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1 TRAIT MEDIATED EFFECTS OF PREDATION RISK: HOW DOES IT INFLUENCE FOREST BIRD HABITAT RELATIONSHIPS? By FANGYUAN HUA A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 201 3
2 201 3 F angyuan H ua
3 To my beloved parents and grandmother
4 ACKNOWLEDGMENTS I thank my committee Drs. Doug Levey, Lyn Branch, and Colette St. Mary for invaluable input in the design, implementation, and completion of the dissertation. Dr. Scott Robinson provided helpful insigh ts on the research design. I thank various faculty members at the University of Florida for inspiring discussions and guidance : Drs. Bob Dorazio, Peter Frederick, Susan Jacobson, Kaoru Kitajima, and J ack Putz James Colee provided much appreciated help with statistical analyses. Burung Indonesia, Harapan Rainforest, Wildlife Conservation Society Indon esia Program, Universit as Andalas, the Indonesian Institute of Sciences, and the Bukit Barisan Selatan National Park provided critical support for field work in Indonesia. I also thank Steve Coates and the Ordway Swisher Biological Station for use of field sites in Melrose, Florida. I thank Dr. Bas van Balen for providing crucial bird call recordings, without which my work in Indonesia would not have been possible. I am indebted to numerous colleagues and field assistants for assistance with field work: Dwi Mulyawati, Muhammad Nazri Janra, Belry Zetra, Emma Yustikasari, Aadrean, Liza Meini Fitri Maman, I rina Skinner C hloe Wright I also thank numerous under graduate assistants who helped to process nesting videos. Graduate study funding was provided by the Alumni Fellowship of the School of Natural Resources and Environment, and the Graduate Teaching Assistantship of the Department of Wildlife Ecology and Conservation at the University of Florida. Research funding was provided by the University of Florida, Animal Behavior Society, Inter national Foundation for Science Conserv ation Leadership Programme, and Idea Wild. I thank my committee member Dr. Rob Fletcher for immense guidance and support for my dissertation research. My major advisor Dr. Katie Sieving has been inspiring, encouraging, supportive, and patient beyond measure during all these years
5 of my doctoral program an d I have the deepest gratitude for her guidance as my advisor and mentor. I thank the Sieving lab and fellow students in the Department of Wildlife Ecology and Conservation for inspiring conversations and comforting friendship. I also thank my many friends for providing me with constant care and support: Nilmini Jayasena, Kai Lu, Jun Feng, Qing Liu, YuMin Su, Jing Yuan, Wei Wei, Minjia Xu, and Lulu Xing. Special thanks go to Tingting Wu, for being such a faithful friend that I shall always treasure with th e deepest gratitude. Finally, I thank my beloved parents and grandmother for their unfailing love, support, and faith in me.
6 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 9 LIST OF FIGURES ........................................................................................................ 10 ABSTRACT ................................................................................................................... 11 CHAPTER 1 GENERAL INTRODUCTION .................................................................................. 14 2 FOREST DEGRADATION ALTERS THE PERCEPTION OF PREDATION RISK OF UNDERSTORY BIRDS IN TROPICAL LOWLAND SUMATRA ........................ 21 Introduction ............................................................................................................. 21 Methods .................................................................................................................. 23 Study Area ........................................................................................................ 23 Study Design .................................................................................................... 24 Data Collection ................................................................................................. 32 Statistical Analyses .......................................................................................... 37 Results .................................................................................................................... 40 Understory Bird Mobbing Behavior Intensity Differed among Study Locations ....................................................................................................... 40 Vegetation Structure Explained Mobbing Intensity Differences among Locations ....................................................................................................... 41 Discussion .............................................................................................................. 43 Predation Risk Perception of Understory Birds Increase in Degraded Locations ....................................................................................................... 43 Vegetation Structure Explained Increased Risk Perception in Degraded Locations ....................................................................................................... 45 Conclu sion ........................................................................................................ 47 3 TOO RISKY TO SETTLE: PERCEIVED PREDATION RISK ON ADULTS AND OFFSPRING ALTERS AVIAN COMMUNITY STRUCTURE .................................. 55 Introduction ............................................................................................................. 55 Methods .................................................................................................................. 56 Study A rea ........................................................................................................ 56 Experimental D esign ........................................................................................ 57 Data Collect ion ................................................................................................. 59 Statistical A nalyses .......................................................................................... 60 Results .................................................................................................................... 66 Discussion .............................................................................................................. 67
7 Effect of Predation Risk on Community Structure ............................................ 67 Response to Predation Risk in Relation to Functional Traits ............................ 70 Conclusion ........................................................................................................ 71 4 NONLETHAL PREDATION RISK TO ADULT AND OFFSPRING ALTERS AVIAN REPRODUCTIVE STRATEGY AND REDUCES REPRODUCTIVE OUTPUT ................................................................................................................. 76 Introduction ............................................................................................................. 76 Methods .................................................................................................................. 79 Study Area and Species ................................................................................... 79 Experimental Design ........................................................................................ 79 Data Collection ................................................................................................. 80 Statistical Analyses .......................................................................................... 82 Results .................................................................................................................... 86 Discussion .............................................................................................................. 88 APPENDIX 5 CONCLUSIONS ................................................................................................... 105 A LIST OF SPECIES RECORDED DURING MOBBING PLAYBACKS (CHAPTER 2) .......................................................................................................................... 108 B LIST OF BEST MODELS FOR SPECIESLEVEL ANALYSES OF THE DIFFERENCE OF AVIAN MOBBING INTENSITY AMONG STUDY LOCATIONS (CHAPTER 2) .................................................................................. 111 C LIST OF BEST MODELS FOR SPECIESLEVEL ANALYSES OF THE RELATIONSHIP BETWEEN AVIAN MOBBING INTENSITY AND VEGETATION STRUCTURE ................................................................................ 113 D DETAILS OF PLAYBACK SCHEME (CHAPTER 3) ............................................. 115 E LIST OF FOCAL SPECIES FOR ANALYSES OF RESPONSE TO PLAYBACKS (CHAPTER 3) ....................................................................................................... 118 F DETAILS ON DATA AND DATA SOURCES FOR SPECI ES LIFE HISTORY AND NATURAL HISTORY TRAITS (CHAPTER 3) .............................................. 119 G SPECIFICATION OF N MIXTURE ABUNDANCE MODELS AND SITE OCCUPANCY MODELS (CHAPTER 3) ............................................................... 121 H BEST N MIXTURE ABUNDANCE AND SITE OCCUPANCY MODELS FOR EACH FOCAL SPECIE (CHAPTER 3) ................................................................. 126 I UNEXPECTED RESPONSES OF FOCAL SPECIES TO PREDATION RISK TREATMENT (CHAPTER 3) ................................................................................ 127
8 J EFFECT OF TREATMENT ON RAW COUNTS OF PLOT LEVEL SPECIES RICHNESS (CHAPTER 3) .................................................................................... 128 K EFFECTS OF PREDATOR TREATMENT ON PARENTAL INVESTMENT IN EGG PRODUCTION, WITHOUT ADJUSTMENT BASED ON SUCCESS OF THE FIRST NESTING ATTEMPT (CHAPTER 4) ................................................. 1 29 LIST OF REFERENCES ............................................................................................. 132 BIOGRAPHICAL SKETCH .......................................................................................... 150
9 LIST OF TABLES Table page 2 1 Aspects of mobbing behavior scored during focal sampling to quantify mobbing intensity / conspicuousness ................................................................. 48 2 2 Structure of the best models for the guildlevel difference of avian mobbing intensity among study locations .......................................................................... 49 2 3 Structure of the best models for the guildlevel analyses of the relationship between avian mobbing intensity and vegetation structure ................................ 50 3 1 Bird species that contributed the most to community dissimilarities (based on SIMPER analysis) ............................................................................................... 73 4 1 List of reproductive response measures tested in this study and their definition ............................................................................................................. 96 4 2 Reproductive response of the Eastern bluebird to increased perception of predation risk ...................................................................................................... 97 A 1 List of species that responded to mobbing playback by approaching within 15m from the owl model with mobbing / inspection behavior ........................... 108 B 1 Structure of the best models for the species level diffe rence of avian mobbing intensity among study locations ......................................................... 111 C 1 Structure of the best models for the species level analyses of the r elationship between avian mobbing intensity and vegetation structure .............................. 113 E 1 List of the 18 focal prey species and their common and Latin names .............. 118 F 1 Life history and natural history trait data for all 18 focal prey species. ............. 119 G 1 Defi nition of symbols used in N mixture models and site occupancy models ... 121 H 1 Best hierarchical models selected for the 13 focal species that fitted N mixture models and the one other focal species that fitted occurrence models 126 I 1 List of species with unexpected responses (in terms of abundance and/or detection probability) to increased perception of predation risk ........................ 127 K 1 Response i n egg laying of the eastern bluebird to increased perception of predation risk, without adjustment based on success of the first nesting attempt ............................................................................................................. 129
10 LIST OF F IGURES Figure page 2 1 Theoretical relationship between avian mobbing intensity and two levels of predation risk ...................................................................................................... 51 2 2 General study design .......................................................................................... 52 2 3 Rela tionship between mobbing intensity of understory gleaning species and study location with different degree of habitat degradation ................................. 53 2 4 Relationship between vegetation structure and study location with different degree of habitat degradation ............................................................................. 54 3 1 Treatment effects on community structure, in terms of species richness and composition ........................................................................................................ 74 3 2 Relationship between species response to treatments in terms of abundance and body size (estimates and model predictions, + 95% CI) .............................. 75 4 1 Effects of predator treatments on the total number of fledglings produced on plot over both nesting attempts ........................................................................... 99 4 2 Effects of predator treatm ents on parental investment in egg production ......... 100 4 3 Effects of predator treatments on the average egg hatching rate ..................... 102 4 4 Effects of predator treatments on the number and body condition of nestlings at 14 days of age .............................................................................................. 103 D 1 Illustration of playback scheme ......................................................................... 117 J1 Effects of playback treatment on the number of focal species on each plot as tallied from field surveys (all 18 focal species considered) ............................... 128 K 1 Effects of predator treatments on parental investment in egg production ......... 130
11 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy TRAIT MEDIATED EFFECTS OF PREDATION RISK: HOW DOES IT INFLUENCE FOREST BIRD HABITAT RELATIONSHIPS? By Fangyuan Hua May 2013 Chair: Kathryn E. Sieving Major: Interdisciplinary Ecology Understanding animal habitat relationships remains one of the key inquiries of ecology. In studying animal habitat relationships, the traditional approach has adopted a resource centered view, relying heavily on directly measuring attributes of a habitat itself. However, ecological constraints that may influence access to and limit the use of resources are equally important, but have received much less attention. In my dissertation, I attempted to test the role of predation risk, specifically the non lethal form of predation risk acting via trait mediated effects mechanisms, in influencing forest birdhabitat relationsh ips as an ecological constraint, from three different angles. These angles pertained to: (1) the relevance of trait mediated predation risk ( TMPR ) in birds response to habitat degradation; (2) the role of TMPR, particularly different types of TMPR (e.g., risk for adults versus for offspring), in influencing forest birds habitat selection, community assembly, and reproduction. I first assessed whether and to what extent habitat degradation alters the perception of predation risk (and hence TMPR) in understory forest birds in the tropical lowland rainforest of Sumatra, Indonesia (Chapter 2). I n four forest locations that differ ed in the degree of habitat degradedness I used playback techniques to induce
12 the mobbing / inspection behavior of small bodied understory forest birds and quantified their behavioral intensity as a measur e of their risk perception. I found that understory gleaning species had significantly increased perception of predation risk in degraded forest habitats, and such increase was at least partially attributable to forest vegetation changes I then experiment ally tested whether and to what extent TMPR altered (1) the habitat settlement decisions of forest breeding birds and in turn shaped the structure of the breeding bird community (Chapter 3) and (2) the reproductive output and strategy of a focal forest bi rd species the Eastern bluebird Sialia sialis and how such effects differed between risks to the adults versus to offspring (Chapter 4) I experimentally increased the level of perceived adult, offspring, or both adult and offspring predation risk for san dhill forest birds with playbacks of predator vocalizations, and studied birds responses from the perspectives of avian community structure and reproduction. I found that increased perception of predation risk tended to negatively affect the abundance, oc currence and / or detection probability of most prey forest birds in ways generally explainable by body size, and largely reduced community species richness and shifted communi ty composition I also found that increased perception of predation risk strongl y influenced the reproduction of the focal species Eastern bluebird Sialia sialis by impairing nesting performance and reducing reproductive output. Taken together, my findings demonstrated that: (1) TMPR was likely involved in understory forest birds response to habitat degradation; (2) TMPR could strongly alter breeding birds habitat selection and lead to shifts in community assembly; and (3) TMPR could strongly influence avi an reproductive strategy and output. These results
13 provide strong evidence that TMPR can strongly shape the relationship of forest birds with their habitat. They further suggest that ecological constraints other than resources, such as predation risk, are critical for assessing animal habitat relationships.
14 CHAPTER 1 GENERAL INTRODUCTION Characterization of animals relationships with their habitat has had a long history in ecology (Grinnell 1917, MacArthur et al. 1962, James 1971, Whittaker et al. 1973). T he t raditional approach to studying animal habitat relationships uses a resource centered view, relying heavily on directly measuring attributes of the habitat itself ( Morrison et al. 2006) Under this traditional approach, most studies link animal distribution or demography with vegetation features and / or food resources ( Scott et al. 2002, Morrison et al. 2006) However, habitat encompasses more than the vegetation and resources surrounding an animal (Johnson 2007) ; a s defined by Hall et al. (1997), the operational definition of habitat includes the resources and conditions present in an area that produce occupancy including survival and reproduction by a given organism. Therefore, o f equal importance to animals are the ecological relations hips that influence fitness by constraining the availability and access to resources including predation risk, competition, and facilitation (Morrison et al. 2006, Johnson 2007) In my dissertation, I attempt ed to test the role of predation risk as an ec ological constraint acting on forest birds; specifically I address the non lethal form of predation risk that acts via trait mediated mechanisms P redation risk is a critical factor shaping the ecological and evolutionary processes in animals (Lima and Dil l 1990, Endler 1991). Aside from its direct effect on the density of prey via prey removal (aka density mediated effects), prey perceptions of predation risk based on predator encounters or the anti predator behavior of other individuals, can also strongly shape prey behavior in ways that influence prey fitness and popul ation dynamics (aka behavior, or trait mediated or non lethal effects ) I ndirect predation effect are now recognized as
15 ubiquitous in predator prey systems often with fitn ess and population impact s of greater magnitude than direct (lethal) effects of predation (Lima 1998, Agrawal 2001, Preisser et a l. 2005, Cresswell 2008). As such, predation risk can no longer be viewed solely in its lethal form; indirect effects of predat ion should be an essential requisite in the examination of animal habitat relationships ( e.g., Evans 2004). In my dissertation, I focus ed on the nonlethal component of predation risk, and attempt to address gaps in understanding how trait mediated effects of predation risk (TMPR) can shape forest bird habitat relationships as detailed below. The f irst gap pertains to the relevance of TMPR in birds response to habitat degradation. Specifically, I asked whether and to what extent habitat degradation altered the perception of predation risk (and hence TMPR) in understory forest birds in the tropical lowland rainforest of Sumatra Indonesia (Chapter 2) Understanding how forest biodiversity persists in degraded habitat is of critical importance to inform ing conservation strategies (Bawa and Seidler 1998). However, the vast majority of work to date is focused on animals numerical responses ( Bawa and Seidler 1998, Lambert and Collar 2002, Barlow et al. 2006) largely ignoring n onnumerical responses, such as organisms behavioral and physiological changes that may also be of important ecological significance ( van Horne 1983, Hall et al. 199 7 ). H abitat struc ture is important in determining the dynamics of predation (Verdolin 2006, Quinn et al. 2008). For example, the structural complexity of vegetative layers is highly correlated with predator prey encounter and captur e rates across animal taxa ( e.g., Savino and Stein 1989, Beukers and Jones 1998, Whittingham and Evans 2004, Andruskiw et al. 2008) Therefore, it is understandable how and why habitat structure can profoundly influence
16 prey risk perception and consequent risk sensitive behavior s ( Lazarus an d Symonds 1992, Lima 1993, Thiollay 1999, Boinski et al. 2003, Devereux et al. 2008, Whittingham et al. 2006, Andruskiw et al. 2008, Orpwood et al. 2008). In the context of forestry practices and timber extraction, forest habitat structure undergoes sig nificant changes most of which can be characterized in terms of canopy closure and under story or subcanopy structural complexity or density (Ganzhorn et al. 1990). Each forest stratum has a community of birds uniquely adapted to its food, lighting, and physiognomy Therefore, degradation of the structure of forest habitat under silvicultural practices will alter many aspects of bird habitat relationships, and certainly their perception of predation risk and consequently how they adapt their anti predator behaviors (Lima 1993) Therefore, forest birds may have drastically changed perception of predation risk in degraded habitats and subsequent TMPR could constitute an important nonnumerical response that has heretofore been overlooked. In Chapter 2, I used playback technique to assess understory forest birds perception of predation risk across four forest locations that differ in degree of degradedness in lowland Sumatra, and related birds risk perception to key vegetation structural f eatures. I used mobbing playbacks to elicit the mobbing / inspection behavior of small bodied understory forest birds (typically with body length < 25 cm), and quantified their behavioral intensity as a measure of their risk perception. The use of mobbing behavioral intensity under standardized stimulus as the measure of birds perception of predation risk was based on the considerations that (1) classic approaches of quantifying animals perception of predation risk were not readily applicable to my study situation, and that (2) avian mobbing behavior is constrained by
17 and reflects birds perception of predation risk. I analyzed birds mobbing behavioral intensity at the levels of both individual species and ecological guilds defined by species foragi ng tra its. My results show ed that understory gleaning species had significantly increased perception of predation risk in degraded forest habitats, and such increase was at least partially attributable to forest vegetation changes. The s econd gap pertains to the role of TMPR particularly different types of TMPR (e.g., risk for adults versus for offspring) in influencing forest birds habitat selection and community assembly. Specifically, I asked whether and to what extent TMPR altered the habitat settlement de cisions of forest breeding birds and in turn shaped the structure of the breeding bird community and how species response relate d to life history and natural history traits (Chapter 3). Despite a long history of research, most empirical test s of the role of predation risk in shaping community assembly have focused on aquatic systems, and to a lesser extent, terrestrial invertebrate systems ( earlier studies reviewed in Sih et al. 1985). Tests of whether predation risk can structure terrestrial vertebrate co mmunities remain scarce with only a handful of observational studies ( e.g., Martin 1988a,1988b, Norrdahl and Korpimaki 1998, Forsman et al. 2001, Banks et al. 2008) and even fewer experimental tests (Suhonen et al. 1994, Fontaine and Martin 2006). In addi tion, experimental tests of whether species traits can predict community changes to variation in perceived predation risk remain absent In Chapter 3, I experimentally increased the level of perceived predation risk for forest birds in the sandhill habita t of Southeastern United States I did this by manipulat ing vocalization cues of three avian predators th at preferentially prey on bird
18 nests (blue jay Cyanocitta cristata), or adult birds (Eastern screech owl Megascops asio ), or both (Coopers Hawk Accipiter cooperii ), over an entire avian breeding season. I conducted repeated community surveys and used hierarchical modeling to quantify species level responses, based on which I made inferences on community level responses. I also asked how these spec ies level responses related to species functional traits including body size and fecundity My results suggested that increased perception of predation risk affected the abundance, occurrence and / or detection probability of most species in generally negative ways, and thereby largely shifted community composition. In addition, species level responses in abundance were largely explainable by species body size. The third gap pertains to the role of TMPR, particularly different types of TMPR (e.g., risk for adults versus for offspring), in influencing forest birds reproduction Specifically, I asked whether and to what extent TMPR altered the reproductive output and strategy of a focal forest bird species the Eastern bluebird Sialia sialis and how such effe cts differed between risks to the adults versus to offspring (Chapter 4). Empirical studies of breeding birds reproduction under heightened perception of adult or offspring predation risk have largely support ed theoretical predictions that birds tended to reduce reproductive investment and output ( e.g., Scheuerlein et al. 2001, Eggers et al. 2006, Fontaine and Martin 2006, Thom son et al. 2006, Riou and Hamer 2008). However, experimental tests of the broad collection of theoretical predictions pertaining to avian reproductive strategy and output remain scarce In addition, to my knowledge, no studies have compared avian reproductive response to different types of predation risk (e.g., risk to adult versus to offspring) under the same context yet such
19 comparison is crucial for understanding the nature of avian reproductive responses and testing life history theory predictions. In Chapter 4, I used the same experimental set up as that of Chapter 3. Over the breeding season, I closely monitor ed the reproductive behavior of bluebirds nesting in artificial nest boxes on my study plots. My results suggested that increased perception of predation risk profoundly affected the reproduction of the Eastern bluebird, by significantly reducing parental investment in egg laying, impairing subsequent nesting performance, suppressing nestling feeding behavior, and generally altering breeding strategy. This array of responses culminated in considerably reduced reproductive output under simultaneous adult and offspring pr edation risk (i.e., treatment with the Coopers hawk cues), and qualitatively reduced reproductive output under either adult or offspring predation risk (treatment with Eastern screechowl or blue jay cues, respectively) In addition, by comparing breeding birds reproductive response to different types of predation risk under the same context, my studied provided especially important insights on how breeding birds may respond differently in reproductive strategy when perceiving increased risk to themselves versus that to their offspring. In summary, my dissertation provided an assessment of the role of trait mediated effects of predation risk (TMPR) in influencing forest birdhabitat relationships from three different a ngles I demonstrated that TMPR was li kely involved in understory forest birds response to habitat degradation in the lowland tropical rainforest of Sumatra, through birds increased perception of predation risk in degraded forest habitat (Chapter 2). I also demonstrated that TMPR could strongly alter breeding birds habitat selection and settlement decisions, which culminated in shifts in community assembly
20 (Chapter 3). Finally, I demonstrated that TMPR could strongly influence the reproductive strategy and output of a focal forest bird speci es, leading to substantial fitness and demographic consequences (Chapter 4). These results suggest that TMPR can strongly shape the relationship of f orest birds with their habitat. They further suggest that ecological constraints other than resources, such as predation risk are critical for assessing animal habitat relationships
21 CHAPTER 2 FOREST DEGRADATION ALTERS THE PERCEPTION OF PREDATI ON RISK OF UNDERSTORY BIRDS IN TROPICAL LOWLAND SUMATRA Introduction As tropical rainforests across the globe face mounting pressures from resource extraction and land conversion, degraded forests are becoming an increasingly vital frontline for conservation (Bawa and Seidler 1998, Putz et al. 2001, Edwards et al. 2011) Understanding how tropical forest biodiversity persists in degraded habitat is of critical importance to inform ing conservation strategies The vast majority of work to date on tropical forest biodiversit y responses to habitat degradation documents organisms numerical responses, i.e., patterns of species loss and population change (for reviews, see Bawa and Seidler 1998, Lambert and Collar 2002, Barlow et al. 2006). N onnumerical responses, such as organisms behavioral and physiological changes have received much less attention, even though they are also critical for assessing biodiversity habitat relationships, particularly when such changes bear potential fitness and demographic consequences ( van Horne 1983, Hall et al. 199 7 ) Animals anti predator behavior constitutes one such important, yet over looked nonnumerical response to habitat degradation. This is because of t wo main r easons that pertain to the strong fitness and demographic consequences of predation risk and in particular animals risk sensitive behavior and the potential susceptibility of animals risk sensitive behav ior to alterations of habitat. First, predation risk is a critical factor shaping the ecological and evolutionary processes in animals (Lima and Dill 1990, Endler 1991, Barbosa and Castellanos 2005). Aside from its direct effect on the density of prey by prey removal (aka density mediated effects), predation risk can also strongly shape prey behavior and lead to trait mediated / nonlethal effects (Lima 1998, Agrawal
22 2 001, Preisser et al. 2005, Cresswell 2008) The latter indirect effect in particular, acts via prey perception of predation risk and consequent risk sensitive trait responses that are mostly behavior al and is increasingly recognized to exert ubiquitous ecological and evo lutionary consequences (Preisser et al. 2005, Zanette et al. 2011, Chapters 3 and 4 of this dissertation) Secondly, under the context of habitat degradation, the level of predation risk its assessment / perception by prey and in turn prey risk sensitive behavior, are l ikely to be affected through changes in the predator community (Jul l ien and Thiollay 1996) and vegetation structure (e.g., Johns 1986, Thiollay 1992, Sekercioglu 2002) These changes can i nfluence predator prey interactions via both density and trait mediation (Verdolin 2006, Quinn et al. 2008) V egetation structural complexity influences prey enc ounter rates (Crowder and Cooper 1982, Anderson et al. 1984, Savino and Stein 1989, Lazarus and Symonds 1992, Andruskiw et al. 2008) and capture rates (Schooley et al. 1996, Beukers and Jones 1998, Boinski et al. 2003, Whittingham and Evans 2004, Andruskiw et al. 2008) In addition, vegetation structure determines prey risk perception and in turn their risk sensitive behaviors, including escape tactics (Enstam and Isbell 2002, Devereux et al. 2008) foraging and vigilance patterns (Boinski et al. 2003, Mandelik et al. 2003, Verdolin 2006, Whittingham et al. 2006, Andruskiw et al. 2008) and gregarious group behavior (Thiollay 1999, Boinski et al. 2003, Sieving et al. 2004, Orpwood et al. 2008) Therefore, in a degraded forest that likely has an altered predator community and altered vegetation structure (Johns 1986, Thiollay 1992, Jul lien and Thiollay 1996) prey that persist in these disturbed ecosystems should be expected to exhibit altered perce ptions of risk and ri sksensitive behaviors.
23 In the lowland rainforest of Sumatra, Indonesia I test ed the hypothesis that understory birds exhibit altered perception of pr edation risk in degraded forest s. I chose understory birds as the focal taxa of study for two reasons. Fir st, birds have among the most complex anti predation and predation behaviors, and the nonlethal effect of predation risk may be especially prominent in their ecology and evolution (Cresswell 2008) Secondly the numerical response of tropical forest birds to forest degradation is relatively better understood than other taxa, particularly in Southeast Asia (Bawa and Seidler 1998, Lambert and Collar 2002) U nderstory birds are generally considered as particularly sensitive to forest degradation in the tropics (Thiollay 1992, Lambert and Collar 2002, Barlow et al. 2006) This knowledge will provide relevant background for interpreting findings on their risk sensitive behavioral responses to forest degradation. I used playback techniques to quantitatively assess understory birds perception of predation risk across four forest locations that differ ed in the degree of habitat degradation. I addition ally tes t ed whether forest vegetation structural changes characteristic of habitat degradation could explain the changes in birds perception of predation risk in degraded forest habitats I assessed birds perception of predation risk at the level s of both individual species and ecological guilds. Methods Study A rea I conducted field work in two main forest sites in lowland Sumatra, Indonesia: the Way Canguk Biological Station inside the Bukit Barisan Selatan National Park in Lampung Province (BBSNP hereafter; S5 39, E104 24, 30~60m asl), and the Harapan Rainforest ecosystem restoration site in Jambi Province (HRF hereafter; S2 08, E103 22, 50~8 0m asl; Figure 22 A ). BBSNP covers 3,568 sq km of both montane and
24 lowland for est habitat. I ts lowland part at the southern tip of Sumatra is probably the last sizable patch of primary lowland rainforest that remains of Sumatra after years of rampant forest destruction and degradation (O'Brien and Kinnaird 1996) I used Way Canguk as my location for primary, undegraded forest and referred to it as PRIM hereafter. HRF is a forest restoration concession that covers 98,554 ha of post logging secondary lowland rainforest (Burung Indonesia, Royal Societ y for Protection of Birds and BirdLife International 2010, Hua et al. 2011). Previous industrial logging activities have left a mosaic of secondary forest habitats in different states of degradedness and stages of r egeneration. I used three locations inside HRF generally along the spectrum of habitat degradation as my degraded forest locations: DEG 1 (relatively degraded forest w ith medium to high canopy), DEG 2 (another relatively degraded forest with medium to hi gh canopy), and DEG 3 (most degraded forest with mostly low canopy). Although not closely adjacent, all sites share the same avifauna characteristic of lowland Sumatra rainforest (MacKinnon and Phillips 1993). Study D esign Overall design I first tested t he hypothesis that understory forest birds perception of predation risk differed in forest habitats with different degrees of degradation (Hypothesis 1), by measuring and comparing birds perception of predation risk across four study locations. This is b ased on the assumption that the four study locations were comparable with regard to understory birds perception of predation risk other than for the habitat degradation factor. My study satisfied this assumption by comparing the behaviors of the same spec ies and guilds (see below) in the same ecosystem (i.e., Sumatran lowland rainforest). I then tested whether forest vegetation structural changes could explain the difference in forest birds mobbing intensity among
25 study locations (Hypothesis 2) I did this by (1) testing for the difference in crucial aspects of vegetation structure among study locations, and (2) testing for the relationship of birds perception of predation risk with these vegetation structural aspects If the observed difference of avi an perception of predation risk among study locations (Hypothesis 1) w as consistent with predictions from the combined results of (1) and (2), my study would provide a strong support for H ypothesis 2 Measure of birds perception of predation risk. I used playback techniques to elicit the mobbing behavior of small bodied, understory forest birds, and quantified the intensity of birds mobbing behavior as a measure of their perception of predation risk. Mobbing is the joint assault of animals usually on perc hed predators in order to harass it and / or drive it away (Curio 1978, Shedd 1983, Arnold 2000, Desrochers et al. 2002) I t is a widespread behavior particularly among forest birds, typically characterized by a congregation of excited individuals with loud vocalizations and / or conspicuous visual displays (Altmann 1956, Curio 1978, Shedd 1983, Deppe et al. 2003) The se characteristics of avian mobbing, i.e., prey birds are drawn to a perched predator and exhibit highly conspicuous behavior, imply that two types of predation risk are involved that could influence prey mobbing behavior (Figure 21) First, the level of threat represented by a perched (or immobile) predator motivates prey birds to engage in mobbing (Curio 1978, Shedd 1983). For prey to approach and mob a perched predator, the predation risk posed by this predator should not be minimal (otherwise prey would not be concerned about its presence) nor should it be too high (otherwise it would be too dangerous for prey to approach and mob ( e.g., Templeton and Greene 2007 ) T he relat ionship between avian mobbing intensity and the level of predation risk posed by
26 the perched predator thus should be a bell shaped cu rve (Figure 2 1 A). On the other hand, predation risk from predators other than the perched predator that could be present but undetected at a mobbing event (referred to as ambient predation risk hereafter) should con strain the conspicuousness of avian mobbing behavior. This is because the nervous and focused nature of mobbing behavior almost certai nly distracts mobbing birds from effective vigilance against predator s other than the predator being mobbed and its conspicuous nature can attract predator attention (Sordahl 1990) Anything that influences birds perception of potential attack will define their response to ambient predation risk (e.g., vegetation structure or presence/absence of vigilant alarm calling species; Sieving et al. 2004 ). T he relationship between avian mobbing intensity and the perception of ambient predation risk should thus be a monotonic curve (Figure 2 1 B ; Forsman and Mnkk nen 2001, Sieving et al. 2004, Hua et al. in review). Both types of risk and therefore the perceived vulnerability of prey during mobbing will vary; thereby constraining prey behavioral responses during mobbing (Hogstedt 1983, Sordahl 1990, Sieving et al. 2004, Krams et al. 2007). Three fact ors other than predation risk can also affect avian mobbing intensity. Prey reproductive condition / cycle can dictate behavioral priorities and hence, mobbing motivation, for breeding birds B irds in breeding activities may more reliably exhibit intense mobbing behaviors toward the same perched predator stimulus than nonbreeding birds (Altmann 1956, Shedd 1982, 1983) probably because the cost benefit tradeoff of mobbing changes with birds reproductive s tatus ( Sordahl 1990, Krama and Krams 2004). In addition, body condition and / or energy state of mobbing birds can affect mobbing intensity ; as mobbing is an energy intensive activity (Shedd 1982,
27 Krama and Krams 2004) that is not immediately essential for birds survival (Cuthill and Houston 1997) Final ly, social information can also affect mobbing intensity (Sieving et al. 2004, Hua et al. in review), much as it is influen tial on other aspects of animal behavior in general (Seppnen et al. 2007). I used forest birds mobbing intens ity as a measure of th eir perception of ambient predation risk (as influenced by forest vegetative structure) and so control led the other factors using the following procedures. (1) I us ed standardized playback stimuli and selected playback locations with similar vegetation structure in the immediate area around the playback (as that can influence prey perception of predation risk near a perched predator ; Hendrichsen et al. 2006) (2) I conducted the playback study in a short span of time (36 days) during the end of avian breeding season (June and July 2011; Whitten 199 7 ) and additionally randomized the dates on which the three degraded forest locations located inside HRF ( DEG1, DEG2, and DEG3) were visited. Therefore small bodied forest birds were in a comparable breeding phase across the study locations during the study. (3) I tested for the presence of sys tematic body mass (surrogate for body condition; Green 2001) differences among study locations for four of the mobbing species for which data were available (see be low). I used body mass data from a mist netting study I conducted between December 2010 and March 2011, in the same study plots in three of the four study locations (PRIM, DEG2, and DEG3) Three of the four species showed no body mass differences across study locations, while one species had significantly larger body mass in PRIM than in DEG2 and DEG3 ( Appendix A) Based on these results, I generally assume d that mobbing birds did not differ in body condition and energy state across study locations (but see Discussion below) I
28 also included time of playback as a covariate in the analyses to account for diurnal variation in energy state and interest in mobbing (Sieving et al. 2004) (4) I included the number of mobbing birds as a covariate in the analyses (see below) as a n indicator of variable amounts of social information on mobbing intensity In these ways I controlled major alternative influences on avian mobbing intensity other than ambient predation risk as determined by site level variation in vegetative degradation. Mobbing stimuli. I used two vocal and one visual stimulus in combination to elicit forest birds mobbing behavior. The main stimulus was a recording of a naturally occurring avian mobbing event from the lowland rainforest of northern Sumatra (Lamno, Aceh Province; Bas van Balen, pers. comm.) While the vocalization of a perched predator is the ideal stimulus for eliciting avian mobbing for some other forest bird systems (e.g., the E astern screech owl Megascops asio in Northeast America, and the collared owlet Glaucidium brodiei in montane Southeast Asia; Gehlbach and Leverett 1995, Eames et al. 2002) it does not apply to the lowland rainforest of Southeast Asia (Bas van Balen, pers. comm.). However, as a form of social information (Danchin et al. 2004) the noisy, agitated vocalizations of mobbing participants during naturally occurring mobbing events prove to be a consistently effective stimulus in inducing mobbing behaviors of forest birds in this ecosystem (Bas van Balen, pers. comm.). The recording I used consist ed of the simultaneous agitated mobbing / scolding vocalizations of five small bodied understory bird species that also occurred in all of my study locations. These species were: spectacled bulbul Pycnonotus erythrophthalmos black naped monarch Hypothymis azurea, striped titbabbler Macronous gularis dark necked tailorbird Orthotomus atrogularis and buff vented bulbul Iole olivacea. The
29 cause of the natural mobbing event was unclear, but was probably an avian predator (Bas van Balen, pers. comm.). I additionally used one visual stimulus and one vocal stimulus of owl predator ( collared scops owl Otus lempiji) at playback locations t o simulate a more natural array of cues representing the presence of a perched predator, and to provide a focal point for avian mobbing behavior (Sieving et al. 2004) The collared scops owl is a small owl typical o f the lowland rainforest of Sumatra and is an important avian predator of small bodied forest birds (MacKinnon and Phillips 1993). Its predatory role, nocturnal habit, and small size make it a good candidate predator for forest birds to mob, and indeed it is known to be a target of avian mobbing behavior (Bas van Balen, pers. comm., F. Hua, pers. obs.). I used a custom made wooden model of the collared scops owl to provide the visual stimulus and one recording of the species typical territorial call reco rded from West Kalimantan (catalogue number XC46912, downloaded from XenoCanto: http://www.xeno canto.org/46912 ) to provide the vocal stimulus Measures of vegetation structure I quantified canopy cover and understory density as predictor variables to test whether forest vegetation structural changes were related to forest birds perception of predation risk in degraded forest habitats. C hanges in these structural aspects tend to be the most characteristic of forest habitat degradat ion (e.g., in the case of degradation as a result of selective logging and subsequent forest regeneration, which is relevant to my study system ; Johns 1986, Thiollay 1992) Moreover, these structural feat ures s hould crucially determine understory birds risk perception and risk sensitive behavior. Specifically, a more open / broken canopy will likely render prey birds more susceptible to predators aerial attack
30 from above (Castro Arellano et al. 2009) ; and a denser understory will likely alter the availability of perches and cover (protective or obstructive) for prey birds that would influence predatory detection and escape (Lazarus and Symonds 1992) Ec ological guild classification and expected responses in risk perception I assessed birds mobbing behavior and perception of predation ris k for both individual species and ecological (foraging) guilds. Ecological guilds delineated by species functional traits are reasonable general predictor s of species sensitivity to habitat degradation, because ecologically similar species may be expected to respond to habitat disturbances in similar ways (Woltmann 2003, Barlow et al. 2006, Wunderle et al. 2006, Cleary et al. 2007, Gray et al. 2007 Felton et al. 2008) In the context of risk sensitive behavioral respons e to forest degradation and particularly altered forest vegetation structure for understory birds I used foraging technique ( i.e. gleaning or sallying) as the criteri on for guild classification. The re ason is that foraging technique effectively predict s major activity pattern s of understory birds and, i n turn, their relationship with vegetation structural features when engaged in predator detection and escape (Lima 1993) Thus, not considering influences from possible predator community differences, the perception of predation risk in understory species with similar foraging technique should be expected to respond to habitat degradation and vegetation structural change in similar ways I did not explicitly consider species traits pertaining to escape behavior as Lima (1993) suggested for two reasons: (1) understory species expected to respond to mobbing stimuli in this study are all small bodied species that depend on forest vegetation, and are thus expected to share the same dive to vegetation escape technique identified by Lima (1993); (2) to some extent,
31 species escape behavior is implicitly considered in delineating foraging guilds because it is at least partially dictated by species foraging technique (Lima 1993). Definition of understory species and assignment of a n understory species to the gleaning or sallying foraging technique were based on information from field guides for the region (Smythies 1981, MacKinnon and Phillips 1993, Jeyarajasingam and Pearson 1999) and personal observation during this study To differentiate nonunderstory species, especially undergrowth species, and to assign foraging technique category, I followed the following principles: (1) a species is considered an undergrowth species only if it typically skulks in bushes or other forms of undergrowth vegetation that are usually below 2m in height; (2) i f a species is noted by field guides to use more than one foraging height (i.e. canopy or undergrowth in addition to understory ) or technique category I assigned it to the height or technique category that is more typically used according to my field experiences. For the focal study taxa of this study, I therefore classified two foraging guilds: understory gleaner, and understory sallier. I expected the percepti on of predation risk of the two foraging guilds, as well as species belonging to these guilds, to respond to fores t degradation and in particular altered forest vegetation structure in distinct ways. Their response should be in accordance with: (1) their dependence on different aspects of vegetation structure for predation avoidance, and (2) the likely changes in thes e vegetation aspects following habitat degradation. A denser canopy should confer more protection to understory birds from aerial attacks, and should result in reduced perception of predation risk, for both u nderstory gleaning and sallying guilds (Lima 1993) On the other hand, due to their foraging technique, understory gl eaners should prefer intermediate understory density
32 f or optimal predation avoidance: too sparse an understory may not provide enough protective cover, while too dense an understory may obstruct birds view in detecting predators (Lazarus and Symonds 1992, Lima 1993) In contrast, understory sallying species likely prefer open understory for optimal predat or avoidance, because t hey typical ly have extended periods of vigilance while perched, unmoving and searching for prey (Lima 1993). Thus they can tolerate less cover because they are cryptic (unmoving) much of the time which enhances their ability to detect predators without bei ng seen. Thus, a n intermediate understory density should minimize perception of predation risk for understory gleaning species, while intermediate to dense understory is expected to heighten perception of predation risk for understory sallying species Sam pling design. At each of the four study locations, I laid out a 250m x 250m grid system in a 1,000m x 1,250m ( i.e., 125 ha) plot, with 30 points as my sampling units ( Figure 22 B ). At / around these sampling points (see below), I conducted mobbing playbacks during mostly morning hours following a standardized protocol, and recorded the number and identity of mobbing participants, as well as their behavioral intensity. I additionally quantified canopy cover and understory density at these s ampling points to test for the role of vegetation structural changes in mediating possible mobbing behavioral differences between forest locations. Finally, I recorded the time of playback and the ambient temperature at the time of playback, to account for the possi ble effect of dail y energy state variation and times other effect s on mobbing intensity (Sieving et al. 2004) Data C ollection Playback protocol. My playback set up included the owl model as the playback center a nd two spe akers each connected via extension cords to an iPod player. I used
33 one speaker to play the main mobbing stimulus (i.e., the natural mobbing event recording), and the other speaker to simultaneously play the collared scops owl territorial call. The rate s of vocalizations in the playback s followed natural rates according to the original recording s, and I fixed the volumes of playbacks at a consistent volume that was judged by my experience to mimic natural volumes. Both recordings were put on a ten min loop. I always set up the owl model and its accompanying speaker on top of a pole at 3m above ground in a conspicuous position (i.e., the center of playback) and the other speaker in vegetation immediately close by at ~2m above ground facing the owl model. I camouflaged both speakers to minimize the influence of their presence on bird behavior. I conducted the vast majority ( 97.0% ) of playback s between 08:30 and 1 3 : 0 0 on day s without rain or strong wind. This insured that mobbing response from forest birds could be readily elicited and natural lighting allowed good observation of species identities and mobbing behavior (Hua et al. in review). Around each sampling point, I located a spot with immediate vegetation structure conforming to a set of standards to set up the playback system. Playback centers were thus not necessarily located on the sampling points, although they were close by. These standards for vegetation structure included : (1) there was a generally complete canopy cover, such that forest birds would not be deterred from participating in mobbing for fear of high risk of predatory attack from above; (2) mid and under story are relatively open, to allow the detection of the owl model by mobbing participants and to facilitate my field observation; and (3) there was some vegetation in the midand under story immediately surrounding the owl model, such that mobbing participants had access to perches should they decide to
34 approach the owl model close. I tried to keep consistent the immediate vegetation structure surrounding the playback center across all my sampling points in all study locations, even though the overall vegetation structure at the plot scale was expected to differ substantially across study locations. After setting up the pla yback system, I retreated to 10m away from the playback center, waited for two minutes before conduct ing the playbacks for ten minutes. I conducted all playback experiments following this same, standardized protocol across all sampling points. I conducted field work in HRF in mid to late June 2011, and in BBSNP in early to mid July 2011. Quantifying mobbing behavior intensity. I quantified the intensity of forest bird mobbing behavior only during the tenminute playback periods, using two approaches: (1) by gauging the propensity of an individual bird to approach close to the playback center (i.e. the owl model and its corresponding speaker ) assuming close approach to the perched predator represents intense mobbing from prey (e.g., Hurd 1996, Sieving et al. 2004, Langham et al. 2006) ; and (2) by scoring the conspicuousness of an individual birds mobbing behavior via focal sampling. For (1) I counted the number of bird sp ecies and individuals that at any point within the tenminute playback period came within a 15m radius from the owl model (hereafter referred to as the 15m scale) and a 3 m radius sphere centering on the owl model (hereafter referred to as the 3m scale) I considered the individuals entering the 15m scale as participating in mobbing and / or predator inspection behavior Of these individuals, the propensity of an individual to enter the 3m scale was represented as binary data in the form of 0 ( i.e., di d not enter 3m scale) or 1 ( i.e., entered 3m scale). I use d propensity rather than raw counts of
35 individuals entering the 3m scale because the latter was likely dependent on the bird community composition of the forest location (Turcotte and Desrochers 2002) which l ikely differe d among study locations To quantify mobbing intensity (2) I conducted focal sampling of bird individuals in an opportunistic way, due to the chaotic nature of mobbing events where birds were constantly moving around and hence rarely amenable to prolonged or standardized length of focal observation. Whenever I located a bird within the 15m scale, I visually followed it and described its behavior, until I lost it from sight or until enough information had been collected to allow the description of the birds typical behavior at that sampling point. I scored five aspects of each focal individuals behavior during its focal sampling span, including measures of movement and vocalization behaviors that collectively characterize the conspicuousness of the focal individual ( Table 21 ) I then tallied scores across these aspects to form a composite score of the birds overall mobbing behavioral intensity / conspicuousness. Because of the opportunistic nature of observations, the length of focal span and the moment when the bird was observed during the playback differed from bird to bird. For the purpose of comparison across forest locations, I expected no systematic bias in mobbing intensity / conspicuousness quantification as I used the same approach for all sampling points across the four study locations. The resultant composite mobbing intensity / conspicuousness score for each bird individual was a nonnegative integer within the range of 0 to 15. Quantif ying forest canopy cover and understory density To quantify forest canopy cover and understory density, I set up two concentric circular plots centering on each sampling point with a 5 m radius and a 10m radius, respectively I measured
36 p ercentage canopy cover at the center of plots with a typeA densiometer, taking four readings at every 90 from which I calculated the average reading (in percentage). I measured the average understory vegetation density at five height levels ( i.e., 1m, 2m, 3m, 4m, and 5m above ground). I measured vegetation density using a 100cm x 100cm density board that was divided eq ually into 36 squares, held vertically at different height levels and its number of cells that were blocked by vegetation counted f rom ten meters away (modified from Nudds 1977). Within each 10m plot, I measured v egetation density eight times: w ith the board held at plot center and the observer counting from the four diagonal positions on the 10m plot edge, the first of which was chosen randomly ( resulting in four readings ); and with the board held at the four diagonal positions on the edge of the 5m plot, the first of which was chosen randomly, and the observer counting from the opposite diagonal positions on the edge of the 5m plot, again ten meters away ( resulting in another four reading s). I then took the average of these eight readings for each height level to represent the average unders tory density of the plot at that height level (in percentage). I averaged the understory density across 1m and 2m heights to obtain the density for lower understory, and across 3m, 4m, and 5m heights to obtain the density for upper understory. To provide a meaningful measure of the canopy cover and understory density at a scale that was relevant in influencing forest birds perception of predation risk I averaged measures from individua l sampling points across the neighboring sampling points ( i.e., the eight immediately neighboring sampling points surroundi ng each sampling point in question; sampling points at the edge of the study plots had fewer neighboring points). The rationale for this was that by incorporating information on
37 vegetation structure from immediately surrounding sampling points, the resultant average measures provided a better representation of the habitat in which birds participated in mobbing at each sampling point were living in and influenced by. Statistical A nalyses I first tested for the difference in mobbing intensity of forest birds (at the species and guild levels) among the four study locations, to test the hypothesis that avian perception of predation risk differed in forest habitats with different degrees of degradation (Hypothesis 1) I used generalized linear mixed models (GLMM ) or linear mixed models (LMM) for these analyses (Zuur et al. 2009). I additionally tested whether forest vegetation structural changes in canopy cover and understory density could explain the difference in forest birds mobbing intensity (again at the species and guild levels) among study locations (Hypothesis 2) by : (1) testing for the difference in canopy cover and understory density among study locations, and (2) testing for the relationship of birds mobbing intensity with canopy cover and understory density. I f the observed mobbing intensity difference among study locations (of Hypothesis 1 ) was consistent with what would be predicted from the combined results of (1) and (2) there would be strong support for Hypothesis 2. I used analysis o f variance (ANOVA) for (1), and again used GLMM or LMM for (2). For species level analysis, I focused only on understory species that had at least six individuals entering the 15m scale tallied across all sampling points in at least two of the study locat ions. F or guild level analysis, for the inclusion of each understory species into the two foraging guilds, I included the same number of indi viduals across the four study locations Since the number of individuals was determined by the lowest number of the four study locations, for locations with more indi viduals I selected
3 8 individuals that were t o be included in the guilds randomly. This was to ensure that any difference observed in the guildlevel perception of predation risk among study locations was due to difference in birds mobbing behavior, rather than differences in the identity and composition of species making up the guild. For GLMM, I used binomial error distribution and a logit link function to analyze the propensity of individuals to enter the 3m scale given that they were within the 15m scale. To analyze the b ehavioral conspicuousness of individuals on which I did focal sampling, I use d LMM for species level analysis, but used GLMM with poisson error distribution and a log link function for guildlevel analysis for its better fit of data structure. Global model structure for fixed effects is given in Equation s 2 1 2 2, and 23 In all models, I used the time of playback (represented by the number of minutes since sunrise, centered and scaled) to account for the potential effects of birds energetic state and other timerelated effects on mobbing motivation. I did not include ambient tem perature at the time of playback because it was highly correlated with the time of playback ( rPearson: 0. 7342). I used the number of bird individuals that entered the 3m scale at any point during the tenminute playback period (except for the bird individual in question) to account for the effects of social information. The number of birds at 15m scale was highly correlated with that at the 3m scale ( 116 SE = 0.0 53 P = 0.03 1 ) so only the number at 3m scale was used (using the number at 15m scal e did not influence the results) In addition, in models testing the relationship between avian mobbing intensity and vegetation s tructure I included the quadratic term of understory density in the global models for understory gleaning species and guild ( Equation 23) to detect the possibility of an optimal level of understory density that confers the lowest
39 level of predation risk perception for this foraging guild For random effects, I included the identity of sampling point as a random effect for all species level analyses, because individuals recorded at the same sampling point were likely to behave in similar ways. I included the identity of sampling point and the identity of species as two random effects for all guild level analyses, because individuals of the same species were also likely to behave in similar ways. Mobbing intensity ~ Time + Group size + Study location(Equation 21) Mobbing intensity ~ Time + Group size + Ca nopy cover + Understory density .(Eq uation 22) Mobbing intensity ~ Time + Group size + Canopy cover + Understory density + Understory density 2 (Equation 23; only for understory gleaning species / guild) For ANOVA, I used the upper understory density to be an overall representation of und erstory density, because it was highly correlated with, and should represent, the lower understory density ( rPearsonI used Akaike information criterion (AIC) for model selection, and considered as best models those that were within 2.0 AIC from the lowest AIC score (Burnham and Anderson 2002). I based my conclusion on the models with the smallest AIC values, unless the covariates left out were shown to have a significant effect by other best models. In that case, I based my conclusion on the best model with the smallest AIC that included the covariates in question. I conducted all analyses in R.2.13.1 (R Development core team 2011), with the packages lme4 ( versio n 0.99937542; Bates et al. 2011) for GLMM an 2012) for AICc based model selection. : 0.7271). I used the identity of study locations as the only predictor variable in the ANOVA.
40 Results In all, I conducted mobbing playbacks at 30, 22, 24 and 25 sampling points in each of the 125ha sampling plots at PRIM, DEG1, DEG2, and DEG 3, respectively. A total of 9 92 understory bird individuals belong ing to 45 species responded to playbacks by entering the 15m scale with inspection / mobbing behavior ( Appendix A; the total number of bird individuals and species that responded to playback, i.e., including canopy and undergrowth birds, were 1,142 bird individuals and 72 species respectively ; all results and discussion below focus solely on understory species ) With t he exception of very few individuals, most of the responding species were of small to medium body size (typically with a body length below 25cm ) I obtained focal sampling on 565 indi viduals belonging to 42 species. In all, I was able to conduct species l evel analyses o f birds mobbing intensity on nine species (six gleaners and three salliers) in addition to g uild level analyses on the two foraging guilds. The list of responding species, their foraging guild category, tallied numbers at each study locati on, and the species and number of individuals that were included in the species and guildlevel analyses are given in Appendix A Understory Bird Mobbing B ehavior I ntensity D iffered among Study Locations Two of the n ine individually analyzed species showed differences in mobbing intensity among study locations: one understory gleaner and one understory sallier. Both species exhibited less intense mobbing behavior in more degraded forest locations ( Appendix B ). The time of playback and group size also partially explained mobbing intensity in several cases, with a notable positive effect of group size (Appendix B ). Understory gleaning guild exhibited significantly more intense mobbing intensity in the undegraded location PRIM than all the degraded l ocati ons (DEG1, DEG2, and
41 DEG3). Birds in PRIM were significantly more likely to enter 3m scale and thus approach the predator owl model at close range than birds in all the degraded forest locations ; birds mobbing intensity did not differ among the degrade d locations (Table 22 Figure 23 A) Analysis of birds behavioral conspicuousness produced two competing best models (Table 22 ). One model suggests that birds in PRIM exhibited significantly more conspicuous mobbing behavior than birds in all the degraded forest locations while mobbing behavior conspicuousness did not differ among the degraded locations (Table 22 Figure 23 B). The other model suggests that birds mobbing behavioral conspicuousness did not differ among locations, but was explained by g roup size: a bird would exhibit more conspicuous mobbing behavior if more of its mobbing companions entered the 3m scale (Table 22 ). The latter best model however, should actually support the former best model, because group size was strongly associated with location in a pattern consistent with the former best model ( results not shown) U nderstory sall ying guild did not exhibit betweenloc ation differences in mobbing intensity whether it was measured by the propensity of birds to enter the 3m scale, or the behavioral conspicuousness (Table 22 ) However, g roup size had a significant negative effect on the behavioral conspicuousness of mob bing birds (Table 22 ). V egetation Structure E xplained Mobbing I ntensity D ifferences among L ocations Canopy cover and understory density differ ed among study locations. Canopy cover and und erstory density both showed strong differences between study locati ons. More degraded forest location had significantly lower canopy cover ( Figure 24 A) Understory density showed pronounced differences among all study locations, with the most degraded location having the lowest understory density but one of the
42 degraded locations in more advanced regeneration (DEG1) having the highest understory density (Figure 24 B) Canopy cove r and understory density explained understory bird mobbing behavior intensity C anopy cover and / or understory density explained the mobbi ng beh avioral intensity of six of the n ine individually analyzed species (App endix C ). B irds mobbing intensity tended to increase with increasing canopy cover and decreasing understory densit y. The time of playback and group size als o explained mobbing intensit y in many cases, with a notable positive effect of group size (Appendix C ). Understory gleaning guild exhibited significantly more intense mobbing intensity with the increase of canopy cover and decrease of understory density. In forest habitats with higher canopy cover, birds were significantly more likely to (1) enter 3m scale and (2) exhibit more conspicuous mobbing behavior (Table 23). In forest habitats with increased understory density, birds were significantly more likely to enter 3m scale, and m arginally more likely to exhibit more conspicuous mobbing behavior (Table 23). I did not find a nonlinear relationship between birds mobbing intensity and understory density (i.e., quadratic term of understory density was not relevant ; Table 23 ). Understory sallying species exhibited marginally less conspicuous mobbing behavior in forest habitats with increased canopy cover but their conspicuousness did not change with understory density (Table 23) Birds propensity to enter the 3m scale did not change with either canopy cover or understory density (Table 23) Group size again had a significant negative effect on birds behavioral conspicuousness, and time had a marginally negative effect (Table 23).
43 Discussion Predat i on R isk Perception of Understory B irds I ncrease in D egraded Locations My results showed that the mobbing behavioral intensities of understory gleaning birds and one understory sallying bird species were substantially lower in degraded forest s than in primary forest This sugges ts that forest habitat degradation has led to an increased level of predation risk perception in these bird species An altered perception of predation risk can constitute an important nonnumerical response of animals to habitat degradation, which may in turn contribute to and / or intensify animals numerical responses Animals adjust risk sensitive behaviors in accordance with their perception of predation risk (Lima and Steury 2005). Under a heightened risk perception, animals responses via behavioral, physiological and other mechanisms are expected to mediate substantial effects of predation risk even without them being directly killed by predators leading to t rait mediated or nonlethal effects of predation risk ( Lima 1998, Agrawal 2001) Such tr ait mediated effects are shown by an increasing volume of literature to have profound influences on a diverse array of animal ecology (Pressier et al. 2005, Cresswell 2008) including habitat selection, movement foraging efficiency, body condition, reproduction, etc. (e.g., Whittingham et al. 2006, Creel et al. 2007, Creel et al. 2009, Morosinotto et al. 2010, Zanette et al. 2011). Many of these influences are almost certain to produce negative consequences at the population and community levels (Lind and Cresswell 2005, Cresswell 2008, Zanette et al. 2011, Chapters 3 and 4 of this dissertation) Thus, when habitat degradation changes animals perception of predation risk like in the case of understory birds in my study, the likely resultant trait mediated predation risk effects may bring about further
44 numerical impacts in addition to those mediated through other pathways such as resource changes ( e.g., Johns 1986, Thiollay 1992). In assessing animal responses to habitat degradation, the vast majority of studies have focused on animal numerical responses (i.e., changes i n occurrence or abundance) N on numerical responses particularly those that are likely to produce fitness and demographic consequences like the altered risk perception demonstrated by my study should also be an indispensable area of inquiry In addition to helping provide a fuller understanding of animals response to habitat degradation ( van Horne 1983, Hall et al. 199 7 ) an understanding of animals nonnumerical response s may help to shed crucial lights on the mechanisms by which animals exhibit numerical responses. A potential caveat against using avian mobbing intensity to represent birds perception of predation risk in my study pertains to the assumption that mobbing birds did not differ in body condition and energy state across study locations. Mist netting records from the same study locations on birds body mass (as a surrogate for body condition; Green 2001) allowed the test of this assumption in only four understory species o ne of which showed significantly heavier body mass in the undeg raded habitat (PRIM). It is unknown whether and to what extent the mobbing individual s (or the species in general) included in the analyses differed in body condition and energy state among the study locations. If many of the individuals from degraded habi tats had poorer body conditions than those from the undegraded habitat, as some studies have suggested ( Olupot 2000, Lucas et al. 2006) their reduced mobbing intensities in degraded habitats may be a result of stronger energy constraint rather than of inc reased perception of predation risk. However, as shown in Table 23 (also see below), birds
45 mobbing responses were strongly predicted by vegetation structure, in ways consistent with the biological predictions when mobbing intensity was assumed to represent birds perception of predation risk. Such a strong relationship between mobbing intensity and vegetation structure would be unlikely to occur if the observed mobbing responses were largely driven by birds energetic constraints. This should lend strong support to the representation o f predation risk perception by avian mobbing intensity. V egetation Structure E xplained Increased Risk Perception in D egraded L ocations My results also showed that forest vegetation structure at lea st partially explained the increase in perception of predation risk for understory gleaning birds in degraded forest habitats. This is because (1) degraded f orest s had significantly reduced canopy cover and altered understory density compared to undegraded forest; and (2) the perception of predation risk in understory gleaning birds was strongly predicted by canopy cover and understory density: birds perceived increased perception of predation risk under reduced canopy cover and increased understory density (1) and (2) thus combined to predict that understory gleaning species would have increased perception of predation risk in degraded forest habitats that have reduced canopy cover and increased understory density, which was strongly confirmed in the degr aded location DEG1 Locations DEG2 and DEG3 however, had reduced canopy but also reduced understory density than the undegraded location, which would respectively predict increased and reduced perception of predation risk These conflicting expectations of birds perception of predation risk probably accounted for the less pronounced and / or more ambiguous differences of the risk perception of understory gleaning species between these locations and the undegraded location (Figure 2 3 ) The fact that unders tory gleaning species largely exhibited increased perception of predation risk in
46 DEG2 and DEG3, also suggests that canopy cover was probably more dominant in determining birds perception of predation risk. Unlike understory gleaning species and contrary to predictions the mobbing behavior of understory sallying species did not differ among forest habitats with difference degrees of habitat degradation (Table 22), nor did it exhibit much dependence on forest canopy cover and understory density ( Table 23 ). Because of their sallying technique for foraging, which entails spending a large proportion of their time sitting quietly on perches and scanning the environment, sallying species also depend on such still scanning technique for vigilance behavior to a large extent (Lima 1993) This behavior stereotype held true for these species even during mobbing events: while gleaning species tended to move around in vegetation incessantly, simultaneously engaging in excited mobbing behaviors and vigilance behaviors, sallying species tended to show distinct spans of still scanning and mobbing that were much more clear cut. In addition, the mobbing behavior of sallying species tended to be predominantly characterized by sallying fl ights, coupled with some visual display and vocalization, all of which were typically much less frequent in occurrence compared to the constant rustling around typical of gleaning species (Hua, pers. obs.). These behavioral characteristics probably allowed sallying species to efficiently maintain an effective vigilance level during mobbing events that to a large extent does not depend on habitat structure Animals perception of predation risk is not only determined by habitat structure, but is to a large extent also determined by the predation pressure from the predator community. Importantly, in the context of habitat degradation, predators may exhibit
47 numerical responses that lead to a changed predator community ( e.g., Jullien and Thiollay 1996) and thus changed predation pressure on prey. In addition, in the case of my study, there may be some intrinsic differences in the predator community among my study locations other than for the habitat degradation factor. T he undegraded location PRIM in particular, was at least 400km apart from the degraded locations inside HRF, and had different edaphic and hydrological features (Hua pers. obs.). The observed differences in avian risk perception between degraded and undegraded forest habitats could therefore also i nvolve contribution from difference s in the predator community in addition to differences in vegetation structure as discussed above. This alternative explanation probably accounted for some apparent discrepancies in my results: in several species, while mobbing behavior intensity had a clear dependence on vegetation structure, it did n ot differ among study locations ( Appendices B and C ). It thus appeared that for these species, potential differences in mobbing behavior predicted from vegetation structure had been confounded by other factors, which could likely be the among site differences in the predator community. Conclusion F orest habitat degradation led to an increased level of predation risk perception in understory birds, which could be at least part ially explained by forest vegetation structural changes. This suggests that animals could respond to habitat degradation in behaviorally mediated, nonnumerical ways that are likely to produce important fitness and demographic consequences I conclude that animals non numerical responses should also be an indispensable area of inquiry for studies that aim to assess animal s responses to habitat degradation
48 Table 21 Aspects of mobbing behavior scored during focal sampling to quantify mobbing intensity / conspicuousness Behavioral aspect Definition Score range Proximity Whether the focal individual approach within 3m and 1m of the owl model 0, 1, or 2 Movement Frequency of body movement, including perch changes and flights 0, 1, 2, or 3 Flicking Frequency of wing flicking and tail flicking behavior 0, 1, 2, or 3 Vocalization Frequency of overall vocalization, irrespective of vocalization type 0, 1, 2, or 3 Other behavior Frequency of other behavior types, including preening and / or foraging 0, 1, 2, or 3 Overall conspicuousness Tallied score across all five behavioral aspects 0 ~ 15 ** : All behaviors that occurred at any moment during the focal sampling were counted. : I used nonnegative integers to score the intensity of different aspects of birds mobbing behavior. With the exception of Proximity measure, the range of scores for each behavioral aspect was between 0 and 3: 0 zero frequency (behavior did not occur), 1 low frequency (behavior occurred occasionally), 2 medium frequency (behavior occurred relatively frequently), 3 high frequency (behavior occurred incessantly). : Score for the Proximity measure: 0 bird did not approach within 3m from the owl model, 1 bird approached to within 3m but outside of 1m from the owl model, 2 bird approached to within 1m from the owl model. : If the focal individual produced harsh, agitated, and easily locatable scolding calls at any moment during the focal sampling, I would add 1 more point to the Vocalization score, to account for the fact that such scolding calls would potentially make the mobbing individual even more conspicuous *: Because behaviors under this category are not ty pical mobbing behaviors, it is hard to say whether their presence indicate a more intense mobbing behavior. I included them in the assessment of behavioral intensity / conspicuousness for the reason that regardless of birds intention in displaying them, t heir presence added to the conspicuousness, and therefore potential exposure to predation risk, of the focal individual. **: Because of the added 1 point for scolding call in the Vocalization aspect, the total, composite score of an individuals behavioral intensity fell in the range of 0~15, with larger scores indicating stronger mobbing behaviors.
49 T able 22 Structure o f the best models for the guildlevel difference of avian mobbing intensity among study locations Guild Mobbing intensity measure Sample size Time Group size Location PRIM DEG1 DEG2 DEG3 Understory gleaners Entry into 3 m scale 71 0.397 (0.343) a 2.424 (0.495) b* 2.663 (0.522) b* 1.709 (0.417) b* Conspicuousness 32 1.303 (0.141) a 0.931 (0.154) b* 1.069 (0.148) ab 1.014 (0.151) b Conspicuousness (Competing best model) 32 0.049 (0.024) Understory salliers Entry into 3 m scale 9 Conspicuousness 6 0.186 (0.091 ) Table lists predictor variables that entered the best model for each analysis, showing covariate values and their standard errors (in parenthesis) for those variables whose effect was at least marginally significantly different from zero ( i.e., Wald test P P indicates that the variable did not enter the best model. : Sample size refers to the number of bird individuals included in the respective guild at each location. : Differences in mobbing intensity between study locations are represented by different letters to the upper right of each covariate estimate, at the P ically different from each other. indicates differences with P
50 Table 23 Structure of the best models for the guildlevel analyses of the relationship between avian mobbing intensity and vegetati on structure Guild Mobbing intensity measure Sample size Time Group size Canopy cover Understory density Understory density ^2 Understory glean ers Entry into 3 m scale 71 0.741 (0.247) 0.600 (0.252) Conspicuousness 32 0.180 (0.063 ) 0.105 (0.060) Understory sall iers Entry into 3 m scale 9 NA Conspicuousness 6 0.250 (0.148) 0.238 (0.087) 0.289 (0.161) NA Table lists predictor variables that entered the best model for each analysis, showing covariate values and their standard errors (in parenthesis) for those variables whose effect was at least marginally significantly different from zero ( i.e., Wald test P P indicates that the variable did not enter the best model NA indicates that the variable did not apply to the guild in question. : Sample size refers to the number of bird individuals included in the respective guild at each location.
51 Figure 21 Theoretical relationship between avian mobbing intensity and two levels of predation risk. A) predation risk from the perched predator that is the target of mobbing. B) ambient predation risk, i.e., predation risk from predators other than the perched predator
52 Figure 22 General study design. A) Map of study locations. B) L ayout of sampling design.
53 Figure 23 Relationship between mobbing intensity of understory gleaning species and study location with different degree of habitat degradation. A) Mobbing intensity as measured by the propensity of birds to enter 3m scale (N = 71 / location) B) mobbing intensity as measured by the behavioral conspicuousness of birds (N = 32 / location) PRIM: primary forest; DEG 1 and DEG 2 : less degraded forest s in more advanced state of regeneration; DEG 3 : most degraded. Statistically significant differences are represented by different letters ( P 0.1), with indicating P + SE.
54 Figure 24 Relationship between vegetation structure and study location with different degree of habitat degradation. A) canopy cover. B) understory density. (N = 30, 22, 24 and 25 for PRIM, DEG1, DEG2, and DEG3, respectively.) PRIM: primary forest; DEG1 and DEG2: less degraded forests in more advanced state of regeneration; DEG3: most degraded. Statistically significant differences are represented by different letters ( P and # indicating P + SE.
55 CHAPTER 3 TOO RISKY TO SETTLE: PERCEIVED PREDATION RISK ON ADULTS AND OFFSPRIN G ALTERS AVIAN COMMUNITY STRUCTURE Introduction Understanding factors determining community structure has long been a focus of ecology. Predation risk was proposed i n early theoretical models as a critical force driving the patterns and dynamics of animal communities (Connell 1975, Menge and Sutherland 1976), and its role has subsequently been tested, and supported, in a variety of ecological systems. Most of the empirical test s however, have focused on aquatic systems, and to a lesser extent, terrestrial i nvertebrate systems ( earlier studies reviewed in Sih et al. 1985). Tests of whether predation risk can structure terrestrial vertebrate communities remain scarce: there are only a handful of observational studies ( e.g., Martin 1988a,1988b, Norrdahl and Kor pimaki 1998, Forsman et al. 2001, Banks et al. 2008) and even fewer experimental tests (Suhonen et al. 1994, Fontaine and Martin 2006). Predicting how predation risk can structure prey communities can be facilitated by understanding how species traits may alter risk perception and the costs and benefits of responding to such risk. Both theoretical and empirical studies suggest that life history and natural history traits are linked to animals sensitivity to predation risk (Blumstein 2006). Lifehistory th eory predicts that compared with shorter lived and more fecund species (aka faster species), longer lived and less fecund species (aka slower species) should value future reproduction over current reproduction because they have higher residual reproduc tive values (sensu Williams 1966; Pianka and Parker 1975). Consequently, when faced with the tradeoff between their own survival and that of their offspring, slower species should value their own survival over that of their offspring,
56 while the opposite is expected of faster species (Ricklefs 1977). Empirical work with a variety of taxa confirms these predictions for reproductive behaviors ( e.g., Ghalambor and Martin 2000, 2001). In addition, natural history traits such as body size, competitive advantage, and social behavior can often predict prey response to predation risk ( e.g., Menge and Sutherland 1976, Gotmark and Post 1996, Blumstein 2006). Nonetheless, experimental tests of whether species traits can predict community changes to variation in perceiv ed predation risk remain absent. I present a community level, experimental study to test the effects of different types of predation risk on forest birds of the southeastern United States. I ask: (1) can perceived predation risk shape breeding bird commu nity structure, and (2) can life history and natural history traits predict prey response to perceived risk and hence help to explain community responses in terms of species richness and composition? I manipulated cues of three avian predators that preferentially prey on either nests, adult birds, or both, and conducted repeated community surveys to characterize species and community level responses. I tested for the effects of perceived predation risk on community richness and composition by estimating the abundance and / or occurrence of target species. I further tested for the relationship between species response and slow/fast life history trait and body size as predictors that may explain community responses to risk of predation. Methods Study A rea I conducted fieldwork in the sandhill habitat of northcentral Florida (29.4N, 82.0W), situated within the Ordway Swisher Biological Station, a 3,700ha managed research preserve, between late February and mid August, 2010. Forest vegetation was
57 dominated by longleaf pine Pinus palustris and to a lesser extent turkey oak Quercus laevis with wiregrass Aristida beyrichiana in the understory. Other typical species included a variety of oaks, herbs, and grasses (FNAI 2010). These habitats received prescribed burning in units of ~20 ha every two to five years to maintain longleaf pine and wiregrass dominated conditions. Experimental D esign In 24 9ha (300m x 300m) plots, I applied predator vocalization playbacks daily throughout the avian breeding season to incr ease the perceived predation risk of the prey bird community. I applied vocalizations of three species: Coopers hawk Accipiter cooperii (Hawk hereafter), blue jay Cyanocitta cristata (Jay hereafter), and E astern screech owl Megascops asio (Owl hereafter). In forested parts of the southeastern United States, all three species are important predators on other birds, and have discernible, relatively frequent vocalizations. Importantly, they represent contrasting types of predation risk to prey, spanning the gamut of being primarily an adult predator (Owl) or nest predator (Jay), or both adult and nest predator (Hawk), and differ in preferred prey species. The Coopers hawk is a major predator of adult birds (Lima 1993, Curtis et al. 2006), and in my study site also regularly rai ds opencup nests of passerines and account s for the majority of nest predation for at least one species (Stracey 2010). The blue jay is an important nest predator of opencup nesting birds, while its predation on adult birds is infrequently observed (Tarvin and Woolfenden 1999). The E astern screech owl is primarily a predator of adult birds when it preys on bird s, and less frequently depredates nestlings. It targets mostly small birds but occasionally attacks larger prey (Gehlbach 1995).
58 I assigned plots to one of four treatments (playback with Hawk, Jay, or Owl vocalizations, versus no playback i.e., Control; N = 6/treatment) using a randomized block design, with care taken to ensure that plots within blocks were in a similar post fire recovery stage with similar vegetation features. I delineated a central 4ha (200m x 200m) core within each plot, with which I applied predator vocalization playbacks and conducted bird surveys (see below). Each plot was t edge, and spaced I did not attempt to remove natural predators or eliminate predation events. I used two playback stations on each plot's core area. Each playback station consisted of a portable CD player mount ed in a camouflaged box at 3m above ground wired to a deepcycle marine battery and a timer (Fletcher 2007, 2008). Within plots, stations were spaced ~100m apart and faced toward the plot center. To minimize habituation to playback, I changed the location of stations biweekly throughout the breeding season (also see below). Control plots had stations that produced no sound, which were shifted in the same way as treatment plots. I chose to provide no sound for Control plots instead of using procedural playback control because of unknown effects from potential procedural control vocalizations ( e.g., Nocera et al. 2008, Fletcher 2009). If species were similarly responding to playbacks in general ( i.e., the rationale for using procedural playback controls), I wo uld expect species to respond to all playback treatments in a similar way, but this was not observed (see Results). I prepared unique playback files for each plot, with vocalization from no more than three individuals for each file to avoid mimicking the presence of too many predators (Kroodsma 2001). Each playback file consisted of a primary vocalization
59 type ( i.e., the most frequent vocalization used by the species, such as territorial calls), supplemented by a lesser amount of a secondary vocalization type ( i.e., vocalization that is less frequently used but that still advertises the presence and / or activity of the species ) The amount of vocalization provided in each playback file and provided daily on each plot was determined according to natural vocalization rates suggested by the original recordings. T he daily amount of playback on each plot was 232 primary vocalizations plus 122 secondary vocalizations for the Hawk treatment, 720 primary vocalizations plus 160 secondary vocalizations for the Jay treatment, and 260 primary vocalizations plus 144 secondary vocalizations for the Owl treatment. I circulated playback files between plots within a given treatment biweekly to further reduce habituation to playback. The detailed playback scheme is provided in Appendix D I played vocalizations daily from March 5th until July 23rd, spanning the entire breeding season and the prebreeding settlement phase of most bird species in this forest system (ACA 2010). I considered the following lifehistory and natural history traits as relevant to prey sensitivity to predation risk: annual fecundity, adult survival rate, and adult body mass. Fecundity and adult survival are major components of species slow/fast lifehistory strategy (Gaillard et al. 1989). I con sidered body mass ( as a proxy for body size) because the relative size of predator versus prey generally determines prey vulnerability (Vezina 1985, Dial et al. 2008). Hawk is likely to favor medium sized prey (Gotmark and Post 1996), while the smaller Owl probably prefers smaller prey (Gehlbach 1995). Data C ollection I collected plot level avian community data throughout the study using repeated transect surveys (6:00 11:00 am, 20 April 21 July, 2010; Bibby 2000). I surveyed
60 each plot five times (approximately three weeks apart), except for three plots (one each for Hawk, Owl and Control) and two additional plots (one for Hawk and Jay each) for which surveys were halted after the 2nd and 3rd surveys, respectively, due to prescribed burning. On the core 4 ha portion of each plot, I surveyed birds along two parallel transects placed 100m apart, at a standard speed of 10m/min (Bibby 2000). I recorded all adult birds seen or heard from the transect, the surveyors location at the time of detection, and birds angle and distance from the surveyor. I used bird records within the boundaries of each 9ha plot to insure adequate inclusion of plot resident birds exposed to treatments and to accommodate possible georeferencing errors. I collected life history and n atural history trait data on natural populations of prey from literature and other sources. Details on data and data sources are provided in Appendix F A generally inverse relationship exists between fecundity and adult survival (Bennett and Harvey 1988, Saether 1988); this was supported by data on my study species (rPearsonStatistical A nal yses : 0.44). I thus used only annual fecundity to represent species slow/f ast life history trait because fecundity data are probably more reliable than survival estimates. I made the following predictions based on predator prey and lifehistory theories. (1) Predator treatments should reduce plot level species richness; (2) treatments should cause shifts in community composition. In terms of species level response to pr edation risk that contributed to community structure changes, I predicted that (3) slower species should respond more negatively to adult predation risk and faster species to nest predation risk; and (4) medium sized species should respond more negatively to Hawk
61 (than smaller and larger species) and smaller species to Owl (than larger species). I first analyzed species response to treatment in abundance/occurrence and detection probability, based on which I tested for community responses. I further tested the relationship between speci es response in abundance and life history and natural history traits. I did all analyses using R.2.13.1 (R Development Core Team 2011). Specieslevel re s ponse to treatment I used N mixture models that explicitly accoun t for imperfect detection to test for species plot level abundance response to treatments (Royle and Dorazio 2008). Birds may respond to treatments with changes in detection probabil ity rather than abundance. T hese two responses should be separated to understand how species respond, especially for making community level inferences. N mixture models separate these responses by separately parameterizing the process component that represents the abundance of animals, and the observation component that represents the detection of animals (Royle and Dorazio 2008) The process and observation components then combine to specify the number of detected animals based on which o bserved data are used to estimate the values of model p arameters via maximum likelihood estimatio n (Royle and Dorazio 2008) A critical consideration of N mixture models that dictates model structure pertains to the assumption of population closure, i.e., to what extent the surveyed population size remains unchanged (aka, population remains closed) across repeated surveys (Rota et al. 2009). To avoid unsupported assumptions about population closure, I contrasted three model categories with decreasing strictness of population closure and thus increasing model complexity: (1) Closed models (assuming closure across all surveys; Royle 2004); (2) Robust Design models (assuming closure within but not between clusters of survey periods
62 aka, primary periods ; sensu Pollock 1982; Royle 2004); and (3) Open models (assuming no closure across all surveys ; Dail and Madsen 2011). For all models, I included treatment as the only candidate covariate for abundance (i.e., the process component of the N mixture models) For detection probability (i.e., the observation component of the N mixture models) I includ ed treatment as the only candidate covariate, and additionally included time, Julian date, and the quadratic term of Julian date of survey as nuisance covariates. The inclusion of the quadratic term of Julian date was because birds may be the most detectable during territory establ ishment and more elusive during subsequent nesting activities ( Wilson and Bart 1985, Royle et al. 2005) Each model category thus had four candidate models that I considered, depending on whether the candidate covariates were incl uded. For each species, I started with Closed models, and moved to the next more complex model category only when the simpler models did not fit. I used three criteria to evaluate the fit of successfully converged models. First, because N mixture models tend to overestimate abundance and underestimate detection probability when closure is inappropriately assumed (Dail and Madsen 2011), I accepted only models that produced estimates that largely aligned with my biological knowledge of the species ( e.g. know n territory sizes of species). Second, I accepted only models that produced estimates with coefficient of variation < 5.0. Third, if models passed the above steps, I used Akaikes Information Criterion (AIC) to select the best models that were within two A IC units from the top accepted model (Burnham and Anderson 2002). For species that did not fit N mixture models but were amenable to occurrence analysis ( e.g., did not occur in all plots), I analyzed their occurrence using similar hierarchical models (Royle
63 and Dorazio 2008) and the same model categories, structures and fitting process as the N mixture models (MacKenzie et al. 2005). Of the total 36 breeding bird species detected during my stu dy (including 24 prey species), I focused on eighteen regular prey species that had at least ten detections across all surveys on al l plots combined (see Appendix E for species list). I fitted Robust Design models using my own R codes and the unmarked package (version 0.94) for Closed and Open models. Detailed model specifi cation is provided in Appendix G Community level response to treatment. I tested for community responses to treatment based on species level Nmixture and occurrence models. I estimat ed plot level abundance or occurrence state for each species based on the best abundance/occurrence models and observed count /occurrence data (see Appendix G for details). This produced abundance estimates for thirteen species, and occurrence state estimat es for one other species (Results, and Appendix H ). I further derived the plot level occurrence states for the thirteen species from abundance estimates ( Appendix G ). To analyze bird community species richness response to treatments, I tallied occurrence s tates across species to obtain plot level species richness, with community defined as the collection of these fourteen species. As ten out of these fourteen species were best explained by Robust Design models (Results, and Appendix H ), which stipulated two primary periods within which population was closed, each plot had two richness estimates, one for each primary period. I then used generalized linear models (GLMs) with treatment as the only candidate covariate, using a log link, Poisson error distributio n, and an exchangeable correlation structure to account for the repeated
64 measures of species richness within a plot ( i.e., Generalized Estimating Equations, GEE). I conducted analysis using the yags package (version 4.02.1), and used the Quasi likelihoo d under the Independence model Criterion (QIC) for model selection, selecting the models with the lowest QIC scores (Pan 2001). To analyze community composition response to treatment, I constructed community profiles with abundance estimates of the thirtee n species, with community defined as the collection of these thirteen species. Again, each community had two profiles, one for each primary period. I then used twodimensional, nonmetric multidimensional scaling (NMDS; Kenkel and Orloci 1986), and analyzed treatment effects using analysis of similarity (ANOSIM) based on the Bray Curtis dissi milarity index without data transformation, with 1,000 permutations (Bray and Curtis 1957, Clarke 1993). After finding a treatment effect on community composition (see Results), I additionally used the similarity percentage analysis (SIMPER) to evaluate which species contributed the most to community differences (Clarke and Warwick 2001). Species identified by SIMPER analysis would either be those with variable abundance within treatments, or those that contributed to betweentreatment differences (Warton et al. 2012). It was these latter species that I was interested in. I conducted analysis with the vegan package (version 2.119). Relationship between species response and traits. I tested for the relationship between species life history and natural history traits and their response to predation risk that could explain the community level responses, again based on the species level Nmixture models. For each species, if the model indicating a treatment effect on abundance was among the best models, I obtained from it the species
65 response in the form of the ratio between the mean plot level abundance of treatment over that of Control, on a log scale (Appendix H ). Otherw ise, its response in abundance would be zero (Appendix H ). I then used linear regression to analyze the relationship between species response and functional traits, unless the regression assumptions were not satisfied, in which case I used GLM with a gamma distribution, on exponentiated ( i.e., back transformed) response data. I conducted the analysis using two approaches: 1) by comparing how response to individual predator types relate to functional traits; and 2) by first calculating the pair wise difference in a species response to different predator types (Hawk Jay, Hawk Owl, and Owl Jay), then comparing how this pair wise difference related to functional traits. The second approach was necessary for assessing the sensitivity to different types of predat ion risk within a species ( e.g., risk against its own survival versus that of its offspring). I excluded the brownheaded cowbird from analysis because it is a nest parasite with ext remely high fecundity, which likely make s it respond to predation risk in a different way than other species (Lowther 1993). All linear regressions included annual fecundity and body mass in the global models. I additionally included a quadratic term of body mass in the global models involving Hawk treatment because the Coopers hawk likely prefers medium sized prey over small or big prey (Gotmark and Post 1996). I log transformed fecundity and body mass to reduce the influence of extreme trait values. I used the Akaike information criterion corrected for small sample size (AI Cc) for model selection, and chose the best model with the lowest AICc scores (Burnham and Anderson 2002).
66 Results O ut of the eighteen focal species I wa s able to analyze abundance response to perceived predation risk for thirteen species and occurrence res ponse for one other species E leven species responded to perceived predation risk by having altered abundance, occurrence, and / or detection probability, mostly in negative ways, although some unexpected positive responses also occurred, especially under Hawk and Jay treatments (Appendices E and F). In addition, responses of these eleven species were attributable to altered abundance for one species altered occurrence for one other species altered detection probability for three other species and either altered abundance or altered detection probability for six other species according to similar AIC values of competing models (Appendix H ). Confirming prediction (1) that perceived predation risk should lower plot level species richness, Jay and Owl treatments significantly reduced plot level species 0.099, SE = 0.035, P 0.108, SE = 0.029, P < 0.001), respectively. Hawk treatment did not appear to have a strong effect 0. 030, SE = 0.037, P = 0.407; Figure 3 1 A ). Confirming prediction (2) that perceived predation risk should alter community composition, t re atment strongly shifted community composition: for both primary sampling periods, ordinated community profiles showed distinct clusters for all treatments and Control (Figures 3 1 B and 3 1 C ). Analysis of similarity (ANOSIM) suggested a strong treatment effect on community dissimilarity for both primary time periods (F3, 20 = 3.52, P < 0.001; F3, 15 = 2.93, P < 0.005, res pectively). Similarity percentage (SIMPER) analysis identified these dissimilarities to be predominantly driven by species exhibiting abundance responses to treatments according to N mixture models (Table 3 1).
67 Contrary to prediction (3) that slower species should respond more negatively to adult predation risk and faster species to nest predation risk I did not find support for a relationship between species response to perceived predation risk and fecundity On the other hand, my results confirmed prediction (4) that body size could predict species response to perceived predation risk but only under Owl treatment (Figures 3 2 A and 3 2 B ). S maller bodied species showed a significantly stronger negative response to the Owl treatment compared to larg er bodied species (Figure 3 2 A ). In addition, smaller bodied species show ed a stronger negative response to the smaller bodied Owl than to the larger bodied Hawk, while the opposite applied t o larger bodied species (Figure 3 2 B ). I found no relationship between species response to Hawk and prey body size. Discussion Effect of P redation R isk on C ommunity S tructure My study s uggests that perceived adult, offspring or both adult and offspring predation risks profoundly shaped the structure of breeding bird communities, by reducing species richness, shifting community composition, and generally reducing species abundance and/or occurrence. These results add evidence for the role of predation ri sk in structuring ecological communities, particularly terrestrial vertebrate communities for which there are very limited experimental tests so far (Suhonen et al. 1994, Fontaine and Martin 2006). Factors affecting community assembly typically act via two mechanisms: (1) by altering species colonization through habitat selection; and (2) by altering species key demographic rates post colonization (Vonesh et al. 2009). Because my study spanned one breeding season, the observed community changes should predominantly be the result of community wide alteration in avian habitat selection under heightened
68 predation risk. Due to its direct fitness impacts, predation risk (both on adult birds and offspring) is often considered to be a central factor governing avian breeding habitat selection ( e.g., Martin 1988a, 1988b, Fontaine and Martin 2006, Thomson et al. 2006, Morosinotto et al. 2010). My study adds strong experimental evidence for consistent effects at the community level. My findings also suggest, however, that predation risk could affect avian communities in ways other than habitat selection (Cresswell 2008). More than half of the focal community (nine of fourteen species) exhibited altered, generally lower detection probabilities under heightened predation r isk (Appendix H ), suggesting a variety of behavioral adjustments could have come into play; e.g., reduced calling rates and more cryptic activities. Such behavioral changes have been noted elsewhere ( e.g., Mougeot and Bretagnolle 2000, Krama et al. 2008) and could comprise effective anti predator strategies especially when shifting habitat is not possible or not optimal (Lima 2009). In addition, demographic rates of prey populations, such as reproductive output, are likely to respond to increased predation risk (Julliard et al. 1997, Lima and Dill 1990, Thomson et al. 2006, Zanette et al. 2011, Chapter 4 of this dissertation). These behavioral shifts could potentially exert strong and profound effects at the population and community levels beyond those of habitat selection (Mougeot and Bretagnolle 2000, Zanette et al. 2011). While negative response to predation risk was prevalent among prey species, there were some unexpected responses of species to perceived predation risk, especially under Haw k and Jay treatments (Appendix I ). Several mechanisms involving potentially complex inter and intraspecific interactions could be involved in such
69 unexpected effects of predation risk (sensu Sih et al. 1985). First, prey live in a multi predator world in which respo nse to one predator may be influenced by risks from other predators (Lima 1992). Some avian predators could potentially provide protection against other predators via intraguild predation (Sih et al. 1985, Polis et al. 1989), and may be sought as neighbors by their prey (Mnkknen et al. 2007, Greeney and Wethington 2009). This mechanism could be involved in many of the nonnegative responses of prey species toward the Hawk treatment. Second, when competition exists among prey, negative effects of predation risk on the competitively dominant prey may release the poor er competitor via the keystone predator effect (sensu Paine 1966; Sih et al. 1985). This is well documented in aquatic systems (Addicott 1974, Menge 1976), and has been shown in some vertebrate system s (Kullberg and Ekman 2000). In addition, when habitat availability is limited, part of the population would be forced to settle in suboptimal habitats, causing the apparent absence of predation risk effects, while prey may have a range of other negative responses with resulting fitness consequences (Rodenhouse et al. 1997). Lastly, the fact that the Jay treatment had the highest number of unexpected prey responses (Appendix I) may involve the blue jay being a heterospecific attractant for at least some of the prey species (Mnkknen et al. 1999). Highly active, aggressive and vocal, the blue jay may provide potential benefits to some prey species that override the predation risk it poses, such as reducing predation risk, indicating habitat quality, and/or mitigating competition. Of the three treatments, Owl had the strongest negative effect on species richness, while Hawk did not show a significant effect. However, it should be noted that the analysis was on a subset of the prey bird community ( i.e., the collection of fourteen
70 species that fitted N mixture or occurrence models, out of the eighteen regular prey species, and the total of 24 prey species), and that the effect may be different if I were able to include all prey species in the analysis. For example, analysis on the raw richness data of the eighteen regular prey species ( i.e., the number of species tallied from field surveys) suggested that all treatments had significant negative effects on the number of species detected (Appendix J ). Response to P redation R isk in R elation to F unctional T raits Under the context of habitat selection, I did not find support for the lifehistory theory prediction that fecundity should predict species response to adult versus nest predation risk. However, my finding that smaller bodied species responded more negatively to vocalizations of the small bodied Owl treatment supported the prediction that body size should determine prey sensitivity to adult predation risk (Figures 3 2 A and 3 2 B ; Vezina 1985, Dial et al. 2008). The life history tradeoff between current and future reproduction of an organism broadly predicts greater aversion to adult predation risk over offspring predation risk for slower species, and the opposite for faster species (Pianka and Parker 1975, Montgomerie and Weatherhead 1988). Empirical tests in birds of this widely held prediction have largely focused on nest defense and other risk taking behaviors around the nest, due to the obvious tradeoff parents must make in those situations (Rick lefs 1977, Ghalambor and Martin 2000, 2001). I designed this study to provide new types of support, by (1) assessing predationrisk sensitivity in the context of avian breeding habitat selection, a behavior that operates over larger spatiotemporal scales w ith demonstrable population level consequences (Jones 2001); and (2) contrasting adult versus offspring predation risks under the same context. The latter is an essential
71 aspect of testing this life history theory prediction but has only rarely been implem ented within the design of single studies (Ghalambor and Martin 2000, 2001). The lack of support for the above lifehistory theory predictions in the context of breeding bird habitat selection should be considered in view of the following issues. First, sp ecies may display their sensitivity to predation risk in ways other than habitat selection, as suggested by their responses in terms of detection probabi lity (see Results and Appendix H ). Indeed, at least three species in my analysis that had a zero response in abundance actually responded to predation risk with an altered detection probability (Appendix H ). Therefore, the fact that lifehistory variation failed to predict species response to predation risk in terms of habitat selection does not mean that t his trait bears no relevance to species sensitivity to predation risk in general. Second, compared with the body mass trait that can be easily and reliably measured, the fecundity trait is much more difficult to estimate reliably for each species. Difficul ties in obtaining reliable data are large ( e.g., collected over full breeding cycles or across environmental gradients; Nylin and Gotthard 1998); and my design would have been stronger if I had reliably estimated vital rates for populations inhabiting my p articular sites. It thus may not be so surprising that I was able to detect body mass effects but not fecundity effects on species sensitivity to predation risk. Conclusion The role of predation risk in shaping community assembly has had a long history in ecology (Connell 1975). However, in comparison with aquatic systems, the gap of empirical evidence from terrestrial systems acknowledged by Sih et al. (1985) still largely lingers. To my knowledge, there are only two experiments testing the effect of predation risk on shaping avian community structure, one in structurally simplified
72 agricultural landscapes (Suhonen et al. 1994), the other looking only at nest predation ri sk (Fontaine and Martin 2006). My study experimentally demonstrated that perceived adul t, offspring or both adult and offspring predation risks could profoundly shape avian prey communities in natural forest landscapes, by reducing species richness, shifting community composition, and generally reducing species abundance and / or occurrence in ways that could largely be predicted fro m species body size.
73 Table 31. Bird species that contributed the most to community dissimilari ties (based on SIMPER analysis) Period Species Contribution Abundance response to treatment Hawk Jay Owl 1 Summer tanager Piranga rubra 25.13 14.91 24.38 0.72 (Hawk); 0.82 (Owl) Pine warbler Dendroica pinus 10.10 13.26 19.94 0.71 (Owl) Red headed woodpecker Melanerpes erythrocephalus 9.37 11.37 0.73 (Hawk); 0.71 (Jay) Mourning dove Zenaida macroura 8.89 16.58 13.40 0.57 (Jay) 2 Summer tanager Piranga rubra 19.47 12.28 12.36 0.72 (Hawk); 0.82 (Owl) Pine warbler Dendroica pinus 19.02 14.17 39.26 0.71 (Owl) Bachmans sparrow Peucaea aestivalis 11.00 0.94 (Owl) Eastern bluebird Sialia sialis 10.20 9.83 Mourning dove Zenaida macroura 19.14 0.57 (Jay) This table shows the list of species that combined to contribute at least 50% of the community dissimilarities compared to Control for both primary sampling periods, and their response to treatment in abundance as indicated by N mixture models. : Response is represented in the form of the ratio of mean plot level abundance between treatment and control, on log scale (see Appendices D and E). Only responses with a P value below 0.1 are presented.
74 Figure 31. Treatment effects on community structure, in terms of species richness and composition. A): Effects of playback treatments on plot level species richness as estimated by N mixture and occurrence models. Effects are presented as the log of treatment level species richness ratio ( 1.96SE) between treatment and Control. (N = 6 / treatment.) B) Two dimensional nonmetric multidimensional scaling (NMDS) of Bray Curtis community similarity index among plots for the fir st time period. C) Two dimensional nonmetric multidimensional scaling (NMDS) of Bray Curtis community similarity index among plots for the second time period. For the second time period, five plots were lost due to prescribed burning, including two for Hawk on e for Jay, Owl and Control each
75 Figure 32. Relationship between species response to treatments in terms of abundance and body size (estimates and model predictions, + 95% CI). A ) Species response to Owl treatment as predicted by body mass (brown headed cowbird was excluded from the analysis). B ) The difference between species response to Hawk treatment and Owl treatment as predicted by body mass (brownheaded cowbird was excluded from the analysis) (N = 12.)
76 CHAPTER 4 NONLETH AL PREDATION RISK TO ADULT AND OFFSPRING ALTERS AVIAN REPRODUCTIVE STRATEGY AND REDUCES REPRODUCTIVE OUTPUT Introduction Predation is long recognized to play a major role in shaping ecological and evolutionary processes (Lima and Dill 1990, Endler 1991, Barbosa and Castellanos 2005). Such effects have traditionally been ascribed solely to the direct removal of prey by predators (aka densitymediated effects). Only recent ly have ecologists begun to recognize that predation risk may lead to potent ecological and evolutionary consequences by strongly shaping prey behavior, resulting i n nonlethal effects (Lima 1998, Agrawal 2001, Preisser et al. 2005, Cresswell 2008). Birds have among the most complicated anti predation and predation behaviors, and the nonlethal effect of predation risk may be especially prominent in their ecology and evolution (Lima 1993, Cresswell 2008). Reproduction provides a particularly relevant and important context to look at avian response to nonlethal predation risk, because it renders parent birds more susceptible to predation risk, and its outcome carries direct fitness consequences (Rickle fs 1969, Magnhagen 1990). A vian reproductive strategies and output should thus be strongly influenced by nonlethal pr edation risk to both adult birds and offspring, and such nonlethal effects should be considered in addition to density mediate effects (Roff 1992, Bennett and Owens 2002, Martin 2004, Cresswell 2008, Lima 2009, Martin and Briskie 2009). Hereafter, I will use the term perception of predation risk and nonlethal predation risk interchangeably, because nonlethal predation risk is mediated by prey perception of risk and subsequent responses (Lima and Steury 2005) Theoretical models provide a suite of predictions of how breeding birds should respond reproductively to nonlethal predation risk. I ncreased perception of offspring
77 predation risk may lead to reduced clutch size, egg mass, clutch mass, nest attentiveness (the percentage of time females spend inc ubating), and / or feeding rate (Martin and Briskie 2009). These responses could happen as breeding birds seek to (1) avoid nest predators by increasing nest crypsis and shortening the susceptible nesting period, if nest conspicuousness increases with more off spring and the ensuing increase in trips to the nest (Skutch 1949, Safriel 1975, Redondo and Castro 1992, Martin et al. 2000a, b); and / or 2) reduce parental investment in current clutches as a means of bet hedging to save energy for possible renesti ng, as egglaying and nest attending both are energy intense activities (Slagsvold 1984, Roff 1992, Martin 1995). Alternatively, increased perception of offspring predation risk may lead to increased nest attentiveness and / or feeding rate, as parent birds seek to more actively attend to and thus defend nest (Montgomerie and Weatherhead 1988, Martin 1992), and / or to speed up offspring development to reduce the length of time when offspring are most vulnerable (Martin 2002, Martin et al. 2007). Compared t o offspring predation risk, the e ffect of non lethal adult predation risk has received much less theoretical investigation. It was predicted by a single modeling study to result in a smaller clutch size, reduced feeding rate of parents to offspring and inc reased food limitation suffered by offspring (Lima 1987). This response is based on the assumption that by shortening the nesting period and reduc ing foraging time, parents reduce exposure to predators (Lima 2009). Empirical studies of breeding birds reproduction under heightened perception of predation risk largely support the above theoretical predictions H eightened perception of adult predation risk was found to reduce clutch size and reproductive output (Scheuerlein et al. 2001, Thomson et al. 2006), while heightened perception of offspring
78 predation risk generally r educed clutch size, clutch mass, and parental feeding rate (Eggers et al. 2006, Fontaine and Martin 2006, Riou and Hamer 2008; see also Doligez and Clobert 2003; but see Greenwood and D awson 2011, Mnkknen et al. 2009 for examples of increased clutch size). However, experimental tests of the broad collection of theoretical predictions pertaining to avian reproductive strategy and output remain scarce : there are only five experimental st udies to my knowledge (Eggers et al. 2006, Fontaine and Martin 2006, Doligez and Clobert 2003, Mnkknen et al. 2009, Greenwood and Dawson 2011 ; also see Zanette et al. 2011 for an experiment with a mix of adult and offspring predation risks ) In addition, I know of no study that has compared avian reproductive response to differ ent types of predation risk (e.g ., risk to adult versus to offspring), yet such compar ison is crucial for understanding the nature of avian reproductive responses and testing life h istory theory predictions. I experimentally test ed whether nonlethal predation r isk to adult or offspring alter ed the reproductive strategy and output of the cavitynesting Eastern bluebird Sialia sialis I manipulated cues of three avian predators that preferentially prey on either adult s or offspring in sandhill forest habitats where the artificial nest boxes for the bluebirds were located I monitored bluebirds reproduction throughout the breeding season to characterize their reproductive responses to elevated perception of predation risk. Specifically, I looked at two broad aspects of reproduction, both of which are predicted by theory to be sensitive to elevated perception of predation risk: parental reproductive investment and success, and incubation and nestling feeding behaviors.
79 Methods Study Area and S pecies The s tudy area was the same as that of Chapter 3 (see above). The Eastern bluebird Sialia sialis is a small (~ 30g ) insectivorous resident species in my study site. I t depends on naturally occurring cavities or cavities built by other species (Gowaty and Plissner 1998), and readily uses artificial nest boxes (DeLuca 2008). In my study site, nesting attempts typically begin in mid April and most bird individuals engage in two nesting at tempts during a breeding season (DeLuca 2008). Adult birds are susceptible to predation from a variety of predators including hawks, falcons, owls, and snakes N est predation is usually caused by raccoons, snakes, jays, and other bird species that compete for nesting cavities (Gowaty and Plissner 1998). Experimental D esign The e xperimental design was the same as that of Chapter 3 (see above). O n each plot, I erected four artificial nest boxes in January 2010, roughly three months before the earliest nest co nstruction began, and recorded the nesting investment and output, and nesting behavior of the breeding bluebirds under the different predation risk treatment s. Each nest box was mounted on top of a single metal pole ~ 1.5m abo ve ground, and was spaced ~ 100m from plot edge and from each other, in locations with relatively open habitat that was preferred by the bluebirds (Gowaty and Plissner 1998). Typical territory size of Eastern bluebirds can go down to 1.1ha (Gowaty et al. 1998); and birds in my study site are known to use artificial nest boxes spaced 100m apart or closer (Katie Sieving, pers. comm.). I focused on testing the nonlethal effects of predation risk throughout. To do so, I limited potential lethal / consumptive effects of nest predation events through: (1) using narrow poles to mount the nest boxes; (2)
80 setting up the nest boxes away from any standing structure that could provide climbing support to potential nest predators; and (3) keeping the poles greased for the entire length, except ~ 30cm below the nest boxes (USDA Natural Resources Conservation Service 1999). These measures proved effective in excluding most nest predation events. I removed from analyses all predated nests and nests that failed due to other external factors I did not conf irm the survival of parent birds to preclude the possibility of adult predation. However, because both parents are needed for the initiation of nesting (i.e., the laying of fertilized eggs) adult predation can be ruled out and hence effects regarding adult predation risk can be considered as entirely nonlethal effects, at least up until th e egg stage of the second nesting attempt. Data C ollection Reproductive success. I monitored each nest box closely throughout the nesting season. Because parent bluebirds were not individually banded, I made the following assumptions regarding nesting attempts after the first nesting attempt: (1) if the same nest box was used for more than one nesting attempt, it was being used by the same pair; (2) if nes t ing shifted between the first and subsequent nesting attempts on a given plot, the new nest belonged to the pair that previously nest ed in the abandoned box on the same plot. (T here was no incidence of more than one nest shift on the same plot .) Throughout the study, I measured parental investment in egg production, including clutch size, egg size, egg mass, and clutch mass (Martin and Briskie 2009). All eggs were measured on the second day following clutch completion (Fontaine and Martin 2006). For six nests I missed the second day (due to equipment failure); therefore, in subsequent analyses I used a derived relative egg mass index calculated from egg length and width, following Equations 4 1 and 4 2 in subsequent
81 analyses ( below ); ( Hoyt 1979). I also measured parental nesting performance past the egg stage, including egg hatching rate, fledging rate, the number and body condition of nestlings on the 14th day since hatching ( i.e., about two days prior to fledging), and the number of fledglings. I used nestling body mass to represent nestling body condition (Freeman and Jackson 1990), and us ed it to calculate the brood mass on the 14th E gg volume = 0.51lengthwidth2 ..... (Equation 4 1) day since hatching Table 41 lists all the above reproductive success measures and their definitions. Most nesting pairs (N = 30) engaged in two nesting attempts, yet some (N = 5) only had one nesting attempt in my plots. I thus took data on both nesting attempts when applicable. All mass and length measurements were accurate to the nearest 0.01g and 0.01mm, respectively. Egg mass = 1.005egg volume. ...... (Equation 4 2) Reproductive behaviors. In addition to the above measurements pertaining to reproductive success I also assessed parental behaviors by videotaping the incubation and offspring feeding activities of parent birds for first nesting attempts. For each nest I videotaped, I set up the recording system next to the nest boxes at least one day before videotaping such that parental behavior was minimally affected by the presence of the recording system. I videotaped incubation activities of at least five nests for each trea tment on around the 3rd day ( + 2 days) since clutch completion, from before sunrise until 11.5 hours after sunrise Due to equipment limitations, I videotaped offspring feeding activities of at least four nests for all but Jay treat ment on around the 12th day ( + 1 day) since hatching, from sunrise until 13.5 hours after sunrise. For incubation recordings I recorded the following behaviors : female nest activity female nest attentiveness, female onand off bout length, male feeding rate, and total nest
82 attentiveness. I scored the nestling feeding reco rdings for nestling feeding activity ( Martin et al. 2000b, Fontaine and Martin 2006). Additionally, as increased perception of predation risk may alter the way parent birds distribute their activities throughout the day ( e.g., by reducing activities when the predator was likely to be more active and compensating during other times; Riou and Hamer 2008), I also calculated the time distribution of female nest ac tivity and nestling feeding activity during each threehour block after sunrise. Although I was not able to completely standardize the time when videotaping was carri ed out due to equipment limitations, there was no systematic bias in terms of the number of days that elapsed since egg laying or hat ching until videotaking among treatments (F3,18 = 0.96, P = 0.432 for incubation videos; F2,9Statistical A nalyses = 0.50, P = 0.622 for nestli ng feeding videos). In addition, I measured nestling body mass right before I took nestling feeding videos to control for the influences of the number of nestlings in nest and nestling development on feeding rates. In correspondence to these behavioral measures during the first nesting attempt, I also measured the length of the incubation and nestling feeding (i.e., brood rearing) stages of the nesting cycle for the first nesting attempt. Table 41 lists all the above reproductive behavior measures and thei r definitions. Reproductive success. The lethal effects of predation risk were precluded across the two nesting attempts for Jay treatment, and only up until the egg stage of the second nesting attempt for Hawk and Owl treatments. To t est for the non lethal effect s of predation risk, I therefore analyzed the effects of playback treatment s on nesting success measures differently for Jay than for Hawk and Owl treatments. For Jay treatment, I analyzed treatment effects across two clutches for all nesting success
83 measures. For Hawk and Owl treatments, I analyzed treatment effects across two clutches only for egg stage measures and focus ed on the first clutch of the breeding pairs that engaged in two nesting attempts for all measures past the egg stage. Breeding birds investment in subsequent nesting attempts tends to depend on the cumulative success of previous nesting attempts (Smith et al. 1987, Linden 1988). Hence bluebirds parental investment in the second clutch was likely affected by the success of the first clutch. T o tease out non treatment influences from external factors that could have cascaded onto the second clutch, I adjusted the egg production measures (i.e., clutch size, egg mass, and clutch mass) for the s econd clutch according to the success of the first clutch I did this by: (1) running a linear regression of each egg production measure against the number of fledglings from the first clutch ; (2) predicting values of the egg production measure assuming fi ve fledgling s were produced from the first clutch ; (3) and adding the predicted values to the corresponding regression residuals. I did not make adjustments to other nesting measures past the egg stage, assuming that parent birds would be stro ngly motivated to successfully rear each egg laid to fledg ling once the eggs was laid. For measures across two clutches I used generalized linear models with generalized estimation equations (GEE) to account for the correlated nature of data (Liang and Zeger 1986). For continuous data I used an identity link, normal error distribution, and an exchangeable correlation structure. For binary data, I used a logit link, binomial error distribution, and an exchangeable correlation structure. All GEE models began with the sa me global model structure that included treatment, the identity of each clutch ( i.e., clutch 1 or 2), and the interaction term between these two variables
84 to allow possible different treatment effects on the two nesting attempts. In addition, the GEE model for hatching success also included clutch size as a candidate variable, as a larger clutch size likely has negative effects on hatching rate (Thomson et al. 1998). I then used the q uasi likelihood under the Independence model criterion (QIC) for model sel ection, selecting the models with the lowest QIC scores (Pan 2001). For measures only for the first clutch, I used generalized linear models (GLM) with the same model structure as the GEE models specified above, except for removing the second clutch and the correlated data structure. I then used Akaike s information criterion corrected for small sample size (AI Cc) to select the best model with the lowest AICc score (Burnham and Anderson 2002) Finally, to analyze the effect of playback treatments on the other nonrepeated nesting measures, namely, the timing of nesting initiation (i.e., the laying of the first egg of the season), and the total number of fledglings produced over the breeding season, I used GLM with treatment as the only candidate variable (i.e., analysis of variance ANOVA) I used AICc to select the best model with the lowest AICc score (Burnham and Anderson 2002). Reproductive behaviors. To analyze effect of playback treatment s on the incubation and feeding behaviors during the first nest ing attempt, I used linear regression for all measures except for the analysis of parental activity distribution dat a ( see below). F or regression models on incubation behavior, I included treatment Julian date when the video was recorded, and number of eg gs (i.e., clutch size) as candidate variables. I included Julian date and clutch size because incubation behavior was likely to be influenced by season progression as ambient temperature increased (White and
85 Kinney 1974, Haftorn 1988, Conway and Martin 2000); and larger clutches would likely require more incubation effort (Thomson et al. 1998). In addition, for female incubation behavior, I also included malefeed ing rate as a candidate variable, because increased male feeding could likely allow the female to spend less time foraging and more time attending the nest, and could have an influence on the incubation process in general (Nilsson and Smith 1988). In regression models for nestling feeding behavior, I included treatment Julian date, and brood mass at the start of r ecording as candidate variables. I included brood mass because more and / or larger nestlings would likely require more feeding effort I used AICc to select the best model with the lowest AICc score (Burnham and Anderson 2002). To a nalyze the time distribut ion of female incubation activity and parental nestling feeding activity, I first converted the data on number of activities during each threehour block into proportions of activities out of the total number of activities. I then analyzed treatment effects using analysis of similarity (ANOSIM) based on the Bray Curtis dissimilarity index without data transformation, with 1,000 permutations (Bray and Curtis 1957, Clarke 1993) Time distribution data resembles community composition data, to which ANOSIM is typically applied, in that the object of analysis is the information on the relative frequency of activities (resembling abundance of each species) under each time block (resembling the identity of each species) I included treatment Julian date, a nd clutch size as candidate variables for the incubation behavior; and I included treatment, Julian date, and brood mass as candidate variables for the feeding behavior. Because ANOSIM is not amenable to AIC based model selection, I conduc ted model selection based on progressive removal of variables that did not have statistical significance.
86 Finally, m any of the reproductive measures tested in this study are likely t o be part of an overall response of bluebirds breeding strategy to increased perception of predation risk. In that case, a multivariate analysis of these measures may be able to provide additional insight s o n bluebirds overall breeding strategy response, for example a principal component analysis (PCA) on these measu res followed by an analysis of variance (ANOVA) on the most important principal components (PCs) While a PCA on the reproductive success measures ( listed in Table 4 1 ) produced biologically i nterpretable PC s, the first four of which explained over 90% o f the variance (results not shown), the number of nests for which PCs could be produced was reduced by over half compared to original data. This was caused by the presence of missing values of some original measures for many of the nests, and by the fact that the presence of a single missing value for a nest would preclude a PC to be produced for that nest I therefore chose not to conduct a multivariate analysis on the reproductive data. I conducted all statistical analyses using R.2.13.1 (R Development C o re Team). I used the yags package (version 4.02.1) for GEE analyses the vegan package (version 2 .1 19) for ANOSIM analyses, and the MuMIn package (version 1.7.2) for model selection with AICc. Results A total of 35 bluebird pairs nested in nest boxes on my plots during the 2010 breeding season (N = 7, 11, 9, 8 for Control, Hawk, Jay, and Owl, respectively). Of these, 30 pairs engaged in two nesting attempts (N = 7, 9, 7, 7 for Control, Hawk, Jay and Owl treatments, respectively), and the other five pairs engaged in one nesting attempt on my plots (N = 0, 2, 2, 1 for Control, Hawk, Jay and Owl treatments, respectively). I obtained videotaping of nesting behavior for 22 bluebird pairs during
87 incubation (N = 5, 7, 5, 5 for Control, Hawk, Jay, and Owl, respectively) and 12 pairs during brood rear ing (N = 4 each for Control, Hawk, and Owl ). Three pairs (two for Control, one for Owl) failed for both nesting attempts and one pair failed for the second nesting attempt (for Hawk), potentially due to nest predation, prescribed burning, and /or parents being infertile. I excluded these nests from all but egg stage analyses. Two additional nests sustained partial egg predation by possible avian cavity competitors such as Carolina chickadee Poecile carolinensis ( Gowaty and Plissner 1998, Maier and DeGraaf 2000). I excluded these nests from analysis on egg hatching success. Overall, by the end of the breeding season, nests under the Hawk treatment produced 35.40% fewer fledglings compared to Control (Table 42, Fig ure 41). While Jay and Owl treatments did not have a significant effect on total fledg ling production, the trends were both negative (Figure 41 ). Predator treatments did not affect the timing of nesting activity initiation (Table 42) but led to signif icant reduction in bluebird investment in egg laying. Specifically, after adjusting for influences from the success of the first nesting attempt, all treatments resulted in significantly smaller clutch size compared to Control over two nesting attempts ( Ta ble 42 Figure 4 2 A). Only the Owl treatment reduced clutch mass ( Table 4 2 Figure 4 2 B). I detected no significant effect of treatments on egg mass (Table 42) although there was a trend for bluebirds under predator treatments to lay slightly larger eg gs, particularly for Hawk and Jay treatments during the second nesting attempt (Figure 4 2C ). Without adjustment of influence from the success of the first nesting attempt, treatments had even more pronounced negative effects on clutch size and clutch mass (Appendix K ). Treatments did not have an effect on parental behavior
88 during incubation of the first nesting attempt nor did it affect the length of the incubation period ( Table 42 ). Treatments also had significant, largely negative impacts on nesting performance past the egg stage. Specifically, Jay treatments significantly reduced egg hatching success, but this effect was not detected in Hawk or Owl treatment ( Table 42 Figure 43 ). At 14 days of nestling age, Hawk and Jay treatment s resulted in marginally significant reductions in the number of nestlings but did not affec t total brood mass ( Table 42 Figure s 4 4 A and 44 B ). Accounting for this apparent inconsistency between fewer nestlings and equal brood mass under Hawk and Jay treatments, is the finding that both Hawk and Jay treatment s resulted in significantly larger nestlings ( T able 42 Figure 44 C). Owl treatment did not affect nestling number, nestling mass, or total brood mass (Table 42 Figure 44 ) I detected no treatment effect on nestling fledging rate ( Table 42 ). Unlike for the incubation period, I detected some strong effects of treatment on the nestling feeding behavior of bluebirds. Hawk and Owl treatments significantly reduced nestling feeding rates during the first nesting attempt compared to Control, after effects of brood mass were controlled for ( Table 42 ). Jay treatment resulted in a marginally shorter brood rearing period for the first nesting attempt, reducing the length of the brood rearing period by an average of 1 .4 days c ompared to the average 17.2day period of Control (Table 42 ) Discussion My results suggest that i ncreased perception of adult, offspring or adult and offspring predation risk profoundly affected the reproduction of Eastern bluebirds, both behaviorally and demographically. Significant effects included reduced parental
89 investment in egg laying (all treatments), impaired post egg nesting performance (Hawk and Jay treatments), suppressed nestling feeding rates (Hawk and Owl treatments) a nd expedited brood rearing process (Jay treatment) This array of responses culminated in considerably reduced reproductive output under the Hawk treatment and qualitatively reduced reproductive output under the Jay and Owl treatments although this resul t pertaining to Hawk and Owl treatments should be taken with the caveat that some lethal predation risk effects may also be involved (but see below) My study provides the first experimental evidence for the nonlethal effect s of distinctly different types of predation risk (i.e., to adults versus offspring) on avian reproduction, and largely confirms major predictions from lifehistory theory. The direct comparison of distinctly different types of predation risk in my study allowed an especially important insight that previous studies looking at only one type of individual or mixed predation risk was unable to provide, on how breeding birds may respond differently in reproductive strategy when perceiving increased risk to themselves versus that to their off spring. Three sets of distinctly different responses to increased perception of adult predation risk alone (Owl) versus offspring predation risk (Hawk or Jay) are noteworthy. First, while all treatments reduced clutch size, only Owl treatment reduced bluebirds total egg laying investment (i.e., clutch mass) Birds maintenance of an unaffected total egg laying investment under Hawk and Jay treatments when clutch size was reduced could only happen through producing larger, heavier eggs which was qualitativ ely supported by my results Secondly, Jay and to a lesser extent, Hawk treatments expedited the brood rearing stage, i.e., the stage that nestlings were exposed to nest centered predator detection T his response was not
90 present in birds under Owl treatment A faster brood rearing stage was evident from the shorter length of brood rearing period under Jay treatment, and suggested by the heavier body mass of nestlings at close to fledging under both Jay and Hawk treatments Finally, Jay treatment had generally stronger negative effects on bluebird reproductive performance compared to Owl treatment, culminated in a lower overall reproductive output ac ross the breeding season. It thus appears that the Eastern bluebird may be more sensitive to offspr ing predation risk than to adult predation risk under the context of reproduction. The first two sets of bluebirds disparate responses to perceived adult versus offspring predation risk can be understood in light of how breeding birds should be expected to ma ximize fitness under different risk situations via behavioral mechanisms B reeding birds perceiving heightened predation risk to themselves should reduce the probability of gett ing killed by reducing nesting related activities and hence the attention of p redators (Lima 2009) B reeding birds perceiving heightened predation risk to their offspring though, should (1) conserve their residual reproductive value (sensu Williams 1966) from possible failure of current reproduction for future reproduction (Martin and Briskie 2009), and (2) to reduce the probability of their offspring getting killed These responses can be achieved by reducing investment in the current reproduction (Slagsvold 1984, Roff 1992, Martin 1995) shortening the nestling stage when offspring are most vulnerable ( Martin 2002, Martin et al. 2007) and / or keeping the nestlings better fed and hence l ess noisy in foodbegging that can attract nest predators (Grodzinski et al. 2008, Lima 2009). Different types of predation risk should therefore lead to distinct responses in at least some aspects of birds reproduction. In my study
91 the observed heavier individual eggs ( which can confer nestlings a growth headstart) and speedier brood rearing stage under Hawk and Jay but not Owl treatment, are consistent with and probably exhibitions of predictions pertaining to shortened nestling stage and better fed nestlings under increased perception of offspring predation risk The third set of bluebirds disparate reproductive response to perceived adult versus offspring predation risk, i.e., the apparent higher sensitivity to offspring predation risk (Jay) than to adult predation risk (Owl) is probably explainable by the slow/fast lifehis tory trait of the species and lifehistory predictions ( Pianka and Parker 1975, Ricklefs 1977) Of the avifauna in the sandhill forest ecosystem investigated in this dissertat ion ( Chapter 3) the Eastern bluebird has a relatively fast lifehistory strategy : its clutch size ranks the 5th largest out of 18 species, and its annual fecundity the 7th highest ( Appendix F ). S horter lived and more fecund, such fast species is predict ed by lifehistory theory to value current reproduction over future reproduction because it tends to have lower residual reproductive values (sensu Williams 1966; Pianka and Parker 1975). Consequently, it is expected to be more sensitive to the survival of its offspring ( representing outputs from current reproduction) over that of itself ( representing opportunity of future reproduction) My finding thus provides empirical support to this life history theory prediction (Ricklefs 1977 ), from the perspective of avian reproductive behavior and output that has not been investigated by previous empirical tests of this prediction ( Ghalambor and Martin 2000, 2001). Finally, w hile not relevant to supporting the slow/fast lifehistory theory, it is worth noting that t he Hawk treatment consistently had more pronounced negative effects on bluebird reproduction than Jay or Owl treatment This may be explained by the fact that the Cooper s hawk posed high risk for
92 both adult s and offspring (Curtis et al. 2006, Stracey 2010), and that as an adult predator, it generally has higher levels of threat than the Owl due to its diurnal hunting habit and excellent hunting efficiency (Lima 1993, Curtis et al. 2006). My results showed reduce d clutch size and hence reduced number of dependent offspring under increased perception of both adult and offspring predation risk providing strong evidence for theoretical predictions that have only received limited empirical test Life history theory predicts a smaller clutch size and / or parental investment in egg laying under increased perception of offspring predation risk, primarily because smaller broods may (1) shorten the period when the nest is susceptible and reduce the number of parental nest visits that could attract the attention of nest predators (Skutch 1949, Safriel 1975, Martin et al. 2000a, b); and (2) allow parent birds to save energy and maintain an increased survival prospect for future renesting (Slagsvold 1984, Roff 1992, Martin 1995). Theoretical predictions a bout the effect of increased perception of adult predation risk on clutch size and clutch investment in general are less well established, although some models suggest a smaller clutch size and / or reduced clutch investment such that parent birds could reduce foraging time and thus their own risk of predation (Lima 1987). There have been only four experimental tests of these predictions, with three pertaining to nonlethal offspring predation risk (Eggers et al. 2006, Fontaine and Martin 2006, Mnkknen et al. 2009), and only one to nonlethal adult predation risk (Thomson et al. 2006). These empirical studies tested and largely upheld theoretical predictions of reduced clutch size (Eggers et al. 2006, Thomson et al. 2006) and / or clutch investment (Fontai ne and Martin 2006) under increased perception of offspring predation risk and adult predation risk. Zanette et al. (2011) provided
93 another relevant experimental test supporting reduced clutch size under increased risk perception, although they manipulated a mix of non lethal adult and offspring predation risk, hence it was unclear to what extent the observed responses were attribut able to different risk types. My results provide unequivocal support for theoretical predictions, suggesting that increased per ception of predation risk to adults reduces avian investment in egg laying (via reduced clutch size) and, thereby, subsequent nesting effort, as does increased perception of offspring predation risk (Lima 2009, Martin and Briskie 2009). My results also showed that parent birds reduced feeding rates und er both Hawk and Owl treatments, consistent with breeding birds expected response to reduce nesting related activities and hence the attention of adult predators (Lima 2009). However, nests on reduced food provisioning in Hawk or Owl treatments not only fledged nestlings no later than those in Control treatment, but in the case of Hawk treatment, actually sustained faster growth of nestlings. This apparent contradiction is likely explained by the fac t that n ests under Hawk (but to a lesser extent, Owl) treatment had fewer nestlings, such that even with reduced feeding activities, nestlings could still get enough food and grow faster than those on Control plots. I unfortunately lack information on the nestling feeding rate of bluebirds under Jay treatment but the brood rearing stage is shown to have been expedited by the treatment R egardless of how bluebirds may or may not adjust their nestling feeding rate under Jay treatment, the smaller clutches and broods under Jay treatment probably accounted for the faster brood rearing stage: nestling number is found to be a stronger predictor of nestling mass and length of brood rearing stage under Jay treatment (results not shown). These
94 findings provide additional su pport for the adaptive value of smaller clutches and lower number of dependent offspring under increased perception of adult or offspring predation risk ( Lima 2009, Martin and Briskie 2009). Combining output from two nesting attempts, my results showed a significant, 35.4% reduction in the number of fledged offspring produced by bluebirds under the Hawk treatment, in addition to similar qualitative trends under Jay and Owl treatments. As noted above, the preclusion of actual adult predation on breeding blue birds, and hence the certainty that the detected reproductive responses under heightened perception of adult predation risk were nonlethal predation risk effects, could only be ascertained for up until the egg stage of the second nesting attempt in this s tudy. Therefore, the detected negative effects of treatment on total fledgling production under the Hawk and Owl treatment s potentially also involved lethal predation risk effects, i.e., the predation of one of the parent s, especially if predator treatment increased the presence of adult predators on plot. While such potential lethal effects should be taken as a caveat for this particular result, two issues should lend strong support to this results being mostly if not entirely of nonlethal predation risk effects. The f irst issue pertains to the rarity of actual predation events. Throughout the breeding season, I conducted repeated avian surveys on the study plots ( Chapter 3), and not once did I record the presence of the Coopers hawk or the Eastern screech owl suggesting the rarity of occurrence of these predators on plot. In addition, all breeding pairs that initiated a second clutch (30 out of 34 pairs; one other pair came in to a plot only for the second nesting attempt, and its whereabouts for t he first nesting attempt was unknown) were confirmed to be alive until at least two months into the start of the playback
95 experiment (from early March to mid May) further suggesting the rarity of adult predation events The second issue pertains to the fi nding of strong negative treatment effects on egg production that were confirmed to be entirely nonlethal under Hawk and Owl treatments Even if Hawk and Owl treatments did not exert further negative effects on nesting performance past the egg stage (the y were unlikely to positively affect nesting performance) the reduced fledgling output at the end of the breeding cycle would already have been determined, by the reduced propagule (i.e., egg) output. It is thus highly likely that the observed strong negative effect of Hawk treatment on bluebird fledgling production represented real reproductive consequences of nonlethal predation risk rather than being artifacts of lethal predation risk. One potential caveat of my findings is that the observed responses to predation risk treatment could be due to differences in parent birds body condition among treatments, rather than as a result of treatment per se. This situation could happen if more dominant birds settled in better habitats ( i.e., Control plots) whil e subordinate birds were forced to settle in more risky habitats ( i.e., treatment plots), and if subordinate parent birds also tended to have poorer body conditions and consequently poorer reproductive performances (Prt 1991, 1994). I did not assess the b ody condition of parent bluebirds. However, if there was competition among breeding pairs for better habitat such that breeding territories on Control plots were occupied first, I should expect to see an earlier onset of nesting activities on Control plots than on treatment plots. This was not observed in my study, and should lend strong support to the conclusion that the observed reproductive responses in Eastern bluebirds were caused by the nonlethal effect of predation risk.
96 Table 41. List of r eproductive response measures tested in this study and their definition Reproductive response Definition Reproductive success measures: Clutch size The number of eggs laid in a nest Egg mass The mass of individual eggs in a nest Clutch mass The total mass of all the eggs in a nest Hatching rate The percentage of eggs that hatched in a nest 14 th The number of nestlings in a nest on the 14 day nestling number th 14 day since hatching th The mass of individual nestlings in a nest on the 14 day nestling mass th 14 day since hatching th The total mass of all nestlings in a nest on the 14 day brood mass th Fledging rate day since hatching The percentage of nestlings that fledged after hatching in a nest Total fledgling reproduction The total number of fledglings produced by a breeding pair over two nesting attempts Reproductive behavior measures: Timing of nesting initiation The date on which the first egg of the breeding season of a breeding pair was laid Incubation length The length of the incubation period (in days) Brood rearing length The length of the brood rearing period, i.e., between hatching and fledging (in days) Female nest activity The total number of entries and exits at nest by the female Incubation on bout length The average length of female on nest bouts Male feeding rate The total number of nest visits by male Female nest attentiveness The tota l length of time female was present at the nest Total nest attentiveness The total length of time at least one parent attended (i.e., was present at) the nest Distribution female nest activity Time distribution of female nest activity during each three hour block after sunrise Nestling feeding activity The total number of times that either parent fed the nestlings Distribution nestling feeding activity Time distribution of nestling feeding activity by either parent during each three hour block after sunrise : Represented as derived relative mass calculated from egg length and width (accurate to 0.01mm) using Equations 41 and 4 2
97 Table 42 Reproductive response of the Eastern bluebird to increased perception of predation risk Reproductive response Model type Model structure Treatment effect (over Control) Treatment Mean SE p value Reproductive success measures: Clutch size GEE Treatment + Clutch identity Hawk 0.436 0.149 0.004 Jay 0.365 0.154 0.018 Owl 0.413 0.192 0.032 Clutch mass GEE Treatment + Clutch identity Owl 1.167 0.620 0.060 Egg mass GEE Treatment Clutch identity Hatching rate GEE Treatment + Clutch identity Jay 2.076 0.999 0.038 GLM 1 ( ) 14 th day nestling number GEE Treatment + Clutch identity Jay 0.533 0.309 0.085 ANOVA Treatment Hawk 0.767 0.437 0.088 14 th day brood mass GEE Clutch identity ANOVA 1 ( ) 14 th day nestling mass GEE Treatment Jay 1.437 0.460 0.002 ANOVA Treatment Hawk 0.923 0.462 0.048 Fledging rate GEE 1 (lowest QIC score) GLM Treatment Total fledgling production ANOVA Treatment Hawk 3.044 1.095 0.011 Reproductive behavior measures: Timing of n esting initiation ANOVA 1 ( ) Incubation length ANOVA 1 ( ) Brood rearing length ANOVA Treatment Jay 1.400 0.736 0.075 Female nest activity Linear regression Julian date + Male feeding Incubation on bout length Linear regression Julian date Male feeding rate Linear regression 1 ( ) Female nest attentiveness Linear regression 1 ( ) Total nest attentiveness Linear regression Julian date + Clutch size Distribution female nest activity ANOSIM 1 Nestling feeding activity Linear regression Treatment + Brood mass Hawk 57.819 13.544 0.002 Owl 35.590 13.717 0.029 Distribution nestling feeding ANOSIM Julian date + Brood mass
98 T able show s results from the best model on eac h reproductive measure in addition to treatment effects that had a P < 0.1 (Wald test). Statistically s ignificant treatment effect s with a P < 0.05 (Wald test ) are highlighted in bold. : Model structure lists the predictor variables retained in the best models. Models that do not include any predictor variables are indicated by 1 (i.e., null model) with whenever AICc could be calculated. : The adult predation risk e ffects of Hawk and Owl treatments observed in this study may not be entirely nonlethal after the egg sta ge of the second nesting attempt (see main text). Therefore, analyses of reproductive success measures past the egg stage differed for Jay treatment (using GEE analyses with data across two clutches) than for Hawk and Owl treatments (using GLM or ANOVA analyses with data only from the first clutch when adult birds were confirmed to be alive). : Total nesting production analyzed the number of fledglings produced over two nesting attempts. E ffects detected for Hawk and Owl treatments may thus also involve l ethal adult predation risk effects, because the possibility of adult predation could not be entirely precluded for the second clutch past the egg stage. Result from this analysis should thus be taken with this caveat in mind (but see Discussion) *: Abbrev iate of Distribution nestling feeding activity due to space limitations Sample size for each analysis is provided in Figures 41 through 44.
99 Figure 41 Effects of predator treatments on the total number of fledglings produced on plot over both nesting attempts Hawk treatment significantly reduced the total number of fledglings produced compared to Control. This translates into a 35.40% reduction in fledgling output compared to Control, over the two nesting attempts combined. Jay and O wl treatments did not led to statistically significant reduction in fledgling production over two nesting attempts ( 1.886, SE = 1.150, P 1.600, SE = 1.242, P = 0.211 for Owl ). Values are mean + SE.
100 Figure 4 2. Effects of pred ator treatments on parental investment in egg production. A) Clutch size. B) Clutch mass. C) Egg mass. All treatments significantly reduced clutch size compared to Control. Owl treatment marginally reduced clutch mass compared to Control; effects of Hawk and Jay were not statistically significant. No treatment significantly affected egg mass compared to Control, although Hawk and Jay treatments may have led to heavier eggs particularly during the second nesting attempt. Values are mean + SE, of unadjusted egg investment data. All statistical analyses were performed on data across two nesting attempts after adjusted for influence from the success of the first nesting attempt.
102 Figure 43 Effects of predator treatments on the average egg hatching rate. Jay treatment led to significantly reduced egg hatching probability compared to Control, across two nesting attempts. This translates into a 91.67% reduction in the odds of egg hatching probability compared to Control. Analyses for Hawk and Owl treatments were performed on data from first nesting attempt only, and treatment effects were not shown to be significant. Values are mean + SE.
103 Figure 44 Effects of predator treatments on the number and body condition of nestlings at 14 days of age. A) Number of nestlings. B) Brood mass. C) Nestling mass. Hawk and Jay treatments marginally reduced the number of nestlings compared to Control. Hawk and Jay treatments significantly increased the body mass of nestlings. Values are mean + SE.
105 CHAPTE R 5 CONCLUSIONS In my dissertation, I attempt ed to address several important gaps in our understanding of how trait mediated effects of predation risk (TMPR) could shape forest bird habitat relationships as an ecological constraint (Morrison et al. 2006, Johnson 2007) These knowledge gaps pertained to: (1) the relevance of TMPR in birds response to habitat degradation; (2) the role of TMPR, particularly different types of TMPR (e.g., risk for adults ver sus for offspring), in influencing forest birds habitat selection, community assembly, and reproduction. In Chapter 2, I showed that the percept ion of predation risk of small bodied understory forest birds (typically with a body length < 25cm) in the lowl and rainforest of Sumatra was altered by habitat degradation, in ways largely explainable by forest veget ation structural changes In Chapter 3 and Chapter 4, I demonstrated with experimental evidence the effect of an increased perception of predation risk on the population density, community structure, and reproductive strategy of forest birds in the sandhill forest of Southeastern United States. Forest birds were found to respond to an increased perception of predation risk with altered population abundance, occurrence, and / or detection probability, in ways largely predictable from the type of predation risk ( i.e., predation risk on adult versus offspring), and their functional traits including body size and fecundity (Chapter 3). The outcome was reduced community species richness and altered community composition, providing strong evidence that perceived predation risk could potently shape forest bird community structure. Chapter 4 showed that the focal species Eastern bluebird Sialia sialis responded to an increased perception of predation risk with altered reproductive strategy (represented especially by a reduced
106 investment in egg laying), and a drastically reduced reproductive output. In addition, bluebirds reproductive response to different types of predation risk was different, in ways consistent with lifehistory theory predictions. Chapter 4 thus provided strong evidence that perceived predation risk could influence forest birds reproduction, hence influencing key aspects of species population dy namics. In summary, my dissertation provided an assessment of the role of trait mediated effects of predation risk (TMPR) in influencing forest birdhabitat relationships from three different angles. I demonstrated that : (1) TMPR was likely involved in und erstory forest birds response to habitat degradation; (2) TMPR could strongly alter breeding birds habitat selection and lead to shifts in community assembly ; and (3) TMPR could strongly influence avian reproductive strategy and output. These results pro vide strong evidence that TMPR can strongly shape the relationship of forest birds with their habitat. They further suggest that ecological constraints other than resources, such as predation risk, are critical for assessing animal habitat relationships. F inally, d espite a long recognition and a more recent rediscussion of the important link between behavioral ecology and conservation (Sutherland 1998, Caro 1999, Buchholz 2007, Caro 2007, Angeloni et al. 2008), relatively few studies have explicitly incorp orated behavioral perspectives in addressing conservation issues. By providing an example that explicitly considers animal risk sensitive behavior under the context of habitat degradation in one of the most endangered forest ecosystems I propose that anim al risk sensitive behavior, and animal behavior in general, should be given more consideration while we seek to understand the ecological impacts of habitat disturbances and degradation ( e.g., Johns 1986, Andruskiw et al. 2008, CastroArellano
107 et al. 2009) Such an ecological process oriented approach may provide a fuller and more nuanced understanding of how biodiversity persists in degraded habitats, compared to studies that only assess animals numerical responses (van Horne 1983, Henry et al. 2007, CastroArellano et al. 2009). I t may a lso allow us to gain important insights into the mechanisms involved in such responses that will serve as critical basis for effective management and conservation measures.
108 APPENDIX A LIST OF SPECIES RECORDED DURING MOBBING PLAYBACKS (CHAPTER 2) Table A 1. List of species that responded to mobbing playback by approaching within 15m from the owl m odel wit h mobbing / inspection behavior Common name Latin n ame Number at each study location Inclusion in analyses PRIM DEG1 DEG2 DEG3 Understory gleaning species Rufous piculet Sasia abnormis 1 0 1 1 Checker throated woodpecker Picus mentalis 0 0 2 0 Banded woodpecker Picus miniaceus 0 0 0 1 Buff necked woodpecker Meiglyptes tukki 0 0 0 1 Red eyed bulbul Pycnonotus brunneus 25 21 30 59 21 | 4 Spectacled bulbul Pycnonotus erythrophthalmos 46 12 10 37 10 | 6 Hairy backed bulbul Tricholestes criniger 10 23 44 7 7 | 6 Grey cheeked bulbul Alophoixus bres 15 12 20 21 12 | 4 Cream vented bulbul Pycnonotus simplex 3 10 10 22 3 | 1 Grey bellied bulbul Pycnonotus cyaniventris 0 17 19 4 Black headed bulbul Pycnonotus atriceps 0 1 1 16 Black crested bulbul Pycnonotus melanicterus 9 2 0 5 Yellow bellied bulbul Alophoixus phaeocephalus 0 1 2 3 Streaked bulbul Ixos malaccensis 3 1 0 0 Buff vented bulbul Iole olivacea 1 0 0 0 Crested jay Platylophus galericulatus 0 0 1 0 Moustached babbler Malacopteron magnirostre 47 4 7 5 4 | 4 Chestnut backed scimitar babbler Pomatorhinus montanus 1 7 11 4 1 | 0 Rufous crowned babbler Malatopteron magnum 4 5 8 4 4 | 3 Scaly crowned babbler Malacopteron cinereum 6 4 1 6 1 | 1 Striped tit babbler Macronous gularis 11 0 1 2 Brown fulvetta Alcippe brunneicauda 0 5 7 1 Chestnut rumped babbler Stachyris maculate 0 1 6 2 Black throated babbler Stachyris nigricollis 2 0 0 0
109 Table A 1. Continued. Grey breasted babbler Malacopteron albogulare 0 1 0 0 White chested babbler Trichastoma rostratum 1 0 0 0 Rufous tailed shama Trichixos pyrrhopygus 0 3 3 2 Spotted fantail Rhipidura perlata 0 13 3 0 Ashy tailorbird Orthotomus ruficeps 1 0 0 0 Plain sunbird Anthreptes simplex 5 7 19 7 5 | 2 Purple naped sunbird Hypogramma hypogrammicum 5 0 3 2 Ruby cheeked sunbird Anthreptes singalensis 0 2 4 1 Little spiderhunter Arachnothera longirostra 36 10 2 1 1 | 1 ** Grey breasted spiderhunter Arachnothera affinis 2 2 1 1 1 | 0 Long billed spiderhunter Arachnothera robusta 2 1 1 0 Crimson breasted flowerpecker Prionochilus percussus 2 7 4 9 2 | 1 Yellow breasted flowerpecker Prionochilus maculatus 3 4 4 0 Orange bellied flowerpecker Dicaeum trigonostigma 1 1 0 1 Understory sallying species Scarlet rumped trogon Harpactes duvaucelii 4 3 4 5 3 | 2 Red bearded bee eater Nyctyornis amictus 0 1 1 1 Greater racket tailed drongo Dicrurus paradiseus 10 2 12 1 1 | 1 Grey chested jungle flycatcher Rhinomyias umbratilis 2 12 13 1 1 | 1 Black naped monarch Hypothymis azurea 3 5 4 7 3 | 2 Asian paradise flycatcher Terpsiphone paradise 4 0 0 0 Rufous winged philentoma Philentoma pyrhopterum 4 11 12 1 1 | 0 Sunda blue flycatcher Cyornis caerulatus 0 0 0 1 Canopy species Chestnut breasted malkoha Phaenicophaeus curvirostris 2 0 0 0 Raffless malkoha Phaenicophaeus chlorophaeus 1 0 0 0 Red throated barbet Megalaima mystacophanos 2 0 0 0 Brown barbet Calorhamphus fuliginosus 0 1 0 0 Red crowned barbet Megalaima rafflesii 0 0 1 0 Green broadbill Calyptomena viridis 1 13 10 0 Black and yellow broadbill Eurylaimus ochromalus 1 0 0 1
110 Table A 1. Continued. Banded broadbill Eurylaimus javanicus 0 1 0 0 Black winged flycatcher shrike Hemipus hirundinaceus 4 0 2 1 Large woodshrike Tephrodornis gularis 0 1 1 0 Blue winged leafbird Chloropsis cochinchinensis 5 0 5 12 Lesser green leafbird Chloropsis cyanopogon 3 2 6 9 Green iora Aegithina viridissima 1 3 2 4 Dark throated oriole Oriolus xanthonotus 2 1 3 0 Black magpie Platysmurus leucopterus 1 0 3 0 Bronzed drongo Dicrurus aeneus 4 1 0 0 Asian fairy bluebird Irena puella 2 0 0 2 Red throated sunbird Anthreptes rhodolaema 1 0 1 2 Spectacled spiderhunter Arachnothera flavigaster 3 1 1 0 Yellow eared spiderhunter Arachnothera chrysogenys 0 0 2 0 Scarlet breasted flowerpecker Prionochilus thoracicus 0 1 0 0 Undergrowth species Chestnut winged babbler Stachyris erythroptera 0 2 0 1 Striped wren babbler Kenopia striata 0 2 0 0 Ferruginous babbler Trichastoma bicolor 0 0 1 0 Fluffy backed tit babbler Macronous ptilosus 1 0 0 0 Dark necked tailorbird Orthotomus atrogularis 2 6 0 1 Rufous tailed tailorbirds Orthotomus sericeus 6 1 1 2 : This column indicates if the species was included in the species level or guild level analyses ( included in species level analysis; the number s before and after the bar | indicate the number s of individuals from each location that were included in the guildlevel analysis of the propensity to enter 3m scale and of behavioral conspicuousness respectively ). For four of these species, I analyzed their body mass difference across three study loc ations (PRIM, DEG2, DEG3) based on data from a mist netting study I conducted in the same study plots between December 2010 and March 2011. *: Three of these four species did not show a body mass difference among locations. **: One of these four species s howed a significant body mass difference among locations, with PRIM having heavier body mass than DEG2 and DEG3. : The only incidence of crested jay did not enter 15m scale, hence it was not included in the analyses. I record it here to indicate the attr action of this species to the playback.
111 APPENDIX B LIST OF BEST MODELS FOR SPECI ESLEVEL ANALYSES OF THE DIFFERENCE OF AVIAN MOBBING INTENSITY AMONG STUDY LOCATIONS (CHAPTER 2) Table B 1. Structure of the best models for the species level difference of avian mobbing intensity among study locations Table lists predictor variables that entered the best model for each analysis, showing covariate values and their standard errors (in parenthesis) for those variables whose effect was at least marginally different from zero ( i.e., Wald test P Species Mobbing intensity measure Time Group size Location PRIM DEG 1 DEG 2 DEG 3 Red eyed bulbul Entry into 3 m scale NA NA NA NA NA NA Conspicuousness Grey cheeked bulbul Entry into 3 m scale 0.6434 (0.2106) Conspicuousness Moustached babbler Entry into 3 m scale NA NA NA NA NA NA Conspicuousness 0.4081 (0.1547) Plain sunbird Entry into 3 m scale 0.6213 (0.3708) 4.5156 (2.7706) a NA 0.1556 (0.8384) b 2.2497 (1.6088) a b Conspicuousness Cream vented bulbul Entry into 3 m scale 0.6111 (0.3137) Conspicuousness Rufous winged philentoma Entry into 3 m scale NA NA NA NA NA NA Conspicuousness Grey chested jungle flycatcher Entry into 3 m scale NA NA NA NA NA NA Conspicuousness 2.1685 (0.9094) 1.1688 (0.5013) Crimson breasted flowerpecker Entry into 3 m scale Conspicuousness Greater racket tailed drongo Entry into 3 m scale NA NA NA NA NA NA Conspicuousness (+) 6.3552 (0.9008) a 3.7955 (1.0819) bc* 4.9075 (0.5966) b 1.0985 (1.3897) c*
112 Covariates with P indicates that the variable did not enter the best model, that the variable entered the best model (with + or in parenthesis indicating the direction of effect), but its covariate was not significantly different from zero ( i.e., P > 0.1). : Differences in mobbing intensity between study locations are represented by different letters to the upper right of each covariate estimate, at the P same letter are not statistically different from each other. indicates differences with P Sample size for each species at each study loc ation is provided in Appendix A.
113 APPENDIX C LIST OF BEST MODELS FOR SPECIESLEVEL ANALYSES OF THE RELATIONSHIP BETWEEN AVIAN MOBBING INTENSITY AND VEGETATION STRUCTURE Table C 1. Structure of the best models for the species level analyses of the relationship between avian mobbing intensity and vegetation structure Species Mobbing intensity measure Time Group size Canopy cover Understory density Understory density ^2 Red eyed bulbul Entry into 3 m scale NA NA NA NA NA Conspicuousness Grey cheeked bulbul Entry into 3 m scale 0.6718 (0.2151) ) Conspicuousness 0.5826 (0.3271) 0.5759 (0.3166) Moustached babbler Entry into 3 m scale Conspicuousness 0.3857 (0.1457) 0.7215 (0.3803) Plain sunbird Entry into 3 m scale 1.5566 (0.8344) 1.7015 (0.9368) Conspicuousness Cream vented bulbul Entry into 3 m scale 0.6111 (0.3137) Conspicuousness Rufous winged philentoma Entry into 3 m scale NA NA NA NA NA Conspicuousness 2.1258 (1.1823) 2.0076 (1.0572) NA Grey chested jungle flycatcher Entry into 3 m scale ) NA Conspicuousness 1.8656 (0.9734) NA Conspicuousness 2.1685 (0.9094) 1.1688 (0.5013) NA
114 Table lists predictor variables that entered the best model for each analysis, showing covariate values and their standard errors (in parenthesis) for those variables whose effect was at least marginally significantly different from zero ( i.e., Wald test P th P indicates that the variable did not enter the best model, indicates that the variable entered the best model (with + or in parenthesis indicating the direction of effect), but its covariate was not signific antly different from zero ( i.e., P > 0.1). Sample size for each species at each study location is provided in Appendix A. Table C 1. Continued. Crimson breasted flowerpecker Entry into 3 m scale Conspicuousness 1.2840 (0.5712) Greater racket tailed drongo Entry into 3 m scale NA NA NA NA NA Conspicuousness 1.0099 (0.4971) NA
115 APPENDIX D DETAILS OF PLAYBACK SCHEME (CHAPTER 3 ) P layback Scheme I obtained predator vocalization recordings from various sources including museums, audio libraries, and other online databases. Whenever possible, I used recordings taken from near Southeastern United States, but some recordings from other parts of the United States were also used due to inadequate number of recordings from local regions. I processed the recordings to remove noise and augment volume using program Audacity (version 1.3 Beta), and then compiled them into unique playback files to be burnt onto CD for each plot, using vocalizations from no m ore than three individual birds for each playback file. Each playback file consisted mainly of a primary vocalization type ( i.e., the most frequent vocalization used by the species, such as territorial calls), supplemented by a lesser amount of a second ary vocalization type ( i.e., vocalization that is less frequently used but that still advertises the presence and/or activity of the species; however, in the case of COHK, I still used territorial calls for its relative lack of variability of vocalization repertoire; Gehlbach 1995, Tarvin and Woolfenden 1999, Curtis et al. 2006). With the help of programmable timers, I created playback schemes whereby vocalizations were played in bouts throughout the day (or night for ESSO) at rates similar to the natural vocalization behavior of the species as suggested by original recordings. Play back schemes are displayed in Figure D 1 along time axes, showing numbers of primary vocalizations (P) and secondary vocalizations (S) during each onehour interval. It should be noted that as I used two playback stations on each plot that played the same playback files, the actual playback amount was twice the amount provided by a playback file. The two playback stations on each
116 plot followed the same playback scheme, except that one station lagged five minutes behind the other.
117 Figure D 1. Illustration of playback scheme
118 APPENDIX E LIST OF FOCAL SPECIES FOR ANALYSES OF RESPONSE TO PLAYBACKS (CHAPTER 3) Table E 1. List of the 18 focal prey s pecies a nd their common and Latin names Species c ode Common n ame Latin n ame BASP Bachmans s parrow Peucaea aestivalis BGGN Blue gray g natcatcher Polioptila caerulea BHCO Brown headed c owbird Molothrus ater BRTH Brown t hrasher Toxostoma rufum DOWO Downy w oodpecker Picoides pubescens EABL Eastern b luebird Sialia sialis EATO Eastern t owhee Pipilo erythrophthalmus GCFL Great crested f lycatcher Myiarchus crinitus MODO Mourning d ove Zenaida macroura NOCA Northern c ardinal Cardinalis cardinalis NOMO Northern m ockingbird Mimus polyglottos PIWA Pine w arbler Dendroica pinus PIWO Pileated w oodpecker Dryocopus pileatus RBWO Red bellied w oodpecker Melanerpes carolinus RHWO Red headed w oodpecker Melanerpes erythrocephalus SUTA Summer t anager Piranga rubra TUTI Tufted t itmouse Baeolophus bicolor YTVI Yellow throated v ireo Vireo flavifrons I included as focal species only species that had at least ten total detections across five surveys on all plots combined. I also removed nonprey species that had enough detections, namely the American Crow Corvus brachyrhyncos and Blue Jay Cyanocitta cri stata
119 APPENDIX F DETAILS ON DATA AND DA TA SOURCES FOR SPECIES LIFEHISTORY AND NATURA L HISTORY TRAITS (CHAPTER 3) Table F 1. Lifehistory and natural history trait data for all 18 focal prey species Species Clutch size Broods /Y ear Egg l ength (mm ) Egg w idth (mm) Annual f ecundity Adult survival Body m ass (g) Reference BASP 3.90 2.0 19.80 15.60 0.9173 0.4200* 21.00 Martin 1995, Dunning 2006 BGGN 4.40 1.5 14.60 11.38 1.0660 0.5100 6.00 Ellison 1992, Martin 1995, Nott et al. 2008 BHCO** 21.45 16.42 3.0008 0.4500 42.75 Lowther 1993, Nott et al. 2007 BRTH 3.72 1.5 26.90 19.70 0.4340 0.7130 68.80 Cavitt and Haas 2000, IBP 2012 DOWO 4.81 1.0 19.43 15.24 0.4541 0.6000 24.50 Martin 1995, Jackson and Ouellet 2002, IBP 2012 EABL 4.41 2.0 20.88 16.50 0.8426 0.4500 30.00 Martin 1995, Gowaty and Plissner 1998 EATO 2.40 2.0 22.60 17.50 0.4054 0.4510 41.50 Martin 1995, Greenlaw 1996, IBP 2012 GCFL 5.00 1.0 22.60 17.20 0.5115 0.4600 33.50 Lanyon 1997, IBP 2012 MODO 2.00 6.0 28.00 22.00 3.1160 0.4300 127.00 Otis et al. 2008 NOCA 3.12 2.5 24.88 18.58 0.7631 0.5320 45.00 Martin 1995, Halkin and Linville 1999, IBP 2012 NOMO 3.40 3.0 24.40 18.60 0.8929 0.3160 49.00 Martin 1995, Farnsworth 2011, IBP 2012 PIWA 3.50 2.5 18.10 13.50 1.0959 0.4700 13.50 Rodewald et al. 1999, Nott et al. 2008 PIWO 3.60 1.0 32.90 24.72 0.1421 0.6500 280.50 Martin 1995, Bull and Jackson 2011 RBWO 4.25 1.5 25.20 19.00 0.4044 0.6600 73.50 Karr et al. 1990, Martin 1995, Shackelford et al. 2000, Nott et al. 2008 RHWO 4.65 1.5 25.14 19.17 0.4494 0.6200 73.50 Martin 1995, Smith et al. 2000 SUTA 3.20 1.5 23.55 17.20 0.5714 0.4400 30.00 Robinson 1996, Nott et al. 2008 TUTI 5.85 1.5 18.40 14.10 0.7479 0.5000 22.00 Grubb and Pravasudov 1994, Martin 1995, IBP 2012 YTVI 3.60 1.0 20.80 14.80 0.4670 0.5700 18.00 Rodewald and James 2011, IBP 2012 Pearson correlation coefficient between annual fecundity and adult survival rate: 0.44. : I classified brood number per breeding season to the nearest 0.5 broods following Martin 1995. : Due to incomplete published data on egg mass, I used egg volume to calculate egg mass assuming an average egg density of 1.005 g/cm3 (Hoyt 1979). Annual fecundity was then calculated using Equations F 1 to F 3 (Ackerman et al. 2006, Hoyt 1979, Mar tin 1995): Egg volume = 0.51lengthwidth2 (Equation F 1); Egg mass = 1.005egg volume (Equation F 2); Fecundity = (egg massclutch sizebrood number per season) / female body mass (Equation F 3). : I used adult survival rate estimates from (close to) Southeastern United States whenever available. Otherwise, available estimates from other regions were used. : Whenever available, I used data from within or near southeastern United States. Whe n data from different sources
120 conflicted, I preferentially used data that have more support ( e.g., based on larger sample sizes, closer to estimates from most sources, etc.). *: I was unable to find an annual survival rate estimate for BASP (Bachmans spar row), but instead only found a monthly survival rate estimate (during the breeding season). Adult survival rate here was approximated by exponentiating monthly survival rate 12 times. **: BHCO (Brownheaded Cowbird) is a nest parasite. Instead of having re gular clutches and nesting attempts, its female continues to lay eggs in the nests of other birds throughout the breeding season. Clutch size and brood number per year therefore do not apply to this species, and annual fecundity is calculated based on total number of eggs a female lays per breeding season on average, obtained directly from the literature.
121 APPENDIX G SPECIFICATION OF N MIXTURE ABUNDANCE MODELS AND SITE OCCUPANCY MODELS (CHAPTER 3) Table G 1. D efinition of symbols used in N mixture models and site occupancy m odels Model t ype Symbol Definition N mixture Abundance Models Mean plot level abundance of focal species for treatment TRT at time t Ni.t Abundance of focal species on plot i at time t (each plot can be classified into one of four TRT types) pTRT.t Detection probability of individuals of focal species; it is a function of treatment TRT and other time effects yi.t Observed abundance of focal species on plot i at time t Mean plot level recruitment rate (through immigration) of individuals of focal species for treatment TRT at time t (specific for Open models) Mean plot level survival rate (through avoiding emigration or predation) of individuals of focal species for treatment TRT at time t (specific for Open models) Dependence coefficient between mean plot level abundances of focal species in the second primary period and that in the first primary period (specific for Robust models) Site Occupancy Models TRT.t Mean plot level occurrence probability of focal species for treatment TRT at time t Yi.t True occurrence state of focal species on plot i at time t (each plot can be classified into one of four TRT types) pTRT' Detection probability of focal species for treatment TRT across the breeding season* yi.t' Observed occurrence state of focal species on plot i at time t Mean plot level recruitment rate (through immigration) of focal species for treatment TRT at time t (specific for Open models) Mean plot level extinc tion rate (through emigration or predation) of focal species for treatment TRT at time t (specific for Open models) Dependence coefficient between mean plot level occurrence of focal species in the second primary period and that in the first primary period (specific for Robust models)
122 Basic M odel Structure of N Mixture Models and S ite Occupancy M odels F or abundance models, the basic N mixture model structure is: TRT.10 1TRT 2TRT 3 logit (p TRT Owl ..... (Equation G 1 ) TRT0 1TRT 2TRT 3TRT 4Time of day + 56Julian date2 N ... (Equation G 2 ) i.t ~ Poisson ( TRT.t) ... (Equation G 3 ; TRT t y will be defined below) i.t ~ Binomial (Ni.t, pTRTFor occurrence models, the basic model structure is: ) (Equation G 4 ) logit ( TRT.1 logit (p 0 1 TRT 2 TRT 3 TRT Owl (Equation G 5 ) TRT 0 1 TRT 2 TRT 3 TRT Owl + 4 Time of day + 5 6 Julian date2 Y i.t ~ Bernoulli ( (Equation G 6 ) TRT.t) ..(Equation G 7 ; TRT t y will be defined below for individual model categories) i.t ~ Bernoulli (Yi.t, pTRTIt should be noted that treatment effects on p and p are represented as relative effects as compared to those of Control, with individual predator treatment types coded as dummy variables. I therefore have ) (Equation G 8 ) 0 = log ( Control.10 = logit (pControl0 = logit ( Control.10 = logit (pControl); and the other coefficients represent the effect of p and p are considered to be independent of treatment ef fects, then these equations simplify to: log ( TRT.10, logit ( TRT.10, logit (pTRT0 45Julian date + 6Julian date2, and logit (pTRT0456Julian date2.
123 Close d P opulation Abunda nce and O ccurrence M odels For each species, assuming population to be closed throughout the breeding season, I have TRT.t = TRT.1, TRT t = TRT.1; and Ni.t and Yi.t remain constant, denoted as Ni and Yi.Robust D esign Abundance and O ccurrence M odels Observed abundance counts and occurrence patterns as repeated observation of the same closed populations can therefore be defined based on the above equations, and allow the estimation of equation parameters ( i.e., model parameters) through maximum likeli hood estimation (MLE; MacKenzie et al. 2002, Royle 2004, Royle and Dorazio 2008). For each species, I ran four models for abundance data and four models for occurrence data, allowing p and p to be either dependent on treatments, or independent from treatments. I ran all models in unmarked package in R (version 0.94; Fiske and Chandler 2011), using functions pcount and occu. I used a K value (integer upper index of integration for N mixture; Royle 2004, Fiske and Chandler 2011) of 100 for all abundance models of all species. For Robust Design models, I divided the primary survey periods at the end of the 3rd survey, for the reason that it was around the end of the first nesting attempt for most speci es (ACA 2010). For each species, assuming population closure within primary periods (Surveys 1 through 3 as the first primary period, and Surveys 4 through 5 as the second primary period), I have two abundance measures ( TRT.1, TRT.2) and two occurrence pattern measures ( TRT.1, TRT.2 ) for each plot. As a correlated relationship is expected of populations between the two primary periods, I use a single coefficient to define this correlation structure: TRT.2) = + log ( TRT.1 logit ( ) (Equ ation G 9 ) TRT.2) = + logit ( TRT.1) ... ....(Equation G 10 )
124 where and are taken to be constants, regardless of the treatment: 0 (Equation G 11 ) 0 .. (Equation G 12) Observed abundance counts and occurrence patterns as repeated observation of the same closed populations within each primary period can therefore be defined based on the above equations, and allow the estimation of equation parameters ( i.e. model parameters) through MLE (Pollock 1982, MacKenzie et al. 2003, Royle 2004). For each species, I ran eight models for abundance data and eight models for occurrence data, allowing p, p, and to be either dependent on treatments, or independent from treatments. I ran all models using my own R code. I used a K value of 100 for all abundance models of all species (Royle 2004). OpenP opulation Abundance and O ccurrence M odels For each species, population is assumed to be open, i.e., allowing population abundance and occurrence patterns to change in between surveys. However, a dependence structure of populations between adjacent surveys is assumed, defined by TRT.t, TRT.t, TRT.t' and TRT.t. Dependence structures for plot level abundance and occurrence pattern are defined according to MacKenzie et al. 2003, and Dail and Madsen 2011. Observed abundance counts and occurrence patterns as repeated observation of the open populations can therefore be defined based on equati ons, and allow the estimation of equation parameters ( i.e., model parameters) through MLE ( MacKenzie et al. 2003, Dail and Madsen 2011). For each species, I ran four models for abundance data and four models for occurrence data, allowing p, p to be either dependent on treatments, or independent from treatments, with and constants, regardless of treatment I ran all models in unmarked package in R (version
125 0.9 4; Fiske and Chandler 2011), using functions pcountOpen and colext. I use d a K value of 80 and a constant dynamics structure for all abundance models of all species (Dail and Madsen 2011, Fiske and Chandler 2011). Estimation of P lot L evel A bundance (aka B est U nbiased P redictor of Ni.tFor each species, I estimated the true pl ot level abundance based on the best N mixture abundance model. For species with more than one best model, I based the estimation on the model with models. For Closed models, I estimated N ) i.t using the r anef function in unmarked package (Fiske and Chandler 2011), which adopts an empirical Bayesian approach. For Robust models, estimation of Ni.t used the classical integration as outlined in Royle and Dorazio 2008 (p. 138). I used the mode rather than the mean for abundance estimates, therefore abundance estimates all took the form of integers.
126 APPENDIX H BEST N MIXTURE ABUNDANCE AND SITE OCCUPANCY MODELS FOR EACH FOCAL SPECIE (CHAPTER 3) Table H 1. Best hierarchical models selected for the 13 focal species that fitted N mixture m odels and the one other focal species that fitted occurrence m odels Species Model c ategory Model s tructure Treatment e ffect (over Control) Treatment Mean SE p value BASP Robust design Owl 0.9411 0.5318 0.0768 Owl p 1.4954 0.7293 0.0403 BGGN Robust design (TRT).p( ) BHCO Closed Hawk 2.1129 1.0604 0.0463 Owl 2.0078 1.0576 0.0576 Hawk p 2.5118 1.4973 0.0934 Owl p 4.6206 2.7442 0.0922 BRTH Closed p() EABL Closed Owl p 0.9194 0.5020 0.0670 EATO Closed Owl p 3.5656 1.9559 0.0683 p() MODO Robust design Jay 0.5664 0.3369 0.0928 p() NOCA Robust design PIWA Robust design Owl 0.7088 0.2088 0.0007 Owl p 0.9065 0.2847 0.0015 PIWO Robust design p() RBWO Robust design p() RHWO Robust design p() Hawk 0.7272 0.4080 0.0747 Jay 0.7131 0.3943 0.0705 p() SUTA Robust design Hawk 0.7239 0.3534 0.0405 Owl 0.8182 0.3316 0.0136 Hawk p 0.8093 0.4145 0.0509 Owl p 0.9316 0.3953 0.0184 p() YTVI Robust design Hawk 1.1572 0.6775 0.0876 Owl 1.3437 0.6527 0.0395 p() Accepted models within two AIC units from the top model were included, resulting in more than one best models for some species. The table additionally lists effects of individual treatments that had a P < 0.1 (Wald test). Significant results of individual treatment effect that had a P < 0.05 ( Wald test ) are highlighted in bold. Full name and Latin names of species are provided in Appendix B.
127 APPENDIX I UNEXPECTED RESPONSES OF FOCAL SPECIES TO PREDATION RISK TREATMENT (CHAPTER 3) Table I 1. L ist of species with unexpected responses (in terms of abundance and/or detection probability) to increased perception of predation r isk Species Hawk Jay Owl BASP NA BGGN BHCO NA BRTH EABL NA EATO NA MODO NOCA PIWA NA PIWO RBWO RHWO SUTA NA NA YTVI NA NA NA: Species that did not display unexpected response. : S pecies that should not be vulnerable to the predation risk in question and that indeed did not exhibit a response. Species that did not respond to perceived predation risk to which they should be vulnerable; : Species that responded positively to perceived predation risk to which they should be vulnerable; : Species that responded positively to perceived predation ri sk to which they should not be vulnerable. *: EABL was initially thought to not be sensitive to Jay because of the safety of the nest boxes. However, detailed study on its reproduction (Chapter 4 of this dissertation) suggested strong sensitivity of the sp ecies to Jay treatment, probably because under the natural cavities EABL uses for nesting under natural situations do not provide sufficient protection from nest predation by the blue jay.
128 APPENDIX J EFFECT OF TREATMENT ON RAW COUNTS OF PLOT LEVEL SPECIES RICHNESS (CHAPTER 3) Figure J 1. Effects of playback treatment on the number of focal species on each plot as tallied from field surveys (all 18 focal s peci es considered). Effects are presented as the log of treatment level species richness rati o between treatment and Control. This translates into a 17.72%, 13.06% and 15.71% reduction in species richness by Hawk, Jay, and Owl treatments, respectively. Graph shows mean 1.96SE. Double asterisk and triple asterisk indicate a statistical differenc e from zero at P < 0.01 and P < 0.001 levels, respectively.
129 APPENDIX K EFFECTS OF PREDATOR TREATMENT ON PARENTAL INVESTMENT IN EGG PRODUCTION, WITHOUT ADJUSTMENT BASED ON SUCCESS OF THE FIRST NESTING ATTEMPT (CHAPTER 4) Table K 1. Response in egg laying of the eastern bluebird to increased perception of predation risk, without adjustment based on success of the first nesting attempt Reproductive response Model type Model structure Treatment effect (over Control) Treatment Mean SE p value Clutch size GEE Treatment + Clutch identity Hawk 0.486 0.1 51 0.00 1 Jay 0. 44 3 0.1 61 0.0 06 Owl 0.4 70 0. 2 10 0.02 5 Clutch mass GEE Treatment + Clutch identity Owl 1.306 0.6 89 0.0 58 Egg mass GEE Treatment Clutch identity Table shows results from the best model on each of the reproductive measures, in addition to treatment effects that had a P < 0.1 (Wald test). Statistically s ignificant treatment effect s with a P < 0.05 (Wald test ) are highlighted in bold. : Model structure lists the predictor variables retained in the best models.
130 Figure K 1. Effects of predator treatments on parental investment in egg production. A) Clutch size. B) Clutch mass. C) Egg mass. All treatments significantly reduced clutch size compared to Control Owl treatment marginally reduc ed clutch mass compared to Control ; effects of Hawk and Jay were n ot statistically significant. No treatment significant ly a ffect ed egg mass compared to Control although Hawk and Jay treatments may have led to heavier eggs particularly during the second nesting attempt. Values are mean + SE, of unadjusted egg investment data. All statistical analyses were performed on unadjusted egg investment data across two nesting attempts.
132 LIST OF REFERENCES Ackerman J. T., J. M. Eadie, and T. G. Moore. 2006. Does life history predict risk taking behavior of wintering dabbling ducks? Condor 108:530546. Addicott J. F. 1974. Predation and prey community structure experimental study of effect of mos quito larvae on protozoan communities of pitcher plants. Ecology 55:475492. Agrawal A. A. 2001. Phenotypic plasticity in the interactions and evolution of species. Science 294:321326. Alachua County Audubon (ACA). 2010. Alachua county birding calendar. R etrieved from Alachua County Audubon website: http://www.flmnh.ufl.edu/aud/birding.htm Altmann S. T. U. A. R. T. A. 1956. Avian mobbing behavior and predator recognition. Condor 58:241253. Anderson O. R., N. R. Swanberg, and P. Bennett. 1984. An estimate of predation rate and relative preference for algal versus crustacean prey by a spongiose skeletal radiolarian. Marine Biology 78:205207. Andruskiw M., J. M. Fryxell, I. D. Thompson, and J. A. Baker. 2008. Habitat mediated variation in predation risk by t he American marten. Ecology 89:22732280. Angeloni et al. 2008. A reassessment of the interface between conservation and behaviour. Animal Behaviour 75: 731737. Arnold K. E. 2000. Group mobbing behaviour and nest defence in a cooperatively breeding Australian bird. Ethology 106:385393. Banks P. B., M. Nordstrom, M. Ahola, P. Salo, K. Fey, and E. Korpimaki. 2008. Impacts of alien mink predation on island vertebrate communities of the Baltic Sea Archipelago: review of a long term experimental study. Boreal Environment Research 13:316. Barbosa P., I. Castellanos. 2005. Ecology of Predator prey Interactions. Oxford University Press. New York. Barto K. 2012. Multi model inference. URL: http://cran.r project.org/web/packages/MuMIn/index.html Bates D., M. Maec hler, and B. Bolker. 2011. Linear mixedeffects models using S4 classes. URL: http://lme4.r forge.r project.org/
133 Barlow J., C. A. Peres, L. M. P. Henriques, P. C. Stouffer, and J. M. Wunderle. 2006. The responses of understorey birds to forest fragmentation, logging and wildfires: An Amazonian synthesis. Biological Conservation 128:182192. Bawa K. S., R. Seidler. 1998. Natural forest management and conservation of biodiversity in tropical forests. Conservation Biology 12:4655. Bennett P. M., P. H. Harvey. 1988. How fecundity balances mortality in birds. Nature 333:216. Bennett P. M., Owens I. P. F. 2002. Evolutionary Ecology of Birds: Life Histories, Mating Systems, and Extinction. Oxford University Press. Betts M. G., A. S. Hadley, N. Rodenhouse, and J. J Nocera. 2008. Social information trumps vegetation structure in breeding site selection by a migrant songbird. Proceedings of the Royal Society B Biological Sciences 275:22572263. Beukers J. S., G. P. Jones. 1998. Habitat complexity modifies the impact of piscivores on a coral reef fish population. Oecologia 114:5059. Bibby C. J., N. D. Burgess, D. A. Hill, S. Mustoe, S. Lambton. 2000. Bird census techniques. Academic Press, London, U. K. 302 p. Blumstein D. T. 2006. Developing an evolutionary ecology of fear: how life history and natural history traits affect disturbance tolerance in birds. Animal Behaviour 71:389399. Boinski S., L. Kauffman, A. Westoll, C. M. Stickler, S. Cropp, and E. Ehmke. 2003. Are vigilance, risk from avian predators and group s ize consequences of habitat structure? A comparison of three species of squirrel monkey ( Saimiri oerstedii S. boliviensis and S. sciureus). Behaviour 140:14211467. Bray J. R., J. T. Curtis. 1957. An ordination of the upland forest communities of souther n Wisconsin. Ecological Monographs 27:325349. Brown J. S. 1988. Patch use as an indicator of habitat preference, predation risk, and competition. Behavioral Ecology and Sociobiology 22:3747. Buchholz R. 2007. Behavioural biology: an effective and relevant conservation tool. Trends in Ecology and Evolution 22: 401407. Bull E. L., J. A. Jackson. 2011. Pileated Woodpecker ( Dryocopus pileatus ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Burnham K. P., D. Anderson. 2002. Model selection and multi model inference. Springer Press, New York. U.S.A. 496 p.
134 Caro T. 1999. The behavior conservation interface. Trends in Ecology and Evolution14:366369. Caro T. 2007. Behavior and conservation: a bridge too far? Trends in Ecol ogy and Evolution 22:394400. Castro Arellano I., S. J. Presley, M. R. Willig, J. M. Wunderle, and L. N. Saldanha. 2009. Reducedimpact logging and temporal activity of understorey bats in lowland Amazonia. Biological Conservation 142:21312139. Cavitt J. F., C. A. Haas. 2000. Brown Thrasher ( Toxostoma rufum ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Clarke K. R. 1993. Nonparametric multivariate analyses of changes in community structure. Australian Journal of E cology 18:117143. Clark K. R., R. Warwick. 2001. Change in marine communities: an approach to statistical analysis and interpretation (2nd ed.). England: PRIMER E, Plymouth. Cleary D. F. R., T. J. B. Boyle, T. Setyawati, C. D. Anggraeni, E. E. Van Loon, a nd S. B. J. Menken. 2007. Bird species and traits associated with logged and unlogged forest in Borneo. Ecological Applications 17:11841197. Connell J. H. 1975. Some mechanisms producing structure in natural communities: a model and evidence from field ex periments. pp. 460490. In M. L. Cody and J. Diamond [ed.] Ecology and evolution of communities. Belknap Press, Cambridge, Massachusetts. 545 p. Conway C. J., T. E. Martin. 2000. Effects of ambient temperature on avian incubation behavior. Behavioral Ecology 11:178188. Creel S., D. Christianson, S. Liley, and J. A. Winnie Jr. 2007. Predation risk affects reproductive physiology and demography of elk. Science 315:960. Creel S., J. A. Winnie Jr., and D. Christianson. 2009. Glucocorticoid stress hormones and the effect of predation risk on elk reproduction. Proceedings of the National Academy of Sciences of the United States of America 106:1238812393. Cresswell W. 2008. Non lethal effects of predation in birds. Ibis 150:317. Crowder L. B., W. E. Cooper. 1982. Habitat structural complexity and the interaction between bluegills and their prey. Ecology 63:18021813. Curio E. 1978. Adaptive significance of avian mobbing .1. Teleonomic hypotheses and predictions. Zeitschrift Fur TierpsychologieJournal of Comparat ive Ethology 48:175183.
135 Curtis E., R. N. Rosenfield, and J. Bielefeldt. 2006. Cooper's hawk ( Accipiter cooperii ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Cuthill I C, A. I. Houston. 1997. Managing time and energy. Pp. 97 120 in Krebs J R and N. B. Davies eds. (4thDail D., L. Madsen. 2011. Models for estimating abundance from repeated counts of an open metapopulation. Biometrics 67:577587. Edition). Wiley Blackwell. Malden, MA. Danchin E., L. A. Giraldeau, T. J. Valone, and R. H. Wagner. 2004. Public information: From nosy neighbors to cultural evolution. Science 305:487491. DeLuca J. J. 2008. Reproduction of Eastern bluebirds ( Sialia sialias ) in relation to farmland and management and food resources in northcentral Florida. A thesis presented to the Graduate School of the University of Florida in partial fulfillment of the requirements for the degree of Master of Science University of Florida. Gainesville, Florida, U.S.A. 17 p. Deppe C., D. Holt, J. Tewksbury, L. Broberg, J. Petersen, and K. Wood. 2003. Effect of Northern pygmy owl ( Glaucidium gnoma) eyespots on avian mobbing. Auk 120:765771. Desrochers A., M. Belisle, and J. Bourque. 2002. Do mobbing calls affect the perception of pr edation risk by forest birds? Animal Behaviour 64:709714. Devereux C. L., E. Fernandez Juricic, J. R. Krebs, and M. J. Whittingham. 2008. Habitat affects escape behaviour and alarm calling in Common Starlings Sturnus vulgaris Ibis 150:191198. Dial K. P. E. Greene, and D. J. Irschick. 2008. Allometry of behavior. Trends in Ecology & Evolution 23:394401. Doligez B., J. Clobert. 2003. Clutch size reduction as a response to increased nest predation rate in the collared flycatcher. Ecology 84:25822588. Dun ning J. B. 2006. Bachman's Sparrow ( Peucaea aestivalis ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Eames J. C., F. D. Steinheimer, and R. Bansok. 2002. A collection of birds from the Cardamom Mountains, Cambodia including a new subspecies of Arborophila cambodiana. Forktail 18:6786. Edwards D. P., T. H. Larsen, T. D. S. Docherty, F. A. Ansell, W. W. Hsu, M. A. Derhe, K. C. Hamer, and D. S. Wilcove. 2011. Degraded lands worth protecting: the biological
136 importanc e of Southeast Asia's repeatedly logged forests. Proceedings of the Royal Society B Biological Sciences 278:8290. Eggers S., M. Griesser, M. Nystrand, and J. Ekman. 2006. Predation risk induces changes in nest site selection and clutch size in the Siberia n jay. Proceedings of the Royal Society B Biological Sciences 273:701706. Ellison W. G. 1992. Bluegray Gnatcatcher ( Polioptila caerulea), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Endler J. A. 1991. Interactions between predators and prey. In: Behavioural Ecology: an Evolutionary Approach (Krebs J. R., N. B. Davies, eds). Oxford: Blackwell, pp. 169196. Enstam K. L., L. A. Isbell. 2002. Comparison of responses to alarm calls by patas ( Erythrocebus patas ) and vervet ( Cercopithecus aethiops ) monkeys in relation to habitat structure. American Journal of Physical Anthropology 119:314. Evans K. L. 2004. The potential for interactions between predation and habitat change to cause population declines of farmland birds Ibis 146:113. Farnsworth G., G. A. Londono, J. U. Martin, K. C. Derrickson, and R. Breitwisch. 2011. Northern Mockingbird ( Mimus polyglottos ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Felton A., J. Wood, A. M. Felton, B. Hennessey, and D. B. Lindenmayer. 2008. Bird community responses to reducedimpact logging in a certified forestry concession in lowland Bolivia. Biological Conservation 141:545555. Fiske I. J., R. B. Chandler. 2011. Unmarked: An R Package f or Fitting Hierarchical Models of Wildlife Occurrence and Abundance. Journal of Statistical Software 43:123. Fletcher R. J.,Jr. 2007. Species interactions and population density mediate the use of social cues for habitat selection. Journal of Animal Ecology 76:598606. Fletcher R. J.,Jr. 2008. Social information and community dynamics: Nontarget effects from simulating social cues for management. Ecological Applications 18:17641773. Fletcher R. J.,Jr. 2009. Does attraction to conspecifics explain the patc h size effect? An experimental test. Oikos 118:11391147. Florida Natural Areas Inventory (FNAI). 2010. Guide to the natural communities of Florida: 2010 edition. Florida Natural Areas Inventory, Tallahassee, Florida, U.S.A.
137 Fontaine J. J., T. E. Martin. 2 006. Parent birds assess nest predation risk and adjust their reproductive strategies. Ecology Letters 9:428434. Forsman J. T., M. Mnkknen, and M. Hukkanen. 2001. Effects of predation on community assembly and spatial dispersion of breeding forest birds Ecology 82:232244. Forsman J. T., M. M nkk nen. 2001. Responses by breeding birds to heterospecific song and mobbing call playbacks under varying predation risk. Animal Behaviour 62:10671073. Freeman S., W. M. Jackson. 1990. Univariate metrics are not adequate to measure avian body size. Auk 107:6974. Gaillard J. M., D. Pontier, D. Allaine, J. D. Lebreton, J. Trouvilliez, and J. Clobert. 1989. An analysis of demographic tactics in birds and mammals. Oikos 56:5976. Ganzhorn J. U., A. W. Ganzhorn, J. P. Abraham, L. Andriamanarivo, and A. Ramananjatovo. The impacts of selective logging on forest structure and tenrec populations in Western Madagascar. Oecologia 84:126133. Gehlbach F. R. 1995. Eastern screech owl ( Megascops asio), The Birds of North Americ a Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Gehlbach F. R., J. S. Leverett. 1995. Mobbing of Eastern screech owls predatory cues, risk to mobbers and degree of threat. Condor 97:831834. Ghalambor C. K., T. E. Martin. 2000. Parental inv estment strategies in two species of nuthatch vary with stagespecific predation risk and reproductive effort. Animal Behaviour 60:263267. Ghalambor C. K., T. E. Martin. 2001. Fecundity survival tradeoffs and parental risk taking in birds. Science 292:494 497. Gotmark F., P. Post. 1996. Prey selection by sparrowhawks, Accipiter nisus : relative predation risk for breeding passerine birds in relation to their size, ecology and behaviour. Philosophical Transaction of the Royal Society of London Series B Biological Sciences 351:15591577. Gowaty P. A., J. H. Plissner. 1998. Eastern Bluebird ( Sialia sialis ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Gray M. A., S. L. Baldauf, P. J. Mayhew, and J. K. Hill. 2007. The response of avian feeding guilds to tropical forest disturbance. Conservation Biology 21:133141.
138 Green A. J. 2001. Mass/length residuals: measures of body condition or generators of spurious results? Ecology 82:14731483. Greeney H. F., S. M. Wethington. 2009. Proximity to active Accipiter nests reduces nest predation of black chinned hummingbirds. Wilson Journal of Ornithology 121:809812. Greenlaw J. S. 1996. Eastern Towhee ( Pipilo erythrophthalmus ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Greenwood J. L., R. D. Dawson. 2011. Risk of nest predation influences reproductive investment in American kestrels ( Falco Sparverius ): an experimental test. Journal of Raptor Research 45:1526. Grinnell J. 1917. The nicherelationships of the California thrasher. Auk 34:427433. Grodzinski U., I. Erev, and A. Lotem. 2008. Can hungry nestlings be trained to reduce their begging? Behavioral Ecology 19:116125. Grubb T. C. Jr., V. V. Pravasudov. 1994. Tufted Titmouse ( Baeolophus bicolor ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Haftorn S. 1988. Incubating female passerines do not let the egg temperature fall below the physiological zero temperature during their absences from the ne st. Ornis Scandinavica 19:97110. Halkin S. L., S. U. Linville. 1999. Northern Cardinal ( Cardinalis cardinalis ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Hall L. S., P. R. Krausman, M. L. Morrison. 1997. The habitat concept and a plea for standard terminology. Wildlife Society Bulletin 25:173182. Hendrichsen D. K., P. Christiansen, E. K. Nielsen, T. Dabelsteen, and P. Sunde. 2006. Exposure affects the risk of an owl being mobbed experimental evidence. Journal of Avian Biology 37:13 18. Henry M., J. Cosson, and J. Pons. 2007. Abundance may be a misleading indicator of fragmentationsensitivity: the case of fig eating bats. Biological Conservation 139:462467. Hogstedt G. 1983. Adaptation unto death function of fear screams. American Naturalist 121:562570. Horne B. V. 1983. Density as a misleading indicator of habitat quality. Journal of Wildlife Management. 47:893901.
139 Hoyt D. F. 1979. Practical methods of estimating volume and fresh weight of bird eggs. Au k 96:73 77. Hua F., W. Marthy, D. Lee, and M. N. Janra. 2011. Globally threatened Sunda blue flycatcher Cyornis caerulatus : synthesis of global records and recent records from Sumatra. Forktail:8385. Hua F., E. A. Silva Rod rguez J. Frechette, K. E. Sieving. In review. An experimental test of the use of risk based personal and social information by forest birds. Submitted to Behavioral Ecology. Hurd C. R. 1996. Interspecific attraction to the mobbing calls of black capped chickadees ( Parus atricapillus). Behavioral Ecology and Sociobiology 38:287292. Institute of Bird Populations (IBP). 2012. NBII/MAPS avian demographics query interface. Online at: http://www.birdpop. org/nbii/surv/default.asp?SpecSel=brth&strSurv=surv Jackson J. A., H. R. Ouellet. 2002. Downy Woodpecker ( Picoides pubescens ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. James F. C. 1971. Ordinations of habitat relationships among breeding birds. Wilson Bulletin 83:215236. Jeyarajasingam A., and A. Pearson. 1999. A Field Guide to the birds of West Malaysia and Singapore. Oxford University Press, New York, NY. Johns A. D. 1985. Behavioral responses of 2 Malaysian primates ( Hylobates lar and Presbytis melalophos ) to selective logging vocal behavior, territoriality, and nonemigration. International Journal of Primatology 6:423433. Johns A. D. 1986. Effects of selective logging on the behavioral ecology of west Malaysian primates. Ecology 67:684694. Jones J. 2001. Habitat selection studies in avian ecology: A critical review. Auk 118:557562. Julliard R., R. H. McCleery, J. Clobert, and C. M. Perrins. 1997. Phenotypic adjustment of clutch size due to nest predat ion in the great tit. Ecology 78:394404. Jullien M., J. M. Thiollay. 1996. Effects of rain forest disturbance and fragmentation: Comparative changes of the raptor community along natural and humanmade gradients in French Guiana. Journal of Biogeography 23:7 25.
140 Karr J. R., J. D. Nichols, K. Klimkiewicz, and J. D. Brawn. 1990. Survival rates of birds of tropical and temperate forests: will the dogma survive? American Naturalist 136:277291. Kenkel N. C., L. Orloci. 1986. Applying metric and nonmetric multi dimensional scaling to ecological studies: some new results. Ecology 67:919928. Kotler B. P., J. S. Brown, and O. Hasson. 1991. Factors affecting gerbil foraging behavior and rates of owl predation. Ecology 72:22492260. Krama T., I. Krams. 2004. Cost of mobbing call to breeding pied flycatcher, Ficedula hypoleuca. Behavioral Ecology 16:3740. Krama T., I. Krams, and K. Igaune. 2008. Effects of cover on loud trill call and soft seet call use in the crested tit Parus cristatus Ethology 114:656661. Krams I ., T. Krama, K. Igaune, and R. Maend. 2007. Long lasting mobbing of the pied flycatcher increases the risk of nest predation. Behavioral Ecology 18:10821084. Kroodsma D. E., B. E. Byers, E. Goodale, S. Johnson, and W. C. Liu. 2001. Pseudoreplication in pl ayback experiments, revisited a decade later. Animal Behaviour 61:10291033. Kullberg C., J. Ekman. 2000. Does predation maintain tit community diversity? Oikos 89:4145. Lambert F. R., N. J. Collar. 2002. The future for Sundaic lowland forest birds: long term effects of commercial logging and fragmentation. Forktail 18:127146. Langham G. M., T. A. Contreras, and K. E. Sieving. 2006. Why pishing works: Titmouse (Paridae) scolds elicit a generalized response in bird communities. Ecoscience 13:485496. Lanyon W. E. 1997. Great Crested Flycatcher ( Myiarchus crinitus ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Lazarus J., M. Symonds. 1992. Contrasting effects of protective and obstructive cover on avian vigilance. Animal Behaviour 43:519521. Liang K. Y., S. L. Zeger. 1986. Longitudinal dataanalysis using generalized linear models. Biometrika 73:1322. Lima S. L. 1987. Clutch size in birds a predation perspective. Ecology 68:10621070. Lima S. L. 1992. Life in a multipredator environment some considerations for antipredator vigilance. Annales Zoologici Fennici 29:217226.
141 Lima S. L. 1993. Ecological and evolutionary perspectives on escape from predatory attack a survey of NorthAmeri can birds. Wilson Bulletin 105:147. Lima S. L. 1998. Nonlethal effects in the ecology of predator prey interactions what are the ecological effects of anti predator decision making? Bioscience 48:2534. Lima S. L. 2009. Predators and the breeding bird: behavioral and reproductive flexibility under the risk of predation. Biological Reviews 84:485513. Lima S. L., L. M. Dill. 1990. Behavioral decisions made under the risk of predation a review and prospectus. Canadian Journal of Zoology 68:619640. Lima S. L., T. D. Steury. 2005. Perception fo risk: the foundation of nonlethal predator prey interactions. In The Ecology of Predator prey Interactions (eds. Barbosa P., I. Castellanos), pp. 166188. Oxford University Press, Oxford. Lind J., W. Cresswell. 200 5. Determining the fitness consequences of antipredation behavior. Behavioral Ecology 16:945956. Linden M. 1988. Reproductive tradeoff between first and second clutches in the great tit Parus major an experimental study. Oikos 51:285290. Lowther P. E. 1993. Brownheaded Cowbird ( Molothrus ater ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Lucas J. R., T. M. Freeberg, J. Egbert, and H. Schwabl. 2006. Fecal corticosterone, body mass, and caching rates of Carolina chickadees (Poecile coralinensis) from disturbed and undisturbed sites. Hormones and Behavior 49:634643. MacArthur R. H., J. W. MacArthur, and J. Peer. 1962. On bird species diversity II: prediction of bird census from habitat measurements. American Nat uralist 96:167174. MacKenzie D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. A. Royle, and C. A. Langtimm. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83:22482255. MacKenzie D. I., J. D. Nichols, J. E. Hines, M. G. Knutson, and A. B. Franklin. 2003. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84:22002207. MacKenzie D. I., J. D. Nichols, J. A. Royle, K. H. Pollock, L. L. Bailey, and J. E. Hines. 2005. Occupancy estimation and modeling. Academic Press. Burlington, Massachusetts, U.S.A.
142 MacKinnon J., and K. Phillipps. 1993. A Field Guide to the Birds of Borneo, Sumatra, Java, and Bali, the Greater Sunda Islands. Oxford University Press, New York, NY. Magnhagen C. 1990. Reproduction under predation risk in the sand goby, Pomatoschistus minutus and the black goby, Gobius niger : the effect of age and longevity. Behavioral Ecology and Sociobiology 26:331335. Maier T. J., R. M. DeGraaf. 2000. Pr edation on Japanese quail vs. house sparrow eggs in artificial nests: small eggs reveal small predators. The Condor 102:325332. Mandelik Y., M. Jones, and T. Dayan. 2003. Structurally complex habitat and sensory adaptations mediate the behavioural respons es of a desert rodent to an indirect cue for increased predation risk. Evolutionary Ecology Research 5:501515. Martin T. E. 1988a. Habitat and area effects on forest bird assemblages is nest predation an influence. Ecology 69:7484. Martin T. E. 1988b. Processes organizing opennesting bird assemblages: competition or nest predation? Evolutionary Ecology 2:3750. Martin T. E. 1992. Interaction of nest predation and food limitation in reproductive strategies. Current Ornithology 9:163197. Martin T. E. 19 95. Avian lifehistory evolution in relation to nest sites, nest predation, and food. Ecological Monographs 65:101127. Martin T. E. 2002. A new view of avian lifehistory evolution tested on an incubation paradox. Proceedings of the Royal Society B Biolog ical Sciences 269:309316. Martin T. E. 2004. Avian lifehistory evolution has an eminent past: does it have a bright future? Auk 121:289301. Martin T. E., P. R. Martin, C. R. Olson, B. J. Heidinger, and J. J. Fontaine. 2000a. Parental care and clutch sizes in North and South American birds. Science 287:14821485. Martin T. E., J. Scott, and C. Menge. 2000b. Nest predation increases with parental activity: separating nest site and parental activity effects. Proceedings of the Royal Society of London Ser ies B Biological Sciences 267:22872293. Martin T. E., J. V. Briskie. 2009. Predation on dependent offspring: a review of the consequences for mean expression and phenotypic plasticity in avian life history traits. Year in Evolutionary Biology 2009 1168:20 1 217.
143 Martin T. E., S. K. Auer, R. D. Bassar, A. M. Niklison, and P. Lloyd. 2007. Geographic variation in avian incubation periods and parental influences on embryonic temperature. Evolution 61:25582569. Menge B. A. 1976. Organization of New England rock y intertidal community role of predation, competition, and environmental heterogeneity. Ecological Monographs 46:355393. Menge B. A., J. P. Sutherland. 1976. Species diversity gradients synthesis of roles of predation, competition, and temporal heterogeneity. American Naturalist 110:351369. Mnkknen M., J. T. Forsman, T. Kananoja, and H. Ylonen. 2009. Indirect cues of nest predation risk and avian reproductive decisions. Biology Letters 5:176178. Mnkknen M., M. Husby, R. Tornberg, P. Helle, and R. L. Thomson. 2007. Predation as a landscape effect: the trading off by prey species between predation risks and protection benefits. Journal of Animal Ecology 76:619629. Mnkknen M., R. Hrdling, J. Forsman, and J. Tuomi. 1999. Evolution of heterospecifi cs attraction: using other species as cues in habitat selection. Evolutionary Ecology 13:91104. Montgomerie R. D., P. J. Weatherhead. 1988. Risks and rewards of nest defense by parent birds. Quarterly Review of Biology 63:167187. Morosinotto C., R. L. Th omson, and E. Korpimaki. 2010. Habitat selection as an antipredator behaviour in a multi predator landscape: all enemies are not equal. Journal of Animal Ecology 79:327333. Morrison M. L., B. G. Marcot, and R. W. Mannan. 2006. Wildlifehabitat relationshi ps: concepts and applications. 3rdMougeot F., V. Bretagnolle. 2000. Predation as a cost of sexual communication in nocturnal seabirds: an experimental approach using acoustic signals. Animal Behaviour 60:647656. ed. Island Press, Washington, D. C. Nilsso n J. A., H. G. Smith. 1988. Incubation feeding as a male tactic for early hatching. Animal Behaviour 36:641647. Nilsson S. G. 1984. The evolution of nest site selection among holenesting birds: the importance of nest predation and competition. Ornis Scandinavica 15:167175. Nocera J. J., P. D. Taylor, and L. M. Ratcliffe. 2008. Inspection of mobcalls as sources of predator information: response of migrant and resident birds in the Neotropics. Behavioral Ecology and Sociobiology 62:17691777.
1 44 Norrdahl K., E. Korpimaki. 1998. Fear in farmlands: how much does predator avoidance affect bird community structure? Journal of Avian Biology 29:7985. Nott M. P., K. Gordon, D. Kaschube, and T. Morris. 2007. Landbird demographic monitoring in Virginia: an analysis of historical MAPS data in Virginia and surrounding region. Retrieved from Institute of Bird Populations at: http://www.birdpop.org/DownloadDocuments/VAM_MAPS_Report07.pdf Nott P. P. Pyle, and D. Kaschube. 2008. The 2007 report of the Monitoring Avian Productivity and Survivorship (MAPS) Program at Fort Bragg. Point Reyes Station, CA: The Institute for Bird Populations. Retrieved from Institute of Bird Populations at: http://www.birdpop.org/downloaddocuments/bragrep07.pdf Nudds T. D. 1977. Quantifying the vegetative structure of wildlife cover. Wildlife Society Bulletin 5:113117. Nylin S., K. Gotthard. 1998. Plas ticity in life history traits. Annual Reviews in Entomology. 43:6383. O'Brien T. G., M. F. Kinnaird. 1996. Birds and mammals of the Bukit Barisan Selatan National Park, Sumatra, Indonesia. Oryx 30:207217. Olupot W. 2000. Mass differences among male Mangabey monkeys inhabiting logged and unlogged forest compartments. Conservation Biology 14:833843. Orpwood J. E., A. E. Magurran, J. D. Armstrong, and S. W. Griffiths. 2008. Minnows and the selfish herd: effects of predation risk on shoaling behaviour are dependent on habitat complexity. Animal Behaviour 76:143152. Otis D. L., J. H. Schulz, D. Miller, R. E. Mirarchi, and T. S. Baskett. 2008. Mourning Dove (Zenaida macroura), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithol ogy. Paine R. T. 1966. Food web complexity and species diversity. American Naturalist 100:6575. Pan W. 2001. Akaike's information criterion in generalized estimating equations. Biometrics 57:120125. Prt T. 1991. Philopatry pays: a comparison between col lared flycatcher sisters. American Naturalist 138:790796. Prt T. 1994. Male philopatry confers a mating advantage in the migratory collared flycatcher, Ficedula albicollis Animal Behaviour 48:401409.
145 Pianka E. R., W. S. Parker. 1975. Agespecific repro ductive tactics. American Naturalist 109:453464. Polis G. A., C. A. Myers, and R. D. Holt. 1989. The ecology and evolution of intraguild predation potential competitors that eat each other. Annual Review of Ecology and Systematics 20:297330. Pollock K. H. 1982. A capturerecapture design robust to unequal probability of capture. Journal of Wildlife Management 46:752757. Preisser E. L., D. I. Bolnick, and M. F. Benard. 2005. Scared to death? The effects of intimidation and consumption in predator prey i nteractions. Ecology 86:501509. Putz F. E., G. M. Blate, K. H. Redford, R. Fimbel, and J. Robinson. 2001. Tropical forest management and conservation of biodiversity: an overview. Conservation Biology 15:720. Quinn J. L., S. J. Reynolds, and R. B. Bradbu ry. 2008. Birds as predators and as prey. Ibis 150:18. R Development Core Team. 2011. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: http://www.R project.org/ Redondo T., F. Castro. 1992. The increase in risk of predation with begging activity in broods of magpies Pica pica. Ibis 134:180187. Ricklefs R. E. 1969. An analysis of nesting mortality in birds. Smithsonian Contributions to Zoology 9:148. Ricklefs R. E. 1977. Reactions of so me Panamanian birds to human intrusion at nest. Condor 79:376379. Riou S., K. C. Hamer. 2008. Predation risk and reproductive effort: impacts of moonlight on food provisioning and chick growth in Manx shearwaters. Animal Behaviour 76:17431748. Robinson W D. 1996. Summer Tanager ( Piranga rubra), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Rodenhouse N. L., T. W. Sherry, and R. T. Holmes. 1997. Sitedependent regulation of population size: a new synthesis. Ecology 78:20252042. Rodewald P. G., R. D. James. 2011. Yellow throated Vireo ( Vireo flavifrons ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology.
146 Rodewald P. G., J. H. Withgott, and K. G. Smith. 1999. Pine Warbler ( Setophaga pinus ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Roff D. A. 1992. The evolution of life histories: theory and analysis. New York: Chapman & Hall. Rota C. T., R. J. Fletcher Jr., R. M. Dorazio, and M. G. Betts 2009. Occupancy estimation and the closure assumption. Journal of Applied Ecology 46:11731181. Royle J. A. 2004. N mixture models for estimating population size from spatially replicated counts. Biometrics 60:108115. Royle J. A., R. M. Dorazio. 2008. H ierarchical modeling and inference in ecology: the analysis of data from populations, metapopulations and communities. Academic Press, London, U. K. Royle J. A., J. D. Nichols, and M. Kry. 2005. Modelling occurrence and abundance of species when detection is imperfect. Oikos 110:353359. Saether B. E. 1988. Pattern of covariation between lifehistory traits of European birds. Nature 331:616617. Safriel U. N. 1975. Significance of clutch size in nidifugous birds. Ecology 56:703708. Savino J. F., R. A. Stein. 1989. Behavioral interactions between fish predators and their prey effects of plant density. Animal Behaviour 37:311321. Scheuerlein A., T. J. Van't Hof, and E. Gwinner. 2001. Predators as stressors? Physiological and reproductive consequences o f predation risk in tropical stonechats ( Saxicola torquata axillaris ). Proceedings of the Royal Society of London Series B Biological Sciences 268:15751582. Schooley R. L., P. B. Sharpe, and B. VanHorne. 1996. Can shrub cover increase predation risk for a desert rodent? Canadian Journal of Zoology Revue Canadienne De Zoologie 74:157163. Scott J. M, P. J. Heglund, M. L. Morrison, J. B. Haufler, M. G. Raphael, W. A. Wall, and F. B. Samson. [eds]. 2002. Predicting species occurrences. Island Press, Washingto n, D. C. Sekercioglu C. H. 2002. Effects of forestry practices on vegetation structure and bird community of Kibale National Park, Uganda. Biological Conservation 107:229240. Sekercioglu C. H., P. R. Ehrlich, G. C. Daily, D. Aygen, D. Goehring, and R. F. Sandi. 2002. Disappearance of insectivorous birds from tropical forest fragments.
147 Proceedings of the National Academy of Sciences of the United States of America 99:263267. Seppnen J T ., J. T. Forsman, M. Mnkknen, and R. L. Thomson. 2007. Social inform ation use is a process across time, space, and ecology, reaching heterospecifics. Ecology 88: 16221633. Shackelford C. E., R. E. Brown, and R. N. Conner. 2000. Redbellied Woodpecker ( Melanerpes carolinus ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Shedd D. H. 1983. Seasonal variation and function of mobbing and related antipredator behaviors of the American Robin ( Turdus migratorius ). Auk 99: 342346. Shedd D. H. 1983. Seasonal variation in mobbing intensity in the black capped chickadee. Wilson Bulletin 95:343348. Sieving K. E., T. A. Contreras, and K. L. Maute. 2004. Heterospecific facilitation of forest boundary crossing by mobbing understory birds in NorthCentral Florida. Auk 121:738751. Sih A., P. Cro wley, M. Mcpeek, J. Petranka, and K. Strohmeier. 1985. Predation, competition, and prey communities a review of field experiments. Annual Review of Ecology and Systematics 16:269311. Skutch A. F. 1949. Do tropical birds rear as many young as they can nourish. Ibis 91:430458. Slagsvold T. 1984. Clutch size variation of birds in relation to nest predation on the cost of reproduction. Journal of Animal Ecology 53:945953. Smith H. G., H. Kallander, and J. A. Nilsson. 1987. Effect of experimentally altered brood size on frequency and timing of second clutches in the great tit. Auk 104:700706. Smith K. G., J. H. Withgott, and P. G. Rodewald. 2000. Red headed Woodpecker ( Melanerpes erythrocephalus ), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Smythies B. E. 1981. The Birds of Borneo. Sabah Society with the Malayan Nature Society, Kota Kinabalu and Kuala Lumpur, Malaysia. Sordahl T. A. 1990. The risks of avian mobbing and distraction behavior: an anecdotal review. Th e Wilson Bulletin 102:349352.
148 Stracey C. M. 2010. Pattern and process in urban bird communities: what makes the northern mockingbird an urban adapter? A dissertation presented to the Graduate School of the University of Florida in partial fulfillment of t he requirements for the degree of Doctor of Philosophy University of Florida. Gainesville, Florida, U.S.A. 62 p. Suhonen J., K. Norrdahl, and E. Korpimaki. 1994. Avian predation risk modifies breeding bird community on a farmland area. Ecology 75:1626163 4. Sutherland W. J. 1998. The importance of behavioural studies in conservation biology. Animal Behaviour. 56:801809. Tarvin K. A., G. E. Woolfenden. 1999. Blue jay ( Cyanocitta cristata), The Birds of North America Online (A. Poole, Ed.). Ithaca: Cornell Lab of Ornithology. Templeton C. N., E. Greene. 2007. Nuthatches eavesdrop on variation in heterospecific chickadee mobbing alarm calls. Proceedings of National Academy of Sciences 104:54795482. Thiollay J. M. 1992. Influence of selective logging on bird species diversity in a Guianan rain forest. Conservation Biology 6:4763. Thiollay J. M. 1999. Frequency of mixed species flocking in tropical forest birds and correlates of predation risk: an intertropical comparison. Journal of Avian Biology 30:282294. Thomson D. L., P. Monaghan, R. W. Furness. 1998. The demands of incubation and avian clutch size. Biological Reviews 73:293304. Thomson R. L., J. T. Forsman, F. SardaPalomera, and M. Mnkknen. 2006. Fear factor: prey habitat selection and its consequenc es in a predation risk landscape. Ecography 29:507514. Turcotte Y., A. Desrochers. 2002. Playbacks of mobbing calls of Black capped Chickadees help estimate the abundance of forest birds in winter. Journal of Field Ornithology 73:303307. USDA Natural Res ources Conservation Service. 1999. Eastern Bluebird ( Sialia sialis ). Fish and Wildlife Habitat Management Leaflet. 12 p. Verdolin J. L. 2006. Metaanalysis of foraging and predation risk tradeoffs in terrestrial systems. Behavioral Ecology and Sociobiology 60:457464. Vezina A. F. 1985. Empirical relationships between predator and prey size among terrestrial vertebrate predators. Oecologia 67:555565.
149 Vonesh J. R., J. M. Kraus, J. S. Rosenberg, and J. M. Chase. 2009. Predator effects on aquatic community assembly: disentangling the roles of habitat selection and post colonization processes. Oikos 118:12191229. Warton D. I., T. W. Wright, and Y. Wang. 2012. Distance based multivariate analyses confound location and dispersion effects. Methods in Ecology and Evolution 3: 89101. White F. N., J. L. Kinney. 1974. Avian incubation. Science 186:107115. Whittaker R. H., S. A. Levin, and R. B. Roor. 1973. Niche, habitat, and ecotope. American Naturalist 107:321338. Whitten T. 1997. The Ecology of Sumatra. Eric Oey Tuttle Publishing. North Clarendon, VT. Whittingham M. J., C. L. Devereux, A. D. Evans, and R. B. Bradbury. 2006. Altering perceived predation risk and food availability: management prescriptions to benefit farmland birds on stubble fields. Journal of Applied Ecology 43:640650. Whittingham M. J., K. L. Evans. 2004. The effects of habitat structure on predation risk of birds in agricultural landscapes. Ibis 146:210220. Williams G. C. 1966. Natural selection costs of reproduction and a refinement of Lack' s principle. American Naturalist 100:687690. Wilson D. M., J. Bart. 1987. Reliability of singing bird surveys: effects of song phenology during the breeding season. Condor 87:6973. Woltmann S. 2003. Bird community responses to disturbance in a forestry c oncession in lowland Bolivia. Biodiversity and Conservation 12:19211936. Wunderle J. M., L. M. P. Henriques, and M. R. Willig. 2006. Short term responses of birds to forest gaps and understory: an assessment of reducedimpact logging in a lowland Amazon f orest. Biotropica 38:235255. Zanette L. Y., A. F. White, M. C. Allen, and M. Clinchy. 2011. Perceived predation risk reduces the number of offspring songbirds produce per year. Science 334:13981401. Zimmerman E. A. 2009. Sialis.org, Woodstock CT. Retriev ed from Sialis online: http://www.sialis.org/predatorid.htm (November 14, 2012). Zurr A. F., E. N. Ieno, N. Walker, A. A. Saveliev, and G. M. Smith. 2009. Mixed effects models and extensions in ecology with R. Springer, New York. NY.
150 BIOGRAPHICAL SKETCH F angyuan Hua was born and raised in the city of Leshan, Sichuan Province, China. She obtained her b achel ors degree in s cience (major of b iology) from Be ijing Normal University in 2003, and went on to obtain her m aster s degree in s cience (major of biochemistry and molecular biology) from Peking Union Medical College in 2006. A love for nature and outdoors, and a passion for conservation cultivated from her bird watching hobby eventually led her down the path of becoming a wildlife ecologist (although she is still yet to work up her professional skills with critters that do not have feathers). Sh e started her Ph.D. program in wildlife ecology and c onservation and interdisciplinary ecology at the University of Florida in Fall 2006, under the mentorshi p of Dr. Katie Sieving. Over the years, her professional interest has gradually developed, and her vision of a professional career as a wildlife ecologist and conservation scientist has gradually taken shape. Fangyuan is intensely interested in nature cons ervation in Asia and her home country China, and would like to continue working in that region for the conservation of natural habitat and biodiversity after finishing her Ph.D. degree.