University of Florida Thesis Or Dissertation

FLORIDA SCRUB-JAY MOVEMENT THROUGH UNSUITABLE HABITAT AND ITS RELATIONSHIP TO OCCUPANCY IN OCALA NATIONAL FOREST

AMANDA M. ABEL

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2014

To my Mom who has always encouraged me to be a lifelong learner

ACKNOWLEDGMENTS

I would like to begin by thanking my family and friends for their support and encouragement throughout my time at the University of Florida. I thank my advisor,

Katie Sieving, and committee members, Karl Miller and Tom Hoctor, for helping me develop a well thought out thesis project. I thank Liz White for helping me with field work in Ocala National Forest. I thank Jennifer Gillett-Kaufman and Thomas Walker for hiring me as a graduate assistanct at the Natural Area Teaching Lab. Lastly, I thank the

School of Natural Resources and Environment for the tuition waivers and the Provost for the stipend I received for my graduate assistantship.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF FIGURES ...... 7

LIST OF ABBREVIATIONS ...... 8

ABSTRACT ...... 9

CHAPTER

1 INTRODUCTION ...... 11

2 MOVEMENT THROUGH UNSUITABLE HABITAT...... 13

Research Design ...... 15 Study Species: Florida Scrub-jay (Aphelocoma coerulescens) ...... 16 Study Site: Ocala National Forest ...... 17 Methods ...... 18 Sampling Design ...... 18 Field Techniques ...... 19 Remote Sensing and Land Cover Classification ...... 20 Calculation of Percent Pine, Oak, and Open Space Within Stands ...... 20 Data Analysis ...... 21 Results ...... 22 Discussion ...... 23

3 CLEAR-CUT PATCH OCCUPANCY ...... 30

Research Design ...... 31 Study Species: Florida Scrub-jay (Aphelocoma coerulescens) ...... 32 Study Site: Ocala National Forest ...... 33 Methods ...... 34 Survey Stand Identification and Definition of Suitable Stands ...... 34 Data Analysis ...... 37 Results ...... 38 Discussion ...... 38

4 CONCLUSIONS ...... 46

LIST OF REFERENCES ...... 47

BIOGRAPHICAL SKETCH ...... 51

LIST OF TABLES Table page

2-1 Sampling Design ...... 27

2-2 Predictor (X) and Response (Y) Variables in Analyses ...... 27

2-3 Principal Components Variance Explained and Loadings for Matrix Vegetation and Age ...... 27

2-4 Principal Component Variance Explained and Loadings for Habitat Vegetation and Age ...... 28

2-5 Logistic Regression of Enter/Not Enter (Seasons Combined) ...... 28

2-6 Linear Regression on Distance Followed (Seasons Combined) ...... 28

3-1 Predictor (X) and Response (Y) Variables of Occupancy Study ...... 42

3-2 Logistic Regression of Patch Occupancy ...... 42

LIST OF FIGURES

Figure page

2-1 Examples of clustered and isolated functional patches...... 29

3-1 Demonstration of verifying unoccupied stands, functional stands, and distance to closest optimal patch ...... 43

3-2 Classification and regression tree analysis (CART) of stands before merging to make "functional patches" ...... 44

3-3 Classification and regression tree analysis (CART) of the functional patches with all predictor variables included ...... 45

LIST OF ABBREVIATIONS

DFP Distance to closest functional patch

DFPDP Distance to closest functional patch in the direction being pulled

FLSJ Florida scrub-jay

Functional Patch Stands which are presumably suitable for FLSJ occupancy

ONF Ocala National Forest

Stand Forest management units for logging activities

YSH Years since last harvest

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

FLORIDA SCRUB-JAY MOVEMENT THROUGH UNSUITABLE HABITAT AND ITS RELATIONSHIP TO OCCUPANCY IN OCALA NATIONAL FOREST

Amanda M. Abel

December 2014

Chair: Kathryn E. Sieving Major: Interdisciplinary Ecology

For this study we used Florida Scrub-jay (FLSJ) survey data and behavioral experiments to determine how occupancy and movement through harvested clear-cut stands in Ocala National Forest (ONF) is affected by characteristics of the stands and surrounding stands. We conducted a replicated playback study utilizing territorial calls of

FLSJ to elicit approach and following responses in target jay groups. In this way we could compare birds’ willingness to enter and move through adjacent stands exhibiting a range of age, vegetation structure, and other stand metrics using a standardized motivation to approach a potential intruder. We found that many FLSJ in ONF were willing to move into stands unsuitable for FLSJ occupancy (the matrix). Regression models showed us that FLSJ are more likely to enter a matrix stand which is relatively old, has high percent pine, and low percent oak and open space. Importantly, the distance to the nearest suitable habitat patch was also a significant factor. Playback

‘pulls’ into matrix stands which had suitable habitat nearby elicited longer movements into the matrix, suggesting that FLSJs are aware of habitat availability and configuration outside of their own occupied stand. Next, we used clear-cut patch occupancy survey data to create statistical models to determine characteristics which most affect

occupancy. We found that occupancy of clear-cut stands in ONF is negatively related to distance to the closest stand suitable for occupancy, positively related to area, and negatively related to edge:area ratio (a reflection of shape). A classification and regression tree analysis of survey data allowed us to create recommendation for ONF forest managers. We recommend that clear-cut stands should be within 540 meters of other suitable stands. If stands exceed this distance, they should be greater than 26 hectares in size. As edge:area ratio negatively affects occupancy, creating stands with small edge:area ratios (large core space) may positively FLSJ occupancy. As ONF is home to the largest remaining population of this federally threatened species, effective management this habitat is essential to the species persistence. We believe the findings of this study may help forest managers more effectively manage FLSJs in this ecosystem.

CHAPTER 1 INTRODUCTION

Ocala National Forest (ONF) protects the largest contiguous sand pine scrub forest and is home to the largest population of Florida scrub-jays (FLSJ). Sand pine scrub ecosystems are adapted to a cycle in which wildfires kill all vegetation above ground, the vegetation grows back, and burns again in 20 to 80 years. Native species, such as the FLSJ, depend on this cycle of opening and regrowth. Natural wildfires are suppressed in ONF and openings are created primarily by timber harvesting. Timber harvest clear-cutting produces a similar effect as wildfires, but the stands are usually much smaller than would be naturally (USDA 1999). Harvesting involves clear cutting stands in forest which have not been harvested in around 50 years. The result is a mosaic of small stands suitable for FLSJs (functional patches), embedded in a matrix of unsuitable habitat. FLSJ begin to use clear-cut stands shortly after harvesting, and may use the stands until they are around 15 years old (Miller 2012). As ONF is home to the largest remaining population of this federally threatened species, effective management this habitat is essential to the species persistence.

For this study we used FLSJ survey data and behavioral experiments to determine how occupancy and movement through harvested clear-cut stands in ONF is affected by characteristics of the functional patch and surrounding stands. We conducted a replicated playback study utilizing territorial calls of FLSJ to elicit approach and following responses in target jay groups. In this way we could compare birds’ willingness to enter and move through adjacent stands exhibiting a range of age, vegetation structure, and other metrics using a standardized motivation to approach a potential intruder. Next, we used clear-cut patch occupancy survey data to create

statistical models to determine stand characteristics which most affect occupancy.

These finding have allowed us to create a list of recommendations for forest managers to consider when planning FLSJ management strategies.

CHAPTER 2 MOVEMENT THROUGH UNSUITABLE HABITAT

Habitat fragmentation, the subdivision of continuous habitat into smaller patches, occurs naturally through fire, windfall, and other natural disturbances. Anthropogenic fragmentation causes loss of habitat, habitat patch size reduction, and increasing isolation of patches that, together, contribute to declines in biological diversity relative to the original habitat (Andren 1994). Consequences to wildlife vary strongly with patch size, distance from other patches, and the degree of connectivity among patches and the ecology of the species in question (Ewers and Didham 2006). Physical habitat changes due to fragmentation are also dependent on the shape, size, and position of the patches within the landscape matrix, as well as the characteristics of the matrix itself

(Saunders et al. 1991). Because effective dispersal (immigration followed by successful breeding) is important to metapopulation maintenance, connecting isolated populations in fragmented landscapes and understanding how habitat patches within a mosaic landscape influence each other is essential to long-term conservation efforts. For many species the matrix may be made up of unsuitable and possibly hostile habitat, but it is rarely a complete barrier to dispersal (Witt and Huntly 2001). Distinct habitat characteristics within the matrix (commonly defined by vegetation type and structure) may be differentially permeable to a specific species. Understanding how the matrix structure affects its permeability for animals is crucial to managing species in fragmented landscapes (Ricketts 2001). Dispersal itself is very difficult to directly quantify, so alternative measures are sometimes necessary to efficiently determine matrix resistance (Castellon and Sieving 2006; 2012).

Experimental methods are an effective approach to quantifying the relative resistance to animal dispersal that different matrix types represent. One such method is the experimental translocation of birds or small mammals to evaluate gap-crossing decisions during ‘forced homing’ behaviors (Castellon and Sieving 2006). Experiments of this kind can demonstrate that species are averse to traveling through specific

"hostile" matrix types, and corridors or patches of suitable cover can facilitate movement across hostile matrix (Bowman and Fahrig 2002; Desrochers and Hannon 1997; Grubb and Doherty 1999). In studies of federally listed species or rare species, translocations may be inappropriate for various reasons. Another less disruptive study design to test for matrix aversion is the playback study. This method involves the use of recorded conspecific vocalizations to stimulate birds to enter or cross habitat gaps which are unsuitable for occupancy (Sieving et al. 1996). Comparisons of findings from translocations and playbacks suggest the playback results are consistent with translocations in determining species relative vagility in unsuitable habitats. For example, Sieving et al. (1996) used playback to assess behavioral reactions to specific matrix types adjacent to habitat patches occupied by five species of resident, terrestrial, insectivorous birds; a guild known for their fragmentation sensitivity. This study revealed species specific matrix ‘permeability’ estimates consistent with findings of other studies estimating relative vagility of the same species (Cornelius et al. 2000; Castellón and

Sieving 2012). For example, within the suite of birds studied, the Magellanic Tapaculo

(Scytalopus magellanicus) was the only species that entered stumpy pastures in the playback study, and is also the most widespread of the tapaculos studied. A variety of works now confirm that playback tests of birds’ willingness to enter unsuitable matrix

adjacent to their preferred living habitats can reflect more general aspects of their movement and dispersal capabilities (Awade and Metzger 2008; Robertson and

Radford 2009).

In this study, we tested the willingness of a threatened species, the Florida scrub- jay (Aphelocoma coerulescens; FLSJ), to enter and move in matrix areas outside their occupied habitat stands within a large mosaic of managed forest in the Ocala National

Forest (ONF) in Central Florida. The ONF landscape mosaic supports the largest remaining population of the Florida scrub-jays and is, therefore, central to the species’ long-term protection. The species is adapted to mid to early successional stages of sand pine scrub forest (after wildfire). In ONF clear-cutting is used as a management tool which creates a shifting mosaic of uneven aged ‘stands’ of scrub harvested for sand pine (Pinus clausa) on a 50 year rotation. Balancing harvest needs with species protection is, therefore, is a practice of optimizing inter-patch connectivity through matrix that is permeable to jay movement, optimal habitat patch size, and an overall high availability of suitable habitat that fosters the jays’ highest productivity (FWS 1990;

USDA 1999; FWS 2012). Using playback we sought to reveal patterns of jay aversion to specific habitat types within the ONF mosaic in the interest of providing recommendations for maximizing behavioral connectivity (this chapter) and to help parameterize models regarding FLSJ occupancy patterns in the ONF (Chapter 2).

Research Design

The multi-factorial hypothesis tested is that FLSJ willingness to enter and move through forests adjacent to occupied "functional patches" is affected by the following factors: age of the functional patch (time since last harvest); the age of the adjacent stand; the percent pine, oak, and open space (bare sandy ground) within habitat and

matrix stands; the time of year; and the distance to the closest functional patch in the direction being pulled (DFPDP). We conducted a replicated playback study utilizing territorial calls of scrub jays to elicit approach and following responses in target jay groups. In this way we could compare birds’ willingness to enter and move through adjacent stands of matrix exhibiting a range of age, vegetation structure, and other metrics using a standardized motivation to approach a potential intruder. If matrix stand characteristics influence willingness to enter and move through matrix stands, then we predicted consistent patterns in probability of entering and in maximum distance moved into stands would be associated with certain matrix stand characteristics. We selected a priori a variety of stand metrics to quantify in each playback sample; metrics that were suggested or demonstrated in previous studies to be important constraints on movement by birds and other animals with similar mobility and habits to FLSJ. The significance of this study design is that tests such as this one can reveal fundamental limitations on population connectivity in.

Study Species: Florida Scrub-jay (Aphelocoma coerulescens)

Florida scrub-jays are cooperative breeders, hold territories year round, and only occur in scrub, scrubby flatwoods, and sand pine scrub (FWS 2012). The jays live in family groups ranging from two to eight adults and one to four juveniles. Fledglings may remain in their natal territory as "helpers" for up to 4 years. Helpers participate in territorial defense, sentinel duties, predator mobbing, and feeding of young. Their sentinel system involves one jay watching for territory intruders or predators from a well exposed perch. When a predator is sighted, the sentinel gives a warning call and all members of the group take cover in the vegetation. FLSJ defend the borders of their territories by flying towards the intruding jay and scolding loudly (Woolfenden and

Fitzpatrick 1977). Territory availability is a limiting factor for FLSJs, so once a male establishes a territory he will stay there until the habitat becomes unsuitable or he is removed via death or competition (Woolfenden and Fitzpatrick 1984). In some cases he may remain there after the stand becomes unsuitable if there are no other options available. New territories may also be established in areas which have recently become suitable, such as after a disturbance. Historically wildfire was the primary source of new suitable habitat. In anthropogenically fire suppressed ecosystems, habitat management efforts such as prescribed fire or mechanical clear-cutting are the primary source of new suitable habitat (USDA 1999; FWS 2012).

Study Site: Ocala National Forest

Ocala National Forest (ONF) protects the largest contiguous sand pine scrub forest and is home to the largest population of FLSJs. Sand pine scrub ecosystems are adapted to a cycle in which wildfires kill all vegetation above ground, the vegetation grows back, and burns again in 20 to 80 years. Native species, such as the FLSJ, depend on this cycle of opening and regrowth. Natural wildfires are suppressed in ONF and openings are created primarily by timber harvesting. Timber harvest clear-cutting produces a similar effect as wildfires, but the stands are usually much smaller than would be naturally (USDA 1999). Harvesting involves clear cutting stands in forest which have not been harvested in around 50 years. The result is a mosaic of small stands suitable for FLSJs (functional patches), embedded in a matrix of unsuitable habitat. FLSJ begin to use clear-cut stands shortly after harvesting, and may use the stands until they are around 15 years old (Franzreb and Zarnoch 2011; Miller et al

2013.).

ONFs 1999 Land and Resource Management Plan contains a forest-wide objective to "maintain a dynamic system of at least 45,000 to 55,000 acres of habitat capable of supporting scrub-jays on the Ocala NF ". The Forest Service follows guidelines developed by USFWS's Florida Scrub Jay Recovery Plan to protect FLSJ habitat (USDA 1999). Previous studies on jays in ONF have identified stands with stand ages between 2-10 years old as optimal habitat (Miller et al. 2012). Foresters choose stands to harvest based on the need for FLSJ replacement habitat. The current technique is to harvest stands nearby to 3-6 year old occupied stands. By the time the newly harvested stands become suitable for the jays, the occupied stands are approaching the age at which the jays find them less suitable. Presumably, the jays in the occupied patch will then have the opportunity to claim new territory in the nearby newly harvested stand (Franzreb and Zarnoch 2011).

Studies on Florida’s Atlantic coast have suggested that proximity to hardwood forest edges negatively affect FLSJ occupancy (Burgman et al. 2001). The effects of sand pine forest edges, like in ONF, have not yet been analyzed. This study was intended to be used as an indicator of the jay's willingness to travel through forests of unsuitable habitat to colonize new suitable territory and provide a better understanding of the permeability of the matrix.

Methods

Sampling Design

Using ArcGIS and a shapefile of clear-cut stands in Ocala National Forest, we identified suitable habitat by selecting stands harvested in the last 0-13 years. We used the erase tool to remove the Florida Fish and Wildlife Conservation Commission's survey blocks to avoid interfering with their study. In order to find trends related to the

distance to the nearest presumably functional patch, we used ArcMap buffer and intersect tools to identify functional patches which were "clustered" or "isolated". Our definition of an "isolated patch" is a functional patch, or small group of functional patches (up to 3), which is greater than 1 km from other functional patches. Our definition of a "clustered patch" is a functional patch which is less than 500 meters from

5 or more functional patches (Figure 2-1). From these two categories we randomly chose 3 functional patches to represent each of the following categories: adjacent forest stand age between 11-20 years, 21-30 years, 31-40 and >40 years (Table 2-1).

Sample sites were stratified across both the latitudinal and longitudinal extents of the

ONF.

Field Techniques

At a fixed playback points along each forest edge of interest, we conducted playback experiments to test FLSJ willingness to approach and enter forests adjacent to occupied functional patches. Experiments were conducted in July and December. Each month we returned to the playback point, we played a different version of the territorial calls recorded in ONF. In July we distinguished between the adult and juvenile birds by looking for the juvenile's distinctive gray plumage on the neck and back. In December the birds had molted and it was not possible to identify juveniles. Measurements were taken in the morning (between sunrise and 11 am), when the birds are most likely to be responsive. At the forest's edge, we played a recording of territorial calls to elicit responses from FLSJs. We played the recording for 15 minutes or until birds approached. If FLSJs approached, we attempted to lure them into the forest edge by walking into the forest while playing the recording. We did not stop walking into the forest until the bird(s) retreated back to their territory. When the bird(s) retreated, a

GPS coordinate was recorded. GIS was later used to measure the distance from the starting point at the edge of the habitat to the point in which they retreated. For each trial we recorded the number of respondents, if the respondents entered the matrix, the proportion of respondents where were juvenile, and the distance the birds followed us into the matrix (meters). A list of the predictor and response variables measured can be found in table 2-2.

Remote Sensing and Land Cover Classification

Remote sensing is the noncontact recording of information about the earth, both on the surface and in the atmosphere. Aerial sensors detect reflected electromagnetic radiation (eg. visible, infrared) in order to detect and classify objects or phenomenon

(Jensen 2005). NASA's Landsat satellite remote sensors are designed to be of use to a variety of fields like geology, agriculture, land-use planning, and forestry. Every 16 days

Landsat 8 revisits each spot on the earth and collects 15 meter resolution data.

("Landscape Toolbox" 2013). Images acquired by Landsat satellites can be used for land cover classification and vegetation index calculation.

Supervised land cover classification involves the specification of certain known land cover types within an image by physically visiting the site. The use of statistics about the wavelengths reflected off the Earth's surface in these areas are then used to categorize the entire image. The result is an image where each pixel is categorized as a specified land cover type. This is an efficient way of calculating the amount of different vegetation types within an area (Jensen 2005).

Calculation of Percent Pine, Oak, and Open Space Within Stands

Remotely sensed images and supervised land cover classification were used to calculate the percent pine, oak, and open space (bare sandy ground) within each

habitat and matrix stand type (identified in training samples taken in ONF that used areas of homogeneous pine, oak, sand, and water land cover; Jensen 2007). Using the

USGS Earth Explorer, we acquired Landsat 8 data for June 2013 (USGS 2013). We digitized the training samples by creating a vector file in the program ENVI by outlining the areas in the Landsat image which we found to be homogenous land cover. Next we used the Maximum Likelihood Supervised Classification tool in ENVI to classify the image using the digitized training samples. After importing the supervised classification map to ArcMap, we used the raster to polygon tool to convert the image to a shape file.

We then used the intersect tool to create polygons of the land covers within each clear- cut stand (defined by the US Forestry shape file). Next we used the dissolve tool to combine polygons of each land cover classification in each clear-cut into one polygon.

We used the calculate geometry tool to find the area of each land cover within the stands. Finally, the field calculator was used to divide the area of each land cover within a stand by the total area of the stand.

Data Analysis

Matrix and habitat descriptors to be used as predictor variables were correlated with one another (Pearson’s correlation), so we used principal component analyses

(PCA) to characterize matrix (habitat FLSJ entered) and functional patch characteristics

(habitat FLSJ were encouraged to leave) in two different analyses because habitat and matrix characteristics were not functionally interrelated. For the matrix PCA, we included stand age, and percent cover of pine, oak, and open space. The analysis produced 2 significant components (eigenvalues > 1; using Varimax rotation with Kaiser

Normalization; Table 2-3). The habitat PCA included stand age, and percent cover of oak and open space within the functional patch (percent pine cover was negligible and

not included; Table 2-2). One significant component was obtained in this analysis

(eigenvalue>1; Table 2-4). Component scores for all three principal components were then used as predictor variables in subsequent analyses.

We used the information theoretic approach with generalized linear models to determine the factors that influence 1) the bird's choice to enter or not enter the matrix

(binomial distribution, logit link) and 2) the distance they traveled into the matrix (normal distribution, log link). Factors in these models included the two matrix PCs, the habitat

PC, and the distance to closest presumably functional patch in the direction being pulled

(DFPDP). Models were run for July and December separately and with the months combined.

Results

The two matrix PCs explained 84.71% of the variance in the first PCA (Table 2-

3). The first matrix PC expresses the contrast between percent pine and percent oak.

This PC is positively related to percent pine and negatively related to percent oak. The second matrix PC expresses the contrast between stand age and percent open area.

This PC is positively related to matrix age and negatively related to percent open area

(Table 2-3). The habitat PC explained 70.2% of the variance (Table 2-4) and expresses the contrast between oak and open area. This PC is positively related to percent oak and negatively related to percent open area (Table 2-4). As habitat develops post- harvest, becoming highly suitable for the jays, oak cover increases and pines have not yet emerged, which is why pine percent cover was negligible (and not included in the

PCA).

Of the three logistic regressions conducted (July, December, and seasons combined) we found no covariates to be significant with a 95% confidence level but with

both seasons combined, the model achieved 90% confidence level. The two matrix PCs

(Matrix Pine-Oak Contrast and Age-Open Area Contrast; p-values = 0.091 and 0.08 respectively; Table 2-5); suggest that FLSJ were more likely to enter a matrix stand with relatively high percent pine and low percent oak (Matrix Pine-Oak Contrast) and that were relatively older (time since last harvest) with low percent open sand on the ground

(Matrix Age-Open Area Contrast).

Of the three linear regressions conducted on distances that birds followed the playback into the matrix (July, December, and seasons combined), one significant model was identified (p=0.034; table 2-6). This had both seasons data combined and identified DFPDP and the second matrix PC (Age-Open Area Contrast) as significant predictor variables (p=0.015 and p=0.056 respectively; table 2-6). This models suggests that FLSJ followed the playback stimulus farther into matrix stands that were relatively older, with little open sand, and where the distance to the closest presumably functional patch (in the same direction) was relative short.

Discussion

We found that many Florida Scrub-jays in ONF were willing to move into unsuitable habitat, where clear-cutting had occurred more than 13 years ago. Similar to other playback studies utilizing territorial calls to motivate following behavior, FLSJ movement through the matrix was sensitive to specific vegetative structural elements

(Sieving et al. 1996, 2004). Regression models show that FLSJ are more likely to enter a matrix stand which is relatively old, have high percent pine, and low percent oak and open space. The distance to the nearest functional patch was also a significant factor.

Playback ‘pulls’ into matrix stands which had suitable habitat nearby elicited longer movements into the matrix, suggesting that FLSJs are aware of habitat availability and

configuration outside of their own functional patch. As fledglings from previous years may remain in their natal territory as "helpers" for up to 4 years, waiting for new territory to become available, they have likely explored surrounding stands while looking for a place to establish territory (Woolfenden and Fitzpatrick 1977). If they are aware that jays reside in a nearby stand, our results suggest that they will follow the "intruder" farther towards that functional patch.

The fact that FLSJ were more willing to enter and follow in stands that were relatively old, had high percent pine, and low percent oak and open area may indicate that they have a visual advantage while following the "intruder". Tall sand pine trees allowed the jays to stay in the canopy while following the playback (Abel, Personal

Observations). Studies on other species have documented birds retreating upward when confronted with intruders (Carothers 1986; Slagsvold et al. 2014; Hedenstrom and

Rosen 2001).

Potential caveats to consider in the interpretation of these results includes an issue with using remotely sensed land cover classification. As these measurements are only from an aerial perspective, they cannot capture the extent of the vertical space between the understory and canopy. Therefore, the resolution of our analysis did not allow for precise identification of this type of habitat feature a priori. If we could have identified these stands (and distinguished them from stands with pine cover but more open understory, for example) we may have achieved better model fits. Nonetheless, we were able to identify FLSJ affinity for moving in this type of matrix. In future study of

FLSJ movement habitat suitability, this combination of low shrub-layer with tall pine canopy above should be considered as a potentially useful vegetative structural

combination for provision of movement pathways between suitable habitat stands. In addition, in combining data from the two seasons in our analyses, we included (for some of the sites) samples taken from the same locations. Presumably some of the same jay groups were tested twice; therefore some data points represent non- independent data. However, because models based on a single season’s data (for both logistic regression and GLMs) identified the same trends as the combined models in identifying influential predictors, we are confident that this did not influence our interpretation of important variables.

The significance of these findings pertains to potential management of habitat connectivity within the Ocala National Forest, where the largest remaining population of the federally threatened FLSJ resides. Creation and maintenance of the heterogeneous mosaic of clear-cut and regenerating stands of sand pine scrub in ONF has focused primarily on maintaining sufficient area of suitable-aged stands for FLSJ (USDA 1999).

Our results suggest that although habitat suitability for occupancy and reproductive activities declines in stands older than 10 years old, FLSJ are willing to travel through older stands. In terms of a recommendation based on this finding, we can suggest that when given a choice of older stands to leave in between currently functional patches, those with low scrub in the understory and emergent pines forming a canopy that could be used for high perches will be permeable to jay movement. Moreover, our study detected an effect of inter-patch distances on willingness to leave a home patch and travel into the matrix. Therefore, while this finding should be independently confirmed, we recommend that when managers choose stands to clear-cut they should consider minimizing the distances between functional patches to enhance the permeability of

surrounding matrix to jay movement. Given that the US Fish and Wildlife Service has been considering larger clear-cuts in the ONF as an adjustment that may better support long-term jay population viability for various reasons (Miller 2012), our findings suggest that, larger clear-cut areas would, by default, help minimize interpatch distances thereby enhancing permeability of the ONF mosaic to FLSJ interpatch movements.

Table 2-1. Sampling Design. From the patches categorized as "isolated" or "connected" we randomly chose 3 functional patches to represent each of the following categories: adjacent forest stand age between 11-20 years, 21-30 years, 31- 40 and >40 years. 11-20 YSH 21-30 31-40 >40 Isolated 3 3 3 3 Connected 3 3 3 3

Table 2-2. Predictor (X) and Response (Y) Variables in Analyses X or Y Name Variable Type x S Y Number of Respondents Count 3.17 ± 0.22 Y Matrix ntry by ≥ 1 Respondent (yes/no) Categorical - Y Distance Followed Into Matrix (meters) Continuous 137 ± 24.47 Y Proportion of Juvenile Respondents Proportion 0.12 ± 0.03 X Occupied Patch Stand Age (years) Continuous 6.057± 0.43 X Matrix Stand Age Continuous 30.37 ± 2.43 X DFPDP* (meters) Continuous 1718.7 ± 357.62 X Season Categorical - X Percent open space within habitat patch Proportion 0.71 ± 0.06 X Percent oak within habitat patch Proportion 0.29 ± 0.06 X Percent pine within habitat patch Proportion <0.01± 0 X Percent open space within matrix stand Proportion 0.18± 0.03 X Percent oak within matrix stand Proportion 0.54± 0.04 X Percent pine within matrix stand Proportion 0.27 ± 0.05 * DFPDP = distance to closest optimal stand in direction pulled

Table 2-3. Principal Components Variance Explained and Loadings for Matrix Vegetation and Age Principal Components Component Eigenvalue % Variance Cum. % Variance 1 1.76 43.99 43.99 2 1.62 40.73 84.71 Variable Loadings Variable Component 1 Component 2 Matrix Age 0.19 0.76 Matrix % Pine 0.86 0.45 Matrix % Oak -0.99 0.16 Matrix % Open 0.08 -0.91

Table 2-4. Principal Component Variance Explained and Loadings for Habitat Vegetation and Age Principal Component Component Eigenvalue % Variance 1 2.11 70.2 Variable Loading Variable Component 1 Habitat Age 0.43 Habitat % Oak 0.98 Habitat % Open -0.98

Table 2-5. Logistic Regression of Enter/Not Enter (Seasons Combined) B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B) Lower Upper Matrix PC1 1.01 0.59 2.86 1 0.091 2.74 0.85 8.82 Matrix PC2 0.85 0.48 3.07 1 0.080 2.33 0.91 5.99 Habitat PC1 0.17 0.47 0.13 1 0.722 1.18 0.47 2.98 DFPDP -0.001 0.001 1.59 1 0.208 0.99 0.99 1 Constant 2.23 0.95 5.52 1 0.019 9.32

Table 2-6. Linear Regression on Distance Followed (Seasons Combined) Model Summary R R Square Adjusted R Square Std. Error of the Estimate .55 0.31 0.21 129.41 ANOVA Sum of Squares df Mean Square F Sig. Regression 203527 4 50881.75 3.04 0.034 Residual 468906 28 16746.64 Total 672433 32 Coefficients Unstandardized Standardized Coefficients Coefficients t Sig. B Std. Error Beta (Constant) 227.12 38.43 5.91 0 DFPDP -0.06 0.03 -0.42 -2.61 0.015 Matrix PC 1 34.56 22.73 0.24 1.52 0.140 Matrix PC 2 46.02 23.09 0.32 1.99 0.056 Habitat PC 1 -6.66 23.30 -0.05 -0.29 0.777

Figure 2-1. Examples of clustered and isolated functional patches. Our definition of an "isolated patch" is a functional patch, or small group of functional patches (up to 3), which is greater than 1 km from other functional patches. Our definition of a "clustered patch" is a suitable stand which is less than 500 meters from 5 or more functional patches.

CHAPTER 3 CLEAR-CUT PATCH OCCUPANCY

Classic metapopulation theory provides a framework for understanding how species populations persist (or go extinct) when they are constrained to exist in a landscape made up of suitable habitat patches within a matrix of unsuitable habitat.

Sub-populations inhabiting semi or completely isolated patches interact through the movement of individuals between patches. If the overall sub-population extinction and colonization rates are balanced, a stable equilibrium is predicted to exist with some fraction of the patches being occupied. If the overall patch-level extinction rate is greater than the colonization rate, extinction of the entire metapopulation can occur

(Hastings and Harrison 1994). Therefore, a species is more likely to persist if individuals are able to, and frequently do, disperse among suitable habitat patches

(Bergl et al. 2008; Ortego et al. 2008; Smith and Green 2005). Metapopulation models can appear overly simplistic but they provide a framework to address how animal movements, especially dispersal, may connect breeding individuals and sustain populations. As such, metapopulation theory can provide guidance for obtaining insightful empirical data necessary for designing conservation areas so that important habitat areas, such as source patches, are protected (Castellon & Sieving 2007;

Sonsthegen et al. 2012).

Statistical models that estimate the probability that a patch of habitat is occupied by a particular species are known as patch occupancy models. The most basic approach to modeling the effect of landscape variables on wildlife populations is based on the analysis of species distribution throughout a fragmented landscape. By using this "incidence" based approach, differences in patch occupancy for a particular species

are assumed to reflect how combined landscape factors affect population processes, resulting in the presence or absence of a species in a patch. Incorporating the effects of patch size, distance from other patches, and degree of connectivity into patch occupancy models can help parameterize and enhance the predictive capability of the models (Hanski 1994; Rizkalla et al. 2009).

The Florida scrub-jay is the state’s only endemic bird species, and is listed as a threatened species at the state and federal level due to the fragmentation, loss, and degradation of scrub habitats in Florida. The total population has declined by an estimated 25-50%, due primarily to fire suppression, agriculture, and urbanization (FWS

2012). As ONF is home to the largest remaining population of FLSJs, effective management of this habitat is essential to the species persistence. Additionally, fragmentation of scrub habitat is a problem throughout the state so expanding knowledge about habitat permeability and dispersion could benefit other populations as well. In 2009, the US Fish and Wildlife Service proposed an amendment to the ONF's

Forest Plan which would increase the minimum clear-cut stand size from 120 acres

(48.56 ha) to 800 acres (323.75 ha; Miller et al. 2012). Research providing evidence that FLSJs have higher rates of occupancy in larger stands would provide empirical evidence to support this change.

Research Design

The overall hypothesis we tested is that FSJ stand occupancy is affected primarily by the proximity to functional patches and by size. Other factors that may affect occupancy include the configuration features of the intervening matrix as well as shape, and quality (years since harvest). For purposes of habitat identification for establishing our sampling design, we assumed that stands that had been clear-cut

within 13 years were suitable for ONF jays (presumably functional patches). We designed a comparative study to test explicitly for the effects of habitat isolation and stand size. By including various representative interconnected functional patches and all of the smallest and most isolated functional patches in ONF, we obtained causal inference about size and isolation effects as ONF. In addition, presumably functional patches of all dimensions were stratified across both the latitudinal and longitudinal extents of the ONF to the extent possible. We used playbacks of territorial calls to census stands in a manner that minimized false negatives and maximized the probability of detecting jays in a given survey patch. Given the marked improvement in detectability of FLSJ’s with the use of playback (Johnson et al. 1981), and the protocol we followed, we could assume that detection probability of jays on our design was near

100% (Castellon and Sieving 2006).

We predicted stands that were relatively far from other functional patches and those that were very small and/or disconnected would be less likely to be occupied. We initially assumed that stands harvested in the last 10 years are most suitable for FSJ occupancy, if accessible to the jays (but our design allowed us to test the validity of this assumption). Extraneous influential variables were quantified and included in statistical analyses (Table 3-1).

Study Species: Florida Scrub-jay (Aphelocoma coerulescens)

sentinel system involves one jay watching for territory intruders or predators from a well exposed perch. When a predator is sighted, the sentinel gives a warning call and all members of the group take cover in the vegetation. FLSJ defend the borders of their territories by flying towards the intruding jay and scolding loudly (Woolfenden and

Study Site: Ocala National Forest

habitat. FLSJ begin to use clear-cut stands shortly after harvesting, and may use the stands until they are around 15 years old (Miller 2012; Franzreb and Zarnoch 2011).

ONF's 1999 Land and Resource Management Plan contains a forest-wide objective to sustain at least 45,000 to 55,000 acres of habitat suitable for FLSJ. The

Forest Service follows guidelines developed by USFWS's Florida Scrub Jay Recovery

Plan to protect FLSJ habitat (USDA 1999). Studies on jays in ONF have identified stands with stand ages between 2-10 years old as optimal habitat (Miller et al. 2012).

Foresters choose stands to harvest based on the need for FLSJ replacement habitat.

The current technique is to harvest stands nearby to 3-6 year old occupied stands. By the time the newly harvested stands become suitable for the jays, the occupied stands are approaching the age at which the jays find them less suitable. Presumably, the jays in the occupied stand will then have the opportunity to claim new territory in the nearby newly harvested stand (Franzreb and Zarnoch 2011).

Methods

Survey Stand Identification and Definition of Suitable Stands

For this study we identified stands to survey using an ArcGIS shapefile of all clear-cut management units (stands) in Ocala National Forest. In this way we could explore how characteristics of the stands affect occupancy throughout the width and length of the forest. We identified presumably suitable habitat by selecting stands harvested in the last 0-13 years. In order to find trends related to the distance to the nearest suitable stand, we identified stands which are "clustered" or "isolated". Our definition of an "isolated" stand is a functional patch, or small group of functional patches (up to 3), which is greater than 1 km from other functional patches. Our definition of a "clustered" stand is a functional patch which is less than 500 meters from

5 or more functional patches. We identified a total of 31 isolated stands and 247 clustered stands. We chose to survey all of the isolated stands and 31 of the clustered stands which had similar size and edge:area ratio as each of the isolated stands. We also chose 20 of the smallest stands which were in the suitable age range to survey. In addition to the stands that we surveyed, we received census data from the Florida Fish and Wildlife Commission's (FWC) FLSJ monitoring program in ONF. The FWC sampling design involves surveying stands within four 3 mi2 blocks located throughout

ONF. These blocks were chosen based on variables such as size, interspersion of functional patches, the amount of potential habitat, and the proximity to landscape features such as highways and water bodies (Miller 2012).

Originally, we chose to survey stands that were 2-10 YSH based on the findings of Miller and Franzreb and Zarchnoch that these ages were optimal for FLSJs. After a few weeks of censusing we found a large percentage of these stands were occupied.

We then decided to increase the survey to include stands 0-2 and 10-13 YSH. In the beginning we also planned to survey the 10 largest and 10 smallest stands which were

2-10 YSH. After a few weeks of censusing we found that all of the large stands were occupied. We then decided to survey the smallest 20 stands that were 0-13 YSH.

To survey the stands for occupancy, we used the FWC's FLSJ survey technique which involves playing a recording of territorial calls to elicit responses from the jays.

Measurements were taken in the morning (between sunrise and 11 am), when the birds are most likely to be responsive. We played recordings at the edge of the patch for 15 minutes. If FLSJ approached we recorded the patch as occupied. If we did not detect jays we used GIS to create survey points spaced approximately 50 meters apart,

forming a grid over the patch (Figure 3-1). Using GPS coordinates from the map we played the recording at each points for 15 minutes or until FLSJ were detected. If we found a patch to be unoccupied, we rechecked it 2 more times (on different days) to make sure there were definitively no jays residing there.

In order to confirm our assumption that stands in the ONF providing optimal habitat for FLSJ fall into the range of 10 years since last harvest (YSH), we conducted a classification tree analysis (CART). CART is a powerful approach to analyzing ecological data sets providing efficient screening of diverse predictor variables (De'ath and Fabricius 2000; Castellón and Sieving 2006). Classification tree analyses iteratively split a dataset with a categorical response variable into 2 homogeneous subsets. Each bifurcation uses only one most influential variable to create two homogeneous subsets; each of which may be split again using a different (or the same) predictor variable. The end result is a tree diagram that can function as a decision tree that conveys predictive power concerning the explanatory variables that best identify case membership in a given sample subset (or node; Breiman et al. 1984). Using CART with YSH as the only predictor of FLSJ occupancy in the stands we surveyed, we found that stands up to a maximum age of 12.5 years supported FLSJ occupancy with 74% probability (and stands older than 12.5 years were empty with an 84% probability; Figure 3-2). For this reason we revised our operational definition of optimal habitat in ONF as most likely to occur in stands less than 13 YSH.

Given that stands in ONF are areas defined by forest managers as management units for logging activities, and not based on jay use of space, we distinguished functional patches from stands in the following way. Given that we set up our sampling

regime based on stands, in order to define functional patches associated with censused stands we combined stands of suitable age (< 13 YSH) that were adjacent to censused stands into ‘functional patches’ of suitable habitat (Figure 3-1). If a stand of known occupancy was not touching a stand of suitable age (now defined as less than 13 YSH based on the findings of our CART analysis), it was left as a single entity. If an adjacent stand was touching another stand of optimal age (not touching the original), it was also merged. This process continued until the new patch conglomeration was not touching any stands of optimal age. New areas and edge:area ratios were calculated for these merged stands. Additionally, we calculated weighted ages for the new functional patches by summing the age x proportion of area of each individual stand in the new merged patch. For example, if a complex consisted of one patch that was 5 ha and 10 years old, and another that was 10 ha and 8 years old (total 15 ha when patches combined), we would calculated the weighted age like this: (5/15)*10+(10/15)*8= 8.67.

For each functional patch the distance to the nearest presumably functional patch (0-13

YSH) in any direction (DFP) was calculated using the measure tool in ArcMap (Figure 3-

1). If any stand within a functional patch complex was known to be occupied, the complex was categorized as occupied. Complexes with no known occupancy were categorized as unoccupied.

Data Analysis

With the newly defined functional patches we conduced another classification tree (CART) analysis. All predictor variables were included in this analysis (Table 3-1).

We additionally used the information theoretic approach with generalized linear models to determine the factors that influence stand occupancy (binomial distribution, logit link) and their effects.

Results

The CART analysis of the occupancy of patches, with all variables included

(Table 3-1) found that distance to closest functional patch (DFP) best divided the data at the first split (Figure 3-3). Of functional patches less than 539 meters DFP 78.5% of them were occupied while 45.5% of functional patches greater than 539 meters DFP were occupied. Of the functional patches with DFP less than 539 meters, edge:area ratio (meters:hectares) best split the data. Of the functional patches with an edge to area ratio less than 191, 84% of them were occupied while 41.7% of functional patches with an edge:area ratio greater than 191 were occupied. Of the functional patch/patch complexes with DFP greater than 539 meters, total area best divided the data. Those greater than 26 hectares were more likely to be occupied (70% occupied) than those less than (25% occupied).

Two significant binomial logistic regression models were obtained. The first includes the variables area and DFP (p=0.026 and p=0.033 respectively; Table 3-2).

Area was positively related to occupancy while DFP was negatively related. The second model includes the variables DFP and edge:area ratio (p=0.014 and p=0.025 respectively; Table 3-2); both were negatively related to occupancy in this model. These finding are in agreement with the findings of the CART analysis of patch occupancy.

Discussion

In this study we found that FLSJ occupancy of clear-cut stands in ONF is negatively related to distance to closest functional patch (DFP), positively related to area, and negatively related to edge:area ratio. These results are similar to other studies on occupancy of a limited mobile species and its relationship to landscape structure, such as Castellon and Sieving 2006. In their study, patch occupancy of the

Chucao Tapaculo (Scelorchilus rubecula) in South American forests of varying fragmentation was used to create occupancy models. They also found that occupancy was positively related to patch size and negatively related to the distance to suitable habitat. Numerous other studies have found similar relationships (Ferraz et al. 2007;

Knick and Rotenberry 1995; McCollin 1993). A variable that many of these studies included, and we did not, is the permeability of the matrix between functional patches. In the future, findings from chapter 1 about movement of FLSJ through stands unsuitable for occupancy could increase the predictive capabilities of our models.

Although we did not account for detectability in our analyses we are confident that this important bias was minimally influential in our results because of the responsiveness of this species to playback (Fitzpatrick et al. 1991). Like Castellon and

Sieving (2006), however we made every attempt to avoid false negatives by returning up to two additional times to unoccupied stands after the initial survey to insure that lack of response was due to lack of occupancy. In fact 38% of all birds in occupied stands that were first undetected were subsequently detected in the second visit and only 3% were detected in the third and final visit. This pattern suggested that 3 visits were sufficient to push our detection probability near to 100%.

Our findings that patch occupancy is affected by distance to the closest optimal patch, size, and edge:area ratio allows us to make some inferences about FLSJ decision making processes when establishing new territories. First and foremost, if jays do not have to travel far distances from their natal territory to establish new territory, they will be more likely to occupy an available functional patch. If FLSJ must travel far distances to find an available functional patch, they are more likely to choose a large

patch. These stands may be easier to find purely due to their size (Damschen et al.

2014). Large, isolated stands may be perceived as more suitable than small isolated stands because there is more room for territorial budding. Budding involves a helper male becoming a breeder in a section of its natal territory (FWC 2012). Small, isolated stands may not have enough room for budding, forcing fledglings to travel far distances to find new available habitat and mates. If there are stands of optimal age close to the natal territory, the jays may be more choosey about the shape of the patch. The CART analysis of patch occupancy demonstrates that they are more likely to selective one that has a small edge:area ratio. Stands with small edge:area ratios have large core areas and less edge area. Studies on Florida’s Atlantic coast have suggested that proximity to hardwood forest edges negatively affect FLSJ occupancy (Burgman et al. 2001). Our findings suggest jays in Ocala may have the same aversion to habitat edges.

The significance of these findings pertains to potential management of habitat connectivity within the Ocala National Forest, where the largest remaining population of the federally threatened FLSJ resides (USDA 1999). The CART analysis and regression models of patch occupancy allow us to make some recommendation for forest managers. We recommend that clear-cut stands should be within 540 meters of other presumably functional patches. If stands exceed this distance, they should be greater than 26 hectares in size. As edge:area ratio negatively affects occupancy, creating stands with small edge:area ratio (large core space) may benefit FLSJs. In the future our models could help estimate the total population of the ONF, with the additional information about functional patch densities (Miller 2012) and the permeability of the intervening matrix. As the last official estimate of the species total population was in

1994 by Fitzpatrick et al., these estimates could additionally help to create a more up to date estimate of the total population in the state.

Table 3-1. Predictor (X) and Response (Y) Variables of Occupancy Study X or Y Name Variable x S Type Y Occupancy Categorical 0.72 ± 0.04 X Stand Age/ Stand Complex Age Categorical 8.06 ± 0.28 X Patch area (hectares) Continuous 39.36 ± 4.03 X Edge:area ratio (meters:hectares) Continuous 137.59 ± 6.54 X Distance to closest optimal patch Continuous 409.71 ± 60.44 (DFP; meters)

Table 3-2 Logistic Regression of Patch Occupancy Covariates: area and DFP 95% C.I.for B S.E. Wald df Sig. Exp(B) EXP(B) Lower Upper Area 0.22 0.01 4.99 1 0.026 1.02 1 1.04 DFP -0.001 0 4.54 1 0.033 0.99 0.99 1 Constant 0.64 0.37 3.1 1 0.079 1.89 Covariates: DFP and edge:area ratio 95% C.I.for B S.E. Wald df Sig. Exp(B) EXP(B) Lower Upper DFP -0.001 0 6.02 1 0.014 0.99 0.99 1 Edge:Area -0.007 0 5.01 1 0.025 0.99 0.99 Constant 2.337 0.55 17.79 1 0 10.35

Figure 3-1. Demonstration of verifying unoccupied stands, functional stands, and distance to closest optimal patch A) Black dots are census points for verifying unoccupied stands. If we did not initially detect FSJs in a stand, we used the measure tool in ArcMap to create census points spaced approximately 50 meters apart, forming a grid over the patch. We then used GPS coordinates from the map to verify unoccupied stands. (See methods section) B) Stands of optimal age (0-13 years since harvest (YSH)) touching a surveyed stand were conglomerated to form a newly defined patch C) Distance to the closest functional patch (DFP) was calculated using the measure tool in ArcMap.

Figure 3-2. Classification and regression tree analysis (CART) of stands before merging to make "functional patches". % Total is the percentage of the total data set within the node, n is the number of samples within the node, and % Node is the percentage of the samples within the node.

Figure 3-3. Classification and regression tree analysis (CART) of the functional patches with all predictor variables included. % Total is the percentage of the total data set within the node, n is the number of samples within the node, and % Node is the percentage of the samples within the node. ,

CHAPTER 4 CONCLUSIONS

In the second chapter of this thesis we found that many FLSJ in Ocala were willing to move into stands unsuitable for FLSJ occupancy (the matrix). Regression models showed us that FLSJ are more likely to enter a matrix stand which is relatively old, has high percent pine, and low percent oak and open space. Importantly, the distance to the nearest presumably functional patch was also a significant factor.

Playback ‘pulls’ into matrix stands which had suitable habitat nearby elicited longer movements into the matrix, suggesting that FLSJs are aware of patch availability and configuration outside of their own occupied patch.

In the third chapter of this thesis we found that occupancy of clear-cut stands in

ONF is negatively related to distance to closest presumably functional patch, positively related to area, and negatively related to edge:area ratio (a reflection of shape). A classification and regression tree analysis of survey data allowed us to create recommendation for ONF forest managers. We recommend that clear-cut stands should be within 540 meters of other presumably functional patches. If stands exceed this distance, they should be greater than 26 hectares in size. As edge:area ratio negatively affects occupancy, creating stands with small edge:area ratios (large core space) may positively FLSJ occupancy

LIST OF REFERENCES

Andren, H. 1994. Effects of Habitat Fragmentation on Birds and Mammals in landscapes with Different Proportions of Suitable Habitat- A Review. Oikos 71:355-366.

Awade, M., and J. P. Metzger. 2008. Using gap-crossing capacity to evaluate functional connectivity of two Atlantic rainforest birds and their response to fragmentation. Austral Ecology 33:863-871.

Bergl, R. A., B. J. Bradleys, A. Nsubuga, and L. Vigilant. 2008. Effects of habitat fragmentation, population size and demographic history on genetic diversity: The Cross River gorilla in a comparative context. American Journal of Primatology 70:848-859.

Bowman, J., and L. Fahrig. 2002. Gap crossing by chipmunks: an experimental test of landscape connectivity. Canadian Journal of Zoology 80:1556–1561.

Breiman, L., J. H. Friedman, R. A. Olshen, and C. G. Stone. 1984. Classification and Regression Trees. Wadsworth International Group, Belmont, California, USA.

Burgman, M. A., D. R. Breininger, B. W. Duncan, and S. Ferson. 2001. Setting reliability bounds on habitat suitability indices. Ecological Applications 11:70-78.

Carothers, J.H. 1986. The effect of retreat site quality on interference-related behavior among Hawaiian Honeycreepers. Condor 88(4):421-426.

Castellon, T. D., and K. E. Sieving. 2006. An experimental test of matrix permeability and corridor use by an endemic understory bird. Conservation Biology 20:135- 145.

Cornelius, C., H. Cofre, and P.A. Marquet. 2000. Effects of Habitat Fragmentation on Bird Species ina Relict Temperate Forest in Semiarid Chile. Conservation Biology. 14:534-543.

Damschen, E.I., D.V. Baker, G. Bohrer, R. Nathan, J.L. Orrock, J.R. Turner, L.A. Brudvig, N.M Haddad, D.J. Levey, and J.J. Tewksbury. 2014. How fragmentation and corridors affect wind dynamics and seed dispersal in open habitats. Proc Natl Acad Sci 111(9): 3484–3489.

De'ath, G. and K.E. Fabricius. 2000. Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology 81(11): 3178-3192.

Desrochers, A., and Hannon, S.J. 1997. Gap crossing decisions by forest songbirds during the post-fledging period. Conservation Biology 11: 1204-1210.

Ewers, R. M. and R. K. Didham. 2006. Confounding factors in detection of species responses to habitat fragmentatin. Biological Reviews 81:117-142.

Ferraz, G., J.D. Nichols, J.E. Hines, P.C. Stouffer, R.O. Bierregaard, T.E. Lovejoy. 2007. A large-scale deforestation experiment: Effects of patch area and isolation on Amazon birds. Science. 315(5809):238-241.

Fitzpatrick, J.W., M.T. Kopeny and G.E. Woolfenden 1991. Ecology and development- related habitat requirements of the Florida scrub Jay (Aphelocoma coerulescens coerulescens). Nongame Wildlife Program, Technical Report. Florida Game and Fresh Water Fish Commission 8:49.

Franzreb, K. E., and S. J. Zarnoch. 2011. Factors Affecting Florida Scrub-Jay Nest Survival on Ocala National Forest, Florida. Journal of Wildlife Management 75:1040-1050.

Grubb, T.c., Jr., and Doherty, P.f., Jr. 1999. On home-range gap-crossing. Auk, 116: 618-628.

Hanski, I. 1994. A practical model of metapopulation dynamics. Journal of Animal Ecology 63:151-162. Jimenez, J. E. 2000. Effect of sample size, plot size, and counting time on estimates of avian diversity and abundance in a Chilean rainforest. Journal of Field Ornithology 71:66-87.

Hastings, A. and S. Harrison.1994. Metapopulation dynamics and genetics. Annual Review of Ecological Systems, 25, 167–188.

Hedenstrom, A. and M. Rosen. 2001. Predator versus prey: on aerial hunting and escape strategies in birds. Behavioral Ecology 12(2):150-156.

Jensen, J. R. 2007.Remote Sensing of the Environment: An Earth Resource Perspective. Upper Saddle River, NJ: Pearson Prentice Hall, 2007. Print.

Johnson, R. R., B. T. Brown, L. T. Haight, and J. M. Simpson. 1981. Playback recordings as a special avian censusing tool. Studies in Avian Biol. 6:66-75.

Knick, S.T. and J.T. Rotenberry. 1995. Landscape characteristics of fragmented shrubsteppe habitats and breeding passerine birds. Conservation Biology 9:1059-1071

Landsat Data Continuity Mission (Landsat 8) Landscape Toolbox. 2013. http://wiki.landscapetoolbox.org/doku.php/remote_sensor_types:landsat_8?s[]=la ndsat

McCollin, D.1993. Avian distribution patterns in a fragmented wooded landscape (North Humberside, UK): the role of between-patch and within-patch structure. Global Ecology and Biogeography Letters. 3(2):48-62.

Miller, K.E. 2012. Annual Report- Florida Scrub-Jay Monitoring Protocol for Ocala National Forest. Florida Fish and Wildlife Conservation Commission,

Ortego, J., J. M. Aparicio, P. J. Cordero, and G. Calabuig. 2008. Individual genetic diversity correlates with the size and spatial isolation of natal colonies in a bird metapopulation. Proceedings of the Royal Society B-Biological Sciences 275:2039-2047.

Ricketts, T. H. 2001. The matrix matters: effective isolation in fragmented landscapes. The American Naturalist 158:87–99.

Rizkalla, C. E., J. E. Moore, and R. K. Swihart. 2009. Modeling patch occupancy: Relative performance of ecologically scaled landscape indices. Landscape Ecology 24:77-88.

Robertson, O. J., and J. Q. Radford. 2009. Gap-crossing decisions of forest birds in a fragmented landscape. Austral Ecology 34:435-446.

Saunders D. A., R. J. Hobbs, and C. R. Margules. 1991. Biological consequences of ecosystem fragmentation: a review. Conservation Biology 5:18-32.

Sieving, K. E., M. F. Willson, and T. L. DeSanto. 1996. Habitat barriers to movement of understory birds in fragmented south-temperate rainforest. Auk 113:944-949.

Sieving KE, M.F. Willson, and T.L. De Santo 2000. Defining corridor functions for endemic birds of south-temperate rainforest. Conservation Biology 14: 1120- 1132.

Slagsvold, T.,J. Husek, and J.D. Whittington. 2014. Antipredator behavior: escape flights on a landscape slope. Behavioral Ecology. 25(2):378-385.

Smith, M. A., and D. M. Green. 2005. Dispersal and the metapopulation paradigm in amphibian ecology and conservation: are all amphibian populations metapopulations? Ecography 28:110-128.

Sonsthagen, S. A., McClaren, E. L., Doyle, F. I., Titus, K., Sage, G. K., Wilson, R. E., Gust, J. R. 2012. Identification of metapopulation dynamics among Northern Goshawks of the Alexander Archipelago, Alaska, and Coastal British Columbia. Conservation Genetics,13(4), 1045–1057.

U.S. Department of Agriculture Forest Service (USDA). "Management Area Goals, Desired Future Conditions, Standards, and Guidelines". LAND AND RESOURCE MANAGEMENT PLAN, 1999. Web. 10 Jan. 2013.

U.S. Fish and Wildlife Service (FWS). "Florida Scrub-jay." South Florida Multi Species Recovery Plan, 2012. Web. 10 Oct. 2012.

U.S. Fish and Wildlife Service (FWS). "Florida Scrub-jay." South Florida Multi Species Recovery Plan, 1990. Web. 10 Oct. 2012.

USGS Earth Explorer. 2013. http://earthexplorer.usgs.gov/ Web. 10 Nov. 2013.

Witt, W. C., and N. Huntly. 2001. Effects of isolation on red-backed voles (Clethrionomys gapperi) and deer mice (Peromyscus maniculatus) in a sage- steppe matrix. Canadian Journal of Zoology 79:1597–1603.

Woolfenden, G. E., and J. W. Fitzpatrick. 1977. Dominance in Florida Scrub-jays. Condor 79: 1-12.

Woolfenden, G. E., and J. W. Fitzpatrick.1984. The Florida Scrub Jay: Demography of a Cooperative- breeding Bird. Science. 227(4694):1573-1574.

BIOGRAPHICAL SKETCH

Amanda Abel was born in Highland Park, Illinois in 1988. She lived in Illinois and

Wisconsin until age 18 when she moved to Tampa, Florida to attend the University of

South Florida (USF). She received a Bachelor of Science degree in the field of environmental science and policy in 2012 at USF. That fall she began a Master of

Science degree in the school of Natural Resources and the Environment at the

University of Florida. She will graduate from UF in December 2014 and plans to begin a career in wildlife conservation shortly after.