• TABLE OF CONTENTS
HIDE
 Title Page
 Table of Contents
 Abstract
 Methods
 Results and discussion
 Conclusions
 Acknowledgement
 Literature cited
 Tables
 Figures






Group Title: Florida Cooperative Fish and Wildlife Research Unit Technical Report no. 19
Title: Status survey and habitat evaluation of the Cape Sable seaside sparrow in East Everglades, Florida
CITATION PAGE IMAGE ZOOMABLE
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00073814/00001
 Material Information
Title: Status survey and habitat evaluation of the Cape Sable seaside sparrow in East Everglades, Florida
Series Title: Technical report
Physical Description: 19, 28 p. : ill., maps ; 28 cm.
Language: English
Creator: O'Meara, Timothy E
Marion, Wayne R
Publisher: Florida Cooperative Fish and Wildlife Research Unit, U.S. Fish and Wildlife Service, University of Florida
Place of Publication: Gainesville Fla
Publication Date: [1985]
 Subjects
Subject: Seaside sparrow -- Florida   ( lcsh )
Birds, Protection of -- Florida   ( lcsh )
Genre: non-fiction   ( marcgt )
 Notes
Bibliography: Includes bibliographical references (p. 18-19).
Statement of Responsibility: by Timothy E. O'Meara and Wayne R. Marion.
General Note: Cover title.
General Note: "Final report submitted to U.S. Army Corps of Engineers, Jacksonville, Florida 32232 and Endangered Species Field Office, U.S. Fish and Wildlife Service, Jacksonville, Florida 32250 from Florida Cooperative Fish and Wildlife Research Unit, U.S. Fish and Wildlife Service, University of Florida, Gainesville, Florida 32611."
General Note: "Research Work Order No. 28."
General Note: "October, 1986."
Funding: This collection includes items related to Florida’s environments, ecosystems, and species. It includes the subcollections of Florida Cooperative Fish and Wildlife Research Unit project documents, the Sea Grant technical series, the Florida Geological Survey series, the Coastal Engineering Department series, the Howard T. Odum Center for Wetland technical reports, and other entities devoted to the study and preservation of Florida's natural resources.
 Record Information
Bibliographic ID: UF00073814
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved, Board of Trustees of the University of Florida
Resource Identifier: aleph - 002190361
oclc - 35865909
notis - ALD0143

Table of Contents
    Title Page
        Title page
    Table of Contents
        Page 1
    Abstract
        Page 2 (MULTIPLE)
        Page 3
    Methods
        Page 4
        Data collection
            Page 4
            Page 5
        Data analysis
            Page 6
            Page 7
    Results and discussion
        Page 8
        Sparrow distribution
            Page 8
            Page 9
        Habitat analysis
            Page 10
            Page 11
        Habitat suitability
            Page 12
            Page 13
    Conclusions
        Page 14
        Page 15
        Page 16
    Acknowledgement
        Page 17
    Literature cited
        Page 18
        Page 19
    Tables
        Page 20
        Page 21
        Page 22
        Page 23
    Figures
        Page 24
        Page 25
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
        Page 31
        Page 32
        Page 33
        Page 34
        Page 35
        Page 36
        Page 37
        Page 38
        Page 39
        Page 40
        Page 41
        Page 42
        Page 43
        Page 44
        Page 45
        Page 46
        Page 47
Full Text
















TECHNICAL REPORT NO. 19


Status Survey and Habitat Evaluation
of the
Cape Sable Seaside Sparrow
in
East Everglades, Florida




by
Timothy E. O'Meara
and
Wayne R. Marion (Principal Investigator)
Department of Wildlife and Range Sciences
University of Florida


Final Report
Submitted to:
U. S. Army Corps of Engineers
Jacksonville, Florida 32232

and

Endangered Species Field Office
U. S. Fish and Wildlife Service
Jacksonville, Florida 32250

From

Florida Cooperative Fish and Wildlife Research Unit
U. S. Fish and Wildlife Service
University of Florida
Gainesville, Florida 32611


Research Work Order No. 28
October 1985










CONTENTS



ABSTRACT...................................................... .... 2

INTRODUCTION.......................................... ...... 2

METHODS...................................... ....... .......... .. ... 4

Data Collection................................................ 4

Data Analysis................................................. 6

RESULTS AND DISCUSSION.......................................... 8

Sparrow Distribution ......... ... .................. .... 8

Habitat Analysis... ........................................... 10

Habitat Suitability........................................... 12

CONCLUSIONS.................................................... 14

ACKNOWLEDGMENTS ..................................... ..............17












Abstract: Cape Sable Seaside Sparrows were detected at 26% of 131

points sampled in East Everglades. Sparrows were found in greater

numbers, and in many cases at different points, than in a 1981 survey.

Changes in distribution could possibly be due to successional changes in

vegetation in the intervening years. Vegetation measurements describing

cover, shrub density, and distance to trees or tree islands appeared

most useful for predicting occurrence of sparrows at a point.

Vegetation variables describing the amount of clumping in grass and

grass-like vegetation appeared most useful for predicting number of

sparrows at a point. A habitat suitability index model previously

derived was significantly correlated with an index based on "expert

opinion," but apparently could be improved to better predict sparrow

occurrence. Data resulting from this survey should be useful for

refining the habitat-suitability index model to more accurately predict

occurrence of Cape Sable Seaside Sparrows.






The Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) is

federally listed as an endangered species due to its restricted

distribution and specific habitat requirements. The species was

discovered in 1918 and, as a result of the inaccessibility of its

habitat, the extent of its range was largely unknown for several

decades. Stimson (1956) described its former and current distribution

based on records and sightings. Werner (1971) reported the species'

rediscovery on Cape Sable and surveyed known populations (Werner 1975).









More recently, historical records of the species' distribution were

summarized and its range during the period 1978-1981 was systematically

surveyed (Bass and Kushlan 1982, Kushlan and Bass 1983).

Vegetation changes and fire have been identified as primary factors

influencing the distribution of the Cape Sable Seaside Sparrow (Kushlan

et al. 1982). 'In general, the species occurs in "vast brushless

graminoid seasonally flooded interior prairies" (Werner 1975:206). In a

recent census of the sparrow, muhly (Muhlenbergia filipes) prairie and

mixed prairie accounted for 96% of the habitat occupied (Bass and

Kushlan 1982, Kushlan and Bass 1983). Fire affects quality of these

habitats as a result of changes in cover and biomass of living and dead

vegetation (Taylor 1983). Schroeder and Armbruster (1985) derived a

habitat model for evaluating habitat quality based on vegetation

characteristics.

Continued developmental pressures and habitat changes necessitate

up-to-date information on the status of the Cape Sable Seaside Sparrow

for evaluation of developmental impacts and water management practices,

for assessment of critical habitat boundaries, and for recovery

planning. This report describes a survey to identify distribution of

the species in East Everglades. Data describing vegetative

characteristics also were collected to enhance our ability to predict.

habitat suitability.


Objectives:


1. To determine the current distribution of Cape Sable Seaside

Sparrows in East Everglades.









2. To describe selected vegetative characteristics at points

surveyed for Cape Sable Seaside Sparrows.

3. To identify vegetative characteristics correlated with

distribution of Cape Sable Seaside Sparrows.

4. To evaluate the utility of the habitat model developed by

Schroeder and Armbruster (1985) for predicting occurrence of

Cape Sable Seaside Sparrows.


METHODS

Data Collection

Surveys were conducted in known and suspected sparrow habitat in

East Everglades. The survey included treeless freshwater marshes and

prairies; urban and agricultural areas were excluded. Areas sampled

during the 1981 survey were inventoried as well as additional areas of

potential habitat within and around critical habitat boundaries (Figs. 1

and 2). The survey area was gridded into blocks 1 km on a side and

plotted on U. S. Geological Survey 7.5 minute quadrangle maps. Sparrows

were inventoried at locations indicated by intersection of the grid

lines between 3 April and 6 May, 1985.

Each sample consisted of landing at a point by helicopter and

listening for singing Cape Sable Seaside Sparrows for 12 minutes.

Additional sparrows detected when points were revisited for vegetation

sampling were recorded. Also, 31 points where sparrows had not been

detected on the first 2 visits were each revisited for 5 minutes to

increase the completeness of the distribution survey. Numbers of birds

seen or heard were recorded and distances to the birds identified by

sight were estimated at each point. Bird surveys were continued for 3.0









2. To describe selected vegetative characteristics at points

surveyed for Cape Sable Seaside Sparrows.

3. To identify vegetative characteristics correlated with

distribution of Cape Sable Seaside Sparrows.

4. To evaluate the utility of the habitat model developed by

Schroeder and Armbruster (1985) for predicting occurrence of

Cape Sable Seaside Sparrows.


METHODS

Data Collection

Surveys were conducted in known and suspected sparrow habitat in

East Everglades. The survey included treeless freshwater marshes and

prairies; urban and agricultural areas were excluded. Areas sampled

during the 1981 survey were inventoried as well as additional areas of

potential habitat within and around critical habitat boundaries (Figs. 1

and 2). The survey area was gridded into blocks 1 km on a side and

plotted on U. S. Geological Survey 7.5 minute quadrangle maps. Sparrows

were inventoried at locations indicated by intersection of the grid

lines between 3 April and 6 May, 1985.

Each sample consisted of landing at a point by helicopter and

listening for singing Cape Sable Seaside Sparrows for 12 minutes.

Additional sparrows detected when points were revisited for vegetation

sampling were recorded. Also, 31 points where sparrows had not been

detected on the first 2 visits were each revisited for 5 minutes to

increase the completeness of the distribution survey. Numbers of birds

seen or heard were recorded and distances to the birds identified by

sight were estimated at each point. Bird surveys were continued for 3.0






5


hours following sunrise or until increasing.wind velocity inhibited

singing by territorial males.

Vegetation sampling was conducted to describe parameters used in

the habitat model derived by Schroeder and Armbruster (1985). All

points, with the exception of 11, were revisited in late morning and

early afternoon for vegetation sampling. The 11 points that were not

revisited occurred in agricultural areas (4), on roads (1), and in areas

where tree and shrub densities precluded use by Cape Sable Seaside

Sparrows (6).

Herbaceous cover, soil depth, and woody plant densities were

described in each vegetation sample. Distance to the nearest tree

island in each quadrant was estimated to assess openness of the habitat.

Trees were defined as woody plants > 4 m tall. To assess herbaceous

cover and soil depth, a 1-m2 frame was placed at 24 sample points evenly

spaced at 8-m intervals around a square 48 m on a side and centered at

the survey point. At each placement of the frame, 5 parameters were

measured: 1) The most abundant 3 or 4 plant species were recorded in

order of abundance based on percent cover. 2) Percent cover of

herbaceous vegetation, living and dead, was estimated. 3) Number of

clumps of vegetation were counted where a clump was defined as a

bunch-grass-type growth form with a basal diameter.> 5 cm. 4) Percent

of total herbaceous vegetation cover in clumped growth form was

estimated. 5) Soil depth was measured by inserting a sharpened steel

rod (120 cm long, 5 mm in diameter, and calibrated in cm) into the soil

to bedrock at each of the 4 corners of the frame. Soil depths > 110 cm

were recorded as 120 cm.









Shrub densities were estimated by counting shrubs within the 48-m x

48-m squares. Additionally, the helicopter was flown to an elevation of

120 m (400 ft) over each survey point and vertical photos were taken in

an attempt to provide supplementary data on shrub densities. Two film

types were used, color IR and Kodachrome 64.

Subsequent to completion of all sampling, 49 points were revisited

with 0. L. Bass, Jr. (South Florida Research Center, Everglades National

Park) to get an independent assessment of habitat suitability for Cape

Sable Seaside Sparrows. Points were selected that represented a variety

of vegetation conditions over the survey areas. At each point, Bass'

recorded his estimate of relative habitat suitability on a scale of 1 to

10.


Data Analysis

Fifteen variables were derived from the soil and vegetation data

(Table 1) and their values were determined for each point for comparison

with sparrow occurrence. COVER was derived to quantify the amount of

herbaceous vegetation at each point. CLUMPNO, CLUMPCOV, and CLFREQ were

calculated as alternative indicators of the amount of vegetation in

bunch-grass-type growth form. COVERCV and COVFREQ were used as

alternative methods of assessing the "patchiness" of herbaceous

vegetative cover or the "evenness" with which cover was distributed.

Similarly, CLCOVCV and CLNOCV were used as indicators of the

distribution of clumped vegetation. SHRUBDEN was. determined from the

number of shrubs within the 48-m x 48-m square. Resolution of the

aerial photos proved inadequate for estimating shrub densities.









Three alternative indices of tree or tree-island density were used.

TREEMEAN and TREEMIN were measures of distance from the sampling point

to nearest tree or tree island while TREEDENS served as an index to the

area around .each point without trees or tree islands (i.e. density).

SOIL was recorded as a possible indicator of vegetative characteristics.

HSI was calculated from COVER, COVFREQ, CLFREQ, SHRUBDEN, and TREEMIN as

described by Schroeder and Armbruster (1985). SHSI was an index of

habitat suitability as determined by 0. L. Bass, Jr.

Correlation and regression were used to identify relationships

between the 15 variables and the number of sparrows detected at each

point. Two correlation analyses were done: one using all sample points

and one using only sample points where birds were detected. Similarly,

stepwise multiple regression was used to identify the variables most

useful for predicting the number of birds at a sample point using (1)

all sample points and (2) sample points where birds were detected.

Significance levels were set at P 0.15 for entering and retaining

variables in both regression models. Models were constructed using only

9 of the 15 habitat variables. CLUMPNO and CLUMPCOV were omitted from

this analysis because they were highly correlated with CLFREQ. When no

clumps were detected at a sample point, CLNOCY and CLCOVCV could not be

calculated. As a result, CLNOCV and CLCOVCV were omitted from the

regression analyses to maximize sample sizes. HSI and SHSI were not

included in these analyses because they did not represent vegetative

characteristics that could be used to classify sites.

T-tests and discriminant function analysis were used to identify

variables that may be useful for distinguishing between sites with and

without sparrows. Non-paired t-tests were used to compare mean values









for each of the variables between the 2 site classifications. Stepwise

discriminant function analysis was done to produce a discrimination

model that uses a subset of the variables to classify points. The same

9 variables used in stepwise multiple regression were employed in this

analysis. Significance levels for entering and retaining variables in

the model were set at P < 0.15.

Correlation and stepwise regression identify linear trends in data.

The variables measured, however, may not vary in a linear fashion with

habitat suitability for Cape Sable Seaside Sparrows (see Schroeder and

Armbruster 1985). To better examine the relationships between habitat

suitability and the variables described, sample points were grouped

based on values of each variable. The percentage of sample points in

each group at which birds were found was determined. In other words,

the range of data values for each variable was divided into 3 to 13

groupings. Sample points were then categorized into these groupings

based on the value of the respective variable. The percentage of sample

points in each grouping at which birds were detected was then plotted as

a histogram for each variable (e.g. Fig. 5).

Data-were analyzed on an Amdahl 470 V/6-11 computer at the

Northeast Regional Data Center, the University of Florida. The

Statistical Analysis System (Freund and Littell 1981, Ray 1982) was

utilized for all analyses.


RESULTS AND DISCUSSION

Sparrow Distribution

Cape Sable Seaside Sparrows were detected at 34 of 131 (26%) points

sampled (Figs. 1 and 2). Between 1 and 7 birds were detected at each of









for each of the variables between the 2 site classifications. Stepwise

discriminant function analysis was done to produce a discrimination

model that uses a subset of the variables to classify points. The same

9 variables used in stepwise multiple regression were employed in this

analysis. Significance levels for entering and retaining variables in

the model were set at P < 0.15.

Correlation and stepwise regression identify linear trends in data.

The variables measured, however, may not vary in a linear fashion with

habitat suitability for Cape Sable Seaside Sparrows (see Schroeder and

Armbruster 1985). To better examine the relationships between habitat

suitability and the variables described, sample points were grouped

based on values of each variable. The percentage of sample points in

each group at which birds were found was determined. In other words,

the range of data values for each variable was divided into 3 to 13

groupings. Sample points were then categorized into these groupings

based on the value of the respective variable. The percentage of sample

points in each grouping at which birds were detected was then plotted as

a histogram for each variable (e.g. Fig. 5).

Data-were analyzed on an Amdahl 470 V/6-11 computer at the

Northeast Regional Data Center, the University of Florida. The

Statistical Analysis System (Freund and Littell 1981, Ray 1982) was

utilized for all analyses.


RESULTS AND DISCUSSION

Sparrow Distribution

Cape Sable Seaside Sparrows were detected at 34 of 131 (26%) points

sampled (Figs. 1 and 2). Between 1 and 7 birds were detected at each of










these 34 points for a total of 68 sparrows. We found 19 birds at 9 of

59 points included on the Grossman Hammock Quadrangle Map, and 49 birds

at 25 of 72 points included on the Royal Palm Ranger Station SE

Quadrangle Map.

Bass and Kushlan (1982) reported sparrows occurring at 23 of 106

(22%) of the points we sampled (Figs. 3 and 4). If we consider only

sparrows detected during our 12 minute sampling period for comparison

with their data, the number of points at which sparrows were detected

are comparable between the 2 surveys, but number of birds detected was

greater during the 1985 survey. Bass and Kushlan (1982) found 7

sparrows at 5 of 38 points on the Grossman Hammock Quadrangle Map,

compared to 13 sparrows at 5 of the same 38 points in our survey. They

reported 25 sparrows at 18 of 68 points on the Royal Palm Ranger Station

SE Quadrangle Map, compared to 41 sparrows at 20 of the same 68 points

in our survey. Greater numbers of sparrows detected during the 1985

survey were not necessarily a result of greater densities compared to

1981. The 12 minutes we spent at each point may have averaged .greater

than the 10-15 minutes spent at each point during the 1981 survey (0. L.

Bass, pers. commun.).

Although sparrows were found at approximately the same number of

points during the 2 surveys, distributions differed. On the Grossman

Hammock Quadrangle Map, only 1 of the points at which we found sparrows

was coincident with a point where Bass and Kushlan found sparrows.

Other points with sparrows were up to 3 km from the nearest point where

sparrows were found in the 1981 survey. Sparrow distributions on the

area covered by the Royal Palm Ranger Station SE Quadrangle Map showed

considerable overlap between the 2 surveys, but sparrows were still









found at points up to 3 km from the nearest location at which sparrows

were found in the 1981 survey. Sparrows also were more widespread on

this area during the current survey than during the 1981 survey.


Habitat Analysis

Insufficient data were obtained on sparrow detection distances to

quantify detectability and to estimate densities. Detectability,

however, should not have differed appreciably among points and relative

comparisons of numbers of sparrows detected with vegetation variables

therefore could be made.

Of the 15 variables tested, only SHSI was correlated (P < 0.05)

with numbers of sparrows per point over all points (Table 2). Two other

variables, TREEMIN and CLUMPNO, had P values < 0.1. When the 15

variables were correlated with numbers of sparrows only at points where

sparrows occurred, 3 correlations with P < 0.5 were identified (Table

3). CLUMPNO, CLUMPCOV, and CLFREQ were positively correlated with

sparrow numbers across these points, indicating that amount of clumping

in the vegetation influenced habitat quality.

Stepwise regression resulted in only 1 variable being included in

each of the 2 models derived with significance levels set at P 0.15.

Coefficients of determination were low in both cases. The regression

for bird numbers at all points resulted in the model:

y=0.0026 (TREEMIN) + 0.37, (r2=0.02, P=0.143).

The regression for bird numbers only at points where birds were detected

resulted in the model:

y=0.059 (CLFREQ) + 1.34, (r2=0.16, P0.022).









Both the correlation and multiple regression analyses indicated

that some measure of vegetation clumping is a predictor of habitat

quality where sparrows occur. TREEMIN was the variable most useful for

predicting sparrow numbers, but only when all points were included.

TREEMIN may be more useful for separating sites with suitable habitat

from unsuitable sites than for predicting habitat quality. The

relatively high correlation of SHSI with bird numbers at all points

suggests that "expert opinion" can identify suitable sparrow habitat.

Values from the HSI model, however, were not significantly correlated

with sparrow numbers.

Results of the t-tests identified 5 vegetation variables that were

at least marginally significant (P < 0.1) and may be useful for

separating points with and points without sparrows (Table 4). Values

for COVFREQ, SHRUBDEN, TREEMEAN, TREEMIN, and TREEDENS differed between

the 2 point classifications. No variables describing vegetative

clumping differed between the 2 sets of points. Again, SHSI exhibited

the greatest difference between the point classifications, while HSI

values did not differ between the 2 point classifications.

Classification criteria in the discriminant function analysis were

based on the pooled covariance matrix, despite the fact that a

likelihood-ratio test indicated non-homogeneity of the within-group

covariance matrices. Although homogeneity of covariance matrices is an

underlying assumption for discriminant function analysis, violation of

this assumption is typical for ecological data and does not necessarily

negate the derivation of biologically meaningful results (Green 1971).

Stepwise discriminant analysis resulted in a significant 3 variable

model for distinguishing points with and without sparrows:





12


y=0.032 (COVFREQ) 0.025 (CLFREQ) 0.008 (TREEMIN) 2.988,

[F (3,106) = 3.55, P = 0.107]
Points with a solution to this equation < 0 were classified as "sparrows

present," points with y > 0 were classified as "sparrows absent."

The discriminant model did not serve as a very good predictor of

point classification. Forty of 124 (32%) points were misclassified as a

result of this model. Thirty points where birds were absent were

classified as "present"; 10 points where birds were present were

classified as "absent." Most (75%) of the misclassifications predicted

sparrows to occur where they did not. The poor performance of this

model may have been the result of the low utility of the variables

measured for predicting sparrow occurrence. Alternatively, birds may

have been absent from, or present but not detected at, some points with

suitable habitat.


Habitat Suitability

If the purpose of a habitat suitability model is to predict habitat

quality for a species, then some objective measure of habitat

suitability should be defined for evaluating effectiveness of the model.

The ultimate measure of habitat suitability is the ability of the

habitat to support a population that will contribute to the future gene

pool of the species (Fretwell 1972). Assessment of the productivity of

a species in a habitat often requires difficult and time consuming

measurements; most often, species density is used as an alternative

indicator of habitat suitability. If the utility of a habitat

suitability index is to predict occurrence of a species in a habitat,

then frequency of occurrence of the species in habitats with given





13


characteristics may be a suitable measure for evaluation. In this

context, a habitat suitability index with a scale of 0-1 should

represent the probability of finding the species at a site with given

characteristics, and the regression of percent occurrence of the species

on the index should have an intercept of 0 and slope of 1. Bass's

subjective index (SHSI) proved to be a good predictor of sparrow

occurrence (Fig. 6) and approached our criteria for optimal performance

of an index for predicting sparrow occurrence. Regression of percent

frequency of sparrow occurrence on SHSI resulted in the equation:


y-0.9 (SHSI) 1.9, (r=0.87).


These results suggest that the goal of the workshop to derive a habitat

suitability index model that simulated "expert opinion" of habitat

suitability was appropriate. HSI was significantly correlated with SHSI

(P < 0.01), but the low correlation coefficient (r=0.46) suggests that

the model could be improved to better mimic "expert opinion." Only 1

point had an HSI value > 0.29, while SHSI values ranged from 0.2-0.8.

Apparently, some of the suitability index curves that comprise the

habitat suitability index models need to be modified or shifted to the

left to make the resulting indices better predictors of species

occurrence.

Histograms of sparrow frequency of occurrence versus habitat

variable values were plotted (Figs. 7 to 19) to facilitate evaluation of

these models. Caution must be exercised in interpreting these charts;

sample sizes from which percentages were calculated should be

considered. In most cases, sample size declined toward one or both ends








of the x-axis, resulting in lower reliability on the extremes of the

histograms. These charts should, however, be useful for refining

habitat suitability indices.

For example, the histogram for COVER (Fig. 7) followed the pattern

of the suitability index curve for SIV1 derived by Schroeder and

Armbruster (1985). If the suitability index SIV1 of Schroeder and

Armbruster was interpreted to represent habitat suitability based on

frequency of occurrence, then maximum frequency of occurrence would be

expected to occur at between 50 and 70% cover based upon their model.

The maximum percent occurrence of sparrows, however, occurred in the

30-39% COVER class, suggesting that the curve for SIV1 may function

better if shifted to the left. Alternative interpretations are that our

subjective appraisal of percent cover was biased relative to that of

Werner (1975), which was used for deriving the models, or that percent

cover requirements may differ.between the mixed-prairies we sampled and

the muhly prairies that Werner sampled.

Although the histogram for COVER was easily interpretable, COVER

was not indicated by the statistical tests we conducted as a variable

useful for predicting sparrow occurrence or sparrow numbers, Our

analyses would be useful for identifying linear relationships, but not

curvilinear or non-linear relationships. CLUMPNO (Fig. 10), CLFREQ

(Fig. 14) and SOIL (Fig. 19) may be additional variables that are useful

for predicting sparrow occurrence, but in a non-linear fashion.


CONCLUSIONS

Results of the survey suggest that Cape Sable Seaside Sparrow

numbers have not decreased, and may have increased in the study area









since 1981. That sparrow distributions have changed on the study areas

over the 4 intervening years is not surprising since habitat suitability

depends on burning history and habitat quality can decline as early as 4

years after the most recent burn (Taylor 1983).

Since the primary objective of the study was to document current

distribution of Cape Sable Seaside Sparrows in East Everglades,

vegetation and soil sampling were conducted within the constraints of

meeting this objective. The number of points to be censused for

sparrows precluded extensive vegetation sampling. As a result, a number

of factors may have contributed to lack of correlation between

vegetation measurements and sparrow detections. 1) Habitat sampling was

restricted to a 48-n4 x 48-m square around the bird sampling point while

a number of sparrows were detected outside this square (Fig. 20).

Vegetation sampled at a point, therefore, was not necessarily

characteristic of vegetation inhabited by all nearby sparrows. 2) The

minimal number of visits to each point to census birds may not have

always resulted in sparrow counts that were related to habitat quality.

Counts may have differed as a result of weather, time of day, and period

of the season, as well as habitat quality. 3) Ahumber of sites where

sparrows were not detected during the first 2 visits were revisited for

additional sampling, resulting in an uneven sampling procedure that may

have biased the analyses. These revisits, however, should have

augmented the completeness of the survey and should have improved our

correlations between vegetation characteristics and habitat use. 4) A

one-year survey of birds may not have been of long enough duration to

accurately reflect long-term habitat characteristics to which sparrows

were responding.









Despite these sources of variation, some of the variables selected

for sampling did prove useful for predicting sparrow counts or

distinguishing points with and without sparrows. Variables describing

clumping attributes of the grass and grass-like vegetation proved most

useful for predicting sparrow counts. TREEMIN, COVFREQ, and CLFREQ were

most useful for classifying points. TREEMIN in this analysis was not

the same as the suitability index for distance to trees or tree islands

(SIV5) in Schroeder and Armbruster (1985). TREEMIN, in the correlation

analyses used here, served as an index of tree or tree island density,

whereas SIV5 represented the minimum distance from a tree or tree island

at which Cape Sable Seaside Sparrows would be expected to occur.

Although a sampling point may have been less than 50 m from a tree

island, for example, sparrows detected from that point may have been

more than 50 m from the tree island.

The lack of correlation between HSI values and sparrow numbers and

the failure of HSI values to differ between points with and without

sparrows may have been due to several factors in addition to those which

affected correlations between vegetation measurements and sparrow

occurrence. Variables measured for the HSI model may not have

represented vegetation characteristics to which the sparrows were

responding. A second possibility is that our ocular estimation of

subjective vegetation measurements, such as % cover, may have differed

from those on which the model was based. Model variables describing

herbaceous cover were derived from Werner's (1975) data. If our

subjective estimates differed from his, or if the habitats we sampled

were sufficientlydifferent from the ones from which the model was

derived, the model may have resulted in an inconsistent quantification









of habitat suitability. A final possibility is that the relationships

between the variables measured and habitat suitability may have been

misrepresented in the model.

Although the HSI model did not prove very effective for predicting

sparrow numbers or predicting sparrow occurrence, it was significantly

correlated with Bass's subjective evaluation of habitat suitability. In

this respect, it did approach accomplishing the goal for which it was

derived. Further attempts to refine the model might focus on modifying

the model to mimic Bass's indices for the 49 points he evaluated. The

refined model could then be tested through correlation with sparrow

occurrence at other points sampled in this study or at additional points

sampled in the future.

The purpose of this study was not to derive a better habitat

suitability index model for Cape Sable Seaside Sparrows than the one

previously proposed, and no attempt was made to do so herein.. Sampling

procedures employed in the study, by necessity, were not optimal for

deriving a habitat suitability index. The results, however, in

conjunction with other published data and "expert opinion,," should be

proficuous for refining the suitability index model to better predict

Cape Sable Seaside Sparrow occurrence.


ACKNOWLEDGMENTS

We are grateful to D. David and G. Pullen for their assistance in

data collection. T. Edwards provided helpful advice on data analyses.

The cooperative spirit of the helicopter pilots, D. Mitchell and J.

Gomez, was greatly appreciated. We thank 0. Bass, Jr. and H. Werner for

recommending improvements in our data collection techniques, and also 0.









Bass, Jr. for visiting sampling points to give his subjective evaluation

of habitat suitability. J. Moulding, D. Palmer, and R. Schroeder

reviewed an earlier draft of the manuscript. This project was funded by

the Jacksonville Endangered Species Office of the U.S. Fish and Wildlife

Service and the U.S. Army Corps of Engineers under Research Work Order

No. 28 with the Florida Cooperative Fish and Wildlife Research Unit,

University of Florida, Gainesville.


Literature Cited


Bass, O.L., Jr., and J.A. Kushlan. 1982. Status of the Cape Sable

Sparrow. .South Florida Research Center Report T-672. 41 pp.

Fretwell, S.D. 1972. Populations in a seasonal environment. Princeton

Univ. Press, Princeton, New Jersey.

Freund, R.J., and R.C. Littell. 1981. SAS for linear models. SAS

Institute Inc., Cary, North Carolina. 231 pp.

Green, R.H. 1971. A multivariate statistical approach to the

Hutchinsonian niche : bivalve molluscs of central Canada. Ecology

52:543-556.

Kushlan, J.A., and O.L. Bass, Jr. 1983. Habitat use and the

distribution of the Cape Sable Sparrow. Pages 139-146 in The

Seaside Sparrow, its biology and management. North Carolina Biol.

Surv. and North Carolina State Mus., Raleigh, North Carolina.

Kushlan, J.A., O.L. Bass, Jr., L.L. Loope, W.B. Robertson, Jr., P.C.

Rosendahl, and D.L. Taylor. 1982. Cape Sable Sparrow management

plan. South Florida Research Center Report M-660. 37 pp.

Ray, A.A., ed. 1982. SAS user's guide: statistics. SAS Institute









Inc., Cary, North Carolina. 584 pp.

Schroeder, R., and M. Armbruster. 1985. A habitat model for the Cape

Sable Seaside Sparrow. Review Draft (May). U.S. Fish and Wildl.

Serv., Fort Collins, Colorado. 13 pp.

Stimson, L.A. 1956. The Cape Sable Seaside Sparrow: its former and

present distribution. Auk 73:489-502.

Taylor, D.L. 1983. Fire management and the Cape Sable Sparrow. Pages

147-152 in The Seaside Sparrow, its biology and management. North

Carolina Biol. Surv. and North Carolina State Mus., Raleigh, North

Carolina.

Werner, H.W. 1971. Cape Sable Sparrows rediscovered on Cape Sable.

Auk 88:432.

Werner, H.W. 1975. The biology of the Cape Sable Sparrow. Everglades

National Park, Homestead, Florida. 215 pp.









Table 1. Variables derived from soil and vegetation measurements and

calculated for each sampling point.


Variable Definition (units)


COVER Mean % cover of herbaceous vegetation

COVERCV Coefficient of variation of COVER (%)

COVFREQ Percent of frames with COVER > 75%

CLUMPNO Mean number of clumps of bunch-grass-type growth

CLNOCV Coefficient of variation of CLUMPNO (%)

CLUMPCOV Mean % of total cover in bunch-grass-type growth

form

CLCOVCV Coefficient of variation of CLUMPCOV (%)

CLFREQ Percent of frames with CLUMPCOV > 50%

SHRUBDEN Shrub density (shrubs/ha)

TREEMEAN Mean of distances to closest tree or tree island

in each quadrant (m)

TREEMIN Distance to closest tree or tree island (m)

TREEDENS 10,000/TREEMEAN2 (tree islands/ha)

SOIL Mean soil depth (cm)

HSI -Habitat suitability index value calculated from

Schroeder and Armbruster (1985) (index)

SHSI Subjective valuation of habitat quality for Cape
Sable Seaside Sparrows (index)









Table 2. Correlations of habitat variables with numbers of Cape Sable

Seaside Sparrows detected per point for all points.



Variable r n P


COVER

COVERCV

COVFREQ

CLUMPNO

CLNOCV

CLUMPCOV

CLCOVCV

CLFREQ

SHRUBDEN

TREEMEAN

TREEMIN

TREEDENS

SOIL

HSI

SHSI


0.001

-0.011

-0.107

0.174

-0.012

0.137

-0.060

0.101

-0.080

0.118

0.166

-0.131

-0.097

0.104

0.471


120

120

120

120

79

120

79

120

121

123

124

123

111

124

49


0.991

0.905

0.243

0.057

0.918

0.135

0.599

0.273

0.385

0.194

0.066

0.148

0.309

0.248

0.001









Table 3. Correlations of habitat variables with numbers of Cape Sable

Seaside Sparrows per point for points where sparrows were detected.



Variable r n P


COVER

COVERCV

COVFREQ

CLUMPNO

CLNOCV

CLUMPCOV

CLCOVCV

CLFREQ

SHRUBDEN

TREEMEAN

TREEMIN

TREEDENS

SOIL

HSI

SHSI


0.223

-0.242

0.046

0.486

-0.314

0.449

-0.335

0.396

0.070

-0.034

-0.061

0.116

-0.133

0.101

0.306


0.205

0.167

0.797

0.004

0.103

0.008

0.081

0.020

0.700

0.848

0.734

0.515

0.462

0.570

0.268









Table 4.

with Cape


Results of t-tests comparing habitat variables between points

Sable Seaside Sparrows and points without sparrows.


Variable t n P


COVER

COVERCV

COVFREQ

CLUMPNO

CLNOCV

CLUMPCOV

CLCOVCV

CLFREQ

SHRUBDEN

TREEMEAN

TREEMIN

TREEDENS

SOIL

HSI

SHSI


0.98

-1.05

2.06

-0.48

-1.31

-0.34

-0.85

-0.13

1.71

-1.91

-2.86

3.25

1.33

-1.38

-4.06


120

120

120

120

79

120

79

120

121

123

124

123

111

124

49


0.331

0.294

0.042

0.634

0.195

0.738

0.399

0.901

0.091

0.059

0.005

0.002

0.187

0.172

0.001



































Fig. 1. Locations of points sampled and points where Cape Sable Seaside

Sparrows were located (closed circles) on Grossman Hammock Quadrangle

Map., April-May, 1985. Total number of sparrows detected at each point

as well as points that were revisited (R) for sparrow censusing are

indicated. Points where sparrows were detected during vegetation

sampling (V) and number of additional sparrows added to the total (in

parentheses) also are indicated.















czY.


0.














VO) e3
S "' ; --


o *
A ,1 ,/* ", .-
-- -






--".- -r 7!: -- 4 -T-- -------.



A
-L









O -


"P~
0 0 0


0 0 0




o0




O O
I 0 0 0


Pip


0


E V-' G









O f
1*1



0
*/*


L A D


V(O)


E


0 .
L P A


O


2
R


o0
A R


070
*7 0 0






I


I 0


F'


II



HI



3.j..


*1 I
I


0 0
R


o O




































Fig. 2. Locations of points sampled and points where Cape Sable Seaside

Sparrows were located (closed circles) on Royal Palm Ranger Station SE

Quadrangle Map, April-May, 1985. Total number of sparrows detected at

each point as well as points that were revisited (R) for sparrow

censusing are indicated. Points where sparrows were detected during

vegetation sampling (V) and number of additional sparrows added to the

total (in parentheses) also are indicated.












." " :,,. -
% 'A I A D IS I KIS V "
.1 1
.- *^ I'.
: -., ? *' t 9 ,lt, ..
-, "- .. .


0 ,I .r
*-'
$r *' 59


(S



* ,, **I
4, r

.4 '^ If'


- A


-4
o *- A '



,o ii '-

^*y *:
'- ' 7.. '

P 0 :-,.


9
6 9


I. "


*: ,+ _
. '. o ..o. .:

* *
o*,'' l ;." ; .**- .' ,.*
'; ^ .:* ,,* *. "-
..
9'''"
. V** .., ,. .* .-.
*i.; *- .r ^ -. ,
' */ -* .r *".-' -/
* ** :,*- ^ /... *. ,*'*..
"* "I 'i :*
S, .. .6 ; 0*



I -",-,b ,x0
.1. I~~~l e

..
.3 ~ '- A A'i*
A .'. ..
...
'** ** ^ ." 1 .V

-'"-p.



A k"


.'M.
o <' :'1
;Y ,". -1?




; -.-> i-.n rS.Vw ?.-^.r *


Ik


1i


1022)0 .6


.9 t -t. ..: '

0. I-~:o :d~
~ p


-* .r. .
S' .- *. '


"- ,' '' "* *
,, 2 f. _
*01,. *5 os ^1^. /

-,' ., ; .,"P ..:
-^ ^^.** ". j^ ^ 5:.





































Fig. 3. Locations where sparrows were found and numbers detected during

the 1981 survey on the Grossman Hammock Quadrangle Map. Data are from

Bass and Kushlan (1982).











h; -" j, M .'',


, .,,., d.s
A Al 1


j t ,0 .,f l -I
'."** .*"


; -,-, .ia.-.w, .: '.-
Ar '
. .


. .," + -.
I4. -' .,.: - .
* r r ,.






. t .. ... .
* j' t-" ,. r- .Z:'" '



. .' '.. .: -.-'i






IAi A
' *.-',r
.,, ;+.,- :.;..-. .










.t *^'- f"


. o .. .
. '; : '. *' '" '' "- -" : "/' '


.. '
u *" *' *

---,'. -a -*-. rL>,,
', : ; !, .... .


~C o r0


I,: S '
At ~


,' r.,























60 30


50-


40-
12
00 -











HSI (midpoints)
20- 75


10 A


0o- 1 9 1
0.05 0.15 0.25 0.35 0.45 0.55 0.65
HSI midpointss)

Fig. 5. Percentage of points at which Cape Sable Seaside Sparrows were
detected, grouped by HSI values, and number of points in each grouping.














NJ'3 3.U


.T. ;


,'. 1 N "- -'. "'' -

.. .; ; _: ..





-:-: :
S- *flj _




1." 1 0"'- 0"
-: -,, f .. -

* ; --'. .-- :. o o




" :'" -. *,- 0 0
1 .

3 -'




-, A L ,-
,'' -'-.. .*. 0 0 0 "











'O O O*, O .-
O -.:O O
-.--.". :(0 o ,0 0- 0 I -.

_-. : I

-" '0 .




--. 0 0. A 0- I3 A
I..








I1 -











- -


b~n


2..3..


II I




til


i I .


~aa., ,
.3 .




































Fig. 4. Locations where sparrows were found and numbers detected during

the 1981 survey on the Royal Palm Ranger Station SE Quadrangle Map.

Data are from Bass and Kushlan (1982).




















wu -- -- --- -.-

80
4

70-
5 5
60-

\- 4 2
450

a. 40-

30 a

20- 6

10

0 a 7
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.55
SHSI midpointt)


Fig. 6. Percentage of points at which Cape Sable Seaside Sparrows were
detected, grouped by SHSI values, and number of points in each grouping.



















50- 29


40- 21

C



20


10
16
4 4
0- ,. l l 0/ 1l



5 15 25 35 45 5 65 75 85
COVER midpointss)

Fig. 7. Percentage of points at which Cape Sable Seaside Sparrows were
detected, grouped by COVER values, and number of points in each grouping.




















50-


40 -

C
*
i 30-


20-


10-


0-


. .7. i
Ir7 IZI VZ IIIV
wi r /j r Z, rz/lIfJ
/ /,, /.. .../" / / /
^ ^ ^ ^^ ^ / y
2 7 MH 0 ^


15 25 35 45 55 S 75 85 95


COVElCV midpointt)

Fig. 8. Percentage of points at which Cape Sable Seaside Sparrows were
detected, grouped by COVERCV values, and number of points in each grouping.


105 115


6- I




















40

35- 74


30 14


25
19 5
o 20

15


10

5

/ / 4 1 2 / 1
I I
5 15 25 35 45 55 65 75
COVFREQ midpointss)


Fig. 9. Percentage of points at which Cape Sable Seaside Sparrows were
detected, grouped by COVFREQ values, and number of points in each grouping.













24


ii


11








I/Z


tPzo


40-

30

20-

10-

0


2 1


0.75 1.25 1.75 2.25 2.75


3.25 3.75


CLUMPNO midpointss)
Fig. 10. Percentage of points at which Cape Sable Seaside Sparrows were
detected, grouped by CLUMPNO values, and number of points in each grouping.


50


a'


I-- A & -I .-I._ I -a I -I I A


0.25
0.25




















711-
18





(9
40 -


50 -
9

140 -


.30 ,
20 4




10

2 VA

25 75 125 175 225 275 325 375 425 47S
CLNOCV (mrdpoints)


Fig. 11. Percentage of points at which Cape Sable Seaside Sparrows
were detected, grouped by CLNOCV values, and number of points in each
grouping.























69



Vo


00n004

00


S


- ....I... -- r ---~ r~


CLUMPCOV midpointt)

Fig. 12. Percentage of .joints at which Cape Sable Seaside Sparrows were
detected, grouped by-CLUMPCOV values, and number of points in each
grouping.


'I


50 -


40-
C
30-

20-

10 -

0-


E L


- 1 -


23



















70


s0 1

8 8
50-

5
. 40-










// 3
-0 1.






25 75 125 175 225 275 325 375 425 475
CLCOVCV midpointss)

Fig. 13. Percentage of points at which Cape Sable Seaside Sparrows
were detected, grouped by CLCOVCV values, and number of points in each
grouping.






















16
Is
60 -



14
.. 40-


30-
75
20* 6
10 -



3 5 0 0 1





Fig. 14. Percentage of points at which Cape Sable Seaside Sparrows
were detected, grouped by CLFREQ values, and number of points in each
grouping.




















40-


3"-
1 22

30


25


S 20 22
a.S
15-

10 10





5 15 25 345 04
SHRUBOEN midpointss)


Fig. 15. Percentage of points at which Cape Sable Seaside Sparrows
were detected, grouped by SHRUBDEN values, and number of points in each
grouping.




















110


100-

90 -

80 -
7
7 0


40- 20





20 -
10 VJ
V IV -


4 10 204
25 75 123 175 225 275 325 375 425 475 525 575 >600
TREEMEAN midpointss)


Fig. 16. Percentage of points at which Cape Sable Seaside Sparrows were
detected, grouped by TREEMEAN values, and number of points in each
grouping.


___ ~
































39
31


PA M-


29



If


0 0


-. ________________ z
I I I I ~ 1~~r


I I
12.5 37.5 75 125 175 225 275
TREEMIN midpointss)


____ m


325 375 >400


Fig. 17. Percentage of points at which Cape Sable Seaside Sparrows
were detected, grouped by TREEMIN values, and number of points in each
grouping.


110


100-
90-
80so
70-
80-
50-
40
30-


20 -
10 -


1
























40



30
C
S31
20


9
10



015
0.25 0.75 1.2 1.7S >2.0
TREEDENS midpointss)


Fig. 18. Percentage of points at which Cape Sable Seaside Sparrows
were detected, grouped-by TREEDENS values, and number of points in
each grouping.













*1


50-

40-

1 30-

20-

10-

0


37


16


14


16










I


9



//00


5 2S I 4 I i
5 15 25 35 45 SS >'0


SOIL midpointt)

Fig. 19. Percentage of points at which Cape Sable Seaside Sparrows
were detected, grouped by SOIL values, and number of points in each
grouping.
















4 _


7



I:


I4
'2

2




25. 7 125 175 225 >250
Distance (midpoints)

Fig. 20. Distance from sample points versus number of sparrows detected.
Only sparrows for which accurate distance could be estimated are included.




University of Florida Home Page
© 2004 - 2010 University of Florida George A. Smathers Libraries.
All rights reserved.

Acceptable Use, Copyright, and Disclaimer Statement
Last updated October 10, 2010 - - mvs