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1 EFFECTS OF AGING AND THE ENVIRONMENT ON SNAIL KITE DEMOGRAPHY: A REASS ESS MENT OF SNAIL KITE (Rostrhamus sociabilis plumbeus ) VITAL RATES By BRIAN ENGEBOS REICHERT A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSIT Y OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2009
2 2009 Brian E Reichert
3 To Conservation
4 ACKNOWLEDGMENTS First of all, I would like to thank my committee members: Wiley Kitchens, Robert Fletcher, and William Kendall. Their guidance and advice have been integral in the refining of my skills and to my understanding of the principles of ecology and conservation. Also, I want to especially thank Julien Martin and Chris Cattau. Although one might consider us peers, they have served as my mentors providing me with technical support, encouragement, and patience throughout this entire process. This work could not have been completed wi thout the hard work a nd ingenuity of those who designed the Snail Kite monitoring study: Robert Bennetts, Vicky Dreitz, Wiley Kitchens, and James Nichols. I am also indebted to the field biologists who ha ve helped collect and manage data over the years, especially Juli en Martin, Chris Cattau, Andrea Bowling, Sara Stocco, Zach Welch, Christa Zweig, Wesley Cr aine, Bridget Deemer, William DeGravelles, Lauren Soloman, Kyle Pias, Jean Olbert, Emily Bu tler, Jeremy Wood and the rest of the Florida Coop F&W ResearchUnit. Franklin Percival, Joan Hill, and Amanda Burnett provided invaluable administrative support. I would also like to thank Kevin Doherty, Matt Reiter, David Andersen, and the Minnesota Cooperative Fish and Wildlife Unit for supporting me and facilitating op portunities that will continue to open doors for the rest of my career as an ecologist. Lastly, Id like to express my gratitude to my family and friends who have provided unwavering support and positive encouragement. Financial support was provided by the U.S. Ar my Corps of Engineers, US Fish and Wildlife Service, St. Johns River Wate r Management District, and U.S.G.S.
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................................... 4 LIST OF TABLES ................................................................................................................................ 7 LIST OF FIGURES .............................................................................................................................. 8 ABSTRACT .......................................................................................................................................... 9 CHAPTER 1 INTRODUCTION ....................................................................................................................... 11 2 INTERACTIVE EFFECTS OF SENESCENCE AND NATURAL DISTURBANCE ON THE ANNUAL SURVIVAL PROBABILITIES OF SNAIL KITES .............................. 16 Introduction ................................................................................................................................. 16 Methods ....................................................................................................................................... 20 Data Collection and Field Methodology ............................................................................ 20 Statistical Modeling ............................................................................................................. 21 Annual resight probabilities ......................................................................................... 21 Age specificity and senescent declines in survival probabilities .............................. 22 Age class -specific drought effects .............................................................................. 23 Goodness of Fit .................................................................................................................... 24 Resu lts .......................................................................................................................................... 24 Time and Age Class -Specific Resight Probabilities .......................................................... 24 Decreasing Survival Probabilities with Age ...................................................................... 25 Evidence of Age Class -Drought Interaction ...................................................................... 25 Discussion .................................................................................................................................... 26 Conservation Implications .................................................................................................. 27 3 EFFECTS OF AGE AND THE ENVIRONMENT ON SNAIL KITE BREEDING PROBABILITIES: TESTING FOR COSTS OF REPRODUCTION ..................................... 35 Introduction ................................................................................................................................. 35 Age Effects ........................................................................................................................... 38 Environmental Effects ......................................................................................................... 39 Objectives ............................................................................................................................. 41 Methods ....................................................................................................................................... 41 Field Methods and Data Collection .................................................................................... 41 Statistical Modeling ............................................................................................................. 42 Results .......................................................................................................................................... 46 Aging Effects ....................................................................................................................... 46 Drought Effects .................................................................................................................... 47 Effects of Long Term Habitat Degradation ....................................................................... 48
6 Discussion .................................................................................................................................... 49 Breeding Probabilities and Breeder Proportions ............................................................... 51 Post Hoc Assessments ......................................................................................................... 52 4 CONSERVATION IMPLICATIONS ....................................................................................... 65 LIST OF REFERENCES ................................................................................................................... 70 BIOGRAPHICAL SKETCH ............................................................................................................. 79
7 LIST OF TABLES Table page 2 1 CMR models expressing hypotheses about annual, re -sight probabilities (detection) of Snail Kites in Florida, 1976 2008. Models used one single parameterization to constrain survival by age class and time. .............................................................................. 29 2 2 CMR models describing age class -specific, apparent annual survival probabilities of Snail Kites banded in Florida, 1976 2008. Age classes were determined a priori using biologically relevant criteria (see Methods). .............................................................. 30 2 3 CMR models expressing hypotheses pertaining to age class -specific drought effects on adult Snail Kites. ............................................................................................................... 31 3 1 Snail Kite behavior warranting designation of breeder state. ........................................... 53 3 2 Examples of two occasion mark recapture histories, associated probabilities, and notation definitions. ................................................................................................................ 53 3 3 Models representing hypotheses on the effects of age, droughts, and habitat degradation on the range -wide breeding probabilities of Snail Kites in Florida, USA. .... 54 3 4 Models representing hypotheses of environmental effects on the range -wide breeding probabilities, and age class dependent survival of Snail Kites in Florida, USA. ............... 55
8 LIST OF FIGURES Figure page 2 1 Age class specific apparent annual range-wide survival estimates for the Snail Kite population in Florida, USA. Estimates are from the best fit age -class model (Table 2, model 2). The non-overlap ping error bars (95% CI) for the declining estimates of the two adult age classes (2 12 years and 13+ years) suggest senescence in survival beginning with 13 years of age. ............................................................................................. 32 2 2 Annual survival probabilities as a function of age ( from the fully age dependent model. Solid line represents estimates from model incorporating the Weibull function beginning at age 13 (Phi (.)1 (.)2 3 W~(4)). ............. 33 2 3 Model averaged estimates and 95% CI (Table 3) of age class specific apparent annual survival for the two adult Snail Kite age classes (2 12 years, years, o 2002) and non-drought (19921999; 20032008). ............................................................................................................................. 34 3 1 Population size of Snail Kites estimated using the super -population approach (Cattau et al. 2008). ............................................................................................................................. 57 3 2 Wetland units included in annual surveys; also representative of Snail Kite breeding areas. ....................................................................................................................................... 58 3 3 Age class -specific probabilities and associated standard err or of adult Snail Kite nonbreeders attempting to breed in subsequent years. ............................................................... 59 3 4 Age class -specific probabilities and associated standard error of adult Snail Kite breeders not attempting t o breed in subsequent years. ........................................................ 59 3 5 Cumulative probability of an individual Snail Kite attempting to breed through -out life span. .................................................................................................................................. 60 3 6 Evidence of droughts increasing the costs of reproduction on Snail Kite survival probabilities. ........................................................................................................................... 61 3 7 Effects of habitat degradation on adult Snail Kite conditional breeding probabil ities. ..... 62 3 8 Effect of habitat degradation on Snail Kite survival probabilities (model 8, Table 3 4) .............................................................................................................................................. 62 3 9 Snail Kite population in different breeding states. ............................... 63 3 10 Numbers of Snail Kites attempting to breed since 1998. .................................................... 64 4 1 Age di stribution of banded Snail Kites. Numbers of adults were adjusted for age specific annual detection probabilities (Chapter 2). ............................................................. 69
9 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requi rements for the Degree of Master of Science EFFECTS OF AGING AND THE ENVIRONMENT ON SNAIL KITE DEMOGRAPHY: A REASSESSMENT OF SNAIL KITE ( Rostrhamus sociabilis plumbeus ) VITAL RATES By Brian Engebos Reichert December 2009 Chair: Wiley M. Kitchens Major: Wildlife Ecology and Conservation The Snail Kite (Rostrhamus sociabilis plumbeus ) is a long lived species In this work, banded individuals were observed at age 25 and successfully reproducing until 18 years of age The Snail Kites life -history pattern has been described by relatively high, stable adult survival, variable juvenile survival, and low annual adult fecundity These patterns are generally characteristic for populations whose reproductive output is closel y tied to environmental stochasticity. Snail Kites, like most wild populations, face risks associated with both intrinsic (i.e. aging) and extrinsic (i.e. envir onmental) sources of mortality. The objecti ve of this work was to assess their relative impact s on Snail Kite vital rates Thus, w e u se a capture -mark -recapture framework to model age -dependent survival and breeding. The results of which provide evidence of senescent declines in both survival and the breeding probabilities of adult kites As a dieta ry specialist, the Snail Kite is heavily dep endent upon its primary food resource, the freshwater Florida apple snail (Pomacea paludosa) whose availability varies according to local hydrologic conditions. Therefore, the Snail Kite population provides an e xcellent opportunity to evaluate hypotheses related to the interactions of aging and the environment U sing range -wide drought event s (20002002 and 2007) and the occurrence of range -wide
10 habitat degradation we p rovide convincing evidence that the annual s urvival probabilities of senescent kites are dis proportionately affected by drought s relative to the survival pr obability of prime aged adults, and that the cost of reproduction which decreases Snail Kite survival is exacerbated during periods of low resource availability. Accounting for these additional sources of variation within vital rates may be pa rticularly important to accur ately compute estimates of population growth rate, and probabilities of quasi -extinctions. Finally, we use our results to assess the current population age structure and discuss the potential implications of an aging Snail Kite population on future conservation objectives
11 CHAPTER 1 INTRODUCTION The Florida Snail Kite ( Rostrhamus sociabilis plumbeus ) is an endangered raptor whose population is confined to the remaining freshwater marshes and littoral wetlands of central and south ern peninsular Florida (Bennetts and Kitchens 1997) Simila r to most species native to the South Florida Environment the Snail Kite population has been adversely affected by human alterations in the landscape specifically widespread drainage and changes to the natural hydrologic regime (Bennetts and Kitchens 1997; USFWS 1 999; Martin et al. 2008; Sykes 1983) In a ddition to drainage efforts impoundments, invasive species, and nutrien t loading have severally degraded the natural processes within the Snail Kite s historical range : the Kissimmee OkeechobeeEverglades watershed (Sykes 1979; Duever et al. 1994; Sklar et al. 2002; Sklar et al. 2005) The life history strategies of the Everglades flora and fauna have been shaped in response to extreme spatial and temporal climatic variation, the same precipitation driven pat terns which dictated the natural hydrology of the system. As it has been recognized that a successful restoration of the Everglades ecosystem is contingent upon reestablishing the natural hydrologic regime (CERP) species, such as the Snail Kite have been sel ected as performance measures of restoration efforts because they exhibit numerical changes in population size, movement probabilities and vital rates in response to changes in habitat quality (i.e. hydrology) (RECOVER 2005) Although reliable estimates of population size were not avail able until 1996 (Dreitz et al. 2002) early ac counts and recent studies both provide convincing evidence that the changes in Snail Kite population size correspond directly to environmental conditions especially hydrology (Sykes 1983; Beissinger 1988; Martin et al. 2006; Cattau 2008) After reaching an estimated
12 level of 3,400 individuals in 1999, the Snail Kite population has subsequently halved twice in the last decade : once from 20002002 and again from 20062008 (Cattau 2008) The population was estimated at 685 individuals in 2008, and preliminary calculations suggest no significant change in 2009 (Cattau, unpublished) As t he Snail Kite s population growth is a function of its vital rates long term monitoring has focused on how these vital rates change in relation to the Snail Kite s environment. If changes in Snail Kite demography provide a reliable barometer for Everglade s restoration, recent dramatic declines in population size, fecundity, and juvenile survival are indications of an ailing eco system. The vital rates, or demographic parameters, addressed in this body of work are of interest from both ecological and conser vation perspectives. Generally speaking, vital rates can be used to describe and predict the life-cycle of a population of interest (Caswell 2001) They can also be used as a qualitative means for gaining insight into the relationships between the individuals of a population and their surroundings. For example, assessing the trends in Snail Kite survival and breed ing probabilities over a period of habitat degradation or environmental disturbance can be used as an indicator of how individuals respond to changes in habitat quality Furthermore, i dentifying how changes in the populations environment may adversely a ffect the vital rates of one age cohort more than another provide s important information about Snail Kite population dynamics, which are critical for predicting population responses to management alternatives (Ralls et al. 2002) As a dietary specialist, the Snail Kite s behavior and thus its demography are strongly dependent upon the availability of its prima ry food resource, a freshwater ap ple s nail ( Pomacea paludosa) (Beissinger 1995; Bennetts 1998a; Mooij et al. 2002; Martin et al. 2007a; Cattau 2008) W ide -spread drying event s decrease apple snail availability, requiring Snail Kite s to
13 increase their effort in search for adequate foraging habitat, or refugia (Benn etts and Kitchens 1997) The longer the duration and greater the spatial extent of a drying event the greater its potential impact can be on Snail Kite survival (Martin et al. 2006; Bennetts and Dreitz 1997) Thus, a gene ral lack of refugia, resulting from habitat degradation or conversion during times of resource de pletion (i.e. drought) exacerbates this effect by extending the n ecessary distance to travel, and increasing the individual fitness necessary to arrive at suitable habitat. I ndividuals less than a year old, who are less physiologically fit, and/or less experienced in finding refugia experience higher rates of mortality (Bennetts and Kitchens 1997; Martin 2007) Th ese phenomena are thought to have played an important role in the populations recent dramatic declines (Martin et al. 2008) In Chapter 2 we model this drought effect on the survival on yet another potentially less adept portion of the population, senescent adults We compare models testing hypotheses with regard to senescence and drought, t he results o f which suggest that both aging and age -specific drought effects limit overall adult survival. This is a finding of significant importance, as changes in adult survival have the most affect on the population growth rate of long lived avian species (Saether and Bakke 2000; Stahl and Oli 2006) In addition to survival, Snail Kite reproductive effort is also thought of to be affected by resource availability (Beissinger 1988; Bennetts and Kitchens 1997) As an itero pa rous species, an individual makes the decision whether or not t o attempt breeding at multiple occasions during their life span Cued in part by environmental factors, it must make a trade off between attempting to breed and maximizing their probability of survival (Curio 1988; Erikstad et al. 1998) Sources of individual heterogeneity, such as age and experience, interact with the se
14 environmental conditions, and become factors in determining the outcome of their decisions and their life -history patterns. In Chapter 3 we use known patterns in Snail Kite life -history to generate a set of candidate mode ls which test for the effects of aging and environment on the probability of individuals attempting to breed. Util izing a robust multistate design that adjusts for the misclassification of breeding state designations we provide robust estimates of breeding probabilities for the first time. We assess the direct and indirect cost s of reproduction on Snail Kite survival and future breeding, assess recent trends in breeding probabilities and provide an age -specific distribution for the breeding proportion of the population. Chapter 4 assesses the significant findings in the context of Snail Kite conservation We discuss potential concerns related to recent changes in the age structure of the Snail Kite population and provide recommendations for future analyses The early inclusion of the Snail Kite to the Endangered Species Act, in 1967, and its use as a performance meas ure for the management of wet land habitats, especially the Comprehensive Everglades R estoration Project (RECOVER 2005) has generated an unusually large amount of information relative to most endangered species, of which we tend to have limited demographic data. The longterm Snail Kite mo nitoring project was implemented with the intent of providing future researchers the opportunity to test predictions and adjust models by incorporating new information about long-term processes, such as aging (Bennetts and Kitchens 1997) This iterative modeling process and the adaptive approach to management are critica l for the successful recovery of the Snail Kite (Nichols et al. 1980; Bennetts and Kitchens 1997; Martin et al. 2007a)
15 Adult fecundity and survival have the most impact on the rate of population growth, and theref ore the persistence of the Snail Kite in Florida (Nichols et al. 1980; Beissinger 1995, Martin et al. 2008) Nichols et al. (1980) suggested that further research should focus primarily on the clarification of rela tionships between these rates and environmental factors which affect their magnitude. The following study is an attempt to further our understanding of these relationships by testing hypotheses regarding the effects of aging and environment on adult Snail Kite surviva l ( Chapter s 2 and 3 ) and breeding probabilities ( Chapter 3 ). The public dissemination of this information is absolutely critical for its use in future conservation tools and by managers interested in Snail Kite conservation and its eventual recovery.
16 CHAPTER 2 INTERACTIVE EFFECTS OF SENESCENCE AND NA TURAL DISTURBANCE ON THE ANNUAL SURVIVAL PROB ABILITIES OF SNAIL KITE S Introduction On an individual level, senescence can be defined as a decrease in survival and/or reproductive output that re sults from physiological deterioration with increasing age (Partridge and Barton 1993; Abrams 1993) Jones et al. (2008) disentangled these two manifestations of senescence and concluded that addressing survival al one provides reliable infor mation on overall senescence. Understanding the role of senescence, and age -specific survival in nature is important for numerous issues in ecology (condition-dependent mortality; (Willia ms et al. 2006; Caswell 2007; Roach et al. 2009) evolut ion (e.g., life -history theory; (Jones et al. 2008) and conservation (e.g., population projections ; (Beissinger and Westphal 1998) Nonetheless, empirical tests of the evolutionary theories of aging first presented by Medawar (1952) Hamilton (1966) and Williams (1957) have relied heavily on controlled populations in laboratory settings (Rose and Charlesworth 2002; Charlesworth and Hughes 1996; Charlesworth 2000 ; Kirkwood and Austad 2000; Partridge and Gems 2002; Carey et al. 1992; Kawasaki e t al. 2008) These studies elucidated many of the physiological mechanisms responsible for senescence and demonstrated several important tradeoffs inherent to different life history strategies. However, several authors have recently questioned whether the inferences drawn from laboratory studies of senescence are applicable when studying patterns of survival in naturally occurring populations (Kawasaki et al. 2008; Monaghan et al. 2008; Williams et al. 2006; Roach et al. 2009) Developments in the techniques of data collection and analyses, specifically those involving marked individuals, have allowed researchers to conduct formal tests for the effects of aging on the survival probabilities of individuals in wild pop ulations (Nichols et al. 1997;
17 Gaillard et al. 2004) A growing consensus in the current literature favors the use of longitudinal data over cross -sectional data when evaluating senescence at the individual level (Monaghan et al. 2008; Nussey et al. 2008; Roach et al. 2009) A commonly cited reason is the inability of cross -sectional data, such as that used in life tables, to incorporate imperfect detection probabilities (Ni sbet and Cam 2002; Nichols et al. 1997; Gaillard et al. 2004) Longitudinal studies (i.e. those that follow multiple cohorts of marked individuals through time via capture mark recapture or mark resight methods) generate capture histories of individuals, and thus, provide a framework for dealing with re -sight probabilities that are less than one (Nicho ls et al. 1997) The use of longitudinal analyses has lead to a increasing number of studies that have demonstrated the presence of senescence in wild populations of birds (Newton and Rothery 1997; Loiso n et al. 1999; Gaillard et al. 2004; McDona ld et al. 1996; Pugesek et al. 1995) mammals (Loison et al. 1999) and fish (Reznick et al. 2004) However, a paucity of studies exists that explore the ecological consequ ences of senescence, particularl y the interactive effects between aging and natural disturbance (Williams et al. 2006) Sou rces of mortality in individuals of wild populations can be divided into intrinsic mortality, which results from agingrelated factors (e.g. cancer, stroke, cardiovascular failure), and extrinsic mortality, which results from factors external to the or ganism (e.g. extreme weather, predation (Abrams 1993) Condition -dependent mortality may occur when there is an interaction betw een ageing related factors and external stressors (Abrams 1993) Therefore, extrinsic mortality, which is truly independent of age, is differentiated from condition -dependent mortality, in which external factors adversely affect one age cohort more than another. In general, wild populations are exposed to higher levels of external stressors than captive
18 populations (Ricklefs, 2000) thus wild populations may face greater risks of both extrinsic and condition-dependent mortality. Although several authors have hypothesized that condition dependent mortality is pervasive in natural systems, few studies have provided empirical evidence showing that senescent individuals of wild avian populations become more vulnerable to environmental stressors as they age (Williams et al. 2006) In fact, earlier studies provided evidence to the contrary (Ricklefs 2000; Ricklefs and Scheuerlein 2001; Coulson and Fairweather 2001) which supported the hypothesis that senescence in survival of wild avian populations is driven by catastrophic intrinsic causes of death that kill indep endently of the external environment implying that birds in wild populations do not become more vulnerable to extrinsic mortality factors with increasing age (Ricklefs 2000 ; Ricklefs 2008) The intent of this study is not to separate the intrinsic and extrinsic causes of mortality, but rather to identify the interaction phenomenon, termed as conditiondependent mortality, in a wild avian population. The Snail Kite (Rhostrhamus sociabilis plumbeus) population in Florida provides an excellent opportunity to evaluate hyp otheses related to senescence and condition -dependent mortality in a natural context. The Snail Kite is an endangered species whose range is limited to the remaining freshwater wetlands of central and southern Florida (Kitchens et al. 2002) A long term monitoring study of Snail Kite demography and movement, which includes marking birds with individual alpha -numeric bands, has been ongoing since 1992 (Martin et al. 2007a) and other less intensive studies that also involved marking individuals date back to the mid 1970s (Bennetts et al. 1999) T he Snail Kite is a long lived species, with early studies discovering that Snail Kite s could live and reproduce up to at least 18 years of age; however only a small number of such old individuals were identified (Bennetts and Kitchens 1997) Some of these same
19 individuals have now been re -sighted at age 25. Obtaining an adequate sample size of older individuals is commonly reported to be the limiting factor when testing for the presence of actuarial senescence in wild populations (Nichols et al. 1997; Nussey et al. 2008) Until now sufficient data has been lac king to test for age -specificity in Snail Kite survival rates among older individuals (>12 years) compared to prime aged adults (112 years). To obtain a sufficient sample size of knownage individuals in the older age class, w e utilized resight data collected since 1992 that included knownage individuals marked as nestlings during intermittent studies from 1976 to 1991 and individuals that were marked during the longterm monitoring study that has been ongoing since 1992. Past studies have shown that adult survival does not change substantially over time except during catastrophic events (Bennetts and Kitchens 1997; Martin et al. 2006) As a dietary specialist, the Snail Kite is depende nt upon the availability of its primary food resource, the freshwater Florida apple snail (Pomacea paludosa) During droughts, the availability of apple snails to kites decreases (Beissinger and Takeka wa 1983; Bennet ts and Kitchens 1997) and previous authors have shown that droughts negatively affect Snail Kite survival probabilities (Beissinger 1995; Bennetts and Kitchens 1997; Martin et al. 2006) During the period of 20002002, the majority of the Snail Kite range in Florida experienced a period of extremely dry conditions, characterized as a drought by Martin et al. (2006) who found that apparent survival of adults (> 1 year old) decreased by 16%, while the apparent survival of juvenile Snail Kite s (< 1 year old) decreased by 86%; however, these authors only considered two ag e classes that did not distinguish potential effects on older individuals This environmental perturbation provides us the opportunity to evaluate the hypothesis of conditiondependent mortality in senescent individuals.
20 The objectives of our study were t wo -fold. First, we employed capture -mark recapture (CMR) methodologies on longitudinal data, to test for age -dependent annual survival among adult Snail Kite s. Authors have noted that inadequate sample sizes (Nichols et al. 1997) and the effects of individual heterogeneity (Nisbet and Cam 2002) may mask evidence of senescence. In order to address these concerns, we take advantage of a large, longitudinal data set (2,084 individuals, 3,746 re -sights) spanning a 33 year period (19762008) from multiple cohorts of a long -lived bird. Second, we used a range -wide drought event, already shown to have a negative effect on Snail Kite survival probabilities (Martin et al. 2006) to test for an interactive effect betwee n aging and an environmental disturbance, as evidence of condition -dependent mortality in a naturally occurring population. We predict that the range -wide drought will have a disproportionate effect on the annual survival probabilities of senescent Snail K ite s compared to younger adults. Methods D ata Collection and Field Methodology Resight data from marked individuals was collected annually from 1992 to 2008 as part of a long -term monitoring project of the Snail Kite subspecies population in Florida U.S.A The entire range of the Snail Kite in Florida was systematically searched via airboat during each of the four to six intra annual survey occasions, and marked individuals were identified using a 15 60x spotting scope. Detailed descriptions of the field methodologies and study site can be found in Bennetts & Kit chens (1997; 1999) Dreitz et al. (2002) and Martin et al. (2006) For the purpose s o f this study, we only included individuals captured and banded as nestlings in our analyses, which limited our sample to known age Snail Kite s. Individuals were banded just prior to fledging (approxima tely 1827 days post hatching), using unique alphanumeric colored leg bands which can be easy identified in field with the use of a spotting
21 scope (Martin et al. 2007b) We also in cluded 74 individuals captured and banded as nestlings (149 re -sights) from 1976 to 1991 even though re -sight data for these birds were not available until systematic surveys began in 1992. Of the 74 individuals, 50 survived past age 12 and added to the sa mple size of the oldest age class (13 years and older). We assumed a 50:50 sex ratio (Bennetts and Kitchens 1997; Martin 2007) Statistical Modeling We modeled encounter histories in Program MARK 5.1 (White and Burnham 1999) using e xtensions of the CormackJolly -Seber (CJS) model for open populations (Cormack 1964; Jolly 1965; Seber 1965; Lebreton et al. 1992) which accounted for varying age -classes. The models included parameters for both apparent survival (surviving and returning to the sample area) and detection (the probability that an individual that is alive and i n the study area is re-sighted) (Cooch and White 2008) To include the individuals banded prior to 1992, we fixed detection probabilities to zero for occasions when the population was not sampled (Cooch and White 2008) The model notations and their biological meanings are explained in Tables 2 1, 2 2, and 23. Model selection was based on Akaikes Information Criterion adjuste d for small sample sizes, AICc (Burnham and Anderson 2002) Annual r esight p robabilities We modeled survival in a step-wise manner, where we first parameterized resighting probability, and then subsequently used the most parsimonious paramet erization of resighting probability to further model variation in apparent survival (Nichols et al. 1997) To find the most parsimonious model for resight probabilities, we used the general parameterization of survival, which included all possible time and age class specific survival parameters for the period 19762008.
22 As first discussed by Loison et al. (1999) and later by Nisbet and Cam (2002) one commonly overlooked source of bias in senescence anal yses is related to the theory of agespecific emigration. In other words, the probability of an individual temporarily leaving an area during sampling efforts can be co linear with age (either increasing or decreasing). T o address this potential pitfall, w e included models that allowed re -sight probability to vary by time and age -class. Similarly, we included models with drought effects identical to those used to test for age -drought interactions on survival. A comparison of estimates and associated 95% con fidence intervals of the drought effects parameters was used to assess whether the resight probability of older individuals decreased as a function of drought. The most parsimonious structure on resight probability was retained for all subsequent modeling of survival. For this reason, descriptions of models in Tables 2 2 and 2 3 d o not explicitly list re sight probability parameterizations. Another commonly overlooked phenomenon in senescence analyses is the potential confounding between age -relat ed marker loss and senescence. In other words, markers used to band individuals in the early stages of a study may be more susceptible to deterioration and eventually falling off. Having no direct means for testing or controlling for this hypothesis, we acknowledge this potential confounding in regards to estimates of senescence. Nevertheless, marker loss would not confound estimates of the interaction between drought and senescence, as we have no reason to believe that older individuals would lose their bands at a higher rate than prime aged individuals during droughts. Age s pecificity and senescent declines in survival p robabilities Models which incorporate mathematical functions that make specific assumptions about the declines in survival (increase in mortal ity) with age are used as a means to test hypotheses about senescence (Nichols et al. 1997; Loison et al. 1999; Ricklefs and Scheuerlein 2002; Gaillard et al. 2004) The Weibull, a power function which assumes an ad ditive effect of
23 mortality, has been described as the most appropriate function for modeling senescent trends in avian populations (Ricklefs and Scheuerlein 2001). Therefore, as a formal test for a senescent decline in the survival probabilities within the Snail Kite population we compared a model which constrained survival to be a function of a two parameter Weibull model (Gaillard et al. 2004) against a model with constant adult survival. Models which allowed the Weibull to start at different ages were included to test for the onset of senescence. Model averaged survival estimates were used to test for age -dependent adul t survival. In addition, a set of models were also developed that made no assumptions about the rate that survival changes with age. Understanding the age structure underlining vital rates such as survival probabilities provides important information for modeling population dynamics (Caswell 2001; Charlesworth 1980) Thus, we tested for age class -specific survival probabilities by comparing models which constrained apparent survival to 2 and 3 age classes against a fully age -dependent model and a model for which adult survival is held constant for all ages (Table 2 2) (Loison et al. 1999; Gaillard et al. 2004) A ge c lass -specific drought e ffects Hypotheses re garding the potential effects of the 20002002 drought on the survival probabilities of the oldest age classes of Snail Kite s were tested using the age -class structure of the most parsimonious model determined by the aforementioned test of age specificity in survival probabilities. Models were compared which constrained age -class specific survival by 2 time periods (drought and nondrought). The years 20002002 were modeled as a 2 parameter time -specific drought effect. Testing for age -specific drought effe cts, we compared models with no drought effects against models with additive and interactive drought age class interactions. The additive model assumed the effect of drought to be equal for both adult age classes (years 1 12; and 13+). We compared this model to an interactive, or condition -dependent, model which
24 included an additional drought parameter assigned to the oldest age class. Beyond model selection, we assessed the significance of this additional parameter as further evidence to either support or reject the hypothesis that the drought had greater negative consequences on the older individuals than compared to the prime aged individuals. Goodness o f Fit To assess goodness of fit we utilized the median c -hat approach (Cooch and White 2008) on a subse t of the data which included only the years 1992 2008, during which there was a continuous search effort, using the model (Phi (t)1 (.) 2 3, (.)4; p (t)1 (t)2 4). The approach yielded a c -hat < 1. Therefore there was no need to adjust for over -dispersion. Results Time and Age Class Specific Resight Probabilities The most parsimonious model was one that allowed detection to vary by time, yet differ between age classes 1 and (2 3), which correspond to ages 0 and 1+ respectively (Table 2 1, model 1). Models wh ich included a drought effect for years 20002002 on detection probabilities scored high AICc values and were therefore poorly supported by the data. However, models that allowed detection to vary by time performed consistently better than models which ass umed detection to be constant over time (Table 2 1). Model (4) which constrained the detection of the oldest age class to a 2 parameter drought effect, did not perform well in model selection. Additionally, the estimated coefficient for the drought effect in model (4) indicated that drought DROUGHT = 0.6435, 95% CI = 1.52 to 0.239). Based on the results of the analysis, we used the two age class, fully time -dependent model par ameterization of detection probabilities without drought effects for all subsequent tests on these data.
25 Decreasing Survival Probabilities with Age Consistent with general avian life -history theory, we found a decrease in survival probabilities beginning l ater in life, in this case at age 13 (Table 2 2 ) (Figures 1 and 2). The most parsimonious model constrained survival probabilities as a function of the Weibull curve (Table 2 2 model 1), and model selection indicated that this model fit the data better th an discrete age class models. Consequently, model averaged survival estimates were strongly influenced by the model that incorporated the Weibull function, as it received 78.7 percent of the AICc weight (Table 2 2 Figure 2 2 ). However, important differenc es a mong discrete age -class models were also elucidated during model selection. Namely, discrete age -class models that allowed survival to vary between prime aged and senescent adult s (Table 2 2 models 2 and 3) out -performed those that held adult survival constant (Table 2 2 models 5, 6, and 10) Furthermore, models that assumed unique survival parameters for each age (in years) (Table 2 2, models 7, 8, and 9) were all poorly supported by the data (Table 22 ). Evidence of Age Class-Drought Interaction Ge nerally, models which included drought effects outperformed those without drought effects (Table 2 3). The best fit model (Phi (t) 1 DE*2 3,4) includes an additional interactive drought parameter for the oldest age class (13+ years). This model received 72 .4 % of the AICc weight versus 24.6 % model weight which was given to the additive drought model. Analyzing the beta parameter of the interaction variable reveals a significantly negative interactive DROUGHTXOLDAGE = 0.4727, 95% CI = 0.9255 to 0.0199). Model averaged estimates for both adult age classes during drought and non-drought years are presented in Figure 3.
26 Discussion The present study offers two major findings. First, we found evidence of senescence in survival using both the point estimates of differing adult age classes (Figure 1) and by using a traditional survival function, the Weibull model, as a direct test for a senescent decline in survival probabilities in a naturally occurring avian population Secondly, we found evidence that the survival of older individuals was considerably lower than younger adults during a drought. This result suggests an interaction between age dependent survival probabilities in older age classes and natural disturbance events one form of condition -dependent mortality. In the past, comparative analyses have yielded results which allow evolutionary biologists to make generalizations about the pervasiveness of senescence among taxonomic groups (Jones et al. 2008; Holmes and Austad 1995; Ricklefs and Scheuerlein 2001) When compared to mammals, birds are considered to have relatively slower rates of aging which are expressed later in their lifetime. For some avian populations, indivi duals simply do not live long enough to express senescent declines in survival. A relatively large data set of a long -lived species, such as the Snail Kite provides investigators with the opportunity to test for senescent declines in survival probabilitie s. However, the lack of a consensus in the results of similar longitudinal studies on senescence in wild avian populations suggests that additional sources of variation exist which determine the degree to which senescence is expressed beyond that of taxono mic classes (Jones et al. 2008) In fact, our results suggest that the expression of senescence may also be dependent upon the ecological interactions (i.e. drought) specific to a population of interest. Sudden env ironmental perturbations (e.g. hurricanes, droughts, freezes) undoubtedly have fitness consequences for individuals in wild populations. Depending on the resiliency of the population, these consequences can have widespread, longlasting impacts on its demo graphy. The strong relationship between Snail Kite life history and the current water levels in its critical
27 habitat, through its dependency on the freshwater apple snail as its sole food resource, provides an opportunity to test hypotheses about the effec ts of natural disturbances on senesc ence. To date, few studies (e.g. Waide 1991; Swilli ng et al. 1998; Labisky et al. 1999 ; Jones et al. 2001; Gaillard et al. 2003) have addressed the effects of environmental distur bances on the demography of vertebrate populations. Of these studies, only Gaillard et al. (2003) directly tested hypotheses regarding disproportional impacts on senescent versus prime aged adults. For their analysis, they utilized CMR data to assess the impacts of a hurr icane on the survival and reproduction of a roe deer population in Europe. No evidence was found to suggest that the extreme weather event had greater impacts on the senescent cohort than to the rest of the adult population. O ur results suggest that an i ncreasing vulnerability to extrinsic causes of mortality may be an important causal factor of ageing related mortality in wild avian populations. A ging related mortality in avian populations may indeed be a result of increasing vulnerability to catastrophi c intrinsic mortality (Ricklefs and Scheuerlein 2001; Coulson and Fairweather 2001) However, according to our data, environmental disturbances exacerbate an individuals vulnerability to these catastrophic events. Our results provide empirical evidence of conditiondependent mortality in a naturally occurring avian population. Conservation Implications In addition to its implications regarding our understanding of aging processes, our study emphasizes the importanc e of examining the interaction between senescence and natural disturbance. Indeed, we found evidence that survival of older ind ividuals was considerably lower than younger adults during the drought. The magnitude of the difference among these estimates sug gests that future estimates of population growth rate and probabilities of quasi -extinction should account for these sources of variation (Beissinger 1995; Martin et al. 2009) In addition,
28 given the growing interest in applying decision theory to solve manage ment problems (Bakke r and Doak 2009; Martin et al. 2009) and given the fact that the Snail Kite has been selected as one of the performance measures of Everglades restoration, estimates accounting for senescence and condition -dependent mortality may contribute to the improvement of existing management models (Mooij et al. 2002)
29 Table 2 1. CMR models expressing h ypotheses about annual, re -sight probabilities (detection) of Snail Kites in Florida 19762008. Models used one single parameterization to constrain survival by age class and time. Model AICc Delta AICc AICc Weight # Par Biological Hypothesis (1) p ( t)1 (t)2 3 9567.967 0 0.99394 107 Detection varies by time and ages (0), (1+). (2) p (t)1 (t)2 (t)3 (t)4 9578.2131 10.2461 0.00592 125 Detection varies by time and ages (0), (1 4), (5 12), (13+) (3) p (t)1 (t)2 (t)3 9586.131 18.164 0.00011 122 Dete ction varies by time and ages (0), (1 12), (13+) (4) p (t)1 (t)2 3 (DE)4 9589.3536 21.3866 0.00002 109 Detection varies by time for ages (0), (1 12); drought effect for ages (13+) (5) p (t)1 3 9604.0352 36.0682 0 91 Detection varies by time only (6) p (.)1 (t)2 3 9617.9748 50.0078 0 92 Detection is constant for age (0) and varies by time for ages (1+) (7) p (t)1 (.)2 3 9696.4237 128.4567 0 92 Detection varies by time for age (0) and in constant for ages (1+) (8) p (.)1 (.)2 (.)3 (.)4 9710.4357 142.468 7 0 79 Detection is constant but different between ages (0), (1 4), (5 12), (13+) (9) p (.)1 (DE)2 3 9739.7377 171.7707 0 78 Detection is constant for a ge (0); drought effect for ages (1+) (10) p (.)1 (.)2 3 9740.3208 172.3538 0 77 Detection is constant but differen t between ages (0) and (1+) (11) p (DE)1 (DE)2 3 9740.6865 172.7195 0 79 Separate drought effects for ages (0) and (1+) (12) p (.)1 (.)2 (.)3 9742.393 174.426 0 78 Detection is constant but different between ages (0), (1 12), and (13+)
30 Table 2 2 CMR models describing age class specific, apparent annual survival probabilities of Snail Kite s banded in Florida 19762008. Age classes were determined a priori using biologically relevant criteria (see Methods). Model AICc Delta A ICc AICc Weight # Par Biological Hypothesis (1) Phi (.)1 (.)2 3 W~(4) 9990.96 0 0.78712 38 Survival is constant and different between ages (0), (1 12); survival is function of Weibull for ages (13+) (2) Phi (.)1 (.)2 3 (.)4 9995.16 4.20 0.09642 37 Survival is constant yet different between ages (0), (1 12), (13+) (3) Phi (.)1 (.)2 (.)3 (.)4 9996.46 5.50 0.05030 38 Survival is constant yet different between ages (0), (1 4), (5 12), (13+) (4) Phi (.)1 W~2 4 99 96.53 5.57 0.04850 37 Survival is constant for age (0); survival is function of Weibull for ages (1+) (5) Phi (.)1 (.)2 (.)3 4) 9999.55 8.59 0.01074 37 Survival is constant yet different between ages (0), (1 4), (5+) (6) Phi (.)1 (.)2 4) 100 00.48 9.52 0.00673 36 Survival is constant yet different between ages (0), (1+) (7) Phi (.)1 (.)2 3 (Jones et al. ) 4 10007.76 16.80 0.00018 49 Survival is constant yet different between ages (0), (1 12); survival is fully age dependent beginning with age 13, (13 26) (8) Phi (.)1 32 (full age) 10014.22 23.27 0.00001 60 Survival varies with all ages (9) Phi W~(full age) 10075.26 84.30 0.00000 36 Survival is function of Weibull starting at age (0) (10) Phi (.)1 4 10241.78 250.82 0.00000 35 Survival is constant for all ages
31 T able 2 3 CMR models expressing hypotheses pertaining to age class specific drought effects on adult Snail Kite s. Model AICc Delta AICc AICc Weight # Par Biologi cal Hypothesis (1) Phi (t)1 (DE* 2 3, 4) 9621.03 0 0.724 62 Time specific survival for age class 1; interactive drought/age class effect (2) Phi (t)1 (DE+ 2 3, 4) 9622.96 1.93 0.276 61 Time specific survival for age class 1; additive drought/age clas s effect (3) Phi (t)1 (DE)2 3 (.)4 9648.00 26.98 0 61 Time specific survival for age class 1; drought effect for age class (2 3); constant survival for age class 4 (4) Phi (t)1 (.)2 3 (DE)4 9746.88 125.85 0 61 Time specific survival for age class 1; drought effect for age class 4; constant survival for age class (2 3) (5) Phi (t)1 (.)2 3 (.)4 9764.33 143.30 0 60 Time specific survival for age class 1; constant but different survival for age classes (2 3) and 4 Notes: Drought effects are 2 paramet er time effects combining drought (2000 2001) and nondrought years (1992 1999, 20022007). Age class structure taken from model (2) in Table 2. (see Table 2 for age class descriptions)
32 Age (years) 0-1 2-12 13+ Annual survival probability 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Figur e 2 1. Age -class spe cific apparent annual range -wide survival estimates for the Snail Kite population in Florida USA. Estimates are from the best fit age-class model (Table 2, model 2). The nonoverlapping error bars (95% CI) for the declining estimates of the two adult ag e classes (2 12 years and 13+ years) suggest senescence in survival beginning with 13 years of age
33 Age (years) 0 5 10 15 20 25 Annual survival probabilities 0.0 0.2 0.4 0.6 0.8 1.0 Figure 2 2. Annual survival probabilities as a function of age ( from the fully age dependent model. Solid line represents estimates from model incorporating the Weibull function beginning at age 13 (Phi (.)1 (.)2 3 W~(4)).
34 Non-drought Drought Annual survival probabilities 0.2 0.4 0.6 0.8 1.0 Figure 2 3. Model averaged estimates and 95% CI (Table 3) of age -class specific apparent annual survival for the two adult Snail Kite age classes (2 12 years, years, 000 to 2002) and non-drought (19921999; 20032008).
35 CHAPTER 3 EFFECTS OF AGE AND T HE ENVIRONMENT ON SNAIL KITE BREEDING PROBABILITIES: TESTI NG FOR COSTS OF REPR ODUCTION Introduction Identifying sources of variation wit hin reproductive vital rates has b een a central theme in studies of life -hi story theory, population ecology, and conservation biology (i.e.Williams 1957 ; Charlesworth 1980; Gaillard et al. 1998) Vital rates such as breeding probabilities or proje cted proportions of individuals attempting to breed are commonly used in stage or age based matrix modeling procedures (Caswel l 2001; Fujiwara and Caswell 2002) F actors influencing t hese rates (i.e. age -structure and the environment ) can be important determinants of popula tion dynamics, which make them of great interest to both wildlife demographers and conservation biologists Changes in the proportion of individuals attempting to breed in a small population can have pronounced effects on its population dynam ics. W hen availabl e precise estimate s should be used to make predictions of population growth rates (Coulson et al. 2001) However, this requirement is rarely met as producing precise estimates can require long -term datasets and labo r intensive field sampling. These res trictions can result in researchers having to utilize estimates based on theoretical models and small sample sizes for use in management tools (i.e. population viability analysis) (Beissinger 1995) This is especially true for endangered specie s whose demographic data is typically limiting Therefore long term datasets that allow estimation of breeding probabilities, especially those that exist for small populations, may not only improve our general understanding of population dynamics and evolutionary ecology, but also provide robust estimates of vital ra tes for conservation practitioners. The urgency for an iterative process which incorporates the re -examination of demographic parameters using long term datasets increases when confronted with a federally endangered species whose population has recently undergone
36 dramatic declines (Ralls et al. 2002) as is the case for the Snail Kite (Rostrhamus sociabilis plumbeus ) (Figure 3 1) in Florida USA (Cattau et al. 2008) The Snail Kite is a longlived species with individuals recently observed nesting and successfully reproducing up to 18 years of age (this work ). Its life -history pattern s have been described with relatively high, stable adult survival, variable juvenile survival, and low annual fecundity rates (Nichols et al. 1980) T hese patterns are generally characteristic for populations whose reproductive output is closely tied to environmental stochastici ty (Schaffer 1974; Nichols et al. 1980) Although Snail Kite reproductive output is thought to be strongly affected by local hydrology and thus resource availability (Beissinger 1995; B ennetts and Kitc hens 1997; Martin, 2007; Bennetts 1998a) very little is known about the degree to which reproductive effort varies in response to these same factors. From our knowledge, Bennetts et al. (1998a) has been the only other attempt to estimate Snail Kite breeding proportions from empirical data. Earlier estimates relied on anecdotal observations and theoretical Snail Kite models (Nichols et al. 1980; Beissinger 1 995) which defined a breeding attempt to begin with nest construction. Bennetts et al. (1998) defined Snail Kite breeding attempts in the context of estimating nest success. Because nest buildin g is a part of male courtship (Beissinger 1988) and one male may build several nests prior to est ablishing a pair bond (Bennetts et al. 1998) including uninitiated nests (those found before egg layin g) would produce biased estimates of nest success Therefore, Bennetts et al. (1998) considered Snail Kite breeding attempts to occur only once eggs had been laid For the purpose of this study, we define attempting to breed as a decision process with a behavior al outcome. A n individual begins to re allocate energetic resources for breeding long before egg laying is initiated. Even before pair bonds are established an individual must make
37 the decision to breed. As Snail Kite s must co p e with a limited energetic resource energy that is invested in courtship (e.g. nest construction ) limits the time and effort available to forage for ones own surv ival. Th is is especially relevant for Snail Kite s who may initiate courtship and build nests for as many as five separate females (Bennetts et al. 1998). The definition for a breeding attempt used in this study is a more sensitive indicator of the decision to ini tiate breeding behavior which can be viewed as the trade -off b etween allocating resources for breeding or maximizing self survival (Stearns 1989) Evolutionarily, this process and its outcomes are infl uenced by the costs of reproduction on survival and future breeding potential that are thought to vary with age (Sedinger et al. 2001) and environmental conditions (resource availability) (Hecht Orzack and Tuljapurkar 2001) In addition to parental care and nest success, breeding probabilities can be a necessary component fo r assessing reproductive effort (Nichols et al. 1994) In this case, breeding probabilities pro vide information about behavioral responses to environmental conditions; information that cannot be assessed when only considerin g parameters of reproductive output M ode ls that generate breeding probabilities can be used to test hypotheses about the costs of reproduction (Nichols et al. 1994; Orell and Belda 2002) as well as evolutionary ecology (Nichols and Kendall 1995; Cam and Monnat 2000) A thorough u nderstanding of these relationships is important for testing hypotheses on how population dynamics will be affected by management activities especially when efforts are to be f ocused on impro ving habitat to stimulate reproduction. E mpirical studies which test for sources of variation in Snail Kite breeding probabilities such as age structure and environmental effects are sorely lacking. And in general few studies have assessed breeding probabilities that account for the misclassification of breeding status assignment, an assumption frequently violated when states are based on field
38 observations. In this study, we take advantage of a longterm dataset (1976-2009) to assess the effects of a ge and the environment on the costs of reproduction in Snail Kite s through the use of a recent development in robust multi -state mark recapture models that adjust s for misclassification bias. Age Effects Most avian populations ex hibit age -specif ic reproduction These patterns typically begin with relatively low proba bilities of breeding for young or inexperienced individuals, until th eir first attempt (Wooller and Coulson 1977; Weimerskirch 1992; Cezilly et al. 1996; Sedinger et al. 2001) In longer lived species, this period of low breeding probability tends to be prolonged (Bell 1980) as seen in wandering albatross (Weimerskirch 1992) and Greater Snow Geese (Reed et al. 2003) Breedin g probabilities and reproductive performance increase with age (Cam and Monnat 2000; Reed et al. 2003) and experience until they reach a plateau that corresponds to prime aged adults, when individuals can maximize reproductive effort during peak individual fitness. In some populations, t his plateau is followed by a senescent tren d in reproductive output resulting from physical deterioration or decreased foraging ability (Catry et al. 2006) ; as well as a decline in an individuals breeding probability (Cam et al. 2002) Age -specific reproductive patterns have been attributed to either direct or indirect costs of reproduction on survival and future breeding, resulting in a life -history trade -off S tudies suggest that long lived species tend to maximize their lifetime reproductive success by restricting annu al reproduct ive efforts t o levels below that of their full potential (Stearns 1976) especially during early life stages when fitness and breeding experience are low (Ricklefs 1977; Saether et al. 1993) This ensures that individuals minimize the direct cost of reproduction on survival for any given season, which results in sustained high adult survival, thus increasing their chances of
39 future breeding events and maximizing their lifetime reproductive success (restraint hypothesis) (Curio 1988; Charlesworth 1980) We use Snail Kite age -classes (Chapter 2) to model the effects of age on breeding probabilities and test for age -dep endent costs of reproduction on survival. Given the relatively low rate of juvenile survival and the occurrence of actuarial senescence within the Snail Kite population ( Chapter 2 ) we predict an age -dependent pattern of breeding probabilit ies similar to that described above. Environmental Effects Although the observed patterns would differ, it is reasonable for one to expect that reproductive restraint may a lso occur when populations of longlived species are subjected to harsh breeding en vironments When environmental conditions result in resource depletion young breeders may delay breeding (Tuljapurkar 1990) and experienced individuals may skip breeding u ntil conditions improve (Erikstad et al. 1998) For those individuals who do not restrict reproduction in response to harsh environments, one would expect direct costs of reproduction on survival and future breeding attempts as a potential trade off (Williams 1966) A lack of consensus exists among the few empirical studies that have test ed for environmental e ffects on the costs of reproduction as it relates to breeding and survival probabilities especially when survival is known to be age -dependent ( Chapter 3). For example, a lthough snow cover affected the reproductive output (i.e. breeding propensity, nest success) of Greater Snow Geese, no evidence was found to support that it af fected the breeding probabilities of first time breeders (Reed et al. 2003). In contrast breeding experience and sea surface height, as an index of available food resources, were s hown to have effects on both their survival and breeding probabilities of Blue Petrels (Barbraud and Weimerskirch 2005)
40 Similarly, when environments remain permanently varied, and unpredictable some theoretical models suggest that reproductive effort may become fixed at low levels (Stea rns 1977; Hastings and Caswell 1979) At this point only the fittest individuals may attempt to breed. If Snail Kite s are required to reach a physiological threshold before they consider al l ocating resources for breeding, quality e nvironmental conditions with high availability of their primary food resource, the freshwater Florida apple snail ( Pomacea paludosa), must exist during the time leading up to breeding. As with the case for many end angered species, habitat loss and degradation have played significant roles in its population dec line (Martin et al. 2008). While recent widespread droughts (20002001 and 2007) have negatively affected adult survival by forcing kites to make long distance movements in search of more suitable habitat (Martin et al. 2006) the c onversion of vegetative communities in response t o water management (Zweig and Kitchens 2008) has greatly reduced the reproductive potential of primary Snail Kite breeding areas (Martin et al. 2008). As a result, recent reproductive distribution has shifted away from the traditionally most productive wet land units ( Water Conservation Area 3A ) (Figure 3 2 ). This shift in spatial distribution corresponds temporally to a decrease in adult fertility and juvenile recruitment (Cattau et al. 2008) Base d on these trends, we hypothesize that the total breeding ca pacity of the Snail Kite s range has decreased, and as a result we predict that range -wide breeding probabilities have decreased over time as well. As Snail Kite s rarely nest over less than 20 cm of water (Sykes 1987) and dry conditions tend to cause apple snails to aestivate (Kushlan 1975) effectively decreasing resource availability to foraging Snail Kite s w e also predict a decrease i n breeding probabilities during low water years such as those characterized as droughts by Martin ( 2007). Because Snail Kite survival is known to decrease during drought years (Martin 2007) we predict that kites nest ing during
41 drought years will have even lower survival than those that do not and will be less likely to nest in the following season. Objectives Our first objective was to test for direct and delayed costs of reproduction on Snail Kite su rvival and breeding probabilities. Then, by assess ing how aging, drought, and habitat degradation affect these costs we attempt to describe the life time reproductive strategy of the Snail Kite The proportions of breeding Snail Kite s are estimated direct ly for the years 1996 to 1999 and compared against theoretically derived estimates The results are intended for use in future conservation tools, such as comparative population viability analysis. Methods Field Methods and Data Collection Snail Kite s were banded just prior to fledging (18 27 days post hatching) from 1976 through 2008. The entire Snail Kite breeding range (Figure 3 2 ) was systematically surveyed via airboat during each of the four to six intra annual survey occasions from 1992 through 2009, as part of a longterm monitoring project of the Snail Kite subspecies population in Florida U.S.A (Bennetts & Kitchens 1997; Dreitz et al. 2002; Martin et al. 2006). Begi nning in 1996, information on a banded individuals breeding status was collected. Corresponding to the height of the Snail Kite breeding season, surveys were conducted between February 28th and June 30th annually, in which it is assumed that all individuals return to breeding areas Individuals were identified by alphanumeric colored l eg bands which can be easy identified in field with the use of a spotting scope (Martin et al. 2007b) Encounter histories w ere generated in the style of the multistate robust des ign (MS RD) An individual was recorded as attempting to breed if they were observed actively displaying breeding, nesting, or courtship behavior (Table 3 1). For the purposes of this study,
42 we only included individuals captured and banded as nestlings in our analyses, which limited our sample to knownage Snail Kite s (2,197 individuals, and 2,577 recaptures), but allowed for tests of age -specific breeding probabilities. It is important to note that the Snail Kite population in Florida is considered to be a closed population. Currently, no evidence exists to suggest that individuals actively emigrate to other Snail Kite populations (i.e. Cuba). Thus the inferences made can be applied at the population level, as the areas surveyed incorporate the extent of the populations breeding habitat. Statistical Modeling Breeding probabilities can be viewed as either the probability that an individual will transition into a specific breeding state conditional on its current breeding status, or as a projected proporti on of breeding individuals within a population, unconditional to prior breeding status The breeding probability of most iteroparous vertebrate populations is best described through the use of long-term mark recapture data implemented into a multistate mod el that incorporates some variation of at least two states, either breeding or non breeding (Nichols et al. 1994). Model developments have provided researchers with the capacity to account for less than perfect recapture rates (Nichols et al. 1994) and mor e recently unobservable states (for a review, see Kendall 2004). For the purposes of this study we utilize a full likelihood version of the multi -state mark -recapture model (Schwarz et al. 1993; Brownie et al. 1993) in the framework of the closed robust design (CRD) (Pollock 1982) that adjusts for bias attributed to the misclassification of individuals into a particular breeding state (Kendall et al. 2003; 2004, unpublished manuscript ) The model is a variation of Kendall et al. (2003; 2004) that uses individual encounter histories. As in the CRD, m ultiple secondary sampling occasions (range wide surveys) which are c onducted within each primary session (years) are used t o estimate the probability of detecting an individual (pij B or pij N) and the probability of detecting an individual is
43 breeding, given you detected the individual ij B) separately (Kendall 2004) Similar to the original CRD, sub -sampling parameters are then used to derive detection proba bilities for the primary sessions The expanded number of parameters required in this model type demand s a significant amount of data. In order to avoid problems with paramete r estimation we collapsed surveys down to two intra annual efforts The assumptions of this model are similar to those of the origina l CRD (Pollock 1982) Kendall (1999) outlines specific cases when these assumptions can be relaxed without introducing additional bias. To meet the assumptions of geographic and demographic closure within primary sessions, the end of the first sub -sampling period for each year occurs after all individuals have come into the sampling area (Kendal l 1999) Adjusting for misclassification bias is important when an individual s state is determined based on field observations and the assignment of an individual to a biological state is less than perfect. For example, in this study the breeding state of an adult Snail Kite is determined through field observations based on Snail K ite behavior. Individuals observed demonstrating reproductive behavior can be unambiguously classified as attempting to breed. While individuals that are not exhibiting these behaviors are classified as non breeding, but in reality may in fact be breeding or non -breeding. This scenario is not unlike the one presented in Kendall et al. (2004) in which adult female manatees are designated as breeders with complete accuracy due to the presence of an attendant calf, while females without calves are apparent non -breeders (Kendall et al. 2004). In both cases, not accounting for misclassified state assignments can produce breeding probabilities which tend to be biased low and underestimate the difference in survival probabilities between breeding states (Kendall et al. 2003; 2004) (i.e. the cost of reproduction on survival, Nichols 1994).
44 T he misclassification model s treat individuals whose breeding status is not known with complete a ccuracy as a mixture. The likelihood accounts for the probabilities associated wit h both breeding states. In a scenario with two breeding states, breeder and non -breeder, where misclassification is unidirectional, (i.e. only individuals observed displaying breeding behavior are known to be att empting to breed with certainty) the mixture is comprised of both nonbreeders and breeders who were not actively exhibiting breeding behavior when they were observed in the field For complete model explanations see Kendall et al. (2003; 2004) and Kendall (2004) For the purpose of including age structure in the analysis we used only the encounter histories generated by Snail Kite s banded as nestlings. As kites are not known to breed until at least nine months post fledging, all individuals were assigned to a young state upon their initial captu re (banding). Biologically, the null model assume s stage -based reproduction, where young are not capable of breed ing until the onset of their first breeding season at which time all young from the previous year become either breeding or non -breeding with the same probability as all other adults. For modeling procedures we fixed the probability of a young individual remaining as a young to equal zero, and the detection of this state to equal one for all sub sampling and primary ses sions Because it is no t possible for breeders or nonbreeders to become young, we fixed these transition probabilities to equal zero as well. Additionally, since the breeding state of young is known with complete accuracy, we were also able to fix the probability of ascertainin g its state ij J = 1 ). Table 3 2 provides an example of the encounter histories used and their associated probabilities modified from those presented in Kendall (2004). Program MARK 5.1 (White and Burnham 1999) was used for model im plementation.
45 A set of candi date mo dels was developed to test hypotheses on the effe cts of age and the environment (drought and long term hab itat degradation) on Snail Kite survival (S) and breeding (Table s 3 3 and 3 4 ). Hypotheses for age class -specific breeding proba bilities were adopted from previous analyses which indicated age structure in survival probabilities, and specifically senescent declines in adult survival beginning at age 13 (Chapter 2). The models in Table 33 include both full age (i.e. one parameter for each age) and parameters for two, three, and four age classes (AC) adopted from Chapter 2 In addition to ageing effects, Table 3 4 includes models which address hypotheses about the effects of drought and habitat degradation. For these models, notati on where DE = Drought Effects; parameter/s were used to distinguish between specified drought years and non -drought years, PDE = Post Prought Effect; parameter/s were used to distin guish between the year after a d rought occurred (2002 and 2008 only) an d other years, DP99 = Degradation Period E ffects beginning in 1999 ; o ne parameter was used for years 19961998; one par ameter was used for years 19992009, and AC = age class; when survival (S) or state transitions ( When DP99 and DE were used in combination for the s ame model, drought years after 1999 were not incl uded as post degradation years but were k ept as drought years. Refer to Tables 3 3 and 3 4 for complete model description s and their associated biological hypotheses. AICc model selection was used to compare the fit between models and thus the relative influence of a ge, drought (DE), and habitat degradation (DP99) on the transition probabilities between breeder st ates (young, breeder, and non -breeder) Model estimates were used t o assess the costs of reproduction on survival and the probability of attempting to breed during and one year after a harsh environment al disturbance (i.e. droughts and habitat degradation) It is important to note that the costs of reproduction to Snail Kites could be realized in demographic
46 parameters beyond the scope of this study. For example, we did not test for responses of body condition, clutch size, or long term breeding probabiliti es to increased reproductive effort. Testing for these relationships may provide additional insight into the actual mechanisms driving the costs of reproduction, such as energy allocation or longterm life history trade offs. Along with the estimation of breeding and survival probabilities, the model we used also directly estimates the proportion s of individuals in each state, at a certain time, assuming that individuals from all states are equally likely to be in the study area (Kendall 2004) Model averaging was used to obtain estimates of breeding proportions for each year, which we a pplied to robust estimates of population size obtained from an earlier analysis (Martin 2006; Cattau et al. 2008) and calculated the annual number of Snail Kite s breeding from 1998 through 2008. Results Aging Effects Mo dels that ig nored environmental effects, but constrained breeding probabilities by age out performed those which held breeding probabilities constant (T able 3 3). Model selection suggested that environmental effects explain ed more variation in breeding pr obabilities than age alone ( T able 3 3), yet model estimates elucidate patterns of age -specific variation in the costs of reproduction on breeding probabilities and survival Mo del selection within age models suggests an age -dependent trend in the probabi lity of attempting to breed, as seen i n the estimates from model 1, t able s 3 3 (Figures 3 3 and 3 4 ). This model constrains adult Snail Kite breeding probabilities to 3 age classes: ages 2 4 years, 5 12 years and 13+ Of note is a significant declining trend in the transition of non-breeders to attempt breeding in the subsequent year ( Figure 3 3) and the simultaneous increase in the probabilities of breeders becoming non-breeders with age (Figure 3 4). The s e results suggest that as individual s age they ar e more likely to become non -breeders and stay in the non -breeding
47 state. Age class -specific decrease s in breeding attempts provide evidence of reprod uctive senesce nce, which begins at age five, seven years earlier than the onset of actuarial (survival) sen escence in adult Snail Kite s Drought Effects A model which included drought effects on breeding probabilities for the years 2001 and 2007, with one year post drought effects (2002 and 2008) (model 1, Table 3 -4) was best support ed by the data (AICc weight = .989). Generally models that included drought effects for years 2001 and 2007, performed better than comparable models that considered 2000 as a drought year for example ( model 1 versus model 2). Despite that model 1 (Table 3 4 ) received nearly all o f the Akaike weight (.941) separate breeding probabilities for the 2007 drought were not estimable. W e suspect that this problem was an artifact of sparse data during the 2007 drought T he relatively poor fit of models which equated the drought in 2007 a s havi ng the same effect on breeding probabilities as the conditions in 2001, and the declines in estimated breeding proportions (Figure 3 8) suggest that the drought in 2007 had less effect on range -wide breeding than the 2001 drought. W e agree with previ ous au thors that the 2007 drought did in fact have negative effect s on Snail Kite survival (Figure 3 5) (Catta u et al. 2008). But we also hypothesize that the 2007 drought had less effect on breedi ng probabilities than the 2001 drought because greater than 70 percent of the reproduction for tha t year occurred in the Kissim m ee Chain of Lakes, an area of the Snail Kite range traditionally considered to serve as a refuge during harsh conditions (Martin et al. 2006) We assessed the impact that drought years had on the cost s of reproduction by comparing the survival of breeders versus nonbreeders during drought years (2001 and 2 0 07) and non drought years (19962000; 20032006) (Figure 3 5 ). As fully time dependent detection
48 probabilities did not allow for the estimation of survival during drought years, we had to include a model that assumed equal detection probabilities for bree ding and nonbreeding adults respectively, during drought years (2001 and 2007) (model 3 Table 3 4). The results from this model indicate that the drought exacerbated the costs of reproduction on survival probabilities of the breeding individuals especia lly for the oldest adults Models which included post drought effects (PDE) were well supported by the data (Table 3 4 ). However, a comparison of est imates for those years to non-drought years reveals no significant lag effects from droughts on breeding pr obabilities or survival. Effects o f Long -Term Habitat Degradation Models that included degradation effects (DP99) were also well supported by the data. In fact, the best fit model (model 1, Table 3 4 ) included effects of habitat degradation for breeding st ate transitions E stimates from this model suggest a decrease in the probability of individuals attempting to breed beginning in 1999 (Figure, 3 7 ). The figure illustrates that the decrease in individual adult breeding probabilities in the post degradation period (after 1998) is largely due to a lack of n on -breeders attempting to breed PreDP99 NB PostDP99 NB = 0.06), as the individuals who were already breeding continued at the same rate ( PreDP99 BB PostDP99 BB = .619). An evaluation of a ge cla ss-dependent survival estimates suggests a disproportionate decrease i n the survival of older breeding adults after 1998 (Figure 3 8 ). Survival e stimates between breeders and non -breeders did not differ significantly. Therefore, the decrease in adult survival can be attributed to ha bitat degradation and not to additional cost s of reprodu ction A finding that is supported by previous work ( Chapter 2) suggesting that less fit Snail Kite s experience lower survival during times of resource limitation, whe ther it be a result of droughts or habitat degradation.
49 Discussion Compared to shorter lived, more fecund avian species, long lived birds provide a convenient framework to test for the costs of reproduction, as effects can be manifested over a long er per iod of time (Weimerskirch 1992) If c osts of reproduction are drivin g the decision to breed, one would expect to observe decreased survival and/or breeding probabilities in the breeding proportion of a population (Clutton Brock 1984; Nichols et al. 1994) We indeed found evidence that indicate s a direct cost of reproduction on Snail Kite survival which become s exacerb ated during d roughts and disproportionatel y affect s older individuals. Our results did not suggest any cost of reproduction on subsequen t breeding probabilities, as the best fit models estimated the probability of breeders attempting to breed in a subsequent year to be gre ater than not breeding. A n e xcept ion occurs only during drought years when the proportions of ki tes attempting to breed declines significantly. A s we predicted Snail Kite breeding probabilities decrease d during tim es of low resource availability, which suggests that some Snail K ite s have the capacity to persist through droughts by forgoing breeding attempts and allocating the remaining resources for their own survival. We did not find an increased cost of reproduction due to habitat degradation, although adult survival has decrea sed since 1998. Habitat degradation and other long term processes seem to decrease individual physiological fitness (as seen in survival) and cause kites to forgo breeding attempts. Although t his is the first time that flexibility in Snail Kite reproducti ve effort has been formally address ed, several authors have noted similar hypotheses, although typically in regards t o nest abandonment (Beissinger 1988; Bennetts and Kitchens 1997) For other avian species, t heoretical models have predicted similar respon se s, such as seabirds (Erikstad et al. 1998; Hecht Orzack and Tuljapurkar 2001) but here we provide empirical evidence to support these models
50 Another important result of this study is the description of age -specific breeding probabilities. A profile of the cumulative annual probabilities of becoming a bree der throughout a Snail Kite s life (Figure 3 5) illustrates that the highest probability of attempting to breed happens within the first 5 years. Although we did not test individual fitness directly our results suggest that aging in Snail Kite s results in a physiological deterioration as a result of allocating resourc es for early breeding, which decreases an individuals attempts to breed and probability of surviving later in life ( Chapter 2) Most studies of reproductive senescence focus on declining rates of reproductive performance, such as inefficient foraging fecundity, or nest success (Catry et al. 2006) These results provide additional insight into reproductive senescence, suggesting a decrease in reproductiv e effort As evolutionary life history models suggest that reproductive effort should increase with age (Charlesworth and Leon 1976) a decr ease in effort seen in breeding attempts may reflect the failure of older individuals to reach a fitness threshold required for breeding (Tuomi et al. 1983) or an environment with limited resources where only the fittest individuals are able to gain access to breeding opportuni ties. We identified a decreasing proportion of breeders in the Snail Kite population after 1998. This may indicate an aging population, with older individuals less likely to breed (Figure 3 3 ) however, it may also be indicative of a system with increasin gly limited resources for breeding. As discussed, Snail Kite reproduction has geographically shifted away from the traditionally most productive regions, such as the Everglades and Lake Okeechobee, and is now limited to the Kissimmee Chain Of Lakes; Lake T ohopekaliga in particular (Cattau et al. 2008). It is not unreasonable to suspect that the reproductive capacity of the system has decreased. The population, which was once supported by potential reproductive units throughout its range, is now restricted t o breeding in much fewer areas. As resources would limit the number of potential
51 breeders only the most fit may gain access to breed, where less fit individuals, (i.e. young and old) become non -breeders (Rodenhouse et al. 1997) An alternative hypothesis is that the adult breeders from areas no longer able to support reproduction (i.e. Water Conservation Areas Figure 3 2), forgo breeding and move north to the Kissimmee Chain of Lakes or other marginal habitats. Once again, the resources in these wetlands cannot support the same level of breeders as their range once could. The strong natal site fidelity exhibited in Snail Kite s suggests that this process could continue year after year until natal areas or refugia provide suitable habitat for reproduction, or kites senescence and experience increased mortality associated with making continued long di stance movements (Martin et al. 2006; 2007a ). Breeding Probabilities and Breeder Proportions Addressing sources of variation in Snail Kite breeding probabiliti es has also al lowed us to model annual breeding probabi lities and directly estimate breeding proportion s (Figure 3 9 ) and thus, the number of breeding Snail Kite s over time (Figure 3 10). Until now robust estimates of Snail Kite breeding pro portions have n ot been available. Early efforts to model the stochastic growth rate of the Snail Kite population h ave assumed reductions in breeding probabilities during drought years based on theoretical models (Nichols et al. 1980; Beissinger 1995). Using estimates ba sed on a sample of 23 birds fixed with radio telemetry backpacks over a period of two years (Bennetts 1998b) the most recent modeling efforts assumed adult breeding probabilities to be equal to 1 during wet years and .33 during drought years (Martin et al. 2008) Comparatively, our results indicate that breeding proportions are far l ower than previously assumed, and that more variation exists within estimates than can be explained by drought and non-drought years alone.
52 Figure 3 9 illustrates the proportions of breeding Snail Kite s by year from 1996 to 2009. Of note is the relatively high proportion of breeders in 2000, a year that was considered to b e a drought year in some of our models. The relatively poor fit of these models suggests that although the drought began in 2000, dry conditions did not begin until after the 2000 nestin g season had finished. Therefore, dry conditions in 2000 would have affected juvenile and adult survival (Cat tau et al. 2008), but not the breeding probabilities. As we had expected, these results provide evidence that Snail Kite breeding probabilities are i n fact more sensitive to conditions earlier in the breeding season. We recommend that future analyses of Snail Kite breeding probabilities take this into consideration. Post Hoc Assessments Assessing Snail Kite breeding proportions elucidate s that the high est breeding proportion between 1996 and 2009 occurred in 2003, when greater than 70 percent of the population attempted to breed (Figure s 3 9 and 3 10). This may have been a result of the dry conditions which occurred in 2001, which were conducive to sna il egg laying and hatching, w hich in turn, may have increase d snail abundances in 2002 and le d to a proliferation of available resources during the onset of the 2003 breeding season (Darby, personal communication). This work provides evidence that the cond itions leading up to the breeding season are important for promoting Snail Kite breeding attempts. Understanding not only the causes of negative effects on breeding probabilities, but environmental effects which promote breeding attempts will be a major co mponent for restoring Snail Kite reproduction and thus the recovery of the Snail Kite population in Florida
53 Table 3 1. Snail Kite behavior warranting designation of breeder state. Behavior category Field observation Nesting Flushing from or occu pying visible nest. Flushing from perch and circling 12 m above, giving alarm calls. Feigning attacks at observers with talons extended, while giving alarm calls Courtship Males carrying sticks to nest building site Males feeding females, or fledglings Females feeding fledglings Reprodu ction Copulation Notes: 'Breeder' state refers to individuals attempting to breed, and makes no assumptions about the degree of energetic investment, only that an individual has decided to attempt breeding during a particular breeding season. Tabl e 3 2. Examples of two occasion mark recapture histories, associated probabilities, and notation definitions. Encounter History Likelihood BN S 1 B 1 BB p 2 B(1 1 BN p 2 N ) NB 1 S 1 N 1 NB p + (1 1 ) S 1 B 1 BB p 2 YB S 1 Y 1 YB p 2 YN S 1 Y 1 YB p 2 B(1 1 YN p 2 N ) Notes: S 1 B 1 NB = probability of individu al transitioning from a non-1 = probability that a kite seen for the first time in year 1 and not exhibiting breeding behavior is in fact a non -breeder. p= probability that a breeder is detected and seen exhibiting breeding behavior pB (1 = probability that an individual attempting to breed is detected, but not observed exhibiting breeding behavior.
54 Table 3 3. Models representing hypotheses on the effects of age, droughts, and habitat degradation on the rang e wide breeding probabilities of Snail Kite s in Florida USA. # Model # Parm Akaike Weight Biological Hypothesis (1) S (AC (0,1 12,13+)) Psi (AC (0,1 4, 5 12, 13+)) 1 12 0.00 1 Survival varies by age class; bre eding probabilities vary by age classes (0, 1 4, 5 12, 13+ years). (2) S (Full age) Psi (Full age) 149 79.15 0 Survival varies by individual ages; breeding probabilities vary by individual ages. (3) S (AC (0 ,1 12,13+)) Psi (Full age) 133 183.73 0 Survival varies by age class; breeding probabilities vary by individual ages. (4) S (AC (0,1 12,13+)) Psi (.) 114 211.22 0 Survival varies by age class; breeding probabilites were constant across time and for all ages (5) S (AC (0,1 12,13+)) Psi (AC (0,1 12,13+)) 118 237.40 0 Survival varies by age class; breeding probabi lities vary by age class. (6) S (Full age) Psi (Full age) p(NB=B) 130 417.13 0 Survival varies by individual ages; breeding probabilities vary by individual ages; detection is the same for breeders and nonbreeders.
55 Table 3 4. Models representing hypotheses of environmental effects on the range -wide breeding probabilities, and age class dependent survival of Snail Kite s in Florida USA. # Model # Parm AICc Akaike Weight Biological Hypothesis (1) S (AC DE(00 01,07)) Psi (DP99; DE(01,07); PDE(02,08)) 143 0 0 .941 Survival varies by age class and is drought dependent (2000 01 differ 2007); breeding probabilities differed beginning in 1999, were drought dependent (2001 differ 2007), with one year lag effects (2002 differ 2008). (2) S (AC DE(00 01,07)) Psi (DP99; DE(00 01,07); PDE(02,08)) 142 5.5 0 .059 Survival varies by age class and is drought dependent (2000 01 dif fer 2007); breeding probabilities differed beginning in 1999, were drought dependent (2000 01 differ 2007), with one year lag effects (2002 differ 2008). (3) S (AC DE(00 01,07) Psi (AC (1 4, 5 12, 13+) (DP99) DE(01=07) PDE(02=08) pB(01) = pNB(07)} 146 27.67 0 Survival varies by age class and is drought dependent (2000 01 differ 2007); breeding probabilities differed beginning in 1999, were drought dependent (2001 = 2007), with one year l ag effects (2002 = 2008), adult detection was equal during droughts. (4) S (AC DE 00 01=07) Psi (DE (01,07)} 119 39.27 0 Survival varies by age class and is drought dependent (2000 01 = 2007); breeding probabilities we re drought dependent (2 001 differ 2007) (5) S (AC DE 00 01) Psi (DE (01 07) 122 56.46 0 Survival varies by age class and is drought dependent (2000 01 = 2007); Breeding probabilities were drought dependent (2001 = 2007) (6) S (AC DE 00 01=07) Psi (AC DE (01,07)} 124 99.76 0 Survival varies by age class and is drought dependent (2000 01 = 2007); Breeding probability is age class dependent and varied between 2001 and 2007drought (7) S (AC DE (00 01)) Psi (DE (00 01); PDE(02)) 124 102.2 0 Survival varies by age class and 2000 01 drought; Breeding probability varied by 200001 drought and one year drought lag effect (2002). (8) S (AC DE 00 01) Psi (DE (01 02) 124 103.9 0 Survival varies by age cla ss and 2000 01 drought; Breeding probability varied by 200102 drought years (9) S (AC DE 00 01) Psi (DE (00 01) 123 105.4 0 Survival varies by age class and 2000 01 drought; Breeding probability varied by 200001 drought years
56 Table 3 4. Contin ued (10) S (AC DE (00 01)) Psi (AC DE (00=01) 130 139.7 0 Survival varies by age class and is dependent on 200001 drought; Breeding probability is age class dependent and varied by 2000 01 drought. (11) S (AC DE (00 01)) Psi (AC DE (01=02) 132 141.7 0 Survival varies by age class and is dependent on 200001 drought; Breeding probability is age class dependent and varied by 2000 01 drought. (12) S (AC DE (00 01=07)) Psi (AC DP99; DE (00 01=07); PDE (02=08)) 137 147.9 0 Survival varies by age class and is drought dependent (2000 01 = 2007); breeding probabilities are age class dependent, differed beginning in 1999, were drought dependent (2000 01 = 2007) with one year lag effects (2002 = 2008). (13) S (AC DE (00 01=07)) Psi (DP99; DE(00 1=07); PDE (02=08)) 134 150.9 0 Survival varies by age class and is drought dependent (2000 01 = 2007); breeding pr obabilities differed beginning in 1999, were drought dependent (2000 01 = 2007), with one year lag effects (2002 = 2008). (14) S (AC DE (00 01=07)) Psi (DE (00 01=07); PDE(02=08)) 131 156.8 0 Survival varies by age class and is drought dependent (2000 01 = 2007); breeding probabilities were drought dependent (2000 01 = 2007), with one year lag effects (2002 = 2008). (15) S (AC DP99) Psi (AC DP99) 122 181.6 0 Survival varies by age class and differed beg inning in 1999; breeding probabilities vary by age class and differed beginning in 1999. (16) S (AC) Psi (DP99) 110 218.9 0 Survival varies by age class; breeding probabilities differed beginning in 1999. (17) S (AC) Psi (AC DE(00 01=07)) 109 347.2 0 Survival varies by age class, breeding probabilities vary by age class and were drought dependent (2000 01 = 2007). (18) S (AC) Psi (DE (00 01=07)) 103 350.7 0 Survival varies by age class, breeding probabilities were drought dependent (2 000 01 = 2007).
57 Year 1996 1998 2000 2002 2004 2006 2008Number of Snail Kites 0 1000 2000 3000 4000 5000 Figure 3 1. Population size of Snail Kite s estimated using the super -population approach (Cattau et al. 2008).
58 Figure 3 2. Wetland units included in annual surveys ; also representative of Sn ail Kite breeding areas. K. Pias, 2009
59 Age Class (years) 1-4 5-12 13+ Probability 0.05 0.10 0.15 0.20 0.25 0.30 0.35 Figure 3 3. Age class -specific probabilities and associated standard error of adult Snail Kite nonbreeders attempting to breed in subsequent years. Age Class (yrs) 1-4 5-12 13+ Probability 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34 Figure 3 4. Age class -specific probabilities and associated standard error of adult Snail Kite breeders not attempting to breed in subsequent years.
60 Age (years) 0 5 10 15 20 25 Cumulative breeding probability 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Figure 3 5 Cumulative p robability of an individual Snail Kite atte mpting to breed through -out life span.
61 Non-drought years Drought years Annual Adult Survival Probability 0.2 0.4 0.6 0.8 1.0 Non-breeder Adults (1-12 yrs old) Non-breeder Adults (13+ yrs old) Breeder Adults (1-12 yrs old) Breeder Adults (13+ yrs old) Figure 3 6 Evidence of droughts increasing the costs of reproduction on Snail Kite survival probabilities.
62 1996-1999 1999-2009 Conditional breeding probability 0.0 0.2 0.4 0.6 0.8 Breeders continuing to breed Non-breeders becoming breed Figure 3 7 Effects of ha bitat degradation on adult Snail Kite conditional breeding probabilities. Age Classes (years) 0 1-12 13+ Survival probabilities 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1999-2009 1996-1998 Figure 3 8 E ffect of habitat degradation on Snail Kite survival probab ilities (model 8, Table 3 4 )
63 Years 1996 1998 2000 2002 2004 2006 2008 Proportions within Snail Kite Population 0.0 0.2 0.4 0.6 0.8 1.0 Breeders Non-breeders Young Fi gure 3 9 Proportions of Snail Kite population in different breeding states.
64 Year 1996 1998 2000 2002 2004 2006 2008 Snail Kites 0 1000 2000 3000 4000 Breeders Non-Breeders Figure 3 10. Numbers of Snail Kite s attempting to breed since 1998.
65 CHAPTER 4 CONSERVATION IMPLICA TIONS A ge -S tructure and Snail Kite Demography The findings in this work provide convincing evidence of an age structured life history pattern in Snail Kite s Here, we put these findings into the conte xt of the current state of the Snail Kite population, and discuss what age dependent surv ival and breeding means for the f uture of Snail Kite population dynamics As stated, the Snail Kite population has declined rapidly since 1999. Efforts to model the stochastic growth rate of the Snail Kite population found that the growth rate fell substa ntially after 1998 (.90), and that > 80% of this decrease could be attributed to a reduction in Snail Kite fertility (Martin et al. 2008) The sensitivity analysis indicated that changes in adult survival and fertility had the largest effect on the populat ion growth two parameters which have yet to be considered for Snail Kite population dynamics. We suggest that these concepts be include in a future population viability analysis. Without such an analysis it is difficult to make accurate quantitative assessments of population level effects. Figure 4 1 illustrate s the relative changes in Snail Kite age distribution over time using the proportions of banded individuals re -sighted annually and adjust ed for agespecific detection probabilities. The number of young (< 1 yr old) in this figure is the number of banded fledglings per year, which is not included into annual population estimates, as recruitment is variable and does not occur until the beginn ing of an individuals second breeding season ( Bennetts and Kitchens 1997; Martin 2007). However, we included banded fledglings because we think it is telling of several
66 important issues in Snail Kite demography and has been the focus of recent conservati on efforts. T he number of fledglings produced in a given year provides an idea of the relative potential for recruit ment in the following year. As a general trend, the number of younger birds in the population has decreased since 1998, supporting the conc lusion s made by Martin et al. (2008). Beginning in 1998, a decreasing proportion of Snail Kite s are in the (1 4 ) year old age class, and the population becom es dominated by older individuals. We suspect that prior to 1998, recruitment of younger birds ha d been supplementing the population I n the second half of 2000, adult survival ( especially that of the oldest adults see Chapter 2 ) decreased as a result of the range -wide drought This drought effect corresponded to decreasing breeding probabilities ( Ch apter 3). Reduced reproductive effort failed to supply the population with new individuals. Recent reports suggest that depressed reproduction has continued through 2008 (Cattau et al. 2008) and 2009 (Cattau unpublished) As the population becomes older, t he majority of Snail Kite s, who are currently middle aged will enter a senescent state and experience decreased survival rates ( Chapter 2) and be potentially less capable of breeding ( Chapter 3). We hypothesize that if adult fecundity does not increase ov er the next five years, the Snail Kite population could experience yet anot her dramatic decline resulting from the demographic process associated with Snail Kite aging, which were described in this body of work The general aging of the population is also evident in the increase of the average age of breeding adults, which we estimated using band re -sights from individuals identified as breeders to have increased from 5.8 in 1996 to 10.0 in 2009. Although we
67 recognize this to be an ad hoc methodology, w e r ecommend that additional studies be c onducted that investigate age -specific parameters of reproductive out put (i.e. nest success, fecundity, breeding attempts per year, etc.) and factors that may promote reproduction of older individuals such as improving the nesting substrate or for a ging base in the natal areas of older individuals (Martin et al. 2006). Recently, criticism has been expressed regarding the validity of the documented demographic trends. It is important that these issues are addressed so as to assure that doubt does not exist regarding the current emergency state of the Snail Kite population among those in positions to effectively manage for its recovery. Considering that the Snail Kite population in Florida is closed to demographic and geographic movement, and that banded individuals have the same mortality rate as un banded individuals, there is no re ason to think that our sample is not an adequate representation of the total Snail Kite population. Therefore, there is no reason to b elieve that the observed, decreasing trends in population size, fertility, adult survival, juvenile survival, and now breeding probabilities are not in fact real and occurring at the population level. As the Snail Kite population in Florida continues to decrease, providing managers and conservation biologist s with reliable estimates of survival and reproduction becomes increasingly important as a means of accurately project ing the effects of competing management strategies on the Snail Kite population as a guide to species recovery Unfortunately, the information provide d here elucidates sources of variation which are limiting Snail Kite vital rates. It is difficult to predict the true quantitative impacts of these results on the projected Snail Kite popula tion Nonetheless, continuing to test
68 predictions and redefining our hypotheses accordingly, is an imperative step in the adaptive process of conserving the Snail Kite population in Florida
69 Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Snail Kites 0 500 1000 1500 2000 2500 Banded Fledglings (< 1 yr olds) 1-4 yrs old 5-12 yrs old +13 yrs old Figure 4 1. Age dist ribution of banded Snail Kite s Numbers of adults were adjusted for age -specif ic a nnual detection probabilities ( Chapter 2)
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79 BIOGRAPHICAL SKETCH Brian Engebos Reichert was born in M ilwaukee, Wisconsin in 1982. He obtained a B.S. in Fisheries, Wildlife, and Conservation at the University of Minnesota, St. Pa ul in 2005. Beginning in 2003, Brian worked as a field technician with the Minnesota Cooperative Fish and Wildlife Research Unit on several projects including the investigations into: the fall movements, habitat use, and s urvival of the American Woodcock i n the Western Great Lakes Region long -term monitoring of breeding Eastern Prairie Population Can ada Geese at Cape Churchill, Ma nitoba, Canada; compiling the breeding bird and master avian species lists for Wapusk National Park, Cape Churchill, Ma nitoba, Canada; Wo od Frog and Boreal Chorus Frog distribution and habitat a ssociations in Wapusk National Park. In 2006, Brian moved to Florida and began working as a field ecologist for the Florida Fish and Wildlife Cooperative Research Unit on the long t erm monitoring of Snail Kite demography and its habitat.