Title Page
 Table of Contents
 Global change and ecological...
 Scaling ecological dynamics: Self-organization,...
 Fire, seed dispersal and multiple...
 Fire in boreal forest: Emergent...
 A general model of contagious...
 Ecological resilience, biodiversity...
 Biographical sketch

Title: Contagious disturbance and ecological resilience
Full Citation
Permanent Link: http://ufdc.ufl.edu/UF00100664/00001
 Material Information
Title: Contagious disturbance and ecological resilience
Physical Description: Book
Language: English
Creator: Peterson, Garry D., 1969-
Publisher: State University System of Florida
Place of Publication: <Florida>
Publication Date: 1999
Copyright Date: 1999
Subject: Zoology thesis, Ph. D   ( lcsh )
Dissertations, Academic -- Zoology -- UF   ( lcsh )
Genre: government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )
Summary: ABSTRACT: Managing the consequences of anthropogenic transformation of the biosphere requires understanding the processes that create, maintain, destroy, and restore ecological organization. In this dissertation I examine the ability of contagious disturbance processes, such as fire and insect outbreaks, to create and destroy ecological resilience. Using computer models I examine the consequences of changes in fire regime on the organization of two specific ecosystems. I demonstrate that in north Florida forests both wildfire and the current management practice of periodic controlled burning are insufficient to maintain the landscape, but a policy that applies controlled burns based upon the vegetative condition of an area could be effective. I compare the responses of climate and fuel-accumulation driven models of fire in the boreal forest. I demonstrate that climate models are more sensitive to climate change than fuel accumulation models, but are more vulnerable to abrupt reorganization. I use these specific explorations to construct two general conceptual models: the first of contagious disturbance, and the second of ecological resilience. My model of contagious disturbance defines disturbance agents, stress accumulation and release, chain reactions and evolutionary epidemics as four key sub-types of contagious disturbance. I identified initiation, reproduction, contagion, virulence, and recovery as the five key properties of a contagious disturbance regime, and show how a change in the relationship of these variables alters the behavior of contagious disturbances.
Summary: ABSTRACT (cont.): These models provide general insights into the consequences of specific ecological organizations, allowing qualitative predictions to be made about the behavior of a poorly understood disturbance process. My model of cross-scale resilience proposes that ecological resilience is generated by diverse, but overlapping, functions within a scale and by apparently redundant species that operate at different scales. The distribution of functional diversity within and across scales allows regeneration and renewal to occur following ecological disruption over a wide range of scales. The consequences of species or ecological process loss may not be immediately visible, but it decreases ecological resilience to disturbance or disruption. It produces ecosystems that are more vulnerable to ecological collapse, and reduces the variety of possible alternative ecological organizations.
Summary: KEYWORDS: fire, scale, disturbance, boreal forest, longleaf pine
Thesis: Thesis (Ph. D.)--University of Florida, 1999.
Bibliography: Includes bibliographical references (p. 248-260).
System Details: System requirements: World Wide Web browser and PDF reader.
System Details: Mode of access: World Wide Web.
Statement of Responsibility: by Garry D. Peterson.
General Note: Title from first page of PDF file.
General Note: Document formatted into pages; contains ix, 261 p.; also includes graphics.
General Note: Vita.
 Record Information
Bibliographic ID: UF00100664
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 47679825
alephbibnum - 002456724
notis - AMG2055


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Table of Contents
    Title Page
        Page i
        Page ii
        Page iii
    Table of Contents
        Page iv
        Page v
        Page vi
        Page vii
        Page viii
        Page ix
        Page 1
        Page 2
        Page 3
        Page 4
    Global change and ecological resilience
        Page 5
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    Scaling ecological dynamics: Self-organization, hierarchical structure and resilience
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    Fire, seed dispersal and multiple stable states in longleaf pine forest
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    Fire in boreal forest: Emergent landscaped dynamics
        Page 138
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    A general model of contagious disturbance
        Page 177
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    Ecological resilience, biodiversity and scale
        Page 209
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    Biographical sketch
        Page 261
Full Text








Many people helped sustain, enrich and strengthen this dissertation. Without them

this document would not exist. Buzz Holling encouraged me to avoid the deceptively safe

traps of stable, small-scale science and pursue research that was difficult and dangerous,

but ultimately general and practical. He greatly assisted my development as a scientist by

continually confronting me with new ideas, and introducing me to exciting scientists. He

also ensured that his lab was an excellent place to work.

The collegial atmosphere and fellowship of the Arthur R. Marshall Jr. Ecological

Sciences Laboratory encouraged discussions that developed this research as well as

providing the friendship necessary to support my work, and for this I would like to thank

my fellow graduate students Craig Allen, Jan Sendzimir, Rusty Pritchard, Carla Restrepo,

Joe Fragoso, Paul Marples, and Mark Hostetler. Frequent discussions with Lance

Gunderson helped shape my dissertation and clarified my developing ecological ideas.

Toni Carter guided and protected me on my navigation through the ever shifting twists and

turns of the University of Florida's bureaucracy, as well as making the Arthur R. Marshall

Lab an excellent place to work.

My dissertation committee along with a number of other professors provided me

with a wide variety of assistance. Steve Sanderson introduced me to the human

dimensions of land-use/land-cover change, and greatly enriched my thinking on human

dimensions of resilience and disturbance. Colin Chapman encouraged an interest in the

scaling of seed-dispersal and provided clear, helpful critiques of many manuscripts. Jack

Putz encouraged me to learn more about tropical and sub-tropical ecology, and was always

willing to engage in ecological conversation. Lauren Chapman thoughtfully helped me

clarify many proposed portions of this document. Doria Gordon greatly enhanced my

understanding of north Florida ecosystems. I enjoyed working with them all.

The work on longleaf pine dynamics, described in Chapter 4, was conducted with

the collaboration and support from the Florida Nature Conservancy in partnership with

Eglin Air Force Base. Many people within both institutions helped build and test this

model. I would particularly like to thank Jeff Hardesty and Doria Gordon for their help in

developing this model, and the fire managers of Jackson Guard at Eglin Air Force Base for

testing and refining it. The work on fire in the boreal forest, in Chapter 5, was greatly

improved by discussions and data sharing with Paul Marples. Many discussion and grant

proposals written with Craig Allen produced the ideas that lead to Chapter 7.

Finally, I would like to thank my friends in Gainesville, Lance Gunderson, Jan

Sendzimir, Rusty and Joanna Pritchard, Kathleen 'Netjes' Ragsdale, Jessica Anders, Hank

Green, Joe Fragoso, Kirsten Silvius, and Ravic Nijbroek, whose friendship and support

helped me research and then write this dissertation. Most of all I thank Marieke Heemskerk

for her love and encouragement.

Finally, I would like to acknowledge that this work was supported by a Post-

Graduate Fellowship from the National Science and Engineering Research Council of

Canada and an Earth System Science Fellowship from NASA.


A C K N O W L E D G M E N T S .................................................................... ii

ABSTRACT ............................................................................... viii

IN TROD U CTION ................................................................. ...... 1

Ecological Reorganization .................................................... ...... 1
Resilience and Disturbance ................................................... ...... 1
E cological Scale .............................................................. ....... 2
A D issertatio n O u tlin e ............................................................ ....... 3

GLOBAL CHANGE AND ECOLOGICAL RESILENCE .............................. 5

G lobal C change ................................................................. ...... 5
E arth Sy stem s ............................................................ ....... 7
E arth and the Sun ............................................ ............ ....... 7
Earth's Internal Dynamics ..................................................... 8
Origin of Earth and the Biosphere............................................. 9
Early Life the Archean .............................................. ......... 9
Transition to an Oxygen Atmosphere......................................... 10
Land Plants ....................................... .................. ............... 11
Expansion of Carbon Cycling ............................................... 12
D evelopm ent of the Biosphere................. .................... ......... 12
Multiple Climate States ......................................................... 13
Expansion of Humanity..................................... 14
Malthus & Darwin: Limits & Adaptation..................................... 15
H um an R egim es ............................................ ................. ........ 16
H um an D om ination......................... ........................................ 19
Land-use/Land Cover Change.............................................. 19
A tm ospheric C change .................................................. ......... 20
E cological A ppropriation............................................. ......... 21
Modification of Biogeochemical Cycles...................................... 21
Loss of Biological Diversity ...................................... ............... 22
Comparing Human and Historic Global Change................................. 22
Sum m ary ...................................................................... ........ 2 5

HIERARCHICAL STRUCTURE & RESILIENCE ................................ 32

In tro d u c tio n ...................................................................... ......... 3 2

Scaling Problems .......................................................... ........ 32
Non-Linearity and Heterogeneity.............................................. 33
Different Processes Dominate at Different Scales ............................. 34
Cross-scale Connections ....................................................... 34
Em ergent Processes ............................................................. 35
Coping with Scaling Problems ............................... .............. 37
Cross-Scale Interactions ............................................................. 37
Hierarchy Theory ...................................................... ......... 38
H ierarchical O organization ............................................... .... 38
Dynamic Hierarchy ............................................................. 41
C ross-Scale C change ................................................... ......... 42
Cross-Scale Resilience ............................................................... 44
Avian Predation and Spruce Budworm....................................... 45
Scaling and Ecological Reorganization ....................................... 48
C o n c lu sio n s ...................................................................... ........ 4 8

LONGLEAF PINE FOREST .............................................. 56

Introduction ............................................................................... 56
Ecological Management.............................. ... ............. 57
Longleaf Pine Forest on Eglin Air Force Base................................... 58
Ecological Caricatures and Management Alternatives ............................. 60
Caricatures of Ecological D ynam ics ............................................... 61
Modeling Approach................................................... ............ 62
Succession and Fire Temporal Dynamics....................................... 64
Modeling Vegetation Dynamics................................................ 64
Likelihood of States...................................................... ......... 73
Succession and Fire Spatial Dynamics.......................................... 75
M odel Organization .............................................................. 76
M odel Spatial Processes ......................................................... 79
Landscape Resilience ........................................................... 82
M odel T testing ............................................ ..... ..................... 84
Spatial Dynamics Management Options......................................... 87
Management Vegetation Classification........................................ 88
M managing Prescribed B urns....................... .................. ......... 88
Burning Strategy ................................................................ 89
Management Results ........................................................... 92
D is c u s s io n ............................................................................ 9 5


In tro du action ................................................................. . . ..... 13 8
Fire in the Boreal Forest ........................................... ...... .......... 140
Topography and Dominant Tree Species..................................... 141
C lim ate .............................................................................. 14 1
Fire in the Forest of Southeastern Manitoba .................................. 142
A approach .............................................................. .... ............. 142
Modeling Approach ................................ ................. ........... 143
M odel Organization ................................................... ......... 144
Fire & F orest M odels ........................................ ............ .......... 145
N ull M odel ............................................................. ......... 146

In teractio n M o d el ......................................................... ........... 14 6
Fuel M odel ............................................................. ......... 146
Alternative Models ...................................... ......................... 147
M odel B behavior ............................................................ ......... 147
S elf-O rg an iz atio n ......................................................... ........... 14 7
F ire P attern s ............................................................ ... ......... 14 9
Summary of Model Behavior.................................................. 150
E m pirical C om prison ....................................... ............ .......... 151
M ethods .......................................................................... .... 151
R e su lts ....................................... .................. . . ............ 1 5 1
Summary of Comparison between Models and Manitoban Data ............ 153
Climate Change and The Self-Organizing Forest ................................ 154
C constant Clim ate Sim ulations....................................... ........... 155
Climate Change Experiments ................................ ........... ..... 155
Clim ate M odel .......................................................... ......... 155
E xperim ents ............................................................ ......... 156
R e su lts ................................................................................ 1 5 6
Summary of Climate Change Modeling Experiments........................... 158
C o n c lu sio n s .............................................................................. 1 5 9


Contagious Disturbance........................ .. ............. 178
A General Model of Contagious Disturbance......................................... 179
Model States ........................................................... .......... 179
M odel Processes........................ ......................................... 180
Types of Contagious Disturbance.................................................. 182
Disturbance Agents ............................................ ................. 183
Stress Accumulation and Release.............................................. 183
Chain Reaction .................................................................... 185
Evolutionary Epidem ic ........................................ ....... ............ 186
Comparisons of Contagious Disturbances........................................ 189
Disturbance Spread................ .... ................. 190
Disturbance Behavior: Constant vs. Adaptive ............................... 190
Disturbance and Ecological Dynamics........................................ 192
Disturbance Patterns..................................................................... 193
Stress Accumulation and Release.............................................. 193
Chain Reaction ................................................... .... ............... 195
Response to Environmental Change .......................................... 196
Summary .................................................................... .......... 197


In tro d u c tio n ............................................................................... 2 0 9
Models of Ecological Organization .............................................. 210
Sp ecies-D iv ersity ..................................................... .......... 2 11
Idiosyncratic ........................................................... .......... 211
R iv ets ............................................................ .. ....... . . ...... 2 12
Drivers and passengers ............................. ................ ........... 213
Model Synthesis ...................................................... .......... 213
Resilience .................................................................. ........... 216
Multiple Stable States ................................... ............................. 218

Irreversible State Transitions............................................... 221
Slow Dynamics ....................................................... .......... 222
Scale ........ ..... ............................... ... ......... 223
Species, Scale and Ecological Function............................... ............ 225
Avian Predation of Insect Defoliators............................. ............ 226
Mammalian Seed Dispersal in an African Tropical Forest.................... 228
Potential Tests of Cross-scale Resilience ............................... .......... 229
C conclusions ................................................................. ......... 2 30

SUM M ARY .................................................................... .......... 244

Translating Across Scales ............................. .................. .......... 244
Floridan Fire M anagem ent ........ ........................... .......... ... 245
Boreal Forest Fires and Forest Resilience .............................. ........... 245
Contagious Disturbance ................................. ................ ........... 246
C ross-scale R esilience ................................................... .......... 247

BIBLIOGRAPHY ............................................................. .......... 248

BIO GRAPH ICAL SKETCH ...................................................... 261

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



Garry Peterson

May 1999

Chairman: C.S. Holling
Major Department: Zoology

Managing the consequences of anthropogenic transformation of the biosphere

requires understanding the processes that create, maintain, destroy, and restore ecological

organization. In this dissertation I examine the ability of contagious disturbance processes,

such as fire and insect outbreaks, to create and destroy ecological resilience.

Using computer models I examine the consequences of changes in fire regime on

the organization of two specific ecosystems. I demonstrate that in north Florida forests

both wildfire and the current management practice of periodic controlled burning are

insufficient to maintain the landscape, but a policy that applies controlled burns based upon

the vegetative condition of an area could be effective.

I compare the responses of climate and fuel-accumulation driven models of fire in

the boreal forest. I demonstrate that climate models are more sensitive to climate change

than fuel accumulation models, but are more vulnerable to abrupt reorganization. I use

these specific explorations to construct two general conceptual models: the first of

contagious disturbance, and the second of ecological resilience.

My model of contagious disturbance defines disturbance agents, stress

accumulation and release, chain reactions and evolutionary epidemics as four key sub-types

of contagious disturbance. I identified initiation, reproduction, contagion, virulence, and

recovery as the five key properties of a contagious disturbance regime, and show how a

change in the relationship of these variables alters the behavior of contagious disturbances.

These models provide general insights into the consequences of specific ecological

organizations, allowing qualitative predictions to be made about the behavior of a poorly

understood disturbance process.

My model of cross-scale resilience proposes that ecological resilience is generated

by diverse, but overlapping, functions within a scale and by apparently redundant species

that operate at different scales. The distribution of functional diversity within and across

scales allows regeneration and renewal to occur following ecological disruption over a wide

range of scales. The consequences of species or ecological process loss may not be

immediately visible, but it decreases ecological resilience to disturbance or disruption. It

produces ecosystems that are more vulnerable to ecological collapse, and reduces the

variety of possible alternative ecological organizations.


Ecological Reorganization

The Earth is currently undergoing a biospheric reorganization, as the expansion of

human constructed or dominated ecosystems at the expense of natural ecosystems is

altering climate, mineral cycles, land cover, and biotic communities (Vitousek et al. 1997).

Human ecological expansion is mainly occurring at the meso-scale, between the global and

the local. The meso-scale is the scale at which people live and transform ecosystems

through activities such as agriculture, human settlement, and forestry (Harris 1984). It is

these activities that drive much of global change and it is through changes to these activities

that people will experience much of the impacts global change.

Anticipating and coping with the surprises that these anthropogenic transformations

will bring requires an understanding of how the interaction of ecological dynamics across

scales shapes ecological organization (Kates and Clark 1996). It requires understanding

the processes that create, maintain, and restore resilient ecosystems, as well as

understanding the processes that disrupt and destroy them. In this dissertation, I examine

the cross-scale relationship between ecological resilience and disturbance.

Resilience and Disturbance

Ecological resilience is the ability of a system to persist despite disruption (Holling

1973). Ecological resilience assumes that an ecosystem can exist in alternative self-

organized or 'stable' states. It is a measure of the amount of change or disruption that is

required to transform a ecosystem from being maintained by one set of mutually reinforcing

processes and structures to a different set of processes and structures.

A disturbance is an event that disrupts the functioning of an ecosystem. A

disturbance process produces disturbances at a frequency slower than the time it takes the

disturbed system to recover. I differentiate between contagious and non-contagious

disturbances. Non-contagious disturbances have a fixed temporal frequency and spatial

impact that is externally imposed upon the disturbed system. Contagious disturbances are

disturbances that propagate through a system, such as fire or a flood. Consequently, their

size and behavior emerges out of the interaction of the disturbance process and the system

being disturbed. In this dissertation I disregard non-contagious disturbances and focus

upon contagious disturbances, because contagious disturbance processes connect

ecosystems across scales.

Disturbance and resilience are intimately intertwined with one another. Disturbance

disrupts ecosystems. This disruption can overwhelm an ecosystem's resilience causing it

to reorganize. However, resilience is often maintained by the diversity and heterogeneity

produced by disturbance. Disturbance both produces and destroys resilience. The key to

this apparent paradox is the scaling relationship of disturbance and resilience.

Ecological Scale

I define a scale as a range of spatial and temporal frequencies. This range of

frequencies is defined by resolution below which faster and smaller frequencies are noise,

and the extent above which slower and larger frequencies are background. Different

ecological processes occur at different scales. For example, forest fires occur at different

scales in space and time from a deer mouse's foraging behavior. Processes that operate at

the same scale interact strongly with each other, but the organization and context of these

interactions are determined by the cross-scale organization of an ecosystem. It is this type

of cross-scale interaction that typically occurs between disturbance and an ecosystem.

Small, frequent disturbance often helps an ecosystem, survive, and recover from large,

severe disturbance. An example of this dynamic between disturbance and resilience

recently occurred in the South Florida. During Hurricane Andrew in 1992, mature

mangrove trees were killed by wind damage, however many young mangroves survived.

Many of these young mangroves were located in gaps caused by lightning. Local lighting

disturbances provided the mangrove population with increased resilience to the large-scale

disturbance produced by Hurricane Andrew (Smith et al. 1994). It is these cross-scale

interactions between resilience and disturbance that I explore in this dissertation.

A Dissertation Outline

This dissertation investigates the dynamics of resilience and disturbance from a

combination of theoretical and empirical perspectives. I begin the dissertation by providing

a motivation for this investigation of disturbance and resilience.

In the second chapter, I discuss the ecological dynamics of global change. I

distinguish between natural and anthropogenic global change, provide a brief history of

natural and anthropogenic global change, and place current anthropogenic global change in

a historical context. This chapter illustrates how a variety of cross-scale processes have

provided the resilience that has allowed the biosphere to recover from past global changes,

and that humanity's present combination of global changes is reducing the adaptive capacity

of the biosphere. Along with the general concern for the health of the biosphere, this also

identifies the importance of understanding the resilience of ecosystems and the dynamics of


In the third chapter, I discuss problems with translating data and understanding

across scales. Assessing the impact of change on ecosystems requires the use of scaling

methods, but the resilience of ecological organization to changes in key processes

determines the situations in which scaling methods apply, require adjustment, or break

down. I propose that ecological resilience can be used to develop scaling methods that

incorporate ecological reorganization.

In chapter four, I move from the theoretical considerations of chapter three to

detailed cross-scale investigation of forest dynamics in northwest Florida. Resilience and

scaling are used to analyze the consequences of contagious fire and seed dispersal across a

meso-scale landscape. These concepts are used to compare alternative models of forest

dynamics and assess the consequences of alternative land management strategies.

Chapter five, addresses another investigation of forest dynamics, this time in the

boreal forest. The organization of the landscape under several alternative models is tested

against empirical data, and the sensitivity of the alternative models to climate change is


Chapter six compresses and generalizes the disturbance models developed in

chapters four and five, to produce a general model of contagious disturbance. I identify the

key features of contagious disturbance, the variables that define those features, and outline

how different relationships among these variables produce different disturbance dynamics.

I use these attributes to define four sub-models of my general model, which represent a

variety of abstract types of contagious disturbance. These models provide general insights

into the consequences of specific ecological organizations, allowing qualitative predictions

to be made about the behavior of a poorly understood disturbance process by fitting it into

the classification of rates and their relationship.

Chapter seven returns to investigate resilience and its linkages to disturbance.

In this chapter, I present a cross-scale model of resilience that integrates disturbance and

ecological processes. The model proposes that ecological resilience is generated by

diverse, but overlapping, function within a scale and by apparently redundant processes

that operate at different scales, thereby reinforcing function across scales. The distribution

of functional diversity within and across scales allows regeneration and renewal to occur

following ecological disruption over a wide range of scales.


This chapter illustrates how a variety of cross-scale processes have provided the

resilience that has allowed the biosphere to recover from past global changes, and that the

diverse nature of present day anthropogenic global changes is reducing the adaptive

capacity of the biosphere. This decline in the general adaptive capacity of the biosphere

will likely manifest itself far more seriously in specific regions. This loss of adaptive

capacity supports my argument that understanding the dynamics of ecological resilience and

disturbance should lie at the center of applied ecology.

In this chapter I discuss the history and dynamics of natural and anthropogenic

global change, before comparing modern anthropogenic global change to past global

changes, and presenting a simple model of how global change is eroding adaptive capacity.

Global Change

Global change is different from ecological change at smaller scales. The dynamics

of a local or regional ecosystem may be strongly influenced by fluxes and migrations from

neighboring systems, but there is little interaction of the global biosphere with its external

environment (Barlow and Volk 1992). The globe receives energy from the sun, and emits

heat to space. It receives heat and minerals from beneath the crust, and returns reorganized

materials through the ocean floor. However, these fluxes are tiny compared to the fluxes

of material and energy in Earth's biogeochemical cycles (Schlesinger 1991).

The closed nature of the Earth means an accumulation or decline of material at a

global scale will not be counteracted by external fluxes. This is not true at a local scale.

For example, as a plant removes CO2 from the air near the plant that CO2 is rapidly replaced

by CO2 from the surrounding air. At the global scale, CO2 emissions and fixation have to

match, or the stocks of CO2 begin to change.

At the global scale, change that occurs is largely due to endogenous dynamics.

Evidence suggests that an asteroid may have been precipitated the Cretaceous mass-

extinctions, but the other mass-extinctions appear not to have had an external trigger

(National Research Council 1995). Similarly, recent glacial cycles are probably driven by

changes in the Earth's orbit around the sun. However, the magnitude of the global change

caused by relatively minor changes in solar input is due to the amplifying power of Earth's

endogenous dynamics (Bartlein and Prentice 1989). These internal dynamics are

dominated by the biosphere.

Over the history of the Earth, the expansion and diversification of the biosphere

have caused massive changes in Earth's physical, chemical, and biological organization

altering the Earth's response to external variation (Schlesinger 1991). Today, the

expansion of the Earth's human population and the enormous amplification in the power of

human technology that has accompanied it is driving a transformation of global flows of

energy, materials and biota that overshadows natural global change (Vitousek et al. 1997).

This anthropogenic change is combined and intertwined with the natural dynamics of the

Earth system. In one sense this combination is trivial, in that for those impacted by global

change the cause of that change does not matter. But, understanding how the Earth works,

and designing effective policies to mitigate and adapt to global change requires

distinguishing the natural and anthropogenic drivers of global change.

Understanding consequences of the human transformation of the Earth system

requires understanding the natural dynamics of the Earth system itself. The study of the

dynamics of the Earth system is a prerequisite for disentangling the natural and

anthropogenic components of global change. In the following sections I outline these two

interacting components of global change. I begin with a discussion of the evolutionary

dynamics of the Earth system, before describing the expansion of humanity within the


Earth Systems

The evolution of Earth's biosphere has driven periods of revolutionary global

change. The study of history provides a rich set of past world organizations that represent

alternative configurations of the Earth system. The exploration of how the Earth system

responded to past global change helps provide clues to how the Earth will respond to

present day global change. In this section I outline Earth's past global changes to place

present day ecosystems and ecological change in perspective. I discuss the Earth's

relationship with the sun, its internal geophysical dynamics, and the evolution of the


Earth and the Sun

The sun has changed over the history of the Earth. It has gradually become

brighter, about 25% brighter over the past 4 billion years, and this change has increased the

amount of solar radiation reaching the Earth (Lovelock 1988). Over shorter periods, Earth

experiences significant variations in the distribution of solar energy upon its surface and

timing of that energy. Along with this gradual increase in luminosity, the Earth's orbit

around the sun introduces cyclical variation into the amount of sunlight the Earth receives.

Variation in Earth's orbit and a wobble in the tilt of its axis vary the sunlight the

Earth receives (Bartlein and Prentice 1989). There are three cycles occurring at different

time scales. The slowest change is in the eccentricity of the Earth's orbit. Over 100,000

years its varies between being near circular and being more elliptical. This change alters the

difference between Earth's seasons. Over a period of 41,000 years the tilt of the Earth's

axis varies. The degree of tilt alters the Earth's seasonality, and therefore temperature

differences between the northern and southern hemispheres. The fast cycle, with a period

of 23,000 years, corresponds to change in the seasonality in Earth's orbit of Earth's closest

approach to the sun. This change either mitigates or enhances seasonality and differences

between the Northern and Southern Hemisphere. It appears that these orbital variations

interact in some way with one another and Earth's biosphere, but the interaction is not

clear. Variations in polar ice volume are synchronized with these cycles. Until 800,000

years ago, ice ages corresponded strongly with the frequency of axis wobbles, but after

that time, ice ages correspond better with the variation in the shape of Earth's orbit

(National Research Council 1995). The reasons for this shift in the process driving ice

ages are unclear, but either changes in the Earth's biosphere or global oceanic or

atmospheric circulation patterns could alter the relative importance of different forms of


Earth's Internal Dynamics

The long term cooling of the Earth drives continental drift (Westbroek 1992). The

difference between the hot molten core of the Earth and its surface produces convective

currents within the Earth. These currents gradually move the pieces of Earth's crust apart

and against one another, generating continental drift. These forces continually rework

Earth's surface, splitting continents apart and crashing continents together (Schlesinger

1991). Continental drift has had a major impact on the history of life, bringing together

biota that are novel to one another, as well as encouraging speciation by dividing

populations and ecologies (Briggs 1995). Also continental drift has driven climate change,

by changing the configuration of ocean basins and the location of mountain ranges has

altered global oceanic and atmospheric circulation patterns (Ruddiman 1990). Over long

periods, continental drift has altered atmospheric composition by producing gas-emitting

volcanoes. This effect was strongest early in the history of the Earth.

The development of the Earth's biosphere has diversified Earth's biogeochemical

cycles. Mutual reinforcement among these processes tends to self-organize sets of

processes that are relatively stable, because interactions among processes that are unstable

tend to extinguish themselves (Lovelock 1988). However, the biosphere has periodically

been reorganized as new biogeochemical pathways have been created by interaction of

biotic evolution with changes in Earth's geochemical environment. These revolutions have

produced fundamentally different biospheres.

One of the central aims of global change research is to assess to what degree human

alteration of the Earth is eroding the ability of the current Earth system to maintain itself

despite disruption. By examining past reorganizations of the biosphere, current changes

and their future consequences can be placed in context. In the following sections, I

summarize the history of different Earth system regimes, and the transitions between them,

focusing on the development of the biosphere and evolution of novel biogeochemical


Origin of Earth and the Biosphere

The Earth was formed about 4.5 billion years ago, as planets condensed into orbit

around the sun. During it initial development it was bombarded with cosmic debris, which

increased its size, and heated the planet to such an extent that it was liquefied, and denser

substances migrated towards the core of the Earth. Over time, collisions reduced the

amount of the solar debris and the rate at which the Earth was pummeled by meteorites. At

this time, about 3.8 billion years ago, the first traces of life are found (National Research

Council 1995).

The composition of the Earth and its position in the solar system define internal and

external bounds on Earth's biota. The Earth receives the bulk of its energy from the sun.

The Earth itself provides heat from radioactive decay, but its main contribution to life is the

materials that comprise the Earth, and the chemical energy that they contain. Between the

rock of Earth and the space of the solar system the biosphere developed. In the following

sections I sketch a brief history of the biosphere, and then discuss how variations in Earth

and Solar dynamics have altered the dynamics of the biosphere.

Early Life the Archean

The biosphere covers the surface of the Earth, and extends at least several

kilometers in the Earth's crust and up into the Earth's atmosphere is supported by energy

from the sun, mineral energy from the Earth (Pedersen 1993). The earliest life was almost

certainly dependent upon chemical energy, but photosynthetic bacteria soon evolved

directly linking life to incoming solar energy (Volk 1998).

While the early history of life on Earth is difficult to reconstruct, one of the earliest

consequences of life was a reduction in atmospheric CO2, as bacteria sequestered carbon.

This decrease in CO2 liberated oxygen. Rather than increasing atmospheric oxygen levels,

this oxygen reacted with oxidizable material in the Earth's oceans, atmosphere, and crust.

The early Earth was probably a reducing environment, and certainly there were many

oxidizable materials on the early Earth. The oxidization of iron dissolved in seawater,

produced iron deposits that provide the earliest evidence for photosynthesis (Schlesinger


Transition to an Oxygen Atmosphere

Between about 2.2 and 1.9 billion years ago (bya) the oxygen increased from being

just a trace gas to composing as much as 3% of the atmosphere (Volk 1998). The cause of

this rise in oxygen is unclear. It could have been due to increased rock weathering burning

more carbon, a decrease in emissions of reducing gases and CO2 from volcanoes as the

Earth cooled, or an increase in the biomass of aerobic bacteria.

It is suggestive that this period corresponds with an expansion of the biosphere.

During this time eukaryotes evolved from prokaryotes. Eukaryotes are more efficient

photosnythesizers than prokaryotes. Whether an increase in oxygen facilitated the

evolution of eukaryotes, or eukaryotes facilitated the rise in oxygen levels in unclear.

However, the proliferation of eukaryotes established a positive feedback, as aerobic

bacteria encouraged the growth of more aerobic bacteria.

A combination of these processes may have produced the rise in oxygen, because

processes such as rock weathering and biotic oxygen production are not mutually

exclusive, rather they are mutually reinforcing. As an increase in bacteria populations,

particularly if they colonized land and rock fractures, would have accelerated rock

weathering (Schwartzman and Volk 1989).

There was another pulse in atmospheric oxygen between 1 0.6 bya. This oxygen

pulse also occurred synchronously with a diversification of life. In this case, the rise of

oxygen accompanied the development of metazoan life (Canfield and Teske 1996).

Another suggestive feature common to both these revolutionary reorganizations of the

biosphere is that both rises of oxygen were accompanied by declines in CO2 and severe

glaciations (Hoffman et al. 1998).

The paleoecological record suggests that as the biosphere became more diverse, the

gradual accumulation of oxygen triggered global ecological crises. However, these crises

were likely overcome by the negative feedback effect of biospheric cycles. For example, if

an increase in oxygen lowered global atmospheric CO2, reducing the warming produced by

the greenhouse effect, this cooling would reduce the rate of plant growth, allowing CO2 to

increase again established a climate more favorable to life (Lenton 1998).

Land Plants

Following the origin of metazoan life, the next major reorganization of the Earth

system was the colonization of land by plants. Plants accelerated weathering by a variety

of means. Plants increased the surface area of rock weathered through their root networks.

By breaking apart rocks they facilitated the creation of organic soil, which increased the

ability of water to weather minerals in the soil. These increases in weathering probably

removed atmospheric CO2 (Berner 1997).

Furthermore, this period coincides with the evolution of lignin by plants. It appears

that plants used lignin for several million years before fungi developed the means to break

apart lignin (Robinson 1990). During this lag period, large amounts of lignin were buried,

removing large amounts of carbon from the atmosphere until fungi evolved the ability to

decompose lignin. This process may have allowed oxygen levels to climb to their highest

levels ever, perhaps even up to 30% of the atmosphere (Lovelock 1988). Beyond this

oxygen concentration, organic matter becomes extremely combustible, and fire would act to

decrease oxygen concentrations.

Expansion of Carbon Cycling

Another key ecological transformation was the evolution of calcium carbonate

precipitating plankton. These plankton fundamentally altered Earth's carbon cycle, and

perhaps even plate tectonics when they evolved 200 million years ago (mya). By

producing calcium carbonate (limestone) from CO2 and calcium they removed CO2 from the

atmosphere. By moving carbon deposition into the deep sea these plankton, surprisingly,

made CO2 more available to the atmosphere. Large volumes of limestone precipitated by

plankton were pushed underneath continental plates. Some of the carbon in this limestone

was returned to the surface through volcanoes, recycling carbon through a slow cycle

driven by continental drift. Limestone deposition may even accelerate continental drift, and

consequently carbon cycling (Lovelock 1988, Westbroek 1992).

Development of the Biosphere

The functional elaboration and diversification of life on Earth over geological time

has increased the number of biogeochemical pathways in the biosphere. These pathways

often perform similar functions, but do so at different scales. For example, carbon is

cycled between the ocean and the atmosphere over minutes, while carbon moves from the

seabed to the atmosphere over hundreds of millions of years.

A general pattern in the development of the biosphere is that the gradual

accumulation of low entropy, high-embodied energy waste products, such as oxygen or

lignin, accumulated to the extent that they provided a utilizable resource. In turn the

evolution of new functional groups to take advantage of this concentrated energy lead to the

accumulation of new waste products, containing ever more embodied energy. This ratchet

of complexity has allowed the elaboration and articulation of Earth's biosphere.

The existence of a diverse set of ecological functions, such as the various steps of

biogeochemical cycles, that are replicated at many temporal scales, such as the many

separate pathways in the carbon cycle, suggests that the development of the biosphere may

have produced an increasing ability to respond to variation or disturbance across wide

variety of scales. Whether this is indeed the case, or not, is unclear. Life has been able to

maintain itself and the Earth's temperature, in the face of a 25% change in the sun's output

(Lenton 1998, Lovelock 1988). The response of the present globe to more subtle changes

in the in Earth's distance from and orbit around the sun, and continental drift is more


Multiple Climate States

Biospheric reorganizations appear to have interacted with the Earth's geophysical

dynamics and solar forcing to produce alternative stable states in the Earth system. At the

scale of hundreds of millions of years the Earth appears to have alternated between periods

in which glaciation was possible, what have been termed 'icehouse' worlds, and those in

which it is not, 'greenhouse' worlds.

The most recent transition between these organizations of the world was 34 mya.

At that time continental drift caused a change in the world ocean circulation triggering a

planetary cooling, shifting the Earth from a 'greenhouse' to an 'icehouse' state. This

occurred when continental drift caused Australia to separate from Antarctica, allowing

water to circulate uninterrupted around Antarctica at the South Pole. Prior to this event, the

Earth's oceans were broken into a series of basins, in which cooler and warmer water

mixed. The circulation of cool water around the south pole, cut off polar water from

mixing with warmer temperate water, and established a steep temperature gradient between

pole and equator which lead to the deep water circulation of cold Antarctic water across the

ocean floor (Prothero 1994).

The cooling of Antarctica may have established a positive feedback cycle, because

as Antarctica cooled it began to accumulate ice, which in turn increased the albedo of

Antarctica, causing it to absorb less heat from the sun, cooling the continent further,

allowing glaciers to develop and spread (Briggs 1995). Another possible cause of global

cooling was the uplift of the Tibetan plateau, which probably lead to increased weathering,

removing CO2 from the atmosphere (Ruddiman 1990). Regardless of the exact cause or

combination of causes, these changes moved the world from a 'greenhouse' to an

'icehouse' state.

During this current icehouse regime, the world has been repeatedly glaciated. It has

alternated between, short warmer inter-glacial periods and longer, cooler glacial periods.

During the course of this recent 'icehouse' regime, oceans have shrunk, novel plant

metabolic pathways formed, and new animal communities emerged (Cerling et al. 1997).

It was in this low CO2, cool, and dry world that humanity's ancestors evolved, and

humanity came into being.

Expansion of Humanity

One hundred thousand years ago the ancestors of modern humanity were a

population of several hundred thousand large hunter-gathers living upon the African

savanna. Today, a population of almost 6 billion people live in all over the world, in

highly organized aggregations, in ecosystems that have been largely shaped by human

action (Cohen 1995). During this time, an ice age ended, global sea levels have changed -

altering the configuration of continents, a wide variety of large mammals, birds and reptiles

became extinct, and entire ecosystems have reorganized.

The greater part of humanity's expansion occurred during the past few centuries

(Figure 2-1), whether measured in absolute population size, population growth rate, or

energy and material use per capital. In the two hundred years from 1600 to 1800 the

world's population increased about 1.7 times, while in the two centuries from 1800 to

1995 the world's population increased 6.3 times (Figure 2-2). Similarly, the amount of

non-animal or plant energy used per person/year increased about 28 times from 1800 to

1900, and another four times from 1900 to 1987 (Cohen 1995).

Malthus & Darwin: Limits & Adaptation

All living things are subject to limits to their growth. Malthus observed that

populations often grow faster than their ability to acquire new resources, leading to

resource shortages (Malthus 1798). These resource limitations bound the increase of a

population. This Malthusian relationship suggests that populations should grow until they

reach their limits, and then remain or oscillate around that steady state. Darwin's theory of

natural selection extended this Malthusian dynamic (Darwin 1859). He noted that not all

individuals in a population are identical. If some individuals have traits that enable them to

obtain resources better than other individuals, then those individuals are more likely to

reproduce than others. Darwin's theory of natural selection, showed how a population

could adapt to a set of environmental limits and possibly find ways to escape from that set

of limits.

Human societies being able to learn and reorganize their means of production are

more able than most species to adapt and innovate to escape from Malthusian pressures.

Indeed, the demographic expansion of humanity has progressed hand-in-hand with the

expansion of humanities ability to manipulate and construct ecosystems. Locally increased

population densities, put more pressure on local ecosystems. This pressure causes

ecological degradation and, in some cases, inspires innovative forms of ecological

engineering. While many societies become caught in a Malthusian trap some societies

managed to innovate to temporarily escape from Malthusian pressures, by reorganizing

their local ecologies to increasingly support humans rather than other non-human ecologies,

and these innovations usually spread to other groups (Goldstone 1991, Spicer 1996).

While these reorganizations have enabled phenomenal growth in humanity, by increasing

the amount of ecological production aver by people, they have removed resources and

support from non-human ecosystems.

Human Regimes

Many innovations have expanded the resources available to people. To organize

my discussion I consider four expansions of anthropogenic ecological domination: the

domestication of fire, agriculture, long distance trade, and public health. These ecological

innovations were made possible by combinations of technological and social innovations

that did not occur simultaneously around the globe, but rather occurred unevenly as

innovations spread out from various centers. However, the spread of these ideas by

conquest and trade makes these transitions meaningful at the coarse global scale (Spicer

1996), and despite some regions of poverty and disorder, the major proportion of the

world's population has passed or is passing through these ecological transformations.

There is a suggestion of these transitions in the population data of Figure 2-1 and

Figure 2-2. When the same data is displayed on a log-log graph, the ever accelerating

nature of these transitions can be seen (Figure 2-3). On a log-log graph, exponential graph

produces a curve that initially climbs steeply, before flattening off. Therefore, any portion

of a log-log graph with an increasing or constant slope indicates an accelerating growth

rate. On Figure 2-3, there is an acceleration at about 10,000 years ago that coincides with

the introduction of agriculture. Similarly, the expansion of global trade 500 years ago, and

global public health measures of the past 50 years both boost growth until the fertility

declines of the past decade, which is more clearly shown in Figure 2-4. While the quality

of the data plotted in Figure 2-3 is low, the figure does suggest that humanity as a whole

has been quite successful at evading Malthusian limits. I discuss each of the expansions of

human dominated ecosystems in more detail below.


Fire has large ecological effects, strongly influencing vegetation growth and

succession, and consequently animal community dynamics (Bond and van Wilgen 1996).

Humans probably domesticated fire about half a million years ago (Goudsblom 1992).

Fire may not have been the first, but it was certainly one of the most powerful early

ecological engineering tools that humanity mastered. The effect of this event on human

population growth is hidden in the distant past, but it is reasonable to assume that it was


People have used fire for hunting, war, and agriculture all over the world. Indeed,

on of the most common paleoecological records of human settlement is an increase in

charcoal in lake sediments (Goudsblom 1992). Records from Australia (Kershaw 1988),

New Zealand (Flannery 1994), and North America (Cronon 1983) all show evidence of

increases in fire frequency with the arrival of people. The domestication of fire also aided

in food preparation, allowing people to expand the range of food items that they could



Agriculture probably was invented about 10,000 to 12,000 years ago, following the

last ice age (Diamond 1998). Agricultural domestication of plants and animals allowed

people to develop novel variations of natural ecosystems that channeled their production

into resources for people.

Agriculture is unremarkable in a fashion; after all ants have domesticated aphids and

fungi. However, people have relentlessly innovated expanding the range of plants and

animals domesticated, and physically restructuring places to channel energy and resources

away from non-human ecosystems towards human-dominated ecosystems.


Agricultural innovation was expanded through trade. The local diffusion and

spread of technologies and domesticated plants and animals has occurred for a long time, as

is evidenced by the spread of the banana from New Guinea to Africa (Diamond 1998).

However, ecological engineering received a large leap forward following the European

discovery of the Americas, which integrated the world's population into a single world

system (Wallerstein 1974). Between 1650 and 1850s, this system became tightly

integrated through trade, and migration. The transport of domesticated plants and animals

among disparate regions greatly increased agricultural yields (Cohen 1995). The impact of

the potato and maize upon Europe and the sweet potato and Manioc upon the tropics

provide two examples of the greatly increased agricultural yields that followed these

agricultural modifications (Crosby 1972, Diamond 1998).

Public health

Humanity's novel ecosystems produced novel problems. By requiring denser

human settlement agriculture changed conditions for human ecosystem parasites and

diseases (McNeill 1976). Global trade and transport connections also encouraged the

development of epidemic diseases. While novel human ecosystems provided support for

humanity, they also created new ecological niches for parasites, such as rats and

cockroaches, and pathogens, such as tuberculosis and smallpox (Crosby 1986). While

medicine improved during the 19th century, it was only in the 20th century that public

health programs managed to massively decrease mortality from epidemic diseases such as

smallpox, malaria, and tuberculosis. At the same time, sanitation measures re-engineered

urban ecosystems reducing mortality from infection. Following the Second World War

massive worldwide immunization and sanitation programs public began to have great

success (McNeill 1976). These programs can be regarded as ecological engineering. By

eliminating disease populations, by immunization, and reorganizing human ecosystems

through sanitation and other public health measures public health programs greatly reduced

or even eliminated many of humanities parasites. This reduced human mortality, allowing

population growth rates to increase to their highest levels (Figure 2-4).

Recently, a fifth transition appears to be occurring. In the past decade fertility rates

have declined (Figure 2-4). The phenomena is diverse and not well understood, however it

may represent an important advance in the ability of people to regulate their own

reproduction, or it may be a temporary side-effect of an increasing average age (Cohen

1995, Lutz et al. 1997).

Despite this recent decline in the fertility rates population growth is expected to

continue until the world's population is near 10 billion people, six times the world's

population at the beginning of the 20th century. This growth in absolute population size,

the human ability to innovate, and the desire for an improved material standard of living is

expected to increase the human domination of Earth above its already high levels.

Human Domination

Humans currently dominate the structure and functioning of Earth's ecosystems.

The human impact on the Earth has increased at an accelerating rate due to the increase in

human population and concomitant increases in per capital use and consumption of natural

resources, expanded technological capacity to access resources, and the expansion of

human settlement over the globe (Turner et al. 1993). These increases mean that humanity

has, and will probably continue, to expropriate an ever-increasing share of the Earth's

renewable and nonrenewable resources and increasingly rearrange and reengineer the

Earth's physical, chemical, and biological systems.

Anthropogenic global change is usual divided into a number of separate classes that

are usually considered in isolation, despite the well recognized links that exist among them.

Below I provide a quick outline of the dimensions of several key types of anthropogenic

global change, specifically land-use/land-cover change, atmospheric change, ecological

appropriation, biodiversity loss, and the modification of biogoechemical cycles. I follow

this discussion with my approach to integration.

Land-use/Land Cover Change

Humanity currently uses 2/3 of vegetated land and an estimated 40% of net primary

productivity (NPP) of this land (McNeely et al. 1995). The ability of humans to transform

landscape is ancient, perhaps beginning with the domestication of fire thousands of years

ago. The invention of agriculture, and irrigation amplified the role of humans as ecological


Land use change involves conversions from one type of land cover to another, as

well as changes in management practices. For example, the area of forest converted to

agriculture or grassland since pre-agricultural times is estimated to be about 8 million km2,

with 3/4 of this land cleared after 1680 (Turner et al. 1993). Similarly, about 7 million km2

of grassland have been converted to agriculture, and 0.5 million km2 has been submerged

beneath artificial lakes (Turner et al. 1993).

Land-use/land cover change directly alters the amount and distribution of various

ecosystems, and biota. It also alters ecological dynamics such as the dispersal of biota

(Harris 1984), the provision of ecological services to neighboring locations (Forman

1995), and the interactions of between vegetation and the atmosphere (Pielke et al. 1993).

Atmospheric Change

Anthropogenic emissions are altering the composition of the atmosphere. Land use

change has resulted in net addition of large amounts of CO2 and methane, along with other

gases to the atmosphere (Walker and Steffen 1997). More importantly, the combustion of

large volumes of fossil fuel has also increased atmospheric concentrations of CO2 and

nitrogen compounds.

The atmosphere's current CO2 concentration is about 360 ppm, and increasing

(Walker and Steffen 1997). In the centuries before industrialization, the atmosphere

maintained a concentration near 280 ppm (Schlesinger 1991). Global average temperature

is closely correlated to atmospheric CO2 levels. The Intergovernmental Panel on Climate

Change 1995 assessment (Intergovernmental Panel on Climate Change 1995) concludes

that increased concentrations of CO2, and other greenhouse gases, have probably already

altered the world's climate, and that this alteration is likely to become more extreme in

forthcoming decades.

Furthermore, the production and emission of CFCs has caused decreases in

stratospheric ozone, as well as being a strong greenhouse gas. These decreases in ozone

have increased the amount of ultraviolet radiation reaching the Earth's surface, particularly

near the North and South poles.

Ecological Appropriation

Humanity uses a significant proportion of the Earth's total ecological production.

Recent estimates put humanity's use of terrestrial ecological production at between 39 %

and 50 % and our use of oceanic production at about 8% (Vitousek et al. 1997). This

ecological production is unavailable to non-human ecosystems, and is suggestive of both

the large stress that humanity is placing on the global biosphere, and of the limits to further

growth in the amount of ecological production that humanity appropriates.

Modification of Biogeochemical cycles

Human action has for most of history altered biogeochemical cycles through the

modification of the environment, for example by burning a grassland. However, the ability

of modem industrial societies to mine and synthesize minerals and chemicals has caused

humans to directly alter global chemical cycles.

The synthesis of phosphate and nitrogen fertilizer has doubled the global cycle of

these key ecological nutrients (Vitousek 1994), driving a global eutrophication of terrestrial

and aquatic ecosystems.

Mining and fossil fuel use has greatly magnified the amount of poisonous heavy

metals in the globe's biogeochemical cycles. For example, the amount of lead has

increased twenty five times, cadmium seven times and mercury ten times (Turner et al.


The success of synthetic chemistry has unleashed a massive wave of novel

synthetic substances into the global environment that have produced novel biogeochemical

processes. These introductions have often had, unexpected negative impacts on people and

animals. The most recent danger is the potential consequences of ubiquitous endocrine

disrupting chemicals (Colbom et al. 1996).

Loss of Biological Diversity

Humanity's spread across the world over the past tens of thousand years appears to

have initiated a mass extinction episode. The spread of people to the New World (Martin

1984), Australia, and New Zealand (Flannery 1994), the Pacific archipelago (Steadman

1995), and other islands (Crosby 1986, Diamond 1992) all instigated regional mass

extinctions. Estimates of current extinction rates suggest that the current rate of species

loss is 100 to 1000 times greater than the average pre-human rate of species loss (Lawton

and May 1995).

Currently several different forces appear to be driving biodiversity loss: habitat

loss, harvesting, and biological invasions. Habitat loss is due to land-use/land cover

change, which was mentioned above, as well as ecological appropriation of the resources

that animals and plants require to survive and reproduce. Secondly, harvesting and hunting

of animals is due to ecological appropriation of the animal and plant populations

themselves. Thirdly, biota has been purposely and accidentally transported within human

transport networks. Transported biota can compete with, prey upon, or modify the

physical environment of native biota, frequently producing large ecological impacts (Lodge

1993). Furthermore, biodiversity loss may occur due to previous extinctions. A species

creates habitat or provides food for other species, its extinction can lead to the extinction of

its dependent species.

Comparing Human and Historic Global Change

The magnitude of modem anthropogenic change appears to be roughly comparable

to past revolutionary global changes. The biosphere has been able to adapt to these

changes. For example, about 6 to7 million years ago, the continuing decline in

atmospheric CO2 lead to the evolution of C4 metabolism plants, which are able to survive

warm temperatures and moisture stress better than C3 plants at current atmospheric CO2

concentrations. However, these adaptations have all occurred on an Earth in which the

biosphere has been unconstrained in its response to these changes.

Present day anthropogenic global change is disrupting a disrupted biosphere. The

current biosphere is not as free to adapt as in the past. Humanity demands a variety of

ecological services, such as food production, from human ecosystems thereby constraining

the degree to which these ecosystems can be allowed to adapt to global change. Humanity

and our constructed ecosystems have greatly reduced the extent of non-human dominated

ecosystems. This reduction may have been as much as 50% (Vitousek et al. 1997), but it

appears to be at least 10% (Gorshkov 1995). This elimination of much of the Earth's

adaptive capacity will likely reduced the ability of the biosphere to respond to

anthropogenic global change.

Past innovations in biogeochemical cycling appear to have often resulted in crises in

global biogeochemical regulation. As discussed above, the development of an oxidizing

atmosphere and the evolution of land plants, lead to periods in which global regulation of

climate broke down, leading to global glaciations.

Present day anthropogenic global change is reorganizing global cycles to a degree

comparable to past ecological revolutions. Human alteration and acceleration of

biogeochemical cycles, the burning of fossil fuel, and the synthesis of novel chemicals has

produced a wide variety of new biogeochemical pathways, many of which are significant

proportions of global fluxes (e.g. N, P, Hg) or are new materials with significant effects

(e.g. CFCs). The synchrony and extent of these changes increases the possibility that

another collapse of Earth's homeostatic processes may occur. The biosphere has managed

to survive past collapses at the cost of mass extinctions and ecological reorganization.

Probably, humanity could also survive an ecological collapse, but such a collapse would

have immense human, economic, and spiritual costs.

A simple conceptual model of the impacts of global change illustrates the risk of the

simultaneous degradation and alteration of global ecological processes. The integrated

impact of various global change processes depends upon the degree of interaction among

these separate changes. If these processes are independent of one another, then their

combined impacts can be obtained by adding the individual impacts together. If global

change processes are not independent of one another, but rather positively interact with one

another, then their integrated impacts can be obtained by multiplying the impacts together.

I illustrate this model by considering the linkages among the global change

processes. Usually, the response or adaptation to a global change process depends upon

the flexibility of other components of an ecosystem. For example, more reliable ecosystem

services appear to be produced by diverse ecosystems (Holling et al. 1995). Reductions in

the diversity of species inhabiting a watershed may reduce the ability of that watershed to

reliably provide an ecological service, such as clean water. Similarly, the adaptation of

ecosystems to climate change, depends upon the presence of a diverse pool of species that

can flourish under different sets of conditions. If biodiversity loss and land-use/land-cover

change, eliminate species or their ability to migrate across a landscape, the historical ability

of ecosystems to adjust to climate variation will be greatly reduced.

The different risks associated with the independent and interaction models can be

clearly illustrated by representing the using a Gaussian probability distribution to

characterize the impact of a global change process. For example, consider the interaction of

four different processes (Figure 2-5). A comparison of the independent and interactive

models of integrative global change reveals two key differences between them (Figure 2-6).

First, the uncertainty surrounding the integrated impacts of global change is much less if

processes are independent than if they interact with one another. Secondly, severe impacts

are much more likely if processes interact with one another. This suggests that the

framework used to integrate global change processes strongly influences the prediction of

risk. While this model of system responses to change is simple, it illustrates the

importance of interaction between processes, and the importance of taking an integrated



Anthropogenic global change appears to be of the same magnitude as Earth's

previous periods of revolutionary ecological change. However, the biosphere and

humanity are probably in a more brittle state during this transition than during past

transitions, due to the increasing proportion of the Earth's biosphere that is used by

humanity. The likelihood that humanity's domination of the biosphere is reducing the

resilience of the Earth to anthropogenic global changes underscores the importance of

analyzing the resilience of Earth's biosphere.

The meso-scale is also the scale at which people live and structure the landscape.

These meso-scale activities drive much of global change, and it is through changes to these

activities that people will experience much of the impact of global change. In the following

chapters I analyze how changes in larger scale processes impact the dynamics of the meso-

scale, and the interactions that meso-scale processes have with larger and smaller scale



World 5.00 -
Population 4.00 -
(billions) 3.00 -
2.00 -
1.00 -
0.00 , i .--..-..----
-1000000 -800000 -600000 -400000 -200000 0
Years Before Present (1998)

Figure 2-1. The size of the global human population over the past million years. The
early data is estimated with much less accuracy than the most recent data, but on the scale
of this graph even large relative errors in estimating the past and dwarfed by the magnitude
of current human population. Data are from Cohen (1995).

World o.uu -
Population 4.00 -
(billions) 3.00 -
2.00 -
1.00 -
0.00 .
-2000 -1500 -1000 -500 0 500 1000 1500 2000

Figure 2-2. The size of the global human population over the last four thousand years.
Examining the recent past more closely indicates the most of human population growth has
occurred quite recently. Data are from Cohen (1995).



1E+08 Human


l 1E+05
1000000 100000 10000 1000 100 10 1
Years Before Present (1998)

Figure 2-3. Population of the Earth over the one past million years. The data is plotted on
a log-log scale, diminishing relatively recent large absolute changes. This presentation
shows that the population growth rate has varied greatly over time. On this graph simple
exponential growth follows a curve with a decreasing slope. Data are from Cohen (1995).


Per Year



1.00 t


0 .0 0 I i -I I I i I I I I i I I I I i i I
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000
Years Before (1998)

Figure 2-4. Estimated yearly growth rate of humanity over the last five hundred years.
The global population growth rate has clearly increased over the past few centuries, and
especially over the last fifty years. While recent growth rates are lower than the 1950's or
1960's they are still higher than any time before then. Data are from Cohen (1995).

* .











- Change A
- Change B
-- Change C
Change D

-2 -1 0 1 2
Individual Impact

3 4 5 6

Figure 2-5. Example estimated probability distributions of impacts of four imaginary
global change processes. The impact of A is low and quite certain. The impact of B is less
certain, but expected to be larger. C is also expected to produce a large impact, but less
certainly than B. It could even have a positive impact. Similarly, the impact of D is
expected to be large, but it is highly uncertain. Its impact may quite substantial, or it may
be insignificant.

0.9 ---
0.8 ---

Probability 0.6 -
0.5 -



0.1 Inleraclion
-20 -10 0 10 20 30 40 50 60
Integrated Impact

Figure 2-6. The integrated effect of the global change processes A, B, C, and D is quite
different depending upon whether they interact following the Interaction or the Independent
model. The cumulative probability distribution for the integrated impacts, shows that
minimal or extreme impacts are much more likely with process interaction case, than with
process independence. If the processes are independent, they have a 95% chance of having
a cumulative impact less than 10. However, if the processes are interactive they have an
only a 70% chance of having a cumulative impact less than 10, and a 95% chance of having
a cumulative impact of less than 24.



Global change research centers much of its efforts on understanding the global

consequences of local actions, and how global changes impact specific, local sites. These

efforts require techniques that are able to translate understanding developed at one scale to

other scales. Unfortunately, it is usually quite difficult to 'upscale' results from small

scales to large scales and 'downscale' the results of global models to local sites (Ahl and

Allen 1996, Levin 1992, Wiens 1989).

Finding useful ways of translating across scales requires understanding the

problems that scaling presents, which I outline in the next section. I then discuss cross-

scale interactions, focussing on the analysis of hierarchical structure, the identification of

hierarchical reorganization, and the search for the potential alternative organizations.

Finally, I discuss how ecological resilience and scaling are intertwined using an example

from the boreal forest. But first, I discuss the problems of scaling.

Scaling Problems

Global change research continually confronts upscaling and downscaling problems.

Upscaling involves applying the results of a local study to a wide region, such as

extrapolating the effects of an experiment on CO2 fertilization of trees in a forest plot to the

response of the entire boreal forest to an increase in global CO2 levels (Korner 1996).

Downscaling involves moving in the opposite direction. Downscaling attempts to

determine the local consequences of a large scale change on a local site, such as the impact

of global climate change on agricultural production in a field (Parry et al. 1996). Most

global change research has used simple scaling methods, such as averaging or

interpolation, to translate data across scales.

Unfortunately, these methods often fail to adequately map processes from one scale

to another. For example, variation of ecosystem productivity to CO2 fertilization is not the

sum of the variation in the response of individual species, but due to compensation and

complementarity among species the response is far less (Korner 1996). These methods fail

for four related reasons: non-linearity and environmental heterogeneity, dominance of

different processes at different scales, cross-scale connections, and emergent processes.

Non-Linearity and Heterogeneity

First, and perhaps fundamentally, ecological processes are usually non-

linear.Local non-linearity combined with the spatial heterogeneity of the world means that

the aggregate large-scale behavior of local processes is difficult to predict (Levin 1976).

For example, the process of fire interacts with the ecological structure provided by tree

species in North Florida sandhill communities. Either oak (Quercus spp.) or longleaf pine

(Pinuspalustris) tends to dominate these communities. The interaction of fire with fire-

suppressing oaks and fire-encouraging pines produces a non-linear relationship between

the time since a site has burned and it combustibility. A lack of fuel tends to suppress fire

in oak stands, encouraging the growth of more oaks, while fuel accumulation in stands of

pine tend to encourage fire, suppressing oaks and encouraging the growth of pine

(Abrahamson and Hartnett 1990, Rebertus et al. 1993).

The ability of fire to spread, and consequently the stability of patches of oak or

pine, is determined by the distribution of oaks and pine across the landscape. The

combination of spatial heterogeneity and positive feedbacks make the dynamics of forest

difficult to predict from the study of a local site, since processes that define a site are

determined by the properties of its neighbors.

Different Processes Dominate at Different Scales

Different processes dominate at different scales. This observation can be illustrated

by examining vegetative pattern in the North American boreal forest (Figure 3-1). The

growth of an individual tree is determined by vegetative processes influenced by factors

such as light availability, soil moisture, nutrient availability, and temperature. Studies of

tree growth, in small 1 m2 plots over several years, have produced a great deal of

knowledge of tree growth, and these studies have been synthesized in simulation models.

These models predict that shade-intolerant tree species should gradually be replaced by

shade-tolerant trees (Bormann and Likens 1981). However, this replacement does not

occur in the boreal forest, because large-scale spruce budworm (Choristoneurafumiferana)

outbreaks periodically defoliate and kill large areas of shade-tolerant balsam fire (Blais

1983). These outbreaks require large areas of susceptible trees to begin, but the presence

or absence of susceptible areas cannot be detected by small-scale studies. Therefore, up-

scaling local observations requires the accounting for the presence of budworm dynamics at

larger scales.

The presence of different dominant processes at different scales means that as a

scaling method attempts to span a wider ranger of scales, it needs to incorporate the effects

of an increasing number of processes. Scaling methods can either ignore or incorporate

shifts in dominant processes across scales, but both approaches have problems. Ignoring

changes in structuring processes can produce an inaccurate scaling method, however more

complex scaling methods that incorporate multiple processes may be too complicated or

costly to use effectively.

Cross-scale Connections

Processes at different scales do not function independently of one another.

Processes operating at small and fast scales are constrained by processes that operate at

slow and large scales, but these large and slow processes are constructed and organized by

the interactions of many small fast processes (Ahl and Allen 1996). These cross-scale

connections make scaling difficult, because scaling relationships only hold if organization

at other scales remains constant. However, change occurs at all scales.

The dynamics of spruce budworm illustrate the importance of cross-scale

connections. Large scale budworm outbreaks are connected to local forest dynamics via

tree mortality. At the local scale, an individual tree is connected to the large scale by

providing a rich food source and refuge from predation that are required for outbreaks to

occur (Ludwig et al. 1978). In this case, these cross-scale connections allow the forest to

be organized into either a cycle of budworm outbreaks or local low-level budworm

infestation (Clark et al. 1979). In the forests of New Brunswick, logging and spraying to

suppress the budworm changed the existing state of the forest from large even-aged stands

with periodic large outbreaks, to smaller and denser even-aged stands with chronic shifting

budworm infestations (Baskerville 1995).

Alternative behaviors, emerging from the same processes, demonstrate how

understanding developed at one scale may only be relevant or useful as long as the

structures and processes at other scales that are maintaining system organization persist.

These cross-scale connections suggest that scaling can only apply over specific ranges of

scale and in specific situations. Forest pattern would scale differently in a forest that has

periodic budworm outbreaks, than it would in a forest experiencing chronic budworm

infestation. By recognizing alternative ecological organizations and assessing what drives

the shifts among them, analysts could improve scaling methods. Separate scaling methods

could be developed for each alternative organization, and the rules for transitions among

alternatives could be used to synthesize these separate scaling methods into an aggregate

scaling method that incorporates the behavior of the ensemble of states.

Emergent Processes

Occasionally the interaction of process and pattern at one scale produce emergent

organization at a larger, slower scale. For example, the interaction of air and water

circulation over tropical oceans can produce a self-reinforcing vortex, which strengthens

itself as it draws in increasing volumes of warm air. The continued growth of this vortex

produces a hurricane (Barry and Chorley 1992). Emergent processes form due to non-

linearity across heterogeneous space (Nicolis and Prigogine 1977). Many small fast

processes repeatedly interact to produce a larger slower structure that constrains the

behavior of the small processes in such a way that they mutually reinforce one another.

Such emergent processes are self-organized. They are not created by some outside force,

but are created from the mutual reinforcement of their component processes. Emergent

processes present the strongest challenge to scaling theories, because they demonstrate that

in some situations systemic change is not only non-linear, but also structural.

Emergent processes are common in ecology (Perry 1994). They exist in all of the

previous examples. For example, the interaction of tree growth, avian predation, and

budworm population dynamics can create either budworm outbreaks or chronic budworm

infestations. Slight changes in avian predation can shift budworm dynamics from

infestation to outbreak behavior (Figure 3-2). The ecological consequences of outbreaks

are qualitatively different from dispersed chronic budworm infestation. Budworm

outbreaks increase budworm densities over a thousandfold above non-outbreak budworm

densities. At these high densities budworm defoliation kills trees over large areas. The

death of a large number of trees during a short period of time over large area alters nutrient

cycling, tree growth, forest combustibility, and create large even-aged stands that have the

potential to trigger future budworm outbreaks (Clark et al. 1979).

The production of emergent pattern and process complicates scaling, for it suggests

that the organization that is being analyzed can abruptly reorganize, rendering previously

developed scaling relationships invalid as the structures and processes that they incorporate

cease to exist. This issue is particularly important in global change research, as global

change will change ecological organization and processes in novel ways leading to the

production of unexpected ecological processes and structures (Kates and Clark 1996).

Coping with Scaling Problems

Simple scaling, using addition or integration fails when the scaling problems

discussed above apply. Attempting to scale processes by adding new processes and

variables as the scale range of a model increases will work across small ranges of scale, but

will become unmanageably complex across broader ranges of scale. Both of these

approaches are static. They are based upon the assumption that the current organization of

structure and processes will persist over time. The existence of emergent processes

demonstrates that cross-scale organization is dynamic. Translating across these scales in a

dynamic manner requires understanding existing cross-scale ecological organization,

possible alternative organizations, and pathways by which systems can reorganize (Figure


Self-organizing systems appear to be structured by a few key variables (Holling et

al. 1996), which suggests that identifying these variables, and constraints under which they

interact will allow a dynamic, parsimonious method of translating across scales. In the

following sections of this paper, I present a framework for the analysis of cross-scale

interactions based upon self-organization, and then apply this framework to the analysis of

cross-scale resilience.

Cross-Scale Interactions

Ecological organization emerges from the interaction of structures and processes

operating at different scales. Traditionally, the effects of scale on the organization of

ecological systems have been analyzed using hierarchy theory. Hierarchy theory focuses

upon the consequences of hierarchical organization, but translating information across

spatial and temporal scales also requires understanding how hierarchical organization is

constructed and how it falls apart.

Hierarchy Theory

Hierarchy theory is an extension of systems theory that attempts to analyze the

effects of scale on the organization of complex systems (Simon 1974). Hierarchy theory

does not assume that a system is necessarily hierarchically constructed. Rather, it divides

the world into hierarchical levels to simplify the analysis of cross-scale interactions (Ahl

and Allen 1996, Allen and Starr 1982, O'Neill et al. 1986).

Hierarchy theory regards the world as systems that are comprised of components.

It views a system as a set of interacting components (i.e., lower-level entities) that is itself a

component of a larger system (i.e., a higher level entity). A hierarchical system is

composed of a set of coupled subsystems (O'Neill et al. 1986). Hierarchical organization

removes or attenuates interactions between system components. In a non-hierarchical

system any components may interact with any other; however in a hierarchical system

components are grouped into sub-systems that interact strongly internally, but only weakly

with other sub-systems (Simon 1974).

Hierarchy theory analyzes ecological patterns by separating the large from the

small, and the fast from the slow. Hierarchy theory has been used to organize analysis at

different scales, both spatially and temporally. Ecologists have analyzed how vegetation

patterns and the processes organizing pattern change with spatial scale (Krummel et al.

1987, O'Neill et al. 1991). Similarly, the constraints of slower temporal dynamics on

faster processes have been analyzed at a variety of scales. For example, fire frequencies in

the southwest United States are constrained by El Nifio fluctuations (Swetnam and

Betancourt 1990). This approach is useful for analyzing existing ecological configurations,

but it is not as useful for understanding the formation, dynamics, and reorganization of


Hierarchical Organization

The concept of a hierarchy is a human construct, but natural hierarchical

organizations may have arisen because hierarchy offers life a more stable alternative to

'flat', non-hierarchical organization. Because hierarchical construction increases stability,

systems with hierarchical organizations should be able to form and persist (Holling 1992a,

Simon 1974).

Simon (1974) uses a parable of watchmakers to illustrate the advantages of

hierarchy. He describes one watchmaker who uses a hierarchical watch construction,

while another does not. When the work of the two watchmakers is periodically disrupted

each watchmaker loses his work, but the watchmaker using hierarchical construction, loses

only that component. Therefore, even if the costs of hierarchical construction are high and

the disturbance rate low, the hierarchical watchmaker is able to produce far more watches

than the non-hierarchical watchmaker. This parable illustrates the virtue of modularity as a

defense against disturbance. It argues that the ideal complexity of a module depends upon

the disturbance rate, and the cost of isolating function within a module. A low disturbance

rate encourages complex modules, while a high disturbance rate encourages simple


Ecological organization occurs as processes interact with structure across space and

time. For example, the mutual reinforcement between the process of fire and structure

provided by longleaf pine will only occur if the fires are started frequently and fires are able

to spread across a large area. Otherwise, sites will bum infrequently and fire-encouraging

vegetation will be replaced by fire-suppressing vegetation. The mutual reinforcement of

fire and vegetation pattern produce spatial patterns of fire, longleaf pine and hardwoods, at

a scale larger and longer lasting than any individual fire or mix of vegetation.

Simon's argument can be inverted to understand why and how self-organized

hierarchies form in ecosystems. Ecosystems are hierarchically organized, because they are

composed of individual organisms and abiotic processes that operate at a variety of scales.

The persistence of a particular organization depends upon the degree to which the

interactions are mutually reinforcing relative to the degree to which the organization is

disrupted. If conditions are favorable, spatial-temporal interaction of structures and

processes produces an emergent pattern at a larger and slower scale than the scale of the

processes and structures themselves. These self-organized patterns and processes can,

through interaction with other larger and slower processes, organize still larger and slower

sets of pattern and process. This type of interaction produces nested hierarchies in nature.

I claim that a nested hierarchy exists in the boreal forest. The forest is composed of

stands, which are composed of trees. Patterns a higher scales emerge from interactions at

the smaller levels. At the scale of a tree, interactions over tens of meters between trees

determine ecological structure. The cumulative impact of a tree and its neighbors, shapes

fuel accumulation over a larger area. The larger scale process of fire interacts with this fuel

accumulation, to produce stand creating, tree-destroying forest fires. The pattern of

patches across the landscape influences larger scale processes, such as moose foraging

(Pastor et al. 1993) and weather (Knowles 1993), shape the forest at larger scales. The

emergence of new structuring processes at larger scales means that it is inappropriate to

consider larger scales simply as aggregations of a large number of small-scale entities.

Hierarchy theory suggests that the rules that organize a hierarchy are the ones that

should be used to translate across scales. For example, if higher hierarchical levels average

changes in the lower levels, then simple scaling methods, such as interpolation or

averaging, are likely to be successful. While, if non-linear processes produce the higher

levels then a scaling rule that is appropriate to those specific non-linear interactions can be

discovered. For example, the frequency of forest fires of different sizes can be appears to

be a function of fire size raised to an exponent (Malamud et al. 1998). This type of power

law relationship can be used to scale forest or fire dynamics.

While hierarchy theory can explain why hierarchical organization offers advantages

over flat organization, it does not provide much insight into how hierarchical structure

forms, changes, or dissolves. Understanding these processes requires examining process


Dynamic Hierarchy

The conventional view of hierarchies is static and structural (Pickett et al. 1989, Wu

and Loucks 1995), however this approach ignores the processes that build and destroy

hierarchical structure. In the past decade, complexity theory has expanded and enriched

systems theory, producing a dynamic conception of hierarchy. Holling (1986) has

proposed a general model of ecological change that I use to organize my discussion of

dynamics in hierarchies.

Holling's adaptive cycle model proposes that the internal dynamics of systems cycle

through four phases: rapid growth, conservation, collapse, and re-organization. As

unorganized processes interact, some processes reinforce one another, rapidly building

structure, or organization. This organization channels and constrains interactions within

the system. However, the system becomes dependent upon structure and constraint for its

persistence, leaving it vulnerable to either internal fluctuations or external disruption.

Eventually, the system collapses leaving disorganizing structures and processes to

reorganize a new set of structures. This dynamic can be seen in a forest stand. As a young

stand grows it gradually becomes denser, accumulates fuel, and becomes increasingly

susceptible to fire. Following a fire, the stand is reorganized as plants resprout from roots

or seeds, producing a new forest stand.

The key point of this model is that during the development of a system, its

organization changes, and due to these changes there are times when it is either more or

less vulnerable to internal and external fluctuations. This point of view acknowledges the

importance of top-down constraint on processes, but it also recognizes that during some

periods large-scale processes are unstable and are vulnerable to change from below. For

example, in a dense old forest damage to an individual tree may allow bark beetles

(Coleoptera: Scolytidae) to reach densities at which they can attack and kill nearby healthy

trees, initiating an outbreak that can alter forest pattern over a large area (Berryman et al.

1984). Similarly, after clear-cut logging, quick small-scale processes, such as seed

dispersal and competition between seedlings, shape the subsequent development of that site

for decades (Bormann and Likens 1981). During other times, slower and larger scale

processes are stable and resilient, constraining the lower levels and integrating noisy

variation of small, faster processes. It is during the periods of destruction and

reorganization that a system is most vulnerable to small, fast processes (Gunderson et al.


I propose that there are three ways that change propagates through dynamic

hierarchies (Figure 3-4). First, change at a higher level alters a lower level due to the

constraints that it places upon it. Second, small-scale disturbance can trigger larger scale

collapse if the larger system is at a brittle stage in its adaptive cycle. Third, following the

collapse of a system, small-scale organization drives the formation of large-scale structure

during periods of reorganization. These changes occur within a dynamic hierarchy that is

embedded in a larger environment, that is itself composed of other dynamic hierarchies.

By considering these hierarchical dynamics a richer set of scaling methods can be

developed. For example, a forest that is vulnerable to a bark beetle outbreak will be better

represented by different scaling methods than a forest that is not. Identifying the

reorganization events that cause an ecosystems scaling to change allows an integrated

scaling method to be developed that identifies the conditions in which different scaling

relationships are appropriate.

Cross-Scale Change

Ecological organization across scales is the result of the interaction of processes

operating at different scales. For organization to persist these interactions must not

immediately disrupt one another, rather they should be independent of one another or

reinforce one another. But as these processes alter ecological structures, the interactions

that are possible change. For example, the accumulation of oak leaves in a forest can

suppress the growth of understory vegetation, reducing the possibility of fire. Changes in

ecological organization can: alter the scale at which a process functions, eliminate a process

by disrupting the interactions that maintain it, or create new interactions that enable a new

process to occur.

The spatial and temporal scale of a fire regime is defined by the cross-scale

interaction of fire with relatively slower vegetative processes and faster atmospheric

processes (Figure 3-1). The key processes defining a fire regime are the rate of fire

ignition, the rate of forest recovery following a fire, and the rate of fire spread. If fire

spreads faster than a forest recovers from fire, and forest recovery is faster than the ignition

of another fire, then the average fire size is proportional to the rate of recovery divided by

the rate of ignition (Drossel 1997). These rates emerge from the interaction of vegetative

and atmospheric processes. Changes in these processes that alter these key variables will

alter the fire regime. For example, a change in climate that increases the frequency of

thunderstorms could increase the rate of fire initiations, which would produce smaller and

more frequent fires. Alternatively, a region invaded by pyrogenic grass can recover from

fires far quicker, and this would increase the frequency and spatial extent of fire

(D'Antonio and Vitousek 1992).

Changes in the scale of fire regime will alter the scale range over which a scaling

method works. Incorporating the variables that drive these changes into a scaling method

allows scaling methods to become dynamic, because it incorporate changes in scaling

relationships into a scaling method. For example, given a scaling law that describes forest

fire frequency as a power law of forest fire size (Malamud et al. 1998), simulation

modeling could be used to assess the sensitivity of the scaling exponent to changes in fire

ignition, forest recovery, and fire spread. This type of analysis could potentially identify

when the scaling behavior of an ecosystem would break down.

The emergence, maintenance, and destruction of ecological organization are due to

cross-scale interactions. The stability of a given organization over time and space depends

upon the nature of these interactions. The strength with which they reinforce one another,

their sensitivity to disruption, and the presence of alternate configurations of interactions.

These attributes of ecological organization can be integrated and analyzed from the

perspective of cross-scale resilience.

Cross-Scale Resilience

Ecological resilience is a measure of the amount of change or disruption that is

required to cause a ecosystem to switch from being maintained by one set of mutually

reinforcing processes and structures to a different set of processes and structures (Holling

1973). As long as an ecological organization maintains its resilience, its organization

persists. If the scaling behavior of a given ecological organization can be represented by a

scaling method, maintaining resilience also means maintaining the ability to use a scaling

method. The limits of an ecosystem's resilience are also the limits to the applicability of a

given scaling method. A loss of resilience causes an ecosystem to reorganize, and the

scaling behavior of a new ecological organization should be represented by a new scaling

method. Resilience emerges from both cross-scale and within-scale interactions (Peterson

et al. 1998).

Cross-scale resilience is produced by the replication of process at different scales.

The apparent redundancy of similar function replicated at different scales adds resilience to

an ecosystem, because disturbances are limited to specific scales, functions that operate at

other scales are able to persist. For example, Fragoso (1997) has shown that in Brazil's

Maraca Island Ecological Reserve palm seeds are dispersed across a range of scales by a

variety of species. Seed dispensers range in size from small rodents, which typically

disperse seeds within 5m of parent trees, to tapirs (Tayassu tajacu), which disperse seeds

up to 2 km. Seed dispersal at multiple scales allows the palm population to persist despite a

variety of disturbance processes occurring at different scales, because the trees are

distributed across the landscape at different scales. Cross-scale resilience is complemented

by within-scale resilience.

Within-scale resilience is produced by compensating overlap of ecological function

between similar processes that occur at the same scales. In the previous example a variety

of species, including deer, pecaries, primates, and rodents, disperse palm seed short

distances (Fragoso 1997). Population fluctuations of a species do not have a large effect

on the rate of short distance seed dispersal, because a decline in seed dispersal by one

species leaves more seeds available for other species. An increase in seed availability

allows other species to increase their seed dispersal compensating for initial decline in

dispersal. This compensating complementarity among seed dispersers reduces the impact

of population fluctuations on ecosystem function (Frost et al. 1995), increasing its


By focussing on the cross-scale and within scale sources of an ecological

organization's resilience one can assess the vulnerability of a particular scaling relationship

to within-scale and cross-scale changes. In the following section I discuss how such an

approach has been applied to boreal forest dynamics.

Avian Predation and Spruce Budworm

In the forests of the Canadian province of New Brunswick, outbreaks of spruce

budworm periodically defoliate and kill large areas of mature boreal fir forest (Clark et al.

1979, Morris 1963). The specific form that the budworm/forest system organizes into is

controlled by a variety of forces including climate, landscape pattern, and avian predation

on budworm. Changes in any of these factors can alter the organization and scaling pattern

of the forest, but I will focus upon the effects of variation in avian predation.

Avian predation on budworm slows budworm population growth, altering the

frequency and extent of budworm outbreaks (Holling 1988). Avian predation can prevent

budworm population growth over a wide range of budworm densities. However, at high

densities avian predation has a minimal impact on budworm population dynamics, because

the per budworm effect of avian predation declines. This decline allows the budworm

population as a whole to grow at a faster rate, escaping from avian predation. The range of

budworm densities over which avian predation can control budworm population growth

controls the frequency and intensity of budworm outbreaks, and the effectiveness of avian

predation is determined by within-scale foraging diversity and cross-scale reinforcement.

Within-scale foraging diversity means that similar sized bird species prefer different

prey items. These differences in preferences increase the robustness of predation, because

if a particular prey item increases in abundance, it becomes easier to capture, and species

that did not previously prey upon it will begin to consume it (Murdoch 1969). These

predators switch from their preferred prey because the increasing relative abundance of a

resource makes its use less costly. This switching behavior increases the effectiveness of

avian budworm predation, because the group of species provides more resilient predation

than any single species.

Cross-scale functional reinforcement occurs because different species of birds

forage for budworm at different scales. A bird's body size has a strong influence on how it

forages, determining the amount of food a bird can consume and the scale at which it

searches for food (Peters 1983). At high population densities, budworms are spatially

aggregated. The concentration of food that is available in these aggregations allows larger

birds to consume budworm. These birds do not eat budworm when it is dispersed,

because searching for individual budworm is not worth the effort. Additionally, larger

birds forage over wider areas than smaller birds. Consequently, the size of birds that are

able to effectively consume budworm and the distance from which they are attracted will

increase as the size of local aggregations of budworm increase.

While within-scale and cross-scale diversity do produce effective predation on

budworm, budworm populations can escape from control by birds. Local concentrations

of budworm can be controlled by bird migration, but budworm increases over a wide area

require a huge amount of predation to control. The density of budworm at which avian

predation is unable to control budworm populations determines the frequency of budworm

outbreaks and their spatial scale. Holling (1988) used Ludwig et al.'s (1978) mathematical

model to calculate that large changes in bird predation, an approximately 50% decline in

effectiveness, would be necessary to significantly to shift the scale of budworm outbreaks.

I used the same model to illustrate how different levels of avian predation alter the temporal

scaling behavior of forest vegetation.

I used Ludwig et al.'s (1978) simple analytical model of budworm outbreaks to

simulate budworm-forest dynamics. This is the same model that I used to generate Figure

3-2. This model simulates budworm-forest dynamics through three coupled differential

equations that represent slow changes in forest volume, medium term changes in tree

foliage, and fast changes in budworm densities.

I used the parameter values of Ludwig et al (1978) as a base case, I altered the

maximum amount of predation that was possible in the model. This alteration is equivalent

to simulating increases or decreases in avian populations. I generated a millennium of

forest dynamics, and then analyzed the variance in forest age using a range of temporal

windows. I normalized these variance relationships to be proportional to the maximum

variance in each simulation run. Changes in the normalized variance reveal the relative

amount of variance present at a scale (Figure 3-5).

The greater the rate of decrease in relative variance at a scale, the greater the

proportion of variance present at that scale. Vertical arrows indicate the time scale at which

the greatest amount of variance has been lost. These scales correspond to the periodicity of

budworm outbreaks at different levels of predation. Reducing predation decreases the scale

over which variation occurs. Increasing the amount of predation increases the scale of

budworm outbreaks. When predation is increased more than 17% above the base level

predation becomes strong enough to prevent outbreaks from occurring, causing forest

dynamics to follow a fundamentally different scaling relationship.

The analysis of the sensitivity of scaling relationships to changes in the processes

that support a specific ecological is useful in showing how changes in underlying processes

shift scaling relationships, and cause them to fall apart. Figure 3-5, shows that the

difference between no predation and 50 % of the base predation is very slight, but that at a

20% increase over the base predation would cause the existing scaling relationship to

collapse. This collapse in the scaling relationship corresponds to the difference between the

chronic budworm infestation and budworm outbreak that are shown in Figure 3-2.

Such a change occurred in the forests of New Brunswick when pesticide spraying,

which by killing budworm is similar to an increase in predation, eliminated budworm

outbreaks causing a chronic budworm infestation (Baskerville 1995).

Scaling and Ecological Reorganization

Scaling relationships are essentially models that are used to predict the behavior of a

system over space and time, given a few measurements. These models are constructed

based upon the analysis of the existing cross-scale dynamics of a system. However, a

given set of scaling relationships is unlikely to persist in a world experiencing global

change. Global changes in climate, land-use/land-cover, and biodiversity will all alter

ecological organization and scaling.

Analyzing the cross-scale dynamics of existing ecological organization allows the

assessment of the degree to which change alters scaling relationships, or causes them to

collapse entirely. As was illustrated in budworm predation example, models can be used to

assess the resilience of ecological organizations. Additionally, if alternative ecological

organizations are known, and the causes of switches between these configurations can be

determined then multiple relatively simple scaling rules can be used in large models,

without explicitly incorporating the dynamics that drive changes in scaling into a scaling

method itself.


The dynamics of ecological organization are an essential component of global

change research. Global change is the convolution of a huge variety anthropogenic

changes in Earth's biosphere, geosphere and atmosphere that all, to some degree, alter

ecosystem functioning. Assessing the impact of change on ecosystems requires the use of

scaling methods, but these scaling methods may not apply in an altered world. The

resilience of ecological organization to changes in key processes determines the situations

in which scaling methods apply, require adjustment, and break down. These analyses can

be used to developing scaling methods that dynamically compensate for shifts in ecological


Translating either data or understanding across scales is often difficult, because the

interaction of ecological processes self-organize dynamic hierarchical structure. Ecological

change can alter the resilience of an ecosystem, cause ecological reorganization, and

eliminate alternative ecological organizations.

Ecological change that alters the resilience of an ecosystem may shift its scaling

relationship, reducing or expanding the scale range over which a relationship applies, as

shown by the analysis of changes in avian predation on budworm. Reduction in predation

leads to more frequent outbreaks, reducing the scale of budworm outbreaks. This changes

the scaling of forest pattern. Similarly, the loss of large-scale seed dispersal would reduce

that scale of forest patterning.

Change may push an ecosystem beyond the limits of its resilience, causing it to

reorganize. Ecosystems as diverse as coral reefs (Done 1992), shallow lakes (Scheffer

1998), and woodlands (Dublin et al. 1990) have been clearly shown to switch between

alternative ecological organizations. Such ecological reorganization alters an ecosystem's

scaling, as new ecological processes operate over different scales.

Furthermore, the addition or subtraction of processes from an ecosystem may

eliminate alternative ecological organizations. This appears to have occurred during the

Pleistocene when the extinction of mega-herbivores eliminated the disturbance processes

that maintained specific ecological organizations (Owen-Smith 1989, Zimov et al. 1995).

The loss of a form of ecological organization is extremely difficult to reverse, particularly if

that organization was generated by a species that has become extinct (Flannery 1994).

I have argued that by focusing upon the cross-scale processes that create, maintain,

and destroy ecological organization scaling relationships can include important ecological

dynamics. By analyzing the cross-scale organization of ecosystems, methods for

translating across scales can be developed. These scaling methods can be improved by

considering the dynamics of cross-scale organization. By perturbing dynamic ecological

models, the effects of change on scaling methods can be calculated, and the limits of a

system's resilience predicted.

Ecological change that alters the resilience of an ecosystem may shift its scaling

relationship; reducing or expanding the scale range over which a relationship applies.

Humanity lives and manages the landscape at the meso-scale. As well as changing

the distribution and organization of ecosystems, these activities have also altered the scaling

of ecological processes. For example, fire suppression and landscape fragmentation have

altered the scaling behavior of fire. In the following chapters I analyze how these types of

larger scale processes impact the dynamics of meso-scale processes, and how, in turn,

these transformed meso-scale processes influence larger scales.

100 1
m km

100 1 000 the
km km Earth

-5 -4 -3 -2 -1 0 1 2 3 4
Log Space (Kilometers)
O Vegetative structure Disturbance processes
0 Atmospheric processes

4* millennium

4 century

-4 decade

4 year

-4 month

o day

-4 hour

Figure 3-1. Time and space scales of boreal forest structures (Holling 1992a) are compared
with disturbance and atmospheric processes which structure the forest. These processes
include spruce budworm (Choristoneurafumiferana) outbreaks, fire, atmospheric
processes, El Niho, and the rapid CO2 increase in modem times (Clark 1985).


10.0 2.0
9.0 Budworm 1.8
80- Forest
8.0 1.6
Log 7.0 1.4
Budworm 6.0 1.2
Density Forest
(larvae/acre) 5.0 1.0 Surface
Area Ratio
4.0 0.8
3.0 0.6
2.0 0.4
1.0 0.2
0.0 0.0
0 50 100 150 200 250 300
A) Time (years)

10.0 2.0
9.0 rBudworm -1.8
-8 Forest
8.0 1.6

Log 7.0 1.4
Budworm 6.0 1.2 Forest
Density Forest
(larvae/acre) 5.0- 1.0 Surface
Area Ratio
4.0 0.8
3.0 0.6
2.0 0.4
1.0 0.2
0.0 0.0
0 50 100 150 200 250 300
B) Time (years)

Figure 3-2. Two plots illustrating alternate relationships between spruce budworm and
boreal forest volume, generated from Ludwig et al.'s (1978) simple analytical model of
budworm outbreaks. Graphs A and B use Ludwig et al's base parameters, except the
maximum avian predation rate is 25% higher than the base in graph A. A) Non-outbreak
spruce budworm, infecting a forest at relatively high densities. Budworm populations are
controlled by higher than observed levels of avian predation. The forest suffers some loss
of productivity from budworm, but little mortality. B) When avian predation is reduced to
observed levels, budworm outbreaks occur. These outbreaks increase budworm densities
a thousandfold over the non-outbreak budworm densities. At these high densities
budworm defoliation kills trees. The outbreaks entrain forest structure, producing a dis-
equilbrium forest that has a periodicity of about seventy years.

Time Since
Fire (years)
0 16-19
Oc m 10-13
:- 7-10

0 10 20 30 40 50 60 70 80 90 100
A) Time (years)

0 10 20 30 40 50 60 70 80 90 100
B) Time (years)

Figure 3-3. Simulation of two longleaf pine forest with the same initial composition of
forest types, but different spatial distributions of those types. The model used is a one
dimensional spatial model of fire and forest dynamics (Peterson In Press). A) Two old
sites are adjacent to one another. The sites remain old, while the other sites remain young.
B) Two old sites are separated by several young sites. Fire is prevented from burning
these sites, and they become old. The other young sites remain young. After 100 years of
simulation, both the mix of site types and their spatial pattern are quite different. If the
behavior of these models had been extrapolated from either the initial conditions of sites, or
the initial mix of sites, those extrapolations would have been inadequate, unless the spatial
relationships among sites are also considered.

larger constrain i constrain constrain
slower V I noise V noise noise

scale constraint constraint
smaller noise V I 'sparks' 'seeds'
faster a: (c b) c: = i >

Figure 3-4. An idealized map of the relationships between a hierarchical level and the
levels directly above and below it, as the nature of the central level is dominated by
different types of processes. a) A system during 'normal' times. Each level is strongly
constrained by the level above it. 'Noise' from the faster levels below each level has a
minimal effect upon higher levels. The system is resilient. b) A 'brittle' system strongly
constrains the level beneath it, but also extremely vulnerable to any change in the level
beneath it. This brittleness allows a small disturbance to rapidly propagate through the
entire system. c) A reorganizing system only weakly constraints its subsystems, making it
prone to organize around any 'seeds' of order that emerge from change in the lower levels.

1 Level
Normalized No
Variance Predation
(V/Vmax) 0.1 _- -50%
---- 25%
0.01 Predation
------ +15%
0.001 Predation
:- .- - >+17%
S V V Predation
1 10 100
Scale (window size years)

Figure 3-5. Changes in the intensity of predation alter the scaling of forest pattern in a
simple model of budworm outbreaks. Ludwig et al.'s (1978) simple analytical model of
budworm outbreaks was used to calculate variance in forest age over a range of temporal
window size. These variances were normalized based upon the maximum variance at each
level of predation. Using the parameter values of Ludwig et al (1978) as a base case, I
altered the amount of avian predation that was possible in the model. Reducing the
predation rate decreases the scale over which variation occurs, by decreasing the frequency
of budworm outbreaks. Increasing the maximum amount of predation, increases the scale
of budworm outbreaks, until outbreaks can no longer occur. Predation levels greater than
15% above the base case are sufficient to prevent outbreaks, causing the forest pattern
follows a fundamentally different scaling relationship. The vertical arrows indicate the time
scale over which almost all the variance has been lost for the 'Base Predation' case and
reduced predation cases. These time scales correspond to the periodicity of outbreaks,
which are respectively: 17 years, 24 years, 35 years, 65 years, 89 years, and no outbreak.



This chapter focuses upon understanding the cross-scale dynamics of human

dominated longleaf pine (Pinuspalustris) ecosystem in northwest Florida. It applies the

general discussion of resilience, ecological scaling, and large-scale global dynamics to a

specific, complicated managed ecosystem.

I use the concept of ecological resilience to construct a set of alternative models of

longleaf pine sandhill forests that are used to explore the ecological behavior of the

managed forest of Eglin Air Force Base in northwest Florida. In this introduction, I

discuss my approach towards ecosystem management and then describe the longleaf pine

forests of Eglin Air Force Base. The remainder of this chapter is divided into four

sections. First, I present the set of simple alternative models, or caricatures, that I use to

explore the structure, dynamics, and management of northwest Florida longleaf pine forest.

Second, I describe and use a non-spatial model to explore the temporal dynamics of

longleaf pine forest. Third, I describe and use a spatially explicit model to explore the

spatial-temporal dynamics of longleaf pine under a number of management regimes. I

conclude by discussing the ecological and management lessons learned from this

exploration. This includes identifying the important areas of uncertainty that remain and

suggesting appropriate management actions.

Ecological Management

Human transformation of the earth has largely occurred at the meso-scale. These

transformations have altered the abundance and distribution of biota. More subtly, human

action has altered the functioning of ecological processes, encouraging some, suppressing

others, and creating others. These changes in ecological processes often have gradual, but

cumulative effects that present a significant challenge to humanity's ability to effectively

manage ecosystems. These changes produce ecosystems that are organized in novel ways.

Learning how to structure human relationships with poorly understood, changing

ecosystems is one of the central challenges of modem ecology.

Applied ecologists are caught in a trap. Ecological change requires that they act, but

ecological change means that they have limited knowledge of how they should act. Applied

ecology, or ecosystem management, is based upon an abstraction of nature. From the vast

complexity of nature, managers abstract a set of system attributes that they feel capture the

structure and dynamics of the system of interest, and then use that set of relationships to

guide their decision making. Usually this decision process is based upon some form of

ecological theory, even if it is unarticulated. Often scientists work with managers with the

aim of improving the ecological model used by managers. However, often there are a

variety of competing ecological models that can not be rejected based upon existing

knowledge of the ecosystem being managed. One approach to this dilemma, which is

advocated by adaptive management (Holling 1978, Walters 1986), is to base management

upon a set of alternative ecological models rather than attempt to discover which model

performs the best according to a set of criteria.

Exploring a broad set of alternative models helps researchers ask better questions

about how ecosystems work. This allows managers and ecologists to engage in an iterative

process of synthesis, reflection, and experimentation that helps them move towards a richer

understanding of ecosystem dynamics. Perhaps more importantly, the continual use and

evaluation of a diverse set of models reminds ecological managers to attend to the

uncertainty that exists over how the system operates, encouraging management that tests

rather than follows models.

Developing such models is difficult, but the general ecological concepts of multiple

stable states, resilience and scaling, as discussed above, can provide a framework for

model construction. Useful models of complex situations compress a great deal of

understanding into simple, yet rich models that exhibit the minimum complexity to describe

important system dynamics. Such models cannot be developed immediately. Often they

can only be developed after many different complicated interactions are explored and tested.

Such a process usually narrows the set of interactions that have significant effects on

ecological dynamics. This process allows complex models to be compressed into minimal

models. I used these concepts to develop multiple ecological models to explore

management alternatives for forest restoration in Northwest Florida.

Longleaf Pine Forest on Eglin Air Force Base

From the end of the Pleistocene until recently, the majority of the coastal plain of

the southeastern U.S.A. was covered by longleaf pine (Pinuspalustris) forest. During the

past two centuries, human activities such as logging, agriculture and fire suppression have

reduced the area covered by longleaf pine from about 250,000 km2 to less than 12 500 km2

(Schwartz 1994). The largest remaining area of contiguous old-growth longleaf pine forest

is located in the northwest Florida, within the 1 870 km2 Eglin Air Force Base.

Either longleaf pine or various oak species (Quercus spp.) can dominate forest sites

located on the sandy soils of northern Florida (Abrahamson and Hartnett 1990). The

frequency of ground fires usually determines which vegetation type dominates at a specific

location (Heyward 1939). Mature stands of longleaf pine often produce open savanna

ecosystems that possess an understory rich in grasses, and herbs. Longleaf pine shed

needles that, in combination with the understory vegetation, provide a combustible medium

for fire. While longleaf pine stands encourage fire spread, oak stands tend to inhibit fire

spread. Mature oaks form dense stands that reduce the amount of light that reaches the

ground, producing a sparse understory. Oaks shed leaves that form a compressed, low

oxygen litter layer that suppresses the accumulation of understory vegetation and other

potentially combustible detritus.

Fire plays a key role in determining what types of tree seedlings survive to become

trees. Longleaf pine, in all but its young sapling state, survives fire. It also drops many

fine needles which ignite. Young oaks, however, are killed by fire. The absence of fire

from oak stands or longleaf pine stands allows the growth of young oaks. However,

regular fires suppress oak growth allowing longleaf pine to thrive (Rebertus et al. 1989).

The difference between fire's relationship with longleaf pine and oaks drives forest

dynamics in northwest Florida. Longleaf pine through the combination of fire resistant and

the production of fine fuels drive a forest site towards a frequently burned longleaf pine

savanna. While oaks through the combination of fire inhibition and rapid growth in the

absence of fire drive forest sites towards infrequently burned oak scrub-forest.

While the tension between oak and longleaf pine dominance occurs over much of

the southern coastal plane, a third tree species, sand pine (Pinus clausa), further

complicates ecological dynamics on Eglin Air Force Base. Sand pine historically occurred

along the coast, but in past decades has spread into areas formerly occupied by longleaf

pine (Provencher et al. 1998). The Choctawhatchee variety of sand pine that inhabits

northwest Florida is ecologically distinct from other sand pine. While interior Florida sand

pine are serotinous, requiring fires to open their cones before seeds can regenerate, the

Choctawhatchee variety is not (Parker and Hamrick 1996). Sand pine is not as fire tolerant

as longleaf pine, but sand pine reaches sexual maturity much faster than longleaf pine.

These differences mean that sites that are not regularly burned can be invaded by

neighboring sand pine within several years.

On Eglin Air Force Base, longleaf pine savanna has been replaced by pine

plantations and invaded by oaks and the Choctawhatchee variety of sand pine. Presently,

the land managers at Eglin Air Force Base are attempted to restore the now rare longleaf

pine savanna ecosystem (Jackson Guard: Eglin Air Force Base 1993).

Restoring the forest requires understanding its ecology and its history. Ecological

understanding is needed to develop useful management plans, while an understanding of

the past is required to understand what processes and states should be encouraged or

discouraged. While there is a large body of knowledge concentrated on longleaf pine forest

(Glitzenstein et al. 1995, Platt et al. 1988), this knowledge has often been developed at the

scale of forest stands over several years. However, ecosystem management operates at the

scale of thousands of square kilometers and decades. This scale mismatch requires

translating understanding and uncertainty across scales. The development of simulation

models provides a means of integrating ecological and management knowledge. By

constructing alternative models, different models of cross-scale dynamics can be

formulated, explored, and tested. It was this approach that I applied to Eglin Air Force


Ecological Caricatures and Management Alternatives

Management alternatives are developed based upon models of how the forest

functions. From meetings and discussions with land managers and ecologists I developed

a set of simple caricatures of Eglin's forest types and dynamics. These caricatures were

developed to articulate the concepts that implicitly underlie most actual and proposed

management strategies. These caricatures assume specific types of landscape change.

Testing these caricatures may eliminate a several of them, along with the management

strategies that they imply. Furthermore, testing the caricatures reveals the key differences

among them, and the situations in which these differences matter to management.

Identifying these points allows a more refined set of alternative hypotheses to be posed and

tested through management actions.

Caricatures of Ecological Dynamics

I identified five caricatures of the changes that have occurred in Eglin's forests.

Two focus on the temporal dynamics of the forest, while three present alternative models of

spatial and temporal interactions. Each caricature and a management strategy associated

with it are defined below.

Out of Tune Forest: Fire frequency determines whether vegetation is maintained

or changed. The frequency of fire in Eglin has decreased, leading to an increased

presence of hardwood tree species and sand pine in the forest.

Suggested Management: Restoring longleaf pine requires returning the forest's fire

frequency to its historical level. This task requires discovering the 'right fire

frequency.' Apply this frequency of fires to the landscape and the forest will be


Transformed Forest: The invasion of hardwoods and sand pine has

fundamentally changed the forest. Fire behaves differently and has different

impacts in this transformed forest.

Suggested Management: Restoring longleaf pine savanna requires intensive fires to

transform the forest. Once the forest is restored it can be maintained with less

frequent fires. This requires policy that varies the fire frequency applied to an area

based upon the vegetation of that area.

Edaphic Forest: Soils and topography limit vegetation to specific locations on the

landscape. Changes in vegetation occur only within a limited set of alternatives

depending upon local site characteristics.

Suggested Management: Some sites will be difficult or impossible to change.

Managers should focus their efforts on the areas that can be improved should areas

and ignore the areas cannot be altered.

Divided Forest: Development has broken the landscape into many pieces,

altering the spread of fire across the landscape, which has decreased the frequency

of fire experienced by sites. This has lead to the loss of longleaf pine and the

invasion of hardwoods and sand pine

Suggested Management: The scale of fire must be expanded from beyond fragments

to cover the entire landscape.

Invaded Forest: Sand pine has invaded the forest, altering its dynamics.

Suggested Management: Fire frequency must be sufficient to prevent sand pine

spread. The areas that are occupied by sand pine will expand over time unless the

spread of sand pine is controlled. Managers should focus upon rapidly removing

areas of sand pine that have the potential to invade substantial areas.

The first two caricatures focus upon the interaction of fire and vegetative processes

through time. The next three caricatures focus upon how changes in landscape pattern

shape fire and seed dispersal, which are the processes that strongly influence the pattern of

the landscape itself. These caricatures are not mutually exclusive. Changes in the forest

are likely due to a combination of these processes, but management actions will be quite

different based upon which of these caricatures best captures the forces shaping Eglin's

landscape. A modeling process was used to test and explore the differences among these


Modeling Approach

Ecological modeling serves of variety of goals. Primarily, a model is a synthetic

hypothesis. It combines many diverse and heterogeneous results, observations, and

theories into a unified framework that proposes a model of how an ecological system


The process of developing a model can be useful in and of itself. By assembling

the concepts surrounding an issue in a system, and attempting to synthesize them is an

opportunity for learning about the system. It provides participants to learn about areas

outside their area of expertise, can reveal hidden connections, and establish important gaps

in existing understanding (Walters 1986).

Once a model is constructed, it is a tool for reflection. When its builders examine it

they may not like what they see, but what they see is what they chose to put there, because

a model embodies their prejudices, and ignorance as well as their knowledge. Models

allows individuals and groups to see if they believe what they thought they believed, by

clearly and explicitly laying out the things and processes that people consider to be

important to the function of a system.

The process of reflection often results in the development of alternative models as

processes that were thought to be important are shown to be peripheral, and marginalized

processes are shown to be important. Changes in the processes considered by a model

usually requires changes in the variables that are included within a model. Often a model

evolves over several years, from discussions that alternate with attempts at synthesis, that

are followed by periods of reflection, criticism, speculation and model reformulation.

Frequently, this process can produces a set of models, or a modeling framework that is

acceptable to the people involved in the modeling processes.

For Eglin Air Force Base, I produced a set of models that embodied the ecological

caricatures above. These models represented what workshop participants felt were key

ecological structures, processes, and management actions. At this point the model's basic

structure, but not the rules describing how vegetation changes, were accepted as being

reasonable by the various workshop participants. The model provides a formal document

of consensus opinion of how, in broad terms, of how and why sandhill forest in north

Florida changes. I used this modeling framework to conduct two sets of simulation

experiments to identify what aspects of forest dynamics have important consequences for

landscape management. The first set of experiments used a non-spatial model to explore

forest dynamics under a wide variety of environmental regimes. The second set of

experiments used a spatially explicit model to explore spatial-temporal forest dynamics

under a narrower set of forest management scenarios.

Succession and Fire Temporal Dynamics

Modeling Vegetation Dynamics

The central abstraction in the model is its representation of forest in Eglin as a mix

of the three dominant tree species. I assumed that an area of forest could be represented as

a mix of longleaf pine (LL), sand pine (SP) and hardwood species (HW). This allows the

character of a forest patch to be characterized by its relative proportion of these three tree

types. This mix of tree types can be plotted as a position within a triangle, because of the

assumption that the proportions of the three types comprise the total canopy (i.e. LL + SP

+ HW = 1.0). Changes at a site in the forest can therefore be thought of as tracing a

trajectory through the triangular state space representing the various potential canopy types

(Figure 4-1).

Vegetation types

The various possible combinations of longleaf pine, sand pine and oaks that are

possible in Figure 4-1, were reduced to a set of discrete states. These states were based

upon translating existing management classifications into the triangular state-space

representation of forest composition. The infinite number of possible mixes of tree types

was reduced to eight distinct vegetation states, each representing a discrete type of forest.

These classes are briefly described below, and illustrated in Figure 4-2.

Longleaf pine (LL) : Longleaf pine with recruitment, hardwood understory,

and continuous herb cover.

Longleaf pine and hardwoods (LLHW) : Longleaf pine canopy without

recruitment, hardwood midstory, and discontinuous herb cover.

Longleaf pine and immature Sand pine (LLSP) : Longleaf pine canopy

without recruitment, sexually immature sand pine present, and some hardwoods.

Hardwood/Longleaf pine (HWLL): Hardwood and longleaf pine canopy

with no longleaf recruitment, sparse herb cover.

Sand pine/Longleaf pine (SPLL) : Sand pine and longleaf pine canopy with

no longleaf recruitment, hardwood mid-story, sparse herb cover.

Hardwood (HW) : Hardwood canopy, with sand pine possibly present.

Sand pine and Hardwoods (SPHW): Mixed co-dominant sand pine and


Sand pine (SP): Sand pine canopy, with hardwood midstory.

These vegetative states can be visualized as representing areas within the triangle

defined by the combination of longleaf pine, sand pine and hardwoods (Figure 4-3).

Vegetation dynamics

Fire and vegetation succession determine the transitions among these states. The

model represents fifteen different transitions among the eight states (Table 4-1). These

rules were derived from a series of workshops and discussions with people from: The

Nature Conservancy, the land managers at Jackson Guard on Eglin Air Force Base, other

longleaf pine managers, and ecologists. The general structure of these transition rules, for

example that unburned longleaf pine sites will be invaded by sand pine and hardwoods, is

well understood and has been reviewed in many publications (Heyward 1939, Platt et al.

1988, Rebertus et al. 1993). However, the specific fire frequencies that are necessary to

shift a site from one state to another are much less well known. What is certain is that these

frequencies vary with soil, topography and climate conditions. Therefore, the rules that

have been proposed to model Eglin Air Force Base will likely be subtly different from the

transition rules that would describe even neighboring stands of longleaf pine in different

soil conditions. For example, Blackwater River State Park located just north of Eglin Air

Force Base has less sandy soils than Eglin making it more difficult for fires to kill

hardwoods, among other differences.

Table 4-1. Vegetation transition rules used in the Eglin Longleaf pine model. Transitions
among vegetation states are based upon a sites time since fire (TSF) and the presence of
seeds from either or both pine species.
--> To
years years &
years years
for last
10 years
years years & >250
for last sand years
three pine
fires seeds
years >250
for last years
years & years &
longleaf sand
pine pine
seeds seeds
S_ years
years &

The transitions among LL, LLHW, LLSP and the processes of sand pine and

hardwood invasion are relatively well understood. The transition rules describing the

conversion of HW, SP, HWSP, SPLL, and HWLL to sites with greater amounts of LL are

the least certain parts of the model. These transitions need to be further explored by field

experimentation. The transitions in the model are based upon the experience of land

managers at Eglin. There burning has been more successful at eliminating hardwoods than

in many other areas of Florida, however the amount of burning to decrease the hardwoods

in a HWLL site to the extent that it can be considered a LLHW is uncertain. In view of this

uncertainty, the model uses the same transition rule for both sand pine and hardwood based

upon the idea that it in the absence of evidence to the contrary these transitions will be

assumed to be equally likely. Below, I explain the derivation of these rules.

Longleaf pine (LL) will remain longleaf pine if it is frequently burnt. Without

frequent fire the longleaf pine will be invaded by hardwoods or sand pine, if sand pine

seeds reach the site. Hardwoods are assumed to be always present at low densities at

Eglin, however after twelve years without fire they reach a size that is sufficient to survive

future fires (Rebertus et al. 1993). Managers in Eglin have observed that sand pine can

quickly invade neighboring sites. If these sites are not burned within 5 years of sand pine

establishment, the sand pine reach sexual maturity and begin producing cones (Parker and

Hamrick 1996). However, if these sand pines are burned within twelve years of

establishment they can be killed, or at least have their growth slowed.

I, ( ,f Pine and Hardwoods
Forest in the longleaf-hardwood (LLHW) state requires three fires within five years

to be restored to a longleaf pine savanna state (LL). One or two fires, within a 3-5 year

interval, are not sufficient to eliminate hardwood trees from longleaf stands (Robbins and

Myers 1992). More fires are required, but how many and at what frequency was uncertain

and debated. For this model, three fires within less than 5 years of one another was settled

upon as a reasonable threshold.

Ii rfPine and Sand Pine
The mixed longleaf-sand pine (LLSP) state is composed of sexually mature, but

young sand pine trees. If this site is burned before the sand pine reach sufficient size the

sand pine will be killed and the site will return to being a longleaf forest (LL). However, if

the sand pine remain unburned for ten years, they will reach sufficient size to be more

difficult to kill with fire, and the site converts to the sand pine-longleaf (SPLL) state

(Provencher et al. 1998).

Hardwoods and I, .'l fPine
The hardwood-longleaf (HWLL) state can only be burned with difficulty. The

model assumes that some hardwoods can be killed with repeated fires. This assumption is

supported by field experience at Eglin Air Force Base (Provencher et al. 1998), however

the periodicity of fires that is necessary is not at all certain. Given this uncertainty, I

decided to require three fires within less than 5 years of one another to convert the site to a

mixed longleaf-hardwood state (LLHW). This transition rule is extrapolated from the

transition rule from LLHW to LL states. It is assume that since sizeable hardwoods are

being killed in the LLHW to LL transition, a similar fire frequency is required to kill some

of the hardwood in a HWLL state. This transition is reasonable, and consistent with field

experience at Eglin Air Force Base, but unsupported by any experimental studies.

However, such studies are on-going at Eglin Air Force Base.

Within an unburned hardwood-longleaf (HWLL) site, longleaf pine cannot

successfully regenerate. The longleaf pine in these sites will eventually die, shifting the site

to the hardwood (HW) state. Longleaf pine can live over four centuries, but most trees do

not (Platt et al. 1988). Over time, older trees are killed by lightning and other events. The

model uses the estimates that 250 years without fire are required to eliminate longleaf from

a hardwood-longleaf site.

Forest in the hardwood-longleaf state can be invaded by sand pine. If sand pine

seeds are available, and the site remains unburned for more than thirty years, these sand

pine accumulate and transform the forest to a hardwood-sand pine (HWSP) state. This rate

is based on observed changes in the Eglin landscape over the past fifty years (Provencher et

al. 1998).

Sand Pine and In ..'lfPine
Forest in the sand pine longleaf (SPLL) state is difficult to bum. Fire is assumed

to be able to kill sand pine and convert a SPLL site to LLSP, but little is known about the

specifics of such a process (Provencher et al. 1998). I assumed that sand pine can be killed

by the same fire frequency that the model requires to reduce hardwood densities, which is

at least three fires with a frequency of less than five years. It is possible that even more

frequent fires, over a longer time are needed. Without fire, longleaf pine will persist along

with the sand pine, but will not regenerate. Without longleaf regeneration a site will

convert to pure sand pine. Longleaf mortality is assumed to be have completed the

transformation from a SPLL to a SP state after 250 years without fire.

Forest dominated by hardwoods (HW) can be invaded by longleaf or sand pine, but

only during specific short periods. Immediately after a fire, longleaf or sand pine can

invade a site if their seeds are present. Longleaf pine is assumed to be only able to invade

if the longleaf pine is producing a mast crop of seeds that year (Platt et al. 1988).

Hardwood Sand Pine
Hardwood-sand pine sites are stable, but without fire sand pine will not be able to

survive in the understory, and as with the hardwood-longleaf sites, after 250 years without

fire a hardwood sand pine site will return to a pure hardwood stand. This transition is

unimportant to the model, because it will almost never have the time to occur during a

model run.

Sand Pine
Sand pine sites can be invaded by longleaf pine immediately after a fire, if longleaf

seeds are available. However unless fires continue to frequently occur, the site will remain

dominated by sand pine with a few longleaf

Fire and vegetation

The transitions rules described above are driven by the frequency of fire. Fire

frequency is itself influence by the time since last fire and the vegetation that is being

burned. This section describes how fire dynamics were represented in the model.

Ground fires in the forest of north Florida consume fine fuels, and kill the above

ground portion of understory and midstory vegetation, but they usually do not burn the

canopy trees of the forest. The top-killed understory vegetation is usually able to

regenerate very quickly. Within a year, or less in the case of spring fires, understory

vegetation is able regrow to such an extent that the accumulated fuel can once again

propagate fire. After a site has gone several years without fire, its combustibility begins to

decreases (Platt et al. 1988, Robbins and Myers 1992).

There are significant differences in the combustibility of different forest states,

however almost all forest states are most combustible in the years immediately following a

fire. Sites containing a greater proportion of longleaf pine are more combustible than sites

that contain less longleaf (Rebertus et al. 1989, Robbins and Myers 1992). Sand pine and

oak dominated sites also differ in their combustibility. Assuming a constant amount of

longleaf, sites with a significant sand pine component are less combustible than sites that

have significant hardwood species present (Provencher et al. 1998).

Vegetation State Combustibility
To quantify the relative combustibility of different vegetation types at different times

since fire, Eglin's fire managers were asked for their assessment of the relative

combustibility of different types of vegetation. While there are eight vegetation types,

several of these states were thought to have similar functional relationships between the

time since fire and vegetation combustibility (Figure 4-4).

Two sets of vegetation states were assumed to have probabilities of combustion that

were independent of the time since last fire. Sites with a significant longleaf component,

LL, LLHW, and LLSP, were assumed to have a constant high probability of combustion:

Pcomb,LL = 0.25.
While hardwood sites were assumed to have a constant low probability of


Pcomb,HW= 0.035.
Two other sets of vegetation states were assumed to have probabilities of

combustion that decreased with the time since last fire. Hardwood-longleaf sites were

assumed to bum well if recently burned, but as time since fire increase their combustibility

decays. This decline in the probability of combustion of a site was represented as a

negative exponential function of time since fire, which eventually reaches the same low

probability of burning as a pure hardwood stand. Specifically, the equation used is:

Pcomb,HWLL(TSF) = P e(-r*TSF) Pcomb,HW
Where P0=0.25 and rHW =0.035.

SPLL, HWSP, and SP, are assumed to be easier to burn following a fire than

HWLL vegetation, but their combustibility decrease, at an increasing rate following after

several years. This relationship was represented by a negative logistic relationship.

Specifically, the equation used is:

Pcomb,sp(TSF) = (1 -1/ ( 1/PO+((K+PO)/(K*PO)) *e(-r*TSF)))/3
Where K=0.9, P0=0.25, and rsp=.125

Fire Season
These probabilities of fire combustion change at the time scale of years. There is a

substantial body of fire management literature on the seasonal effects on burning (Robbins

and Myers 1992). Seasonality of fire is not included in the Eglin longleaf model. There

are two reasons for this omission. The first is that there is a minimal effect of burning

season on longleaf pine (Glitzenstein et al. 1995). Secondly, the model is focussing upon

decadal-scale landscape dynamics rather than short-term consequences of a single fire.

This focus reduces the importance of seasonal variation in bum, since over the long-term

variation should approach a long-term average.

Integrated model

An integrated model of vegetation dynamics was be constructed (Figure 4-5). This

model represents the forest with two sets of variables. The first represents the vegetation

of the forest as one of the eight states (Figure 4-3). The second represents the fire history

of the forest by storing the time since last fire, and the frequency of previous fires. These

two variables describe the state of the forest. Every year, the vegetation transition rules are

applied to the forest (Table 4-4), and the forest vegetation may change state. This forest is

subjected to a fire regime that periodically attempts to ignite the forest. The success of

these ignition events is determined by the probability of combustion functions (Figure 4-4).

This non-spatial model was used to test the Out of Tune Forest, Transformed Forest, and

Invaded Forest caricatures by exploring the dynamics of vegetation under a broad set of fire


Succession and fire regime

Fire frequency determines what changes occur in the landscapes of north Florida.

Alternative visions of this relationship are embodied in the Out of Tune Forest and

Transformed Forest caricatures. The simulation model can be used to distinguish between

these caricatures. The key difference between these two caricatures is in the homogeneity

of a forest's response to fire. The Out of Tune Forest caricature proposes that specific

forest types correspond to specific fire frequencies, while the Transformed Forest

caricature proposes that fire suppression causes fundamental changes in the organization

and combustibility of a forest. These changes produce a hysteretic effect, because shifting

a forest from an unburned forest to a burned forest requires more fire than maintaining a

forest as a burned forest.

These caricatures were tested by experimentally assessing the impact of a wide

variety of fire frequencies on a variety of vegetation types. Each vegetation type was

burned at a range of fire frequencies, and its behavior observed over 100 years. Fire

frequency was controlled using the yearly probability of a site's ignition. The inverse of

the probability of ignition is a sites expected fire frequency. For example, the probabilities

0.01 and 1 correspond to average fire frequencies of 100 years and 1 year respectively.

Using a probability of ignition allows the time between fires at a site to vary, but still

correspond to an average fire frequency. These probabilities were varied from 0 to 1, in

0.01 increments. The state of a site after 100 years of burning was recorded. Five

hundred replicates of each vegetation type were exposed to each fire regime. From these

replicates the likelihood of a vegetation class of maintaining its existing vegetation, or

shifting to another vegetation type can be calculated.

These calculations were conducted for the vegetation types LL, LLHW, LLSP,

HWLL, SPLL, HW, and SP. The vegetation type HWSP was ignored, because it is not

present in the existing landscape, it occurs infrequently in the simulation model, and it does

not change in periods less than 250 years rendering its dynamics uninteresting over the

period of 100 years. Each of these sites was simulated with and without the dispersal of

longleaf or sand pine seeds into the site. These cases explore the impact of neighboring

sites on a site's dynamics. These results clearly show that for all fire frequencies a few

vegetation states are much more likely than others (Figures 4-6, 4-7, and 4-8).

These results reveal that some states are more likely than others across all fire

regimes, that the likelihood of vegetation states is strongly influenced by the presence of

external seed sources, and that the initial vegetation state influences model dynamics.

These results invalidate the Out of Tune Forest caricature, and support the Transformed

Forest caricature.

Likelihood of States

The model output shows that LL is the most likely state when fires are frequent, but

that any of HWLL, SPLL, SP or HW is likely to occur when fire is infrequent. Which of

these states occurs depends upon what a site's initial conditions are, and whether external

seed sources are present. If a site is initially in LL, LLHW, or HWLL and there is no

dispersal of sand pine the site is most likely to end up in either LL, if fires are frequent, or

HWLL, if fires are infrequent. There is a much lower likelihood of the site being in the

intermediate state of LLHW. Similarly, if the site is initially in LLSP or SPLL, or if there

is sand pine seed dispersal into LL, LLHW, or HWLL sites, then the site will likely end up

in LL, at high fire frequencies and SPLL, at low fire frequencies. Although, the situation

is more complex at intermediate fire frequencies on LLHW and HWLL sites that are being

invaded by sand pine.

Without seed dispersal HW and SP are completely stable states, regardless of the

fire frequency they will maintain themselves. However, their behavior becomes more

complicated when there is seed dispersal. Longleaf dispersal without sand pine is not

particularly important for any states except for SP and HW, because all the other states

already contain longleaf pine. However, in the case of HW it can be invaded by LL if a

longleaf seed source, but no sand pine seeds are present. However, longleaf is only

weakly able to invade HW, due to the low probability of a mast year coinciding with a burn

of a hardwood site. Sand pine can also be invaded by longleaf at high fire frequencies.

When both sand pine and longleaf seed sources are present, then longleaf is not able to

invade hardwood sites, being pre-empted by sand pine, but it still manages to invade sand

pine sites.

There are several ecological consequences of these transitions. Firstly, the

important transitions are from the initial states of LL, LLHW, LLSP, HWLL, and SPLL as

the other states (HW, SP, and HWSP) occur over minor areas of the landscape. In these

five states, three states are the most likely to occur. With frequent fires, LL dominates,

while infrequent fires cause HWLL or SPLL to dominate, with SPLL being dominant

when sand pine seeds are available.

Forest hysteresis

Despite the fact that the same small set of states dominates the model's ecological

dynamics there are quite different dynamics depending upon the initial state of the model.

That is, different states respond differently to the same fire frequency. These differences

are clearly shown in Figure 4-9 and Figure 4-10. These figures plot the fire frequencies in

which a state has a greater than 50% chance of maintaining the same proportion of longleaf

pine. For example, if a site containing LL without seed dispersal is burned at a fire

frequency less than one fire every four and a half years, it will not be maintained as LL.

Similarly, if a HWLL site is not exposed to fires more frequently than once every six years

it will remain HWLL. Figure 4-10, reveals how adding longleaf and sand pine seed

dispersal alters the sites ecological dynamics, for example increasing the frequency of fire

needed to maintain LL and decreasing the frequency of fire needed to maintain SP and

HWLL. These graphs show that a similar fire regime has quite different effects upon

different vegetation types, and that there is no fire frequency that will maintain all

vegetation types. These results support the Transformed Forest caricature of Eglin Air

Force Base's ecological dynamics.

Seed dispersal

Comparing these seed dispersal and non-seed dispersal cases (Figures 4-6 to 4-10)

reveals that the strong influence that the presence of seed sources has on the response of

vegetation to fire. These results indicate that applying a specific fire frequency across a

landscape will produce variable results even in the same vegetation type, based upon the

input of other site's seeds into that site. This finding supports the Invaded Forest

caricature, by illustrating that the location of other forest types has a strong impact on

ecological dynamics. This support suggests that the spatial dynamics across the landscape

influence temporal dynamics, and need to be incorporated into management planning. To

do this, a spatially explicit version of the Eglin succession model was constructed.

Succession and Fire Spatial Dynamics

Spatial processes fundamentally influence the modeled ecological dynamics of Eglin

Air Force Base. Understanding the interactions of these processes over time required the

construction of a spatially explicit model that allows the spatial heterogeneity of the

landscape to influence fire and seed dispersal.

As discussed above, there are three spatial caricatures of Eglin Air Force Base that

this model had to address the Edaphic Forest, Divided Forest, and Invaded Forest

caricatures. To explore these caricatures the model had to include topographic variability,

fragmentation and seed dispersal along with the central processes of fire and fire


Model Organization

A forest landscape is represented as a matrix of sites. Each of these sites undergoes

the same successional dynamics as represented in the temporal model, but fire and seed

dispersal are explicitly modeled. Additionally, considering the landscape requires the

addition of some new landscape types to the model. I will now address each of these

components of the spatial model in more detail.


The spatial model divides the landscape into a matrix of sites, each of which is 60 m

on edge (Figure 4-11). This resolution was chosen for two reasons. Firstly, it is a

reasonable scale to develop management alternatives, displaying heterogeneity that is

important to management while not providing overwhelming detail (Jackson Guard: Eglin

Air Force Base 1993). Secondly, tree size appears have most of its variation at scales less

than 28 m (Platt and Rathburn 1993), making 60m an ecologically reasonable scale to

represent a portion of forest.

The forest is modeled using a set of overlying matrices that interact with one

another (Figure 4-12). The model stores information of vegetation, and fire history as does

the succession model, but spatial explicitness also requires a map of seed availability and

topography. Seed availability is generated by seed dispersal from existing sites, a process

that will be explained below. The topography layer is simply used to provide a template of

ecologically active and ecologically inert sites. This layer allows the presence of roads or

developed areas to block the spread of seeds and fires.

New model states

Considering the entire landscape requires a consideration of states other than those

that occur in Florida's sandhill communities, because if other states are intermixed with the

sandhill vegetation they can alter that vegetation indirectly by influencing the spread of

seeds and fire.

Two topographically limited states were added to the model to represent wet forest

sites. The sites are both wet forest, but one contains sand pine (WETSP) and the other

does not (WET). These forests are wetter than usual sandhill habitat occurring in creek

beds. Sand pine is assumed to be able to invade wet forests in the same fashion that it

invades hardwoods. The ability of fire to remove sand pine from this forest type is

uncertain, but Eglin's land managers think that it lies somewhere between the difficulty of

removing sand pine from LLSP and SPLL sites. The rules used in the model are shown in

Table 4-2.

Table 4-2. Vegetation transition rules used for the wet forest sites in the .
--> To
years &
sand pine
WETSP Two fires
within last
ten years

Both WETSP and WET sites impede the spread of fire. WET sites are slightly

easier to bum than WETSP sites. WET sites are assumed to combust similarly to HWLL

sites, and WETSP sites are assumed to combust similarly to SPLL sites (Figure 4-4).

WET sites do not disperse any seeds, while WETSP sites produce sand pine seeds.

Controlled bums of hardwood-longleaf pine sites (HWLL) are difficult to manage,

because the forest is difficult to ignite. Conditions that allow burning can result in locally

intense fires that kill existing old longleaf (Robbins and Myers 1992). To explore the

effects of this process and help assess the degree of longleaf pine mortality due to particular

control burning strategies, a new state consisting of a relatively open stand of hardwoods

that includes a significant herb layer was added to the model. Without fire the hardwoods

on the site will grow larger and come to dominate the site, shifting into the HW state.

Longleaf seeds combined with frequent fires will allow longleaf to reestablish and convert

the site back to HWLL. At an intermediate fire frequency or in the absence of longleaf

seeds the site will remain HHW. These transition rules are shown in Table 4-3.

Table 4-3. Modifications of vegetation transition rules to include the herbaceous-hardwood
vegetation state in the Eglin Longleaf pine model. These additions were added to the
transition rules illustrated in Table 4-1

HWLL TSF<5 TSF> TSF>2 1% chance
years 30 50 of
for last years years transition
three & sand during a
fires pine prescribed
seeds burn
years & years &
longleaf sand
pine pine
seeds seeds
years for 5 years
last three
fires &

Herbaceous hardwood sites do not burn as well as longleaf sites, but they also are

not as difficult to burn as pure hardwood sites. The open structure of these stands allows a

combustible understory to develop. In the model, HHW sites are assumed to combust

similarly to HWLL sites (Figure 4-4).

The temporal vegetation model (Figure 4-3), is expanded by the addition of HHW,

and the wetland states, WET and WETSP. These states and the transitions among them

produce a spatial vegetation model (Figure 4-13).


The topographic layer added to the model was extremely simple. It simply defined

areas where vegetation dynamics could occur and areas that were outside the model. This

layer was used to define the boundaries of the simulation area, which in this case were the

boundaries of Eglin Air Force Base, and of the central watersheds of Eglin. It was also

used to define ecological boundaries within Eglin. Roads and developed areas block the

flow of fire and seeds across the landscape. The topographic layer maps these boundaries

among all the disparate layers of the model. The more subtle effects of regional topography

were implicitly included in the model through the wet forest states (WET, and WETSP) that

were discussed above.

Model Spatial Processes

The spatial dynamics of fire spread and seed dispersal are explicitly incorporated in

the Eglin Landscape Model. I will describe how each of these processes was represented

in turn.

Seed dispersal

Seed dispersal is fairly simply represented with the landscape. While sand pine,

longleaf pine and hardwoods all disperse seeds only the dispersal of longleaf and sand pine

are explicitly modeled. Hardwood is assumed to be always present within a site, due to

resprouting as well as prolific seed production. Both sand pine and longleaf pine disperse

their seeds over short distances. The resolution of the model is 60m, and since most cones

disperse under 60m 70% under 20m (Boyer and Peterson 1983, Burns 1978), the model

only includes seed dispersal to the four nearest cells neighboring a site (Figure 4-14).

Longleaf pine seed production varies from year to year. Infrequently, about once a

decade, longleaf pine produces a large number of cones (Platt et al. 1988). The model,

somewhat conservatively, assumes that these mast events occur once a decade. In other

years no longleaf seed dispersal occurs. Sand pine does experience such fluctuations in

seed production. Unlike southern sand pine, the cones of the Choctawhatchee variety of

sand pine that are native to northwest Florida, do not require fire to open (Provencher et al.

1998), and therefore sand pine seed dispersal occurs continuously.

Fire regimes

Wildfires and prescribed fire were both modeled in this landscape model, but not

necessarily at the same time. Wildfires produce their own spatial pattern through

interaction with vegetation, while land managers impose the pattern of prescribed bums.

Wildfires are ignited by lightning across the landscape, and then spread. The area a fire

bums is the product of chance and the existing landscape. Prescribed fire, or prescribed

bums, are different, because land managers chose an area of the landscape to bum, and

then attempt to burn it. However in prescribed bums fire may not spread into all parts of

the designated burn area, especially in areas that are difficult to ignite in large bums.

Wildfire was modeled as a process that propagates itself across a landscape. Every

year a number of sites are struck by lightning, and ignited. The lightning based fire

initiation rate is about one fire started/ 10 km2 (Henkel 1995, Platt et al. 1988), which is

equivalent to about 17 fire initiations in the central three watersheds of Eglin Air Force Base

every year. A burning site can spread fire to its neighbors. This process is probabilistic

and is dependent upon the vegetation type and time since last fire of the fire's neighboring

sites (Figure 4-15). Sites that fire spreads into can then ignite their neighbors. This

process continues until the existing burning sites fail to ignite any new sites. An example

of this process is provided in Figure 4-16. As fire spreads across the landscape it does not

burn homogeneously. Remenant patches of unbumt vegetation remain with a fire, and fire

spread is impeded by older, less combustible vegetation. The spread of fire can be halted

by roads, or diffciult to bum vegetation. An animation of wildfires in a simulation is

shown in Figure 4-17.

Prescribed Burns
Prescribed burns, rather than wildfires, dominate the Eglin landscape. Prescribed

bums occur when land mangers go out and burn an area of the base. Weather, ignition

pattern, the skill of the bum team and other factors affect the results of any prescribed fire,

however these variations are outside the focus of this model.

Prescribed burns can be ignited both from the air and the ground. Aerial ignitions,

in which fire is dropped from helicopters, allow larger areas to be burned. Depending

upon the spacing of ignition sources and the distribution of fule fire may bums these large

areas unevenly. Ground ignitions are started by using drip torches to establish a fire front

that is allowed to bum over a plot and into another fire. Ground ignitions can provide more

control over fire spread, and may bum more evenly than aerial ignition. However, it is

difficult to start and control large fires from the ground.

The simulated large aerial bums and smaller ground bums. The aerial

bums are set to cover 24,000 acres (9 km2), while the ground burns cover 6,000 acres (2

1/4 km2). The probability of a site burning depends upon its vegetation type and time

since last fire as it does in a wildfire. However, in bum blocks it is assumed that each site

has multiple chances to be ignited. Aerial bums are assumed to provide three chances and

ground bums are assumed to provide four chances. These rates correspond to a fire front,

and an encircling fire respectively. These probabilities produce bum blocks that bum with

completeness similar to bum blocks in actual prescribed bums. When burning a hardwood

invaded longleaf site a ground bum will actually ignite about 75% of the area, while an

aerial bum will ignite about 60% of the area. Choosing which blocks to bum in which year

requires a management strategy, and this strategy can be varied within the simulation.

Landscape Resilience

The ability of a state to persist despite disruption is termed resilience (Holling

1973). Ecological resilience is a measure of the amount of change or disruption that is

required to transform an ecosystem from being maintained by one set of mutually

reinforcing processes and structures to a different set of processes and structures.

The concept of resilience can be illustrated by modeling an ecological 'state' as the

position of a ball on a landscape (Figure 4-18). Gravity pulls the ball downwards towards

pits in the surface. These pits are stable states. The deeper a pit the more stable it is,

because increasingly strong disturbances are required to move an ecological state away

from the bottom of the pit. The steepness of the sides of a stability pit corresponds to the

strength of negative feedback processes maintaining an ecosystem near its stable state.

These are the forces that transform the ecosystem, back towards a stable state following a

disturbance. The width of a pit is the range of change that a state can experience before

transforming to another state. The width of the stability pit corresponds to the ecological

resilience of a state (Figure 4-18).

The concept of resilience can be adapted from the analysis of a system of

continuously varying variables to a system that experiences transitions among discrete

states. In the ball and landscape illustration above, the state of the system was represented

by a single variable. The use of multiple variables is possible, but the analysis and

visualization of the results becomes increasingly complicated. In the discrete case the use

of more than two states is similarly complicated, unless the states are arranged along an

axis and transitions do not jump across intervening states. By dividing the multiple

possible states in two states, the resilience of discrete states can be simply and clearly

assessed by analyzing the likelihood of transitions between states.

In the spatial Eglin model these states are defined on basis of their longleaf density

as 'poor' (P) and 'good' (G). Two states produce four likelihood ratios, because each state

can either remain in its state or switch to the other state. Because a state can either remain

in that state or shift to the other state, these likelihood can be written as:

PtoG + PtoP = 1.0 and

GtoG+ GtoP = 1.0.

The probability of a state remaining within that state (i.e., Ptop and GtoG)

corresponds to the resilience of that state, as this probability represents the chance that the

state will persist given a particular environmental regime. It is important to note that the

probabilities of changing from poor to good, and from good to poor are not necessarily


This model can be applied to data by gathering multiple observations of a system

and calculating the likelihood of the four types of transition. For example, the transition

likelihood given in a table can be converted into a resilience ball and landscape model by

assigning the likelihood of remaining in a state to the width of a state, and the likelihood of

leaving the state as its potential energy. The states are then connected by barrier to state

change that is equal in height to the probability of state change:

Prob. Of State Change = 1.0 the likelihood of remaining within the state.

An example table and diagram are shown in Figure 4-19.

This approach of assessing resilience is observationally based. It does not attempt

to analyze the underlying dynamics of a system. Rather it takes observed state-transitions

and applies the concept of resilience to them. If this process is inappropriate this method

will reveal that the states) have no resilience by producing a flat line. This observational

approach does not easily allow results that are collected from a system under one

environmental regime to be applied to the system under another environmental regime.

However, by focusing on transitions between defined states, this method does provide a

method of analyzing the integrated response of a system to a given environmental regime

that is based upon management criteria.

The state-transition resilience approach can be applied to landscapes. The spatial

distribution of the likelihood of state transitions can be assessed by classifying an initial

landscape into two states, and then repeatedly applying a environmental regime to that

landscape and recording the post-treatment landscape states.

This approached was applied to the Eglin Air Force Base landscape by classifying

Eglin's vegetation-based land management goals. At Eglin managers wish to expand the

area in longleaf pine savanna. I used this goal to classify states as either being 'poor' or

'good' longleaf sites. I classified LL, LLSP, LLHW, and WET as good longleaf sites. All

the other sites were considered poor LL. By running the simulation model many times, the

likelihood of a given site on the landscape changing states can be assessed and plotted on

the landscape. This measure not only allows one to assess the stability of a given

landscape formation, but also measure the type of change. This approach was used to

compare the response of the Eglin landscape to multiple fire management strategies.

Model Testing

The Eglin Landscape Model was tested in a number of fashions. Quantitative tests

of the model were impossible, due to a lack of comparable data. However, because this

purpose of this model is not to predict the future landscape dynamics of Eglin, but rather to

explore the consequences of different sets of assumptions, this lack of quantitative

comparison is not a serious failing. The model's dynamics were tested against its ability to

reproduce historical landscape dynamics, and its behavior was tested against the

expectations of Eglin's land managers. There were two main tests of the model's

credibility: its ability to reproduce and maintain the historical Eglin landscape, and its ability

to reproduce hardwood and sand pine invasion into longleaf sites. These tests were

conducted using the concept of landscape resilience, which I will briefly outline, before

discussing the tests themselves.

Historical test

The landscape of northwest Florida was in the recent past dominated by longleaf

pine savanna (Schwartz 1994). The Nature Conservancy used a variety of historical and

ecological sources to attempt to reconstruct the landscape of Eglin Air Force Base (Kindell

et al. 1997, Provencher et al. 1998). This reconstruction did not have sufficient detail for

the Eglin Landscape Model, so a number of further assumptions on the distribution of sand

pine and hardwoods dominance had to be made before a historical landscape could be

produced for the model. This historical landscape, which I extrapolated from The Nature

Conservancy's work, is shown in Figure 4-20.

Historical Proposition: If the model was accurately mimicking the natural processes

of north Florida, wildfire should be able to maintain this landscape in a shifting

steady state fairly close to this reconstructed historical condition.

This proposition was tested by running the model for one hundred years starting

from the initial conditions of the estimated historical landscape (Figure 4-20). This model

used the estimated historical fire initiation rate, and the vegetation transition rules described

above. Ten model runs were used to assess the resilience of the landscape to the historical

wildfire regime.

The landscape resilience of the historical landscape to wildfire is shown in Figure 4-

21. Most of the landscape area is maintained as either good longleaf or poor longleaf. The

areas of degradation, by sand pine spread, are relatively minimal and since the initial

distribution of sand pine was itself largely unknown, some variation is to be expected.

Therefore, the model successfully maintained the estimated historical landscape.

Fire suppression test

A second test of the model's functioning is its ability to recreate the invasion of

longleaf by hardwoods and sand pine following landscape fragmentation and fire

suppression. We used the following proposition:

Suppression Proposition: Fragmenting the landscape and suppressing fire for a

similar time in the simulation model should produce a situation qualitatively similar

to the existing landscape.

Fire suppression began about fifty years ago. Therefore, the current road network

fragmented the historical landscape, and fire was suppressed to one fifth of its original

level. The model was allowed to run for fifty years, at which time the simulated landscape

was compared to the Eglin Air Force Base's present landscape. An animation of vegetation

changes during 100 years of fire suppression is shown in the Figure 4-22.

The model landscape successfully reproduces the invasion of sand pine out of

creeks, and the limited amount of longleaf pine on the landscape. The vegetation of Eglin

Air Force Base has been fragmented by an undocumented, complex set of logging, tree

planting, and road cutting that makes quantitative comparison between the simulation and

the existing Eglin landscape unreasonable, because these processes are not included in the

model. To test the ability of the model to reproduce larger scale changes in the Eglin

landscape, the landscape dynamics predicted by the model were shown to and discussed

with Eglin's land managers. They felt that the landscape produced by this method

accurately captured the speed of hardwood and sand pine invasion of longleaf pine. While

this test is not a strong one, it is still a test, because the model could have failed to produce

reasonable dynamics. Passing this test does not validate the model, rather it improves the

model's credibility. However, the vegetation pattern produced after fifty years of fire

suppression was considered to be enough a reasonable representation of the current

landscape, that this simulated landscape could be used to represent the current landscape for

the comparison of alternative management scenarios. To distinguish this simulated

landscape from the actual landscape, I refer to it as the 'current' landscape (Figure 4-23).

Testing summary

This model was developed not to predict the future of Eglin Air Force Base, but

rather to serve as a tool to integrate and test existing ecological knowledge. Part of its

utility has been to identify important gaps in that understanding. The Eglin Landscape

Model embodies the consensus ecological understanding of Eglin's managers. By

observing the dynamics of the model and comparing these dynamics to their tacit feeling for

how the landscape functions, managers are presented with an opportunity for learning.

Conflict between expectations and model dynamics means that either the model needs to be

revised, or that the managers understanding of the landscape needs to be changed. In

either case, learning occurs.

The Eglin Landscape Model passed through a series of workshops, in which it was

used, discussed and revised. This process lead to a model in which the land managers had

some confidence, due to their feeling that the modeling accurately captured their

understanding of the forest, and it produced realistic forest dynamics. This confidence

allow the model to be used as a tool to investigate alternative land management strategies.

Spatial Dynamics Management Options

Ecosystem management has been ongoing since the early 1990's. Burning the

forest using prescribed fire has been one of the main components of this activity. This

burning is motivated by both ecological and safety concerns. Ecologically, prescribed

bums are conducted to attempt to increase the area of longleaf pine forest. Additionally,

they inhibit fuel accumulation reducing the risk of uncontrollable wildfire.

The main purpose of modeling the Eglin landscape was to explore and challenge the

ideas underlying management. In this sprit, the model was used to determine the potential

consequences of continuing the current of wildfire suppression without also conducting

prescribed bums. This test was conducted by applying the policy of fire suppression to the

'current' landscape (Figure 4-23). Under the policy of fire suppression the 'current'

landscape is quite unstable. The landscape continues to lose areas of longleaf pine, and

experience sand pine invasion.

Calculating the landscape resilience of fire suppression on the 'current' landscape

reveals that a continued policy of fire suppression would lead to the degradation of large

areas of the base, and the maintenance of longleaf in only a few small areas (Figure 4-24).

Given the management goals of expanding the area in longleaf, this response is not

acceptable. For this reason land managers have been applying prescribed fire to the

landscape and logging areas of sand pine and hardwood that they wish to restore.

Management Vegetation Classification

Alternate management polices were evaluated, and prescribed bums implemented,

based upon a simplification of vegetation into three classes. These classes divided the

vegetation types into three states. Because, an increase in the area of longleaf pine is a

major management goal at Eglin Air Force Base, these three classes rank vegetation as

either being 'best', 'good' or 'poor' based upon the amount of longleaf pine a vegetation

state contains (Figure 4-25).

Together the 'good' and the 'poor' class encompase all the land that can be

potentially occupied by longleaf pine. The 'best' class is a subset of the 'good' class. The

'best' sites are longleaf pine savanna (LL). All the sites in which longleaf pine is a

dominant component (LL, LLHW, and LLSP) are 'good' sites. All the other sites that

could include longleaf are considered 'poor sites' (HWLL, SPLL, HW, HWP, SP and

HHW). The vegetation types WET and WETSP are excluded from these divisions as they

do not contain and cannot change to contain longleaf pine.

Land managers would like to convert the 'poor' sites into 'good' sites, and improve

the 'good' sites into 'best' sites. However, in meeting these goals they also would like to

ensure that they at least maintain the existing area of 'good' sites.

Managing Prescribed Bums

Prescribed bums are difficult to organize. They require trained fire managers, and

prescriptions require specific weather conditions. Eglin managers estimate that ecological

and social conditions are appropriate for burning about 80 days a year. Optimistically, they

suggest that they could conduct two bums per day during about half of these burn days.

These estimates produce a high estimate of 120 prescribed bums a year.

As discussed above, prescribed burns can be ignited from either the ground or the

air. Managers estimate that prescribed bums would be evenly split between aerial and

ground ignition each year. These estimates provide an upper bound of 60 ground and 60

aerial fires per year. The landscape area being used in the simulation model corresponds to

a third of Eglin, which means that the maximum number of prescribed bums in the

modelled area is 20 aerial bums and 20 ground burns per year.

These estimates of the number of prescribed bums that could be applied a year are

optimistic and do not account for extreme weather, military missions or crises in

management such as budget problems or understaffing. Therefore it is useful to observe

what effect lower rates of prescribed burning would have on the Eglin landscape.

Additional, applying prescribed bums is expensive. If fewer fires could restore the

landscape effectively, spending money on over-burning the landscape would not be

sensible. To explore a range of prescribed bum levels, I ran the model with 20, 15, 10,

and 5 pairs of aerial and ground fires a year.

Burning Strategy

Using fire to manage a landscape requires a strategy for deciding when and where

to bum. Part of the purpose of developing a simulation model is help develop such

strategies. Using the model to evaluate land management strategies offers a simple way to

test ideas about management ideas that combine both social and ecological models and

to locate important uncertainties and unintended consequences.

Based upon discussions with land mangers I developed four sets of land

management strategies. As with most of this model, these strategies were not meant to be

realistic, but rather simple charicatures of different management approaches. Three of these

approaches use prescribed fire to manage the landscape. These approaches are named

'maintenance', 'conversion' and 'rotation'. The fourth approach uses wildfire, and is

named 'fire'.

The 'fire' management strategy is used as a base case, against which to compare the

other strategies. The 'fire' strategies do not use prescribed fire, therefore they cannot be

applied at the same frequencies as the prescribed fire strategies. Rather, wildfire is

conducted at two levels the historical rate of wildfire initiation and the current suppressed

rate of forest fires.

The simplest management strategy that uses prescribed fire is 'rotation.' This

strategy divides the landscape into bum blocks. Each block is burned with the same

frequency. This frequency is determined by the number of blocks burned per year divided

by the total number of burn blocks.

Both the 'maintenance' and 'conversion' strategies alter the fire frequency of a bum

block in response to how the block's vegetation compares to the remainder of the

landscape. Both these strategies vary the fire frequency of bum blocks depending upon the

vegetation within a block. These strategies counts the amount of 'best', 'good', and 'poor'

sites in a bum block and then use a formula to rank the blocks. The 'conversion' and

'maintenance' use different forumulas. Each year bum blocks are burned in the order their

ranking, until the available burns for the year are complete. Sites that have been burned

within the last three years are not returned. The 'converison' strategy aims to convert poor

sites to good sites, while also maintaining good sites. The maintenance strategy trys to

maintain the good sites.

The rule used for the 'conversion' strategy is:

Score = Good + Poor 2/3 + Best 3/5

The rule used for the 'maintenance' strategy is:

Score = Good Best/3

These alternative management strategies were used to probe the dynamics of the

managed Eglin landscape. The purpose of these probes is to test the caricatures discussed

at the beginning of this chapter. While these simulation exercises cannot be expected to

predict the details of specific management regime, they can identify which caricatures

appear to better represent the functioning of the sandhill communities of north Florida, and

where there are important gaps in ecological understanding.

Four different management strategies were tested: 'maintenance', 'conversion',

'rotation' and 'wildfire.' These strategies were each tested at several different fire

frequencies. Prescribed bums were tested at levels of 20, 15, 10, and 5 pairs of large and

small bums per year. While the 'wildfire' strategy was tested at 'historical' and

'suppressed' levels. In combination a total of fourteen different management regimes were

tested. The combinations of fire frequency and management strategies tested are shown in

Table 4-5.

Table 4-5. Alternative management regimes combining yearly bum amount and fire
management strategy.
Fire Types Fire Strategy
Aerial and Wildfire
Ground Initiations Rotation Maintenance Conversion Wildfire
20,20 X X X
15,15 X X X
10,10 X X X
5,5 X X X
S Historical X
Suppressed X

The success of these management alternatives was evaluated based upon the

changes they produced in the landscape over fifty years. The pattern of change produced

by each management strategy has a complex temporal dynamic (Figure 4-26). These

experiments, however, focus upon the spatial aspect of northwest Florida forest. To

simplify the analysis of changes in the spatial distribution of vegetation a fixed time period

of fifty years was used for analysis. The 'future' landscape at the end of these fifty years

was compared with the 'present' landscape, and changes calculated.

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