serenoa repens
Stillangia sylvatica
Vaccinium spp


Asin
Cyra
Gadu
Gafr
Ilca
Ilgl
Ilvo
Kahi
Limi
Lylu
Mavi
Myce
Phpy
Qupu
Sere
Stsy
Vacc


Anvi
Arbe
Cacu
Ctar
Cype
Ersp
Dich
Pani
Paer
Pala
Scle
Xyca

Asvi
Asad
Aser
Asre
Asto


Wooly paw paw
Titi
Drawf huckleberry
Dangleberry
Large gallberry
Gallberry
Y aupon
Hairy wicky
Gopher apple
Fetterbush
sweet bay
Wax myrtle
Red choke berry
Running oak
Saw palmetto
Queens delight
Blueberry spp


Bluestem grasses
Wiregrass
Curtis sandgrass
Toothache grass
Sedge spp
Purple lovegrass
Eggleaf witch grass
Panicum spp
Erect leaf witchgrass
Velvet Witchgrass
Nutrush spp
Yellow eyed grass

Southern milkweed
Scaleleaf aster
Thistleleaf aster
White top aster
Dixie aster


Grasses
Andropogan virginicus
Aristida stricta var. beyrichiana
Calanmovilfa curtissii
Cteniun2 aronmaticun2
Cyperus
Eragrostis spectabilis
Dichantheliun2 ovale
Panicun2 Dichanthelium
Dichantheliun2 erectifoliun2
Panicun2 laxf florun2
Scleria
Xyris caroliniana
Forbs
Asclepias viridula
Aster adnatus
Astereryngiifolius
Aster reticulatus
Aster tortifolius











Table A-1. Continued
Carphephorous pseudoliatris
Carphephorus odoratissinmus
Chrysopsis
Conyza canadensis
Coreopsis linifolia
Desnmodinn2 rotundifoliun2
Drosera capillaris
Elephantopus tonmentosus
Eupatoriun2 capillifoliun2
Eupatoriun2 conmpositifoliun2
Eupatoriun2 nohrii
Eupatoriun2 pilosun2
Euthanmia granminifolia
Gelsenmium senmpervirens
Gratiola hispida
Hypericun2 hypericoides
Hypoxis sessilis
Hypoxis spp
LLI huImIIhII caroliniana
Lechea
Lechea pulchella
Liatris gracilis
Liatris tenuifolia
Mimosa quadrivalvis
Oenothera fruticosa
Opuntia hunmifusa
Pityopsis gramn~infolia
Pterocaulon pycnostachyun2
Rhexia alifanus
Rhexia petiolata
Sabatia brevifolia
Seynmeria cassioides
Snmilax laurifolia
Snmilax punmila
Solidago odora
Stylisnma patens
Tragia urens
Verbena brasiliensis
Viola septenmloba
Vitis rotundifolia


Caps
Caod
Chry
Coca
Coli
Dero
Drca
Elto
Euca
Euco
Eumo
Eupi
Eugr
Gese
Grhi
Hyhy
Hyse
Hypo
Laca
Lech
Lepu
Ligr
Lite
Miqu
Oefr
Ophu
Pigr
Ptpy
Rhal
Rhpe
Sabr
Seca
Smla
Smpu
Sood
Stpa
Trur
Vebr
Vise
Viro


Bristleleaf chaffhead
Deer tongue
Silkgrass spp
Canadian horseweed
Texas tickseed
Tricklyfoil
Pink sundew
Devils grandmother
Dog fennel
Yankee weed
Mohr' sthoroughwort
Rough Boneset
Flat top goldenrod
Yellow j essamine
Rough Hedgehyssop
St. Andrews cross
Glossy seed yellow stargrass
Stargrass spp
Carolina redroot
Pineweed spp
Leggett's pineweed
Slender gayfeather
Shortleaf gayfeather
Sensitive brier
Evening primrose
Prickly pear
Silkgrass
Blackroot
Meadow beauty
Fringed meadow beauty
Shortleaf Rosegentian
Yaupon Blacksenna
Laurel green brier
Green brier
goldenrod
Coastal plain dawn flower
Wavyleaf noseburn
Brazilian verv~ain
Blue violet
Muscadine










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BIOGRAPHICAL SKETCH

George McCaskill started his doctoral study at the School of Forest Resources and

Conservation, University of Florida in January, 2003. Before j oining the University of Florida,

he was Associate Faculty teaching multiple courses at the College of the Redwoods in Eureka,

California. Prior to that he worked as a Bilingual Forestry instructor at Mt. Hood Community

College. For three years, he was a State Lands Timber Sales Forester for the Washington

Department of Natural Resources. He spent 3.5 years in the U. S. Peace Corps serving as an

Environmental Program Specialist evaluating Chilean forest practices as applied to their

Monterey pine plantations and their native Nothofagus forests. While working with the Chilean

Forestry Corporation, he served as interpreter/translator/editor during the Sixth Congress on

Criteria and Indicators for the Conservation and Sustainable Management of Temperate and

Boreal Forests. Also known as the Montreal Protocol, he helped to finalize the treaty where all

the Pacific Rim countries signed the document. In 1990, Mr. McCaskill completed his Masters

program at California Polytechnic in San Luis Obispo, California. He is a Registered

Professional Forester in California.





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1 LONGLEAF PINE ( Pinus palustris Mill) ECOSYSTEM RESTORATION ON COASTAL WET PINE FLATS: DEVELOPING A MONITORING PROGRAM USING VEGETATION AND SOIL CHARACTERISTICS By GEORGE L. McCASKILL A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

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2 2008 George L. McCaskill

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3 To my beloved wife Susana, my wonderful son Pedro, my mother, brothers and sisters

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4 ACKNOWLEDGMENTS I would like to thank m y supervisory committ ee members, Drs. Shibu Jose (Chair), Andy Ogram, Alan Long, Wendell Cropper, Eric Jokela and Jack Putz for their advice, time and support. In particular, I want to thank Dr. Shi bu Jose for providing me the opportunity to attend graduate school and providing me with funds to co mplete the project. I would also like to thank Dr. Andy Ogram for his extensiv e advice and laboratory assistan ce concerning the soil microbial analysis procedures. His knowledge and help on soil microbial ecology helped make this study possible. Wendell Cropper with his speci alized knowledge on ecosystem modeling and ecosystems in general provided invaluable improve ments to my research. Thanks to Drs. Alan Long, Jack Putz and Eric Jokela for providing me with the scientific / philosophical continuity necessary for evaluating pine planta tions and natural forests. I woul d also like to thank Dr. Craig Ramsey and Dr. Ashvini Chauhan for their assistance from field work to la boratorial analysis of the project. The efforts of Tim Baxley in data collection work and Chris Dervinis with making the HLPC operational are truly appreciated. I would also like to thank my fellow graduate students, Drs. Susan Bambo, Pedram Daneshgar, Diomides Zamora, and Dawn Henderson for their friendship and support during my studies. In addition, I would also like to thank the Florida Division of Forestry for funding the restoration site, and the personnel of Topsail Hill State Park, St Marks National Wildlife Refuge, and the Chassahowitzka Wildlife Management Area of Floridas Fish & Wildlife Commission for providing permission and assistance in establishing the reference sites. I could not give a full acknowledgement without giving special thanks to my wife Susana and my son Pedro for their suppo rt and patience throughout our time at the UF. I would also like to thank my mother, brothers and sisters fo r always supporting me throughout my extended period of education.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........8 LIST OF FIGURES.........................................................................................................................9 ABSTRACT...................................................................................................................................12 CHAP TER 1 MONITORING LONGLEAF PINE RESTOR ATION IN C OASTAL WET PINE FLAT COMMUNITIES......................................................................................................... 14 Longleaf Pine Ecosystems......................................................................................................14 Monitoring Restoration Success............................................................................................. 15 Monitoring Soil Characteristics.............................................................................................. 16 Developing a Monitoring Program......................................................................................... 17 2 FOREST STRUCTURE AND PLANT SPECIES DIVERSITY IN WET LONGLEAF PINE FLATS ACROSS A CHRONOSEQUENCE ............................................................... 21 Introduction................................................................................................................... ..........21 Materials and Methods...........................................................................................................25 Study Sites.......................................................................................................................25 Forest Age Classes.......................................................................................................... 27 Field Measurements......................................................................................................... 28 Data Analysis...................................................................................................................29 Results.....................................................................................................................................30 Overstory Stand Structure...............................................................................................30 Understory..................................................................................................................... ..30 Discussion...............................................................................................................................31 Overstory.........................................................................................................................31 Plant Species Diversity.................................................................................................... 32 Conclusions.............................................................................................................................34 3 PATTERNS OF SOIL CHEMICAL AND MICROBIAL PROPERTIES ALONG A CHRONOSEQUENCE IN W ET LONGLEA F PINE FLATS OF FLORIDA...................... 45 Introduction................................................................................................................... ..........45 Materials and Methods...........................................................................................................50 Study Areas.....................................................................................................................50 Soil Sampling and Preparation........................................................................................ 50 Soil Chemical Analysis................................................................................................... 51 Net Nitrogen Mineralization............................................................................................ 51

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6 Microbial Biomass........................................................................................................... 52 Data Analysis...................................................................................................................54 Results.....................................................................................................................................55 Soil Types, Soil Organic Matter, and Soil pH................................................................. 55 Net Nitrogen Mineralization............................................................................................ 55 Microbial Properties........................................................................................................ 56 Discussion...............................................................................................................................57 Conclusions.............................................................................................................................59 4 RELATIONSHIP BETWEEN VEGETATION AND SOIL CHARACTERISTICS IN WET L ONGLEAF PINE FLATS AL ONG FLORIDAS GULF COAST............................ 67 Introduction................................................................................................................... ..........67 Materials and Methods...........................................................................................................69 Study Areas.....................................................................................................................69 Field Measurements......................................................................................................... 69 Soil Sampling and Preparation........................................................................................ 70 Soil Chemical Analysis................................................................................................... 70 Mineral Nitrogen Fluxes..................................................................................................71 Bacterial Abundance and Microbial Dyna mics............................................................... 71 Experimental Design and Analysis................................................................................. 73 Results.....................................................................................................................................74 Nitrifying Bacteria and Nitrogen Mineralization............................................................ 74 Overstory.........................................................................................................................75 Understory..................................................................................................................... ..75 Discussion...............................................................................................................................76 Conclusions.............................................................................................................................78 5 MONITORING RESTORATION SUCCESS USING VE GETATION AND SOIL AS KEY INDICATORS: CASE STUDY OF A WET LONGLEAF PINE FLATS RESTORATION PROJECT................................................................................................... 85 Introduction................................................................................................................... ..........85 Materials and Methods...........................................................................................................88 Pt. Washington Restoration Site...................................................................................... 88 Pine Survival and Growth............................................................................................... 90 Vegetation Sampling....................................................................................................... 90 Reference Sites................................................................................................................ 91 Soil Sampling and Preparation........................................................................................ 91 Data Analysis...................................................................................................................92 Pine survival and growth.......................................................................................... 92 Understory................................................................................................................ 92 Biogeochemical indicators....................................................................................... 93 Results.....................................................................................................................................95 Ecological Classification................................................................................................. 95 Pine Growth and Vegetation Control.............................................................................. 96 Treatment Effects-Bioge ochem ical Indicators................................................................ 96

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7 Discussion...............................................................................................................................97 Conclusions...........................................................................................................................100 6 SUMMARY AND CONCLUSIONS...................................................................................112 Research Implications in Coastal We t Longleaf Pine Flats Restoration .............................. 117 APPENDIX SPECIES CODE LIST............................................................................................ 120 LIST OF REFERENCES.............................................................................................................122 BIOGRAPHICAL SKETCH.......................................................................................................138

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8 LIST OF TABLES Table page 2-1 Qualitative classification system for downed and standing deadwood............................. 36 2-2 Forest structural and plant spec ies diversity m eans among age classes............................ 36 2-3 Indicator values for plant species in three forest age classes. ............................................ 37 3-1 Soil and stand properties between reference sites..............................................................61 3-2 Soil chemical and microbial bi om ass means between age classes.................................... 62 3-3 Soil nitrogen mineralization means (Nmi n) for dry season 2002 and wet season 2005. ... 62 3-4 Differences in soil biogeochemical relationships based upon Spearm an rank correlations r as stratified by forest age class (n = 48)...................................................... 63 4-1 MPN enumerations of nitrifying bacteria in young and old longleaf pine forest soils. ..... 80 4-2 Ammonification and nitrification in y oung and old longleaf pine forest soils. .................80 4-3 Soil biogeochemical relationships with stand attributes based upon Spearm an Rank correlations r as stratified by forest age class (n = 48)...................................................... 81 5-1 Correlations and biplot scores for the biogeochem ical variables by pine.......................102 5-2 Plant Indicator Values (IndVal) (percent of perfect indicati on) with associated biogeochem ical variable by pine flat type....................................................................... 102 5-3 Correlations and biplot scores for the biogeochem ical variables by forest age class...... 103 5-4 Plant Indicator Values (IndVal) (percent of perfect indicati on) with associated biogeochem ical variable by forest age class.................................................................... 103 5-5 The means for soil biogeochemical variable s between reference si te locations and the Pt. W ashington restoration site........................................................................................ 104 5-6 Pt. Washington actual vs. predicted indicator values. ..................................................... 104 A-1 Species list.............................................................................................................. ........120

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9 LIST OF FIGURES Figure page 1-1 Floridas Gulf Coast Flatwoods zone wh ere the w et pine flat sites are located................ 20 2-1 Locations of the Pt. Washington Longleaf Pine Restoration site ( ) and the reference sites within Gulf Coast Flatw oods subecoregion of Florida. .............................................38 2-2 Nested plot sampling desi gn applied at three different s ites (age classes) for each reference location............................................................................................................. ..39 2-3 Mean stand density (trees per he ctare) along a 110-y ear longleaf pine chronosequence as m easured from 26 differently aged stands.......................................... 39 2-4 Mean stand DBH, height, BA, and volum e along a 110-year longleaf pine chronosequence as measured from 26 differently aged stands.......................................... 40 2-5 Downed woody debris and standing deadwood (snag) accum ulations along a 110year longleaf pine chronosequence as m easured from 26 differently aged stands............41 2-6 Decomposition levels by forest age cla ss as m easured from 26 differently aged stands..................................................................................................................................42 2-7 Composition of understory vegetation by forest age class. ............................................... 42 2-8 Shannon-Wiener Diversity and Coleman Rarefaction indices along a 110-year longleaf pine chronosequence as m easured from 26 differently aged stands.................... 43 2-9 Mean stand density versus the Shann on-W iener Diversity index and mean stand height versus the Coleman Rarefaction index as measured from the young, mid-age and mature age longleaf pine stands.................................................................................. 44 3-1 Soil organic matter content versus soil m oisture as measured from 26 differently aged stands.................................................................................................................... .....63 3-2 Soil pH versus soil organic matter content (percent) as m easured from 26 differently aged stands.................................................................................................................... .....64 3-3 Total net nitrogen mineralization, amm onification and nitrification rates (m g -1 nitrogen / kg -1 soil / month -1 ) along a 110-year chronosequence as measured from 26 differently aged stands..................................................................................................64 3-4 Trends for microbial biomass carbon (Cmb) and net nitrogen mineralization rates (Nmin) along a 110-year longleaf pine chronosequence as measured from 26 differently aged stands....................................................................................................... 65

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10 3-5 Microbial biomass carbon ve rsus net nitrogen m ineraliza tion rates as measured from 26 differently aged stands..................................................................................................65 3-6 Fungal biomass carbon ( C ) along a 110-y ear longleaf pine chronosequence as m easured from 26 differently aged stands......................................................................... 66 3-7 The fungal-to-microbial biomass ratio and fungal biom ass carbon levels (means) during the earlier and later portions of chronosequence respectively, as measured from 26 differently aged stands along th e 110-year longleaf pine chronosequence.......... 66 4-1 Net nitrogen mineralizati on versus stand volum e as measured from 26 differently aged stands.................................................................................................................... .....82 4-2 The fungal biomass (FB)-to-microbial biom ass (MB) ratio versus stand height as m easured from 26 differently aged stands......................................................................... 82 4-3 Fungal biomass carbon (Cfb) versus stand basal area (BA) as measured from stands grouped within the mid-aged and mature age classes only................................................ 83 4-4 Coarse woody debris accumulation versus fungal biomass carbon (Cfb) as measured from 26 differently aged stands......................................................................................... 83 4-5 Coleman Rarefaction index versus the fungal biom ass (FB)-to-microbial biomass (MB) ratio as measured from 26 differently aged stands.................................................. 84 4-6 Shannon-Wiener diversity H index vers us the fungal biom ass (FB)-to-microbial biomass (MB) ratio as measured fr om 26 differently aged stands.................................... 84 5-1 Pine flat type determined by a threedim ensional ordination biplot derived from Canonical Correspondence Analysis (CCA) of 192 plots usin g understory plant species abundance and soil biogeochemi cal data including the Pt. Washington restoration site..................................................................................................................105 5-2 A threer-dimensional ordination biplot derived from Canonical Correspondence Analysis (CCA) of 192 plots using unde rstory plant species abundance and soil biogeochemical data collected within the young, mid-aged, mature age class, and the Pt. Washington restoration site........................................................................................ 106 5-3 Monthly variation of total nitrogen mine ra lization, ammonification and nitrification rates (mg-1 kg-1 month-1) obtained from field incubation of soils (untreated) during 14 months before and after the 2002 treatments................................................................... 107 5-4 Net nitrogen mineralization means mg (NH4 + + NO3 -) / kg-1 soil / month for the control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometuron methylhexazinone mix and Arsenal: imazapyr........................................................................... 107

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11 5-5 Net ammonification mean monthly rates (mg-1 NH4 + / kg-1 soil / month) for the control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometuron methylhexazinone mix and Arsenal: imazapyr........................................................................... 108 5-6 Net nitrification mg -1 N03 / kg -1 soil / month; for the control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometur on methylhexazinone mix, and Arsenal: imazapyr; applied in different growing s easons, frequencies, and time of year.............. 108 5-7 Microbial biomass carbon (Cmb) mg -1 C / kg -1 soil; for the control, Oust: sulfometuron methyl, Velpar: hexazinone sulfometuron methylhexazinone mix, and Arsenal: imazapyr.....................................................................................................109 5-8 Microbial biomass carbon (Cmb) mg-1 carbon / kg-1 soil from soils treated only one year and two consecutive years of applications............................................................... 109 5-9 Microbial biomass carbon (Cmb) mg -1 carbon / kg -1 soil levels measured at the reference sites and the Pt. Wa shington restoration site.................................................... 110 5-10 Fungal biomass carbon mg -1 carbon / kg -1 soil; for the control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometur on methylhexazinone mix, and Arsenal: imazapyr....................................................................................................................... ....110 5-11 Pools and fluxes of nitrogen in the RE SDYN restoration m ode l. MP, metabolic pool; grass&forbs, holocellulose pool; shru bs, lignocellulosic pool; and CWD, woody pool..................................................................................................................................111

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12 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy LONGLEAF PINE ( Pinus palustris Mill) ECOSYSTEM RESTORATION ON COASTAL WET PINE FLATS: DEVELOPING A MONITORING PROGRAM USING VEGETATION AND SOIL CHARACTERISTICS By George L. McCaskill August 2008 Chair: Shibu Jose Major: Forest Resources and Conservation Longleaf pine ecosystem rest oration should include more than reforestation or the application of prescribed fire. It must include the restoration of all the major functions and processes within the forest eco system along with restoring overs tory and understory species composition. Despite many longleaf pine restorati on projects on coastal pine flats, there is no monitoring protocol in place to evaluate the succe ss of an all-inclusive restoration effort. The goal of this study was to estab lish an ecological trajectory us ing selected indicators for wet longleaf pine flats as a monitoring framework for restoration projects. The first specific objective was to quantify the vegetational attributes of longleaf pine flat ecosystems along a chronosequence (2 -years after stand re placement to 110-years-old) of stands from within the Gulf Coast Flatwoods zone in Florida. Overstory structure and understory plant species diversity were quantified along the chro nosequence. Mean diamet er at breast height (dbh), height, and basal area in creased until 60-70 years, and then declined. Stand volume continued to increase. Stand density decrea sed before reaching a steady state. Coleman rarefaction and Shannon-Wiener diversity indices for understory plants ex hibited opposite trends during early stand development, but reached equilibrium during the mature (> 90 years) phase.

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13 The second objective was to ex amine soil chemical and micr obiological properties along the same chronosequence. Ne t nitrogen mineralization (Nmin), soil microbial biomass carbon (Cmb), and fungal biomass carbon (Cfb) increased from the young to th e mid-aged age stands and declined from the mid-aged through the matu re age stands. Ammonium production dominated nitrogen cycling and ammonium enrichment occu rred on these wet sites by reduction of nitrate (the DNRA pathway). The biogeoche mical attributes showed that Floridas Gulf coastal pine flats reach a self-organizing threshold after 85-90 years. The third objective was to examine the in terrelationships between the structural (vegetative) and functional (so il biogeochemical) attributes. Nmin, Cmb and Cfb increased with increases in dbh, height, basal ar ea, and volume. Plant species di versity decreased as the FB-toMB ratio increased. Nitrate levels and nitrifyi ng bacteria numbers were higher in young forest soils than old forest soils. Based upon the indicat ors, coastal longleaf pi ne flats reach a steady state threshold with a lower and less variab le (tighter) nitrogen cycle at 90 years. The final objective was to determine if observe d structural and functi onal attributes were useful for evaluating restoration projects. An ongoing restoration project at the Pt. Washington State forest was evaluated for its ecological traj ectory following various restoration treatments involving herbicides. The site was determ ined to be a wet flatwoods based upon environmental ordination and plant species indi cator analysis. Herbicide use in creased soil microbial biomass carbon and net nitrogen mineralization rates. Imazapyr was the most effective herbicide treatment for this wet pine flat s site based upon the level of sh rub control, minimum impacts on herbaceous species diversity, and desired st ructural attributes of the overstory. Key words: Longleaf pine, reference communities, monito ring, ecological indicators, herbicides.

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14 CHAPTER 1 MONITORING LONGLEAF PINE RESTORAT ION IN C OASTAL WET PINE FLAT COMMUNITIES Longleaf Pine Ecosystems The longleaf pine (Pinus palustris Mill) ecosystems that historically dominated the lower Coastal Plain from Virginia to Texas currently oc cupies less than 3 % of its original area (37 million ha) (Frost, 2006). This reduction in area has resulted in a great loss of habitat necessary for many plant and animal species (Wade et al 2000; Van Lear et al. 2005). Longleaf pine ecosystems are naturally maintained by frequent fires that reduce vegetative competition during pine seedling and sapling devel opment (Boyer, 1990). Fires, natura l or prescribed, have become severely restricted, especially by urban expans ion because of liability and property damage concerns (Achtemeier et al. 1998; Haines et al. 2001). For the last thirty years, forest industries in the South preferred to replace longleaf pine stands with slash pine ( Pinus elliottii Engelm.) on wet sites and with loblolly pine ( Pinus taeda L.) on upland areas (Croker & Lande rs, 1987). Slash and loblolly pines are considered easier to regenerate and managers have little need to address the long leaf pines unpred ictable period of establishment (grass stage). Furt hermore, they also reach comme rcial size faster than longleaf pine, which shortens the econom ic rotation (Outcalt, 2000). In recent years, there has been a great deal of attention given to the restoration of the extensive and species-ric h longleaf pine ecosystem. There ha ve been attempts to restore 400,000 ha of longleaf pine in the Sout heast during the past decade (W MI, 2006). This effort creates a need for monitoring protocols to be in place fo r evaluating the success of these restoration efforts. While established monitoring guideline s and programs are active for many of the other forest ecosystems in other parts of the U.S. (ERI 2003), the lack of such established directives

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15 can hinder longleaf pine restorat ion projects in establishing functional and self-sustaining ecosystems across its originally exte nsive range (Devries et al. 2003). Monitoring Restoration Success Three established strategies for assessing a restoration effort are direct comparison, attribute analysis, and trajectory analysis (SER, 2004). Perm anent-plot studies have been used to directly identify changes over ti me after stand replacing harvests or other impacts. Attribute analysis uses the measurement of ecological indi cators to evaluate ecosystem conditions without directly considering patterns over time. Trajectory an alysis uses data collect ed over periodic time intervals to identify if restora tion trends are toward a refere nce condition (SER, 2004). We used a combination of attribute analysis and trajectory analysis to monitor our restoration project. Since expensive permanent-plot studies are genera lly limited to less than 30 years, conducting a trajectory analysis employing a space-for-time subs titution or chronosequence can be an efficient alternative for describing general trends over time or to test hypot heses based upon forest succession (Pickett, 1989). Chronosequencial studi es make use of a group of sites that have similar biotic, climatic, soil biogeochemical, and hi storical characteristics, but differ in age since a harvest or other stand replacing disturbance (Pickett, 1989). By comparing the different aged sites, one can identify changes in composition or function between decades, centuries, or even millenniums (Williamson et al. 2005). There is a critical requi rement for the different aged sites to be subjected to the same historical conditions and have the same speci es available over the ch ronosequence to give validity for using the space-for-time substitutions. One must also deal with separating spatial variability from the variability associated w ith time (Veldkamp et al. 1999). A recent chronosequence study examined the relationships between stand development and understory vegetation on 15 stands ranging from 7 to 427 years in a mixed conifer forest along the

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16 California-Oregon border. Regression an alysis showed canopy openness was positively correlated with total understory cover, species richness, diversity, and composition. Surprisingly, no correlations were observed between any of the measured stand attributes. Shrub and graminoid species were negatively correlated, an d forbs were positively correlated, with stand age (Jules et al. 2008). Another study used detailed forest inventory and climatic data from 43 stands along a 250-year chronose quence to assess the effects of disturbance and climate on biomass accumulation patterns across Russia. Regr ession analysis indicated as expected the highest biomass increments in the warmest region s and the lowest in the coldest regions. Spruce ( Picea spp.) and birch (Betula spp.) forests had the highest biomass increments while larch ( Larix spp.) and aspen ( Populus spp.) forests had the lowest biomass accumulation. The faster growing spruce and birch forests had declines in biomass accumulation rates after 150 years whereas the slower growing la rch and aspen never showed declines duri ng the 250-year chronosequence (Krankina et al. 2005). Monitoring Soil Characteristics In addition to vegetative characteristics, m ineral pools, and the mineralization of key elements have been identified as important attrib utes for evaluating restor ation success in recent years (Mller et al. 2000; Mller and Lenz, 2006). Du ring the last decade there has been a major effort at assessing the effects of different forest management practices on the long-term soil productivity of southern pine forests (Burger a nd Kelting, 1999), including coastal wet pine flats (Lockaby and Walbridge, 1998; Lister, 1999; Burg er and Xu, 2001; Burdt, 2003). These studies have assessed treatment effects utilizing a set of so il indicators (Kelting et al. 1999) including soil pH, soil organic matter content, soil moistu re content, and the mi neralization levels of nitrogen, and phosphorus (Reynolds et al. 2000; Redding et al. 2004). For example, a recent chronosequence study examined the relationshi p between biomass accumulation and nitrogen

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17 availability over 87 years in Populus grandidentata forests. Overstory biomass increment increased with stand age while understory bioma ss levels decreased. Net nitrogen mineralization rates were found to decrease during the first 18 years after harvest than increase over the next 70 years (White et al. 2004). In an earlier investig ation, forest floor microbial biomass was studied in a chronosequence of northern hardwood forest stands ranging from 3 years after clearcut to 120 years. Microbial biomass increased during the early successional stage, decreased during the mid-aged stage, and then increased during the late successional stage. Soil organic matter followed a pattern similar to microbial biomass. There was no trend in the fungal-to-bacterial ratio along the chronosequence. Soil moisture was strongly and positively correlated with fungal biomass. Soil pH was negatively correlated w ith fungal biomass. Finally, ammonium (NH4 +) production increased from the early to mid-aged stages and then decreased from the mid-aged to late successional stages (Taylor et al. 1999). Developing a Monitoring Program A good m onitoring program should be well focused on just a few key indicators to provide for statistically sound information (Lindenmay er, 1999). The standards for restoration are obtained from measuring key environmental indi cators at the restoration site and comparing them to established reference communities (SER, 2004). In ecological re storation, the pathway from the degraded condition to the restored, self-s ustaining condition is called the ecological trajectory (Stanturf et al 2001). Predicting the ecological traject ory of a longleaf pine forest is difficult because of the great variet y of disturbance regimes associat ed with southern pine forest ecosystems along the Gulf Coast (Palik et al. 2002). To define when a given ecological trajectory has reached a self-s ustaining state it is important to establish some specific goals for the restoration project (Hobbs & Harris, 2001). Two notable standards are to rest ore viable populations of key nativ e species in natural patterns

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18 of abundance and distribution, and to sustai n key geomorphologic, hydr ological, ecological, biological, and evolutionary processes within the normal range s of variation (ecological integrity; Mller et al. 2000). Forest structur e and plant species com position are two of the indicators being monitored in this study to capture the successional and developmental forces. Soil chemical properties, net ni trogen mineralization, and soil mi crobial dynamics were also included as indicators to insure that key biogeochemical, ecologi cal, and biological processes are also being evaluated (Harri s, 2003; Mller and Lenz, 2006). How does one determine if these goals are being achieved along the successional pattern? The normal range of variation along a spatial s cale can be determined by using a series of reference communities that are evenly distributed along the distinct ecologically identified range, to compare with the restoration site (Harris, 1999). To evaluate changes in restoration along the chronosequence, each reference community had to contain stands representing distinct ages distributed evenly as possible al ong the 110-year scale (Mller, 1998). In summary, the following steps have been recommended to insure that a monitoring plan functions properly: a) Set monitoring goals, b) identify the resources to monitor, c) establish threshold levels, d) develop a sampling design, e) co llect and analyze data, a nd f) evaluate results (Block et al. 2001). The overall goa l of this study was to establis h an ecological trajectory using selected indicators for wet longleaf pine flats so that it can be used as a monitoring framework for restoration projects. The next four chapters will address the following four specific objectives of this study. 1. Quantify the vegetational attributes of longleaf pine flat ecosystems, along a chronosequence (2-years after a stand replaci ng disturbance to 110-ye ars) of stands from within the Gulf Coast Flatw oods zone of Florida.

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19 2. Examine soil chemical (soil organic matter c ontent, pH, plant-available phosphorus, net nitrogen mineralization) and microbiologica l (microbial biomass carbon, fungal biomass carbon) properties along the same chronosequence. 3. Examine the interrelationships between the structural (vegetative) and functional (soil biogeochemical) attributes. 4. Determine if the observed structural and functio nal attributes could be used to evaluate restoration projects.

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20 Figure 1-1. Floridas Gulf Coast Flatwoods zone wh ere the wet pine flat s ites are located (Florida DEP, 2002).

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21 CHAPTER 2 FOREST STRUCTURE AND PLANT SPECIES DIVERSITY IN WET LONGLEAF PINE FLATS ACROSS A CHRONOSEQUENCE Introduction In recent years, there has been a great deal of interest in restorati on of the longleaf pine ecosystem one of the most threatened ecosystems in the United States with less than 3% of its original extent remaining. Ther e have been attempts to restor e 400,000 hectares of longleaf pine in the Southeast during the past decade alone (WMI, 2006). This situatio n creates a need for developing monitoring protocols to evaluate the success of these restoration efforts. While established monitoring programs ar e in place for many forest ecosystems in other parts of the U.S. (ERI, 2003), the lack of such establishe d guidelines can hinder th e restoration of the longleaf pine forest as a functiona l, self-sustaining ecosystem acro ss its former range (Devries et al. 2003). Community structure and species composition are two key attr ibutes often evaluated in restoration projects (Brockway et al. 2005). However, reliable information on the ecological trajectory of longleaf pine ecosystems have hamp ered monitoring of restoration projects in Florida and elsewhere in the Sout heast. Although past research has examined the structure and species composition of upland longleaf pine ecosyst ems, little information exists on the temporal patterns of forest structure and plant species diversity in wet longleaf pine flat communities located along the coastal lowlands of Floridas Gulf Coast (Michener, 199 9). Wet pine flats are pine-dominated, poorly drained, broad plain wetlands (Stout and Mari on, 1993; Harms et al. 1998).

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22 In Florida, plant species ri chness has been found to increas e with soil moisture until an ecotone between wet pine flats and cypress swamps is reached (Huck, 1986; Walker, 1993; Kirkman et al. 2001; Walker and Silletti, 2006). This ecotone is the zone where one finds wet flatwoods and wet savanna subtypes of the coas tal wet pine flat (Mes sina and Conner, 1998). Their overstories are dominated with varying mixtur es of longleaf and slash pines, but also might contain a component of Choctawhatchee sand (Pinus clausa var. immuginata) and/or pond (Pinus serotina ) pine (Parker and Hamrick, 1996). The environment for Floridas wet pine flat s is the 1,240 km-long Gulf Coast, containing sounds, bays, and offshore islands. This coasta l landscape is continuous ly shaped by active fluvial deposition and shore z one processes which promote and maintain the formation of beaches, swamps and wet mineral flats. The lo cal relief ranges from 0 to 20 m in elevation. Annual precipitation ranges fr om 1300 mm and average annual temperatures vary between 19-21 C. Growing seasons are long, lasting 270-290 days (McNab and Avers, 1994). Soil parent material consists of marine deposits containing limestone, marl, sand, and clay. The dominant soils are Aquults, Aquepts, Aquods, and Aquents. These highly acidic soils have thermic and hyperthermic temperature regimes and an aquic moisture regime. The major forest type of this region is the longleaf-sla sh pine flatwoods, while water oak ( Quercus nigra), swamp tupelo ( Nyssa sylvatica var. biflora ), sweetbay (Magnolia virginiana ), and cypress ( Taxodium sp.) are found along the major river drainages and is olated depressions (McNab and Avers, 1994). Floridas subecoregional Gulf Coast Flatwoods (Figure 2-1) covers the majority of this geographical area where both pine savannas and coastal flatwoods occur in close association with cypress ponds (Myers and Ewel, 1990; Gri ffith et al. 1994). Because of the growing

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23 conditions, wet pine flats are hi ghly productive ecosystems, and represent more than one million ha in the Southeast (Burger and Xu, 2001). There are almost 200 rare vascular plant ta xa found in the great variety of habitats classified as longleaf pine ecosystems. In additio n to the majority of them being found in Florida (Collins et al. 2001), the richest sites are found in these wet pine flats and their associated wetlands (Walker, 1993). The uniqueness of Florid as wet pine flat communities make them crucial for plant species diversity and the rarity of plants to be evaluated in any monitoring plan for restoration (Walker, 1993; Collins et al. 2001; LaSalle, 2002). One of the ways by which restoration progr ess can be monitored is by examining the ecological trajectory of the rest ored site and comparing it with the ecological trajectory of reference sites. Post-stand replacement seconda ry forest succession has been well studied in other ecosystems and is considered to follow four stages of development (Peet and Christensen, 1980; Oliver, 1981). Stand Initiation commences after a stand -replacing disturbance has occurred. Plants regenerate from sprouts, seed banks or newly dispersed seeds. Any advanced regeneration (saplings and seedlings) is releas ed by the disturbance and commences accelerated growth. The Stem Exclusion stage is when the individual tree s in the stand come under fierce competition for light, water, and nutrients. Canopy closure results in a great reduction of stand density as the residual stocking c ontains fewer, larger trees. Understory Reinitiation occurs when some of the dominant overstory trees be gin to die forming gaps for new regeneration. Finally, the Old Growth or Steady-State stage is reached when gap dynamics dominates the landscape and the forest is now all-aged. Snag s and downed logs are also found throughout the landscape (Perry, 1994). Gap dynamics is major sour ce of regeneration in na tural longleaf pine forests (Brockway and Outcalt, 1998). Steady-state is tied to the ability of a system to be self-

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24 organizing and resilient (Mlle r et al. 2000; Mller and Lenz, 2006). Self-organizing forces become especially apparent during the understory reinitiation stage of forest succession when the steady-state mosaic begins to form. When identifying patterns of succession along a chronosequence, stand changes caused by disturbance must also be cons idered (Frelich, 2002; Pickett and Cadenasso, 1995). The longleaf pine forest is a pyro-climax ecosystem which relie s on short fire return intervals to maintain the steady-state stage over other woody plant species (Wade et al. 2000). In coastal wet pine flats, wind and precipitation are also major shapers of longleaf pine communities. Hurricanes directly affect the canopy stru cture of longleaf pine stands through gale-forced winds, opening them up to sunlight and changing the compositi on of the flora and fauna that occupy them. Hurricanes also affect longleaf pine stands by the extensive flooding that accompanies the wind. Extended flooding can cause changes in both the above and below ground productivity (Johnston and Crossley, 2002; Palik et al. 2002). Anthropogenic effects caused by human activities in forests can also change the forest structure. Timber harvesting, grazing, and prescrib ed fires can cause changes in the structural complexity of forests having negative effects by exposing surface soils and reducing biodiversity (Redding et al. 2004; Van Lear et al. 2005). In addition, climatic changes such as increased atmospheric CO2 levels may affect the soil biotic co mmunity, adding to th e potential negative feedbacks toward the productivity of abovegr ound plant communities (Peacock, 2001; Frelich, 2002). Measuring forest structural data along a chronosequence will make it possible to evaluate change along the life cycle of coastal wet longleaf pine flats. The objective of this study was to examine stand structural a ttributes and understory plant species diversity along a 110-year chronosequence. We hypothesized that stand DBH, height,

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25 basal area (BA), and volume would increase while stand density and plant species richness and diversity would decrease through the mid-age. We expected th ese parameters to reach a threshold or steady-state during the mature phase when the unde rstory reinitia tion stage of succession has begun. Quantification of this eco logical trajectory w ould help establish monitoring thresholds in terms of stand struct ure and plant species composition for restoration projects. Materials and Methods Study Sites Three study sites were establishe d along a wet pine flats located within three kilom eters of Floridas Gulf Coast. They were Topsail Hill State Park, St. Marks National Wildlife Refuge, and Chassahowitzka Wildlife Management Ar ea. They were found between the cities of Pensacola and Tampa Bay (Figure 2-1), a narrow z one that makes up the majority of the Natural Resource Conservation Services Eastern Gulf Coast Flatwoods ecoregion (MLRA 152A) and the National Oceanic and Atmospheric Associations Panhandle Coast unit of the Louisianan reserve (National Estuary and Ri ver Reserve System). Both of these federal designations make this zone unique from an ecological as well as hy drological perspective. Within the Eastern Gulf Coast Flatwoods zone is the Gulf Coast Flatw oods (75I) subecoregion of Florida (Figure 2-1; Griffith et al. 1994). Any environmental varia tions between these sites were minimized by establishing very specifically defined spatial scales. This was accomplished by stratifying the important segments of the whole system (e.g. Floridas Gulf coastal flatwoods) down to the smallest distinct scale as possible (e.g. wet pine flats with in 3 kilometers of the coast) in order to take meaningful measurements (Chertov et al. 1999; Frelich, 2002; Mller et al. 2000). The first 120 years of longleaf pine succession has been incl uded with wet pine flats situated within 3 km of the Florida Gulf coast as the temporal and spatial scales in this study. These two scales were

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26 determined from an in-depth preliminary survey of stand conditions found within the Gulf Coast Flatwoods zone of Florida a nd at the restoration site. The herbaceous ground cover of wet longleaf pine flats is very diverse due to the warm temperatures and high rainfall. Broomsedge ( Andropogon virginicus) wiregrass ( Aristida stricta var. beyrichiana) witch grass (Dichanthelium spp .), goldenrod ( Solidago odora), meadow beauty ( Rhexia alifanus) fetterbush ( Lyonia lucida) and aster ( Aster adnatus) are found on both subtypes (Brewer, 1998). Pine savannas are distinguished from wet flatwoods by a greater abundance of beak sedge (Cyperus) nut rush ( Scleria cilliata ), bloodroot ( Lachnanthes caroliniana) pitcher plants ( Sarracenia) and orchids ( Calopogon) or ( Platanthera) Coastal flatwoods have a greater presence of titi ( Cliftonia monophylla) swamp tupelo, gallberry ( Ilex glabra) saw palmetto ( Serenoa repens ), and sweetbay. Where fire is restricted, catbrier ( Smilax pumila) can be a prevalent vine species (LaSalle, 2002). All three of the selected sites have a moistu re gradient as represented by cypress swamps, wet pine savannas, and wet pine flatwoods. All th ree sites have active restoration management programs where fire has been used for more th an 20 years on approximately a three-year-return interval. All of the sites are primarily managed to enhance habitat for threatened species associated with longleaf pine ecosystems, and are managed by a state or federal agency. The southern site on the spatial gradient is the Chassahowitzka Wildlife Management Area in Hernando County, FL. It is approxima tely 12,140 ha, and the soils are dominated by Basinger fine sands (sandy, sili ceous, hyperthermic spodic Psammaquents) and Myakka fine sands (sandy, siliceous, hypert hermic aeric alaquods) (Hyde et al. 1977; Spencer, 2004). The St. Marks National Wildlife Refuge in Wakulla and Jefferson Counties, FL consists of 25,900 ha and the majority of the soils are mapped as the Scranton series (sandy, siliceous,

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27 thermic humaqueptic Psammaquents) and the Leon series (sandy, siliceous, thermic aeric Alaquods) (Reinman, 1985; Allen, 1991). Topsail Hill State Park in Sant a Rosa County, FL, contains 610 ha of some of the oldest longleaf pine stands in Florid a. The soils are Pickney sand se ries (sandy, siliceous, thermic cumulic humaquepts) and the Leon series (Overing and Watts, 1989; White, 2001). Forest Age Classes The 110-year chronoseq uence starts from the point of stand replacement to the oldest stands measured in our reference sites. The fo llowing age classes were used to stratify and analyze changes in forest structure and plant sp ecies composition within the different stands at each of the reference sites. There are 12 repli cates per age class for the stand data and 48 replicates per age class for plan t species data. The age classes pr ovide a means to identify the structure of stands within speci fic time periods along the chronos equence and to detect changes from one time period to the next (Mller, 1998; Aravena et al. 2002). In this paper, a tree is defined as a woody plant with a diameter at breast height (DBH) of greater than 10 cm. A sapling is a woody plant with a DBH of less than 10 cm but gr eater than 2.5 cm. Finally, a seedling is a woody plant that is less than 91.5 cm in height (Wenger, 1984). The young age class: A young age stand exists when the majority of the stocking (> 70%) can be found as seedlings and saplings. The average stand age should be < 20 years since replacement. The mid-aged class: The mid-age stand should have stocking (>70%) dominated by a mixture of poles and small sawlog size trees (1 0-30 cm DBH). The average stand age should be between 20 and 55 years old.

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28 The mature age class: A stand is considered wi thin the mature age class when the majority of stocking (>70%) can be found as dominant sawlog trees (30-45 cm DBH). The stand age should be > 55 years old. Field Measurements Each referen ce location had a cluster of th ree one-hectare blocks, containing stands representing each of the three previously defined age classes. Each one-hectare block was subdivided into four ra ndomly placed 400 m2 measurement plots. Tree height and DBH were measured on all trees > 10 cm DBH. At least tw o of the dominant trees were cored at breast height to determine stand age. St and density (trees/ha), basal area (m2/ha) and standing volume (m3/ha) were calculated from thes e data. In addition, the volume (m3/ha) of all snags and downed woody debris (CWD) were also calculated. The e quation used for tree and snag estimates was: V = (0.000078539816*(DBH2))*tree height. The volumes of downed logs were estimat ed with Smalians metric equation: V = [((D2) + (d2))*0.00003927]* log length (m), where D = diameter large end (cm) and d = diameter small end (Wenger, 1984). We adapted the system of five decomposition st ates for snags and downed woody debris used by Spetich et. al. (1999). The decomposition descriptions translated to five levels of decomposition deductions by percent (15, 30, 45, 60, and 75%; see Table 2-1). Each 400 m2 plot contained four smaller 1 m2 subplots randomly placed within the larger plot for understory sampling (Figure 2-3). Perc ent cover of each species was assessed using a modified Daubenmire method incorp orating eight different levels (Daubenmire, 1959). Coleman rarefaction and the Shannon-Weiner diversity indi ces were calculated for each stand (Koellner and Hersperger, 2004; Colwell, 2006).

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29 Data Analysis A three stage balanced nested design was used to integrate the indicators measured at different scales, and among sites (Figure 2-2). Hy pothesis testing for differences between means was accomplished by using two-sample t-test with an alpha of 0.05 and a two-tailed confidence interval. The sampling of nine distinct refe rence locations produced a dataset where the assumptions for analysis of variance (ANOVA) wa s not ensured; therefor e, non-parametric tests were used to detect any significant differences among the reference sites and among the distinct forest age class segments (SAS, 2002). Trends over time and between variables were obtained from linear regression using the general linear model (PROC GL M) (Yang et al. 2006; SAS, 2002). Plant species indicator analysis (IndVal) was used to measure the level of relationship between a given plant species to categorical units such as pine flat subtypes or forest age classes. It calculates the indicator value d of species as the product of the relative frequency and re lative average abundance in each categorical cluster. Indicator species analysis is used to attribute species to particular environmental conditions based on the abundance and occurrence of that species within the selected group. A species that is a perfect indicator is consistent to a pa rticular group without fail. Indicator values range from 0 to 100, with 100 being a perfect indicator score. Because indicator species analysis is a statistical inference, a test of significance is applied to determine if species are significant indicators of the groups with which they are associated (Dufrene and Legendre, 1997). This is achieved by the M onte Carlo permutation test procedure (1000 iterations), where the significance of a P-value is determined by the number of random runs greater than or equal to the inferred value ( =0.10). Accuracy is defined from the binomial 95% confidence interval (Strauss, 1982).

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30 Results Overstory Stand Structure The m ean stand DBH, height, BA, and volume varied significantly among the age classes (Table 2-2). For example, the mean DBH fo r the young stands was 6.0 cm, 23.4cm for the midage stands, and 30.0cm for the mature age sta nds. Height, BA, and volume exhibited similar results. Snags and downed woody debris had similar values for the young and mid-age stands, but was significantly higher for the mature stan ds (Table 2-2). Regression analysis revealed trends over the chronosequence for stand structural variables. Except for stand density, all of the stand variables increased with forest age class. Stand density was highl y variable over time and did not exhibit any specific patterns (Figure 2-3) As expected, stand DBH increased with age until 85-90 years then began to reach a steady-state (Figure 2-4). Stand height also increased over time, but reached an asymptote at 85-90 yrs. Stand basal area and volume followed similar regression curves as with DBH and height (F igure 2-4). Downed wood y debris accumulation levels were highly variable, but in general increased over the 110-year chronosequence (Table 22; Figure 2-5). The volume of standing deadwood (snag) increased through the mid-age stands and then decreased thereafter (F igure 2-6). The level of CWD decomposition remained the same for the young and mid-age stands, but was lower for the mature age stands (Figure 2-7). Understory The abundance of grasses and forbs decreased, and the abundance of shrubs increased over the three forest age classes (p < 0.05; Figure 2-8). The Shannon-W iener diversity index ranged from 1.28 2.40 for the dataset. In general, Shan non-Wiener diversity index decreased over time (Figure 2-9). Shannon-Wiener dive rsity index also decreased w ith stand density during the young age class, but increased with stand density during the mature age cl ass (Figure 2-10). The Coleman rarefaction index ranged from 7.2-22.0 for the dataset (Table 2-9). The Coleman

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31 rarefaction index increased with stand height during the young age class, but decreased with stand height during the midaged class (Figure 2-10). Bluestem grass, blueberry ( Vaccinium spp.), and witchgrass were the dominant plant species indicators for the young ag e stands (p < 0.022). Meadow b eauty, wiregrass, and Carolina redroot were the dominant plant species indicators for the mid-aged stands (p < 0.067). Gallberry, running oak ( Quercus pumila ), and dangleberry were the best plant species indicators for the mature age stands (p < 0.1; Table 2-3). Discussion Overstory The overstory variables of m ean stand DBH, stand height, stand BA, and volume exhibited strong positive relationships with stand age as expected. Even downed logs and snags, heterogeneous variables among the sites and with in age classes, produced a weakly positive trend with stand age (p < 0.042). Stand de nsity showed no clear pattern along the chronosequence, owing to the high variability f ound within density across the age gradient. The data showed most of the gr owth variables reaching an as ymptote around 85-90 years. When the chronosequence stand data were co mpared to growth and yield of natural longleaf pine stands, our stands were found to have lower basal area (14 m2 vs. 25 m2) at age 30, but comparable stand volumes (150 m3 vs. 130 m3) at age 60 (Farrar, 1985). The steady-state phase for these forests is reached around 85-90 year s, may be shorter than the steady state of 110 years for longleaf pine ecosystems in Texas, reported by Chapman (1909). All of the stands over 85 years measured at our reference sites had large gaps containing saplings and some poles. This structure would in dicate that the understory reinitiation stage of secondary succession was well along and the ec osystems self-organization capacity was apparent. Of the aboveground indicators, both stand density and CWD had the greatest

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32 heterogeneous datasets based upon statistical analysis. This great variability in stand density and CWD reflects the differences that must be evaluated when comparing natural versus intensively managed forest stands. Stand growth is high during the early phase, but is slowed during the mid-aged class when the stem exclusion state is reached. Interspecifi c competition results in mortality of individuals and the snag accumulation rates increase. This was evident along the chronosequence when snag accumulation peaked between 60 and 80 years. During the mature phase, stand growth is slowed as the forest reaches a steady state. This indicat es that the mid-aged class was the major period for CWD accumulation brought on by both competi tion and disturbance (Spetich et al. 1999). Downed logs and snags continued to accumulate during the mature phase, but with a decreasing rate of decomposition. Plant Species Diversity The species diversity exhibited interesting patterns along the chronosequence. Stand density had a strong negative relationship with the Shannon-W iener diversity index within the young age class, but a positive relationship dur ing the mature age class (Figure 2-10). The Coleman rarefaction index increased with stand height within the young age class then decreased during the mid-aged class (Figure 2-10). The ch ange in response between stand height and Coleman rarefaction, and stand density w ith the Shannon-Wiener diversity index is due to how these two distinct indices calculate species diversity. The Coleman rarefaction function gives more weight to the rarity than commonness of species. The function looks at the diversity where the turnov er of species between two distinct species pools is measure d. There was an expected number of species E(s) where Monte Carlo iterations were performed to predict which species would be more likely appear (Hurlbert, 1971). In this case, species diversity became related to habitat diversity or habitat heterogeneity

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33 (Hersperger and Koellner, 2004). As habitats beco me more complex (layer ed) with tree growth during early stand development, ra refaction index increases with species turnover rates, and as the number of rare species increase. This approa ch can better assess disturbance changes to the site than the (information theory measure) Sha nnon-Wiener diversity inde x (Gotelli and Colwell, 2001). The Shannon Wiener diversity index responde d positively to a stand with higher tree density during the mature age class. These mature higher density stands may have more habitat homogeneity than a stand with greater openness. Habitat homogeneity influences the ShannonWiener indexs evenness function J. Species evenness may be easier to attain where habitat homogeneity is greater. This is why the Shannon-Wiener indexs H value decreased as habitat heterogeneity increased during ea rly stand development, or may increase in older stands with higher stand density (homogeneity). Since the Shannon-Wiener indexs evenness function gives equal weight to rare and common species, it doe s not measure local patterns of assemblage where disturbance impacts could be assesse d (Pianka, 1966). The Shannon-Wiener diversity index still should be included w ith a rarefaction index during assessments because it is an abundance-based function where the total number of species (richness S) that are found within an area are measured. In addition, a measuremen t of the relative abund ance (N) and degree of equality among species (evenness J) ar e also calculated (Poole, 1974). The young, mid-aged, and mature age classes va ried in the abundance of grasses, forbs, and shrubs. Even with the goal of applying prescribed fire every thre e years at the reference sites, shrub species increased and graminoid species de clined over the age classes. The mature age class changed with running oak for mesic sites and gallberry for the we tter sites. The young age class had blueberry for mesic conditions and bluest em grass for the wetter sites. In this case,

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34 more forest structure brought on by stand maturation could re present a drying effect on soil conditions as represented by a change in plant species. Conclusions Stand DBH, height, and basal area increased until 85-90 years wh en they began to reach a steady state. Coarse woody debris accum ulation levels were highly variable, but tended to increase with age. Standing deadwood also incr eased with age up to 60-80 years and began to decline thereafter. The decomposition levels of CWD were c onstant through the mid-aged class, but declined from the mid-age to the mature stage. The levels of shrub sp ecies were significantly higher in the mature sites than eith er the young or the mid-aged classes. Tree growth during early stand development tran slates to habitat heterogeneity as partial shading brings in new groups of plant species. At this point, stand height had a strong positive relationship with Coleman rarefaction index and stand density had a stro ng negative relationship with Shannon-Wiener diversity index. The plant species turnover rates as indicated by the Coleman rarefaction index were high and the ev enness of plant species as indicated by the Shannon-Wiener were very low. The evenness of plant species was not attained until the mature age class when competition was reduced, and the nu mber of plant species entering the ecosystem was equal to the number of plant species leav ing it. During this time period, Shannon-Wiener diversity index had a strong positive relationship w ith stand density and the Coleman rarefaction index had a negative relations hip with stand height. The results have shown interesting trends along the chronosequence for wet longleaf pine flat communities along the Gulf coast of Florida. The results indica te that Floridas Gulf Coastal longleaf pine flats reach the unders tory reinitiation condition at a pproximately 85-90 years. This would mean the forest is neari ng a steady, self-organizing state, perhaps a threshold point for attaining restoration success in terms of structural attributes.

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35 Three areas of this research warrant further attention. Firs t, investigations concerning coarse woody debris in southern pine forests is lacking, probabl y due to a perception that any accumulation would be limited by prescribed fire. In this research, we found heavy accumulations at sites in each of the forest age classes. Secondly, experiments in plant community assemblage should be conducted to take a closer look at the relationships between the commonly used Shannon-Wiener diversity index and the Coleman rarefaction index. Coleman rarefaction was a stronger index duri ng early stand development, but showed no advantage over the Shannon-Wiener index during la ter stages of stand de velopment. Finally, our research found that shrub species dominated the mature aged stands even with aggressive fire management programs. Many of the plant species that were classified as woody do not have pioneer patterns similar to gallberry, saw-palme tto, or runner oak. They never dominated the site. There should be studies that focus on the less kn own woody species and their benefits to longleaf pine forest ecosystems.

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36 Table 2-1. Qualitative classification sy stem for downed and standing deadwood. Characteristic Decay Class I II III IV V Leaves Present Absent Absent Absent Absent Twigs Present Present Absent Absent Absent Bark Present Present Often Pr esent Often Absent Absent Bole Shape Round Round Round Round to Oval Oval to Flat Wood Consistency Solid Solid Semi-Solid Partly Soft Soft Wood Strength Firm Firm Firm Breakable Fragmented Decomposition Deduction 15% 30% 45% 60% 75% Spetich et al. 1999; Adapted from Spies et al (1988). Table 2-2. Forest structural and plant species diversity means among age classes. Stand Age (years) Age Class Stand Diameter (cm) Stand Height (m) Stand Basal Area (m2/ha) Stand density (Trees/ha) Stand Volume (m3/ha.) CWD (m3/ha) Coleman Rarefaction ES(63) ShannonWierner Diversity 6Young0.30.20.03000.017.510.962.04 8Young0.72.80.12000.013.312.932.24 9Young8.34.90.3501.31.315.781.91 10Young8.56.00.3751.82.615.781.91 17Young16.18.95.625060.811.116.972.25 *Mean6.0a3.3a1.3a175a12.9a9.5a14.51a2.07a24Mid-Aged19.510.72.335026.02.516.692.2927Mid-Aged25.711.75.412562.00.716.692.29 29Mid-Aged13.68.27.340084.04.320.111.91 31Mid-Aged19.810.17.125078.01.517.902.32 34Mid-Aged23.311.510.1225128.076.013.281.72 36Mid-Aged17.211.111.1425125.025.320.111.91 40Mid-Aged29.919.41.87534.03.512.992.14 42Mid-Aged21.910.71.02510.110.012.992.14 46Mid-Aged25.818.020.6425395.880.79.991.28 50Mid-Aged35.020.94.950101.25.89.991.28 52Mid-Aged26.812.311.5275174.51.87.991.78 *Mean23.4b13.0b7.7b216a112.2b9.3a14.41a1.97a60Mature27.719.16.1250115.018.115.551.9461Mature18.412.214.1450192.313.717.902.3262Mature41.717.824.2225433.158.310.911.4168Mature24.912.57.3125122.3105.812.801.6271Mature35.115.510.2225156.161.57.991.78 86Mature26.016.610.7175206.39.612.801.62 95Mature37.518.211.1175202.512.717.891.56 101Mature30.213.213.7175234.111.47.201.16 105Mature33.417.511.0125194.31.017.891.56110Mature34.017.415.9250278.712.317.891.56*Mean30.0c15.5c11.8c217a201.7c30.0b14.25a1.86a Means followed by the same lower case letters are not signif icantly different (alpha=0.05). CWD represents snags and downed logs. The sample size for stand data by age class was n=12 and the understory vegetation data by age class was n=48.

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37 Table 2-3. Indicator values for plant sp ecies in three forest age classes. Age Class Plant Species Age Class Young Mid-Aged Mature SD P-Value Veg Type Young Andropogan virginicus 34 3 1 3.09 0.001 Grass Dichanthelium ovale 33 10 2 3.49 0.002 Grass Vaccinium sp. 25 2 9 3.27 0.019 Shrub Pteridium aquilinum 12 1 0 2.28 0.021 Forb Mid-Aged Rhexia alifanus 0 28 0 3.03 0.001 Forb Cyperus sp. 0 25 0 2.20 0.001 Grass Lachnanthes caroliana 8 25 1 2.98 0.005 Forb Arisitida var. beyrichiana 1 23 0 2.54 0.001 Grass Solidago odora 0 9 1 2.09 0.066 Forb Mature Ilex glabra 20 11 37 3.04 0.007 Shrub Quercus pumila 17 3 29 3.31 0.014 Shrub Gaylussacia frondosa 2 3 13 2.81 0.099 Shrub Licania michauxii 0 0 10 2.17 0.040 Shrub Kalmia hirsuta 0 0 7 1.96 0.094 Shrub INDICATOR VALUES (% of perfect indication based on co mbining the values for relative abundance and relative frequency). The sample size for the understory vegetation data by age class was n=48.

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38 Selected Reference Communities 1. Chassahowitzka Wildlife Management Area 2. St. Marks National Wildlife Refuge 3. Topsail Hill State Park Figure 2-1. Locations of the Pt. Washington Longleaf Pine Restora tion site ( ) and the reference sites within Gulf Coast Flat woods subecoregion of Florida (Griffith, 1994). 3 1 2

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39 Figure 2-2. Nested plot sa mpling design applied at three differe nt sites (age classes) for each reference location. 0 50 100 150 200 250 300 350 400 450 500 020406080100120 Stand Age (Years)Stand Density (trees / ha) Figure 2-3. Mean stand density (trees per hectare) along a 110-year longleaf pine chronosequence as measured from 26 differently aged stands. 1 ha Age Class Site 400 m2Forest p lot 1m2Ve g / soil q uadrat

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40 Figure 2-4. Mean stand DBH, height, BA, and volume al ong a 110-year longleaf pine chronosequence as measured from 26 differently aged stands. y = -0.0043x2 + 0.7571x + 0.4671 R2 = 0.75 p < 0.0013 0 5 10 15 20 25 30 35 40 45 50 0255075100125Mean Stand DBH (cm) y = -0.0026x2 + 0.4109x + 0.9501 R2 = 0.72 p < 0.0004 0 2 4 6 8 10 12 14 16 18 20 22 0255075100125Mean Stand Height (m ) y = -0.0018x2 + 0.3297x 1.9355 R2 = 0.46 p < 0.0025 0 5 10 15 20 25 0255075100125Mean Stand Age (Years)Mean Stand Basal Area (m2 / ha) y = -0.0115x2 + 3.6533x 24.902 R2 = 0.76 p <0 .0004 0 50 100 150 200 250 300 350 0255075100125Mean Stand Age (Years)Mean Stand Volume(m3 / ha)

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41 y = 0.3717x + 0.5181 R2 = 0.16 p < 0.0414 0 20 40 60 80 100 020406080100120Downed Woody Debris (m3 / ha) y = -0.0094x2 + 1.1249x 13.057 R2 = 0.25 p < 0.086 0 20 40 60 80 100 020406080100120 Stand Age (Years)Standing Deadwood (m3 / ha) Figure 2-5. Downed woody debris and standing deadwood (snag) accumulations along a 110year longleaf pine chronosequence as m easured from 26 differently aged stands.

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42 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Forest Stage ClassDecomposition Level (%) Young Mid-Aged Mature Figure 2-6. Decomposition levels by forest age class as measured from 26 differently aged stands. Percent levels repr esent the amount of decay. 0 1 2 3 4 5 6 GrassesForbsShrubsVines Vegetative TypePercent Cover (%) Young Mid-Aged Mature b a a a a b a a b a ab a Figure 2-7. Composition of understory vegetation by forest age class. a a b

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43 Figure 2-8. Shannon-Wiener Diversity and Co leman Rarefaction indices along a 110-year longleaf pine chronosequence as measur ed from 26 differently aged stands. y = 7E-05x3 0.01x2 + 0.3696x + 12.054 R2 = 0.23 p < 0.0373 0.00 5.00 10.00 15.00 20.00 020406080100120 Stand Age (Years)Coleman Rarefaction ES (63) y = 5E-07x2 0.0071x + 2.19 R2 = 0.40 p < 0.0006 1.00 1.20 1.40 1.60 1.80 2.00 2.20 2.40 020406080100120Shannon-Wiener Diversity H'

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44 y = -1E-05x2 + 0.004x + 1.7514 R2 = 0.62 p < 0.0001 1.00 1.20 1.40 1.60 1.80 2.00 2.20 2.40 0100200300400500600Shannon-Wiener Diversity H' Young Age Class y = -0.0609x3 + 0.8965x2 2.5441x + 12.187 R2 = 0.70 p < 0.0001 5 8 11 14 17 20 03691 2Coleman Rarefaction ES (63) y = 0.149x2 5.0778x + 52.855 R2 = 0.61 p < 0.0001 5 8 11 14 17 20 23 691215182124Coleman Rarefaction ES (63) y = 0.4086x2 11.76x + 94.552 R2 = 0.37 p< 0.0024 5 8 11 14 17 20 23 691215182124Stand Height (Meters)Coleman Rarefaction ES (63) y = -6E-06x2 + 0.0023x + 1.7807 R2 = 0.08 1.00 1.20 1.40 1.60 1.80 2.00 2.20 2.40 0100200300400500Shannon-Wiener Diversity H' Mid-Aged Class y = 9E-06x2 0.0023x + 1.6464 R2 = 0.56 p < 0.0032 1.00 1.20 1.40 1.60 1.80 2.00 2.20 2.40 0100200300400500Stand Density (Trees / ha)Shannon-Wiener Diversity H' Mature Age Class Figure 2-9. Mean stand density versus the Shan non-Wiener Diversity index and mean stand height versus the Coleman Rarefaction index as measured from the young, mid-age and mature age longleaf pine stands.

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45 CHAPTER 3 PATTERNS OF SOIL CHEMICAL AND MICROBIAL PROPERTIES ALONG A CHR ONOSEQUENCE IN WET LONGLEAF PINE FLATS OF FLORIDA Introduction Soil nutrient dynamics and their relationshi p to forest stand development have been under investigation for some time (Odum, 1969; V itousek and Reiners, 1975). Studies describing soil nutrient status, in particul ar nitrogen, and its influen ce on stand productivity and canopy nutrient dynamics have focused mostly on plantations (Morris and Boerner, 1998; Kirkman et al. 2001; Allen and Schlesinger, 2003), but research conducted in natu ral stands are also found in the literature (Zak et al. 1990; Vance and Entry, 2000; Arav ena et al. 2002; Chapman et al. 2003; White et al. 2004). Similar studies are rare in the longl eaf pine ecosystem, one of the most threatened ecosystems in the United States (W ilson et al. 2002). For example, patterns of nitrogen mineralization, the relationship between nitrogen levels and soil mi crobes, and how this relationship changes over time, have not been given much attention (Johnston and Crossley, 2003). Such information could aid the efforts of re storation professionals who are interested in not only restoring the structural attributes of the longleaf pine ecosy stem, but its functional attributes as well. One way to study the relationship between forest stand development and soil microbial dynamics is to use a chronosequence of simila r stands having differe nt ages since stand replacement (Pickett, 1989; Williamson et al. 2005) In an earlier investigation, Taylor et al. (1999) studied forest floor microbial biomass of northern hardwood forest stands ranging from 3 years after clearcut to 120 years. These authors reported an increasing tre nd in microbial biomass with age during the early successional stage. Ho wever, microbial biomass decreased with age during the mid-aged stage, but increased again during the late successional stage. Soil organic matter followed a pattern similar to microbial bioma ss. They further reported that fungal biomass

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46 was positively correlated soil moisture and ne gatively correlated with soil pH. Finally, ammonium (NH4 +) production increased from the early to mid-aged stages and decreased from the mid-aged to the late successional stage (Tay lor et al. 1999). In another study, investigators wanted to detect the effects of plant diversity on the levels of fungal microbes by measuring the populations of fungal-feeding nematodes. As the plant community succeeded toward late successional conditions, there was little effect on the numbers of fungal-feeding nematodes (Kardol et al. 2005). The relationship between fungal growth and nematode populations was more complex than the investigators surmised. Recently, increased emphasis has been placed on examining soil microbial communities during soil assessments, especially when monito ring restoration projects (Harris, 2003; Johnston and Crossley, 2003). Some of the measured soil biotic variables have included microbial biomass carbon and nitrogen (Vance and Entry, 2000; Wilson et al. 2002), most probable numbers (MPN) of microbial functional groups (Schmidt and Belser, 1982), fungal biomass estimates (Montgomery et al. 2000), and complete co mmunity profiling (Bailey et al. 2002). Studies from other parts of the U.S. and the world have also contributed to our understanding of the soil community relationships For example, a growing number of studies have indicated that soil microbial communities with distinct f unctional groups inhabit different forest types (Pennanen et al. 1999). A black pine ( Pinus nigra) forest in Austria was found to have higher relative amounts of f ungi and actinomycetes in the soil microbial biomass than were found in a neighboring oak-beech (Quercus petrea Fagus sylvatica ) hardwood forest (Hackl et al. 2005). Researchers conducting a study in England found that soil moisture, pH, and microbial biomass levels decreased along a successional grad ient from moorland to grassland to mature pine forest (Chapman et al. 2003). Researchers in Finland found great variability within soil

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47 microbial populations when measured beyond a 3-4 m radius (Pennanen et al. 1999). This result could imply soil microbial measurements farther than 4 meters apart may need to be considered as independent samples and not replications. A recent study in a longleaf pine ecosystem found that nitrogen mineraliza tion rates declined sign ificantly along a moisture gradient from xeric sandhills through the pine scrub to wet pine flat woods, but the levels of soil carbon, nitrogen, microbial biomass carbon, and aboveground net prim ary productivity (ANPP) had reverse trends (Wilson et al. 2002). These studies illustrate the strong interac tions that exis t between soil biogeochemical properties and vegetative ch anges in the aboveground cover type. Fires are important agents of natural disturba nce in many forest types, and especially in maintaining southern pine forests (Outcalt, 2000). Fires have variable e ffects on soil microbial biomass in forest soils where fire intensity, se ason, and weather play a role (Wilson et al. 2002). Hurricanes, another prominent di sturbance along the Gulf coast, can have major impacts on forest structure by strong winds (Palik et al. 2002), but the associated flooding may have bigger impacts on soil productivity, biogeochemistry and soil microbial popu lations (Lockaby and Walbridge, 1998). Anthropogenic disturbance effects from human activities in fore sts can also have effects on the functioning of forest soils. In a military installation study focused on the effects of different levels of vehicle traffic on the soil microbi al community within a longleaf pine forest, investigators found that increasing levels of tr affic produced a decrease in the fungal biomass (Peacock et al. 2001). Fertilization can have negative feedbacks. In a grassland restoration study, fertilizer additions caused increases in the num ber of bacteria, but a decrease in the fungal population. Sites with no fertil izer had larger fungal biomass levels, a greater number of legumes, and higher plant species richness than th e fertilized areas (Smith et al. 2003). Changes

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48 in soil microbial functional groups caused by natural or human-induced disturbances can have negative impacts on long term soil nutrient cycles. Ecosystem health has been described in terms of nutrient retention or the ability of an ecosystem to prevent nutrient loss (Odum, 1969). Ecosystems have been identified as leaky where nitrate (NO3 -) was found in higher concentrations, or as nitrogen-limited or having a tight nutrient cycle where th e less mobile ammonium (NH4 +) ion was in higher concentrations (Davidson, 2000). The steady-state development st age of succession (Oliver, 1981) has been described as the time period of forest succession when the ecosystems nutrients are held tightly within (Odum, 1969). Vitousek and Reiners (1975) concluded the tightest period of nutrient retention was during the mid-aged period when nutrients are brought into short supply by heavy competition. They further concluded that nutrient retention in an ecosystem actually reflects biomass accumulation patterns. They suggested th at differences between net nitrogen input and output were proportional to the ra te nitrogen was incorporated into net biomass increment. Biogeochemical equilibriu m would be signified when the di fferences between nitrogen inputs and outputs would be equal to ze ro, or during the period of late succession when net biomass accumulation is close to zero (Zak et al. 1990). Given the relationship between biomass accumulation and nutrient retention, the biogeoche mical thresholds should be found when the ecosystem is self-organizing or during the understory reinitiation stage of succession (Oliver, 1981). Recent investigations have f ound an internal mechanism by which excessive nitrate is conserved in wet forested ecosystems. In upla nd forested environments, examined within both the temperate and tropical zones, investigator s have discovered that dissimilatory nitrate reduction to ammonium (DNRA) was a major pa thway for transforming nitrate to ammonium,

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49 and preventing losses from leaching or by the de nitrification pathway (Silver et al. 2001; Huygens et al. 2007). Through 15N tracing, investigators discovered the majority of any surplus nitrate was reduced by DNRA, rather than reduce d by denitrification or immobilized by soils. The common conditions found at the research si tes were wet soils, hi gh organic carbon, and normally nitrogen-limited environments. We conducted our studies within longleaf pi ne ecosystems located along the Gulf Coast Flatwoods zone. This coastal region is found between the panha ndle community of Pensacola and Tampa Bay, Florida. Various ecological studies have investigated the changes in plant community composition along soil moisture gradient s within the Gulf Coast Flatwoods zone, but none have examined the soil chemical and microbial properties al ong a chronosequence. Previous research has concluded that plant species richness increases along a soil moisture gradient until an ecotone betw een mesic flatwoods and cypress swamps is reached (Huck, 1986; Walker, 1993; Kirkman et al. 2001). This ecotone is the interface where one would find the wet flatwoods and wet pine savanna su btypes of the coastal longleaf-slash pine flat (Messina and Conner, 1998). There are almost 200 rare vascular plant taxa found in the great variety of habitats classified as longleaf pine ecosystems. In addition to the majority of them being found in Florida (Collins et al. 2001), the ri chest sites are found in these wet pine flats and their associated wetlands (Walker, 1993). Wet pine flats repres ent more than 1 million ha in the Southeast (Burger and Xu, 2001). Plant species richness of wet longleaf pine communities has been positively correlated with soil productivity (Kirkman et al. 2001), and specific soil properties (Wilson et al. 2002). Soil characteristics need to be included with plant species richness in any restoration assessment of coasta l wet longleaf pine flat ecosyst ems to couple functional with structural attributes (Joh nston and Crossley, 2002).

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50 Our main objective was to measure soil pH, moisture content, organic matter content (SOM), plant-available phosphorus, soil nitrogen mineralization rates (Nmin), soil microbial biomass carbon (Cmb) and fungal biomass (Cfb) along a 110-year chrono sequence to determine the ecological trajectory in terms of soil chemical and microbial characteristics for longleaf pine in coastal wet pine flat communities. We specifically tested our hypothesis that this group of soil biogeochemical indicators measured along a 110year chronosequence would follow a pattern similar to the biomass accumulation curve of fore st succession (Vitousek and Reiners, 1975). In response to rapid increase in grow th during the early years of sta nd establishment, we predicted a similar increase in net nitrogen mineralizati on rates, microbial biomass and fungal biomass levels. We hypothesized that thes e variables would decrease at some point during the late midaged phase and reach a threshold some time during the mature phase. Materials and Methods Study Areas Three representative locations along Floridas Gulf Coast Flatwoods zone (720 km ) were selected for this study. The three locations we re Topsail Hill State Park, St. Marks National Wildlife Refuge, and the Chassahowitzka Wildlif e Management Area of the Florida Fish and Wildlife Conservation Commission. At each location, four 400 m2 plots representing each of early, mid, and mature age classes of longleaf pine stands were laid out for vegetation (reported in Chapter 1) and soil sampling. The different successional ages (age classes) represented a chronosequence of 110-years. Soil Sampling and Preparation Soils sam ples (> 500 g) were taken from four (1m2) quadrats taken in each of the 400 m2 plots during September of 2005 and 2006 for general analysis. The samples were taken from the upper 10 cm of the A horizon, not including the organic layers. An additional sampling was

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51 conducted in August, 2005, at which time a paired-s oil sample was buried in place for incubation (Eno, 1960; described later). The incubated samp les were taken out during the September, 2005 sampling period. All samples were immediately stor ed at 4C until analysis. A sub-sample (20g) from all of the samples was used for determinin g moisture content after oven drying at 105C for 72 hours. Soil Chemical Analysis The soil s amples were analyzed at the Univer sity of Florida soil testing laboratory (UF Analytical Research Laboratories), Gainesville, Florida. Soil water pH was determined from prepared slurries using a soil-to-water rati o of 1-to-2 (EPA method 150.1). Plant-available phosphorus was determined with the use of Mehlich-1 extractant (H2SO4 & HCL) and measured on an inductively coupled plasma (ICP) spect rophotometer (EPA method 200.7; Nelson et al. 1953). Soil organic matter content (SOM %) wa s determined by the Walkley-Black method (Walkley, 1947). Net Nitrogen Mineralization Net nitrogen m ineralization was determined by the buried bag technique (Eno, 1960). Forty eight samples were collected from each reference location and the re storation site for a total of 144. In general, one bag was buried in situ for incubation during August 2005 (Eno, 1960) and the other bag taken to the soil lab for an alysis. The incubated bags were collected and analyzed after 30 days. Mineral nitrogen was extracted from 20 g of both soil samples with 60 ml 2N KCL and placed in a shaker for one hour. They were then filtered through # 42 Whatcom filter papers into 20 ml scillination bottles. The samples were analyzed by the University of Floridas Analytical Research Laboratory fo r ammonium (EPA method 350.1) and nitrate (EPA method 353.2) with a continuous auto-flow analy zer. Net mineralization was calculated as the

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52 difference between incubated-N and initial-N (c orrected for soil moisture) (Keeney & Nelson, 1982). Microbial Biomass Soil m icrobial biomass C was determined by ch loroform fumigation-extraction (Vance et. al., 1987), with the following modifications. Twelve grams of soil were sieved from soil samples stored at 4 C and then placed in 50 ml centrifuge test tubes. Matching 12 g soil samples were set aside in additional 50 ml centr ifuge tubes as the control. The soil samples were fumigated in a desiccator with 40 ml of alc ohol-free chloroform placed into a center beaker, an additional 0.5 ml of chloroform was placed into each centrifuge tube. The top of the desiccator was pressure sealed and vacated until the chloroform began to boil. The tubes were then incubated for 24 hours at 25C. The dessicator was then opened, re sealed, and after the chloroform was reboiled, incubated for an additional 24 hours. The control and fumigated samples were extracted with 36 ml of 0.05 M K2SO4, shaken (360 rpm) on an orbital shaker for 1 hour, and centrifuged @ 6000 rpm for 15 minutes. The supernatant was then filtered through # 42 What com filter papers into 20 ml scintillation vials and frozen until analysis. Levels of total organic carbon (TOC) were determined on a Shimadzu TOC-VCSH analyzer (Vance & Entry, 2000). Microbial biomass carbon was calculated as: [(fumTC ConTC) / 0.51] / (Soil Wt.) = mg C kg-1 dry wt. soil (Joergensen, 1996). The value of 0.51 is the conversion factor equal to the extractable porti on of microbial biomass in a forest soil. Fumigated and non-fumigated blanks were measured to correct for the chloroform and potassium persulfate. Soil fungal biomass levels were determined by a physical disruption method for extraction of ergosterol from soil samples (Gong et al. 2001) with the following modifications. Six grams of soil were mixed with 9 ml of 0C methanol and 1.9 g of glass beads into 20 ml scintillation

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53 vials. The vials were vortexed for 30 seconds, sh aken (360 rpm) on an orbital shaker for 1 hour, and refrigerated over night. An a liquot of 1.8 ml was plac ed into 2 ml micro-centrifuge tubes and centrifuged @ 11,000 rpm for 20 minutes. After extr action, the samples needed to be filtered before running through a High Performance Li quid Chromatography (HPLC) computerized machine. A syringe was used to remove 1.5 ml of the supernatant from the micro-centrifuge and filtered through a 0.20 m filter into amber colored 2 ml glass HPLC vials. The HLPC vials were covered with aluminum foil and stored in the dark at 0C until ready to inject into the HPLC. Each sample was quantified on a Beckman C oulter HPLC equipped with an UV detector, a pump, an auto-sampler, and through a C-18 re verse-phased analytic column (4.6 x 250 mm). The UV detector was set at 282 nm and pure metha nol was used as the mobile phase at a flow rate of 1 ml per minute. Extracts (100 l) were injected while the column pressure was maintained at 1000 psi. Pure ergos terol (Sigma) was recrystalized in pure methanol at different concentrations to establish a set of standards. The standard curve was constructed from a linear regression relationship between peak area and ergosterol concentration Ergosterol recoveries were calculated from the difference between spiked and non-spiked paired samples divided by the amount of ergosterol added Under such conditions, an isolated peak was identified from field samples at approximately 13 minutes, based upon the peaks obtained from the ergosterol standards. Establishe d from results of previo us investigations, an average conversion factor for 3.65 g ergosterol pe r mg of soil is converted to fungal biomass (mg-1 /g -1 soil) when multiplied by 220 (Montgomery et al. 2000). Fungal-to-microbial biomass ratios were determined using a ratio of the calculated soil fungal biomass carbon and the soil microbial biomass carbon for each sample.

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54 Coastal wet longleaf pine flats experience l ong periods of standing water (Harms et al. 1998). This flooding causes changes in the biogeochemical cycling of nutrients. These forested wetlands also contain highly acidic soils that require modifications to standard soil biochemical analysis techniques normally used in moderate pH (6.0) wetlands. The following modifications were necessary in order to produce good laboratory results. The microbial biomass carbon was extracted from soils using a lower 0.05 M K2SO4 extractant instead of the standard molarity (0.5 M) for improved efficiency in these low pH soils (Haney et al. 2001). The samples were centrifuged before filtering to re duce the high amount of woody material found present in the soil samples. A relatively new ergosterol extraction method by physical disruption was utilized to simplify the process for an alyzing fungi in a large number of soil samples (Gong et al. 2001). A lower conver sion factor for fungal biomass was used to account for the flooded conditions on soil fungal growth (Montgomery et al. 2000). Data Analysis A three stage balanced nested design was used to integrate the indicators measured at different scales and among sites. Hypothesis testing for differences between means was accomplished by using two-sample t-test with an alpha of .05 and a two-tailed confidence interval. Since the monitoring of the restoration site with nine distinct reference locations produced a dataset where the assumptions for an alysis of variance (ANOVA) were not ensured, non-parametric tests were used to detect any significant differences among the reference sites and among the distinct age class segments (SAS, 2002). Correlations between soil moisture, soil chemical and microbial abundances were determined using Spearman's rank ( r ) correlations (Dumortier et al. 2002; SAS, 2002; Spyreas and Mathews, 2006). Trends between variables we re obtained from linear regression using the general linear model (PROC GLM) (Yang et al. 2006; SAS, 2002). The chronosequencial trends

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55 were enhanced by incorporating moving average smoothing (MA model) as a data filter to reduce cyclical and seasonal variations found in the datasets fo r a number of the indicators affected by climate ( Platt and Denman 1975; Kumar et al. 2001; Ittig, 2004). The trend analysis was followed by log10 data transformations where necessary to stabilize variances prior to analysis. Partial Canonical Correspondence An alysis with multivariate regression (proc CANCORR) was used to determine the relative c ontributions of the different variables to the relationship (SAS, 2002; Fortin and Dale, 2005). Results Soil Types, Soil Organic Matter, and Soil pH All three ref erence sites contained taxonomically equivalent soil types. All of the soils had similar soil properties (sandy, acidic, thermi c, aquic). The soils were also found to be functionally equivalent even when compared by dr ainage class (Table 3-1). Soil organic matter content (SOM) was found to increase from 1% to 4.5% as gravimetric soil moisture increased from 20% to 60% of soil weight (Figure 3-1). Soil pH decreased from a pH of 5.0 to 4.0 as SOM increased from 1% to 4.5% (Figure 3-2). The plant-availabl e phosphorus tests produced too many non-detectable samples for any m eaningful results (Table 3-2). Net Nitrogen Mineralization Net nitrogen mineralization rates (Nmin) increased during the y oung age class, peaked during the mid-age class, and then decreas ed after 60 years (Figure 3-3). Mean Nmin rates were 12 mg N / kg soil / month for the young age stan ds, 14 mg N / kg soil / month during the midaged class, and 8 mg N / kg soil / month during the mature age class for the reference sites (table 3-2). The pattern for Nmin rates followed microbial biomass levels (Cmb) over the 110-year chronosequence (Figure 3-4). Nmin rates increased from 5 mg N / kg soil / month to 20 mg N / kg soil / month as Cmb increased from 100 mg-1 C / kg soil to 1000 mg-1 C / kg soil (Figure 3-5).

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56 Nitrate production was 13.8 mg-1 NO3 / kg soil during April 2002 and 135.7 mg-1 NO3 / kg soil during August 2002. In comparison, Nitrate production was 4.7 mg-1 NO3 / kg soil during April 2005 and 1.9 mg-1 NO3 / kg soil during August 2005 (Table 3-3; Figure 5-3, Chapter 5). Ammonium production was 13.4 mg-1 NH4 / kg soil during April 2002 and 104.7 mg-1 NH4 / kg soil during August 2002. During 2005, ammonium production was 8.9 mg-1 NH4 / kg soil during April and 9.6 mg-1 NH4 / kg soil during August (Table 3-3). Nmin was positively related to ammonification (NH4 +) (r > 0.810; p < 0.0001) during all three age classes, but not co rrelated with nitrification. Nmin became positively correlated with soil moisture and SOM ( r > 0.460 (p < 0.01) during the mid-aged class and remained so through the mature age class (Table 3-4). Ammonium production was negatively related to nitrate production (NO3 -) during the mid-age and mature ( r = 0.470; p < 0.001) age classes (Table 3-3). Microbial Properties Mean soil microbial biomass carbon (Cmb) levels were 275 (mg C / kg soil) for the young age stands, 416 (mg C / kg soil) during the mid-ag ed class, and 339 (mg C / kg soil) during the mature age class for the reference sites (T able 3-2). Mean soil fungal biomass carbon (Cfb) levels were 102 (mg C / kg soil) for the young age class, 163 (mg C / kg soil) for the mid-aged stands, and 125 (mg C / kg soil) during the mature age class at the reference sites (Table 3-2). Fungal biomass carbon increased during the first 60 years (~200 mg C / kg soil), then decreased down to 110 years (~100 mg C / kg soil; Figure 3-6). The fungal-to-microbial biomass ratio (FB-to-MB) decreased from a mean value of 0.4 to 0.2 during th e first fifteen years after establishment, and then increased to 0.8 at 50 years (Figure 3-7). Microbial biomass (Cmb) had a negative relationship (r > 0.400 (p < 0.01) with soil pH during the mid-aged and mature age classes (Table 3-3).

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57 Discussion The nitrogen m ineralization (Nmin) process in high soil moisture conditions was dominated by ammonium production (NH4 +), with low concentrations of nitrate being measured. The net nitrification rates represente d 50% of the production during 20 02 and less than 25% during 2005. The net nitrogen mineralization rates were 10 magnitudes greater during 2002 compared to 2005 (Table 3-3). Similar results between NO3 and NH4 + were measured in a study comparing xeric, mesic, and wet longleaf pine sites in s outhern Georgia (Wilson et al. 2002). When Nmin became positively correlated with soil moisture and SOM during the mid-age and mature age classes, nitrate levels (NO3 -) became negatively correlated to ammonium (NH4 +) production. The dynamics indi cates a portion of the NO3 was converting to NH4 + during saturated conditions. This condition might be i ndicating the dissimilatory nitrate-reduction-toammonium (DNRA) process is taking place during flooded conditions. Little dinitrogen (N2) gas is lost to the atmosphere or NO3 by leaching when the DNRA pathway is dominant. Flooding causes a lower redox potential (Eh < 0.6), and with a sufficient supply of NO3 and labile carbon, DNRA became the preferred pa thway over denitrification, resulting in the enriched pool of NH4 + (Stevens et al. 1998). Investigators examined the changes in nitrogen and phosphorus availability in longleaf pine sites from wetlands through an ecotone to upland sites, and they measured higher levels of nitrate a nd phosphorus taken from soils in the middle of wetland sites than found in the ecotone or uplan d sites. However, the upland sites had higher amounts of labile nitrate than the we tter sites (Craft and Chiang, 2002). The anaerobic conditions and a high supply of nonlabile nitrate in we t longleaf pine sites are conducive to DNRA. During anaerobic conditions, the DNRA pathway provides NH4 + to plants and microbes, requiring le ss energy to assimilate than NO3 assimilation (Silver et al. 2001). The characteristics favoring DNRA over denitr ification are high rainfa ll, a high C:N ratio,

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58 and a forested ecosystem that is naturally N-limited during dry conditions. The DNRA pathway has been determined to be less sensitive to higher soil temperatures and a lower pH than denitrification. The DNRA pathway is now cons idered a major nitrogen conservation mechanism in humid forest ecosystems (Silver et al. 2001; Huygens et al. 2007). One of the reasons why the nitrogen mineraliz ation patterns closely followed microbial biomass changes over the chronosequence (Figure 3-4) was because the nitrogen mineralization data did not contain significan tly high levels of nitrate during the 2005 growing season. Heterotrophic bacteria and fungi that dominate the soil microbial biomass produce ammonium from organic nitrogen. Only a fe w chemoautotrophic bacteria produc e the majority of nitrite and nitrate (Richards, 1987). Soil nitrogen mineralization ra tes increased during stand esta blishment, but eventually decreased after canopy closure as longleaf pine st ands entered the stem exclusion phase of stand development (Oliver, 1981). Other studies in hard wood forests have found increases in nitrogen mineralization rates after stand re placing harvests up to 60 years, then declining to a constant range (Zak et al. 1990). Investigators evaluating th e affects of ponderosa pine restoration treatments on mycorrhizal fungi, determined treatments prom oting graminoid and herbaceous ground cover had a positive relationship to levels of arbuscu lar mycorrhizal (AM) f ungi (Korb et al. 2003). These authors also discovered a positive re lationship between stand BA and levels of ectomycorrhizal (EM) fungi (Korb et al. 2003). In an earlier experi ment in a slash pine forest, Sylvia and Jarstfer (1997) reported a strong competition that ex ists between AM weeds and EM pine roots (Sylvia and Jarstfer, 1997). The implicat ion would be that AM fungal levels were high at harvest, declined during th e first 15 years as growing EM trees crowded out the AM

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59 groundcover. Eventually the AM fungi were replaced with EM fungi, and the overall fungal biomass levels increased after 15 years. This pattern is simila r to our results (Figure 3-7). Phosphorus availability was very limited in these sites as indicated by the poor results. Similar results have been reported in loblolly pine plantations throughout the South (Martin and Jokela, 2004). Fertilization can dramatically improve biomass accumulation, but unless it is maintained, nutrient-deficient soils can result fr om the fast pine growth (Adegbidi et al. 2005). Phosphorus levels were found to be higher and P-mi neralization rates lower in wet southern pine forests (Grierson et al. 1999; Craft and Chia ng, 2002). In our study, soil or ganic matter content (SOM) was found to increase with soil moisture, and increased levels of SOM caused decreases in soil pH. As soil organic matter increases, it forms complexes with Mg2+ and Ca2+cations in solution, releasing H+ ions into soil solution from organic acids (Brady and Weil, 2002). This relationship was confirmed by a negative relationship between SOM and soil pH. A lower soil pH usually leads to lower nitrogen mineralizat ion rates (Morris and Boerner, 1998). Active bacterial respiration and microbial biomass levels substantially decline below a soil pH threshold of 5.0, resulting in lower rate s of nitrogen mineralization (Baath and Anderson, 2003). Lower mineralization rates results in higher organic matter accumulation. Conclusions Nitrogen cycling was dom inated by ammoni um production during the wet 2005 growing season when compared to a drier 2002. There was ammonium enrichment at the cost of nitrate levels. This probably indicates that the dissimilatory-nitrate reduction-to-ammonium (DNRA) pathway was prominent during the flooded 2004-2005 growing seasons. The net nitrogen mineralization rates, microbial biomass carbon, and fungal biomass carbon increased between the young and mid-aged classes, th en decreased between the mid-ag ed and mature age classes. The FB-to-MB ratios increas ed dramatically up to 60 years, th en decreased to 110 years. Finally,

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60 soil organic matter content (SOM), increased wi th soil moisture. Based upon the results, this group of soil biogeochemical indicators follows biomass accumulation patterns and will attain biogeochemical equilibrium after a stand age of approximately 60-70 years. The threshold would be during the mature age class after the un derstory reinitiation phas e of forest succession has started. Soil biogeochemical studies require a great amount of resources and equipment to conduct an ecosystem-level analysis. The research could have been improved if a series of soil samples were analyzed over a two-year period, at 3-month intervals instead of annual sampling. However, the cost of running net nitrogen mi neralization, microbial biomass and ergosterol determinations would be quite high. Our resear ch has shown some interesting results, but additional research is required to explore the biogeochemistry of wet longleaf pine flats. This would include exploring the soil organic matte r accumulation vs. flooding cycles in facultative wetland pine sites, the relations hip between tree root mass a nd fungal biomass during longleaf succession, and the effects of competition betw een mycorrhizal and saprophytic fungi during longleaf pine development.

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61 Table 3-1. Soil and stand properties between reference sites. LOCATION SOIL GREAT GROUPS SOIL TEXTURE (Top 10 cm) MOISTURE REGIME TEMPERATURE REGIME DRAINAGE CLASS Chassahowitzka Wildlife Management Area Psammaquent Sandy Aquic Hyperthermic Very poorly drained Alaquod Sandy Aquic Hyperthermic Poorly drained St. Marks National Wildlife Refuge Psammaquent Sandy Aquic Thermic Very poorly drained Alaquod Sandy Aquic Thermic Poorly drained Topsail Hill State Preserve Humaquept Sandy Aquic Thermic Very poorly drained Alaquod Sandy Aquic Thermic Poorly drained STAND BASAL AREA AND SOIL BIOCHEMICAL PROPERTIES (Mean Values*) DRAINAGE CLASS STAND BASAL AREA (m2 / ha) pH [ H+] NET NITROGEN MINERALIZATION RATES (mg N / kg soil / month) MICROBIAL BIOMASS CARBON (mg C / kg soil) FUNGAL BIOMASS CARBON (mg C / kg soil) Very poorly drained 6.5a 4.4a 11.6a 374.3a 133.8a Poorly drained 8.3a 4.5a 9.9a 356.1a 135.3a Means followed by the same lower case letter s are not significantly different. (alpha=0.05)

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62 Table 3-2. Soil chemical and microbial biomass means between age classes. Stand Age (years) Age Class Net Nmin (mg-1/kg-1 Soil / year)Cmb (mg-1 / kg-1 / soil) SOM Content (%) Soil pH [ H+] Plant Avail P (mg/kg soil)Cfb mg/kg soil FB to MB Ratio 6Young2.492.22.754.100.4029.07.08 8Young8.333.71.564.70ND31.86.76 9Young23.6253.01.894.500.0457.312.73 10Young22.3448.61.964.60ND72.815.82 17Young5.3674.51.694.30ND102.223.77 *Mean12.0a275.4a1.97a4.44a*101.5a14.48a24Mid-Aged21.81169.50.834.50ND39.80.0327Mid-Aged9.6703.61.294.70ND24.10.03 29Mid-Aged40.8402.13.554.400.0234.00.08 31Mid-Aged9.2762.62.094.30ND83.20.11 34Mid-Aged2.2285.13.024.60ND191.10.67 36Mid-Aged17.0403.82.494.30ND83.50.21 40Mid-Aged-0.730.31.034.60ND98.51.00 42Mid-Aged3.598.61.034.80ND41.90.43 46Mid-Aged16.4321.01.164.001.28117.10.36 50Mid-Aged11.4131.81.295.30ND95.70.73 52Mid-Aged19.1275.83.954.100.14154.40.56 *Mean14.0a416.3b1.98a4.51a*162.9b14.45a60Mature0.833.41.364.900.838.41.0061Mature10.7519.60.704.50ND92.90.1862Mature32.6753.63.624.10ND59.90.0868Mature10.0511.53.554.00ND189.30.3771Mature-0.4132.54.544.00ND36.60.28 86Mature6.4241.80.904.40ND53.90.22 95Mature7.71.01.634.300.040.31.00 101Mature5.4570.21.494.30ND88.00.15 105Mature6.083.01.494.50ND28.50.34110Mature3.782.71.634.700.585.91.00*Mean8.4b338.7ab2.02a4.44a*125.3ab14.25a Means followed by the same lower case letters are not significantly different (alpha=0.05). The sample size for the soil data by age class was n=48. Table 3-3. Soil nitrogen mineralization means (Nmin) for dry season 2002 and wet season 2005. Date Nmin (mg N / kg soil) Nmin (mg NO3 / kg soil) Nmin (mg NH4 / kg soil) Apr-02 27.2b 13.8b 13.4b Aug-02 240.4a 135.7a 104.7a Sep-02 189.1a 128.4a 60.7a Apr-05 7.6b 4.7b 8.9b Aug-05 11.5b 1.9b 9.6b *Means followed by the same lower case letters are not significantly different (alpha=0.05). The sample size for the 2002 soil data was n=180 and for the 2005 soil data was n=144.

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63 Table 3-4. Differences in soil biogeochemical relationships based upon Spearman rank correlations r as stratified by forest age class (n = 48). Net NminNH4 + MinNO3 Min MoistureSoil pHSOM CmbNet Nmin 0.885**** 0. 333* NH4 + Min 0.342* Net NminNH4 + MinNO3 Min MoistureSoil pHSOM CmbNet Nmin 0.805**** 0. 367* 0.468** NH4 + Min -0.310*0.513*** NO3 Min Soil pH -0.411** Net NminNH4 + MinNO3 Min MoistureSoil pHSOM CmbNet Nmin 0.860**** 0. 471***-0.413** 0.470** NH4 + Min -0.528***0.449**-0.329* Soil pH-0.412** Prob > |r| under H0: Rho=0 Young Age Class (6-20) Mid-Aged Class (25-55) Mature Age Class(60-110) Significance of the Spearman rank correlation test: blank: non-significant, *0.05 < p 0.01, **0.01 < p 0.001, ***0.001 < p 0.0001, **** p < 0.0001. y = 6.5506x 0.2389 R2 = 0.49 p < 0.0001 0.0 1.0 2.0 3.0 4.0 5.0 6.0 0.00.10.20.30.40.50.60.70.8 Soil Moisture Content (g H2O / g Soil dwt) Soil Organic Matter content (%) Figure 3-1. Soil organic matter content versus soil moisture as measured from 26 differently aged stands.

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64 y = -0.1615x + 4.7683 R2 = 0.31 P< 0.0029 3.8 4.0 4.2 4.4 4.6 4.8 5.0 0.001.002.003.004.005.00 Soil Organic matter content (%)Soil pH [H+] Figure 3-2. Soil pH versus soil organic matter cont ent (percent) as measured from 26 differently aged stands. y = -0.0016x2 + 0.1273x + 5.7195 R2 = 0.46 p < 0.0258 0 5 10 15 20 020406080100120 Stand Age (Years)Net Nitrogen Mineralization Rates (mg N / kg soil / month) Figure 3-3. Total net nitrogen mineralization, ammonification and nitr ification rates (mg -1 nitrogen / kg -1 soil / month -1 ) along a 110-year chronosequence as measured from 26 differently aged stands. The data was filtered with moving average smoothing to remove seasonal and cyclic effects.

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65 1 10 100 1000 020406080100120 Stand Age (Years)Cmb and Nmin Patterns (mg-1 /kg-1 soil) Microbial Biomass Carbon Net Nitrogen Mineralization Figure 3-4. Trends for microbial biomass carbon (Cmb) and net nitrogen mineralization rates (Nmin) along a 110-year longleaf pine chronosequence as measured from 26 differently aged stands. The data was filtered with moving average smoothing to remove seasonal and cyclic effects. y = 0.0157x + 6.2013 R2 = 0.31 p < 0.004 0 5 10 15 20 25 30 02004006008001,000 Microbial Biomass Carbon (mg C / kg soil)Net N mineralization (mg N / kg soil / month) Figure 3-5. Microbial biomass carbo n versus net nitrogen minerali zation rates as measured from 26 differently aged stands. The data was filtered with moving average smoothing to remove seasonal and cyclic effects.

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66 y = -0.0328x2 + 3.8711x + 57.154 R2 = 0.31 p < 0.0054 0 30 60 90 120 150 180 210 240 270 300 020406080100120 Stand Age (Years)Fungal Biomass (mg-1 C / kg-1 soil) Figure 3-6. Fungal biomass car bon ( C ) along a 110-year longl eaf pine chronosequence as measured from 26 differently aged stands. The data was filtered with moving average smoothing to remove seasonal and cyclic effects. Figure 3-7. The fungal-to-microbial biomass ra tio and fungal biomass carbon levels (means) during the earlier and later portions of chronosequence respectively, as measured from 26 differently aged stands along th e 110-year longleaf pine chronosequence. y = 0.0252x2 5.7258x + 424.05 R2 = 0.40 p < 0.03210 50 100 150 200 250 300 350 485460667278849096102108114Stand Age (years)Fungal Biomass Carbon (mg/kg soil ) y = 0.0011x2 0.0483x + 0.7088 R2 = 0.80 p < 0.00030 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0612182430364248Stand Age (years)Fungal to Microbial Biomass rati o

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67 CHAPTER 4 RELATIONSHIP BETWEEN VEGETATION AND SOIL CHARACTERISTICS IN WET LONGLEAF PINE FLATS ALON G FLORIDAS GULF COAST Introduction There have been m any efforts on assessing th e inter-relationships between community structure, plant species composition and soil bioc hemical attributes of forested ecosystems around the globe (Goebel et al. 2001; Peacock et al. 2001; Wilson et al. 2002; Allen and Schlesinger, 2003). For example, researchers in northwest Spain found that specific herbaceous species assemblages were indicato rs for soil pH, soil organic matte r levels, C: N ratios, and high or low levels of soil nitrogen, phosphorus, potas sium, calcium, and magnesium (Zas and Alonso, 2002). South American researchers co mpared mature natural alerce ( Fitzroya cupressoides ) forests with mature mixed beech ( Nothofagus-Podocarpus ) forests in the Chilean Andes and found that the mixed beech-conifer forests that contained greater tree and plant species biodiversity, had significantly hi gher soil nitrogen minera lization rates (Perez et al. 1998). These and other studies have greatly contributed to ou r understanding of the so il-vegetation community relationships for various ecosystems in the U.S. and other parts of the world (Vance and Entry, 2000; Reynolds et al. 2000; Chapma n et al. 2003; Korb et al. 2003; Hackl et al. 2005). However, the reasons why soil nutrient and microbial dynamics influence community structure and composition of the longleaf pine eco system of the southeastern U. S. still remains unexplored. Longleaf pine ecosystem is one of the most threatened ecosystems in the U.S. Knowledge about the interrelationships among soil chemical, micr obial and vegetational ch aracteristics of the longleaf pine ecosystem may aid in restoring it to a healthy, functiona l ecosystem across its range. The concept of using soil chemical and microbial properties in combination with vegetation attributes for monitori ng restoration projects has gained momentum in the recent past.

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68 For example, researchers monitoring the restoration of ponderosa pine ( Pinus ponderosa ) forests in Arizona explored the relati onship between mycorrhizal and pl ant functional groups (Korb et al. 2003). They discovered that arbuscular mycorrhizal (AM) fungi were highly positively correlated with increases in grasses and forbs, and negatively correlated w ith tree cover and pine litter. Ectomycorrhizal (EM) fungi had no response to the restoration treatments, but had a high positive correlation to stand basal area (Korb et al. 2003). A companion study found that as plant species richness increased primarily due to an increase in legumes and stress tolerant plants, there was a corresponding in crease in soil fungi and an abundance of fungi relative to bacteria (Smith et al. 2003). A growing number of studies have indicated that soil microbial communities with distinct functional groups inhabit different forest types (Pennanen et al. 1999). A black pine forest in northeastern Austria was found to have higher relative amounts of fungi and actinomycetes in the soil microbial biomass than were found in a nei ghboring oak-beech hardwood forest (Hackl et al. 2005). Chapman et al. (2003), investigating nativ e woodland expansion in England, found that soil moisture, pH, and microbial biomass levels decreased along a successional gradient from moorland to grassland to mature pine forest, but the fungal component increased. In beech ( Fagus sp.) forests of Denmark, researchers found that different fractions of coarse woody debris supported distinct fungal species. Larger trees parts contained more f ungal species, smaller pieces had higher densities of a few species, and snags were species-poor. They concluded that coarse woody debris should be left as whole trees compared to smaller or larger pieces to insure high species richness in the fungal community of the forest floor (Heilmann-Clausen and Christensen, 2004). These studies illustrate th e strong interactions that exist between soil biogeochemical properties and aboveground cover type.

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69 The objective of this study was to examine th e relationships between key soil chemical and microbial properties and the oversto ry and understory plant characte ristics of a wet longleaf pine flat community in the Gulf Coastal Plain of Florida. We hypothesized stand volume will show a positive relationship with soil nitrogen mineraliza tion, which, in turn, will be driven by the microbial community dynamics in the soil. We also hypothesized that the fungal biomass will increase as coarse woody debris accumulated on the forest floor and the standing stock increased over time Materials and Methods Study Areas Three reference site locations along a spatial gradient from within the Coastal Flatwoods subecoregion of Florida (Chapt er 1), sub-divided into three one-hectare blocks, representing young, mid-aged, and mature age classes, were used in this study. The different successional age classes represented a chronosequence of 110-year s across a moisture gradient containing mesic flatwoods, wet flatwoods, wet savannas, and bo rdered by cypress ponds. The three hectares established at each reference location was scaled to match the three hectares established at the restoration site (Chapter 5). The three locations are Topsail Hill State park, St. Marks National Wildlife Refuge, and the Chassahowitzka Wildlif e Management Area of the Florida Fish and Wildlife Commission (Chapter 2). Field Measurements Each site had a clus ter of three one-hectare bl ocks, containing stands representing each of the three previously defined age classes. Each one-hectare block was sub-divided into four randomly placed 400 m2 measurement plots. To assess the fo rest structure, tree height and diameter-at-breast height (DBH), were measured on all trees 10cm DBH. At least two of the dominant trees were cored to determine sta nd age. Stand density (trees/ha), basal area (m2/ha)

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70 and volume (m3/ha) were calculated from this data. In addition, the volume (m3/ha) of all snags and coarse woody debris (CWD) were also measured (Spetich et al. 1999). Each 400 m2 plot contained four randomly placed 1 m2 subplots for understory sampling (Chapter 2; Figure 2-2). Percent cover of each species was assessed using the Daubenmire method modified to estimate eight levels of percent cover (Daubenmire, 1959). Coleman rarefaction and the Shannon-Weiner diversity indices were calc ulated for each stand based upon four sub-samples (Colwell, 2006; Koellner and Hersperger, 2004). Soil Sampling and Preparation Soils were sam pled (> 500 grams) from within the vegetation survey (1m2) quadrats taken in the top 10cm of the A horizon. The samp ling took place during August and September of 2005, and September of 2006, at each of the reference sites and the restorati on test site. They were immediately stored at 4C until analysis. A sieved and oven dried (105C) sub-sample (20g) was used for determining moisture content. Soil Chemical Analysis The soil s amples were analyzed at the University of Florida Soil Testing Laboratory (UF ARL), Gainesville, Florida. Soil water pH was determined from prepared slurries using a soil-towater ratio of 1-to-2 (EPA method 150.1). Plantavailable phosphorus was determined with the use of Mehlich-1 extractant (H2SO4 & HCL) and measured on an inductively coupled plasma (ICP) spectrophotometer (EPA method 200.7). So il organic matter content (SOM %) was determined by the Walkley-Black method. The grav imetric soil water content was determined in 2005 and 2006 for all of the samples analyzed.

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71 Mineral Nitrogen Fluxes Net nitrogen m ineralization was determined by comparing collected paired soil samples contained in plastic bags. Forty eight samples we re collected from each reference location for a total of 144. One bag was buried in situ for incubation during August, 2005 (Eno, 1960) and the other bag taken to the soil lab fo r analysis. The incubated bags were collected and analyzed after 30 days. Mineral nitrogen was extracted from 20 g of both soil samples with 60 ml 2N KCL and placed in shaker for one hour. They were then filtered through # 42 Whatcom filter papers and analyzed by the UF ARL for ammonium (EPA method 350.1) and nitrate (EPA method 353.2) with a continuous auto-flow analyzer. Net Mineralization was calculated as the difference between incubated-N and initial-N (corrected for soil moisture) (K eeney & Nelson, 1982). Bacterial Abundance and Microbial Dynamics Enum eration of nitrifying bacteria was de termined by the most probable numbers (MPN) method for densities of ammonium and nitrite oxidizing bacteria using a five tube dilution (Schmidt and Belser, 1982). The ammonium oxidizi ng bacteria were incubated in a medium of di-ammonium sulfate, and the nitrite oxidizing bact eria were incubated in potassium nitrite. The tubes were incubated for 8 weeks for the first read ings and 16 weeks for the final readings. A pH indicator of bromothymol blue was used to determine pH changes caused by increased respiration of the ammonium oxidizing bacteria. Positive readings for the nitrite oxidizing bacteria were determined from a nitrate test r eagent of diphenylamine in sulfuric acid solution (Schmidt and Belser, 1982). Soil microbial biomass C was determined by ch loroform fumigation-extraction (Vance et. al., 1987), with the following modifications. Siev ed 12 grams of soil were taken from soil samples stored at 4 C and then placed in 50 ml centrifuge test tubes. Matching 12 gram soil samples were set aside in additional 50 ml centrif uge tubes as the control. The soil samples were

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72 fumigated in 24 tubes per desiccator with 40 ml of alcohol-free chloroform placed into a center beaker and an additional 0.5 ml of chloroform was placed into each centrifuge tube. The top of the desiccator was pressure sealed and vacated until the chloroform began to boil. The tubes were then incubated for 24 hours at 25C. The de ssicator was then opened, resealed, and after the chloroform was reboiled, incubated for an additional 24 hours. The control and fumigated samples were extracted with 36 ml of 0.05 M K2S04, shaken (360 rpm) on an orbital shaker for 1 hour, and centrifuged @ 6000 rpm for 15 minutes. The supernatant was then filtered through # 42 Whatcom filter papers into 20 ml scintillation vials and frozen until analysis. Levels of total organic carbon (TOC) were determined on a Shimadzu TOC-VCSH analyzer (Vance & Entry, 2000). Microbial biomass carbon was equal to [(fumTC ConTC) / 0.51] / (Soil Wt.) = mg C kg dry wt. Soil -1 (Joergensen, 1996). The value of 0.51 is the conversion factor equal to the extractable portion of microbial biomass in a forest soil. Fumi gated and non-fumigated blanks were measured to correct for the ch loroform and potassium persulfate. Soil fungal biomass levels were determined by a physical disruption method for extraction of ergosterol from soil samples (Gong et al. 2001 ); with the following modifications. Weighed 6 grams of soil were mixed with 9 ml of 0C me thanol and 1.9 grams of glass beads into 20 ml scintillation vials. The vials were vortexed for 30 seconds, shaken (360 rpm) on an orbital shaker for 1 hour, and refrigerated over night. An a liquot of 1.8 ml was plac ed into 2 ml microcentrifuge tubes and centrifuge d @ 11,000 rpm for 20 minutes. A syringe was used to remove 1.5 ml of the supernatant from the micro-cent rifuge tubes and filtered through a 0.20 m filter into amber colored 2 ml glass HPLC vials. Th e HLPC vials were covered with aluminum foil and stored in the dark at 0 degrees C until rea dy to inject into the HPLC. Each sample was quantified on a Beckman Coulter HPLC equi pped with an UV detector, a pump, an auto-

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73 sampler, and through a C-18 reverse-phased anal ytic column (4.6 x 250 mm). The UV detector was set at 282 nm and pure methanol was used as the mobile phase at a flow rate of 1 ml per minute. Extracts (100 l) were injected while the column pressure was maintained at 1000 psi. Pure ergosterol (Sigma) was recrystalized in pur e methanol at different concentrations to establish a set of standards. The standard curve was construc ted from on a linear regression relationship between peak area and ergosterol concentration Ergosterol recoveries were calculated from the difference between spiked and non-spiked paired samples divided by the amount of ergosterol added Under such conditions, an isolat ed peak was identified from field samples at approximately the 13 minutes, base d upon the peaks obtained from the ergosterol standards. An averaged conversion factor for 3.65 g ergosterol per mg of soil translates to a fungal biomass (mg /g -1 soil) when multiplied by (220) (Montgomery et al. 2000). Fungal: microbial biomass ratios were re presented by a ratio of the calcul ated soil fungal biomass, and the soil microbial C biomass levels for each sample. Experimental Design and Analysis A three stage balanced nested design was used to integrate the indicators measured at different scales, and between sites. Since the monito ring of the restoration s ite with nine distinct reference locations produced a dataset where the assumptions for analysis of variance (ANOVA) were not ensured, non-parametric tests were used to detect any signifi cant differences between the reference sites and between the dis tinct forest age classes (SAS, 2002). Inter-relationships between forest structural variables, understory sp ecies diversity indices, and the soil biogeochemical variables we re determined by Spearman's rank ( r ) correlations using SAS 8.2 (Dumortier et al. 2002; SAS, 2002; Spyr eas and Mathews, 2006). Trends between variables were obtained from linear regressi on using the general lin ear model (PROC GLM)

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74 (Yang et al. 2006; SAS, 2002). These trends we re enhanced by incorporating moving average smoothing (MA model) as a data filter to reduce seasonal variations found in the datasets for a number of the indicators affected by climate ( Platt and Denman 1975; Kumar et al. 2001; Ittig, 2004). The trend analysis was followed by log10 data transformations where necessary. Results Nitrifying Bacteria and Nitrogen Mineralization Young forest soils at one of the reference si tes, S t. Marks, had numbers of ammonium oxidizing bacteria (AOB) that were 34 times gr eater (14,690 / g soil) than that found in soils from the mature sites (427 / g soil) (Table 41). The higher AOB number s in the young forest soils corresponded to lower a mmonium production (0.14 mg NH4 + / kg soil/month) and higher nitrate production (Table 4-2). Topsail Hill State Preserve also had numbers of ammonium oxidizing bacteria that were 60 times greater in the young forested soils (240 / g soil) than found in soils from the mature sites (4 / g soil) (Table 4-1). However, the young wet pine savanna had very high ammonium production compared to nitrif ication (Table 4-2). The mesic mature forest soils at Topsail had lower amm onium levels than the wet young forest soils (Table 4-2). The numbers of AOB at St. Marks (14,690) were significantly larger compared to the numbers measured at Topsail Hill (240). The higher AOB numbers in the soil under the young forest at St. Marks resulted in lower ammonification 0.14 mg NH4 + / kg soil/month compared to the soil from the young forest at Topsail Hill 17.9 mg NH4 + / kg soil/month. St. Marks had larger numbers of AOB in the old forest soils (427 vs. 4.0), but the level of ammonium production was smaller 2.98 vs. 4.98 mg NH4 + / kg soil/month when compared with the soils from the Topsail old forest site (Table 4-2). The numbers of nitrite oxidizing bacteria (NOB) showed differences between the age groups (427 / g -1 soil, in young soil vs. 4 / g-1 soil, in old soil), but not between the sites

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75 (Table 4-1). The nitrate production levels in young (2.39 vs. 1.74 mg NO3 / kg soil/month) and old (1.57 vs. 0.9 mg NO3 / kg soil/month) soils were simila r between the sites (Table 4-2). Overstory Stand volume increased with net nitrogen m ineralization (Nmin) until the volume reached 200 m3 / ha, when Nmin decreased substantially (Figure 4-1). All of the overstory stand variables were positively correlated with microbial biomass carbon (Cmb) during the young age class, but mean stand DBH and height were negatively correlated with Cmb during the mid-aged and mature age classes (Table 4-3). Similar to Cmb, all of the forest structural variables were positively correlated with fungal biomass carbon (Cfb) during the young age class, but remained positively correlated with Cfb during the mid-aged class (Table 4-3). FB-to-MB ratios increased with stand height during the mid-aged and mature age classes, when the mean stand height was greater than 7.5 m (Figure 4-2). Cfb increased by more than 130% as stand BA approached 10 m2 / ha. However, Cfb declined by 30 % as the stand BA grew from 10 m2 / ha to 20 m2 / ha (Figure 4-3). Coarse woody debris CWD was positively correlated with Cfb during the mid-aged and mature age class (Table 4-3). Cfb increased by more than 45 % as CWD increased from 1 to 55 m3 / ha (Figure 4-4). Stand density had a positiv e relationship with soil organic matter content (SOM) during the young and mid-aged class, bu t not during the mature age (Table 4-3). Understory Coleman rarefaction was positively correlated with Cmb during the young and mid-aged class, and negatively correlated to Cmb during the mature age class (Table 4-3). The Coleman rarefaction index decreased by 50% and ShannonWierner diversity inde x by 25% as the FB-toMB ratio approached 1.0 (Fi gure 4-5; Figure 4-6) The Coleman rarefaction index and the Shannon-Wiener diversity index were also negatively correlated with soil organic matter content (SOM), during the young age and mature age classes (Table 4-3).

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76 Discussion Net nitrogen m ineralization declin ed at a stand volume of 200 m3 / ha which corresponds to a stand age of 90 years (Chapter 2; Figure 2-9) This could be a stand volume threshold where fungi and actinomycetes have become the major decomposers in the microbial community due to lignin concentrations (Richards, 1987). Even in high soil moisture condit ions, the forest soils from young longleaf pine stands ha d significantly higher levels of nitrifying bacteria than soils from mature pine sites. The nitrifying bacteria data confirmed that nitrif ication rates were higher during the young age class than m easured in the mature aged stands. The AOB numbers were highly variable between sites, bu t the NOB numbers were similar. Nitrate levels were lower and ammonium levels were higher in the soils from the mature forest sites compared to the soils from the young forests. The higher levels of ammonium a nd lower levels of nitrate in mature forest soils could be an indication of a nitrogen c onserving (tighter) ecosystem (Davidson, 2000). There was an exception with the wet young Topsail pine savanna soil that had higher ammonification levels than the mesic mature Topsail soil. Higher ammonium levels and lowe r nitrification levels have been measured in wet longl eaf pine sites when compared to more xeric sites (Wilson et al. 2002). Ammonium production was hi gher and nitrate production was lower in the soils from the unburned Topsail Hill sites compared to St. Marks. The larger numbers of nitrifying bacteria measured at St. Marks NWR compared to Tops ail Hill State Park were probably due to the higher frequency of prescribed fire implemented at St. Marks. Higher ni trification rates after prescribed fire have been meas ured in a number of studies (Cooks on et al. 2007; Hart et al. 2005; Wilson et al. 2002). In addition, researchers studying disturbance in a Norway spruce ( Picea abies ) forest measured large enumerations of ammonium oxidizing bacteria (AOB) in sites recently harvested, but only detected very small numbers (< 10 / gm) in mature undisturbed sites (Paavolainen and Smolander, 1998).

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77 The effect of forest growth on the environmen t represents more than creating a preference for shade tolerant plant species or the creation of a multi-layered architecture. It also represents the evolution of soil organic matter (SOM) input s from an easily decomposed substrate to a SOM complex having a higher portion of recalcitrant material. As the inputs to the soil change, there is a corresponding change in the soil microbial commun ity as ectomycorrhizal and saprophytic fungi play greater ro les. This relationship between the aboveground component and the belowground biological community is important in shaping the ecolo gical trajectory of ecosystems (Hackl et al. 2005). The positive relationship between stand de nsity and soil organic matter (SOM) through the mid-aged class illustrates the effect of site quality on stand productivity. Stand BA and volume had strong positive relationships with Cmb up to the mature age class (60 years+). Correlations also showed strong positive relationships between most of the forest growth variables (DBH, height, BA) and Cfb levels, again up to the mature age class (60 years+). These two relationships reinforce how the rate of stand volume growth is interdep endent on the rate of organic matter decomposition and nutrien t cycling (Vitousek and Reiners, 1975). Regression analysis produ ced a trend showing that Cfb increased dramatically as stand basal area decreased. A ponderosa pine restoration study produced similar results showing positive relationships between increases in forest basal area and higher levels of ectomycorrhizal (EM) fungi (Korb et al. 2003). The relationship be tween the fine root biomass of trees and EM fungal levels has also been well established (Hendricks et al. 2006; Wallander et al. 2001; Sylvia and Jarstfer, 1997). Cfb was also found to increase with higher accumulations of CWD. Researchers in Demark determined that a comb ination of larger DBH logs and the greater surface area of smaller diameter CWD, promoted the highest le vel of fungal species richness

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78 (Heilmann-Clausen and Christensen, 2004). Whether the increase in Cfb during longleaf pine succession was due to the size of a trees root sy stem (mycorrhizal fungi) or in part due to increases in coarse woody debris accumulation (saprophytic fungi), fungal biomass (Cfb) increased as the average stand height incr eased. These results indicate that both Cmb and Cfb are important soil variables for longleaf pine flat development, but the Cfb portion of the biomass becomes more important over time as the eco system requires the decomposition of larger amounts of CWD and the improved cycling of nutr ients (Hackl et al. 2005; Leckie et al. 2004; Pennamen et al. 1999). The relationship between the FB-to-MB ratio and the Coleman rarefaction index was similar to Shannon-Wiener diversity index, species diversity decreased as the fungal component increased. Both Coleman Rarefaction and Shannon-Wiener diversity H indices were also negatively related to SOM during the young and ma ture age classes. Through a restoration study in England, researchers also f ound a negative relationship between native plant species richness and soil fertility. Never the less, in contrast to our results, they found a positive relationship with plant species richness and FB-MB ratios. The inves tigators attributed this positive relationship to a greater presence of legumes in the lo wer fertile soils (Smith et al. 2003). Conclusions The m ajority of the soil biogeochemical indica tors influenced longleaf pine stand growth, and as stands developed, changes in aboveground vegetation influenced the soil biogeochemical indicators. Net nitrogen mineralization increase d with stand volume until a threshold of 200 m3 / ha (stand age = 90 years). Nitrat e was found to be in higher con centrations in the young forest soils than the mature forest soils. Populations of nitrifying bacteria (AOB + NOB) were also found to be higher in the young forest soils. At T opsail Hill, ammonium levels were found to be higher in the wet young pine savanna soils than the mesic mature soil. Higher soil moisture

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79 translates to lower nitrificati on levels. The rela tionships between fungi and increases in stand height or coarse woody debris accumulation indicate a strong c ontinual relationship between the soil biogeochemical indicators and longleaf pine stand development. The dynamics of this relationship might be better unde rstood if the measured fungal biomass could have been identified as arbuscular mycorrhizal (AM) f ungi, ectomycorrhizal (EM) fungi, or saprophytic fungi along the chronosequence. The dominance of fungi negatively affected the Coleman Rarefaction and Shannon-Wiener di versity indices. This may have indicated a decrease in species richness, but the functi onal redundancy component of ecosy stem resilience has probably been strengthened. The strong relationships between forest biomass accumulation and soil biogeochemistry should be assessed in any m onitoring event. Nitroge n cycling appears to become tighter in mature forests at a thres hold of 90 years. This condition is dependent on mycorrhizal and saprophytic fungi do minating the soil microbial biomass.

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80 Table 4-1. MPN enumerations of nitrifying bacter ia in young and old longleaf pine forest soils. Enumerations ( MPN g -1 ) All values are expressed in units of MPN per gram (wet weight) of 0 to 10-cm soil and are averages of three replicates. Lower and upper limits in pare ntheses reflect 95% confidence intervals. St. Marks Mature site (100 yrs.) Topsail Hill Sapling site (19 yrs. Topsail Hill Mature site (100 yr s 0.0040 X 103 (0.005, 0.123) 0.4273 X 103 (0.103, 1.385) 0.0036 X 103 (0.005, 0.123) 0.0427 X 104 (0.103, 1.385) 0.0240 X 104 (0.047, 0.965) 0.0004 X 104 (0.005, 0.123) Ammonium Oxidizers Nitrite Oxidizers St. Marks Seedling site (6 yrs.) Site Locations 0.4273 X 103 (0.103, 1.385) 1.4690 X 104 (0.278, 6.318) Table 4-2. Ammonification and nitrification in y oung and old longleaf pine forest soils. Site Locations Ammonification (mg NH4 / kg soil / month ) Nitrification (mg NO3/ kg soil / month ) St. Marks Seedling site (6 yrs.) 0.14 2.39 St. Marks Mature site (100 yrs.) 2.98 1.57 Topsail Hill Sapling site (19 yrs.) 17.9 1.74 Topsail Hill Mature site (100 yrs.) 5.98 0.9 Values expressed as me an monthly rates and based on the dry weight of soil.

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81 Table 4-3. Soil biogeochemical relationships with stand attributes based upon Spearman Rank correlations r as stratified by forest age class (n = 48). CmbSOMCfbFB-to-MB ratio Stand Height0.524**** 0.543**** Stand Density 0.394** Stand DBH0.513**** 0.510**** Stand BA0.542**** 0.593**** Stand Volume0.540**** 0.564**** CWD Shannon Diversity -0.584**** Coleman Rarefaction 0.464***-0.363**CmbSOMCfbFB-to-MB ratio Stand Height-0.345* 0.296*0.476*** Stand Density0.509***0.581**** -0.358* Stand DBH-0.401**-0.365**0.319*0.539**** Stand BA0.465***0.585****0.348* Stand Volume0.360*0.502***0.457** CWD 0.326*0.339* Shannon Diversity -0.396**-0.290* Coleman Rarefaction 0.351*0.302*-0.322*-0.517***CmbSOMCfbFB-to-MB ratio Stand Height-0.646**** -0.289*0.446** Stand Density Stand DBH-0.429**0.422** Stand BA Stand Volume CWD0.326*0.648****0.293* Shannon Diversit y -0.439** Coleman Rarefaction -0.348*-0.565****Mid-Aged Time Interval ( 2555 ) Mature Time Interval ( 60 110 ) Prob > |r| under H0: Rho=0 Regeneration Time Interval ( 6 20 ) Significance of the Spearman rank corre lation test: blank: non-significant, *0.05 < p 0.01, **0.01 < p 0.001, ***0.001 < p 0.0001, **** p < 0.0001.

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82 y = 1.8739x2 35.731x + 233.45 R2 = 0.44 p < 0.0038 0 4 8 12 16 20 050100150200250300 Stand Volume (m3 / ha)Net Nitrogen Mineralization (mg N / kg soil /month) Figure 4-1. Net nitrogen minerali zation versus stand volume as measured from 26 differently aged stands. y = 0.0041x2 0.0621x + 0.4083 R2 = 0.41 p < 0.0017 0 0.2 0.4 0.6 0.8 1 1.2 51 01 52 02 5 Stand Height (meters)FB-to-MB ratio Figure 4-2. The fungal biomass (FB)-to-microbial biomass (MB) ratio vers us stand height as measured from 26 differently aged stands.

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83 y = 9.5282x + 93.367 R2 = 0.35 p < 0.0154 0 50 100 150 200 250 300 350 400 0510152025 Stand BA (m2 / ha)Cfb (mg C / kg soil) Figure 4-3.Fungal biomass carbon (Cfb) versus stand basal area (BA) as measured from stands grouped within the mid-aged and mature age classes only. y = 1.5077x + 105.9 R2 = 0.33 p < 0.0029 0 50 100 150 200 250 300 -551525354555 Coarse Woody Debris (m3 / ha)Cfb (mg C / kg soil) Figure 4-4. Coarse woody debris accumul ation versus fungal biomass carbon (Cfb) as measured from 26 differently aged stands. The data was filtered with moving average smoothing to remove seasonal and cyclic effects.

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84 y = -10.243x + 18.279 R2 = 0.37 p < 0.0025 5 10 15 20 00.20.40.60.81 FB-to-MB ratioColeman Rarefaction ES(63) Figure 4-5.Coleman Rarefaction index versus the fungal biomass (FB) -to-microbial biomass (MB) ratio as measured from 26 differently aged stands. The data was filtered with moving average smoothing to remove seasonal and cyclic effects. y = -0.9518x + 2.2266 R2 = 0.37 p < 0.0017 1.00 1.50 2.00 2.50 00.20.40.60.81 FB-to-MB ratioShannon-Wiener Diversity H' Figure 4-6. Shannon-Wiener divers ity H index versus the fungal biomass (FB)-to-microbial biomass (MB) ratio as measured from 26 differently aged stands. The data was filtered with moving average smoothing to remove seasonal effects.

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85 CHAPTER 5 MONITORING RESTORATION SUCCESS US ING VE GETATION AND SOIL AS KEY INDICATORS: CASE STUDY OF A WET LONGLEAF PINE FLATS RESTORATION PROJECT Introduction Ecosystem s ecology requires the integration of structural and functional characteristics for developing a holistic understandi ng of ecological change caused by natural or anthropogenic disturbance. However, these two characteristic s of ecosystems have generally been studied separately along vegetative and geochemical gradients (Muller, 1998). Soil chemical and biotic properties need to be included as indicators with forest structural and vegetative compositional measurements for the integration to take pl ace (Johnston and Crossley, 2002). Soil microbial community analysis also provide s a means to measure how respons ive soils are to disturbance and restoration treatments (Harris, 2003). Additionally, the inter-relationships between vegetation and soil characteristics have also been identified and used to assess site quality. In pine plantation research, specifi c soil properties have been found to be associated with the growth of specific tree and plant species. Similarly, certain groups of plant species may indicate specific soil conditions (Burger and Kelting, 1999; Zas and Alonzo, 2002). This combination of above and below ground data can also be used to ecologically verify if a restoration site falls within the spatial gradie nt of the reference sites (Goebel et al. 2001). The research reported in Chapter 3 determin ed that net nitrogen mineralization rates increased until 90 years. It was also determined that the soil fungal-to-microbial biomass ratio increased with stand growth and total woody de bris accumulation. Fina lly, soil fungal biomass increased with mean stand height (Chapter 4). These results show strong relationships exist between stand development and soil biochemical dynamics. This paper examines a case study of a restoration project, hereafter re ferred to as the Pt. Washington restoration project in Florida.

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86 The Pt. Washington restoration project was in itiated in 2001 to convert a slash pine plantation to a longleaf pine ecosystem. The e ffects of low-level herb icide applications on longleaf pine development and understory species richness were evaluated. The central goal of this experimental application of herbicides was to determine which herbicide, as a substitute for fire, would produce the best results for longleaf pine seedling su rvival and growth, understory plant species richness and composition, and soil nitrogen mineralization. Herbicides are currently being used in restoration projects thr oughout the United States for promoting the establishment of native grasslands assisting in the control of exotic invasive species as part of integrated pest management programs, for weed control during early forest stand development, and to combat eutrophicat ion from unwanted plant growth in aquatic ecosystems (Sigg, 1999). Yet, many environmentally-sensitive managers and scientists are hesitant to support the use of herb icides making it imperative that th e correct herbicide is used in the proper environment, with th e lowest feasible application rates (Murphy, 1999; Sigg, 1999). What primary factors make the restoration of coastal longleaf pine flats unique compared to other pine ecosystems? First, longleaf pine regeneration is de pendent on a grass stage when the pine seedlings are able to survive light surface fires and during fierce vegetative competition (Boyer and Peterson, 1983; Boyer, 1990). Longleaf pine seedlings have been known to stay in the grass stage from 5-20 years. This protective state can make th e growth rates of longleaf pines unpredictable (Haywood, 2000). Secondly, altho ugh many longleaf pine ecosystems are found on a variety of upland sites (Peet and Allard, 1993), coastal wet pine flats are unique because they are located on low, rain-fed coastal terr aces where weather patterns maintain high soil moisture conditions for extended periods duri ng the growing season (M essina and Conner, 1998).

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87 The longleaf pine grass stage becomes the critic al factor in the amount of time the pine remains in the seedling stage when prescribed fire is restricted. On e study found that the application of a hexazinone herb icide helped to release longleaf pine seedlings from fierce vegetative competition, enhancing conditions fo r leaving the grass stage (Haywood, 2000). A recent study of herbicides in an old field rest oration project found a hi gher first-year seedling survival rate and a higher percentage of seedlings out of the grass stage (2nd year) than with no herbicide treatment (Ramsey et al. 2003). There have also been studies to control fuel loads by different fuel reduction technique s, including herbicide applicatio ns. An earlier study found that fire and mechanical removal of fuels had imme diate but short-term impacts on reducing fireline intensity levels, but herbicide applications had longer term affects on reducing fuel levels, starting in the second year afte r treatment and lasting up to si x years (Brockway et al. 1998). Herbicides can be the short-term substitute for fire when applied in the correct manner, and within the correct environment. In 2004, the Pt. Washington restoration projec t was expanded to include the development of a monitoring program, the addi tion of bio-indicator s for evaluation incl uding soil microbial dynamics, and the use of reference sites for estab lishing a chronosequence for the restoration site evaluation. We predict the overstory, understory, and soil biogeochemical indicators will be useful for ecologically classifyi ng the Pt. Washington restoration site as a mesic flatwoods, wet flatwoods, or wet savanna. We will use them fo r trying to detect differences among the four herbicide treatment effects applied on the restorati on site. Finally, we will use them to predict the development or ecological trajecto ry in wet longleaf pine flat restoration. The predicted values will be presented with pine growth results on th e effects of herbicide treatments applied in the second year after planting compared to first year only, consecutive herbicide treatments (1st &

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88 2nd Year), and whether an early or late spring application changes the effects (McCaskill data, 2006). Materials and Methods Pt. Washington Restoration Site The longleaf pine restoration project is lo cated on the Point Washington State Forest (30020.04 N, 860 4.22 W) in southern Walton County, Florida. This coastal wet pine flats site was approximately a 4-ha, 26 year-old slash pine plantation having a basal area of 1.85 m2 / ha and an average dbh of 19.1 cm as measured in 1991. It contained scattered residual longleaf pine saplings and poles as part of the stands stocking. The adjacent area makes up approximately 15 ha of mixed slash and l ongleaf pine surrounding a cypress dome, and contained within the greater 6800 ha Pt. Wash ington State Forest. The understory plant community was dominated by broomsedge, a smaller component of wiregrass, and a group of shrub species highlighted by gallberry, saw palmetto, running oak, and dangleberry ( Gaylussacia frondosa). The annual precipitation av erages 1500 mm with most of it occurring during the late summer. The soil belongs to the Leon series and classified as sandy, siliceous, thermic aeric Alaquods. This soil series signi fies that they are very poorly drained soils (Jokela and Long, 1999). Since this pine flats forest is found very close to the coast (within 3 kilometers), its soils were formed on sandy quaternary parent material derived from marine deposits (Stout and Marion 1993). These soils are described as high ly weathered, acidic, infertile substrates (LaSalle, 2002). The surrounding area consists of wet pine sa vannas and wet flatwoods sites that are found within Floridas Gulf Coast Flatwoods zone (Chapter 1; Griffith, 1994). Floridas Gulf coastline is continuously shaped by active fluvial deposition and shoreline processes which promote and maintain the formation of beaches, swamps and mineral flats. The local relief is less than 20 m in

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89 elevation. The annual precipitation ranges from 1300,600 mm, and the average annual temperatures vary between 19 to 21 C. The growing season is long, lasting 270-290 days. The parent material consists of marine deposits containing limestone, marl, sand, and clay. The dominant soils are Aquults, Aquepts, Aquods, and Aquents. These acidic soils have thermic and hyperthermic temperature regimes and an aquic mo isture regime. The soils are poorly drained, deep, and moderately textured. Th e dominant vegetative cover c onsists of longleaf-slash pine forests with a smaller co mponent of Choctawhatchee sand and/or pond pine (McNab and Avers, 1994; Parker and Hamrick, 1996). As the first step towards re storing the Pt. Wash ington site back to longleaf pine, the overstory of slash pine was clearcut during August 2001. The site was roller chopped once and prescribed burned in October 2001. There wa s no existing bedding or any other hydrologicalmodifying practice applied. A randomized comple te-block design (RCB) with six blocks was used to measure the effects of four vegetationcontrol chemical mixtures on the dynamics of the understory plant species and pine growth and survival. Five plot s were randomly located within each of the six blocks. All treatment plots we re 26.6m x 24.4 m, and included at least a 3-m buffer strip between plots. The six blocks with buffers make up approximately 3.5 ha within the 4 ha clearcut. In December 2001, one-year-old containerized longleaf pine seedlings were hand-planted at 3.1 x 1.8 m spacing. Seedlings were planted in rows to facilitate the app lication of herbicides. In March 2002, four herbicide treatments Sulf ometuron methyl (methyl 2-[[[[(4,6-dimethyl-2 pyrimidinyl)amino]carbonyl] amino]sulfonyl]benzoate) at 0.26 ai kg ha-1, Hexazinone (3cyclohexyl-6-(dimethylamino)-1-methyl-1,3,5-triazine-2,4(1H,3H)-dione) at 0.56 ai kg ha-1, Sulfometuron (0.26 ai kg ha-1) + Hexazinone (0.56 ai kg ha-1) mix, and Imazapyr (4,5-dihydro-

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90 4methyl-4(1-methylethyl)-5-oxo-1-H-imidazol2-y l-3 pyridinecarboxylic acid) at 0.21 ai kg ha-1, were applied in a 1.2 m band over th e top of seedlings using a knaps ack sprayer. In each block, one treatment plot was kept herbicide-free as a control plot (Ranasinghe, 2003). Pine Survival and Growth Pine survival and growth (root co llar diameter and height) were monitored at the end of the growing season every year, through 2006. Seedling hei ght was measured using a ruler, from the soil surface to the top of the bud. Root collar diameter (RCD) was measured using a digital caliper. Stem volume index (SV I) was calculated with the measured RCD and height data. Vegetation Sampling A prelim inary vegetation survey was conducted (June 2001) prior to overstory harvest and site preparation to assess the initial per cent cover of understory species. After study establishment and herbicide ap plication, four vegetation surveys were conducted. Two randomly selected 1m2 quadrats were sampled within each tr eatment plot and the same location was revisited for subsequent surveys. In every su rvey, all plants found within the quadrat were identified to species and assigne d to shrub, graminoid, forb, or fe rn vegetation classes. Percent cover was ocularly estimated by species using the modified Daubenmire scale (Daubenmire, 1959). In addition to percent cover, the number of stems and average stem height were collected for the woody understory species. These plant surveys were c onducted concurrently at the reference sites (described below) during the 2004 growing season. Coleman rarefaction and the Shannon-Weiner diversity indices were calculat ed for each stand (Colwell, 2006; Koellner and Hersperger, 2004). The assemblage pathway for th e plant community was determined from these measurements over time using Canonical Corresp ondence Analysis (CCA) ordination (Palmer, 1993).

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91 Reference Sites Three representative locations along a spatial gradient from Pensacola to Tampa Bay (720 km) sub-divided into three one-hectare blocks representing young, mid-aged, and mature age class; were used in this study. Th e different stages (age classes) represente d a chronosequence of 110-years. The three locations are Topsail Hill St ate Park, St. Marks National Wildlife Refuge, and the Chassahowitzka Wildlife Management Area of the Florida Fish and Wildlife Commission. A three stage balanced nested design was used to integrate the indicators measured at different scales, and between site s. Each reference site had a cl uster of three one-hectare blocks containing stands that repr esent young, mid-aged, and 100+ year-old age class. Each one-hectare block was sub-divided into four randomly placed 400 m2 measuring plots where forest structure and coarse woody debris (CWD) were determined. Within each 400 m2 subplot, vegetation was inventoried on four randomly placed 1 m2 quadrat using the same modified Daubenmire scale method utilized at the restoration site. Soil Sampling and Preparation Soils were s ampled from within the vegetation survey quadrats taken from the top 10cm, at each of the reference sites and the restoration test site during August of 2005, and September of 2005 and 2006. They were stored at 4 C until analysis. Sub-samples were sent to the University of Florida soil testing lab for analysis of soil pH by prepared slurries using a soil-to-water ratio of 1-to-2 (EPA method 150.1), organic matter content (%) by the Walkley-Black method, and plant-available phosphorus by the use of Mehlich-1 extractant (H 2SO4 & HCL) and measured on an inductively coupled plasma (ICP) spect rophotometer (EPA method 200.7). Soil microbial biomass was determined by chloroform fumiga tion-extraction extracti on (Vance et. al., 1987). Net nitrogen mineralization rates were estimated from in-situ incubation of soil samples (Eno,

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92 1960). Fungal biomass levels were determined by soil ergosterol analysis (Gong et al. 2001). Fungal-to-microbial biomass ratios were calcula ted along the gradient (Montgomery et al. 2000). A sieved and dried (105C) sub-sample wa s used to determine moisture content. Data Analysis Pine survival and growth Pine survival, RCD, and height data collected during five growing seasons were analyzed using analysis of variance (ANOVA) within the fram ework of a randomized complete block design (RCBD) using JMP IN version 5 (SAS In stitute, Inc.). Height and RCD comparisons were made separately for seedlings in the gras s stage (GS) and out of the grass stage (OOGS) using a threshold height of 12 cm (Haywood, 2000) The study addressed only the main effects of herbicide treatment, and tests of these e ffects were not dependent on the assumption of no treatment x block interaction. Block effects were therefore tr eated as random effects in a univariate ANOVA model with two independent variables: treat ment with Block&Random as a covariate. Data were log-transformed where necessary to meet the assumptions of ANOVA. Significant differences between treatments were se parated with the Tukey-Kramer HSD test. Following a prescribed fire in February of 2007, post-fire seedling mortality was assessed in June 2006. Post-fire survival was analyzed with ANCOVA, using pre-fire su rvival as a covariate (Freeman, 2008). Understory The effects of treatm ents on stem counts and he ights of the major shr ub species were also analyzed using ANOVA for a randomized comple te block design. The study addressed only the main effects of herbicide treatment, and test s of these effects were not dependent on the assumption of no treatment x block interaction. Block effects were therefore treated as random effects in a univariate ANOVA model with tw o independent variable s: treatment with

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93 Block&Random as a covariate. Data were log-transformed where necessary to meet the assumptions of ANOVA. Significant differences between treatments were separated with Tukeys HSD or Hsus MCB. Post-fire treatme nt differences were analyzed with ANCOVA, using pre-fire distributions as a c ovariate (Freeman, 2008; Ranasinghe, 2003). Biogeochemical indicators A three stage balanced nested design was used to integrate the indicators measured at different scales, and between site s. Significant treatment effects on the biogeochemical indicators ( =0.05) were also compared with the contro l using Dunnetts t-test for multiple means comparison. Hypothesis testing for differences between means was accomplished by using twosample t-test with an alpha of .05 and a two-tailed confidence interval. Since the monitoring of the restoration site with nine distinct refe rence locations produced a dataset where the assumptions for analysis of variance (ANOVA) was not ensured, non-parametric multiple and linear regression, and multivariate Canonical Corre spondence Analysis (CCA) tests (ter Braak, 1994) were used to analyze for similarities a nd differences between the reference sites and between the distinct age class segments using SAS version 8.2 (SAS, 2002). For identifying which variables contribute the most to a gi ven relationship, partial Canonical Correspondence Analysis using univariate multiple regression (PROC CANCORR) was used to determine the relative contributions of each indicat or (Fortin and Dale, 2005; SAS, 2002). Trend analysis was enhanced by incorporating moving average smoothing (MA model) as a data filter to reduce cyclical and seasonal variations found in the datasets for a number of the indicators affected by climate (Platt and Denm an 1975; Kumar et al. 2001; Ittig, 2004). The trend analysis was followed by log10 data transformations where necessary.

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94 CCA multivariate analysis functions by relating a primary matrix of plant species abundance data with a secondary matrix of environmental or soil data PC-ORD, a PC-based program (McCune and Meffrod, 1999) containing an algorithm for Canonical Correspondence Analysis (CCA), was used to examine the overall spatial structure of the individual reference sites, the restoration site with the understory plant species along vectors (gradients) for soil chemical, net nitrogen mineralization, and soil microbial values found among the study sites (Heady and Lucas, 2004) (Palmer, 1993). Linear co mbinations of environmental variables are used to maximum the separation of plant species along four dimensional axes. Site scores are derived from the weighted averages of the associ ated species scores. The sites are located in the biplot where the center of the associated spec ies cluster exists. Community structure is illustrated by the influence of different envir onmental variables on its ordination (ter Braak, 1994). Plant species indicator analysis (IndVal) wa s used to measure the level of relationship between a given plant species to ca tegorical units such as pine fl at subtypes or age class. It calculates the indicator value d of species as the product of the relative frequency and relative average abundance in each categorical cluster. In dicator species analysis was used to attribute species to particular environmental conditions based on the abundance and occurrence of that species within the selected group. A species that was a perfect indicator was consistent to a particular group without fail. Indicator values range from 0 to 100 with 100 being a perfect indicator score. Because indicator species analysis is a statistical inference, a test of significance was applied to determine if species are significa nt indicators of the groups to which they are associated (Dufrene and Legendre, 1997). This was achieved by the Monte Carlo permutation test procedure (1000 runs) where the significance of a P-value was determined by the number of

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95 random runs greater than or equal to the inferred value ( =0.1). Accuracy was defined from the binomial 95% confidence interval: p +/accuracy (Strauss, 1982). Growth predictions were determined from linear regression using the general linear model (PROC GLM) (Yang et al. 2006). The multiple regression model selection procedures Rsquared, Backward Elimination, and Mallow Cp we re used to determine the combination of indicators for prediction of each variable. The results from regression analysis were based on best model selection criteria of minimizing Ma llow Cp and maximizing R2 and included only those indicators having a biological si gnificance level of p < 0.05 (SAS, 2002). Results Ecological Classification The Pt. W ashington restoration site can be cla ssified ecologically as a wet pine flatwoods subtype of the coastal pine flat based upon th e results from Canonical Correspondence Analysis (CCA) ordination, indicator species analysis (In dVal), and pre-harvest stand data. Canonical Correspondence Analysis ordination indicated th e majority of the plots measured at Pt. Washington fall in between the environmental pa tterns (moisture05, pH, SOM) (Table 5-1) for mesic flatwoods and wet flatwoods measured at th e reference sites (Figure 5-1). Indicator species analysis produced results showi ng that gallberry was the indicator for both wet flatwoods and the Pt. Washington restoration site (Table 5-2). When the data were analyzed by age class, the ordination produced the same vectors of mois ture05, soil pH, and soil organic matter content (SOM), but with stronger results .(Table 5-3). CCA ordination di d not show any clear separation along age class (Figure 5-2). Indicator species analysis did produce results showing that the restoration site had similar plant species as the young age class of the reference sites (Table 5-4). Witch grass and blue stem grass were species indicators for the young age class, while witch grass and wiregrass were found to be the species indicators of the Pt. Washington restoration site

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96 (Table 5-4). The means for each of the soil biogeoc hemical variables measured at the restoration site were found to be within the range of mean values measured at the reference sites, except for the significantly higher soil microbial biomass levels (Cmb) (Table 5-5). The seasonal trend for nitrogen mineralization fluxes ove r a 14 month period was for the rates to increase from winter through spring, to peak during the middle of August, and to decline throu gh the fall and winter (Figure 5-3). Pine Growth and Vegetation Control A concurrent study produced results from five years of pine growth and four years of vegetation surveys showing imazapyr and sulf ometuron-hexazinone herbicide treatments significantly reduced longleaf pine seedling survival after four growing seasons. Imazapyr, followed by hexazinone treatments produced significantly higher numbers of pine seedlings in the out-of-the-grass stage when compared to the other treatments. Im azapyr also produced significantly taller pines in th e out-of-the-grass stage compared to the other trees. Imazapyr treatments also resulted in the best control of the overall cover (%) and stem counts of the major shrub species, while producing the highest level of herbaceous richness (Freeman, 2008). From the same four years of pine data (2002-2006) both imazapyr and hexazinone produced better pine growth when applied during the second growing season compared to the first (Table 3-6). They both had higher survival rates as indicated by higher stand densities. Imazapyr produced the best pine growth of all the treatments when app lied during April instead of March. Hexazinone produced better pine growth when applied in March (Table 3-6; McCaskill data, 2006). Treatment Effects-Biogeochemical Indicators Im azapyr produced the highest monthly m ean nitrogen mineralization rates while sulfometuron methyl treatments produced the lo west monthly rates. Most of the herbicides increased the nitrogen mineralization rates, but imazapyr was the only treatment to produce

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97 statistically significant higher leve ls of net nitrogen mineralizati on when compared to the control (Figure 5-4). This difference was more pronounced for the ammonification data (Figure 5-5). The sulfometuron methyl mixed with hexazinone treatment produced a higher mean than imazapyr for the nitrification data (Figure 5-6). Only the sulfometuron methyl treatment produced significantly lower microbial biomass leve ls when compared to the control (Figure 57). Two years of herbicide applications resulted in a significant increase in the soil microbial biomass carbon when compared to a single year application (Figure 5-8). The mean microbial biomass carbon levels were higher at the Pt. Washington restoration site than any of the reference sites (Table 5-2; Figure 5-9). Sulfomet uron methyl also produced the lowest levels of fungal biomass, although not significantly different (Figure 5-10). Fungal biomass carbon (Cfb) levels failed to detect significant differences among any of the treatments (Figure 5-10). The predicted values for mean stand DBH, st and density, and stand ba sal area, were close to the actual values (Table 5-7). Predicted valu es involving stand height were different than the actual restoration site. Discussion The vegetative and soil biogeochem ical variab les collected from the reference sites were effective for ecologically classifying the restorati on site at Pt. Washington They were able to determine the pine subtype and the age class. The environmental gradients as evaluated by the soil biogeochemical indicators were stronger dete rminants of ecosystem conditions than was age (Figure 5-1; Figure 5-2). The power of the soil indicators can be realized by the results of the CCA ordination of the sites along the environmental axes and the plant species indicator analysis (Figure 5-1; Table 5-2; Table 5-4). An analysis of all of the treatment effects indicated th at Imazapyr produced the best improvements in pine seedling development and vegetative control while having the smallest

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98 impact on the natural patterns of understory herbaceous richness. These gains are offset by Imazapyr producing one of the lowest pine seedling survival rates. The survival rate can be improved if the herbicide is ap plied at the beginning of the second growing season during April instead of March (Table 5-6). Most herbicides are readily broken down by soil microbes causing an increase in their numbers and activity (Haney et al. 2002). Some researchers have found certain herbicides cause a reduction in microbial biomass accompanied by an increase in nitrogen mineralization rates (Busse et al. 2006). The decrease in microbial biomass was attributed to a corresponding decrease in organic matter inpu ts from the vegetative control, and not from direct microbial mortality. In any case, the genera l response following the application of herbicides has been an increase in the soil nitrogen mine ralization rates (Li et al. 2003). If Imazapyr, a leucine, and isoleucine protei n inhibitor, was the only treatment to produce significantly higher ne t nitrogen mineralization rates when compared to the control, then some factor must have partially interf ered with the affects of the othe r chemical treatments on nitrogen cycling. The factor of interference may be tie d to Imazapyr being the only chemical amongst this group of herbicides to be currently register ed for use in aquatic systems (Langeland et al. 2006). The Leon soil series found on this restorati on site has a moderate soil leaching rating and a high soil runoff rating for pesticide selec tion (Obreza and Hurt, 2006). The concerns for hexazinone, an photosystem II quinone inhibitor, are m obility in soils and persistence in water. It was also found to inhibit ammoni fication and promote denitrifica tion, dominant transformations during flooding events (V ienneau et al. 2004). Sulfometuron methyl, an acetolactate synthase inhibitor, has been found to quickly move off-site when applied to sites in contact with wetlands (Michael et al. 2006). The chemical has

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99 also been found to be toxic in low concentra tions to many strains of pseudomonas, a major heterotrophic bacteria commonly fo und in forest soils (Boldt and Jacobsen, 1998). This mortality was attributed to the acetolactate synthase i nhibition (ALS) property of sulfometuron methyl (Whitcomb, 1999). This finding might explain the reduction in microbial biomass found in our experiment from applying sulfometuron methyl. The chemical properties of hexazinone a nd sulfometuron methyl limited the treatment effects on this coastal wet pine flat when flooding and the associated high water tables were present. Nitrification was impacted more than ammonification by excessive water from flooding. This condition might explain why the sulfometuron methyl-hexazinone treatment had a significant difference with the control in the nitrification data, but not the net nitrogen mineralization or ammonification data. The effect of soil moisture content on herbicides was observed when comparing the ammonification treatment data with the nitrification results (Figure 5-3; Figure 5-5; Figure 5-6). The results show microbi al biomass measurements are able to detect differences between sites where herbic ides have and have not been applied. These Cmb measurements were also sensitive to the number of herbicide applications used on a given site. Fungal biomass carbon did not detect any differe nces among the treatments. Previous studies have failed to detect any herbicide effect s on fungal communities (Busse et al. 2004). A primary reason for the fungal biomass meas urements failing to de tect statistically significant differences between the treatments may be attributed to the time since the treatments were applied. Most of the individual effects of he rbicides on microbial biom ass levels are greatly reduced beyond two years after application (Li et al. 2003). W ith the exception of the Pt. Washington nitrogen studies, the soils were co llected and analyzed for fungal and microbial biomass 40 months after the second year treatmen ts. The predictions were generally good except

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100 for height and volume estimates (Table 5-6). Mean stand height values were skewed due to a group of the 400 m2 forest structure plots measured within the young age class containing naturally regenerated stands. These naturally re generated stands are dominated with larger saplings, poles and some small sawlog-size trees causing the predicted va lues for height and volume in a 6-year old stand to be exaggerated. Why was Imazapyr more effective than the other herbicide treatments in reducing vegetation competition without sign ificantly impacting natural pa tterns of understory succession within this wet flatwoods site? The answer to this question goes back to achieving the central goal of this experiment. The Point Washington State Forest restorati on site suffers from extensive seasonal flooding and dr ought, which adds to pine seed ling mortality and complicates the selection of the proper herbicide for vegetation control. Imazapyr is a broader spectrum herbicide (less selective) and more effective at controlling perennial woody species. These properties are critical in mimick ing fire effects. Secondly, Imazapyr is more persistent in wet sandy soils than the other herbicide treatments. This is also a critical factor in wet longleaf pine flat sites where the water table is constantly near the surface and the effects of herbicide treatments can be reduced by flooding. Conclusions The Pt. W ashington restoration si te contains elements of me sic flatwoods, wet flatwoods, and wet savannas. However, based upon CCA envir onmental ordination, plant species indicator analysis, and pre-harvest stand data it is a wet flatwoods site. These multivariate techniques were also useful in determining similarities between the Pt. Washington rest oration site and the young age class data of the reference s ites. Imazapyr was the best herbicide treatment for this site based on its ability to control shrubs and remain effective during flooding events. In general, herbicide use increased nitrogen mineralization rates, but imazapyr was the only treatment to produce

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101 statistically significant higher le vels of net nitrogen mineralization when compared to the control. Both imazapyr and the sulfometuron methyl-hexazinone treatments had a significant difference with the control in the nitrification data. The herbicide-treated restoration site had higher soil microbial biomass carbo n levels than the reference sites. Two years of herbicide applications increased soil microbial bioma ss carbon over a single application. There was an indication that sulfometuron met hyl treatments caused soil microbi al mortality. Higher nitrogen mineralization rates at Pt. Washington were nega tively correlated with both of the species diversity indices. The net nitrogen mineralization data proved eff ective at detecting differences between the herbicide treatments. Soil microbial biomass carbon was sensitive to the amount of herbicide applied. The predictions were generally good except for height and volume estimates. Mean stand height values were skewed due to a group of the 400 m2 forest structure plots measured within the young age class containing naturally regenerated all-aged stands.

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102 Table 5-1. Correlations and biplot scores for the biogeochemical variables by pine flat type. Variable Axis 1Axis 2Axis 3Axis 1Axis 2Axis 3 Moisture 05-0.7720.245-0.194 -0.351 0.082-0.058 Soil pH 0.358-0.8750.2720.163 -0.292 0.081 SOM -0.8350.136-0.477 -0.380 0.045-0.142 NetNmin -0.087-0.297-0.349-0.039-0.099-0.104 MBc -0.0480.018-0.026-0.0220.006-0.008 FBc -0.2910.1240.539-0.1320.042 0.160 FBc:MBc 0.4450.144-0.3020.2020.048-0.090 *The Pearson correlations are "intraset correlations of ter Braak (1986). Correlations Biplot Scores Table 5-2. Plant Indicator Values (IndVal) (percent of perfect indica tion) with associated biogeochemical variable by pine flat t ype. P-values repres ent the proportion of randomized runs (1000) equal to or less than observed values ( =0.1). Pine Subtype Plant Species Pine Subtype Mesic Wet Flatwoods Wet Savanna SD PValue Veg Type Mesic Smilax pumila 25 1 5 4.69 0.038 Vine Hypericum hypericoides 17 1 0 3.08 0.024 Forb Gaylussacia frondosa 16 0 4 3.30 0.057 Shrub Pteridium aquilinum 12 0 1 3.00 0.066 Fern Wet Flatwoods Lachnanthes caroliana 0 52 4 3.57 0.001 Forb Arisitida beyrichiana 0 36 0 3.51 0.001 Grass Dichanthelium ovale 6 36 7 4.41 0.007 Grass Cyperus 1 11 1 2.67 0.088 Grass Wet Savanna Ilex glabra 19 13 38 3.55 0.009 Shrub Scleria 17 3 29 3.31 0.014 Grass Pt. Washington Blocks 1&2 Blocks 3&4 Blocks 5&6 SD PValue Veg Type Blocks 1 & 2 Arisitida beyrichiana 34 10 7 5.97 0.039 Grass Tragia urens 13 0 0 2.11 0.016 Forb Blocks 3 & 4 Smilax pumila 0 25 0 5.58 0.001 Vine Pteridium aquilinum 2 18 0 3.42 0.013 Fern Blocks 5 & 6 Scleria 0 3 25 6.16 0.078 Grass Lachnanthes caroliana 0 0 25 4.82 0.024 Forb INDICATOR VALUES (% of perfect indication based on combining the values for relative abundance and relative frequency) n=48

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103 Table 5-3. Correlations and biplot scores for the biogeochemical variables by forest age class. VariableAxis 1Axis 2Axis 3Axis 1Axis 2Axis 3 Moisture 05-0.7720.245-0.194 -0.521 0.142-0.106 Soil pH0.358-0.8750.2720.242 -0.505 0.148 SOM-0.8350.136-0.477 -0.563 0.079-0.260 NetNmin-0.087-0.297-0.349-0.058-0.171-0.190 MBc-0.0480.018-0.026-0.0320.011-0.014 FBc-0.2910.1240.539-0.1960.0720.294 FBc:MBc0.4450.144-0.3020.3000.083-0.164 CORRELATIONS AND BIPLOT SCORES (7 Biogeochemical Variables) Correlations Biplot Scores *The Pearson correlations are "intraset correlations of ter Braak (1986). Table 5-4. Plant Indicator Values (IndVal) (percent of perfect indica tion) with associated biogeochemical variable by forest age class. P-values represent the proportion of randomized runs (1000) equal to or less than observed values ( =0.1). Species codes are found in Appendix A. SpeciesAgeGroupIndValp-value Dich-AnviRegen 32.90.0010 Rhal MidAged27.80.0010 Ilgl Mature 28.40.0160 Dich-ArbePt Wash36.40.0010

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104 Table 5-5. The means for soil biogeochemical variab les between reference site locations and the Pt. Washington restoration site. Site Time Interval (years) Stand Age (years) Soil Moisture 2005 Soil Moisture 2006 N et Nmin (mg-1/kg-1 Soil / year)Cmb (mg/kg/ soil) SOM Content (%) Soil pH [H+] Plant Avail-P (mg-1/kg-1 soil)Cfb (mg/kg /soil) FB to MB Ratio St. Marks Seedling60.340.2221152.84.30.24510.44 St. Marks Mid-Aged360.260.28155891.44.6-0.24750.13 St. Marks Mature1100.250.115491.54.60.31871.66 ChassahowSapling90.440.07111862.94.3-0.091050.57 ChassahowMid-Aged450.230.0881451.14.7-0.041611.11 ChassahowMature710.570.22173694.64.10.231560.42 Topsail HillSapling190.450.10205242.94.3-0.361710.33 Topsail HillMid-Aged490.310.1055591.94.5-0.501790.32 Topsail HillMature1010.320.0974902.04.2-0.401900.39 Pt WashSeedling 60.270.072 1198 1.44.6-0.271260.16 Table 5-6. Pt. Washington actual vs predicted indicator values. Predicted Values Age-6 Reference SitesControl Velpar 1st Year Only Velpar 2nd Year Only Velpar March Application Arsenal 1st Year Only Arsenal 2nd Year Only Arsenal April Application DBH (cm) 3.73 2.873.033.143.313.263.233.62 R2 = 0.81 p < 0.0001 Height (m) 2.090.170.180.250.190.260.290.34 R2 = 0.82 p < 0.0001 Density (trees/ha) 265.57 259268298302224244218 R2 = 0.1 p < 0.0083 BA (m2/ha) 0.09 0.170.190.230.260.190.200.22 R2 = 0.56 p < 0.0001 Volume (m3/ha) 17.50 0.030.030.060.050.050.060.07 R2 = 0.52 p < 0.0001Predicted DBH = [(-0.00510*Age 2 ) + (0.82861* Age) 1.06278], Predicted Height = [(-0.00288*Age2) + (0.45277*Age) 0.526 32], Predicted Density = [(-0.83301*Age) + 270.56934], Predicted Basal Area = [(-0.00190*Age2) + (0.3323*Age) 1.95528], Predicted Volume = [(2.46264*Age) + 2.72759]Pt. Washington Actual Values (2006)

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105 Figure 5-1. Pine flat type determined by a thre e-dimensional ordination biplot derived from Canonical Correspondence Analysis (CCA) of 192 plots usin g understory plant species abundance and soil biogeochemi cal data including the Pt. Washington restoration site.

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106 Figure 5-2. A three-dimensiona l ordination biplot derived from Canonical Correspondence Analysis (CCA) of 192 plots using unde rstory plant species abundance and soil biogeochemical data collected within the young, mid-aged, mature age class, and the Pt. Washington restoration site.

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107 -200 -150 -100 -50 0 50 100 150 200 250 300Jan-02 Feb-02 Mar-02 Apr-02 May-02 Jun-02 Jul-02 Aug-02 Sep-02 Oct-02 Nov-02 Dec-02 Jan-03 Feb-03 Mar-03N Mineralization Rate (mg N / kg soil / month) Ammonification Nitrification Figure 5-3. Monthly variation of total nitrogen mineralization, a mmonification and nitrification rates (mg-1 kg-1 month-1) obtained from field incubation of soils (untreated) during 14 months before and after the 2002 treatments. 0 20 40 60 80 100 120 140 160 TreatmentsNitrogen Mineralization (mg (NH4+NO3) / kg soil) Control Oust Velpar Oust-Velpar Arsenal Figure 5-4. Net nitrogen mine ralization means mg (NH4 + + NO3 -) / kg-1 soil / month for the control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometuron methylhexazinone mix and Arsenal: imazapyr. Resu lts are from soil samples collected during 14 months before and after the 2002 treatments. a a a ab b

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108 0 10 20 30 40 50 60 70 TreatmentsNet Ammonification (mg NH4 / kg soil / month) Control Oust Velpar Oust-Velpar Arsenal Figure 5-5. Net ammonification mean monthly rates (mg-1 NH4 + / kg-1 soil / month) for the control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometuron methylhexazinone mix and Arsenal: imazapyr. Resu lts are from soil samples collected during 14 months before and after the 2002 treatments. 0 10 20 30 40 50 60 70 80 90 TreatmentsNitrification (mg N03 / kg soil / month) Control Oust Velpar Oust-Velpar Arsenal Figure 5-6. Net nitrification mg -1 N03 / kg -1 soil / month; for the control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometur on methylhexazinone mix, and Arsenal: imazapyr; applied in different growing s easons, frequencies, and time of year. Results are from soil samples collected during 14 months before and after the 2002 treatments. a a a b b a a a a b

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109 0 200 400 600 800 1000 1200 1400 1600 1800 TreatmentsCmb (mg C / kg soil) Control Oust Velpar Oust-Velpar Arsenal Figure 5-7. Microbial biomass carbon (Cmb) mg -1 C / kg -1 soil; for the control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometuron methylhexazinone mix, and Arsenal: imazapyr. Results are 40 mont hs after second treatment (2006). 0 200 400 600 800 1000 1200 1400 1600 1800 Number of ApplicationsCmb (mg C / kg soil) One Year Two Years Figure 5-8. Effects of one year and two consecutive years of herb icide applications on microbial biomass carbon (Cmb) mg-1 carbon / kg-1 soil from soils. Results are forty months after second treatment (2006). b b b b a a b

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110 St. Marks ChassTopsail Pt. Wash0 250 500 750 1000 1250 1500Cmb (mg Carbon / kg soil) Figure 5-9. Soil levels of Microbial biomass carbon (Cmb) mg -1 carbon / kg -1 measured at the reference sites and the Pt. Washington restor ation site. Results are forty months after second treatment (2006). 0 20 40 60 80 100 120 140 160 180 TreatmentsFungal Biomass (mg carbon / kg soil) Control Oust Velpar Oust-Velpar Arsenal Figure 5-10. Fungal biomass carbon mg -1 carbon / kg -1 soil; for the control, Oust: sulfometuron methyl, Velpar: hexazinone, sulfometur on methylhexazinone mix, and Arsenal: imazapyr. Results are four year s after second treatment (2007). a a b c

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111 Figure 5-11. Pools and fluxes of nitrogen in the RESDYN restoration model. MP, metabolic pool; grass&forbs, holocellulose pool; sh rubs, lignocellulosic pool; and CWD, woody pool. There are distinctive stabilization coefficients for microbial biomass, young soil organic matter (Y-SOM), and old so il organic matter (SOM) (adapted from Corbeels et al. 2005).

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112 CHAPTER 6 SUMMARY AND CONCLUSIONS Ecosystem restoration requires a good monitoring system that allows for the tracking of success by measuring key ecological indicators at th e restoration site and comparing those results with reference communities. The measured ecol ogical indicators must include monitoring changes belowground as well as in the abovegro und vegetation for the coup ling of functional and structural attributes. The overa ll objective of this study was to examine stand structure, understory species composition, and soil ch emical and microbial properties along a chronosequence in longleaf pine wet flats along Floridas Gulf Coast in an attempt to develop an ecological trajectory for this co mmunity. Such an ecological traj ectory would serve as the basis for developing a monitoring framework for restora tion projects in the southern Gulf Coastal Plain. We selected three reference sites within the Gulf Coas t Flatwoods subecoregion to accomplish our objective. Within each reference site we sampled a total of 12 plots, 4 plots each in the early, mid and mature age classes. This experimental design resulted in 29 different age groups representing a chronosequenc e of 2 to 110-year-old stands. The selected reference locations not only represented the highest quality sites that could be found in Florida, but were also located within the specific range for coas tal wet longleaf pine flats found along Floridas Gulf coast. Monitoring this very specific biog eographical area (Gulf Coast Flatwoods subecoregion of Florida) created a spatial gradient pertinent to the restoration site that we wanted to evaluate. The time scale was limited to the oldest available longleaf pine stands (110-year old) distributed along the specified spatial range. The major focus in Chapter 2 was to examine overstory stand structur e data and understory plant species composition along the 110year chronose quence. As expected, stand DBH, height,

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113 and basal area increased with age, but reached a steady state plateau ar ound 80-90 years. when they began to decline. Coarse woody debris accumulation levels were highly variable, but tended to increase with age. The decomposition levels of CWD were constant through the mid-aged class, but declined from the mid-age to the mature age class. The level of shrub species was significantly higher in th e mature sites than found in either the young or the mid-aged classes. Stand growth during early development transl ates to habitat heterogeneity as partial shading brings in new groups of plant species. At this point, stand height had a strong positive relationship with the Coleman rarefaction i ndex and stand density has a strong negative relationship with the Shannon-Wiener diversit y index. The plant spec ies turnover rates as indicated by Coleman rarefaction values were high and the evenness of plant species as indicated by Shannon-Wiener was very low. The evenness of plant species was not attained until the mature stage when the number of plant species entering the ecosystem was equal to the number of plant species leaving it. At this point, Sh annon-Wiener diversity values had a strong positive relationship with stand density and the Colema n rarefaction index had a negative relationship with stand height. The equilibrium between Co leman rarefaction and Shannon-Wiener diversity indices at this stage indicate s a steady state in the overstor y. Based upon the chronosequencial trends, Floridas Gulf Coastal longleaf pine fl ats reach the understory reinitiation stage at approximately 90 years. This would mean the fo rest is self-organizing, a threshold point for restoration. In Chapter 3, Our main objective was to measure soil pH, moisture content, organic matter content (SOM), plant-ava ilable phosphorus, soil nitrog en mineralization rates (Nmin), soil microbial biomass carbon (Cmb) and fungal biomass (Cfb) along the same 110-year chronosequence for determining the ecological traj ectory in terms of soil chemical and microbial

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114 characteristics of longleaf pine in coastal wet pi ne flat communities. We specifically tested our hypothesis that this group of so il biogeochemical indicators measured along the chronosequence would follow a pattern similar to the biomass accumulation curve for forest succession (Vitousek and Reiners, 1975). In response to rapid increas e in growth during the early years of stand establishment, we predicted a similar increase in net nitrogen mineralization rates, microbial biomass and fungal biomass levels. We hypothesized that these variables would decrease at some point during the mid-aged stage and reach a th reshold steady-state some time during the early mature stage when the understory reinitia tion process of forest succession has begun. Nitrogen cycling was dominated by ammoni um production during the wet 2005 growing season when compared to a drie r 2002. Nitrification represented 50% of the production during 2002 and less than 25% during 2005. There was ammonium enrichment by nitrate reduction. This probably indicates that the dissimilatory-n itrate reduction-to-ammonium (DNRA) pathway was prominent during the flooded 2004-2005 growing seasons. The net nitrogen mineralization rates, microbial biomass carbon, and fungal biomass carbon increased between the young and mid-aged classes, then decreased between the mi d-aged and mature age classes. The FB-to-MB ratios increased dramatically up to 60 years, then decreased to 110 year s. Finally, soil organic matter content (SOM), increased with soil moisture. Based upon the results this group of soil indicators follows biomass accumulation patterns and will attain biogeochemical equilibrium after a stand age of approximately 60-70 years. The threshold would be during the mature age class after the understory reinitiation phase of fore st succession has started. The objective of Chapter 4 was to examine the relationships between key soil chemical and microbial properties and the oversto ry and understory characteristic s of a wet longleaf pine flat community in the Gulf Coastal Plain of Flor ida. We hypothesized stand volume will show a

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115 positive relationship with soil nitrogen mineraliza tion, which, in turn, will be driven by the microbial community dynamics in the soil. We also hypothesized that the fungal biomass will increase as coarse woody debris accumulated on the forest floor and the standing stock increased over time The majority of the soil biogeochemical indica tors influenced longleaf pine stand growth, and as stands developed, changes in aboveground vegetation influenced the soil biogeochemical indicators. Net nitrogen mineralization increase d with stand volume until a threshold of 200 m3 / ha (stand age = 90 years). Nitrat e was found to be in higher con centrations in the young forest soils than the mature forest soils. Populations of nitrifying bacteria (AOB + NOB) were also found to be higher in the young forest soils. At T opsail Hill, ammonium levels were found to be higher in the wet young pine savanna soils than the mesic mature soil. Higher soil moisture translates to lower nitrificati on levels. The rela tionships between fungi and increases in stand height or coarse woody debris accumulation indicate a strong c ontinual relationship between the soil biogeochemical indicators and longleaf pine stand development. The dynamics of this relationship might be better unde rstood if the measured fungal biomass could have been identified as arbuscular mycorrhizal (AM) f ungi, ectomycorrhizal (EM) fungi, or saprophytic fungi along the chronosequence. The dominance of fungi negatively affected the Coleman Rarefaction and Shannon-Wiener diversity indices. This may indicate a decrease in species richness, but the functional redundancy component of ecosystem resilience is probably being strengthened. The strong relationships be tween forest biomass accumulation and soil biogeochemistry should always be studied in any monitoring event. Nitrogen cycling appears to become tighter in mature forests at a thres hold of 90 years. This condition is dependent on mycorrhizal and saprophytic fungi do minating the soil microbial biomass.

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116 The objectives of Chapter 5 were to use the i ndicator data to ecological classify the Pt. Washington restoration site as a mesic flatwoods, wet flatwoods or wet savanna. Secondly, to use the soil biogeochemical indicato rs for trying to detect differe nces among the four herbicide treatment effects applied on the restoration site. Finally, we will use both the vegetative and soil biogeochemical data to predict th e development or ecological trajec tory in wet longleaf pine flat restoration. The predicted values will be presented with pine gr owth results on the effects of herbicide treatments applied in the second year after planting compared to first year only, consecutive herbicide treatments (1st & 2nd Year), and whether an earl y or late spring application changes the effects (McCaskill data, 2006). The Pt. Washington restoration si te contains elements of me sic flatwoods, wet flatwoods, and wet savannas. However, based upon CCA envir onmental ordination, plant species indicator analysis, and pre-harvest stand data it is a wet flatwoods site. These multivariate techniques were also useful in determining similarities between the Pt. Washington rest oration site and the young age class data of the reference s ites. Imazapyr was the best herbicide treatment for this site based on its ability to control shrubs and remain effective during flooding events. In general, herbicide use increased nitrogen mineralization rates, but imazapyr was the only treatment to produce statistically significant higher le vels of net nitrogen mineralization when compared to the control. Both imazapyr and the sulfometuron methyl-hexazinone treatments had a significant difference with the control in the nitrification data. The herbicide-treated restoration site had higher soil microbial biomass carbo n levels than the reference sites. Two years of herbicide applications increased soil microbial bioma ss carbon over a single application. There was an indication that sulfometuron met hyl treatments caused soil microbi al mortality. Higher nitrogen mineralization rates at Pt. Washington were nega tively correlated with both of the species

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117 diversity indices. The net nitrogen mineralization data proved eff ective at detecting differences between the herbicide treatments. Soil microbial biomass carbon was sensitive to the amount of herbicide applied. The predictions were generally good except for height and volume estimates. Mean stand height values were skewed due to a group of the 400 m2 forest structure plots measured within the young age class containing naturally regenerated all-aged stands. Research Implications in Coastal We t Longleaf Pine Flats Restoration The monitoring study proved effective at evaluating our restoration site with a set of indicators that integrat ed the structural and functional at tributes of the wet longleaf pine ecosystem. The aboveground vegeta tive variables and the soil biogeochemical measurements produced similar threshold periods. The selectio n process for the reference sites also proved fruitful based upon the sites having similar sta nd, soil properties, and common understory plant species among the locations. It was critical to restrict the location of the reference sites to within the 3 kilometers of the Gulf coast. Our set of reference sites were selected to evaluate southern coastal pine communities that are directly affected by tr opical storms. The restoration of Gulf coastal wet longleaf pine flats is distinct from other longleaf pine communities. Flooding caused by active hurricane seasons can leave these sites inundated for more th an two years. This condition causes two major results in the biogeochemistry of these pinelands. First, extended floodi ng causes the nitrogen cycle to be dominated by ammonium production. When ammonium becomes scarce, nitrate is converted to ammonium thr ough the DNRA pathway conserving nitrogen losses. Secondly, long term flooding results in the accumulation of so il organic matter, causing the pH of the soil medium to drop. This condition favors fungi and anaerobic bacteria over the aerobes. When the conditions become dry, there is a great flush of growth in both the overstory and understory vegetation. The effects of this flooding cycle are greater on younger forests than mature forests

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118 where nitrate is in greater demand because of stand growth requirements. This demand was expressed by the nitrate levels and numbers of nitrifying bacter ia being significantly higher in soils from the young stands compared to the mature stands. When prescribing fire in these sites, it is critical not to burn them during a flooding cycle before the flush of growth is completed. Based upon the conditions at our four sites, that can take 12-14 months af ter the drying process has started. The understory vegetation was also distinct in these wet pine flat s. There are higher densities of facultative wetland grasses and forbs and fewer ha rdwoods, especially the oaks. Very few oaks were measured on any of our site s other than the creepers (running oak). Some of these sites have not been burned in over 5 years. The implication here is th e fire return-intervals can be extended well beyond 2-3 years if flooding conditions exist. The mesic mature sites had a higher composition of shrub species than the young mesic stands, even under fire return-intervals of 3 years. Soil moisture in the terms of extende d flooding can enrich wet l ongleaf pine flat soils, conserve their nitrogen supply, and prevent invasion by shrub species. The flooding cycle can provide as many benefits to coastal wet pine ecosystems as fire does. In summary, monitoring needs to include indicators that meas ure the functions as well as structural attributes of a given ecosystem. This proved to be extremely important in Gulf coastal pine communities where soil cond itions are distinct from in land ecosystems. It was also important to restrict the sites to within the Gulf Coast Flatwoods subecoregion of Florida and to within 3 kilometers of the coast for insuring th e same climatic effects that occurred at the restoration site occurred at each of the reference sites. One result of these stratifications was that all of the sites had 63 understory plant species in common. This may not have been attainable had the spatial scale been broader. This set of indicators and the time scale for the

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119 chronosequence can be utilized at other envir onments where longleaf pine ecosystems are found. The chronosequence approach is strengthened by having as many replications as economically feasible at each of the differently aged sites. A difficult and important aspect to monitoring is selecting the spatial scale for the reference sites. If one is looking to monitor longleaf pine in mountain terrain found in the norther n limit of its range, it would be more effective to restrict the reference sites to within that environment in order to capture the eco logical differences found within the local c limatic and soil conditions. A key directi on for future research is to conduct investigations for improving our understanding of the biogeochemical dynamics that take place in facultative pine wetlands (i.e., wet pine-dominated mineral flats). This research would need to include molecular analysis for the identification species that change in soil microbial community between wet and dry conditions.

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120 APPENDIX SPECIES CODE LIST Table A-1. Species list. Scientific name Code Common name Shrubs Asimina incana Asin Wooly paw paw Cyrilla racemiflora Cyra Titi Gaylussacia dumosa Gadu Drawf huckleberry Gaylussacia frondosa Gafr Dangleberry Ilex coriacea Ilca Large gallberry Ilex glabra Ilgl Gallberry Ilex vomitoria Ilvo Yaupon Kalmia hirsuta Kahi Hairy wicky Licania michauxii Limi Gopher apple Lyonia lucida Lylu Fetterbush Magnolia virginiana Mavi sweet bay Myrica cerifera Myce Wax myrtle Photinia pyrifolia Phpy Red choke berry Quercus pumila Qupu Running oak Serenoa repens Sere Saw palmetto Stillangia sylvatica Stsy Queens delight Vaccinium spp Vacc Blueberry spp Grasses Andropogan virginicus Anvi Bluestem grasses Aristida stricta var. beyrichiana Arbe Wiregrass Calamovilfa curtissii Cacu Curtis sandgrass Ctenium aromaticum Ctar Toothache grass Cyperus Cype Sedge spp Eragrostis spectabilis Ersp Purple lovegrass Dichanthelium ovale Dich Eggleaf witch grass Panicum Dichanthelium Pani Panicum spp Dichanthelium erectifolium Paer Erect leaf witchgrass Panicum laxiflorum Pala Velvet Witchgrass Scleria Scle Nutrush spp Xyris caroliniana Xyca Yellow eyed grass Forbs Asclepias viridula Asvi Southern milkweed Aster adnatus Asad Scaleleaf aster Aster eryngiifolius Aser Thistleleaf aster Aster reticulatus Asre White top aster Aster tortifolius Asto Dixie aster

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121 Table A-1. Continued Carphephorous pseudoliatris Caps Bristleleaf chaffhead Carphephorus odoratissimus Caod Deer tongue Chrysopsis Chry Silkgrass spp Conyza canadensis Coca Canadian horseweed Coreopsis linifolia Coli Texas tickseed Desmodium rotundifolium Dero Tricklyfoil Drosera capillaris Drca Pink sundew Elephantopus tomentosus Elto Devils grandmother Eupatorium capillifolium Euca Dog fennel Eupatorium compositifolium Euco Yankee weed Eupatorium mohrii Eumo Mohrs thoroughwort Eupatorium pilosum Eupi Rough Boneset Euthamia graminifolia Eugr Flat top goldenrod Gelsemium sempervirens Gese Yellow jessamine Gratiola hispida Grhi Rough Hedgehyssop Hypericum hypericoides Hyhy St. Andrews cross Hypoxis sessilis Hyse Glossyseed yellow stargrass Hypoxis spp Hypo Stargrass spp Lachnanthes caroliniana Laca Carolina redroot Lechea Lech Pineweed spp Lechea pulchella Lepu Leggetts pineweed Liatris gracilis Ligr Slender gayfeather Liatris tenuifolia Lite Shortleaf gayfeather Mimosa quadrivalvis Miqu Sensitive brier Oenothera fruticosa Oefr Evening primrose Opuntia humifusa Ophu Prickly pear Pityopsis graminifolia Pigr Silkgrass Pterocaulon pycnostachyum Ptpy Blackroot Rhexia alifanus Rhal Meadow beauty Rhexia petiolata Rhpe Fringed meadow beauty Sabatia brevifolia Sabr Shortleaf Rosegentian Seymeria cassioides Seca Yaupon Blacksenna Smilax laurifolia Smla Laurel green brier Smilax pumila Smpu Green brier Solidago odora Sood goldenrod Stylisma patens Stpa Coastal plain dawn flower Tragia urens Trur Wavyleaf noseburn Verbena brasiliensis Vebr Brazilian vervain Viola septemloba Vise Blue violet Vitis rotundifolia Viro Muscadine

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122 LIST OF REFERENCES Achtem eier, G., Jackson, W., Hawkins, B., Wade, D.D. and C. McMahon. 1998. The Smoke Dilemma: A Head-On Collision. Transcript In: The 63rd North American Wildland and Nature Resource Conference 415-421. Adegbidi H.G., Jokela, E. J. and N.B. Co merford. 2005. Factors influencing production efficiency in intensively managed loblolly pine plantations in a 1-to-4-year old chronosequence. Forest Ec ology and Management 218:245-258. Allen A.S. and W.H. Schlesinger. 2003. Nutr ient limitations to soil microbial biomass and activity in loblolly pine forests. Soil Biology and Biochemistry 36:581-589. Allen M.F., Allen, E.B., Zink, T.A., Harney, S., Yoshida, L.C., and R. MacAller. 1999. Soil Microorganisms, In L. Walker (ed.) Ecosys tems of disturbed ground. 16. Ecosystems of the World. Elsevier Press, New York 22:521-544. Allen, W.J., 1991, Soil survey of Wakulla County, Fl orida: U.S. Department of Agriculture Soil Conservation Service 163 p. Allison, V.J., Condron, E.M., Peltzer D.A., Richar dson, S.J. and B.L. Turner. 2007. Changes in enzyme activities and soil microbial comm unity composition along carbon and nutrient gradients at the Franz Josef ch ronosequence, New Zealand. Soil Biology and Biochemistry 39:1770-1781. Aravena, J. C., Carmona, M.R., Perez, C.A., and J.J. Armesto. 2002. Changes in tree species richness, stand structure and soil properties in a succession al chronosequence in northern Chiloe Island, Chile. Revista chil ena de historia natural 52:1-30. Bth, E., Frostegrd,, A., Pennanen, T., and H. Fritze. 1995. Microb ial community structure and pH response in relation to soil organic ma tter quality in wood-ash fertilized, clear-cut or burned coniferous forest soils. Soil Biology and Biochemistry 27:229-240. Bth, E., and T.H. Anderson. 2003. Compar ison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques. Soil Biology and Biochemistry 35:955-963. Bailey, V.L., Smith, J.L. and H. Bolton Jr. 2002. Fungal-to-bacteria ratio s in soils investigated for enhanced C sequestration. Soil Biology and Biochemistry 34:997-1008. Bailey, V.L., A. D. Peacock, J.L. Smith, and H. Bolton 2002. Relationships between soil microbial biomass determined by chloroform fumigation-extracti on, substrate-induced respiration, and phospholipid fatty acid analys is. Soil Biology and Biochemistry 34:13851389. Barnett, J.P. 1999. Longleaf Pine Ecosystem Restoration: The Role of Fire. Journal of Sustainable Forestry 9:89-96.

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138 BIOGRAPHICAL SKETCH George McCaskill s tarted his doctoral study at the School of Forest Resources and Conservation, University of Florida in January, 2003. Before joining the University of Florida, he was Associate Faculty teaching multiple course s at the College of the Redwoods in Eureka, California. Prior to that he worked as a Bili ngual Forestry instructor at Mt. Hood Community College. For three years, he was a State Lands Timber Sales Forester for the Washington Department of Natural Resources. He spent 3.5 years in the U.S. Peace Corps serving as an Environmental Program Specialis t evaluating Chilean forest pr actices as applied to their Monterey pine plantations and their native Nothof agus forests. While working with the Chilean Forestry Corporation, he served as interprete r/translator/editor duri ng the Sixth Congress on Criteria and Indicators for th e Conservation and Sustainable Management of Temperate and Boreal Forests. Also known as the Montral Prot ocol, he helped to finalize the treaty where all the Pacific Rim countries signed the document. In 1990, Mr. McCaskill completed his Masters program at California Polytechnic in San Lu is Obispo, California. He is a Registered Professional Forester in California.