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Modeling Population Viability and Habitat Suitability for the Endangered Wood Stork (Mycteria americana) in the Southeas...

Permanent Link: http://ufdc.ufl.edu/UFE0024999/00001

Material Information

Title: Modeling Population Viability and Habitat Suitability for the Endangered Wood Stork (Mycteria americana) in the Southeastern United States
Physical Description: 1 online resource (178 p.)
Language: english
Creator: Borkhataria, Rena
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: avian, demography, endangered, habitat, modeling, mycteria, population, stork, survival, viability
Wildlife Ecology and Conservation -- Dissertations, Academic -- UF
Genre: Wildlife Ecology and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: I modeled population dynamics and habitat suitability for the endangered Wood Stork (Mycteria americana) in the Southeastern United States using count data from synoptic aerial surveys and survival and movement data obtained using satellite telemetry. Using a simple, count-based diffusion approximation approach I determined that while the population as a whole has been relatively stable since 1976, population dynamics varied regionally. Georgia and South Carolina were shown to support increasing subpopulations while subpopulations in Florida were either stable or decreasing. High inter-year variability resulted in very wide confidence intervals, however, and I could not eliminate the possibility of long-term population decline in spite of recently measured population increases. The South Florida subpopulation had the highest probability of quasi-extinction, with a 69% probability of declining by 90% over the next 30 years. Quasi-extinction probabilities over the same time period were 34% for North and Central FL, 38% for GA, 0.4% for SC, and 24% for the Southeastern population as a whole. I compared these extinction probabilities to those obtained using a stage-based population matrix model to incorporate observed differences in survival rates among age classes. Data obtained via satellite telemetry was used to estimate age specific survival for Endangered Wood Storks in the Southeastern United States using a live-recovery, live-resight, and tag recovery model. These estimates were incorporated into a stage-based matrix model that was used to estimate the long-term population growth rate and the probability of a population decline of 50% or 90% over the next 50 years. I used an elasticity analysis to determine the stage with the largest influence on the population growth rate and a sensitivity analysis to determine the effects of a simultaneous increase in vital rates or the occurrence of sporadic ?boom? years. Survival was lowest for birds in their first year (0.2772) and highest for adults (0.8612). The stage based model indicated that the population was decreasing (? = 0.94), however, and the population declined by 50% in under 10 years. The model was most sensitive to changes in adult survival rates but a simultaneous increase in vital rates of 10% resulted in population growth (? = 1.03). When survival estimates from the Barker model were used, boom years had to occur approximately 30% of the time for the population to grow. I also examined the impact of environmental conditions on South Florida on Wood Stork population dynamics as a whole. Wood storks in South Florida have shown a marked change in phenology in response to hydrological changes in the Everglades and as a result of drainage and diversion of water from the Florida Everglades, Wood Storks have delayed nest initiation in the Everglades by 3-4 months. As a result, juvenile storks that fledge from these colonies do so at the beginning of the summer wet season, when water levels are high and rising. I used data from juvenile Wood Storks outfitted with satellite transmitters over 4 years to determine whether juvenile survival was influenced by the hydrological conditions into which the birds fledged. Survival rates were higher (0.3673) when storks fledged in low water levels ( < 20 cm) than when they fledged into conditions with high and rising water ( > 30 cm) and survival was low (0.1429). Storks also spent more time foraging in Everglades wetlands in drier years while in wetter years they were more likely to disperse into agricultural settings. I used these results to parameterize a stochastic demographic model in which frequency of wet and dry years varied. I projected a hypothetical population containing 2500 adult females forward 30 years and calculated the end population size under a variety of wet/dry scenarios. I found that dry conditions would have to occur at least 58% of the time for the population to remain stable. Because of the late date at which birds currently fledge, they are unlikely to encounter these conditions and under current conditions the population in south Florida would decline based on juvenile survival alone. This study underscores the need to restore the Everglades so that Wood Storks initiate nesting sooner. Finally, I used location data obtained using satellite telemetry to create a range-wide habitat suitability for the Wood Stork in the Southeastern U.S. Due to their wide range, extreme vagility, and opportunistic use of rapidly changing hydrological conditions, Wood Storks lack a critical habitat determination under the Endangered Species Act. Nonetheless, it is important to identify regions and habitats that are important to the Wood Stork in order to evaluate their current levels of protection and to foster inter-agency management and agreements. I used logistic regression (LR) and Mahalanobis distances (MD) to create habitat suitability maps for the Wood Stork across the Southeastern U.S. using locations obtained from satellite telemetry to identify used habitats. The models were validated using an independent dataset of satellite telemetry locations obtained from Wood Storks captured as part of another study, and compared using Receiver Operating Characteristic (ROC) curves. I found that the LR model provided the best overall fit to the data, but that each model had strengths and weaknesses in different portions of their range. I then used a composite of the 2 models to characterize regions and habitats of importance to the Wood Stork. This represents the first range-wide habitat suitability model for the Wood Stork in the Southeastern U.S.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Rena Borkhataria.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Frederick, Peter C.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-02-28

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024999:00001

Permanent Link: http://ufdc.ufl.edu/UFE0024999/00001

Material Information

Title: Modeling Population Viability and Habitat Suitability for the Endangered Wood Stork (Mycteria americana) in the Southeastern United States
Physical Description: 1 online resource (178 p.)
Language: english
Creator: Borkhataria, Rena
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: avian, demography, endangered, habitat, modeling, mycteria, population, stork, survival, viability
Wildlife Ecology and Conservation -- Dissertations, Academic -- UF
Genre: Wildlife Ecology and Conservation thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: I modeled population dynamics and habitat suitability for the endangered Wood Stork (Mycteria americana) in the Southeastern United States using count data from synoptic aerial surveys and survival and movement data obtained using satellite telemetry. Using a simple, count-based diffusion approximation approach I determined that while the population as a whole has been relatively stable since 1976, population dynamics varied regionally. Georgia and South Carolina were shown to support increasing subpopulations while subpopulations in Florida were either stable or decreasing. High inter-year variability resulted in very wide confidence intervals, however, and I could not eliminate the possibility of long-term population decline in spite of recently measured population increases. The South Florida subpopulation had the highest probability of quasi-extinction, with a 69% probability of declining by 90% over the next 30 years. Quasi-extinction probabilities over the same time period were 34% for North and Central FL, 38% for GA, 0.4% for SC, and 24% for the Southeastern population as a whole. I compared these extinction probabilities to those obtained using a stage-based population matrix model to incorporate observed differences in survival rates among age classes. Data obtained via satellite telemetry was used to estimate age specific survival for Endangered Wood Storks in the Southeastern United States using a live-recovery, live-resight, and tag recovery model. These estimates were incorporated into a stage-based matrix model that was used to estimate the long-term population growth rate and the probability of a population decline of 50% or 90% over the next 50 years. I used an elasticity analysis to determine the stage with the largest influence on the population growth rate and a sensitivity analysis to determine the effects of a simultaneous increase in vital rates or the occurrence of sporadic ?boom? years. Survival was lowest for birds in their first year (0.2772) and highest for adults (0.8612). The stage based model indicated that the population was decreasing (? = 0.94), however, and the population declined by 50% in under 10 years. The model was most sensitive to changes in adult survival rates but a simultaneous increase in vital rates of 10% resulted in population growth (? = 1.03). When survival estimates from the Barker model were used, boom years had to occur approximately 30% of the time for the population to grow. I also examined the impact of environmental conditions on South Florida on Wood Stork population dynamics as a whole. Wood storks in South Florida have shown a marked change in phenology in response to hydrological changes in the Everglades and as a result of drainage and diversion of water from the Florida Everglades, Wood Storks have delayed nest initiation in the Everglades by 3-4 months. As a result, juvenile storks that fledge from these colonies do so at the beginning of the summer wet season, when water levels are high and rising. I used data from juvenile Wood Storks outfitted with satellite transmitters over 4 years to determine whether juvenile survival was influenced by the hydrological conditions into which the birds fledged. Survival rates were higher (0.3673) when storks fledged in low water levels ( < 20 cm) than when they fledged into conditions with high and rising water ( > 30 cm) and survival was low (0.1429). Storks also spent more time foraging in Everglades wetlands in drier years while in wetter years they were more likely to disperse into agricultural settings. I used these results to parameterize a stochastic demographic model in which frequency of wet and dry years varied. I projected a hypothetical population containing 2500 adult females forward 30 years and calculated the end population size under a variety of wet/dry scenarios. I found that dry conditions would have to occur at least 58% of the time for the population to remain stable. Because of the late date at which birds currently fledge, they are unlikely to encounter these conditions and under current conditions the population in south Florida would decline based on juvenile survival alone. This study underscores the need to restore the Everglades so that Wood Storks initiate nesting sooner. Finally, I used location data obtained using satellite telemetry to create a range-wide habitat suitability for the Wood Stork in the Southeastern U.S. Due to their wide range, extreme vagility, and opportunistic use of rapidly changing hydrological conditions, Wood Storks lack a critical habitat determination under the Endangered Species Act. Nonetheless, it is important to identify regions and habitats that are important to the Wood Stork in order to evaluate their current levels of protection and to foster inter-agency management and agreements. I used logistic regression (LR) and Mahalanobis distances (MD) to create habitat suitability maps for the Wood Stork across the Southeastern U.S. using locations obtained from satellite telemetry to identify used habitats. The models were validated using an independent dataset of satellite telemetry locations obtained from Wood Storks captured as part of another study, and compared using Receiver Operating Characteristic (ROC) curves. I found that the LR model provided the best overall fit to the data, but that each model had strengths and weaknesses in different portions of their range. I then used a composite of the 2 models to characterize regions and habitats of importance to the Wood Stork. This represents the first range-wide habitat suitability model for the Wood Stork in the Southeastern U.S.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Rena Borkhataria.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Frederick, Peter C.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-02-28

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0024999:00001


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1 MODELING POPULATION VIABILITY AND HABITAT SUITABILITY FOR THE ENDANGERED WOOD STORK ( MYCTERIA AMERICANA ) IN THE SOUTHEASTERN UNITED STATES By RENA REBECCA BORKHATARIA A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2009

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2 2009 Rena Rebecca Borkhataria

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3 To Colin

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4 ACKNOWLEDGMENTS I am very grateful to the many people who ha ve supported and encouraged my efforts and that made my completion of this project possible. I give special thanks to my advisor, Peter Frederick, for his support on this project and for challenging me to think a bout problems in ways I wouldnt have otherwise. I would also like to thank the me mbers of the Wood Stork Working Group for sharing their ideas and data with me. I am especially grateful to Billy Brooks and Larry Bryan for working so closely with me. Their support has been invaluable. My husband, Colin Saunders, has been endl essly supportive, encour aging me to explore all my options and standing by me as I found the right path on the road toward my doctoral degree. With his encouragement, and that of my masters advisor Jaime Collazo, I was able to find and complete the project of my dreams. I would also like to thank my family for encouraging me to follow my bliss and fo r helping me get to where I am today. I would like to thank a ll of the people who assisted me w ith logistics, stork captures, and field work. Without their help I would never have been able to catch birds from across the Southeast for this study. In part icular, I would like to thank J ohn Simon and Carolyn Enloe, who were a joy to work with and Becky Hylton for show ing me the ropes. I would also like to thank Mary Beth Morrison, Dave Richardson, John Robi nette, Donna Bear-Hull, Warren Stephens, Jason Lauritson, Tom Murphy, Jim Rodgers, Chris Hanson, and Veronica Padula. Finally, I would like to acknowle dge the agencies that funded this research. I received financial support from the Environmental Protec tion Agency in the form of a Science to Achieve Results (STAR) Gradua te Fellowship, as well as from grants awarded by the Army Corps of Engineers, the U.S. Fish and Wild life Service, and the National Park Service.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ............................................................................................................... 4LIST OF TABLES ...........................................................................................................................8LIST OF FIGURES .......................................................................................................................10ABSTRACT ...................................................................................................................... .............15 CHAP TER 1 INTRODUCTION .................................................................................................................. 182 WOOD STORK POPULATION TRENDS IN THE SOUTHEASTERN UNITED STATES: A COUNT-BASED ANALYSIS ........................................................................... 22Introduction .................................................................................................................. ...........22Methods ..................................................................................................................................23Diffusion Approximation ................................................................................................ 23Comparison to Independent Data .................................................................................... 25Analysis of Density Dependence .....................................................................................26Stochastic Population Viability Analysis ........................................................................27Results .....................................................................................................................................29Diffusion Approximation ................................................................................................ 29Comparison to Independent Data .................................................................................... 30Density Dependent Analysis ...........................................................................................30Stochastic Population Viability Analysis ........................................................................31Density independent .................................................................................................31Comparison of density dependent an d density independent analyses ...................... 32Discussion .................................................................................................................... ...........33Conclusion .................................................................................................................... ..........343 AGE-SPECIFIC SURVIVAL IN THE WOOD STORK: IMPLICATIONS FOR LONG-TE RM VIABILITY OF THE SPECIES .................................................................... 52Introduction .................................................................................................................. ...........52Methods ..................................................................................................................................54Satellite Telemetry ........................................................................................................... 54Sexing ........................................................................................................................ ......56Juvenile Fledging Success and Survival ..........................................................................56Adult Survival .................................................................................................................60Demographic PVA ..........................................................................................................61Sensitivity An alysis ......................................................................................................... 62Results .....................................................................................................................................63

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6 Sexing ........................................................................................................................ ......63Juvenile Fledging Success and Survival ..........................................................................63Adult Survival .................................................................................................................64Demographic PVA ..........................................................................................................65Sensitivity An alysis ......................................................................................................... 66Discussion .................................................................................................................... ...........66Conclusion .................................................................................................................... ..........694 SURVIVAL OF JUVENILE WOOD STOR KS IN REL ATION TO EVERGLADES WATER LEVELS AND TIMING OF NESTING .................................................................79Introduction .................................................................................................................. ...........79Study Area and Methods ........................................................................................................ 83Satellite Telemetry ........................................................................................................... 83Water Levels and Recession Rates ..................................................................................84Survival of Young ........................................................................................................... 85Movement Patterns and Habitat Use ...............................................................................86Stochastic Simulation Modeling ......................................................................................87Results .....................................................................................................................................89Water Levels .................................................................................................................. ..89Recession Rates ...............................................................................................................90Survival ...................................................................................................................... ......91Use of Everglades Wetlands ............................................................................................ 92Mortality ..................................................................................................................... .....93Habitat Use ......................................................................................................................93Stochastic Modeling ........................................................................................................ 94Discussion .................................................................................................................... ...........95Conclusion .................................................................................................................... ..........975 A COMPARISON OF TWO RANGE-WIDE HABITAT SUITABILITY MODELS FOR T HE WOOD STORK ( MYCTERIA AMERICANA) IN THE SOUTHEASTERN UNITED STATES ................................................................................................................108Introduction .................................................................................................................. .........108Study Area and Methods ...................................................................................................... 111Spatial Analysis .............................................................................................................112Habitat Suitability Modeling .........................................................................................114Logistic regression .................................................................................................115Mahalanobis distances ............................................................................................116Model Testing ................................................................................................................117Results ...................................................................................................................................118Habitat Suitability Modeling .........................................................................................118Logistic regression .................................................................................................118Mahalanobis distances ............................................................................................119Model Comparison ........................................................................................................119Discussion .................................................................................................................... .........120Conclusion .................................................................................................................... ........124

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7 6 CONCLUSIONS .................................................................................................................. 144APPENDIX A MAPS USED FOR HABITAT ANALYSIS ........................................................................ 146B COMPARISON OF LOGISTIC REGRESSION AND MAHALANOBIS DISTANCE WOOD STORK HABI TAT SU ITABILITY MODELS ...................................................... 162LIST OF REFERENCES .............................................................................................................168BIOGRAPHICAL SKETCH .......................................................................................................177

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8 LIST OF TABLES Table page 2-1 Annual log population growth rates for the inter-census interval for SFL, NCFL, GA, SC and the Southeastern U.S. W ood Stork population as a whole. ................................... 36 2-2 Log population growth rates ( ), var iance in growth rates ( 2), and 95% confidence intervals from Wood Stor k nest count data........................................................................37 2-3 Parameter estimates and model selection c riteria for the dens ity dependent Ricker model and a density independent mode l of Wood Stork population growth. .................... 38 3-1 Locations of tag deployment on juvenile and adult birds included in analyses. Tags were deployed from 2002-2008. ........................................................................................ 71 3-2 Fledging success rates for j uvenile Wood Storks outfitted with satellite tags in 20022005, from approximately 27 d of age to departure from the colony. ............................... 72 3-3 Apparent annual surviv al by age and cohort. ..................................................................... 72 3-4 Apparent survival of Wood Stor ks in each age class from 2002-2007. ............................. 73 3-5 Model structure, AICc values, delta AICc and num ber of parameters for models of survival of juvenile Wood Storks outfitted with satellite tags in SFL (2002-2005) and GA (2005). .........................................................................................................................73 3-6 Survival estimates and confidence intervals for Model S1st yr sex, no cohort, age 2, 3+ .................73 3-7 Model structure, AICc values, delta AI Cc, and num ber of parameters for Barker models of survival of juvenile Wood Stor ks outfitted with satellite tags in SFL (2002-2005) and GA (2005). .............................................................................................74 3-8 Stable age distribution and number of fem ale birds in each age class for a hypothetical population with 10,000 adult fema les for 4 models with different survival parameters. UCL stands for the upper 95% confidence limit for the agespecific survival rate as calcul ated using the Barker model. ............................................. 75 4-1 Mean daily water depths and recessi on rates in May and June of 2002-2004 for Water Conservation Area 3A (CA3AVG) and Everglades National Park (NP-33). Positive rec ession rates indicate receding wa ter levels and negative recession rates indicate rising water levels. ................................................................................................99 4-2 Mean daily recession rate by month and frequen cy of receding vs. rising water levels over a 30 year time period (1978-2007) in Water Conservation Area 3. ........................ 100 4-3 Mean daily recession rate by month and frequen cy of receding vs. rising water levels over a 30 year time period (1978-2007) in Everglades National Park. ........................... 100

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9 4-4 First year survival estimates by cohort for juvenile birds fledged in SFL in 20022005..................................................................................................................................101 4-5 Model structure, AICc values, delta AICc and num ber of parameters for models of first year survival for juvenile Wood Storks outfitted with satellite transmitters in 2002-2005 in SFL. ...........................................................................................................101 4-6 Mean total population size and mean number of breeding fem ales at the end of 30 years for an initial population of 4370 fe male birds (2500 breeding females) when the probability of juveniles fledging into favorable conditions varies from 0-1. ............102 5-1 Locations of tag deployment on juvenile a nd adult birds included in in itial dataset. Tags were deployed from 2002-2008. .............................................................................125 5-2 Results and regression coefficients from logistic regression of used vs. available habitats in relationship to environm ental variables. The land cover types represent proportions of each habitat type in 3.219 x 3.219 grid cells and were arcsin-root transformed. The remaining variables were log + 1 transformed. .................................. 126

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10 LIST OF FIGURES Figure page 2-1 Relationship between the num ber of nests in year t and the population growth rate over the next interval for the Southeas tern Wood Stork population as a whole. ............... 39 2-2 Relationship between the num ber of nests in year t and the population growth rate over the next interval for th e SFL Wood Stork subpopulation. ......................................... 40 2-3 Relationship between the num ber of nests in year t and the population growth rate over the next interval for the NCFL Wood Stork subpopulation. ...................................... 41 2-4 Relationship between the number of nests in year t and the population growth rate over the next interval for th e GA W ood Stork subpopulation. ..........................................42 2-5 Relationship between the num ber of nests in year t and the population growth rate over the next interval for the SC Wood Stork subpopulation. ........................................... 43 2-6 Cumulative probability of the Southeas tern US Wood Stork population d eclining by 50% (gray) or 90% (black) over the next 100 years based on diffusion approximation results. The solid line represents best estimates and the dashed lines represent 95% confidence intervals. ..........................................................................................................44 2-7 Cumulative probability of the SFL subpopulatio n of Wood Storks declining by 50% (gray) or 90% (black) over the next 100 years based on diffusion approximation results. ...................................................................................................................... ..........45 2-8 Cumulative probability of the NCFL subpopulation of W ood Storks declining by 50% (gray) or 90% (black) over the next 100 years based on diffusion approximation results. ...................................................................................................................... ..........46 2-9 Cumulative probability of the GA subpopulatio n of Wood Storks declining by 50% (gray) or 90% (black) over the next 100 years based on diffusion approximation results. ...................................................................................................................... ..........47 2-10 Cumulative probability of the SC subpopulation of W ood Storks declining by 50% (gray) or 90% (black) over the next 100 years based on diffusion approximation results. ...................................................................................................................... ..........48 2-11 Cumulative probability of the SFL subpopul atio n of Wood Storks declining by 90% (black) over the next 100 y ears for the density dependent and density independent models. ....................................................................................................................... ........49 2-12 Cumulative probability of the GA subpopulatio n of Wood Storks declining by 90% (black) over the next 100 y ears for the density dependent and density independent models. ....................................................................................................................... ........50

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11 2-13 Cumulative probability of the SC subpopulation of W ood Storks declining by 90% (black) over the next 100 y ears for the density dependent and density independent models. ....................................................................................................................... ........51 3-1 Locations where satellite transmitters have been deployed on juvenile (triangles) and adult (circles) W ood St orks from 2002-2007. ................................................................... 76 3-2 Future population size over the next 50 years when determ inistic matrix is parameterized using apparent or estimated survival rates. ................................................ 77 3-3 Population size (adult females) after 50 years when the proportion of boom years varied from 0-1. The gray dashed line represents the starting population size of 10,000 adult females. .........................................................................................................78 4-1 Timing of the reproductive cycle of th e W ood Stork. Bars show the approximate timing of fledging for nests initiated in th e first week of each month of the breeding season. If storks began nesting in the firs t week of the month (shown in dark gray), juveniles would fledge approximately 105-130 days later (shown in black). ................. 103 4-2 Map of Florida showing the locations of the 2 colo nies where satellite transmitters were deployed on juvenile Wood Storks, Tamiami West (circle) and the Palm Beach Solid Waste Authority Rookery (square). The dark green area depicts the Water Conservation Areas and Everglades National Park, which contain the wetlands that comprise the remaining Florida Everglades. ................................................................... 104 4-3 Daily water depths during the year for W ater Conservation Area 3A (a) and Everglades National Park (b) in 2002-2004. Arrows indicate the month that the majority of birds fledged in each year. Gaps in the time series indicate missing data. .. 105 4-4 Locations of Wood Storks in 2004 (black circles) and 2005 (white circles) during the months of June and July. The yello w area shows the m inimum convex polygon of locations from 2005. ........................................................................................................106 4-5 Estimated mean total population size after 30 years as a function of the probability that juveniles fledge into optimal hydrologi cal conditions (low and/or receding water levels). The initial population consisted of 4370 individuals. ........................................ 107 5-1 Locations where Wood Storks were captu red outfitted with G PS-enabled satellite transm itters. ................................................................................................................. .....127 5-2 Overlapping 90% kernel hom e ranges for 47 Wood Stor ks created from daily GPS locations obtained via satel lite telemetry, used to repr esent available habitat. ...............128 5-3 Gray cells represent the grid of used locations constructed from the individual location points obtained from juvenile a nd adult Wood Storks outfitted with GPS enabled satellite transmitters from 20 02-2008 (training datase t). Cells are 3.219 x 3.219 km ..................................................................................................................... ....129

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12 5-4 Grid of locations contai ning individual location points obtained from juvenile Wood Storks outfitted with satellite transmitte rs from 2002-2003 (independent dataset). ........ 130 5-5 Habitat suitability model construc ted from logistic regression with P -values indicating the long term probability of use. ..................................................................... 131 5-6 The proportion of cells that were actually used for cells with P -values ranging from 0-0.79. Here P is equal to the probability of use as calculated from the LR model. There were no cells with P -values > 0.79. ....................................................................... 132 5-7 Habitat suitability model constr ucted from Mahalanobis distances. P -values are based on the Chi-Square distribut ion and indicate similarity to ideal habitat, with 1 representing the ideal. ......................................................................................................133 5-8 The proportion of cells that were actually used for cells with P -values ranging from 0-1. Here P is equal to the similarity between each cell and the mean vector of environmental variables representing ide al habitat as calculated from the MD model................................................................................................................................134 5-9 Receiver operating characterics (ROC) plot for the training d ataset comparing the LR (black squares) and MD (gray diam onds) habitat suitability models. ............................. 135 5-10 Receiver operating characterics (ROC) pl ot for the independ ent dataset comparing the LR (black squares) and MD (gray diamonds) habitat suitability models. ................. 136 5-11 Total number of cells in each habitat suitab ility model as the P -value associated with the remaining cells increased ...........................................................................................137 5-12 Habitat suitability maps based on the A) the LR model and B) the MD model. The dark areas represent all cells with a P -value 0.23, while the lighter cells have P values < 0.23. ...................................................................................................................138 5-13 Model overlap for cells with P 0.23 from both the LR and MD models. The dark green areas represent actual overlap, while the blue cells represent nonoverlapping cells from the LR model and the pink cel ls represent nonoverlapping cells from the MD model. ..................................................................................................................... ..139 5-14 Suitable vs. unsuitable habitat for Wood St orks across the Southeastern U .S. using the combined LR and MD habitat suitability models (cells with P 0.23). ................... 140 5-15 Model testing using the training data set upon which the m odel construction was based. The training dataset was constructe d using GPS locations from 47 juvenile and adult Wood Storks that were outf itted with satellite tags in 2004-2008. .................. 141 5-16 Model validation using an independent dataset from 51 juvenile Wood Storks outfitted with satellite tags in 2002-2003. ........................................................................142

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13 5-17 Percentage of cells from the training and independent data sets that were included in the overlapping LR and MD m odels are in dark gray, those occurring within one model or the other but not in both in the bl ack and white crosshatched area, and those that were not included in either m odel (p < 0.23) are in light gray. ................................ 143 A-1 Map of land cover types used in the analyses. ................................................................. 146 A-2 Map showing percent of cell made up of agricultural habitats for 3.198 x 3.198 km cells (2 x 2 miles). .......................................................................................................... ..147 A-3 Map showing percent of cell made up of developed areas for 3.198 x 3.198 km cells (2 x 2 miles). ....................................................................................................................148 A-4 Map showing percent of cell made up of e mergent wetlands for 3.198 x 3.198 km cells (2 x 2 miles). .......................................................................................................... ..149 A-5 Map showing percent of cell made up of forested wetlands for 3.198 x 3.198 km cells (2 x 2 m iles). .......................................................................................................... ..150 A-6 Map showing percent of cell made up of grasslands for 3.198 x 3.198 km cells (2 x 2 miles)......................................................................................................................... .......151 A-7 Map showing percent of cell made up of m arine and estuarine areas for 3.198 x 3.198 km cells (2 x 2 miles). ............................................................................................ 152 A-8 Map showing percent of cell made up of shrub/scrub habitats for 3.198 x 3.198 km cells (2 x 2 miles). .......................................................................................................... ..153 A-9 Map showing percent of cell made up of upland forests for 3.198 x 3.198 km cells (2 x 2 miles)..................................................................................................................... .....154 A-10 Map showing total length of artificial water paths within 3.198 x 3.198 km cells (2 x 2 m iles)....................................................................................................................... ......155 A-11 Map showing total length of canals a nd ditches within 3.198 x 3.198 km cells (2 x 2 miles)......................................................................................................................... .......156 A-12 Map showing total length of streams a nd rivers within 3.198 x 3.198 km cells (2 x 2 miles)......................................................................................................................... .......157 A-13 Map showing local habitat diversity as represented by the numb er of different land cover types within 3.198 x 3.198 km cells (2 x 2 miles). ................................................ 158 A-14 Map showing regional hab ita t diversity as represente d by the mean number of different land cover types within 3.198 x 3.198 km cells (2 x 2 miles) for cells within a 5 cell radius. ..................................................................................................................159 A-15 Map showing mean elevation (m) with in 3.198 x 3.198 km cells (2 x 2 miles). ............. 160

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14 A-16 Map showing variability in elevation as represented by the standard deviation of m ean elevation within 3.198 x 3.198 km cells (2 x 2 miles). .......................................... 161 B-1 Comparison of the Logistic Regressi on (LR) (A) and the Mahalanobis Distance (MD) (B) Wood Stork habitat suitability m odels. The darker cells represent those with a P -value or probability of use 0.1 .......................................................................163 B-3 Comparison of the Logistic Regressi on (LR) (A) and the Mahalanobis Distance (MD) (B) Wood Stork habitat suitability m odels. The darker cells represent those with a P -value or probability of use 0.3. ......................................................................165 B-4 Comparison of the Logistic Regressi on (LR) (A) and the Mahalanobis Distance (MD) (B) Wood Stork habitat suitability m odels. The darker cells represent those with a P -value or probability of use 0.4. ......................................................................166 B-5 Comparison of the Logistic Regressi on (LR) (A) and the Mahalanobis Distance (MD) (B) Wood Stork habitat suitability m odels. The darker cells represent those with a P -value or probability of use 0.5. ......................................................................167

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15 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy MODELING POPULATION VIABILITY AND HABITAT SUITABILITY FOR THE ENDANGERED WOOD STORK ( MYCTERIA AMERICANA ) IN THE SOUTHEASTERN UNITED STATES By Rena Rebecca Borkhataria August 2009 Chair: Peter Frederick Major: Wildlife Ecology and Conservation I modeled population dynamics and habitat suitability for the endangered Wood Stork ( Mycteria americana) in the Southeastern United States using count data from synoptic aerial surveys and survival and movement data obtaine d using satellite telemetry. Using a simple, count-based diffusion approximation approach I determined that while the population as a whole has been relatively stable since 1976, population dynamics varied regionall y. Georgia and South Carolina were shown to support in creasing subpopulations while s ubpopulations in Florida were either stable or decreasing. High inter-year variability resulted in very wide confidence intervals, however, and I could not eliminat e the possibility of long-term population decline in spite of recently measured population increases. The South Florida subpopulation had the highest probability of quasi-extinction, with a 69% proba bility of declining by 90% over the next 30 years. Quasi-extinction probabi lities over the same time period were 34% for North and Central FL, 38% for GA, 0.4% for SC, and 24% for the Southeastern population as a whole. I compared these extinction probabilities to those obtained using a stage-based population matrix model to incorporate observed differences in survival rates among age classes. Data obtained via satellite telemetry was used to estimate age specific survival for endangered Wood

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16 Storks in the Southeastern United States usi ng a live-recovery, live-re sight, and tag recovery model. These estimates were incorporated into a stage-based matrix model that was used to estimate the long-term population growth rate an d the probability of a population decline of 50% or 90% over the next 50 years. I used an elas ticity analysis to determine the stage with the largest influence on the population growth rate and a sensitivity analysis to determine the effects of a simultaneous increase in vita l rates or the occurren ce of sporadic boom years. Survival was lowest for birds in their first year (0.2772) and highest for adults (0.8612). The stage based model indicated that the population was decreasing ( = 0.94), however, and the population declined by 50% in under 10 years. The model wa s most sensitive to changes in adult survival rates but a simultaneous increa se in vital rates of 10% re sulted in population growth ( = 1.03). When survival estimates from the Barker model were used, boom years had to occur approximately 30% of the time for the population to grow. I also examined the impact of environmen tal conditions in South Florida on Wood Stork population dynamics as a whole. Wood storks in South Florida have shown a marked change in phenology in response to hydrological changes in the Everglades and as a result of drainage and diversion of water from the Florida Everglades, Wood Storks have delayed nest initiation in the Everglades by 3-4 months. As a re sult, juvenile storks that fledge from these colonies do so at the beginning of the summer wet season, when wate r levels are high and rising. I used data from juvenile Wood Storks outfitted with satellite transmitters over 4 years to determine whether juvenile survival was influen ced by the hydrological conditions into which the birds fledged. Survival rates were higher (0.3673) when storks fledged in low wa ter levels (< 20 cm) than when they fledged into conditions with high and risi ng water (> 30 cm) and su rvival was low (0.1429). Storks also spent more time foraging in Everglad es wetlands in drier years while in wetter years

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17 they were more likely to disperse into agricultu ral settings. I used these results to parameterize a stochastic demographic model in which frequenc y of wet and dry years varied. I projected a hypothetical population containing 2500 adult females forward 30 years and calculated the end population size under a variety of wet/dry scenarios. I found that dry conditions would have to occur at least 58% of the time for the population to remain stable. Because of the late date at which birds currently fledge, they are unlikely to encounter these conditions and under current conditions the population in south Florida would d ecline based on juvenile survival alone. This study underscores the need to restore the Everglades so that Wood Storks initia te nesting sooner. Finally, I used location data obt ained using satellite telemetry to create a range-wide habitat suitability for the Wood Stork in the Southeaste rn U.S. Due to their wide range, extreme vagility, and opportunistic use of rapidly changing hydrological conditions, Wood Storks lack a critical habitat determination under the Endangered Sp ecies Act. Nonetheless, it is important to identify regions and habitats that are important to the Wood Stork in order to evaluate their current levels of protection a nd to foster inter-agency management and agreements. I used logistic regression (LR) and Mahalanobis distances (MD) to create habitat suitab ility maps for the Wood Stork across the Southeastern U.S. using locations obtained from satellite telemetry to identify used habitats. The models were vali dated using an independent dataset of satellite telemetry locations obtained from Wood Storks ca ptured as part of a nother study, and compared using Receiver Operating Character istic (ROC) curves. I found that the LR model provided the best overall fit to the data, but that each model had strengths and weaknesses in different portions of their range. I then used a composite of the 2 models to characterize regions and habitats of importance to the Wood Stork. This represents th e first range-wide habitat suitability model for the Wood Stork in the Southeastern U.S.

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18 CHAPTER 1 INTRODUCTION The W ood Stork ( Mycteria americana, Ciconiiformes) is distinctive among wetland birds breeding in the United States as the largest wadi ng bird and the only stork. Although the species also breeds in Central and South America, the subpopulation occuring in the Southeastern US is considered separate and distinct from the other subpopulations. In contrast to egrets and he rons, which locate prey primar ily by sight, Wood Storks use a grope-foraging technique, or tactilocation, to locate their pr ey. This technique requires a high density of prey, which generally occur epheme rally as wetlands under go seasonal changes in water levels. In South Florida, the seasonal drying cycle of the Everglades once provided abundant foraging opportunities over the course of the breeding season. Prio r to the 1960s, the majority of Wood Storks nested in the South Fl orida. In the 1960s, however, extensive changes were made to the hydrology of the Everglades as these wetlands were impounded and contained within a series of Water Conservation Areas (WCA) (Light and Dineen 1994). The natural drying cycle has been disrupted as water is now accumulated and held within the WCAs, and although seasonal drying still occurs the timing, du ration, and spatial extent of the drying front have been altered. These changes have result ed greatly decreased nesting in Everglades colonies. The marked decrease in nesting by birds in South Florida led to the Wood Stork being declared endangered by the United States Fish and Wildlife Service in 1984 (U.S. Fish and Wildlife Service 1996, Coulter et al. 1999). While numbers prior to 1960 are subject to debate and may never have exceeded 10,000-15,000 breedi ng pairs (Kushlan and Frohring 1986, Ogden 1994, Coulter et al. 1999), there was a well-documented decrease from an estimated 10,060 pairs in 1960 to approximately 5,110 pairs in 1976 (Ogde n and Nesbitt 1979) and then to a low of

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19 2,520 pairs in 1978 (Coulter et al. 1999, U.S. Fish a nd Wildlife Service 2002). Since then, there has been evidence of an increasing trend in ove rall numbers of nesting storks (Ogden et al. 1987, Brooks and Dean 2008) and in the number of no nbreeding storks counted in Christmas Bird Counts (McCrimmon et al. 1997), suggesting an incr ease in population size. There has been a well documented increase in th e breeding range of the Wood Stork as well (Ogden et al. 1987, Brooks and Dean 2008). Prior to the 1970s, the majority of Wood Storks nested in se veral large colonies in South Florida (SFL) (Ogden and Nesbitt 1979). Since the late 1970s, however, storks have exhibited a considerable northward shift in their breeding range (Ogden et al. 1987) with birds now breeding reliably in Central and North Florida (CNFL) (Rodgers et al. 2008), Ge orgia (GA) (Bryan and Robinette 2008, Winn et al. 2008 ), and South Carolina (SC) (Murphy and Coker 2008). This range expansion, coupled with increasing nest c ounts and high productivity in northern colonies, has led to an optimistic assessment of Wood St ork recovery (Borkhataria et al. 2008, Brooks and Dean 2008) and raised the possibili ty of the reclassifi cation of the species from endangered to threatened in the foreseeable future. In order to make informed decisions regarding the future management of the species, it is important to understand Wood Stork population trends across their range, a nd how the species is likely to be affected by current and future land-use or climate ch anges. This requires knowledge of Wood Stork abundance, survival rates, and ha bitat use across the enti re Southeastern US. Integrating knowledge across their range is especially important because, due to the ephemeral nature of prey concentrations, Wood Storks tend to range widely and can cover large distances over relatively short amounts of tim e, exploiting a variety of habitat types in different portions of their range. Until recently, however, such inform ation was nearly impossible to obtain, but

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20 advances in satellite telemetry now make it possible to follow the fates and movements of individual Wood Storks across th eir entire range and over a sp an of several years. I used satellite telemetry to follow the fates of juvenile and adult Wood Storks across the Southeastern United States from 2004-2008. In 2004 and 2005, I deployed a total of 68 satellite transmitters on juvenile birds at colonies in Sout h Florida and Georgia. I used data from these birds to quantify fledging success and subsequent survival, adding to an existing dataset of juvenile birds collected by Hylt on (2004). I also deployed or co llaborated in the deployment of 40 transmitters on adult Wood Storks in FL, GA, and SC. I used survival data from these birds to model population dynamics for the South eastern U.S. Wood Stork population. To quantify general Wood Stork population tr ends, I used existing data on Wood Stork nesting numbers across their range to determin e whether Wood Storks were increasing or decreasing, and whether these trends varied by region. I then compar ed these results to population projections based on age-specific survival estimates I obtained fo r juvenile and adult birds. Because of the histori cal importance of South Florida colonies, I also examined the influence of Everglades water levels on the survival of chicks fl edging from this region and how those effects influenced populati on dynamics as a whole. Finally, I used locations from both juvenile and adult Wood Storks to build the fi rst range-wide habitat suitability model for this species. This study represents the first complete dem ographic population viabili ty analysis for the Wood Stork in the Southeast U.S., as it incorporates both juvenile and adult survival rates. It is also the first to link juvenile survival to the environmental co nditions encountered by juvenile birds as they disperse from their natal col ony. These advances in the understanding of Wood Stork population dynamics and their vulnerabili ty to environmental conditions will be

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21 instrumental in the management of this species across its range, as will the habitat suitability model for the entire Southeast U.S. These models may also provide guidance for future decisions regarding the status of the Wood Stork as endangered or threatened.

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22 CHAPTER 2 WOOD STORK POPULATION TRENDS IN THE SOUTHEASTERN UNITED STATES: A COUNT-BASED ANALYSIS Introduction Population v iability analyses (PVA) provide a formal and widely accepted means for predicting future population scen arios and likelihood of extincti on under different management or environmental scenarios (Dennis et al 1991, Beissinger and Westphal 1998, Holmes and Fagan 2002, Morris and Doak 2002, Staples et al. 2004) The simplest approach to PVA is the count-based, or diffusion approximation (DA), a pproach. When its assumptions are met, DA allows the use of simple count data to pred ict population growth ra tes and probabilities of decline when detailed information on demograp hic parameters is lacking (Morris and Doak 2002, Holmes 2004). The assumptions of DA are: 1) the population growth rate and its variance do not change over time, 2) there is no e nvironmental autocorrelation, 3) there are no catastrophes or bonanzas, and 4) there is no observation error (Morris and Doak 2002). Modifications of the basic DA are available, how ever, in the event that these assumptions are violated. Count data for the Wood Stork have been co llected intermittently since 1975 by the U.S. Fish and Wildlife Service as part of their synoptic aerial survey monito ring program (Brooks and Dean 2008). Most surveys focused on known colonies, however, with new colonies added opportunistically when encountered along survey routes or reported by citizens or biologists, creating potential biases in population estimates (Frederick and Meyer 2008). Another independent source of count data for the Wood Stork is the National Audubon Societys Christmas Bird Count, which has been used in previous analyses of Wood Stork population trends (McCrimmon et al. 1997).

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23 Here I present a population viability analysis for the southeastern U.S. population of Wood Storks as a whole and in each of four sub-regions using a count based approach. I used this approach to address the following questions at both scales: 1) Is the Wood Stork population or sub-population increasing or decreasing? 2) Are population trends obtai ned from nest count data supported by independent counts of nonbreedi ng birds? 3) Are population dynamics density dependent, and if so, how does density depe ndence influence population growth rates and probabilities of declining over time? 4) Which subpopulation contributes th e most or least to overall population growth? And 5) What are th e longterm probabilities of the population or subpopulation declining by 50 or 90%? Methods Diffusion Approximation I used Wood Stork nest c ount data (Brooks and Dean 2008) to perform a count-based population viability analysis. These data reflect peak nest counts from aerial surveys conducted from 1975-2006 in Florida (FL), Georgia (GA), and S outh Carolina (SC) as part of a range-wide monitoring program. In South Florida (SFL), GA, and SC these counts were conducted annually over this 31 year time frame, while in No rth and Central Florida (NCFL) counts were intermittent. Pooled data for the entire Southeast were therefore intermittent, owing to gaps in the NCFL data. I also considered nest counts of zero for any given region to be a gap in the dataset, since the absence of ne sting did not imply population extinction but rather was likely to reflect an extreme response to stochastic environmental conditions. I conducted PVAs for each of the four regions or subpopulations (SC, GA NCFL, and SFL) and for the Southeast U.S. population (SE) as a whole. For simplicity, I considered each subpopulation to be independent of the others and did not attempt to in corporate movement among subpopulations.

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24 I calculated the density-independe nt population growth rate by regressing the natural log (hereafter referred to as log) of the change in number of nests over th e interval between counts against the time elapsed. To meet the assumpti on of equal variances in the dependent variable over time, a square-root transformation on the time between counts was used, and the change in nest numbers between counts was log transf ormed, to obtain the regression equation: log( Ni+1/ Ni) / iitt 1 = log( Ni+1/ Ni) / xi = log i (2-1) where Ni is the nest count at time i Ni+1 is the count at the next time interval, and ti + 1 ti (or xi) is the time elapsed between count s (Morris and Doak 2002) (see Table 2-1). I calculated the slope and the residual mean square of the regr ession, to obtain the geometric mean of the log population growth rate ( ) and its variance (2), respectively (Dennis et al. 1991, Morris and Doak 2002). I also calculated the 95% confidence intervals for each of these estimates using the equation ( +/t 0.05, q-1) (2-2) where t is the t statistic and q is the total number of transitions between counts. The confidence interval for 2 was calculated as: ((q 1) 2/ 2 0.025, q-1, ( q 1) 2/ 2 0.975, q-1). (2-3) I tested for first-order temporal autocorrelation using the Durbin-Watson test statistic in SAS 9.2. I also performed a preliminary test fo r density dependence by regressing the log of the population growth rate between successive counts [log( Nt+1/ Nt)] against the number of nests at time t ( Nt) (Morris and Doak 2002). If the interval between counts was greater than 1 year ( t > 1), I did not include data from that year ( Nt) in the analysis of density dependence. A significant negative relationship between nest counts and the subsequent popul ation growth rate was taken as evidence of density dependence, indicating that popula tion growth slowed or decreased as the

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25 number of nests increased. I identified outliers in each dataset using the studentized residuals from the regression of log[( Nt+1)/Nt] versus time elapsed and the Di ffits Statistic. The Diffits Statistic measures the influence of each obser vation on the regression parameter estimates by calculating the change in the predicted value of each observation caused by deleting that observation from the dataset. If an observation had a studentized residual > than 2 and a Diffits value > than N /1* 2 it was considered to be an outlier (Morris and Doak 2002). I used my best judgment to decide whethe r to exclude outliers from furt her analyses. If the value represented extreme fluctuations in growth ra tes at low population sizes for subpopulations that were newly established (GA, SC), I did not exclude them from the dataset. If they seemed to represent a very extreme environmental event that resulted in no or severely reduced nesting, I excluded them from further analysis. This resulte d in the exclusion of one outlying value in the South Florida data set (1978). Because Wood Storks have been more actively managed since their listing as endangered, I tested for differences in their popula tion growth rate before and after listing. I calculated and 2 for the Southeast count data for th e two time periods separately (1975-1984, 1985-2006) and tested for differences in their vari ances by calculating the ratio of the two with the larger estimate in the numerator, and the probability of observing such a ratio using the appropriate F statistic (Morris and Doak 2002). Given equal variances, I compared estimates of from the two time periods using a two-sample t-te st and decided a priori that if they differed significantly I would base further analysis on the latter dataset. Comparison to Independent Data I used Christmas Bird Count (CBC) data (NAS 2002) from 1975-2006 as an alternative data source with which to check consistency with the nest count data with respect to the general

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26 trend in population growth for the Southeastern population as a whole. The National Audubon Society conducts CBCs each year, with volunteers c ounting all of the birds seen or heard within a 10.7 m radius count circle. I obtained counts ( N ), represented as number of storks observed per party hour, from the Southeastern U.S. (SC, GA, FL, AL) and used the count data from those 31 years to calculate the log populat ion growth rate, calculating log(Nt+1/ Nt) for each intercensus interval and regressing it against the time elapsed using the standard diffusion approximation approach to obtain and 2 for this dataset. Analysis of Density Dependence To gain insight into whether the observe d relationship between nest counts and population growth rates was likely to be attributable to real de nsity dependence, rather than occurring as an artifact of in terannual variability or observation error, I used nonlinear regression to test whether a density depende nt model would provide a better fit to the count data than a density independent model. I modeled populat ion growth using the Ricker model, which assumes that population growth rates decrease li nearly as the population size increases, as the density dependent model. The Ricker model is expressed as: Nt+1 = Nt exp K N rt1 (2-4) where r is the intrinsic or instantaneous growth rate and K is equal to the carrying capacity of the population (Gotelli 2001, Morris and Doak 2002) The corresponding density independent model was simply Nt+1 = rNt. Because r and K were unknown for both models, I used nonlinear regression to estimate r for several values of K The values I chose represent K were 1) Kaveragethe average number of nests counted over the past 5 years, 2) Kcurrentcurrent population size (nests counted in 2006), 3) Khalf half of the current population size, and 4) Kdoubletwice the current population size.

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27 Because the nonlinear fitting of these population models required an uninterrupted dataset, I performed these analyses for the SFL, GA, and SC subpopulations only. For SFL, I did not use count data from years prior to 1979, in order to exclude the 1978 outli er from the analysis. I compared the Ricker and density inde pendent models by estimating each models maximum log likelihood using: logLmax = 2 q [log(2Vr) + 1] (2-5) where q = the number of data points, and Vr is the residual variance, obtained by dividing the error sums of squares of the regression by the nu mber of data points used in the regression. I then calculated the corrected Akaikes Information Criteria (AICc) for each model using: AICc = -2 logLmax + 2pq / q-p1 (2-6) where q again equals the numb er of data points and p equals the number of parameters in the model. The model with the lowest AICc was considered to provide the best fit for the data. Akaikes weights were also assigned to each model using: wi = R i bestc ic bestc icAIC AIC AIC AIC1 ,)] (5.0exp[ )] (5.0exp[ (2-7) where AICc,i is the AICc for model i AICc,best is the AICc for the best model (the model with the lowest AIC) and R is equal to the number of models being compared. These weights sum to 1 and indicate the relative support for each model. The use of these techniques is described more fully in Morris and Doak (2002). Stochastic Population Viability Analysis I evaluated the long-term population traject ories for each of the subpopulations and the Southeastern population as a whole using and 2 from the corresponding diffusion

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28 approximation analysis. For the th ree subpopulations with complete time series, I also used the nonlinear regression estimates of r and 2 to compare population traj ectories for the four different levels of K and for the density independent model. For the analysis using estimates from th e diffusion approximation models, I calculated the cumulative probability of the popul ation reaching the decline threshold ( x0) during each yearly interval ( Gt) as: Gt = t td d t td 2 exp *2 2 2 (2-8) where d is the natural log of the difference betw een the current populati on size and the decline threshold and is the standard normal cumulative distri bution (Morris and Doak 2002). I used a boot-strap approach, randomly selecting values for and 2 from the confidence intervals of the estimates obtained from the diffusion approxima tion analysis while using a normal probability distribution for and a chi-square distribution for 2. I used the 2006 nest count estimates for my starting population size and then projected the population forwar d in time for 100 years using 50,000 replicates. To evaluate the effects of incorporating density dependence into the population viability analysis, I used the estimates of r obtained by the nonlinear regression analysis to predict future population sizes under each carrying capacity scenario. I added an error term ( ) to the basic Ricker model (Equation 1-4), drawing a new value of t for each year of the simulation from a normal distribution with a mean of 0 and the variance ( 2) estimated by nonlinear regression (Morris and Doak 2002). Again, I projected the population forward in time for 100 years and used 50,000 replicates.

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29 I evaluated the probability of a population declining by 90% (quasi -extinction) over the next 100 years for both the density independent and, when applicable, density dependent models. I also report the probability of the population declining by 90% over the next 30 years. Results Diffusion Approximation The log of the population growth rate () for the entire South eastern population from 1976-2006 was 0.0047, indicating a relatively stable population (Table 2-2). The variance of the estimate ( 2) was 0.13627, indicating high variability in an nual nest counts. The residuals were not significantly autocorrelated (DW = 2.339, P = 0.21) and there were no outliers. There was some evidence of density dependence, however, with the population growth rate declining as the total number of nests increased (Figure 2-1; F1,15 = 5.79, P = 0.0469). Prior to listing, the populati on showed a declining trend ( = -0.0495, 2 = 0.1709, CI = 0.1491-0.6342), while since listing (1984-2006), th e population has shown a positive trend in growth ( = 0.0269, 2 = 0.1143, CI = -0.0234, 0.6839). This difference was not significant, however, owing to the extremely large variance around the estimate d population growth rates for the two periods (t19 = 0.4606, P = 0.3252) and I did not consider the periods separately in further analyses. In SFL, Wood Stork nesting showed a declining trend since 1976 ( = -0.02404, 2 = 0.8560). There was no evidence of negative tem poral autocorrelation in nest count numbers (DW = 2.523, P = 0.0703) but some evidence of density dependence (Figure 2-2; F1,18 = 5.77, P = 0.0232). The CNFL subpopulation was stable ( = 0.0040, 2 = 0.2054), and there was no evidence of temporal autocorrela tion (the outlying nest count from 2001 was removed to obtain this estimate). There was strong eviden ce of density depend ence (Figure 2-3; F1,14 = 19.44, P =

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30 0.0006). The GA subpopulation showed an increasing trend ( = 0.0841, 2 = 0.5500), some evidence of temporal autocorrelation (DW = 0.0563, P = 0.0563) and no density dependence (Figure 2-4; F1,29 = 0.0225, P = 0.4203). There were 3 outliers (1975, 1976, and 1979) but I did not exclude them from analyses because they represented the early years of colony establishment in GA. South Carolina had the highest population growth rate ( = 0.2083, 2 = 0.0331), with no temporal autocorrelation (DW = 2.491, P = 0.1033) but some density dependence (Figure 2-5; F1,23 = 7.6198, P = 0.0111). There were 2 outliers (1984 and 1998). I did not remove these outliers from further analyses because 1984 simp ly reflected an increase from 22 to 74 nests between the 4th and 5th years of the establishment of nesting in SC and because although the number of nests counted in 1999 was much lower than those counted in 1998, declining from 1,093 to 520, the 1999 count was not an extreme valu e compared to other nest counts (median = 688 nests over 25 year time period). Comparison to Independent Data When I regressed the change in number of birds counted per count hour during Christmas Bird Counts against the time elapsed, I found that the log population growth rate of the Southeastern U.S. population of Wood Storks was 0.0313 (CI = -0.0159-0.0785). The variance of the estimate was 0.14311 (CI = 0.0908-0.2586) Density Dependent Analysis Density dependent models of population grow th provided a better f it to the count data than did the density independent model for SFL and SC. For both subpopulations, the models in which carrying capacity was set equal to half of the curren t population size ( Khalf) or to the 5 year average (Kaverage) were indistinguishable from each other in terms of model fit (Table 2-3). For

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31 GA, the 4 density dependent models and the density independent model had AIC values that differed by less than one. Estimates of r for SFL were 0.3839 ( 2 = 0.8176) and 0.5127 ( 2 = 0.8267) for the models in which K was set equal to half of current or to the five year average respectively, while for the density independent model it was 0.0276 ( 2 = 0.9461). For the same two density dependent models for SC, r was equal to 0.4554 ( 2 = 0.1427) and 0.4249 ( 2 = 0.1432), while r for the density independent model was 0.2083 ( 2 = 0.1830). Values of r from the density independent models for the GA subpopulati on ranged from 0.1293-0.2170 with variances ranging from 0.55790.5597. For the density independent model r was estimated to be 0.0841 ( 2 = 0.5690). Stochastic Population Viability Analysis Density independent The density independent model indicated that the cumulative probability of the Southeastern Wood Stork populatio n declining by 90% over the next 30 years was 0.23 (0.310.53) and 0.49 (0.08-0.85) over the next 100 year s (Figure 2-6). The probabilities of the population declining by 50% over those same time intervals were 0.71 (0.37-0.88) and 0.83 (0.41-0.97) respectively. Of the 4 subpopulations, the South Florid a subpopulation had the highest cumulative probability of declining (Figure 8). The cumula tive probability (CI) of a 90% decline was 0.69 (0.38-0.86) over the next 30 years and 0.85 (0.470.99) over the next 100 y ears. The cumulative probability of a 50% decline was 0.91 (0.78-0.97 ) and 0.96 (0.82-1.0) for the next 30 and 100 years respectively (Figure 2-7).

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32 The Central and North Florida subpopul ation was more stable than the SFL subpopulation, with a probability of declining by 90% of 0.34 (0.08-0.64) over the next 30 years and 0.58 (0.13-0.91) over the next 100 years (Figure 2-8). The Georgi a subpopulation was also relatively stable, with a cumula tive probability of 0.38 (0.11-0.67) of declining by 90% over the next 30 years, and 0.47 (0.12-0.82) ov er the next 100 years (Figure 2-9). The South Carolina subpopulation had th e largest population growth rate and had virtually 0 probability of declining by 90% over the next 100 years. Even the probability of a 50% decline over the next 100 years was low (0.19; CI = 0.04-0.48) (Figure 2-10). Comparison of density dependent and density independent analyses Using the nonlinear re gression estimates of r and 2, SFL had the highest probability of decline. The density independent model in this projection yielded probabilities of quasiextinction of 0.5455 over the next 30 years and 0. 7043 over the next 100. These estimates were lower than those obtained using th e modeling approach in the previous section. They were also lower than the quasi-extinction probabilities obtained using parameter estimates from the two best models that incorporated density dependence. For the Khalf and the Kaverage models, the probability of a 90% decline over the next 30 years was 0.9248 and 0.8080 respectively (Figure 2-11). The probability of quasi-e xtinction over the next 100 years for the same two models was 1. The SC subpopulation had virtua lly no probability of quasi-ex tinction over the next 100 years using the independent model. For the Khalf model and the Kaverage models the probability of quasi-extinction was 0.1139 and 0.0265 respectively (Figure 2-12). For GA, the probability of decline was lowest for the density independent scenario and for the Kaverage model than for the other density dependent models. For the density independent

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33 model, the probability of quasi-extincti on after 100 years was 0.4156 and for the Kaverage model it was 0.4120. Quasi-extinction probabilities after 100 years obtained usi ng parameter estimates from the other density dependent models ranged from 0.8586-0.0062 (Figure 2-13). Discussion The Southeastern U.S. Wood Stork populati on appears to be gene rally increasing. Subpopulations in SC, GA, and nothern and central FL were either stable or increasing, while for SFL, the population trend was dependent on the lengt h of the time series used to estimate the population growth rate. When data from the 1975-1978 were included in th e analysis, the best estimate of was negative, while it was positive for th e time series since 1979. Wide confidence intervals around the population growth rate estimates do no t preclude the possibility of a negative population trend fo r any of the regions, but in general the trend appears to be toward an overall population increase. This conclusion is independently supported by the Christmas Bird Count data, which indicated an ev en higher rate of population growth than the nest count data. Density dependence appeared to influen ce population dynamics in all regions except Georgia. This could reflect limitations in suit able nesting habitat or suitable foraging habitat, both of which are listed as possible factors in the Wood Storks steep decline in the latter half of the 20th century (Ogden and Nesbitt 1979). Model fitting suggested that the SFL and SC populations are probably near or above their carrying capacity, and the relatively stable population growth rate for the NCFL subpopulation sugge sts the same for that region. Due to the nature of the nest count data, these estimates of popul ation growth rates and probabilities of decline should not be taken as absolute predictions. Rather, they indicate general trends in population growth or decline. These da ta violated several of the assumptions inherent to count-based PVA. Namely, the assumption that and 2 do not change over time was

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34 violatedthere was evidence of density depend ence in most of the subpopulations and in the population as a whole and catastrophes and bonanzas seem to have occurred, particularly in SFL. The occurrence of catastrophes would have had a negative effect on population growth rates, causing the estimates I have presented to be overoptimistic and possi bly underestimating the probability of serious population declines. Conve rsely, the presence of bonanzas would cause my estimates to be overly pessimistic. Th e incorporation of catastrophes and bonanzas, however, requires information on th eir likelihood of occurrence that I did not possess. This was particularly true for GA and SC, wh ere nesting began relatively recently. Finally, observation error could not be exclude d from this analysis. Potential biases in the nest counts could have been caused by the aerial survey method (Frederick and Meyer 2008) or if the proportion of the populat ion that attempted to breed diff ered from year to year, as clearly occurred. This was reflected in the high variance around the es timated population growth rates. Observation error and the associated infl ated variance generally re sults in a bias towards overly pessimistic assessments of populati on persistence (Morris and Doak 2002) and underscores the needs for count methods that enable the quantification of this source of error. Conclusion The endangered Wood Stork population in the Southeastern United States appears to be stable or increasing, as eviden ced by nest count data and Christ mas Bird Counts. This overall trend can be attributed to increa ses in numbers of birds nesting in Georgia and South Carolina. In Central and North Florida the number of nesting birds seems st able, while in South Florida it appears to be stable or declining. Restorati on plans currently in pl ace for South Florida, including the Comprehensive Everglades Restor ation Plan (CERP), are expected to improve nesting conditions in SFL. Alt hough declines in SFL have been offset by the population increase

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35 to the north, a growing population in SFL would increase the population growth rate for the Southeast as a whole and the probability of re classification from endangered to threatened.

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36 Table 2-1. Annual log population growth rates for the inter-censu s interval for SFL, NCFL, GA, SC and the Southeastern U.S. Wo od Stork population as a whole. Year SFL NCFL GA SC SE 1975 -0.8542 -0.3225 -2.1832 -0.6079 1976 -0.6621 0.2897 2.1547 -0.0089 1977 0.0194 -0.4074 -0.3221 -0.6693 1978 ----0.3108 -0.5978 0.545 1979 -0.0274 0 1.6864 0.0855 1980 0.6841 -0.7174 -0.077 -0.1309 1981 -0.6744 0.2337 -0.7115 0.5978 -0.2171 1982 0.8374 0.228 0.9891 0 0.515 1983 -0.831 0.4734 0.4617 0.0953 0.0429 1984 -0.4448 -0.1566 -0.0335 1.213 -0.1845 1985 -0.216 -0.2812 0.1513 0.4834 -0.0992 1986 -1.861 -0.2474 0.4804 1987 2.0215 -0.4867 -0.0805 1988 -0.3826 0.5573 0.7422 1989 -0.0809 0.2667 0.3545 1990 0.1466 0.3124 0.2141 1991 1.2486 0.4702 0.1186 -0.335 0.355 1992 -1.1835 0.4203 0.5288 1993 0.233 -0.2553 -0.1235 -0.124 -0.1541 1994 0.4308 0.4315 0.0222 0.1521 0.3086 1995 0.0637 -0.1219 -0.0141 0.1394 0.1197 1996 -1.0044 -0.0707 -0.0385 1997 0.0715 0.1885 0.1756 1998 2.3241 -0.3797 -0.7429 1999 -0.2007 -1.5989 -0.6993 0.8658 -0.4107 2000 -0.3247 0.7193 -0.0515 2001 0.1816 1.7204 0.0778 -0.0329 0.3416 2002 -0.6842 0.7073 0.2747 0.177 0.1151 2003 -0.1625 -0.2246 -0.0351 0.4167 -0.0505 2004 -0.9214 -0.6376 0.1297 -0.3798 -0.4101 2005 1.4997 0.9809 0.0593 0.3567 0.7073 ii iitt NN 1 1)/ log(

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37 Table 2-2. Log population growth rates ( ), variance in growth rates ( 2), and 95% confidence interval s from Wood Stork nest count data. Region N Confidence Interval 2 95% Confidence Interval Lower Upper Lower Upper Southeast 21 0.0047 -0.0409 0.0502 0.1363 0.0798 0.2842 South Florida 30 -0.0240 -0.1375 0.0895 0.8560 0.5429 1.5470 North & Central 21 0.00404-0.0802 0.0883 0.4661 0.2728 0.9720 Georgia 31 0.08414-0.0068 0.1751 0.5500 0.3512 0.9827 South Carolina 25 0.0905 0.0155 0.1654 0.0331 0.0202 0.0640

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38 Table 2-3. Parameter estimates and model selection criteria fo r the density dependent Ricker model and a density independent m odel of Wood Stork population growth. Subpopulation N K Model Likelihood AICc AIC weight SFL Kavera g e 26 0.5127 1987 0.8267 -33.9090 72.3398 0.3253 Kcurrent 26 0.4262 2648 0.8688 -34.5542 73.6302 0.1706 Khalf 26 0.3839 1324 0.8176 -33.7640 72.0497 0.3760 Kdouble 26 0.1725 5296 0.9286 -35.4195 75.3608 0.0718 Density Independent 26 0.0276 --0.9462 -35.6631 75.8480 0.0563 GA Kavera g e 30 0.2170 1650 0.5579 -33.3072 71.0589 0.2315 Kcurrent 30 0.1952 1928 0.5597 -33.3543 71.1531 0.2208 Khalf 30 0.2032 964 0.5597 -33.3551 71.1546 0.2207 Kdouble 30 0.1293 3856 0.5652 -33.4999 71.4442 0.1909 Density Independent 30 0.0841 --0.5690 -33.6012 71.6469 0.1361 SC Kavera g e 24 0.4249 1593 0.1432 -10.2227 25.0169 0.3638 Kcurrent 24 0.3768 2010 0.1514 -10.8871 26.3456 0.1872 Khalf 24 0.4554 1005 0.1427 -10.1830 24.9374 0.3786 Kdouble 24 0.2824 4020 0.1686 -12.1817 28.9349 0.0513 Density Independent 24 0.2083 --0.1830 -13.1674 30.9063 0.0191 r 2

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39 R2 = 0.2783 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 020004000600080001000012000 Nests at year t ( Nt)Log( Nt+1/Nt) Figure 2-1. Relationship between the number of nests in year t and the population growth rate over the next interval for the Southeastern Wood Stork population as a whole.

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40 R2 = 0.1709 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 0100020003000400050006000 Nests at year t ( Nt)Log( Nt+1/Nt) Figure 2-2. Relationship between the number of nests in year t and the population growth rate over the next interval for th e SFL Wood Stork subpopulation.

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41 R2 = 0.5813 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 0500100015002000250030003500400045005000 Nests at year t ( Nt)Log( Nt+1/Nt) Figure 2-3. Relationship between the number of nests in year t and the population growth rate over the next interval for th e NCFL Wood Stork subpopulation.

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42 R2 = 0.0225 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 0200400600800100012001400160018002000 Nests at year t ( Nt)Log( Nt+1/Nt) Figure 2-4. Relationship between the number of nests in year t and the population growth rate over the next interval for th e GA Wood Stork subpopulation.

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43 R2 = 0.2489 -1 -0.5 0 0.5 1 1.5 05001000150020002500 Nests at year t ( Nt)Log( Nt+1/Nt) Figure 2-5. Relationship between the number of nests in year t and the population growth rate over the next interval for th e SC Wood Stork subpopulation.

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44 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 05101520253035404550556065707580859095 Years into future ( t )Cumulative probability of decline Figure 2-6. Cumulative probability of the Southeastern US Wood Stork population declin ing by 50% (gray) or 90% (black) over th e next 100 years based on diffusion approximati on results. The solid line represents best estimates and the dashed lines represent 95% confidence intervals.

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45 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 05101520253035404550556065707580859095 Years into future ( t )Cumulative probability of decline Figure 2-7. Cumulative probability of the SFL subpopulation of Wood Storks declining by 50% (gray) or 90% (black) over the nex t 100 years based on diffusion a pproximation results.

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46 Figure 2-8. Cumulative probability of the NCFL subpopulation of Wood Stor ks declining by 50% (gray) or 90% (black) over the ne xt 100 years based on diffusion a pproximation results. 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 05101520253035404550556065707580859095 Time into future ( t )Cumulative probability of decline

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47 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 05101520253035404550556065707580859095 Time into future ( t )Cumulative probability of decline Figure 2-9. Cumulative probability of the GA subpopulation of Wood Storks declining by 50% (gray) or 90% (black) over the next 100 years based on diffusion a pproximation results.

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48 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 05101520253035404550556065707580859095 Years into future ( t )Cumulative probability of decline Figure 2-10. Cumulative probabili ty of the SC subpopulatio n of Wood Storks declin ing by 50% (gray) or 90% (black) over the nex t 100 years based on diffusion a pproximation results.

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49 0 0.2 0.4 0.6 0.8 1 1.2 0 25 50 75 100 Years into future (t)Cumulative probability of decline DI K-Avg K-Current K-Half K-Double Figure 2-11. Cumulative probabili ty of the SFL subpopulation of W ood Storks declining by 90% (bl ack) over the next 100 years f or the density dependent and dens ity independent models.

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50 0 0.2 0.4 0.6 0.8 1 1.2 0 25 50 75 100 Years into future ( t )Cumulative probability of decline DI K-Avg K-Current K-Half K-Double Figure 2-12. Cumulative probabili ty of the GA subpopulation of Wood Storks declin ing by 90% (black) over the next 100 years fo r the density dependent and dens ity independent models.

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51 0.000 0.020 0.040 0.060 0.080 0.100 0.120 02 55 07 51 0 0 Years into future ( t )Cumulative probability of decline DI K-Avg K-Current K-Half K-Double Figure 2-13. Cumulative probabili ty of the SC subpopulatio n of Wood Storks declin ing by 90% (black) over the next 100 years fo r the density dependent and de nsity independent models.

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52 CHAPTER 3 AGE-SPECIFIC SURVIVAL IN THE WOOD ST ORK: IMPLICATIONS FOR LONG-TERM VIABILITY OF THE SP ECIES Introduction While count-based population viability analyses such as the ones in the previous chapter can be important conservation tools, particularly when estimates of vital rates for the species are lacking, the assumptions upon which such analyses are based can limit their applicability. This may be especially true for the Wood Stork. In particular, the assumpti on that observation error does not affect the measured variability in popu lation growth rates (Mor ris and Doak 2002) is almost certainly violated in the case of the stork. The count-b ased analyses for the Wood Stork have been based on synoptic aerial surveys c onducted by the Fish and Wildlife Service (see Brooks and Dean 2008). These counts are subject to 3 sources of observation or sampling error: errors in accuracy of the count itself, which may vary with observer, colony size, and species composition (Rodgers et al. 1995, Rodgers et al. 2005); differences between the number of breeding birds and the peak number of birds counted, which may be due to asynchronous nesting or renesting (Frederick et al. 2006); and differences in the pr oportion of the population counted in any given year, which might occur if bird s do not attempt to nest when conditions are unfavorable, as is likely. Age or stage specific population viability analyses (PVA) are preferable to count-based analyses when individuals vary in their contri bution to the population gr owth rate (Morris and Doak 2002), as is true of the Wood Stork. P VAs are widely used in the conservation and management of endangered or declining species to provide quantitative estimates of extinction risk over a specified time frame and to identify life stages or vital rates that should be targeted for management (Beissinger and Westphal 1998, Heppe ll et al. 2000, Morris et al. 2002, Ellner and Fieberg 2003). Their use is no t without controversy, and a majo r criticism is that parameter

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53 estimates too often lack precision owing to defi ciencies in the available data (Beissinger and Westphal 1998, Ellner et al. 2002, Morris et al. 2002). The simplest demographic model is the de terministic single population model, which requires information on age or stage structure, age or stage at first reprodu ction, and estimates of survival and reproduction for each age class or stage (Beissinger and Westphal 1998). The deterministic model results in a single estimate of the population growth rate, which can be used to determine the population size at some future time. Stochastic popula tion models provide probabilistic estimates of extincti on risk based on the means and variances of vital rates and their distributions. Their accuracy is limited by the le ngth of the time series used to estimate vital rates, however, and precise estimates of extinction risk over t years requires 5t-10t years of data (Fieberg and Ellner 2000, Ellner et al. 2002). In a review on the use of PVAs in the management of endangered species, Beissinger and Westpha l (1998) recommend that PVA be used to generate relative rather than absolute predictions of extinction risk, that predictions be made over short time periods, and that simple models that can be supported by data be used. Until recently, however, not even the simplest demographic models were applicable to the management and recovery of the Wood Stork due to the paucity of knowledge of Wood Stork vital rates. In 2008, Borkhataria et al. publis hed a preliminary model of Wood Stork population dynamics based on apparent survival of juvenile Wood Storks monitored via satellite telemetry. Information on adult survival rates were lacking at that time, and the model was used to estimate adult survival rates ne cessary to maintain a stable or growing population based on information about Wood Stork productivity acro ss the Southeast and juvenile su rvival rates. Without adult survival rates, however, it was impossible to es timate the population growth rate or extinction probabilities.

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54 Here I present age-specific estimates of juve nile and adult survival based on mark-resight analysis of Wood Storks outfitted with satellit e transmitters between 2002 and 2007. I then use these estimates to update the model of Borkhatari a et al. (2008) and to estimate the population growth rate for the Southeastern Wood Stork population for comparison to that obtained from the count-based analyses in Chapter 1. Due to the s hort time series of data available on productivity and survival rates, I maintained the use of a de terministic rather than a stochastic model, and because the predictions of determ inistic population models are very sensitive to the vital rates used in their parameterization, I also use the model to compare the use of apparent survival estimates to those obtained by maximumlikelihood modeling in Program Mark. Methods Satellite Telemetry The birds included in this study were capture d throughout the Southeastern United States (see Figure 3-1 for a map of locations and Table 3-1 for coordinates). Juvenile storks were captured on the nest at 2 Florida and 2 Georgia colonies In Florida, birds were caught at the Tamiami West colony in Everglades National Pa rk, Dade County (Hylton 2004), and at the Palm Beach Solid Waste Authority rookery (SWA) in Pa lm Beach County. In Georgia, I captured young birds at the Harris Neck Na tional Wildlife Refuge, McIntosh County, and at the Chew Mill colony in Jenkins County. In all cases, young birds were caught by climbing to the nest using tree branches or ladders, and simply removing young by hand from the nest. Birds were tagged at four to six weeks of age and the oldest sibling per nest was chosen based on visual comparisons of culmen length and overall size to avoid potential bias associ ated with hatch-order and non-independence of siblings. Prior to attach ing transmitters, birds were also given a brief physical exam, measurements were taken, and we extracted <0.5 ml of blood from the brachial vein for sexing and health analysis.

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55 Adult birds were caught in Fl orida, Georgia, South Caroli na, and Mississippi by myself and collaborators at the University of Georgia an d the Jacksonville Zoo. The vast majority of storks included in this analysis were caught by rocket-netting, with the exception of one stork caught by drop net at the SWA. Ten storks were also caught by drop-net or clap-trap at the Jacksonville Zoo in Florida by our co llaborators. As with the juveni le birds, adults were given a brief physical exam, I measured culmen and tarsus lengths, and 3-4 drops of blood were extracted from the brachial vein for sexing. In 2004 and 2005, I deployed 68 satellite transm itters on juvenile storks, adding to a previous dataset which included 73 juvenile storks tagged in 2002 and 2003 (Hylton 2004). From 2004-2007, I also directly engaged in or co llaborated in the deployment of 34 transmitters on adults. Satellite transmitters weighed 35g (2002-2003) or 45g (2004-2008) and were solarpowered ARGOS-PTT satellite tags (Microwave Te lemetry, Columbia, MD). We attached the transmitters to the birds using a 0.25 cm wide teflon ribbon backp ack-style harness (see Hylton 2004 for a complete description of tagging protocol). In 2002-2005, a 10g VHF transmitter was affixed to the satellite tag on young, whereas satellite transmitte rs deployed on adults for the most part lacked VHF transmitters. I used the VH F signal and/or final satellite transmissions to independently confirm mortality and to attempt to recover tags when birds stopped transmitting satellite locations. If I was unable to confirm mo rtality by relocating a tag for any reason, I used final transmissions from the satellite tags bui lt-in activity counters and final locations to categorize a birds fate as either dead or unknown. A bird was considered to be dead if the activity counter indicated no moveme nt for several days or, for GPS/PTTs, a bird did not change locations for several days. If a bird simply stopped transmitting without displaying either of

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56 these characteristics, I consider ed it possible that the tag had si mply failed, and considered the birds fate to be unknown. Locations for the juvenile birds and 15 a dults were recorded hourly, from 0600-2100, while locations for 19 adults were record ed every two hours, 24 hours per day. Sexing I sent blood samples to Zoogen Services Inc. (Davis, CA) or Avian Biotech International (Tallahassee, FL) for DNA sexing. If blood was not drawn from a juvenile bird for any reason, I did not attempt to assign sex on the basis of mo rphometric characteristic s, but rather excluded that individual from a ny sex-based analyses. For adult bird s, if blood was not drawn from an individual I attempted to assign sex on the basis of culmen and tarsus length. I compared mean culmen and tarsus lengths betw een females and males using a t-test, and assigned sex on the basis of the 95% confidence interv als (CI) around the mean for both se xes. If a bird fell within the confidence intervals for the same sex for both m easurements, I assigned it that sex. If a bird had only one of the two measurements taken (most often tarsus), I assigned sex on the basis of that measurement alone. If the measurement exceeded the upper CI for males, the bird was designated male, and if it was under the lower CI fo r females the bird was designated female. If the measurement fell between the CIs for males and females or within the range in which the CIs overlapped I did not assign sex. Juvenile Fledging Success and Survival I considered a satellite tagged juvenile to have fledged when it flew >0.5 km from the colony and did not return to within 0.5 km of the colony for at least seven days. Birds that died within the colony were not consider ed to have fledged even if they were capable of flight and had previously left the colony. Fledging succe ss was calculated as the number of tagged birds that fledged divided by the number of birds tagged.

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57 I used two approaches to describe the surviv al of birds after they had fledged: proportions surviving from one year to the next (apparent survival), and survival estimates based on the joint modeling of live-recapture, live -resight, and tag-recovery data (Barkers model) in Program MARK (Barker 1997, Cooch and White 2008). Fo r both approaches, I co nsidered first year survival to represent the probabi lity that a bird survived from the day it fledged to the 365-day anniversary of its fledge date. Subsequent annual survival was al so based on this date. I chose to use the anniversary of each birds fledge date rather than hatch date be cause survival estimates were to be used in post-breeding matrix models. I calculated apparent annual survival separately for each year and calculated the mean and variance for each age class across years. Because sample sizes were low, particularly for the later age classes, I also combined data from acr oss years to obtain a si ngle estimate of survival by age group. It was not always possible to determine whether birds that stopped transmitting had died, and while I reported the numbers of bird s known to be dead (i.e., tag retrieved or death confirmed by activity counter) versus the numbe r of birds with unknown fates), for the purposes of calculating apparent annual su rvival, I assumed that all bird s that stopped transmitting had died. This assumption was unlikely to have been true and probably leads to an overly conservative estimate of survival. To incorporate the uncertainty in survival estimates asso ciated with birds with unknown fates, I also used Barkers model to estimate surv ival rates and the associated uncertainty in the estimates. Whereas typical capture-recapture models use data from a single source, such as liverecaptures in the case of the Jolly-Seber model or band returns from dead animals in the Brownie models, Barkers model is appropriate when survival data arise from multiple sources, with some sources coming from periods between the discrete sampling occasions. Barkers model is

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58 typically applied when live capture occurs at di stinct times and animals are resighted at some time in the interval between distinct capture occurrences, and tags from dead animals are sometimes recovered (Barker 1997). This model is especially useful when sample sizes are low, as it allows the incorporation of all available data on a birds fate and the use of these additional data can improve the precision of survival estimat es. I adapted this model to fit the satellite telemetry data by using location histories as pr oxies for physical recapture and resighting. Because a key assumption of capture-recaptu re analysis is that recaptures occur instantaneously (Williams et al. 2001), I considered a bird to have been recaptured if a location was recorded for it on the exact 365 day anniversary of its initial capture a nd the location data indicated that the bird was alive. I considered the bird to have been resighted if live location data were recorded on any days in the time period between annual recaptures. Encounter histories were coded in the livedead format, where each capture occasion and the following interval are paired (LDLDLDLD). In this format, the Ls represent capture occasions and the bird is either alive on that day and coded with a 1, dead on that day and coded with a 0, or alive but not recaptured (i.e., did not transmit a location for that day), also coded with a 0. The Ds represent the interval between capture occasions, and are coded with a 1 if the bird died at any time during the interval, or with a 2 if the bi rd was recorded alive on any day during the interval. For example, a bird that was captured on the first sampling occasion, transmitted at least one location during the firs t 365 day interval, transmitted a location on the exact anniversary of its capture, transmitted a location during the second 365 day interval and again at the next annual capture a nniversary, then died during the next interval w ould be coded: 121211. Had this same bird stopped transmitting during the second interval without any evidence of death, the capture history would have been coded 121200 for the same time frame.

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59 The Barker model in Program MARK estimates the following parameters (Barker 1997): Si = the probability that an animal alive at time i is alive at time i + 1. pi = the probability that an animal is captured at time i given that it is at risk of capture ri = the probability that an animal that dies in the interval between i and i+1 is found dead and reported Ri = the probability that an animal that survives the interval between i and i+1 is resighted alive during the interval Ri = the probability that an animal that dies during the interval between i and i+1 without being found alive is resighted alive during the interval before it died Fi = the probability that an animal at risk of capture at i is at risk of capture at i+1 Fi = the probability that an animal not at risk at i is at risk of capture at i+1. Because there was no boundary to the area cove red by the satellite tags, I considered a tagged stork with a functioning tag to be incapable of leaving the study and to always be at risk of capture (Fi = 1) and because this study did not include marked animals that we considered not to be at risk of capture, I set Fi equal to zero. I also constrained Ri, fixing it at one, because birds invariably transmitted at least once during th e annual interval, regardless of their fate. Due to the small sample size of tagged animals and the loss of degrees of freedom associated with the estimation of multiple parameters, I made the a priori assumption that recapture, reporting and resighting probabilities were unlik ely to vary over time or by sex, and constrained them to be constant. In computing survival rates, I parameterized m odels that I considered to be biologically relevant, rather than computing results for ev ery possible combination of parameters. For juveniles, I compared 10 models with varying para meters associated with survival probabilities

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60 (see Table 3-4), testing for differences among ag e classes and between sexes. I compared models in which survival differed for bird s in their first, second, third and fourth+ year; differed in their first, second, and third+ year; differed only in their first and second+ year, or were constant over the entire birds lives (with no coho rt effect). When years are represented with a +, it indicates that survival was assumed to equal adult survival at that time and to be constant from that point on. I repeated the analyses with survival modeled differently for males and females over all of the years in the analyses a nd also included a model in which sex influenced survival for birds in their first year but not th ereafter. I selected the model with the lowest corrected Akaikes Information Criterion (AICc) (Burnham and Anderson 2002) as the one that best fit the data. If AICc values differed by less than two for two or more models, I chose the model with the lowest number of parameters. Adult Survival Because the sample size for adult birds of known sex was relatively small and uneven among years [2004 (1), 2005 (8), 2006 (23), and 2 007 (2)], I decided a priori to compare only eight models of survival and recapture. I tested for higher mortality in the first year after tagging (tag effects) by assigning one para meter to that year and a different, constant parameter to all other years and compared the model to one in wh ich survival rates were held constant over all sampling periods. I also compared models in wh ich survival rates were allowed to vary by sex or were held constant, for a to tal of four survival scenarios. I only considered two possible recapture scenarios, one in which recapture proba bilities were constant vs. one in which they varied by sex. The two recapture scenarios combined with the four survival scenarios resulted in a total of eight possible models. Again, I select ed the model with the lowest corrected Akaikes Information Criterion (AICc) as the one that best fit the data Because birds tagged as juveniles

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61 exhibited adult survival in their third year and older, I also combined data from birds tagged as juveniles with that of birds tagged as adults to obtain a combined estimate of adult survival. Demographic PVA I used a deterministic matrix model, which I adapted from Borkhataria et al. (2008) to include the revised estimates of j uvenile and adult survival presente d here. As in Borkhataria et al. (2008), the basic model used a birth-puls e, female-only, post-breeding census and was structured as follows: Matrix = 3 3 2 1 353400 00 0 000 **00 ss s s sfsf (3-1) where s1 = first year survival, s2 = second year survival, and s3+ = survival of birds in their third year and beyond. The parameter f4 corresponded to the fertility of first time breeders, which were assumed to breed for the first time at the end of their fourth year, and f5+ to the fertility of experienced breeders. I adopted the same fertility values I reported previously (B orkhataria et al. 2008), based on a pooled analysis of nests of more than 6,000 nests from South Carolina Georgia, and Florida for 2002-2005, with nest success of 0.6375 and 1.99 chicks per successful nest, and multiplied this value by fledging success (d ivided by 2) to obtain the nu mber of female young produced. I assumed that birds bred for the first time at age four, and that first time breeders reproduced with half the success of experienced breeders based on differences in nest success and productivity by age in the White Stork (Ciconia ciconia) (Vergara and Aguirre 2006). I parameterized the matrix above (Eqn. 3-1) fo r 4 scenarios: 1) appare nt juvenile survival; 2) juvenile survival estimates from the Barker model; 3) juvenile surv ival (first and second

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62 years) equal to the upper confid ence limit for those estimates from the Barker model; and 4) all survival rates equal to the upper confidence limit for each estimate from the Barker model For each survival scenario, I calculated the stable age distribution by taking the right eigenvector of the parameterized matrix (Bei ssinger and Westphal 1998, Morris and Doak 2002). I then used the stable age dist ribution to create a population vector with 10,000 breeding females for each scenario. I projected each vector forw ard in time to obtain the population trajectory for the Southeastern population and the time it would ta ke for an initial population of 10,000 birds to decline by 50 and 90%. I compared the population trajectories for each of the four scenarios to that obtained using the population growth rate () obtained from the count based analysis in Chapter 1. To obtain future population sizes using the count-based popul ation growth rate, I used the following equation: Ni = exp()i where Ni is the population size after i years. I then ran the first three models with a range of adult survival values (used for birds three+ years old) and regresse d adult survival against to determine the level of adu lt survival necessary to ma intain a stable population ( = 1). Sensitivity Analysis The proportional sensit ivity or elasticity(ije) of the long-term population growth rate () to small changes in each vital rate was analyzed by calculating the proportion al contribution of each vital rate (ija) to using: ij ij ij ija a a e log log (3-2) where all other elements of the transition matrix are held constant (de Kroon et al. 2000, Caswell 2001).

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63 Because elasticity analyses focus on the c ontribution of a single parameter at a time (Caswell 2001), I also conducted a sensitivity analysis to examine how sensitive the model would be to simultaneous changes in vital rates. I increased the vital rates for all parameters simultaneously by 5% and 10%and calculated for each scenario. Because Wood Storks tend to have boom or bust dynamics, I also analyzed the sensitivity of the model to the sporadic occurrence of boom years, varying the frequenc y of that occurrence from 0-100% of the time using a bootstrap analysis with 5000 repetitions over 50 years. I arbitrarily considered a boom year to have fecundity and first year surviv al double the average and a 10% increase in the survival of older birds. Results Sexing Of 101 juveniles sexed using blood DNA, 40 were female and 61 were male ( 2 = 0.3984, d.f. = 1, p = 0.0360). Of the 26 adult birds for which we obtained blood samples, DNA sexing showed 13 to be females and 13 to be males. Mean culmen length was significantly shorter for adult females (209mm, SE 4.86, CI = 198.84-219.16, N = 11) than for adult males 233.7 (SE 5.09, CI = 223.04-244.36, N =10) ( t19 = 3.5108, P = 0.0023). Mean tarsus length was also significantly shorter for females (194.45mm, SE 5.21, CI = 183.55-205.36, N = 11) than for males (216.10 mm, SE 5.46, 95% CI = 204.66227.54, N = 10) ( t19 = 2.86, P = 0.0099). On this basis, I assigned sex to an additional eight birds: four adult females and four adult males. Juvenile Fledging Success and Survival Fledging success was highly variable among years ranging from 0.50-0.958 (Table 3-2) with a mean of 0.7678 (SE 0.0962) from 2002-2005.

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64 Apparent first year survival rates ranged from 0.0588-0.4444, with the highest and lowest values for birds fledging from a single colony (TW in 2002 and 2003 respectively, Table 3-3). When years were pooled (Table 3-4), the proportion of all chicks survivin g their first year was 0.30, increasing to 0.58 for birds in their second y ear and to 0.69 for birds in their third year. Sample size decreased as age increased, with only ni ne birds surviving into their fourth year and only five into their fifth. The survival model which best fit the data for juvenile storks was the one in which survival rates differed by age for birds one, two, and three years old and older. For this scenario, there was no clear distinction between the models in which there was an effect of sex on first year survival, survival over all years, or no sex effect s (Table 3-5). Survival estimates were slightly higher for females than males, particularly in th eir first year, with firs t year survival of 0.3750 (SE 0.0765) for females and 0.2295 (SE 0.0538) for males (Table 3-6). Overlapping confidence intervals indicated that differences between the sexes were not significant, but the small sample of tagged birds may have precluded statistical ce rtainty. When differences between the sexes were ignored, first year survival was estimat ed as 0.2871 (SE = 0.0450, CI = 0.2074-0.3826). Survival increased with age for the first three years after fledging, then appeared to level out. Second year survival was estimated to be 0.5559 (SE = 0.0931, CI = 0.3742-0.7238) and survival of birds in their third and older wa s estimated to be 0.8671 (SE = 0.0706, CI = 0.66260.9559) using the Barker model. Adult Survival For birds tagged as adults, the best supporte d model was the one in which survival was different in the first year after tagging than in subsequent years, and recapture probability was constant. Models that included sex as a factor affecting survival or recapture received little support (Table 3-7). The estimated survival rates from the preferred model for the first year after

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65 tagging was 0.5440 (SE = 0.0875, CI = 0.3741-0.7042). In subsequent years, survival was estimated to be 0.8612 (SE = 0.0949, CI = 0. 5668-0.9671). The nearly 32% difference in survival indicates a probable dele terious effect of the capture a nd/or tagging process, or of the tag itself. When birds in their third+ year were combined with adults in their second year after tagging, the confidence intervals around adult survival narrowed considerably, to 0.7137-0.9423, with the best estimate of adult su rvival equal to 0.8645. I consid ered this value to reflect the unbiased adult survival rate in further analyses. Demographic PVA When I used apparent survival rates of bird s in their first and second years, and 0.8645 for birds in their third+ year, the long term population growth rate was 0.9381. When estimates from the Barker model were used for first and second year survival, was equal to 0.9333. When the upper confidence limits from the Barker mode l for first year survival were used, was 0.9505. When the upper confidence limits for first and second year survival were used, was 0.9692 and when the upper confidence limit for adult survival was also used, was 1.043. When the best estimates from the Barker model were used fo r first and second year survival and the upper confidence limit for adult survival was used for birds in their third+ year, was 1.008. The stable age distributions are presented in Table 3-8. Adult survival values necessary for the population to remain stable ranged from 0.898 wh en the upper confidence limits from the Barker model were used, to 0.930 when apparent j uvenile survival was used, and 0.935 when the estimates from the Barker model were used. Usi ng juvenile and adult survival values estimated by the Barker model, fertility would have to increase by approximately 150% in order for the population to remain stable.

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66 When I projected the hypotheti cal population vector based on the stable age distribution for each of the four models forward, the populati on decreased in all scen arios except the one in which adult survival was equal to the upper conf idence limits from the Barker model (Figure 32). For the declining populations, when estimates for juvenile and adult survival from the Barker model or apparent survival estimates were us ed, the population declined by 50%, from 10,000 to 5,000 experienced female breeders, in less than 9 years, and by 90% in less than 27 years. When the upper confidence limit for juvenile survival from the Barker model was used, the population declined by 50% in under 21 years and declined by approximately 79% over the 50 years of the projection. The population increase d exponentially when adult survival values were set equal to the upper confidence limit of the Barker estimate. Sensitivity Analysis The elasticity analysis indicated that adult survival had the largest contribution to the population growth rate with elasticity equal to: 7090.00564.0000 000586.000 0000586.00 00000586.0 0564.00022.0000 When vital rates were increased by 5%, the popu lation growth rate incr eased from 0.9358 to 0.9828 and when they were increased by 10% the population experienced a positive growth rate ( = 1.0325). The addition of boom years also ha d an effect on the population trajectory, but they had to occur approximately 32% of the tim e for the population to grow (Figure 3-3). Discussion Contrary to count-based analyses of W ood Stork population trends, the demographic analysis indicated a declining population. Even when the upper confiden ce limit of estimated

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67 juvenile survival was used, the population still declined. Since there is no direct evidence that the population is declining, this raises questions about the accuracy of th e survival estimates or about the predictions of the count-based analysis including the assumpti on implicit in the countbased analysis that the conditions that produced its predictions are persisting. In Chapter One, I found that the long-term population growth rate ( ) for the Southeastern U.S. Wood Stork population as a whole was approximately 0.0047 us ing count based approach, indicating a stable population. The wide confidence in tervals around the estimate did not preclude the possibility of a long term population dec line, and when the density indepe ndent model was used, there was a nearly 60% chance of the population declining by 50% over the next 50 years. Nonetheless, when I used survival estimates from the Barker model the demographic model predicts a much faster decline of approximate ly 50% in under 9 years. It is possible that fledging success and juveni le survival exhibited a negative bias. These rates were based predominately on birds captured at two South Florida colonies over four years (119 of 141 tagged birds). Furthermore, two of t hose years represented p oor foraging conditions for dispersing juveniles owing to high water leve ls in Everglades wetlands (see Chapter Three). If fledging success and first year survival rates estimates are averaged acr oss the two better years (2002, 2004) they increase from 0.7678 and 0.2772 to 0.888 and 0.3589 respectively, and the population growth rate increases to 0.9547. The short time series of data coupled with the limited geographic range and sample size of the sa tellite tagging effort may have resulted in survival estimates that are not representative of the population as a whole. Even estimates of productivity were collected over a short time fram e and were unlikely to represent the range of conditions experienced by breeding birds.

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68 The satellite transmitters themselves may ha ve introduced a negative bias in survival rates. Adult survival was clea rly impacted by the captu re/tagging process, with survival in the first year after capture nearly 32% less than that in subsequent years. It is unclear whether the high mortality in the year following capture is a product of the capture process itself, or whether there is a deleterious effect associated with car rying the satellite transmitter. At 45-55 g, the satellite transmitters were < 3% of the average body weight of adult Wood Storks. Survival estimates for migratory White Storks in Switzer land of 0.850 (Schaub et al. 2004) were actually lower than those obtained in this study (0. 8612), yet that population was deemed to be sustainable. In that model, however, juvenile survival was higher than in this one, birds began breeding one year earlier, a nd survival was assumed to be at adult rates after the first year of life. Population models are sensitive to their underlying assumptions, and the deterministic model I used did not incorporate density depende nce. The incorporation of density dependence can increase or decrease extinction probabilities, with the end result depending on the type and strength of density dependence, environmental va riability, and the population growth rate (Henle et al. 2004). Despite evidence fr om the count-based analysis that the population growth rate for the Wood Stork is density dependent, the mechanis m of density dependence is unclear and it is unknown which vital rates are directly affected. For this reason, I did not attempt its inclusion in the model. This analysis illustrates the need to in crease the geographic and temporal range of research on Wood Stork vital rates. The inclusion of frequent boom years in the model resulted in population growth, but the frequency with which such years occur cannot be inferred directly from the data due to the relatively short time frame over which information was collected. It may take years or even decades to detect population level effects in long-lived species (Arnold et

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69 al. 2006), and this may account for the discrepancy between the demographic and count-based predictions. Predictions from the count-bas ed model were influenced by both current environmental conditions and those that existed more than 20 years ago. In contrast, the vital rates used in the demographic models refl ect Wood Storks responses to environmental conditions over the past five y ears and may take years to manife st themselves as population level effects. Wood Storks are dependent upon coasta l watershed along the southern Atlantic and eastern Gulf Coasts, yet thousands of acres of these wetlands are lo st to development every year. Taken together, the Atlantic and Gulf Coasts of the U.S. lost 361,100 acres between 1998 and 2004 alone (Stedman and Dahl 2008). The loss of th ese habitats may be forcing Wood Storks to use suboptimal habitats such as agricultural areas and aquaculture facilities that may result in higher levels of mortality for these birds. Wood Stork population growth rates were clearly most sens itive to changes in adult survival. For this reason, it is important to id entify the use of suboptimal habitats and specific threats they may pose to adult su rvival. For example, Wood Stork deaths have been known to occur at aquaculture facilities (Borkhat aria, unpublished data; Bill Brooks, personal communication), but the extent to which aquacult ure related deaths occur is unknown. Given the importance of adult survival to the population, iden tifying and eliminating this threat through education or other mitigation might have important population level consequences. Conclusion While the Wood Stork was declared endangere d in 1984, recent increases in numbers of birds nesting across the Southeastern U.S. have led to cautious optimism that the population may be rebounding. The survival estimates presented in this study and the demographic model that incorporated them indicates, howev er, that Wood Stork vital rates would have to be higher than those we have observed in order for the populatio n to remain stable or increase. Wood Stork

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70 survival rates were lowest for birds in their firs t year after fledging and hi ghest for birds in their third year or older (adults). The model was particularly sensitive to changes in adult survival, indicating that management actions that increase adult survival are crucial for the recovery of the species. The model was also sensitive to the occurrence of boom years, in which productivity and survival simultaneously increased. For this reason, future studies should focus on the identification of the primary sources of adult mortality and on understandi ng of variability in productivity and the frequency of extreme recruitm ent and survival events. This information can be easily incorporated into the models I have desc ribed and will allow us to shed further light on the status of the Wood Stork in the Southeastern U.S.

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71 Table 3-1. Locations of tag deployment on juvenile and adult birds in cluded in analyses. Tags we re deployed from 2002-2008. Location Year Latitude Longitude Description # Tags Adults Bear Island WMA, SC 2006 32.574 -80.48 Wildlife Management Area near several SC colonies 1 Harris Neck NWR, GA 2005, 2006 31.63 -81.275 Colony on National Wildlife Refuge in coastal GA 11 Noxubee NWR, MS 2005 33.282 -88.798 Su mmer staging area on National Wildlife Refuge 5 Corkscrew Swamp Sanctuary, FL 2006 26.31433 81.635 Birds caught in flooded ditch < 5 mi SW of Corkscrew colony 5 Welaka National Fish Hatchery, FL 2006 29.433 -81.648 Birds caught after breeding season at fish hatchery 6 White Hall, SC 2006 32.723 -80.697 Colony on private land in South Carolina 1 Palm Beach SWA, FL 2008 26.767 -80.145 Colony at Palm Beach Solid Waste Authority 4 Jacksonville Zoo, FL 20042008 30.405 -81.645 Free-living col ony at the Jacksonville Zoo 6 Juveniles Tamiami Trail West, FL 2002, 2003 25.76 -80.545 Colony in northwestern corner of Everglades National Park 72 Palm Beach SWA, FL 2004, 2005 26.767 -80.15 Colony at Palm Beach Solid Waste Authority 46 Chew Mill Rookery, GA 2005 32.83 -82.098 Colony in private mill pond in GA 11 Harris Neck NWR, GA 2005 31.63 -81.275 Colony on National Wildlife Refuge in coastal GA 11

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72 Table 3-2. Fledging success rates for juvenile Wood Storks outfitt ed with satellite tags in 20022005, from approximately 27 d of ag e to departure from the colony. Year Number tagged Number Fledged Fledging success 2002 33 27 0.818 2003 34 17 0.500 2004 24 23 0.958 2005 44 35 0.795 Table 3-3. Apparent annual survival by age and cohort. Age Cohort # of birds at start Fate at the end of a 1-yr interval Apparent survival Alive Dead Unknown 1s t Year 2002 27 12 5 10 0.4444 2003 17 1 11 5 0.0588 2004 23 8 14 1 0.3478 2005 35 9 20 6 0.2571 2nd Year 2002 12 8 0 4 0.6667 2003 1 0 1 0 0.0000 2004 8 5 2 1 0.6250 2005 9 5 2 2 0.5000 3rd Year 2002 8 5 3 0.6250 2004 5 4 1 0.8000 4th Year 2002 5 5 0 1.0000 5th Year 2002 5 4 1 0.8000

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73 Table 3-4. Apparent survival of Wood Storks in each age class from 2002-2007. Age class # of birds at start Fate at the end of a 1-yr interval Apparent survival Alive Dead Unknown 1st Year 102 30 55 17 0.2941 2nd Year 30 18 5 7 0.6000 3rd Year 18 13 2 3 0.7222 4th Year 9 9 0 0 1.0000 5th Year 5 4 0 1 0.8000 6th Year 4 3 0 1 0.7500 Table 3-5. Model structure, AICc values, delta AICc, and number of parameters for models of survival of juvenile Wood Storks outfitted with satellite tags in SFL (2002-2005) and GA (2005). Model AICc Delta AICc Parameters Ssex-all y ears a g e 1 2 3+ 412.80 0.00 7 Ssex-1st y ea r a g e 1 2 3+ 412.86 0.06 7 Sa g e 1 2 3+ 413.21 0.41 6 Ssex-all y ears a g e 1 2 3 4+ 414.93 2.14 8 Ssex-1st y ea r a g e 1 2 3 4+ 415.00 2.20 8 Sa g e 1 2 3 4+ 415.33 2.53 7 Ssex-1st y ea r a g e 1 2+ 417.11 4.31 6 Ssex-all y ears a g e 1 2+ 417.12 4.32 6 Sa g e 1 2+ 417.47 4.67 5 Sconstant 439.13 26.34 4 Table 3-6. Survival estimates a nd confidence intervals for Model S1st yr sex, no cohort, age 2, 3+ Age Class Sex Survival Estimate SE 95% Confidence Interval Lower Upper 1st Year Female 0.3750 0.0765 0.2403 0.5323 Male 0.2295 0.0538 0.1409 0.3511 Combined 0.2871 0.0450 0.2074 0.3826 2n d Year Survival Combined 0.5559 0.0931 0.3742 0.7238 3rd+ Year Survival Combined 0.8671 0.0706 0.6626 0.9559

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74 Table 3-7. Model structure, AICc values, delta AICc, and number of parameters for Barker models of survival of juvenile Wood Storks outfitted with satellite tags in SFL (20022005) and GA (2005). Model AICc Delta AICc # Parameters Sta g effects Rconstan t 105.64 0.00 6 Sconstan t Rconstan t 107.53 1.90 5 Sta g effects Rsex 107.96 2.33 7 Ssex Rconstan t 109.72 3.44 6 Ssex ta g effects Rconstan t 109.57 3.94 8 Sconstan t Rsex 109.81 4.17 6 Ssex Rsex 111.40 5.76 7 Ssex ta g effects Rsex 112.01 6.38 9

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75 Table 3-8. Stable age distribution and number of female birds in each age cla ss for a hypothetical population with 10,000 adul t females for 4 models with different survival parameters. UCL stands for the upper 95% confidence limit for the agespecific survival rate as calculated using the Barker model. Age class Apparent juvenile survival Barker estimates, juvenile and adult survival Barker 1s t and 2n d year survival = UCL Barker model UCLs for all survival estimates Stable age distribution Population vector Stable age distribution Population vector Stable age distribution Population vector Stable age distribution Population vector 1st year 0.2605 46750.263646880.2424 46030.24934631 2nd year 0.0833 14950.081114420.0957 18170.091517003rd year 0.0515 9240.0483 8590.0715 13580.063511804th year 0.0475 8520.0447 7950.0638 12120.05741066Adult females 0.5572 100000.5623100000.5266 100000.538310000

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76 Figure 3-1. Locations where satellite transmitters have been deployed on juvenile (triangles) and adult (circles) Wood Storks from 2002-2007.

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77 0 5000 10000 15000 20000 25000 30000 35000 40000 05101520253035404550 Years Into FutureNumber of Adult Females Barker Survival, High CI all rates Barker Survival, High CI for Juvenile Survival Barker, Best Estimates Apparent Juvenile Survival Deterministic analysis based on count data Figure 3-2. Future population size over the next 50 years when deterministic matrix is parameterized using apparent or estimat ed survival rates.

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78 1 10 100 1000 10000 100000 1000000 10000000 0.000.100.200.300.400.500.600.700.800.901.00 Proportion of boom yearsPopulation size after 50 years Figure 3-3. Population size (adult females) after 50 years when th e proportion of boom years varied from 0-1. The gray dashed line represents the starti ng population size of 10,000 adult females.

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79 CHAPTER 4 SURVIVAL OF JUVENILE WOOD STORKS IN RELATION TO EVERGLADES W ATER LEVELS AND TIMING OF NESTING Introduction Habitat alteration and climate change are among th e greatest threats to global biodiversity and it is difficult to predict to what extent i ndividual species may adapt to changing conditions via phenotypic plasticity and/or th e rapid evolution of adaptive tr aits. Global climate change has changed the timing of favorable environmental conditions for many organisms and the response has been a change in range or advancement in phenology for many taxa and species (Both et al. 2004, Both and Visser 2005, Marra et al. 2005, Parm esan 2006). For birds, these changes often manifest as earlier arrival of migrants and earlier br eeding in response to warmer spring weather (Walther et al. 2002, Parmesan and Yohe 2003, Both et al. 2004). The fitness consequences of such changes depend on whether crucial interacti ons between species (e.g., predators and prey, herbivores and plants, parasitoids and hosts, etc.) are disrupted by asynchronous shifts or mismatches in phenology (Visser and Both 2005, Parmesan 2006). So far, few temperate avian species have shown a delay in the onset of breeding. A review of 677 species found that only 9% had delayed spring activities, whereas 62% showed spring advancement in response to earlier onset of wa rm spring conditions (Parmesan and Yohe 2003). Phenological changes can have cau ses other than climate change however, and in the Florida Everglades changes in the phenolog y of waterbirds have been li nked to anthropogenic changes in hydrology rather than climate. Retention of water in the Water Conservation Areas and decreased flows to the Florida Ba y estuary (to the south) and Gulf of Mexico (to the west) have led to changes in the timing and lo cation of prey availability for W ood Storks. As a result, rather than initiating nesting in Novemb er or December as they did prior to 1960s, Wood Storks in the Everglades now typically be gin nesting in February and March (Ogden 1994).

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80 The Everglades ecosystem consists of a mix of higher elevation, short hydroperiod wetlands interspersed with deeper water sloughs. Historically, this system flooded during the wet season, inundating the higher elevation ma rshes and moving via sheet flow through the sloughs before draining into Florid a Bay and into estuaries along th e Gulf of Mexico (Fleming et al. 1994). High concentrations of food were available throughout the bree ding season as a result of a seasonal drying pattern (Novemb er April) which concentrated fish and invertebrates into increasingly smaller and shallower pools (Kushlan 1976, Ogden 1994). The deeper sloughs remained flooded and freshwater flows continue d throughout the dry season into the estuaries and Florida Bay (Fleming et al. 1994), supporting producti ve nurseries for fish and shrimp and good foraging areas for wading birds (McIvor et al. 1994, Lorenz 1999). The majority of Wood Stork colonies occurred in the mangrove-f reshwater ecotone during this time period. Drainage, impoundment, conversion to agricu lture and housing, and inappropriate water management during the 20th century (Fennema et al. 1994, Light and Dineen 1994) resulted in the outright or functional loss of many of the short hydroperiod mars hes and coastal habitats that supported nesting by Wood Storks early in the dr y season. Higher elevation marshes remain inundated for longer periods of time, decreasing prey availability for wading birds during the early dry season and salinities in the mangrove -freshwater ecotone and Florida Bay have increased due to the reduction in freshwater inflows, negatively affecting fish and shrimp communities as well as those taxa that prey up them (McIvor et al. 1994, Lorenz 1999). As a result, Wood Stork colonies in the Everglades are a small fraction of their historical size and number and colonies initiate much later they di d historically (Ogden 1994) This sharp decline in Everglades colonies led to the listing of the Wood Stork as Federally endangered in 1984 ((U.S. Fish and Wild life Service 1996).

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81 The initiation and success of Wood Stork bree ding colonies are known to be closely tied to water levels and prey con centrations (Kahl 1964, Kushlan 1976, Ogden 1994, Coulter et al. 1999) and late nesting has had a negative impact on Wood Stork productivity in South Florida (SFL). Ogden (1994) found that colonies initiated after De cember were smaller and less successful than those that were started in N ovember and December, and that both size and success rates decreased as the time before ini tiation lengthened. A major reason for the low success rates of these later colonies is that Wood Storks are unable to finish their nesting cycle prior to the start of the summer wet season that ex tends from May through October. The onset of the summer rains causes water levels to rise across the Everglades, which decreases prey availability as fish and other prey that had b ecome increasingly concentrated over the course of the dry season disperse into the rising waters. As a result, adult Wood Storks often abandon their nests before their young have fledged and nestlings are left to starve en masse (Frederick et al. 2008). The reproductive cycle of the Wood Stork takes approximately 105-130 days (Coulter et al. 1999). When nesting began in November or December (early) young Wood Storks would typically fledge during the hei ght of seasonal drying in March and April, when prey may be super-available (Kahl 1964). Since 1970, however Wood Storks have only initiated nesting as early as December twice (Ogden 1994), and most nesting events begin in February through March (late) (U.S. Fish and Wildlife Service 1999). Late initiated colonies that are successful will fledge young in late May, June, or July (Figure 4-1), during a time of rising water, dispersing prey, and poor food availability. If young birds do survive to fledge, there may st ill be consequences of dispersing so late in the year into such poor conditions. If late nesting and the related ri sing water conditions

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82 during and following fledging have a negative effect on juvenile survival, recruitment of juvenile birds into the population will, by extension, be affected as well. In metapopulation models, similar asymmetries have been shown to in crease the probability of population extinction (Vuilleumier and Possingham 2006, Benard a nd McCauley 2008). Until now, however, the effect of late nesting and wate r levels on juvenile survival and Wood Stork population dynamics has not been addressed. A major goal of Everglades restoration and Wood Stork recovery is an increase in numbers of Wood Storks nesting in sout h Florida. The Wood Stork Rec overy Plan calls for reliable nesting in south Florid a of at least 2500 pair s (U.S. Fish and Wildlif e Service 1996), however it is not currently clear whether the Everglades co ntributes to the larger population or whether it constitutes a population sink for the species in North America. If South Florida acts as a net importer, or sink, for the population (sensu Pu lliam 1988), increased nesting in SFL without earlier nest initiation may be detrim ental to the overall population. Here, I present an analysis of post-fledging dispersal and survival from 2 South Florida Wood Stork colonies in relation to early wet se ason water levels and water recession rates in Everglades wetlands. My objectives were to describe the survival rates, post-fledging movement patterns, and habitat use of juve nile Wood Storks in south Flor ida when fledging into optimal (dry, typical of historic conditions) vs. subop timal (wet, typical of most recent 30 years) conditions. I also present an analysis of the potential population-level eff ects resulting from the interaction between juvenile mortality and the fr equency with which birds fledge into suboptimal conditions. Specifically, I wanted to test the hypothesis that high and rising water levels at the time of post-natal dispersal may cause South Florid a Wood Stork colonies to act as a sink for the larger population.

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83 Study Area and Methods Using satellite telemetry, I followed the fate s of juvenile birds fledged from two South Florida (SFL) colonies in 2002-2005. In 2002 and 2003, work was conducted in the Tamiami West colony (TW) (N25o45, W80o32) in Everglades Nationa l Park, Miami-Dade County, with satellite tags deployed by masters st udent Rebecca Hylton (see Hylton 2004). In 2004 and 2005, this colony failed completely, and I captured birds in the Palm Beach Solid Waste Authority Rookery (SWA) (N26o46, W80o08), Palm Beach County. After birds fledged, they dispersed widely ac ross the southeastern United States. For the purposes of this study, however, I was particularly interested in their acti vities in South Florida (SFL), which includes all areas south of Lake Okeechobee, and in the remaining Everglades wetlands, which are contained within a series of Water Conservation Areas (WCAs) and Everglades National Park (ENP) (Figure 4-1). Satellite Telemetry A total of 113 satellite transmitters were deployed on juvenile birds over four years in SFL. In 2002 and 2003, Hylton (2004) used 35g solar powered Argos/PTT satellite tags (Microwave Telemetry, Inc., Columbia MD, USA, 10h on/24 h off duty cycle) with a 10g VHF transmitter attached (Advanced Telemetry Systems, Isan ti, MN, and American Wildlife Enterprises, Monticello, FL). These tags had an accuracy range of 100-1000 m. In 2004 and 2005, I used 45g solar powered GPS/PTT satellite transmitters (Microwave Telemetry, Inc., Columbia MD, USA, hourly locations, 16h on/8 h off duty cycle) with attached 10g VHF transmitters (Advanced Telemetry Systems, Isanti, MN) and an accuracy of approximately 18 m. In all cases, birds were caught by climbing to the nest using tree branches or ladders, and simply removing flightless or poorly flighted young by ha nd from the nest. The largest sibling from each nest (based on visual comparisons of culmen length and overall size) was tagged at four to

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84 six weeks of age. Transmitters were permanently attached to the birds using a 0.25 cm wide teflon ribbon backpack-style harness sewn through with elastic thread wh ich provided a snug fit to immature birds but expanded as the birds gr ew (see Hylton 2004) for a complete description of tagging protocol). Birds were also given a brief physical exam and blood was collected from the brachial vein. Blood samp les were sent to Zoogen Services Inc. (Davis, CA) or Avian Biotech International (Talla hassee, FL) for DNA sexing. Water Levels and Recession Rates I used daily water level measurements from gauging stations in Water Conservation Area 3A (CA3AVG) and Everglades National Park (Sta tion P-33) to estimate mean water depths and daily recession rates by month for Everglad es wetlands since 1962 (South Florida Water Management District 2008). CA 3AVG represents a 3 gauge average from WCA 3A and is used by the SFWMD to characterize water conditions with in that WCA (Abtew et al. 2009). Station P-33 is frequently used as an indicator of wate r conditions in freshwater marshes of the main drainage in Everglades National Park (Ogden 1975, Abtew et al. 2008). Daily recession rates were calculated by subtracting the water depth at time t +1 from water depth at time t and dividing by number of intervening days. Positive recession rates indicate receding water levels and drying conditions, while ne gative recession rates indicate rising water. I used mean daily water depth and recessi on rate estimates from 2002-2005 to characterize hydrological conditions (i.e., wet vs dry, rising vs. receding) enc ountered by dispersing juvenile Wood Storks in the early wet season (May a nd June) during this st udy. Water depth and recession rate estimates are reported as means (SE), and comparisons among years were made using a nonparametric Kruskal-Wallis chi-square approximation. The entire 30 year period of record for these gauges was used to estimate the frequency with which dispersing storks were likely to en counter rising vs. receding water levels and to

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85 illustrate seasonal differences in water depths and recession rates. I also used mean monthly water depths in November-February fr om 1962-1973 and from 1997-2008 to illustrate differences between historical and modern water levels in WCA3A. Survival of Young I used data from the 113 juvenile Wood Storks that were outfitted with satellite transmitters in the two SFL colonies (Tamiami West and the Palm Beach SWA) to examine the effect of Everglades water le vels on fledging success and postfledging dispersal dynamics. I quantified the number of satellite tagged juveniles that di spersed from the colony and that survived an entire year after dispersing. A bi rd was considered to have started the dispersal process when it did not return to within two km of the natal colony for at least seven days. I again used joint modeling of live-recapture, live-re sight, and tag-recovery data (Barkers model) in Program MARK to estimate survival rates for this subset of SFL birds for their first year after dispersal. To isolate the effects of cohort, water level, and sex on surv ival, I then compared a range of models that allowed for variat ion in survival rates as a function of these three variables. To test for the effects of cohort, I used a model in which first year survival was estimated separately for each of the four cohorts of birds that were tagged. The effect s of water level were built into the model by estimating two first year survival rates, one for the two wetter years combined, and another for the two drier years combined. I consid ered the possibility that survival rates were constant over the years of the study, and could be estimated by a single parameter. I also considered the possibility that su rvival rates under all of the above scenarios varied by sex, for a total of six survival scenarios with no interact ion effects. Recapture rates were modeled as constant, varying by cohort, vary ing by sex, or varying by water level. I did not consider interactions among factors relevant for recapture rates. The comb ination of six survival models

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86 with the four recapture models resulted in a tota l of 24 models for comparison. I chose the best model as the one with the lowest AICc (Burnham and Andersen 1998). Movement Patterns and Habitat Use I analyzed movement patterns, water depths and land cover types at Wood Stork telemetry locations in ArcGIS 9.2 (ESRI 2007). To descri be general movement patterns I used location data from all 4 years, but, due to the lim ited accuracy (100-1000m) of the Argos/PTTs, I confined our analyses of habitat use to locati ons < 50 m altitude obtained from GPS-enabled tags in 2004 and 2005. Given the average hourly step length I observed for the time period in question ( x = 4.38 km, SE = 187.84, n = 7033), and the ability of birds to move more than 100 km in an hour, I considered all locations to be independent for this analysis. To determine the type of habitats in which locations occurred, I used the U.S. Fish and Wildlife Service National Wetland Inventory (NW I) (U.S. Fish and Wildlife Service 2008)to quantify wetland types used by satellite tagged ju venile storks during th eir dispersal from the SWA rookery in June and July of 2002 and 2003. For areas not classified as wetlands by the NWI, I used the National Land Cover Dataset (NLCD) (Multi-Resolution Land Characteristics Consortium 2008) to quantify land co ver types. I collapsed little used categories and included seven categories in the final analysis: forested wetland, emergent wetland, other freshwater (ponds, riverine, open water), marine (deepwate r and wetlands), develo ped, agriculture, and other terrestrial (upland forest shrub/scrub, grassland, and ba rren). I calculated the proportion of locations recorded in each land cover type and compared use between years during the early wet season (June and July) using multi-response permutation procedures (MRPP). MRPP is a nonparametric method for evaluati ng differences between groups, which tests whether two or more sets of locations share the same probabi lity distribution (White and Garrott 1990, McCune

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87 and Grace 2002). The test does not assume that data is normally distributed or that the variances are homogeneous (Zimmerman et al. 1985), rendering it particularly useful for the analysis of ecological data. Because birds in 2004 dispersed broadly acro ss Florida and northward into Georgia and South Carolina, while birds from 2005 remained in central and sout h Florida during those months, I did two comparisons: one in which al l locations from both years were used, and a second, restricted, analysis in which I only used points from 2004 which fell within the minimum convex polygon (Beyer 2004) created from 2005 locations (see Figure 4-4). I used the Everglades Depth Estimation Network (U.S. Geological Service 2008) to estimate mean water depths used by dispersing j uvenile Wood Storks in the Everglades (Figure 4-2). EDEN integrates water levels, ground elevation models, and water surface models to provide scientists with continuous daily spatial interpolations of water stage level and water depth for the freshwater Ev erglades from 2000 through the present on a 400 x 400 m grid (Pearlstine et al. 2007). All lo cations within the area modele d by EDEN were assigned the EDEN daily water depth of the grid cell within which they occurred for the day the location was recorded. I used these values to calculate the mean water depth used by juvenile Wood Storks in 2004 and 2005 from the time they dispersed from the colony through the end of July. Stochastic Simulation Modeling I used a stochastic simulation model to test th e effect of the freque ncy of favorable years on Wood Stork population dynamics. Prior to choosi ng to incorporate dynami cs associated with water levels stochastically, I tested the mean water depth in WCA3A for June since 1962 for autocorrelation and periodicity using spectral an alysis in the JMP 7.2 statistical software package. Since there was no evidence of temporal autocorrelation or periodicity in water depths over this time period, I modeled the frequency of favorable water levels in the Everglades as

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88 independently and ideally distributed (Morris and Doak 2002). I then used a structured population matrix model (Borkhatari a et al. 2008, Chapter Two) to simulate the growth or decline of an idealized hypot hetical South Florida population of 2500 nesting pairs of Wood Storks over 30 years and varied the first year survival values to simulate changes in the frequency with which birds encounter favorab le conditions when fledging, as below. Again, I used the basic female-only, post-breed ing matrix structure of Borkhataria et al. (2008): 43 2 1 0 43 4 3 2 1000 0000 0000 0000 000 ss s s s ff n n n n nyo yo yo yo fl (4-1) where the initial vector represents the numbe r of individuals in each age class at time t as determined for a stable population of 2500 nestin g pairs (represented by female birds only) and the interior matrix represents age specific vital rates. To determine the age structure for the initia l population vector, I initially parameterized the basic stage-structured matrix for this populati on described in the previous chapter, retaining the second year and adult survival rates from th e basic matrix but substituting the first year survival estimate from just the Florida juvenile s averaged across the four years of the study. I derived the value of adu lt survival necessary to sustain a stable populatio n by varying the values for adult survival used in the matrix from 0. 85-0.95 in increments of 0.02 and calculated the resulting population growth rate. I then used linear regression to interpolate the value of adult survival necessary to result in = 1. Using this value for adult survival, I calculated the stable age distribution by taking the right eigenvector of the matrix and used it to create a stage-specific vector of initial population size for a population with 2500 nesting pairs.

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89 I projected this population over th e next 30 years, using one of two matrices. In the first matrix, first-year survival was equal to the estimated survival rate observed for the two drier years while in the second it reflected the mean value for the two wetter years. Because I have shown that second year survival differs from that of birds in their third year or older, I set second year survival equal to the observed value of 0.5775 (Chapter 2). To isolate the effects of first year survival on the population as a whole, however, I used the va lue for adult survival necessary to maintain a stable population rather than the observed rate in both matrices. For each year the population was projected forward, one of these two matrices was drawn at random. I varied the frequency with which eac h matrix was drawn in order to simulate the population level effects of the pr obability of birds fledging in to favorable or unfavorable conditions. I varied the proba bility of encountering favorable conditions from 0-1 in 0.1 increments and used a bootstrap analysis with 5000 replications. Results Water Levels Average daily water depths in the early wet season (May and June) varied significantly among years in both WCA3A and ENP ( 2 = 140.41, 3 df, p < 0.0001; 2 = 86.95, 3 df, p < 0.0001). Conditions were considerably deeper in 2003 and 2005 than they were in 2002 and 2004 (Table 4-1, Figure 4-3). In WCA3A, the aver age depths across May and June in the 2 drier years were 10.68 (0.78) cm and 19.12 (0.72) cm (2002 and 2004 respectively), while in the two wetter years they averaged 34.45 (0.76) cm a nd 31.30 (0.72) cm (2003 and 2005 respectively). While the two wetter years were approximately twice as deep as the 30 year average (19752005) of 16.63 (3.34) cm, they were within 1 standa rd deviation of that mean (std. dev. = 26.30). In ENP, average early wet season depths in the drier years were 15.10 (1.27) and 12.63 (0.26) (2002 and 2004 respectively) while in the two wetter year s they were 29.21 (1.15) and

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90 13.7 (1.54) (2003 and 2005). The 30 year average at this gauge was 11.73 cm (3.14) and in 2003 water levels were more than 1 standard devi ation higher than the mean (std. dev. = 17.24) although in 2005 it did not. The low mean depth in 2005 was due to the rela tively late start of the rainy season in mid-June, however, and water le vels during the last half month of that month averaged 31.28 cm. The conditions encountered by fledging juve niles depended upon the month in which the majority of birds fledged. In 2002 (dry) and 2003 (wet), the majority of birds fledged from the more southerly TW colony in May, encounter ing average water dept hs in WCA3A of 10.68 (0.45) and 34.46 (0.76) cm and average depths in ENP of 7.78 (0.35) and 22.05 (0.99) cm for 2002 and 2003 respectively. In 2004 (dry) and 2005 (wet), the majority of birds fledged from the more northerly SWA colony in June, encount ering average water dept hs in WCA3A of 8.63 (0.37) and 53.68 (2.71) cm and average depths in ENP of 11.98 (0.31) and 22.84 (2.02) cm. The mean EDEN cell depth at used locations in 2004 was 19.23 cm +/15.65 (n = 934, CI = 3.5834.88) while in 2005 it was 43.90 cm +/3.61 (CI = 40.29-47.51). Recession Rates In WCA3A, water levels were still reced ing (positive values) in May of 2002 and 2004, and in June 2004 (Table 4-1) but were rising (negative values) in both May and June of 2003 and 2005 and in June 2002. Daily recession rates enc ountered by storks dispersing into WCA3A in the 2 drier years averaged 0.106 (0.320) cm (May 2002) and 0.163 (0.253) cm (June 2004) and averaged -0.481 (0.345) cm (May 2003) and -1 .687 (0.359) cm (June 2005) in the two wetter years. Average daily recession rates in WCA3A differed signif icantly among years ( 2 = 12.27, 3 df, p = 0.0065; and 2 = 32.49, 3 df, p < 0.0001 for Ma y and June respectively).

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91 At ENP, water levels were generally increas ing in May and June for the years of this study, with the exception being May 2004, when water levels were, on average, still receding (Table 4-1). While average da ily recession rate in May did not differ significantly among the 4 years of this study ( 2 = 0.26, 3 df, p = 0.97), the rate at which water was rising tended to be higher in 2003 and 2005. In June water levels were rising in all year s and differences among years were significant ( 2 = 11.71, 3 df, p = 0.0084) with wate r levels rising most quickly in 2002, 2003, and 2005. Wood Storks dispersing into ENP during the two drier years would have encountered mean daily recession rates of -0.049 (0.155) cm (May 2002) and -0.122 (0.129) cm (June 2004) while those dispersing in the two we tter years would have encountered mean daily recession rates of -0.482 (0.382) cm and -1.128 (0.311) cm in ENP for 2004 and 2005 respectively. When taken in their historical context, daily recession rates in WCA3A averaged by month over the past 30 years, have been mostly positive (receding) in May (22 times or 73% of the time) and negative (rising) in June (24 times or 80% of the time) (Table 4-2). In ENP, mean daily recessions have been mos tly negative (rising) in both May (18 times, 60%) and June (26 times, 87%) (Table 4-3). Survival Of the 113 birds that were tagged in 2002-2004, 86 fledged from their natal colony and the rest died in or around the colony. First year survival estimates by cohort were lowest in 2003 and 2005, and highest in 2002 and 2004 (Table 4-4). The survival m odel that best fit the data was the one in which survival varied with water level and recapture rates were constant. There was little support for sex-based differences in surv ival (Table 4-5). When I combined estimates for male and female birds, survival for years with lower water levels was 0.3673 (SE 0.0689, CI

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92 = 0.2452-0.5093), while for the wetter years the a nnual survival rate was estimated to be 0.1429 (SE = 0.0591, CI = 0.0607-0.3005). When first year survival was constrained to be constant among cohorts, it was estimated at 0.2738 (SE 0.04865, CI = 0.18920.3785). Use of Everglades Wetlands Due to the location of the Tamiami West colony within Everglades National Park (ENP), all birds dispersing from the natal colony in 2002 and 2003 had a strong likelihood of passing through ENP or the WCAs. A total of 99 post-dispersal locations occurred within the WCAs and ENP in May-July, 2002, with 21 of the 27 birds that dispersed from the Tamiami West colony located within these areas at least once. The number of days indivi dual birds used these wetlands ranged from 1-8 (x = 3.09 +/2.21, SE = 0.48), and spanned the period from 16 May16 June. In 2003, however, only 10 post-dispersal locations occurred in the WCAs and ENP in May-July 2003, with only 3 of the 17 birds that dispersed from the TW colony located within these wetlands. The number of days individual birds used these wetlands ranged from 1-3 and occurred on 28 May-01 June. In 2004, seven of the 23 birds that disperse d from the SWA Rookery traveled south and made use of the Water Conservation Areas and/or Everglades National Park. These areas were in use from 12 June through 23 July, with the num ber of days any one bird was present ranging from four to 41. After leavi ng the Everglades wetlands, one bi rd flew to South Carolina, one took up residence in northern Flor ida, and the rest remained in central or south Florida. In contrast, in 2005 only two birds used the Water Conservation Areas and Everglades National Park and only eight locations from only one bi rd occurred within th e area modeled by EDEN (13-14 June).

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93 Mortality In 2002, four dispersing juveniles died before the end of July. Two of these birds used Everglades wetlands and then died in central Flor ida, one moved directly into central Florida and died, and the last moved northward and died around 06 July in South Carolina. Of the birds that survived to the end of July, 10 ended the month in central Florida, two in northern Florida, six in Georgia, three in Alabama, two in Louisiana, an d three in South Carolina. In 2003, only six of the 17 tagged birds survived to th e end of July and of these birds, two ended the month in central Florida, one in northern Florida, and three in Georgia. The rest of the birds died in central and south Florida. Of the other 16 birds that dispersed northw ard or westward from the SWA Rookery in 2004, two ended July in South Carolina, four in GA, seven remained in central and south Florida, one took up residence near Daytona Beach in north ern Florida, and one flew northward to the Florida/Georgia border then returned to central Fl orida. Three birds died in Florida before the end of July, in Brevard, Glad es, and Lafayette counties. Of the 19 birds that dispersed from the colony in 2005, 12 died before the end of July. Of these, eight died in habitats that were classified as agricultural. Of the remaining three deaths, locations at the time of death were classified as emergent wetland, forested wetland, and developed. There was one possible tag failure All deaths occurred in central and southern Florida, with the majority of deaths occurring in Palm Beach (4), St. Lucie (3) and Indian River (2) counties. The 2 remaining deaths occurred in Broward and Hendry counties. No birds ended July further north than Brevar d Co. in Central Florida. Habitat Use When I compared the types of habitats us ed, I found that habita t use was significantly different between 2004 and 2005, for both the range-wide analysis and the re stricted analysis ( A

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94 = 0.0626, P = 0.0093 and A = 0.0828, P = 0.0084 respectively). In 2004, the most commonly used habitats range-wide were emergent wetlands (38.8%), agricultural areas (26.2%), forested wetlands (18.2%), other freshwater habitats (5 .4%), and other terrestrial habitats (5.2%). Developed areas and marine areas were used relati vely little (3.1 and 3.0% respectively). When I restricted the 2004 locations to the areas within the minimum convex polygon of locations from 2005 (south and central Florida), th e same order of use was evident, with emergent wetlands (49.1%), agricultural areas (36.5% ), forested wetlands (9.0%), other freshwater habitats (3.4%) used most commonly. In contrast, in 2005 the majority of locations occurred in agricultural areas (47.4%), followed by emergent wetlands (30.7%), forested we tlands (11.6%), and developed areas (7.6%). Other te rrestrial habitats, other freshwat er habitats, and marine areas made up less than 3% of locations. Stochastic Modeling The adult survival rate nece ssary to support a stable populat ion of Wood Storks in South Florida was 0.9385. The stable age distribution for a population with 2500 adult females (age 4+) was 1207, 326, 174, 163, and 2500 for fledglings and 1-4+ year old birds respectively. This resulted in a total population size of 4370 female birds. When I projected this population forward, I found that the mean population size at the end of 30 years increased exponentially with increasing probability that fledging birds en countered favorable conditions (Figure 4-5). In order for the population to remain stable, young bi rds would have to fledge into favorable conditions approximately 58% of the time. Base d on the above analysis of historical recession rates, birds fledging in April would be likely to encounter favorable c onditions (receding water levels) approximately 80% of the time (averaged across WCA3A and ENP), which would result in population growth over 30 years of approximately 28%. In contra st, birds that fledge in June are likely to encounter favorable conditions on ly 20% of the time. This would result in a

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95 population decline over 30 y ears of nearly 40% (Table 4-6). Birds that fledge in May would encounter favorable conditions only 40% of the time in ENP, but the unavailability of those wetlands might be ameliorated by the probabili ty of encountering favorable conditions in WCA3A nearly 73% of the time. If that is the case, and all birds fledged reliably before June, the South Florida population would increase by approximately 18% or more over 30 years. Discussion First year survival by juvenile Wood Storks in South Florida was strongly influenced by the wetland conditions they encountered on dispersa l. When water conditions in the Everglades were optimal, with water levels still receding and depths average depths below 20 cm, survival rates were high, whereas when water was too deep for Wood Storks to forage and water levels were rising, survival rates were much lower. Although the exact mechanism of their deaths was unknown, I saw significant differences in habita t use in dry vs. wet years which may have impacted survival. Although I only compared habita t use in two years, it was clear that in the drier year emergent wetlands were the primary habitat used by dispersing Wood Storks while in the wetter year dispersing j uveniles moved quickly out of SFL wetlands and were located primarily in agriculture. The time spent foraging in Everglades we tlands during low water conditions may have influenced subsequent juvenile survival by prov iding juvenile storks with a relatively benign setting in which to hone their foraging skills. In contrast, the agricu ltural habitats used by juvenile birds in the wetter years were characte rized by fields of row cr ops dissected regularly by irrigation and drainage ditches. Due to the ac tive management of these ditches, during wetter years agricultural habitats would have provided Wood Storks with lower water levels than those available in SFL wetlands at the tim e that they were dispersing.

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96 The use of agricultural areas in the absence of optimal foraging opportunities is not unique to storks. For example, juvenile oystercatchers ( Haematopus ostralegus ) were much more likely to visit fields during high tides than adults (Caldow et al. 1999). Caldow et al. (1999) surmised that these fields were riskier for juvenile birds due to in creased exposure to cars, trains, and electrical lines on their way to the fields and a higher risk of predation from predators that can take cover in hedgerows and trees. I believe that exposure to toxic chemicals and starvation due to inadequate resources were the more likely causes of high mortality in agriculture for juvenile birds in this study. Subsequent survival may also have been infl uenced by the early use of wetlands if the use of these areas shaped future preferences. Fora ging abilities and preferences may be shaped by the types of habitats used duri ng natal dispersal (Davis and St amps 2004, Benard and McCauley 2008). Young birds that learn to forage in ap propriate wetland environments may have longterm advantages over those birds that are forced almost immedi ately into less productive hunting areas. Diet type and foraging method can have a large impact on survival (Durell et al. 2001), thus fledging into optimal habitats may impr ove both body condition and the ability to find and exploit appropriate resources. Fo r example, in oystercatchers incr eases in foraging efficiency are associated with improvements in body condition a nd survival (Durell et al. 2001, Daunt et al. 2007). Mortality related to water le vels in the Everglades affect ed population dynamics in South Florida on the basis of juvenile survival alone. The frequency w ith which juvenile birds fledged into dry or wet conditions determined whether a target population of 2500 birds would increase or decline. For example, if Wood Storks nest ed reliably in December or January and their young fledged in April the population would grow by approximately 28% over the next 30 years.

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97 However, if birds continue to in itiate nesting in midto late Fe bruary or March, with young birds fledging in June, they will enc ounter rising water levels approx imately 83% of the time and the breeding population will dec line by approximately 38% over the next 30 years. To maintain a population of 2500 birds in Everglades colonies under these conditions, SFL would have to be a net importer of birds. Given recent trends in the timing of colony initiation by Wood Storks in South Florida, these colonies appear to be acting as a sink for the southeastern U.S. Wood Stork popula tion. In order for SFL colonies to export birds to the larger population, Wood Storks must initiate nesting earl ier in the dry season. This will depend on the establishment of a more natural hydrological cycle that restores fr eshwater flows to the estuaries and Florida Bay and allows fo r a strong dry-down and shallower depths in the freshwater Everglades during November December, and January. Wood Storks generally forage in water that is between 15 and 50 cm deep (Kahl 1964). Historically, these depths were usually availabl e from November-January. For example, in the 12 year period between 1962-1973, mean monthly wa ter levels in WCA3A were less than or equal to 50 cm six times in November, eight times in December, and nine times in January. Over the past 10 years, however, average monthly water depths in WCA3A have never been below 50 cm in November and have been less th an or equal to 50 cm only three times each in December and January. It is not until February that there is a reliable drop in water levels. Conclusion The survival and movements of Wood Storks fledging from South Fl orida colonies were strongly influenced by Everglades water levels and the timing of dispersal. Birds that fledged into conditions of high and rising water moved quickly out of the Everglades ecosystem and had lower survival rates than those that fledged wh en water levels were low and receding. When hydrological conditions in the Everglades were fa vorable, juvenile storks were able to spend

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98 more time in this relatively benign environment, learning important foraging skills and building up energy reserves prior to moving out of the system. Although Wood Stork population dynamics are mo st sensitive to adult survival (Chapter Two), this model showed that j uvenile survival alone could influence population dynamics in South Florida and determine whether the region acts as a source or a sink for the overall population. While SFL currently appears to f unction as a population sink (Chapter One), the reliable dispersal of juvenile Wood Storks into favorable hydrological conditions could increase juvenile survival enough that SFL colonies could become self-sustaining. The Everglades is the subject of a multi -billion dollar restoration project aimed at restoring natural hydrologic c onditions and earlier nesting by Wood Storks is one of their recovery goals. If Everglades restoration proceeds as planned and restores a more natural hydrological cycle, Wood Storks may once again flour ish in South Florida. If not, the continued late nesting of birds in SFL colonies is likely to act to the detriment of th e species as a whole.

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99 Table 4-1. Mean daily water depths a nd recession rates in May and June of 2002-2004 for Water Cons ervation Area 3A (CA3AVG) and Everglades National Park (NP-33). Positive recession rates indicate receding water levels and negative recession rates indicate rising water levels. Station Year Mean daily water depths (cm) Mean daily recession rates (cm) May June May June x SE x SE x SE x SE CA3AVG 2002 10.68 0.45 28.88 2.69 0.11 0.32 -1.30 0.38 2003 34.46 0.76 56.03 1.26 -0.48 0.35 -0.63 0.13 2004 19.13 0.93 8.63 0.37 0.43 0.16 0.16 0.25 2005 31.30 0.70 53.69 2.71 -0.22 0.42 -1.69 0.36 NP-33 2002 7.78 0.35 22.66 1.66 -0.05 0.15 -0.59 0.34 2003 22.05 0.99 36.61 0.89 -0.48 0.38 -0.28 0.33 2004 13.24 0.39 11.99 0.31 0.19 0.06 -0.12 0.13 2005 4.85 0.45 22.85 2.02 -0.12 0.19 -1.13 0.31

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100 Table 4-2. Mean daily recession rate by month and frequency of receding vs. rising water levels over a 30 year time period (1978-2007) in Water Conservation Area 3. Month N Mean Daily Recession Rate (cm) SE # of years receding # of years rising January 30 0.20 0.0564 23 7 February 30 0.24 0.0495 26 4 March 30 0.29 0.0491 24 6 April 29 0.34 0.0527 26 3 May 30 0.22 0.0705 22 8 June 30 -0.62 0.1239 6 24 July 30 -0.44 0.0734 4 26 August 30 -0.30 0.0793 7 23 September 30 -0.37 0.0862 8 22 October 30 -0.02 0.0895 16 14 November 30 0.23 0.0635 24 6 December 30 0.27 0.0495 26 4 Table 4-3. Mean daily recession rate by month and frequency of receding vs. rising water levels over a 30 year time period (1978-2007) in Everglades National Park. Month N Mean Daily Recession Rate (cm) SE # of years receding # of years rising January 30 0.18 0.0252 27 3 February 30 0.15 0.0400 24 6 March 30 0.17 0.0418 23 6 April 30 0.13 0.0419 24 6 May 30 0.01 0.0731 12 18 June 30 -0.37 0.0795 4 26 July 29 -0.22 0.0689 7 22 August 29 -0.24 0.0429 4 25 September 28 -0.18 0.0477 9 19 October 28 0.02 0.0423 14 14 November 29 0.07 0.0441 22 7 December 29 0.21 0.0294 26 3

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101 Table 4-4. First year survival estimates by c ohort for juvenile birds fledged in SFL in 20022005. Cohort Survival SE 95% Confidence Interval Lower Upper 2002 0.3846 0.0954 0.2210 0.5793 2003 0.0625 0.0605 0.0087 0.3354 2004 0.3333 0.0962 0.1763 0.5388 2005 0.2105 0.0935 0.0813 0.4455 Table 4-5. Model structure, AICc values, delta AICc, and number of parameters for models of first year survival for juvenile Wood Storks outfitted with satelli te transmitters in 2002-2005 in SFL. Survival Recapture AICc Delta AICc # Parameters Swater level Rconstan t 325.21 0 5 Swater level Rcohor t 326.16 0.46 8 Swater level sex Rconstan t 327.1 1.39 8 Swater level sex Rcohor t 327.76 2.05 11 Swater level Rsex 327.82 2.11 6 Scohor t Rconstan t 328.17 2.46 8 Scohor t Rcohor t 328.76 3.05 10 Sconstan t Rconstan t 328.81 3.1 4 Swater level Rwater level 329.07 3.36 7 Sconstan t Rcohor t 329.2 3.49 7 Swater level sex Rsex 329.28 3.57 9 Scohort sex Rconstan t 329.33 3.62 11 Scohort sex Rcohor t 330.21 4.5 14 Scohor t Rsex 330.33 4.62 8 Ssex Rconstan t 330.41 4.7 6 Swater level sex Rwater level 330.6 4.89 10 Sconstan t Rsex 330.9 5.19 5 Ssex Rcohor t 330.94 5.23 9 Scohort sex Rsex 331.58 5.87 12 Scohor t Rwater level 331.62 5.91 9 Sconstan t Rwater level 332.12 6.41 6 Ssex Rsex 332.55 6.84 7 Scohort sex Rwater level 332.97 7.26 13 Ssex Rwater level 333.82 8.11 8

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102 Table 4-6. Mean total populat ion size and mean number of br eeding females at the end of 30 years for an initial population of 4370 fema le birds (2500 breeding females) when the probability of juveniles fledging into fa vorable conditions varies from 0-1. Probability of Favorable Conditions Total Population Size (Females) Number of Breeding Females x Std. Dev. % Change x Std. Dev. % Change 0 2007 --54.07 1225 --51.00 0.1 2326 182.92 -46.77 1402 105.86 -43.92 0.2 2665 268.19 -39.02 1580 154.51 -36.80 0.3 3018 340.26 -30.94 1774 194.12 -29.04 0.4 3472 393.81 -20.55 2022 214.25 -19.12 0.5 3917 452.4 -10.37 2262 249.83 -9.52 0.6 4454 483.74 1.92 2541 269.46 1.64 0.7 4991 495.21 14.21 2820 267.75 12.80 0.8 5615 472.51 28.49 3146 252.54 25.84 0.9 6324 382.97 44.71 3505 204.47 40.20 1 7060 -61.56 3876 -55.04

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103 Figure 4-1. Timing of the repr oductive cycle of the Wood Stork. Bars show the approximate timing of fledging for nests initiated in th e first week of each month of the breeding season. If storks began nesting in the firs t week of the month (shown in dark gray), juveniles would fledge approximately 105130 days later (shown in black).

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104 Figure 4-2. Map of Florida showing the locations of the 2 colonies where satellite transmitters were deployed on juvenile Wood Storks, Tamiami West (circle) and the Palm Beach Solid Waste Authority Rookery (square). The dark green area depicts the Water Conservation Areas and Ever glades National Park, which contain the wetlands that comprise the remaining Florida Everglades.

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105 Figure 4-3. Daily water dept hs during the year for Water Conservation Area 3A (a) and Everglades National Park (b) in 2002-2004. Arrows indicate the month that the majority of birds fledged in each year. Gaps in the time series indicate missing data.

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106 Figure 4-4. Locations of Wood Storks in 2004 (bl ack circles) and 2005 (wh ite circles) during the months of June and July. The yello w area shows the minimum convex polygon of locations from 2005.

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107 y = 2064e1.2528xR2 = 0.9986 0 1000 2000 3000 4000 5000 6000 7000 8000 00.10.20.30.40.50.60.70.80.91 Probability of fledging into optimal conditionsTotal population size at end of 30 years Figure 4-5. Estimated mean tota l population size after 30 years as a function of the probability that juveniles fledge into optimal hydrologi cal conditions (low and/or receding water levels). The initial population consisted of 4370 individuals.

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108 CHAPTER 5 A COMPARISON OF TWO RANGE-WIDE HAB ITAT SUITABILI TY MODELS FOR THE WOOD STORK ( MYCTERIA AMERICANA) IN THE SOUTHEASTERN UNITED STATES Introduction The protection of critical habitat is often the primary tool used by managers in the recovery of endangered species. Critical habitat is defined in the Endangered Species Act as specific areas within the geographical ar ea occupied by the species on which are found those physical or biological features essentia l to the conservation of the speci es and which may require special management considerations or protection . (Sidle 1987). Critical habitat has not been designated for the Wood Stork, although hab itat management guidelines are in place (Ogden 1990, Brooks and Dean 2008). These guidelines ar e vague, however, identifying freshwater and estuarine wetlands as important foraging, nestin g, and roosting sites without specifying specific wetlands or geographic areas. Th e Wood Stork Recovery Plan ( U.S. Fish and Wildlife Service 1996) identified the need to locate and prioritize important habitats for the Wood Stork and to work with landowners or through existing regulat ory mechanisms to protect and manage these lands. Identifying these areas is difficult, how ever, given the wide ra nge of the Wood Stork across the Southeastern U.S. In order to identify critical habitat needs of Wood Storks, it is necessary to first determine where they are and why they are there (Aarts et al. 2008). Most studie s of Wood Stork habitat use have been local in scope, focusing on where th ey nest or forage within specific geographic regions. Studies have occurred primarily in the wetlands of South Florida (Kahl 1964, Ogden et al. 1976, Browder 1984, Herring 2007), central a nd northern FL (Ogden 1991, Rodgers et al. 1996) or coastal Georgia (Coulter et al. 1987, Gaines et al. 1998, Bryan et al. 2002, Depkin et al. 2005) and have been conducted primarily via direct field observations or by following the flights of foraging Wood Storks by airplane.

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109 These studies have shown that in Florida W ood Storks nest in fr eshwater and marineestuarine forests consisting of mangroves ( Laguncularia racemosa, Rhizophora mangle ), bald cypress ( Taxodium dystichum ), black gum ( Nyssa biflora ), southern willow ( Salix carolinensis ), and pond apple ( Annona glabra), while in Georgia and South Ca rolina they generally nest in cypress (Rodgers et al. 1996, Cou lter et al. 1999). Wood storks also increasingly make use of altered and artificial wetland si tes in Florida, often nesting in non-native species such as Australian pine ( Casuarina australiana) and Brazilian pepper ( Schinus terebinthifolius ) on spoil islands (Ogden 1991, Rodgers et al. 1996). Feeding by Wood Storks was shown to occur in both natural and arti ficial wetlands, with use predicated on prey densities and appropriate water depths. In SFL, Wood Storks are known to feed throughout the Everglades and Big Cypre ss basins, and in flooded agricultural fields as well (Coulter et al. 1999) and the Everglades Water Conservation Areas in Palm Beach, Broward, and Dade counties have been shown to be particularly important foraging habitat, with as much as 55% of the population foraging there during dry years (Bancroft et al. 1992). In southwestern Florida, marshes and wetlands in Collier, Lee, and Hendry counties were shown to be of particular importance for Wood Storks nesting at the Corkscrew Swamp Sanctuary (Browder 1984). In central and northern Florida, little informa tion exists on preferred foraging habitats or wetlands. In the coastal region of Georgia and South Caro lina, Wood Storks were shown to forage in freshwater wetlands and in tidal creeks and pools both during and outside of the breeding season (Gaines et al. 1998, Bryan et al. 2002, Depkin et al. 2005). Foraging success was higher in tidal habitats, but temporally constrained to periods of low tide (Depkin et al. 2005). Birds nesting inland in Georgia foraged primarily in swamps and ponds (Coulter et al. 1987) and in South

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110 Carolina they foraged primarily in palustrine we tlands, intertidal creeks, isolated wetlands, and managed marsh (Murphy and Coker 2008). On the basis of these and similar studies, broad scale habitat suitability models have been generated for the Wood Stork as part of the U. S. Geological Services National Gap Analysis Program (GAP). GAP seeks to identify impor tant areas for conservation by modeling species distributions and diversity using species-habitat relationships and determining whether biological important areas are currently protected (Jennings 2000). Species distribution maps for the Wood Stork are based on land cover type and vary from st ate to state. For example, the South Carolina GAP used 5 broad categories to describe W ood Stork habitat (freshwater, marsh/emergent wetland, swamp, and aquatic vegetation), whereas in Florida, Wood Stork habitats were made up of 33 different land cover types. More mechanistic models have been confined to the ecosystems of South Florida and have been applied primarily to breeding dynamics. In one of the earliest models, (Browder 1976) modeled the relationship between rainfall, water levels, fish production, and the breeding success of Wood Storks in Southwest Florida. (Wolff 1994) used an indi vidual based model to relate Wood Stork breeding dynamics in Everglades wetla nds to dynamic environmental variables, and Herring (2007) used a proportional hazards regre ssion model to predict the use of Everglades wetlands by breeding Wood Storks from three SFL colonies in response to vegetation type and changing hydrology. Advances in telemetry have allowed researchers to broaden the scale of investigations into Wood Storks movements and habitat use. Comer et al. (1987) used VHF telemetry to track 5 adult Wood Storks from their breed ing colony in Georgia to their wi nter range in Florida. More recently, satellite telemetry has been used to document the postbreeding movements of 4 adult

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111 Wood Storks from Georgia, and to investigate population affiliations of Wood Storks captured along the Gulf Coast (Bryan et al. 2008). Hylton (2004) used satellite transmitters to track the movements of juvenile Wood Storks from Sout h Florida, documenting survival and seasonal movement patterns and using their locations to estimate home range size a nd habitat selection. Until now, however, habitat use information obtai ned through satellite telemetry has not been incorporated into predictive models of habitat use. I used locations obtained by satellite telemetr y to create predictive habitat models for the Wood Stork across its range in th e southeastern U.S. My objec tives were to 1) determine whether a temporally and spatially coarse-grained modeling appro ach could be used to predict Wood Stork occurrences in the Southeast and 2) to compare the accuracy of 2 different habitat suitability modeling approaches. Study Area and Methods Wood Storks from the Southeastern U.S. population breed in Sout h Carolina, Georgia, and Florida, and their non-br eeding range also includes Alabama and eastern Mississippi. Elevations in these states range from 0-730 m above sea level, and all 5 states include coastal zones as well as a diverse range of land cover types and land uses. Wood Storks were captured ac ross the southeastern United States (Figure 5-1) from 2004-2008. Juvenile and adult birds were outfitted with GPS-enabled satellite transmitters from 2004-2008. The initial dataset included 77 birds, of which 46 were juveniles and 31 were adults. The juvenile birds were captured at the Palm Beach Solid Waste Authority rookery (SWA) in Palm Beach County, FL (31), the Chew Mill colony in Jenkins County, GA (7), and the Harris Neck National Wildlife Refuge in McIntosh Coun ty, GA (8), (see Table 1 for coordinates of all capture locations). Adult birds were captured in FL, GA, SC, and LA.

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112 Solar-powered ARGOS-PTT satellite tags (Microwave Telemetry, Columbia, MD) were attached to the birds with a backpack-style ha rness of teflon ribbon. Global Positioning System (GPS) locations for the juvenile birds were recorded hourly, from 0600-2100, with an accuracy of approximately 18 m. For the adult birds, 15 birds had locations recorded on the same schedule, and 16 had locations recorded every 2 hours, 24 hours per day. All tags transmitted locations every 3 days. There were a tota l of 384,655 locations in the dataset. I removed all birds from the dataset that tran smitted for 90 days or less, and selected one location per day at random for the 47 remaining bi rds. Birds in the remaining dataset transmitted between 116 and 1,732 days. Although Wood St orks are capable of moving long distances between days, I did not consider these points to be temporally or spatially independent, nor did I consider autocorrelation of locations to be a de triment to the analysis as the alternative of eliminating autocorrelation by removing locations from a dataset has been shown to reduce statistical power and to mask bi ologically relevant information (D e Solla et al. 1999, Cushman et al. 2005). I did assume, however, that each bi rd acted independently of the others. Spatial Analysis I used four existing datasets to categorize la nd cover, elevation, and linear water features across the Southeastern US, and created 18 raster layers using two mile grid cells to represent proportions of land cover types, la ndscape diversity, elevation and va riation in elevation, and the total length of linear water features. I created a map of land cover types using a combination of the 2001 National Land Cover Dataset (NLCD) (Multi-Resolution Land Char acteristic Consortium 2008) and the National Wetland Inventory Polygon dataset (NWIP) ( U.S. Fish and Wildlife Service 2008). The NLCD is a continent-wide land cover database that cat egorizes land cover types into 16 categories using 30 m cells (Homer et al. 2007). I collapsed the land cover classification into 9 categories: open

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113 water, developed (including low, medium a nd high intensity use), barren, upland forest (including deciduous, evergreen, and mixed forests), shrub/sc rub, grassland/herbaceous, agriculture (including hay/pasture and row crops), woody wetlands, and emergent herbaceous wetlands. The National Wetlands Inventory applies speci fically to wetland and deepwater systems in the United States and uses a hierarchical approach to classify these areas into 5 broad systems with nested subsystems and classes. I used the NWI wetland polygon layer to create a raster layer of wetlands across the Southeastern US at the broadest classifi cation scale: marine, estuarine, riverine, lacustrine, and palustrine. To integrate th e two raster layers (NLCD and NWI), I first assigned values from the NWI dataset to a raster map of 30 m cells. I then filled in the non-wetland or other areas not classified by the NWI data w ith values from the reduced NLCD dataset. For ease of interpretation, I consid ered the lacustrine and palustrine categories to correspond with emergent herbaceous wetlands and forested wetland respectively, and classified both marine and estuarine as marine. I used this 12 category map to create 2mi grids characterizing the proportion of each habitat type in each 2 mi gr id cell, the diversity (total number) of habitats within each 2 mi cell, and the mean diversity of cells within a five cell moving window (see Appendix A). I used the U.S. Geological Services Nati onal Elevation Dataset (NED) (U.S. Geological Service 2003) to quantify elevation across the Southeast and th eir National Hydrography Dataset (NHD) (U.S. Geological Service 2007) to quantif y linear water features. The NED provides a 1 arc second (approximately 30 m) resolution map of el evation (m) for the US, I used this dataset to create a 2 mi grid of mean elevation and its variability (as represented by the standard deviation within each 2 mi grid cell). I used the digital line gr aphs available within the NHD to

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114 map linear water features across the Southeast a nd summed their lengths in each 2 mi grid cell, creating three raster maps co rresponding to the th ree feature types: artificial paths, streams/rivers, and canals/ditches. Habitat Suitability Modeling The primary goal of habitat suitability mode ling is to use environmental variables to predict the likelihood that spec ies will inhabit a specific ar ea (Guisan and Zimmermann 2000, Hirzel and Le Lay 2008). Resour ce selection functions (RSFs) us e the spatial di stribution of resources to estimate the distri bution and abundance of animals by yielding probabilities that are proportional to use (Boyce and McDonald 1999, Manly et al. 2002). The goal of resource selection analysis is to determine whether re sources are used dispr oportionately to their availability (Erickson et al. 2001). I used logistic regression (LR) and Mahalanobis distance (MD) models to create rangewide habitat su itability models for the Wood Stork across the Southeastern U.S. Use and availability may be described at the individual or population level. (Erickson et al. 2001, Manly et al. 2002). I estimated both hab itat use and availability at the population level and did not account for temporal va riability in either. A grid cell was considered used if any of the daily Wood Stork locations occu rred within its borders at leas t once. I did not incorporate intensity of use into the analysis so a cell received the same value whether it contained one or 100 locations. To define available habitats, I used the daily locations from the 47 bi rds in the dataset to create individual 90% kernel home ranges. I then combined the overlapping home ranges to create a map of available hab itat (Figure 5-2). The home range s were constructed using the Animal Movement extension in ArcView 3.2. (Hooge and Eichenlaub 1997). I generated

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115 10,000 random points within the available habitat and consider ed all cells in which random points occurred to be availabl e. I recorded the values for the suite of environmental characteristics at each used and available cell and used these values to calculate the coefficients for the regression equation for the LR model, while for the MD m odel I incorporated used sites only. Logistic regression Prior to the regression analysis, I transf ormed the independent variables to better approximate normality, using an arcsin-root transformation on the proportions of land cover types and a logbase 10 + 1 transformation on the other variables. Because logistic regression requires the use of uncorrelate d independent variables (Ott & Longnecker 2001), I used a correlation matrix to identify pairs of va riables with a correlation coefficient ( r ) > 0.60. If a variable was correlated ( r > 0.60) with more than one other variable I dropped it from further consideration. If it was corre lated with only one other variable, I removed each from the regression separately and compared the fit of th e resulting regression usi ng Akaikes Information Criteria (AIC) values (Manly et al 2002), retaining the variables that resulted in the lowest AIC. Because barren areas, ponds, and open water were poorly represented on the landscape, I did not include them in the regression analysis. I used the coefficients of the best regression model to map habitat suitability for the Wood Stork across the Southeast, using the function: Probability of Use = )... exp(1 )... exp(22110 22110pp ppX XX X XX (5-1) where 0 to p were the coefficients estimated from the logistic regression and X1 to Xp were the landscape variables that were significant in the regression (Manly et al. 2002).

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116Mahalanobis distances The Mahalanobis distance statistic differs fr om regression analyses in that only used resources need be identified. Conclusions regarding non-used or available habitats are unnecessary, with the excep tion of the delineat ion of the study area bounda ry (Clark et al. 1993, Manly et al. 2002). The Mahalanobis distan ce measures the dissim ilarity in habitat characteristics between ideal ha bitat (as determined by use), and available habitats (Cayuela 2004). Habitat suitability models are created by assigning values to each grid cell corresponding to a vector composed of the values at that cell of the raster maps of independent variables, and measuring the Mahalanobis distance of the availabl e units to the mean for the used units (Manly et al. 2002). The Mahalanobis distance is determined from the equation: distance = ) ()'(1 uxux (5-2) where x is the vector of environmental char acteristics associated with each cell, u is the mean vector of environmental characteris tics at each telemetry location, and is the estimated covariance matrix (Clark et al. 1993). I used the ADEHABITAT package (Calenge 2006, 2007) in the R st atistical program to generate the Mahalanobis distance surface gr id based on the grids of environmental characteristics, and to recode the grid into P-values based on a Chi-Square distribution. Because the analysis is robust to corre lated variables (Clark et al. 1993, Knick and Rotenberry 1998, Manly et al. 2002), I used 15 grids of environm ental variables to gene rate the Mahalanobis distance surface (% cover for emergent wetlands, forested wetlands, upland forests, marine habitats, shrub/scrub, grasslands, developed ar eas, and agriculture; lo cal and surrounding habitat

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117 heterogeneity; elevation and vari ability in elevation, and total length for streams/rivers, canals/ditches, and artificial paths. Model Testing I tested each model against 2 datasets: th e dataset used to build the model and an independent data set of Wood Storks tagged as juveniles in 20022003 (see (Hylton 2004)for details). There were a total of 51 birds in the independent data se t and a total of 16,958 locations. To evaluate how well each model performed, I used receiver operating characteristic (ROC) plots. ROC plots can be used to compar e model fit by evaluating the proportion of cells that are correctly and incorre ctly classified (Cumming 2000, Boyce et al. 2002). They are constructed by first calculating th e sensitivity and specificity of each model, where sensitivity refers to the proportion of positive cells that are corre ctly classified as used, and specificity refers to the proportion of negative ce lls that are correctly classifi ed as unused (Cumming 2000). Positive cells are those cells which fall at or above a probability threshold, represented by the Pvalue of the habitat suitability model, which vari es from 0-1. Negative cells are those that fall below the threshold. Plotting sensitivity against 1 minus specificity at different thresholds yields the ROC curve, with a well-fitting model risi ng steeply and then leveling off and a random model following a 45o diagonal line from the origin. The sum of the area unde r the curve (AUC) provides a measure of model fit by indicating the probability that a cell selected at random from the initial set of used locations will receive a higher probability of use score than one selected at random from a set of unused or random locations (Cumming 2000).

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118Results Habitat Suitability Modeling Logistic regression There were 4107 used cells across the Sout heastern US for the training dataset, representing a total of 26,759 locat ions (Figure 5-3). The indepe ndent dataset used to validate the models had a total of 16,958 locations and 3200 used cells (Figure 5-4). The correlation matrix indicated that elevati on and its variability we re strongly correlated (r = 0.8145). Upland forests were positively correlated with both mean elevation (r = 0.6671) and variability in elevation (r = 0.7352) and total length of streams/river were correlated with variability in elevation (r = 0.6249). I removed upland forest s from the regression analysis outright, then removed streams and mean elevati on based on the AICs of the regression model. The variables I retained following stepwise regression and model selection procedures were % cover for agriculture, grasslands, forest ed wetlands, shrub/scrub, marine, and emergent wetlands; local and surrounding lands cape heterogeneity, th e total length of canals and ditches, and variability in elevation (see Table 4-2). The association with use was positive for agriculture, forested wetlands, marine habitats, emergent wetlands, length of canals/ditches, and both local and surrounding landscape heterogeneity. It was negative for grasslands, shrub/scrub, and variability in elevation. The habitat suitability map generated from the LR contained 45,682 cells and covered 473,355 km2. The probability of use fo r individual cells ranged from 0-0.7963 (Figure 5-5). There were 14,619 cells with probability of use (P) under 0.1 and 148 with P > 0.7. The percentage of cells classified as used increase d as the probability of us e value went up, for both the training (R2 = 0.7746) and independent datasets (R2 = 0.6451, Figure 5-6). For cells with the

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119 highest probability of use (P 0.7), 54% and 45% were classified as occupied using the training and independent datasets respectively. Mahalanobis distances The habitat suitability map generated from the MD model had 45,947 cells and covered 476,101 km2, with probabilities of use ranging from 0-1 (Figure 5-7). There were 26,927 cells with P < 0.1, and 414 cells with the P = 1. There was no clear lin ear relationship between the probability of use classification and the percentage of cells classified as used for either the training dataset (R2 = 0.4202) or the i ndependent dataset (R2 = 0.2589, Figure 5-8). The relationship was better described by a cubic function, with the percen tage of cells classified as used rising rapidly as the probability of use increased from < 0.1 to 0.2, then leveling off, and rising rapidly again between P = 0.9-1.0 (R2 for cubic fit = 0.6658 and 0.6070 for the training and independent datasets respectively). For cells with the highest probability of use (P 0.9), 25% and 19% were classified as occupied us ing the training and i ndependent datasets. Model Comparison The logistic regression resulted in better overall model fit than the use of Mahalanobis distances for both the training data set and the independent dataset (Figures 5-9 and 5-10). For the training dataset, the LR model had an AUC of 0.8371, while the AUC for the MD model was 0.7310. For the independent dataset, the LR mo del had an AUC of 0.8249 while the MD model had an AUC of 0.6837. When I compared the proportion of us ed cells within each dataset to the P-value of the cell in which they occurred, I found that for the LR model, onl y 1% of used cells from the training dataset and only 2% of those from the independent da taset corresponded to cells with Pvalues < 0.1, while for the MD model, 20% of the used cells from the training dataset and 25% of those from the independent da taset corresponded to cells with P-values < 0.1.

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120 The largest difference between the tw o models was their sensitivity to the P-value used to classify cells as suitable. The number of ce lls in both models decr eased rapidly as the P-value increased from 0-0.1, but the magnitude of the decline was much greater for the MD model, which also decreased more slowly and in a linear fashion for P-values over 0.1 (Figure 5-11). The LR model had more cells with P-values 0.1, but the number of cells in the model decreased exponentially as the prob ability of use increased from 0. 1-0.8 and there were less than 10,000 cells with P-values > 0.3 (See Appendix B). The number of cells in both models was closest for p 0.23, with the LR model having 15,604 cel ls at or above this value and the MD model containing 15,721 (Figure 5-12). The two models combined covered approximately 209,471 km2, with approximately 116,673 km2 overlapping (55.6%) (Figure 5-13). In general, the MD model gave cells in central and northern Florida and in Georgi a a higher probability of use than did the LR model, while the LR model classified cells in SFL as having a higher probability of use than did the MD model. I used the two combined maps, including both ove rlapping and non-overlapping cells to delineate total habitat availability (Figure 5-14). The co mbined LR and MD habitat suitability maps for cells with P 0.23 included 91.5% of locations from th e training dataset (Figure 5-15) and 92% of locations from the independent dataset (Figur e 5-16), while the areas in which the retained cells (P 0.23) from the two models actually overlapped included 72.0% and 58.1% of locations from the two datasets respectivel y (Figure 5-17). Discussion These models represent the first attempt to predict the habitat requirements of the Wood Stork across its entire range in the Southeastern U.S. Although they are based on very coarsegrained habitat characterizations, both models correctly identified regions that are known from independent work to be of high value to the Wood Stork. In particular, both models showed a

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121 high probability of use for cells in Hendry C ounty in southwest FL and along the Georgia coastline. The LR model also identified the Everglades Water Conservation Areas and Everglades National Park as areas with a high pr obability of use and gene rally attributed higher probabilities of use to cells in SFL than the MD model, while the MD model generally gave higher P-values to cells in central and northern FL and GA. Overall, the habitat suitability map ba sed on the LR model provided more accurate predictions of actual Wood Stork usage than the map based on Ma halanobis distances. While both provided a better fit to the data than would a random model, the probability that the LR model would correctly assign a pos itive value to a used location was 11% higher for the training dataset and 14% higher for the independent data set when compared to the MD model. Mahalanobis distance models have receive d increasing use as a method of calculating habitat suitability in wildlife studies. They have been used in studies of Black Bears (Ursus americanus) (Clark et al. 1993, Hellgren et al 2007), Black-tailed Jackrabbits (Lepus californicus) (Knick and Dyer 1997), wolves (Canis lupus) (Corsi et al. 1999, Cayuela 2004), and Timber Rattlesnakes (Crotalus horridus) (Browning et al. 2005). Their advantages relative to regression techniques ar e that characterizations of avai lable habitat are not necessary and collinearity among the independent variables do not strongly affect the results of the model because statistically, the MD is determined from a new set of unco rrelated variables (Clark et al. 1993, Knick and Rotenberry 1998). The model assumes, however, that animals are distributed optimally among habitats in th e landscape (Knick and Rotenbe rry 1998), an assumption that may not have held true for this dataset. Because I used a large dataset of randomly selected daily locations to characterize habitat use and considered use of cells to be equal rega rdless of the number of individuals or locations

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122 that occurred within each cell, use of suboptimal habitats may have been overrepresented in the model. Furthermore, optimal habitats for Wood Storks are known to be stochastic, varying over time and seasonally. I calculated a mean vector of habitat characteristics using a dataset that was pooled over several years and that did not acc ount for seasonal variability in Wood Stork movements and habitat availability, however, and ha bitats that were optimal at one point in time would likely be suboptimal at some later point in time. This is es pecially true given the frequent reliance of Wood Storks on ephemeral wetland re sources. This may account for the relatively low probabilities of use assigned to Everglades wetlands. Although these wetlands are known to be of high value to Wood Storks, particularly dur ing winters when the rest of the southeastern US is relatively dry (Ban croft et al. 1992), they represent a small fraction of the landscape when compared to the entire model, are used during a limited period of time, and are only used when hydrological conditions are favorable. While the LR model performed somewhat better than the MD model, its predictions were very strongly influenced by the probability of use threshold used to define suitable habitats. When the probability of use was 0.1, the model included nearly the entire state of FL and much of the GA coast. While this may reflect the transien t nature of storks and th e possibility that they might occasionally occur in any of these areas, its lack of specificity does not provide particularly meaningful guideline s with regard to regions or ar eas that may provide important Wood Stork habitat. The rapid loss of habitats included in the model as the probability assigned to use increased also limited its applicab ility. When the probability of use was 0.4, northern FL, GA, and SC were poorly represented in the model and AL and MS were scarcely represented at all.

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123 While the LR model may have been influenced by the lack of temporal specificity in my approach it may also have been influenced by th e way in which I defined available habitats. While the use of home ranges is frequently used to delineate available habitats (Erickson et al. 1998, Manly et al. 2002), it can cr eate problems in the analysis, particularly since some used points may fall outside of the available habitat. A better approach may have been to use a minimum convex polygon around all pooled locations to delineate the available habitat. That approach was problematic as well, however, si nce the eastern and west ern edges of the Wood Storks observed ranged contained the northernmost points, resulti ng in the inclusion of a broad swath of land across western SC and northern GA in which no Wood Stork locations occurred and which may have been unavailable due to lack of appropriate habitats. While neither model showed a perfect ability to predict habitat use using the environmental variables I considered, both models did have some predictive value. Both the results of the ROC plots and visual inspection of the models indicated that their fit was better than would be expected at random and that they identified regions that are known to contain important foraging areas for Wood Storks. Particularly impressive was the ability of both models to correctly identify as habitat areas along the Tombigbee, Alabama, and Mobile rivers in MS and AL that contained relativ ely few Wood Stork locations relative to the larger dataset but are important post-breeding summering grounds (C oulter et al. 1999, Hylton 2004, Bryan et al. 2008). While the determination of the P-value required for habitats to be considered suitable is fairly arbitrary, it is probably more instructive to look at the area with the most overlap between models. Both models included similar numbers of cells when their P-values were 0.23, with more than 50% of the cells retained in each model overlapping. For areas which did not overlap,

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124 the MD model was better at predicting use by Wood Storks in the north ern and western portions of their range, while the LR was better at pred icting use in south FL. By combining the 2 models, I was able to improve model fit and to accurately predict more th an 90% of locations from both datasets. These models can provide rough guidelines for managers seeking to understand Wood Stork habitat needs in areas wh ere they have not previously been well-defined. This is particularly true outside of s outh FL. Future refinements to the models should include the incorporation of a temporal component and a refinement of the spatial grain. Models created on a state by state basis and using a grid of smalle r cells might have greater utility for identifying areas that are especially critic al for the conservation and recovery of the species. Conclusion I found that it was possible to use environmental data and Wood Stork locations with a fairly coarse temporal and spatial grain to pr edict approximately 90% of the occurrence of suitable habitat for Wood Storks across their range in the Southeastern U.S. Both logistic regression and Mahalanobis dist ances produced habitat suitability maps with good predictive abilities in at least parts of their range, although each had stre ngths and weaknesses relative to the other. By combining the two approaches, ho wever, I was able to generate a map with good predictive capabilities and enough specificity to be useful for scientis ts, managers and planners.

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125 Table 5-1. Locations of tag deployment on juvenile and adult birds in cluded in initial dataset. Tags were deployed from 20022008. Location Year Latitude Longitude # Tags Adults Bear Island WMA, SC 2006 32.574 -80.480 1 Harris Neck NWR, GA 2005, 2006 31.630 -81.275 11 Noxubee NWR, MS 2005 33.280 -88.798 5 Corkscrew Swamp Sanctuary, FL 2006 26.310 -81.635 5 Welaka National Fish Hatchery, FL 2006 29.433 -81.648 6 White Hall, SC 2006 32.723 -80.697 1 Palm Beach SWA, FL 2008 26.767 -80.145 4 Juveniles Palm Beach SWA, FL 2004, 2005 26.767 -80.150 46 Chew Mill Rookery, GA 2005 32.830 -82.098 11 Harris Neck NWR, GA 2005 31.630 -81.275 11

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126 Table 5-2. Results and regressi on coefficients from logistic re gression of used vs. available habitats in relationship to environmental variables. The land cover types represen t proportions of each habitat type in 3. 219 x 3.219 grid cells and were arcsin-root transformed. The remaining variables were log + 1 transformed. Parameter DF Estimate Standard Error Wald Chi-Square Pr > ChiSq Intercept 1 -2.8415 0.1784 253.84 <.0001 Agriculture 1 0.9038 0.0726 155.14 <.0001 Grasslands 1 -0.3909 0.1662 5.53 0.0187 Forested Wetlands 1 0.9071 0.0815 123.99 <.0001 Shrub/Scrub 1 -1.2265 0.1909 41.27 <.0001 Marine 1 1.1697 0.0969 145.66 <.0001 Emergent Wetlands 1 1.3896 0.0898 239.31 <.0001 Landscape Heterogeneity (Regional) 1 0.3987 0.0945 17.80 <.0001 Landscape Heterogeneity (Local) 1 0.4241 0.0625 46.09 <.0001 Variability in Elevation (Std. Dev.) 1 -0.6272 0.0399 246.57 <.0001 Canals/Ditches (Total Length) 1 0.0927 0.0255 13.19 0.0003

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127 Figure 5-1. Locations where Wood Storks were captured outfitted with GPS-enabled satellite transmitters.

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128 Figure 5-2. Overlapping 90% ke rnel home ranges for 47 Wood St orks created from daily GPS locations obtained via satellite telemetry, used to represent available habitat.

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129 Figure 5-3. Gray cells represen t the grid of used locations constructed from the individual location points obtained from juvenile a nd adult Wood Storks outfitted with GPS enabled satellite transmitters from 20022008 (training dataset) Cells are 3.219 x 3.219 km.

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130 Figure 5-4. Grid of locations containing individual location poin ts obtained from juvenile Wood Storks outfitted with satellite transmitte rs from 2002-2003 (independent dataset).

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131 Figure 5-5. Habitat suitability model c onstructed from logistic regression with P-values indicating the long term probability of use.

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132 0 0.1 0.2 0.3 0.4 0.5 0.6 < 0.10.1-0.190.2-0.290.3-0.390.4-0.490.5-0.590.6-0.690.7-0.79P-valueProportion Used Training Independent Figure 5-6. The proportion of cells that were actually used for cells with P-values ranging from 0-0.79. Here P is equal to the probability of use as calculated from the LR model. There were no cells with P-values > 0.79.

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133 Figure 5-7. Habitat suitability model constructed from Mahalanobis distances. P-values are based on the Chi-Square distribut ion and indicate similarity to ideal habitat, with 1 representing the ideal.

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134 0 0.05 0.1 0.15 0.2 0.25 0.3< 0.10.1-0.190.2-0.290.3-0.390.4-0.490.5-0.590.6-0.690.7-0.790.8-0.890.9-1.0P-valueProportion Used Training Independent Figure 5-8. The proportion of cells that were actually used for cells with P-values ranging from 0-1. Here P is equal to the similarity between each cell and the mean vector of environmental vari ables representing ideal habi tat as calculated from the MD model.

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135 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 00.10.20.30.40.50.60.70.80.91 1 SpecificitySensitivity Logistic Regression Mahalanobis Figure 5-9. Receiver operating characterics (ROC) plot for the training dataset compari ng the LR (black squares) and MD (gray diamonds) habitat suitability models.

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136 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 00.20.40.60.81 1 SpecificitySensitivity Logistic Regression Mahalanobis Figure 5-10. Receiver operating characterics (ROC) plot for the i ndependent dataset comparing the LR (black squares) and MD (g ray diamonds) habitat suitability models.

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137 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000 00.10.20.30.40.50.60.70.80.9 P-valueTotal # of cells Logistic Regression Mahalanobis Figure 5-11. Total number of cells in each habitat suitability model as the P-value associated with the remaining cells increased.

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138 Figure 5-12. Habitat suitability maps based on the A) the LR mode l and B) the MD model. The dark areas represent all cells wi th a P-value 0.23, while the lighter cells have P-values < 0.23.

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139 Figure 5-13. Model ove rlap for cells with P 0.23 from both the LR and MD models. The dark green areas represent actual overlap, while the blue cells represent nonoverlapping cells from the LR model and the pink cel ls represent nonoverlapping cells from the MD model.

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140 Figure 5-14. Suitable vs. unsuita ble habitat for Wood Storks acro ss the Southeastern U.S. using the combined LR and MD habitat suitability models (cells with P 0.23).

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141 Figure 5-15. Model testing using the traini ng dataset upon which the model construction was based. The training dataset was construc ted using GPS locations from 47 juvenile and adult Wood Storks that were outf itted with satellite tags in 2004-2008.

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142 Figure 5-16. Model validation using an indepe ndent dataset from 51 juvenile Wood Storks outfitted with satellite tags in 2002-2003.

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143 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Training dataIndependent data Overlapping Combined None Figure 5-17. Percentage of cells from the training and independent datasets that were included in the overlapping LR and MD models are in dark gray, those occurring within one model or the other but not in both in the bl ack and white crosshatched area, and those that were not included in either model (p < 0.23) are in light gray.

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144 CHAPTER 6 CONCLUSIONS The generally increasing trend in the number of Wood Storks nesting in the Southeastern U.S. have led to some optimism regarding the recovery of the species and have raised the possibility of downlisting the sp ecies from endangered to t hreatened (Brooks and Dean 2008). The population viability analyses I have c onducted as part of this study paint a different picture, however, and suggest that the Wood Stork population c ould still be in danger of dramatic declines even over the next decade. Any understanding of Wood Stor k population dynamics is co mplicated by the occurrence of boom and bust reproductive dynamics. Popul ation dynamics are strongly influenced by environmental stochasticity (Saether et al. 2004) and Wood Stork reproducti on is strongly tied to the stochastic environmental factors such as rainfall and water levels that govern prey availability. Although interannu al population growth rates for the Wood Stork did show the occurrence of infrequent boom years (log Nt+1/Nt > 1), they were more likely to reflect relatively stable population dynamics (-1 < log Nt+1/Nt < 1) or bust years (log Nt+1/Nt < -1) (see Chapter 2). I have shown, however, that boom years would have to occur nearly 32% of the time in order for the population to remain stable (Chapter 3). Long-lived species are known to have signifi cant time lags before extinction occurs, and projections as long as 100 years into the future may still not be adequate to for the prediction of longterm extinction risk (Armbruster et al. 1999). Nonetheless, despite generally increasing nest numbers, both the count-based analysis and the demographic analysis i ndicated that the Wood Stork population in the Southeastern U.S. has a re latively high probability of decline. While the declining trends may have been associated to some degree with uncertainty in the estimates and with negative bias associated with sampling error, it is also possible that they reflect an accurate

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145 picture of the longterm outlook for the species. Fu rther studies to elucidat e the error associated with counting techniques are cr ucial, as is the development of a method for enumerating the proportion of birds that do not nest in a given year. Hydrological conditions in the Fl orida Everglades appear to be unfavorable for juvenile survival following fledging the majority of the tim e. Given current trends in the timing of Wood Stork nesting this could have a negative impact on the population as a whole. Juvenile survival rates were extremely variable and strongly tied to the environmental conditions into which the young birds fledged (Chapter 4). For this reason, it is especia lly important to understand the types of habitats and environmental conditions th at are favorable for juvenile survival. The habitat suitability model I have constructed pr ovides a first step towa rd this understanding (Chapter 5). This map can be used to identi fy high value habitats in relation to Wood Stork colony sites, so that these areas can be managed appropriately dur ing the times that Wood Storks are dispersing from their natal colonies. This study represents a major advancemen t in the knowledge of Wood Stork population dynamics and habitat use. This is the first time adult survival rates have been estimated and have been incorporated into a demographic population vi ability analysis. It is also the first time habitat suitability has been eval uated across the Wood Storks range in the Southeastern U.S. The results of this study can be used by managers to evaluate the current risks to long-term Wood Storks population viability and may be helpful in deciding whether the en dangered status of the species should be maintained or downgraded.

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146 APPENDIX A MAPS USED FOR HABITAT ANALYSIS Figure A-1. Map of land cover types used in the analyses.

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147 Figure A-2. Map showing percent of cell made up of agricultural habitats for 3.198 x 3.198 km cells (2 x 2 miles).

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148 Figure A-3. Map showing percent of cell made up of developed areas for 3.198 x 3.198 km cells (2 x 2 miles).

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149 Figure A-4. Map showing percent of cell made up of emergent wetlands for 3.198 x 3.198 km cells (2 x 2 miles).

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150 Figure A-5. Map showing percent of cell made up of forested wetlands for 3.198 x 3.198 km cells (2 x 2 miles).

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151 Figure A-6. Map showing percent of cell made up of grasslands for 3.198 x 3.198 km cells (2 x 2 miles).

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152 Figure A-7. Map showing percent of cell made up of marine and estuarine areas for 3.198 x 3.198 km cells (2 x 2 miles).

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153 Figure A-8. Map showing percent of cell made up of shrub/scrub habitats for 3.198 x 3.198 km cells (2 x 2 miles).

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154 Figure A-9. Map showing percent of cell made up of upland forests for 3.198 x 3.198 km cells (2 x 2 miles).

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155 Figure A-10. Map showing total length of artific ial water paths within 3.198 x 3.198 km cells (2 x 2 miles).

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156 Figure A-11. Map showing total length of canals and ditches within 3.198 x 3.198 km cells (2 x 2 miles).

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157 Figure A-12. Map showing total length of stream s and rivers within 3.198 x 3.198 km cells (2 x 2 miles).

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158 Figure A-13. Map showing local habitat diversity as represented by the number of different land cover types within 3.198 x 3. 198 km cells (2 x 2 miles).

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159 Figure A-14. Map showing regional habitat dive rsity as represented by the mean number of different land cover types within 3.198 x 3. 198 km cells (2 x 2 miles) for cells within a 5 cell radius.

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160 Figure A-15. Map showing mean elevation (m) within 3.198 x 3.198 km cells (2 x 2 miles).

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161 Figure A-16. Map showing variabil ity in elevation as represente d by the standard deviation of mean elevation within 3.198 x 3.198 km cells (2 x 2 miles).

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162 APPENDIX B COMPARISON OF LOGISTIC REGRESSI ON AND M AHALANOBIS DISTANCE WOOD STORK HABITAT SUITABILITY MODELS

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163 Figure B-1. Comparison of the Logistic Regression (LR) (A) and the Mahalanobis Dist ance (MD) (B) Wood Stor k habitat suitabilit y models. The darker cells represent those with a P-value or probability of use 0.1

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164 Figure B-2. Comparison of the Logistic Regression (LR) (A) and the Mahalanobis Dist ance (MD) (B) Wood Stor k habitat suitabilit y models. he darker cells represent those with a P-value or probability of use 0.2.

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165 Figure B-3. Comparison of the Logistic Regression (LR) (A) and the Mahalanobis Dist ance (MD) (B) Wood Stor k habitat suitabilit y models. The darker cells represent those with a P-value or probability of use 0.3.

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166 Figure B-4. Comparison of the Logistic Regression (LR) (A) and the Mahalanobis Dist ance (MD) (B) Wood Stor k habitat suitabilit y models. The darker cells represent those with a P-value or probability of use 0.4.

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167 Figure B-5. Comparison of the Logistic Regression (LR) (A) and the Mahalanobis Dist ance (MD) (B) Wood Stor k habitat suitabilit y models. The darker cells represent those with a P-value or probability of use 0.5.

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168 LIST OF REFERENCES Aarts, G., M. MacKenzie, B. McConnell, M. Fedak, and J. Matthiopoulos. 2008. Estim ating space-use and habitat preference from wildlife telemetry data. Ecography 31:140-160. Abtew, W., C. Pathak, R. S. Huebner, and V. Ciuca. 2009. Hydrology of the South Florida envrironment. Pages 2.1-2.53 2009 South Flor ida Environmental Report. South Florida Water Management District, West Palm Beach, FL. Armbruster, P., P. Fernando, and R. Lande. 1999. Time frames for population viability analysis of species with long generations: an example with Asian elephants. Animal Conservation 2:69-73. Arnold, J. M., S. Brault, and J. P. Croxall. 2006. Albatross populations in peril: A population trajectory for black-browed albatrosses at South Georgia. Ecol ogical Applications 16:419432. Bancroft, G. T., W. Hoffman, R. J. Sawicki, a nd J. C. Ogden. 1992. The importance of the Water Conservation Areas in the Everglades to the Endangered Wood Stork (Mycteria americana). Conservation Biology 6:392-398. Barker, R. J. 1997. Joint modeling of live-reca pture, tag-resight, a nd tag-recovery data. Biometrics 53:666-677. Beissinger, S. R. and M. I. Westphal. 1998. On the use of demographic models of population viability in endangered species management. Journal of Wildlife Management 62:821-841. Benard, M. F. and S. J. McCauley. 2008. Integra ting across life-history st ages: Consequences of natal habitat effects on dispersal. American Naturalist 171:553-567. Beyer, H. L. 2004. Hawth's Analysis Tool s for ArcGIS. Downloaded 7/14/2005 from http://www.spatialecology.com/htools Borkhataria, R. R., P. C. Frederick, R. Hylt on, A. L. Bryan, and J. A. Rodgers. 2008. A prelim inary model of Wood Stor k population dynamics in the southeastern United States. Waterbirds 31:42-49. Both, C., A. V. Artemyev, B. Blaauw, R. J. Co wie, A. J. Dekhuijzen, T. Eeva, A. Enemar, L. Gustafsson, E. V. Ivankina, A. Jarvinen, N. B. Metcalfe, N. E. I. Nyholm, J. Potti, P. A. Ravussin, J. J. Sanz, B. Silverin, F. M. Slat er, L. V. Sokolov, J. Torok, W. Winkel, J. Wright, H. Zang, and M. E. Visser. 2004. Larg e-scale geographical variation confirms that climate change causes birds to lay earlier. Proceedings of the Royal Society of London Series B-Biological Sciences 271:1657-1662. Both, C. and M. E. Visser. 2005. The effect of climate change on the correlation between avian life-history traits. Global Change Biology 11:1606-1613.

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169 Boyce, M. S. and L. L. McDonald. 1999. Re lating populations to ha bitats using resource selection functions. Trends in Ecology & Evolution 14:268-272. Boyce, M. S., P. R. Vernier, S. E. Nielsen, a nd F. K. A. Schmiegelow. 2002. Evaluating resource selection functions. Ecological Modelling 157:281-300. Brooks, W. B. and T. Dean. 2008. Measuring the bi ological status of th e US breeding population of Wood Storks. Waterbirds 31:50-59. Browder, J. A. 1976. Water, wetlands, and wood storks in Southwest Florida. Ph.D. University of Florida, Gainesville, FL. Browder, J. A. 1984. Wood stork feeding areas in southwest Florida. Florida Field Naturalist 12:81-96. Browning, D. M., S. J. Beaupre, and L. Duncan. 2005. Using partitioned Mahalanobis D-2(K) to formulate a GIS-based model of timber rat tlesnake hibernacula. Journal of Wildlife Management 69:33-44. Bryan, A. L., W. B. Brooks, J. D. Taylor, D. M. Richardson, C. W. Jeske, and I. L. Brisbin. 2008. Satellite tracking large-scale movements of Wood Storks captured in the Gulf Coast region. Waterbirds 31:35-41. Bryan, A. L., K. F. Gaines, and C. S. Eldridge. 2002. Coastal habitat use by wood storks during the non-breeding season. Waterbirds 25:429-435. Bryan, A. L. and J. R. Robinette. 2008. Breed ing success of Wood Storks nesting in Georgia and South Carolina. Waterbirds 31:19-24. Burnham, K. P. and D. R. Andersen. 1998. Model selection and in ference: a practical information-theoretic approach. Springer-Verlag, New York, NY. Burnham, K. P. and D. R. Anderson. 2002. Model selection and multi-model inference: a practical information-theoretic approach, 2nd ed. 2nd edition. Springer, New York, NY. Caldow, R. W. G., J. D. Goss-Custard, R. A. Still man, S. Durell, R. Swinfen, and T. Bregnballe. 1999. Individual variation in the competitive ability of interferen ce-prone foragers: the relative importance of foraging efficiency a nd susceptibility to interference. Journal of Animal Ecology 68:869-878. Calenge, C. 2006. The package "adehabitat" for the R software: A tool for the analysis of space and habitat use by animals. Ecological Modelling 197:516-519. Calenge, C. 2007. Exploring habita t selection by wildlife with adeh abitat. Journal of Statistical Software 22. Caswell, H. 2001. Matrix Population Models: Cons truction, Analysis, and Interpretation. Sinauer Associates, Sunderland, MA.

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177 BIOGRAPHICAL SKETCH Rena Borkhataria was born in Baltimore, Ma ryland. Her love of animals was nurtured by her mother, Carmen, who loved all creatures furred and feathered. After losing her mother at an early age, Rena was eventually fortunate to take up residence with the Richardson family on their horse farm. She took long rides through the fiel ds and forests of the Ma ryland countryside and it was here that her love of wildlife was instilled. Upon graduating from high school, Rena enrolled at St. Johns College, where she studied the classics for one semester. She then tran sferred to the University of MarylandBaltimore County where she majored in classics and learned to read ancient Greek. A fateful visit to a friend in Arizona changed her course, however, and she left the university for life in the desert a short while after. While working at a coffeehouse in Tucson, AZ, Rena made the acquaintance of a wildlife biologist. It was as though the pr overbial light bulb turned on over her head. She enrolled in the wildlife ecology program at the Un iversity of Arizona, where she excelled. During this time she worked as a field technician for the Arizona C ooperative Fish and Wildlife Research Unit and as a research assistant at the Udall Center for St udies in Public Policy. She received several departmental scholarships as well as two prestigious national scholarships: the Harry S. Truman Scholarship and the Morris K. Udall Scholarsh ip. She graduated Su mma Cum Laude and was named Outstanding Graduating Seni or by the wildlife department. Having been awarded a National Science Founda tion Graduate Fellowship, she went on to the masters program in zoology at North Carolina State University, wh ere she worked with Jaime Collazo. For her thesis, entitled Ecological and Politi cal Implications of Conversion from Shade to Sun Coffee in Puerto Rico, she examined differences in biodiversity in sun and

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178 shade coffee plantations and the effect of birds a nd lizards as predators on coffee insects. She also examined the social incentives fo r conversion from shade to sun coffee. After graduating from NCSU and a stint at Duke University, she enrolled in the Ph.D. program in wildlife ecology and conservation at th e University of Florida under the direction of Peter Frederick. Here she rece ived support from an Environmenta l Protection Agency Science to Achieve Results (STAR) fellowship and as a co-pri ncipal investigator on grants from the U.S. Fish and Wildlife Service and the National Park Service. She received her Ph.D. from the University of Arizona in summer of 2009. She is married to fellow scientist Colin Saunders and lives with him, their 4 dogs, 3 cats, a nd 2 horses in West Palm Beach, FL.