University of Florida Thesis Or Dissertation Formatting

FORAGING ECOLOGY AND CALL RELEVANCE DRIVE RELIANCE ON SOCIAL INFORMATION IN AN AVIAN EAVESDROPPING NETWORK

HARRISON HENRY JONES

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

2016

To all those who have given me the inspiration, confidence, and belief to pursue my passion in life

ACKNOWLEDGMENTS

I thank my parents, who have not only provided unconditional support and encouragement during my degree, but also imbued in me an appreciation for nature and a curiosity about the world. My thanks also go to Elena West, Jill Jankowski, and Rachel

Hoang who gave me confidence and the enthusiasm, not to mention sage advice, to apply to a graduate program and follow my passion for ornithology. I would also be remiss to not mention the tremendous support received from the Sieving lab, in particular my amazing officemates Kristen Malone, Willa Chaves, and Andrea Larissa

Boesing who were always available to provide help and perspective when needed.

I would also like to thank my committee for their helpful input. Katie Sieving was a tremendous help in designing and executing the study, in particular through her knowledge of parid vocalizations. Scott Robinson provided in-depth background knowledge about the study species and insight into the results obtained. And Ben

Baiser was invaluable in assisting with the multivariate statistics and generally any other quantitative question I could throw at him. Finally, a big thanks to my many field technicians, Henry Brown, Jason Lackson, Megan Ely, and Florencia Arab, without whom this degree would not be possible.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

ABSTRACT ...... 9

CHAPTER

1 INTRODUCTION ...... 11

Animal Information Networks ...... 11 Factors Determining the Value of Social Information to Eavesdroppers ...... 13

2 FORAGING ECOLOGY AND CALL RELEVANCE DRIVE RELIANCE ON SOCIAL INFORMATION IN AN AVIAN EAVESDROPPING NETWORK ...... 17

Methods ...... 17 Study System ...... 17 Study Design and Predictions ...... 19 Characterizing Foraging Behavior ...... 21 Characterizing Sociality and Call Relevance ...... 22 Alarm Call Playback Procedures ...... 23 Data Reduction of Foraging and Microhabitat Variables ...... 26 Hypothesis Evaluation Using Generalized Linear Models ...... 27 Results ...... 28 Foraging Observations ...... 28 Playback Experiment ...... 29 Defining the Microhabitats Occupied During Playback Trials ...... 31 Defining the Foraging Niches of the Winter Bird Community ...... 31 Modeling Factors Determining Species’ Reliance on Social Information ...... 33

3 CONCLUDING REMARKS ...... 45

The Importance of Foraging Ecology: Aerial Foragers Are Different ...... 47 Call Relevance Matters, Social System Does Not ...... 49 Asymmetric Eavesdropping Networks: Mutualism, Commensalism, or Parasitism? ...... 51

APPENDIX ADDITIONAL TABLES AND FIGURES...... 54

LIST OF REFERENCES ...... 65

BIOGRAPHICAL SKETCH ...... 75

LIST OF TABLES

Table page

2-1 Predictor variables used in the GLMs for overall (Y/N) response and response type...... 35

2-2 Summarized foraging data...... 36

2-3 Summary of playback response for each species...... 37

2-4 Model-averaged results of GLMs of overall response and response type...... 39

A-1 Principal coordinate axes of the microhabitat measures collected before playback...... 54

A-2 Principal coordinate axes from analysis of the foraging ecology data...... 54

A-3 Factor loadings for the foraging ecology principal coordinates used in the analysis...... 55

A-4 Average values for large and small principal coordinate scores of microhabitat data...... 56

A-5 Candidate model set of GLMs for overall response used in the model averaging...... 56

A-6 Candidate set of best models to explain response type (dive versus freeze). .... 59

A-7 Proportions of foraging maneuver and foraging microhabitat use in a winter bird community...... 60

LIST OF FIGURES

Figure page

2-1 Biplots of foraging principal coordinate axes used in the GLM analysis...... 41

2-2 Fitted values for the significant predictors of overall response to the titmouse alarm call...... 42

2-3 Fitted values for the significant predictors of response type (diving versus freezing response)...... 44

A-1 Biplot of principal coordinate axes Edge-MH and Trunk-MH of the microhabitat variables...... 64

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

FORAGING ECOLOGY AND CALL RELEVANCE DRIVE RELIANCE ON SOCIAL INFORMATION IN AN AVIAN EAVESDROPPING NETWORK

Harrison Henry Jones

August 2016

Chair: Kathryn Sieving Major: Wildlife Ecology and Conservation

Vertebrates obtain social information about predation risk by eavesdropping on the alarm calls of sentinel species, which can act as community-wide informants for large numbers of heterospecifics. However, the relative importance of this social information to different eavesdropping species is unknown. We tested the relative importance of four leading hypotheses (foraging ecology, sociality, call relevance based on body size, and local microhabitat) in determining heterospecific reliance on the alarm call of the Tufted Titmouse (Baeolophus bicolor) in a Florida winter bird community. We presented 16 forest species, exhibiting broad social, ecological, and taxonomic variability, with a titmouse alarm call (known to generate strong anti-predator responses in birds) and quantified responses to playback. Predictor metrics representing local microhabitat were assessed during the playback study, whereas species’ foraging ecology and sociality were determined through independent behavioral observations in the study area. Following data reduction procedures applied to 56 total measures, we tested for the effects of 11 final predictor variables (expressing four hypothesized factors plus nuisance variables) on two different response metrics (presence and type of escape response). Using generalized linear modeling we determined best predictors

using model averaging of a candidate set of models (ΔAICc < 2). Overall response was best predicted by foraging ecology and call relevance; relatively larger-bodied canopy foragers and those that use more aerial maneuvers responded less often. Escape behavior type was best explained by a species’ foraging distance from the tree trunk; near-trunk foragers were more likely to freeze in response to playback, whereas foliage gleaners spending time far from trunks were more likely to dive for cover. Our work clearly identifies a large proportion of a winter bird community that relies vigorously on eavesdropping to obtain social information that is critical for predation avoidance.

CHAPTER 1 INTRODUCTION

Animal Information Networks

Individuals must constantly seek information about their surroundings to reduce uncertainty and make adaptive behavioral choices (Danchin et al., 2004; Dall et al.,

2005; Seppanen et al., 2007; Schmidt et al., 2010). Information gained from personal interaction with the environment, or personal information, can be supplemented with cues and signals obtained from other individuals of the same or a different species

(social information). Such publically-obtained information can consist of cues obtained from the environment or another individual, or it can take the form of encoded information transmitted from a signaler to one or more receivers (Dall et al., 2005). The most important potential source of social information is that derived from heterospecifics within a target species’ own trophic level (Goodale et al., 2010). Ecologically similar heterospecifics that share predators, diet items, and other aspects of a target species’ niche space will, collectively, be more abundant than a species’ own conspecifics, yet impose less competition for resources. As such they represent highly available relevant sources of social information at a reduced cost relative to conspecifics (Seppanen et al.,

2007). Heterospecifics might also be better able to detect or signal about relevant threats and opportunities because of differences in sensory physiology, behavior, and habitat use that expand the target species’ effective perceptual range (Goodale and

Kotagama, 2005a).

Among terrestrial vertebrates, the principal mechanism by which heterospecific social information exchange occurs is eavesdropping, when an animal uses information from a signal intended for others (Magrath et al., 2014). The most widespread form of

eavesdropping is interceptive, where the eavesdropper responds very quickly to the signal, and involves alarm calls (Magrath et al., 2014), which are widespread vocalizations in birds and mammals that are used to warn conspecifics about predators

(Caro, 2005). Individuals obtain information about predation risk by eavesdropping on heterospecific alarm calls with encoded information that is relevant, or indicative of danger to the eavesdropper. Calls from specific ‘information-producing’ species can encode highly-complex information on the type of predator (Seyfarth et al., 1980;

Suzuki, 2012) and the degree of danger it represents (Leavesley and Magrath, 2005;

Templeton et al., 2005). Such species often play a sentinel role, and eavesdropping individuals gain immediate benefits from adopting appropriate anti-predator behaviors

(such as fleeing from a mobile predator and mobbing a stationary one) in response to social cues. Additionally, significant indirect benefits are obtained in the form of increased foraging time and efficiency, reduced vigilance, expanded niche breadth, and learning about predators (Dolby and Grubb, 1998; Magrath et al., 2014).

Thus, the production and consumption of social information in a given animal community defines a biological ‘information network’ (Vos et al., 2006; Seppanen et al.,

2007; Goodale et al., 2010; Schmidt et al., 2010; Magrath et al., 2014). Information networks exist independently of trophic networks, and extend beyond antipredator information to, for example, nest site selection based on cues from heterospecifics

(Fletcher, 2008; Jaakkonen et al., 2014). Exchanges of social information are often asymmetric (Magrath et al., 2007; Munoz et al., 2015), where a ‘community informant’ species acts as an information producer serving a diverse audience of heterospecific

‘information consumers’ (Goodale et al., 2010; Schmidt et al., 2010; Contreras and

Sieving, 2011). Such asymmetric information exchanges likely serve as a mechanism underlying large-scale facilitation (Contreras and Sieving, 2011; Hetrick and Sieving,

2012; Szymkowiack, 2013). Because the study of ecological informational networks is in its infancy, most work focuses on descriptions of receiver responses to social information within signaling dyads and ignores the relative importance of social information exchanged (Westrip and Bell, 2015). Due to the asymmetrical nature of information webs, the social information produced by a community informant species may be of varying value to other species in the network (Goodale and Kotagama, 2008;

Magrath et al., 2009; Martinez and Zenil, 2012). Functional knowledge of information networks and, ultimately, their conservation requires characterization of relative strengths of information exchanges as well as asymmetries in species importance within whole communities (Proulx et al., 2005).

Factors Determining the Value of Social Information to Eavesdroppers

A number of factors defined by species traits (physical and behavioral) and environmental constraints will influence the extent to which a species can personally collect all necessary information for successful decision-making or must instead rely on information from other individuals of same or different species (Seppanen et al., 2007;

Parejo and Aviles, 2016). Some species in an information network should be better at detecting threats on their own by virtue of their foraging ecology (Goodale et al., 2010).

For instance, species that forage higher in the vegetation are more likely to detect aerial predators (Morse, 1977; Munn and Terborgh, 1979; Gautierhion et al., 1983). Similarly, birds appear to be better at detecting predators visually than mammals by virtue of their arboreal foraging habits (Rasa, 1983; Lea et al., 2008). Species that forage on

substrates (substrate-based foragers) may have reduced vigilance because of visual occlusion by foliage, while aerially-foraging species (salliers) can scan for prey items and predators simultaneously. Among forest birds, substrate-based foragers respond more readily to heterospecific alarm calls than salliers, indicating greater reliance on social information (Goodale and Kotagama, 2008; Martinez and Zenil, 2012). Species with similar foraging behaviors convergently evolve similar morphological and physiological structures known as ecomorphs (Corbin, 2008; Botero-Delgadillo and

Bayly, 2012); for instance, eye morphology differs significantly between bird species in different foraging guilds (Lisney et al., 2013; Moore et al., 2013). These suites of physiological adaptations to foraging behaviors may result in similar physiological limitations in detection capability, and hence similar degrees of reliance on social information.

Alternatively, sociality may play a key role in determining reliance on social information about predators. Highly gregarious species that live in large conspecific groups may obtain most of their social information from group members, while solitary species that exclude or avoid conspecifics must depend on heterospecifics. Highly social species are more likely to give alarm calls than solitary ones, possibly in order to warn group members or kin about predation risk (Sridhar et al., 2009; Srinivasan et al.,

2010), whereas some solitary species lack alarm calls entirely (Fuong et al., 2014).

Similarly, social species can evolve complex vigilance behaviors such as the sentinel system of the pied babbler, a ground-foraging bird that accurately assesses ambient predation risk in its alarm calls (Ridley and Raihani, 2007; Ridley et al., 2010). Babbler response to heterospecific alarm calls varies in relation to group size, with smaller

groups of babblers eavesdropping more readily than large ones (Ridley and Raihani,

2007). Social primates also seem to only join mixed-species foraging groups when in small numbers relative to conspecific groups (Bshary and Noe, 1997). Solitary species, by contrast, always respond to heterospecific alarm calls of social species in the few systems in which this has been tested (Lea et al., 2008; Ridley et al., 2014).

However, the degree of response to heterospecific alarm calls might also be determined by the degree to which the signaling species’ alarm call signifies a real threat to the eavesdropper. Call relevance, or the proportion of total instances in which the predator eliciting the alarm call represents a threat to the eavesdropper, is an important attribute of an alarm call that can shape heterospecific response (Magrath et al., 2014). The success and likelihood of attacks by predators on prey are strongly influenced by predator-prey body sizes. Predators seek the biggest prey they can successfully subdue, and prey of the same body size range will therefore be vulnerable to the same predators (Rodgers et al., 2015). Therefore, the body size of the alarm- calling species relative to the eavesdropper defines the relevance of the call to the eavesdropper. For example, hornbills are arboreal birds that are vulnerable to eagles, but not leopards. The hornbills respond to the ‘eagle’ alarm calls of a sympatric monkey species, but not the ‘leopard’ alarm call (Rainey et al., 2004a, b). In an Australian passerine community, New Holland honeycreepers (Phylidonyris novaehollandiae) respond to the alarm calls of white-browed scrub-wren (Sericornis frontalis; 18% of alarms given to non-shared predators), but not to those of superb fairy-wrens (Malurus cyaneus; 52% of alarms given to non-shared predators), demonstrating that eavesdroppers learn to respond to relevant alarms only (Magrath et al., 2009).

Finally, the importance of social information to a receiver may depend partly upon local factors. For example, different foraging microhabitats have different associated predation risks for small forest passerines (a ‘micro-landscape of fear’).

Ambush predators that prey on these species attack from above and preferentially target prey further from the trunk (Kullberg, 1995), so forest microhabitats closer to tree trunks and in dense vegetation are thought to be safer (Desrochers, 1989; Suhonen,

1993; Brotons et al., 2000). Similarly, foraging at greater heights may afford higher vigilance and reduced predation risk from predators (Suhonen, 1993; Lee et al., 2005;

Carrascal and Alonso, 2006). Perceived predation risk in mammals, as measured by vigilance levels, differs similarly in relation to small-scale variables such as vegetation density, light intensity, and canopy stratum (Campos and Fedigan, 2014; Lashley et al.,

2014; Kuijper et al., 2015). We therefore predicted that micro-sites defining incident risk of death during an attack would interact with species traits to define prey responses to simulated alarm calls in our study.

In this comparative study, we present a common heterospecific alarm call from a sentinel species to a winter community of forest birds in order to elucidate the determinants of the interaction strengths in a vertebrate eavesdropping network. We compare the relative importance of local, microhabitat factors and three species-level ecological traits (foraging ecology, sociality, or call relevance) in determining the strengths of eavesdropping interactions in an information network.

CHAPTER 2 FORAGING ECOLOGY AND CALL RELEVANCE DRIVE RELIANCE ON SOCIAL INFORMATION IN AN AVIAN EAVESDROPPING NETWORK

Methods

Study System

All field work was conducted on wildlands in the vicinity of Gainesville, Florida,

USA in the North-central portion of the Florida peninsula. Study sites included San

Felasco Hammock Preserve State Park (29º43′44″N 82º26′31″W), Paynes Prairie State

Park (29°34′59″N 82°19′59″W), O’Leno State Park (29°55′01″N 82°35′02″W), Gum Root

Park (29°40′50″N 82°14′17″W), and Newnan’s Lake Conservation Area

(29°40′58″N 82°13′29″W). This area of Florida belongs to the Southeastern conifer forests ecoregion (Olson et al., 2001), which consists of a subtropical mosaic of vegetation types ranging from xeric pine forests to mesic swamplands. In order to standardize the habitat types sampled, we selected only field sites in broadleaf forest, which has the most species-rich forest bird community in winter (Engstrom, 1993).

These upland hardwood forests grow near lakes and spring-fed steams, and the leaf mulch conserves soil moisture leading to mesic conditions (FDNR, 1990). The closed canopy of the forest is structurally diverse and dominated by an assemblage of deciduous trees. Near Gainesville, this mesic forest type is composed of American sweetgum (Liquidambar styraciflua), spruce pine (Pinus glabra), Southern magnolia

(Magnolia grandiflora), swamp chestnut oak (Quercus michauxii), diamondleaf oak

(Quercus laurifolia), bluff oak (Quercus austrina), and pignut hickory (Carya glabra).

Common understory species include American holly (Ilex opaca), Eastern hophornbeam

(Ostrya virginiana), Southern sugar maple (Acer floridanum), saw palmetto (Serenoa repens), and flowering dogwood (Cornus florida).

Florida is home to a more diverse bird community in winter than in summer due to the post-breeding migrations of both short- and long-distance migrants from the North

(Kale II and Maehr, 1990). North-central Florida hosts a great ecological diversity of avian species varying widely in foraging ecology (attack maneuver, foraging microhabitat, height from ground), winter sociality (conspecific group size, degree of territoriality, propensity to join mixed-species flocks), body size (range 6-77 g.), and migratory habit (short- and long-distance migrants, resident species). There are 29 regularly occurring species in hardwood floodplain habitat, including 18 species that are at least partially migratory (Sibley, 2014). In this system, the Tufted Titmouse

(Baeolophus bicolor, hereafter titmouse) is an abundant, year-round-resident, information-producing species that acts as a sentinel species for heterospecific eavesdroppers through high vigilance combined with aggressive mobbing (Sieving et al., 2004) and alarm calling (Gaddis, 1980) directed at predators. Titmice produce complex ‘risk-based’ alarm calls that accurately and reliably encode the size and threat level of a potential predator (Templeton et al., 2005; Sieving et al., 2010) and thus have a community-wide audience of eavesdroppers (Sieving et al., 2004; Langham et al.,

2006). Titmice also act as nuclear species for mixed-species foraging flocks of forest birds (Contreras and Sieving, 2011) that form around small family groups of two to five individuals that hold stable winter territories (Brawn and Samson, 1983; Pravosudova and Grubb, 2000). These foraging flocks are joined by many species of small forest passerines in winter that follow titmouse groups and forage with them (Farley et al.,

2008).

Study Design and Predictions

Given the diverse wintering community in our study system, we can test hypotheses by sampling across the existing range of variation in each factor of interest to obtain causal inference about their importance in determining a species’ reliance on social information (James and McCulloch, 1995). This diverse subtropical winter bird community also has access to a common source of social information in the form of the winter social groups of the Tufted Titmouse, and the alarm calls that they give to aerial predators. As such, we can present the entire community with a familiar and common stimulus, the meaning of which has been well characterized in previous studies, and directly compare species responses. Here we test four hypotheses for winter birds’ reliance on social information from a sentinel species through a comparative study across a full winter bird community. We take local measures of foraging behavior and group size for all species in this system, the natural history of which is well known and the species associations well described, and then present all species with a common titmouse alarm call playback stimulus.

We assumed that species would be very responsive to titmouse high Z alarm calls based on the sentinel role that parids play in Holarctic ecosystems, the high degree of information encoded in their anti-predator calls, the high risk associated with our chosen stimulus, and previous findings (reviewed above). Therefore, patterns of response should vary based on the proposed ecological drivers, with each hypothesis proposing clear, non-mutually-exclusive predictions derived from the literature as follows. If foraging ecology is the main driver of reliance (H1), species with more aerial foraging maneuvers should be less reliant than species that employ more substrate- based maneuvers. Aerial foraging maneuvers allow an individual to scan for prey items

and potential predators simultaneously, increasing the acquisition of personal information and reducing the trade-off between foraging and vigilance. Similarly, species that forage at greater distances from the ground (higher vegetation strata) and in more open microhabitats can also obtain more personal information because of the increased visibility afforded by their foraging niches. Because personal information complements social information, increased visibility while foraging should lead to reduced reliance on titmouse alarm calls.

If, instead, sociality is the main driver (H2), we predicted that conspecifically social species with larger average group sizes in winter should be less reliant on social information from the titmouse than solitary species. Conspecifics can act as a significant source of social information, reducing dependence on heterospecific information; solitary species must rely on heterospecifics for all of their social information because they avoid or exclude members of their own species. Another alternative hypothesis is that call relevance will play an important role in determining response (H3). Because social information is only useful if it is relevant, species should evolve to respond only to the alarm calls of species that are vulnerable to the same set of predators. Using the ratio of titmouse to focal species body size as an index of call relevance (see below), we predicted that species with a body size ratio nearer to 1:1

(having a greater number of shared predators with the titmouse) will be more responsive to alarm calls than those with fewer shared predators (and body size ratios further from 1:1). Finally, local microhabitat that a focal individual occupies (H4) upon detecting an alarm call might affect response because it influences perception of successful predator attack by prey. We predicted increased responsiveness when the

focal individual was located in sites that are closer to the ground, farther from the trunk, in more open habitat, or at farther distances from escape cover, because these factors have been shown to influence vigilance.

Characterizing Foraging Behavior

In order to characterize the winter foraging ecology of the hardwood bird community, we performed foraging observations on wild birds from December 2014 to

February 2015 (winter 1) and from November 2015 to January 2016 (winter 2). A single observer (HHJ) performed the observations with 10x binoculars (Swarovski EL 10x42,

Swarovski Optik, Absam, Austria). We walked trails or transects through hardwood habitat and recorded the foraging behavior of all birds encountered. To ensure independence of the foraging data, we did not resample trails or transects during the same winter sampling period. We performed focal individual sampling, where a single individual was observed until it was lost from sight. When we encountered a mixed- species flock, we only sampled the foraging behavior of a single individual of each species. We recorded each foraging maneuver as the sample unit (following Remsen and Robinson, 1990), along with four associated microhabitat variables for each maneuver observed: height from ground, distance from trunk, vegetation density, and substrate (names of categorical variables in table A-3). Foraging data was taken using a voice recorder in the field and later transcribed into a spreadsheet. We classified foraging maneuvers and substrates according to the Robinson and Remsen typology

(Remsen and Robinson, 1990). Distance from trunk was binned into three categories

(near trunk, medium, and far from trunk), and we estimated height above ground with the help of a laser rangefinder (Raider 600 Digital Laser Rangefinder, Redfield Inc.

Beaverton, OR). Vegetation density in an imaginary 1 m radius sphere around the focal individual was estimated on a 0 to 5 scale after Remsen and Robinson (1990).

Characterizing Sociality and Call Relevance

In order to test the sociality and call relevance hypotheses, we obtained data on winter sociality and body mass from the literature. We used the ratio of titmouse body mass to focal species body mass as a proxy for the degree of overlap in predator suite.

Hawks of the genus Accipiter are the main predators of small forest birds (Storer, 1966;

Lake et al., 2002), and different species preferentially prey on statistically different size classes of birds (Opdam, 1975; Reynolds and Meslow, 1984). Thus, the body mass ratio of a focal bird to the titmouse represents a good estimate of the number of predators they share, with the relevance of the alarm call decreasing as a function of the difference in size. Values closer to 1 indicate a similar mass and more shared predators, while values further from 1 indicate smaller or larger species with fewer shared predators (summarized in Table 2-3). To use a standard measure of body mass, we obtained all body mass estimates in grams for all focal species from Sibley (2000).

For winter sociality data, we used the average number of individuals of each species encountered in flocks as a measure of the degree of heterospecific exclusion of species during the winter. Whereas solitary species exclude conspecifics and join flocks singly, gregarious species join or associate with foraging flocks as a group (authors’ personal observations). We used reported values for this measure from Farley et al. (2008; reported in Table 2-3), a study conducted in the same study areas and habitat.

Alarm Call Playback Procedures

We conducted playback presentations at the same field sites and during the same date ranges as foraging observations, though they were not conducted simultaneously. We used response to presentation of a heterospecific alarm call stimulus as a measure of the degree of reliance on social information from the sentinel species. Free-ranging individuals of each bird species were presented with the high Z call, an alarm call given by titmice in the presence of an aerial predator, typically hawks of the genus Accipiter (Morse, 1970; Gaddis, 1980; Sieving et al., 2010), the main predators of forest birds in winter (Storer, 1966). Birds responding to titmouse Z calls, both conspecific and heterospecific, immediately freeze in place and remain motionless for a period of several minutes (Morse, 1970; Gaddis, 1980; Hetrick and Sieving, 2012).

We selected this stimulus because it is a high-urgency call associated with an attack by the predator, which is associated with the highest degree of responsiveness by eavesdropping heterospecifics (Fallow and Magrath, 2010). Because the call was presented in the absence of an avian predator, the relative responsiveness of each species serves as a measure of the degree to which they rely on heterospecific social information about a predator for which no personally collected information is available.

In other words, species with complete personal information therefore could ‘know’ there was no predator, but species with limited access to personal information should rely more completely on the stimulus to calibrate their perception of threat level.

For our playback presentations, we used known-context alarm calls that were recorded during predator presentation to titmice in aviaries (Sieving et al., 2010). We randomly selected a playback recording for each trial (N=7), and played the recording for thirty seconds regardless of response. To increase realism, the stimulus was

delivered using an extension pole leaned on a tree to mimic the position of a foraging titmouse (3.6 m height). Attenuation of alarm calls over greater signaler-receiver distances have been shown to affect heterospecific response (Murray and Magrath,

2015), so we used focal individuals that were within 30 m of the speaker, and recorded the speaker-to-focal-individual distance using a laser rangefinder. To maintain sample independence, we recorded a GPS point for each playback and separated all playbacks for each species (whether they were conducted on the same or different days) by at least 200 m. This corresponds roughly to the diameter of a Tufted Titmouse flock territory in Florida hardwood habitat (Brawn and Samson, 1983; KE Sieving, unpublished data). Several habitual members of titmouse flocks in our study system exclude conspecifics (Farley et al., 2008), and flocks maintain stable winter territories

(Gaddis, 1983). Therefore our efforts to separate samples of the same species in space minimized the chances of pseudoreplication. Moreover, same-day playbacks conducted

200 m apart were acoustically independent because the signal-to-noise ratio of high Z notes of the titmouse broadcast at the same volumes as we used here degrade to 0 within 60 to 70 m of sound source in hardwood forests of the study region (KE Sieving, unpublished data).

For each playback, we recorded three response variables: a simple yes/no overall response, the type of response (freezing in place or diving for cover), and the length of freezing time if the bird remained motionless. Response to this call is immediate and behavioral changes are both obvious and extended, so classification of response was never ambiguous.

Before each playback, we recorded microhabitat variables that might influence perceived predation risk that we identified from the literature. Density of vegetation and distance from trunk (Desrochers, 1989; Suhonen, 1993; Brotons et al., 2000), height from ground (Suhonen, 1993; Lee et al., 2005; Carrascal and Alonso, 2006), and proximity to escape cover (Lee et al., 2005; Carrascal and Alonso, 2006) may all affect the perceived predation risk of forest passerines. We additionally classified playback locations as either edge or interior sites, based on whether they were located within 50 meters of a hard forest edge. Perceived predation risk by small forest birds may be higher in edge habitat (Rodriguez et al., 2001). The social context of the focal individual before playback was also recorded by noting if titmice were detected aurally or visually.

Heterospecific non-alarm vocalizations may enhance risk perception in birds, and hence, responsiveness to the experimental treatment (Hubbard et al., 2015), so the social context may be another important factor in determining perceived predation risk.

Finally, we determined approximate temperature at time of playback post hoc, using hourly averages at 10 m elevation from the Florida Automated Weather Network

(FAWN), because temperature influences flocking propensity and vigilance levels in

Holarctic parid-led flocks (Brotons et al., 2000; Klein, 1988).

In order to test for an effect of the playback procedure itself, we performed a procedural control using playback of the chorus call of the spring peeper (Pseudacris crucifer), a forest frog, using the same protocol. This small frog is a common resident of hardwood habitat near ephemeral ponds and gives a similar high pitched, repeated call during its breeding season from November to March (Conant and Collins, 1998). As

such, this represents a familiar, non-threatening stimulus with some of the same acoustic qualities as the high Z alarm call.

Data Reduction of Foraging and Microhabitat Variables

All statistical analyses were performed in R (version 3.2.5; R Core Development

Team). We first aggregated all of the foraging observation data (37 variables for each species) for species with more than five independent foraging observations and determined the proportions of each foraging maneuver and foraging microhabitat observed for each species (Table A-7). We removed single observations of a foraging maneuver or substrate for any given species in order to avoid biasing subsequent analyses with outliers. We then ordinated the foraging data at the species level in order to describe covariance patterns and as a variable reduction technique. We used the

Gower dissimilarity index (Gower, 1971) to create a dissimilarity matrix, using the daisy function in the cluster package (Maechler, 2008), which we then analyzed using

Principal Coordinates Analysis (Gower, 2015) using the cmdscale function in the stats package. We selected PCoA because our data do not fit the assumptions of Principal

Components Analysis (lack of multivariate normality, mixed categorical and continuous predictors, more predictor variables than samples) and the relaxed assumptions of this technique allow for unconstrained ordination of such data sets (McGarigal et al., 2000).

We interpreted the PCoA axes by projecting the weighted averages of the scores for each predictor variable onto a biplot using the wascores function (vegan package;

Oksanen et al., 2007). We also performed Principal Coordinates Analysis on the microhabitat variables (5 variables per playback) recorded during playback in order to reduce the number of predictor variables for our final models. Because these data

contained a mix of categorical and continuous predictor variables, they could not be projected onto the biplot. We therefore interpreted these axes using the averages of the predictor variables for extreme values of each principal coordinate axis (Figure A-1). We selected important axes to retain for further analyses by consulting a scree plot, and by retaining only interpretable axes.

Hypothesis Evaluation Using Generalized Linear Models

We ran generalized linear models (glm function, stats package), with a logit link function for the overall (Y/N) response and response-type dependent variables, respectively, to determine the importance of our predictor variables in predicting responses. Models of response type used the subset of the data in which the focal bird responded to the playback. We included eleven predictor variables (Table 2-1), which encompass both species-level ecological traits obtained from the foraging observations and in the literature as well as local microhabitat data recorded in the field before each playback. We included only complete cases in our analyses. We used an information theoretic approach (Burnham and Anderson, 2002) to evaluate our generalized linear models and determine the best models for each of our two response variables. Such techniques are a generally preferred method of model selection over stepwise regression because they examine all possible models instead of a subset (Hegyi and

Zsolt Garamszegi, 2011), and are also more stable and less likely to select different models due to small differences in the data (James and McCulloch, 1990). We feel confident that this approach is not tantamount to ‘data dredging’, because we selected clearly-defined predictor variables already identified in the literature as our predictor variables (Dochtermann and Jenkins, 2011).

For model evaluation, we used the Akaike Information Criterion modified for small sample sizes (AICc). This metric is recommended for data sets where the sample size divided by the number of fitted parameters in the most complex model (n/k) is 40 or less (Symonds and Moussalli, 2011). We first calculated the AICc scores and model weights for the full model set using the dredge function of the MuMIn package (Barton,

2016). Because there was no best model (e.g. model with a ΔAICi of 2 or greater over the second best model; Symonds and Moussalli, 2011), we performed full model averaging over a candidate set of models (model.avg function, MuMIn). We did not consider interactions in our models because the main parameter estimates can be biased by interaction terms when model averaging (Richards et al., 2011). Because model weights were low, the 95% confidence set of models contained over 500 models.

As such, we selected a candidate set of models with ΔAICc of 2 or less, because these models are considered to be as good as the best model (Burnham and Anderson,

2002). We performed a full average of all models in the candidate model set for each predictor. Finally, in order to evaluate the goodness of fit of the models in the candidate set, we calculated McFadden’s R2 value, adjusted for non-linear data sets using the pR2 function in the pscl package (Jackman, 2011).

Results

Foraging Observations

Over two winters of observations, we observed 1274 foraging maneuvers of 327 foraging individuals belonging to 25 species. Of these, 15 species had greater than 10 independent observations of foraging individuals (summarized in Table 2-2, full foraging data available in Table A-7). The number of foraging observations was not biased by

the average foraging height of a species (linear regression, F = 0.091, df =13, p =

0.768) or the average vegetation density at which it forages (linear regression, F=

0.191, df =13, p= 0.669). Similarly, the first foraging maneuver observed did not differ significantly from all foraging maneuvers (Chi squared= 18.49, df= 22, p= 0.679), which indicates that our foraging observations were not biased by more obvious foraging techniques. Generally, winter foraging behavior was specialized, with only two pairs of species not having unique combinations of the main attack maneuver, main substrate used, and main distance from trunk at which they forage (summarized in Table 2-2).

Playback Experiment

We presented the alarm call stimulus to 242 individuals of 31 bird species, representing most of the winter bird community, and all of the species for which we have foraging data (Table 2-3). Of these, 16 species had nine or more playbacks, with the rest representing late southward migrants or rare species in the hardwood habitat.

Generally, response was very strong and nearly universal to the alarm call playback.

Individuals responded 87% of the time (N= 211), and 20 out of 31 species sampled

(65%) responded to every playback stimulus. The notable exception to this trend was the Eastern Phoebe (Sayornis phoebe), a sit-and-wait flycatcher species that almost never responded (6% response rate) and represented 48% of the non-responses to playback. Response type was also remarkably consistent, with the freezing response representing 152 of 211 playback responses (72%; Table 2-3). Response type was generally consistent within a species, but differed substantially between species. A small subset of species responded primarily by diving, including Blue-gray Gnatcatcher

(Polioptila caerulea), Ruby-crowned Kinglet (Regulus calendula), and Yellow-throated

Warbler (Setophaga dominica). Length of freezing response, by contrast, showed strong intraspecific variation and did not vary substantially between species, with the species averages ranging from 100 to 300 seconds (Table 2-3). For our procedural control, we performed 20 frog call playbacks to 11 species over the second winter, with no responses.

There is no indication that our playback protocol affected response rate. Though individuals that responded to the playback were on average closer to the speaker

(mean distance = 16.70 m) than those who did not (mean distance = 18.8 m), this difference was not statistically significant (two sample t-test, t = -1.914, df = 39.59, p =

0.062). Similarly, there was no statistical difference in response between any of the seven alarm call recordings used (Chi-squared test, X2 = 5.86, df = 6, p = 0.44). Our playbacks occurred across a range of local social and microhabitat conditions, allowing us to test for local effects on alarm call response. There were 90 edge and 172 (72%) interior playbacks. Similarly, all three distances from the trunk were relatively equally represented in the playback samples (29%, 21%, and 40% for far, medium, and near, respectively), as were vegetation densities (28% at 1, 32% at 2, 31% at 3, respectively).

We also captured a range of social contexts. The majority of playbacks occurred in mixed-species foraging flocks (N = 131, 54%) and to solitary individuals (N = 74, 31%), though we also performed playbacks to single species flocks (N = 16) and pairs of individuals (N = 21). Titmice were present for 133 playbacks (55%) and absent in 109 cases.

Defining the Microhabitats Occupied During Playback Trials

We selected the first three coordinate axes of the analysis of the microhabitat variables (Table 2-1), accounting for 77% of the observed variance (Table A-1), based on analysis of a scree plot. We interpreted the first axis (36% of variance, Edge-MH) as a measure of forest edge versus forest interior sites. Based on an examination of extreme values (Figure A-1), higher values were associated with sites within 50 m of a hard edge, further distances from the trunk, lower heights from ground, higher vegetation density, and lower distance to cover, while lower values were associated with forest interior sites with lower vegetation density, greater distance to cover, and greater height from the ground (Table A-4). The second axis (25% of variance, Trunk-

MH) represents a measure of distance from trunk in terms of microhabitat. Large values represent sites that are nearer to the trunk, with low vegetation density and greater distances to cover, while smaller values represent sites farther from the trunk, with higher vegetation density and smaller distances to cover (Table A-4). The third axis

(16% of variance, Escape-MH) represents a measure of availability of escape cover.

Large values had high distance to cover, low vegetation density, greater height from ground, and either were located on the trunk or in exposed sites far from the trunk.

Smaller values had low distance to cover, and tended to be located at lower heights in dense vegetation at intermediate distances from the trunk (Table A-4).

Defining the Foraging Niches of the Winter Bird Community

Based on examination of a scree plot and axis interpretability, we selected the first four coordinate axes to use as predictor variables for later analyses. These four coordinate axes describe 68% of variation in the foraging data among them (Table A-2).

The first coordinate axis (hereafter Trunk-F) describes 26% of the variation in the foraging data. Based on the factor loadings (Table A-3), we interpret this axis as a measure of distance of the species’ foraging niche from the trunk. Higher values were associated with foraging nearer to the trunk, use of the trunk as a substrate, and trunk- based foraging maneuvers such as hammer and peck (Figure 2-1a, Table A-3). Lower values were associated with more frequent use of microhabitats further from the trunk, the use of foliage or air as a foraging substrate, and aerial foraging maneuvers (flush- pursue, sally hover). The second axis (Occlusion-F) explains 17% of the variance and represents a measure of the visual occlusion associated with the foraging maneuvers of each species. Higher values were associated with substrates that occlude vision (dead leaves, epiphytes) and probing foraging maneuvers that limit vigilance, while lower values were associated with more frequent use of open sites, such as tree trunks or capturing prey items in the air, as well as aerial or trunk-based foraging maneuvers

(sally, hammer; Table A-3). Species with high values on this coordinate axis consisted of an assemblage of epiphyte-probing birds including Orange-crowned Warbler

(Oreothlypis celata), Pine Warbler (Setophaga pinus), and Yellow-throated Warbler

(Setophaga dominica).

The third foraging axis (Height-F) explains 14% of the variance in the foraging data set, and is a measure of the foraging height of each species. Higher values on this principal coordinate axis were associated with lower foraging heights and increased use of the ground as a foraging substrate, while negative values were associated with greater foraging heights and canopy substrates such as branches, pine needles, and pine cones (Figure 2-1b; Table A-3). Ground-foraging species such as Northern

Cardinal (Cardinalis cardinalis) and Ovenbird (Seiurus aurocapillus) had high values on this coordinate axis. The fourth axis (Aerial-F) explains 10% of the total variance and was more complicated to interpret. Lower values were associated with increased use of microhabitats with high vegetation density (4 on the scale used here), and increased use of substrates associated with dense vegetation (vine tangles, dead leaf clusters). In contrast, higher values were associated with greater use of more open habitats and aerial foraging maneuvers (sallies; Table A-3). As such, we chose to interpret this axis as a measure of degree of aerial foraging behavior for a species. High values on this axis were associated with a sit-and-wait aerial flycatcher (Eastern Phoebe, Sayornis phoebe).

Modeling Factors Determining Species’ Reliance on Social Information

Our information theoretic approach yielded 22 candidate models for overall response with a strong goodness of fit (average McFadden’s pseudo R2 = 0.59; Table

A-5). Model averaging for general response to playback yielded two significant foraging ecology traits, distance from trunk (Trunk-F) and degree of aerial foraging (Aerial-F), as well as a significant effect of call relevance (Table 2-4). The height of the foraging niche from the ground (Height-F) was also marginally significant (p = 0.053), with high relative variable importance. Foraging ecology was important in determining response, with species that forage closer to the trunk, at lower heights above ground, and using fewer aerial maneuvers more likely to respond to the alarm call playback (Figure 2-2).

Conversely, species that forage higher in the forest canopy, farther from the trunk, and using more aerial foraging maneuvers were less likely to respond to the alarm call, indicating reduced reliance on social information. All these traits are also linked to

increased use of aerial foraging maneuvers (Figure 2-1). Finally, call relevance was also significant in explaining overall response to the alarm call; species with smaller masses than the titmouse were more likely to respond than those with an equivalent mass or greater.

The generalized linear models of response type (diving versus freezing) yielded a candidate set of 16 models, with reduced goodness of fit in comparison to that for overall response (average McFadden’s pseudo R2 = 0.25; Table A-6). Model averaging produced a single significant foraging ecology predictor of response type, the distance from the trunk at which a species forages (Trunk-F). The availability of escape cover

(Escape-MH) and the average hourly temperature during playback were also significant, though the effect size of temperature was extremely small (Table 2-4). At this level of response, microhabitat conditions were also important in determining response, though species-level traits still had a larger effect size. Species that forage farther from the trunk were more likely to dive, whereas trunk-foraging species were more likely to freeze (Figure 2-3a). Temperatures also appeared to affect the response, with individuals more likely to freeze in place when the playback took place in colder conditions (Figure 2-3c). Finally, as predicted, individuals located in more exposed microhabitats that were farther from cover at the time of playback were more likely to dive than those located in sites with denser vegetation and closer to escape cover

(Figure 2-3b).

Table 2-1. Predictor variables used in the GLMs for overall (Y/N) response and response type. The first four variables represent principal coordinate axes obtained from ordination of field observations of foraging behavior (37 variables; Table A-3) while the next three variables similarly represent principal coordinate axes obtained from an ordination of 6 microhabitat variables recorded before each playback (Table A-4). Hypothesis Variable name Interpretation Source PCoA axis from Foraging Distance from trunk of Trunk-F foraging Ecology foraging niche of species observations in field Visual occlusion PCoA axis from Foraging associated with foraging Occlusion-F foraging Ecology maneuvers used by observations in field species Height from ground PCoA axis from Foraging associated with the Height-F foraging Ecology foraging niche of each observations in field species PCoA axis from Foraging Degree of aerial foraging of Aerial-F foraging Ecology a species observations in field PCoA axis from Forest edge versus interior Local microhabitat data Edge-MH site at which playback Microhabitat recorded before occurred playback PCoA axis from Local Distance of individual from microhabitat data Trunk-MH Microhabitat trunk during playback recorded before playback PCoA axis from Local Availability of escape cover microhabitat data Escape-MH Microhabitat during playback recorded before playback Was a titmouse aurally or Presence data Local Titmouse visually detected before recorded before Microhabitat presence the playback? playback Average number of Reported in the Sociality Sociality individuals joining MSF of literature (Farley et each species al. 2008) Ratio of focal species body Calculated from Call Call Relevance weight relative to Tufted values in Sibley Relevance Titmouse (2000) Hourly temperature Florida Automated - Temperature average at 10 m Weather Network

Table 2-2. Summarized foraging data. These data were collected in the same habitats and during the same time of year on species that were tested in the playback trials, but were collected independently from playback trials (birds’ behavior was unmanipulated). Species codes correspond to four letter Alpha codes. Foraging height was estimated for each foraging maneuver with the help of a rangefinder. Distance from tree trunk was binned into three categories (near, medium, far) by dividing the branch on which a focal individual was perched into thirds. Vegetation density is calculated based on percentage of light penetrating the foliage on a 0 to 5 scale in a 1 m diameter sphere around the focal individual after Remsen and Robinson (1990). Substrates and attack maneuvers were categorized according to the same typology. Number of Avg. Avg. Avg. Number of Most freq. Most freq. Most freq. Species foraging foraging foraging veg Individuals substrate maneuver distance maneuvers maneuvers Height density BAWW 25 163 6.5200 10.7761 1.2945 Trunk Glean Near BGGN 27 102 3.7778 12.5510 2.2770 Live Leaf Glean Far BHVI 22 42 1.9091 11.2262 1.5001 Branch Glean Medium/Near CACH 13 42 3.2308 12.1750 2.4614 Branch Glean Far DOWO 12 61 5.0833 16.0481 1.1803 Trunk Hammer Near EAPH 13 32 2.4615 9.6250 1.5483 Branch Sally Far ETTI 17 56 3.2941 11.1111 2.6000 Branch Glean/Probe Far MYWA 19 71 3.7368 9.9203 2.2575 Branch Glean Near NOCA 13 47 3.6154 4.2766 1.5316 Ground Reach-down Far OCWA 12 63 5.2500 9.6508 2.2740 Dead Leaf Probe Far PIWA 19 72 3.7895 14.5833 2.0286 Epiphyte Probe Far RBWO 24 100 4.1667 14.6083 1.4167 Trunk Probe Near RCKI 39 159 4.0769 8.3205 2.4831 Live Leaf Glean Far WEVI 11 27 2.4545 7.9074 2.7407 Live Leaf Glean Far YTWA 18 69 3.8333 15.2246 2.2938 Dead Leaf Probe Far

Table 2-3. Summary of playback response for each species. Species codes correspond to four letter Alpha codes. Sample size consists of the number of Z call playbacks for each species. We calculated the proportion of freezing response from the subset of playbacks in which a given species responded; species could either freeze or dive in response to playback. Average freezing time was calculated for species with a minimum of two responses. Titmouse mass ratio was calculated based on masses obtained from Sibley (2000). Average number of individuals per flock is used as a measure of winter sociality taken from Farley et al. 2008, in which the individuals observed in winter mixed-species foraging flocks in our study system were quantified. Both of these last two variables were used as predictors in the GLMs (Table 2-4). Asterisks denote novel species responding to the titmouse alarm call. Sample Proportion Avg. Freeze Freezing Titmouse Avg. Species Size Response Time (sec.) Proportion Mass Ratio Individuals/Flock ACFL 1 0.000 0.605 AMGO 5 1.000 1.000 0.605 3.17 AMRE* 4 1.000 0.500 0.386 AMRO 1 1.000 1.000 3.581 2.87 BAWW 16 1.000 124.31 0.750 0.498 1.20 BGGN 14 0.786 203.56 0.364 0.279 3.89 BHVI 14 1.000 270.38 0.857 0.744 1.32 BLJA 4 0.750 0.667 3.953 1.82 BTWA* 2 1.000 1.000 0.474 CACH 10 0.900 264.00 0.556 0.488 1.64 CAWR 8 0.875 236.00 0.571 0.977 1.43 CHSP 2 0.500 1.000 0.558 8.33 DOWO 9 1.000 183.78 1.000 1.256 1.41 EAPH 16 0.063 0.000 0.930 1.18 EAWP 3 0.000 0.651 GRCA 2 1.000 0.000 1.721 1.33 HETH 10 1.000 451.20 1.000 1.442 1.20 MAWA* 1 1.000 1.000 0.405 MYWA 13 1.000 116.91 1.000 0.572 4.57 NOCA 18 1.000 336.50 0.778 2.093 1.78 OCWA* 11 1.000 259.30 0.818 0.419 1.00 OVEN 6 1.000 0.833 0.907 1.00

Table 2-3. Summary of playback response for each species. Continued. PIWA 9 1.000 170.71 0.778 0.558 3.84 RBWO 11 0.727 250.43 0.875 2.930 1.51 RCKI 19 0.947 200.88 0.278 0.302 4.31 WEVI 10 1.000 163.43 0.700 0.535 1.24 WOTH* 1 1.000 1.000 2.186 YBCU 1 0.000 3.023 YBSA* 10 1.000 143.95 0.900 2.326 1.14 YPWA 1 1.000 0.000 0.479 YTWA 10 1.000 124.75 0.500 0.437 1.05

Table 2-4. Model-averaged results of GLMs of overall response and response type. Italicized factors represent significant predictors, averaged over the candidate model set. Candidate models selected have a ΔAICc of 2 or less. Number of models in the candidate set for each response variable are indicated at the top of each table; for the response type analyses we only included cases in which the individual responded. Reported pseudo R2 values are the average ± SD of the McFadden’s R2 value for the candidate model set. Relative variable importance for each variable is calculated by summing the Akaike weights of the candidate models which include the given variable. Overall Response (Y/N) N= 22 candidate models, Avg. pseudo-R2= 0.59 ± 0.01 Standard Adjusted z Relative variable Coefficient Estimate p Error SE value importance Intercept 6.463 1.629 1.638 3.946 <0.001 - Call relevance -2.567 0.991 0.997 2.575 0.010 1.00 Escape-MH -2.428 3.114 3.124 0.777 0.437 0.55 Trunk-F 26.019 11.559 11.625 2.238 0.025 1.00 Height-F 18.545 9.533 9.588 1.934 0.0531 1.00 Aerial-F -33.084 8.455 8.501 3.892 <0.001 1.00 Edge-MH -1.121 1.817 1.822 0.615 0.538 0.43 Trunk-MH -0.578 1.573 1.578 0.366 0.714 0.22 Sociality 0.094 0.232 0.232 0.405 0.685 0.25 Titmouse 0.131 0.411 0.412 0.318 0.750 0.19 presence Occlusion-F 0.649 2.946 2.958 0.219 0.826 0.13 Response Type (Dive/Freeze) N= 16 candidate models, Avg. pseudo-R2= 0.25 ± 0.01 Standard Adjusted Relative variable Coefficient Estimate z value p Error SE importance Intercept 0.228 0.688 0.691 0.330 0.741 - Call relevance 0.148 0.363 0.365 0.405 0.686 0.27 Escape-MH -4.329 2.094 2.108 2.054 0.040 1.00 Temperature 0.080 0.034 0.034 2.364 0.018 1.00 Trunk-F 12.568 3.967 3.989 3.151 0.002 1.00 Height-F -4.804 4.158 4.187 1.147 0.251 0.04 Aerial-F -6.508 5.499 5.534 1.176 0.240 0.34 Edge-MH 0.661 1.312 1.320 0.501 0.617 0.04 Trunk-MH -0.819 1.542 1.552 0.528 0.598 0.08 Sociality 0.281 0.188 0.189 1.484 0.138 0.51 Occlusion-F -3.497 3.057 3.078 1.136 0.256 0.29

Figure 2-1. Biplots of foraging principal coordinate axes used in the GLM analysis. A) Biplot of Trunk-F and Occlusion-F. These represent the first and second principal coordinate axes derived from 37 variables collected during foraging observations (Table A-3). Blue labels represent weighted averages of predictor variables across all samples and black labels represent the coordinate scores for each sample (bird species sampled). Trunk-MH represents distance from trunk; positive values indicate increased foraging nearer to the trunk, use of the trunk as a substrate, and trunk-based foraging maneuvers, while negative values are associated with more frequent foraging further from the trunk, the use of foliage or air as a substrate, and more aerial foraging maneuvers. Occlusion-F represents a measure of the visual occlusion associated with the foraging maneuvers of each species. Positive values are associated with dead leaf and epiphyte substrates and probing foraging maneuvers, while negative values are associated with more frequent use of air and trunk as substrates and aerial or trunk-based foraging maneuvers (sally, hammer). B) Biplot of Height-F and Aerial-F. These represent the third and fourth principal coordinates derived from the same foraging variables. Height-F is a measure of the foraging height of each species. Positive values on this principal coordinate axis are associated with lower foraging heights and increased use of the ground as a foraging substrate, while negative values are associated with greater foraging heights and canopy substrates such as branches, pine needles, and pine cones. Aerial-F represents a gradient of aerial foraging; positive values were correlated with use of microhabitats with high vegetation density (4 on the scale used here), and increased use of vine tangles and dead leaf clusters. Negative values are associated with greater use of more open habitats and aerial foraging maneuvers.

a. b.

c. d.

Figure 2-2. Fitted values for the significant predictors of overall response to the titmouse alarm call. Significant predictors are obtained from model averaging of a candidate set of 22 generalized linear models with response to Z call playback as the dependent variable (Table A-4). Solid lines show probability of response calculated by inputting random values for the predictor variable of interest into the logistic regression equation for the full model (all 11 predictor variables, see Table 2-1) and using the parameter estimate and intercept values from our model averaging (see Table 2-4). All other predictor variables were set to mean values for the calculation. Plotted points indicate observed values for each predictor (1 = response, 0 = no response). A) Species that forage more frequently using aerial maneuvers were less likely to respond than species those that employ aerial maneuvers less. B) Species foraging further from the tree trunk are less likely to respond than those that forage on or near the tree trunk. C) Canopy foragers were marginally less likely to respond than species that forage closer to the ground. D) Larger-bodied species with respect to the Tufted Titmouse were less likely to respond to the Z call playback than smaller-bodied ones.

Figure 2-3. Fitted values for the significant predictors of response type (diving versus freezing response). Significant predictors are obtained from model averaging of a candidate set of 16 generalized linear models with response type as the dependent variable (Table A-5). We only analyzed cases in which the focal individual responded for this analysis. Solid lines show probability of freezing response calculated by imputing random values for the predictor variable of interest into the logistic regression equation for the full model (all 11 predictor variables, see Table 2-1) and using the parameter estimate and intercept values from our model averaging (see Table 2-4). All other predictor variables were set to mean values for the calculation. Plotted points indicate observed values for each predictor (1 = freeze, 0 = dive response). A) Species with a foraging niche further from the trunk were more likely to dive than those that forage on or near the trunk. B) Individuals foraging in more exposed microhabitats were more likely to dive than those closer to cover. C) Individuals were more likely to dive for cover in colder temperatures.

CHAPTER 3 CONCLUDING REMARKS

We found a strikingly near-universal response to the alarm call of the titmouse, highlighting the important role that this species plays as an anti-predator sentinel.

Responsive species span a range of migratory strategies (residents, passage migrants, winter visitors) and flocking propensities, and we document six responses by species not previously known to respond to parid anti-predator calls (4 passage migrants, 2 winter residents; Table 2-3). Our results agree with previous findings of community-wide responsiveness to the mobbing calls of species of the family Paridae (Hurd, 1996; Gunn et al., 2000; Sieving et al., 2004; Langham et al., 2006). Taken together, this reliance of the near entirety of the avian community on the social information of one species suggests a keystone role for the titmouse (sensu Kotliar et al., 1999) because it is the only species to provide highly reliable social information that encodes complex information about predation risk (Templeton et al., 2005; Sieving et al., 2010; Hetrick and Sieving, 2012). From the perspective of social network theory, the large number of species to which the titmouse is connected in the eavesdropping network also suggests its keystone role (Sih et al., 2009). Attending to the alarm calls of sentinel species can reduce vigilance (Radford and Ridley, 2007; Bell et al., 2009), increase foraging efficiency (Ridley et al., 2014), and expand the foraging niche (Dolby and Grubb, 2000), with pronounced effects on fitness (Dolby and Grubb, 1998) and therefore on community structure. Our results therefore concur with the idea that titmice play a keystone ‘community informant’ role (Szymkowiack, 2013), characterized by high vigilance and conspicuous alarm calling, as has been described for several honeyeater

species in Australian eavesdropping networks (Taylor and Paul, 2006; Magrath et al.,

2009).

Our results demonstrate that species-level traits are more important than local microhabitat factors in determining reliance on social information. Local social and microhabitat factors were not significant predictors in our model for overall response or response type, though the availability of escape cover (Escape-MH) and temperature were significantly correlated with response type, suggesting that these factors influence how an individual responds. Small changes in microhabitat can greatly affect the cost of predation imposed on foraging prey species such as small forest passerines (Brown and Kotler, 2004), and shifts in microhabitat use have been documented under changing predation regimes in both time and space (Suhonen, 1993; Rodriguez et al.,

2001). However, species-level traits may be more important because they affect baseline levels of personal information acquisition, perhaps through ecomorphological adaptations to a given niche that affect vigilance behavior. Species may face a physiological trade-off between adaptations to vigilance and prey detection, which drives reliance on social information. Plasticity in response behavior as a function of microhabitat might therefore serve as a way of reducing the costs of responding to social information for species that are reliant on alarm calls while foraging. Different anti-predator escape tactics are optimized for different substrates (Lima, 1992, 1993), and selecting an appropriate escape behavior for a given microhabitat may minimize the cost of responding to false alarm calls.

The Importance of Foraging Ecology: Aerial Foragers Are Different

Our work empirically tests three competing, species-level hypotheses of the determinants of reliance on social information. Our model averaging results indicate that foraging ecology plays a central role in determining the interaction strengths in eavesdropping networks. Species that forage in more exposed sites (farther from the trunk, higher in the canopy, and in more open vegetation) were less likely to respond, which suggests that perceived predation risk is not driving this trend. Rather, percentage of aerial foraging maneuvers (Aerial-F) was most important in explaining overall response. While it is possible that this trend was driven by the fact that the

Eastern Phoebe was the only species that consistently did not respond to the alarm call, we believe that adaptations to aerial foraging behavior are actually the causative factor.

For one, the proportion of aerial foraging maneuvers alone is significantly associated with the proportion of playback response at the species level (linear regression, F- statistic = 15.79, df = 12, p = 0.002). Similarly, playback also elicited no response from two other species of sit-and-wait tyrannid flycatchers (Acadian Flycatcher, Empidonax virescens; Eastern Wood Pewee, Contopus virens; Table 2-3) that we presented with the alarm call stimulus.

Our findings regarding the importance of aerial foraging behavior in shaping reliance on social information mirror previous findings in avian eavesdropping networks.

Martinez and Zenil (2012) similarly found that foraging guild was an important predictor of response to the alarm call of sentinel species, with aerial foragers responding less often and for less time than substrate-based foragers. However, the response behavior for different types of substrate-based foragers (dead leaf versus live leaf foragers) did not differ, suggesting that aerial foragers may be unique in their limited reliance on

social information. A more recent analysis of the factors influencing response to alarm calls in Amazonian understory bird communities also found foraging strategy to be a significant predictor (Martinez et al., 2016), with gleaners being more responsive than aerial foragers. We argue that the visual adaptations for the sit-and-wait sallying foraging style lead to increased vigilance and reduced reliance on social information.

Inspection of whole mount retinas of sit-and-wait flycatchers (Tyrannidae) shows high foveal neuron densities, as well as a cohort of giant retinal ganglion cells, which are thought to be a specialized eye architecture involved in movement detection (Coimbra et al., 2006). These adaptations should allow for high spatial resolution and visual acuity.

Ecomorphological adaptations to the sit-and-wait aerial sallying foraging style are linked with higher vigilance in other avian systems as well. For instance, Thamnomanes antshrikes are the only genus in their family (Thamnophilidae) to have adapted this foraging behavior (Schulenberg, 1983). They act as sentinel species in mixed-species flocks and even kleptoparasitize heterospecifics by giving false alarm calls (Munn,

1986). Similarly, the drongos (Dicruridae) are a family of sit-and-wait sallying foragers that are vigilant sentinel species in African and Asian flocking systems (Goodale and

Kotagama, 2005a, b) and also kleptoparasitize heterospecifics (Satischandra et al.,

2010). Thus, the high visual acuity afforded by adaptations to sit-and-wait sallying allow for increased personal information, and have led to the exploitation of other species with reduced personal information. Ecomorphological approaches, and visual ecology, may be especially important in determining the position of a species in eavesdropping networks.

The foraging niche of a species also affected the type of response for a given species for the subset of cases in which a response occurred. However, distance from trunk of the foraging niche was the dominant consideration rather than degree of aerial foraging. Species foraging nearer to the trunk were more likely to freeze, while species foraging farther from the trunk were more likely to dive. This mirrors previously reported escape behaviors of foliage-gleaning and bark foraging species that are constant at the species level (Lima, 1993) and suggests that antipredator behavior is highly adapted to foraging substrate, a finding that mirrors previous work in avian systems (Lima, 1992).

Microhabitats located farther from the trunk are more exposed to predator attack, because small raptors preferentially target the exterior of vegetation (Kullberg, 1995).

Thus, these species need to dive for escape cover in order to evade a predator (Lima,

1993). However, species that forage closer to the trunk may be less exposed to direct attack and can freeze to avoid detection by the predator. For trunk-foraging species, the feeding substrate can also act as a refuge by shielding the individual from an attacking predator (Lima, 1992). Indeed, woodpeckers typically freeze against the trunk and place themselves on the opposite side of the trunk from the playback speaker (Sullivan, 1984;

H. Jones, personal observation).

Call Relevance Matters, Social System Does Not

We also found that call relevance plays an important, if secondary, role in determining response. In general, species with a similar body mass to the titmouse were less likely to respond than smaller species, though a couple of very small species also occasionally did not respond (Ruby-crowned Kinglet, 6.5 g.; Blue-gray Gnatcatcher

(Polioptila caerulea), 6 g.). Similar trends were observed in another study to examine an

eavesdropping network, with smaller species being more responsive to the sentinel species’ alarm call (Magrath et al., 2009). However, we did not see the same trend of much larger species consistently not responding. This may be due to the central importance of the titmouse as a sentinel species which is very reliable and encodes more information about the size and threat of a predator in its calls than heterospecifics

(Templeton et al., 2005; Sieving et al., 2010). The sentinel species from the Australian system, the New Holland honeyeater (19 g), is of approximately the same mass as a titmouse (21.5 g), so playback presentations to larger Australian species might yield similar results. The importance of call relevance might be mediated by both call reliability and the information content of alarm calls, such that the number of shared predators might not be the only consideration. If a species is able to encode exceptional amounts of information in its alarm calls, they may be relevant even to species that share fewer predators.

Sociality was not an important factor in determining degree of reliance on social information in our Florida winter community, in contrast to findings from tropical

Africa (Radford and Ridley, 2007; Ridley and Raihani, 2007; Ridley et al., 2014).

However, a majority of these findings come from social systems that comprise kin groups, such as many African primates and the Pied Babbler (Turdoides bicolor). In tropical systems, delayed dispersal and cooperative breeding are more common than in temperate ones (Brown, 1987), and the resulting family groups often become leaders of mixed-species foraging flocks, possibly because of their alarm call systems (Sridhar et al., 2009; Srinivasan et al., 2010). In contrast, the species that form single-species flocks in subtropical Florida (e.g. American Goldfinch, Spinus tristis; Yellow-rumped

Warbler, Setophaga coronata) are short-distance migrants that form seasonal and temporary groups of non-kin individuals in winter (Prescott and Middleton, 1990; Hunt and Flaspohler, 1998). Thus these species may lack the evolutionary drivers of sentinel systems and complex alarm call systems. The only species that exhibits delayed dispersal in our study system is the Tufted Titmouse (Pravosudova and Grubb, 2000).

The Z call may be wholly or partially kin-selected, and we would expect for the titmice to place reduced value on heterospecific information relative to conspecific information in the same way as babblers do (Ridley and Raihani, 2007).

Asymmetric Eavesdropping Networks: Mutualism, Commensalism, or Parasitism?

We document extensive, community-wide eavesdropping on the alarm calls of the tufted titmouse, though our work does not test either the degree to which titmice themselves respond to heterospecific social information or the fitness effects of these interactions on either signaler or eavesdropper. The eavesdropping interactions in parid information networks are highly asymmetrical; responses to non-parid mobbing vocalizations more limited in scope, and parids respond in a more limited fashion to the mobbing calls of other species (Langham et al., 2006). Given the similar trends we observed between mobbing call and alarm call responsiveness, this may also be the case for alarm calls, though this should be experimentally tested in the field. It is unclear, however, if these eavesdropping interactions represent mutualistic interactions, in which both the signaler and the eavesdropper benefit, or whether eavesdropping interactions consist of ‘information parasitism’ or commensalism, where only the eavesdropping species are obtaining fitness benefits. While the benefits to

eavesdropping species, both inside and outside of mixed-species foraging flocks, are obvious, the benefits to titmice are unclear and experimental evidence is lacking.

In our system, the signaler species is a ‘passive’ nuclear species, forming social groups that are joined and followed by other species as they forage, and not actively recruiting satellite species into mixed-species flocks (Contreras and Sieving, 2011).

Instead, heterospecifics may actively search for foraging companions using vocal cues

(Monkkonen et al., 1996). Titmice often (but not always) form kin groups during the winter (Pravosudova and Grubb, 2000), which suggests that alarm calls may be kin- selected. In such cases, satellite species in mixed-species flocks could benefit from eavesdropping on honest signals intended for conspecifics. However, other parid species form flocks of unrelated individuals in winter (Mostrom et al., 2002; Foote et al.,

2010) and give a similar Z call (Morse, 1970). It is possible that sentinel species such as the titmouse suffer a reduction in fitness by associating with ‘information parasites’: anecdotal evidence suggests that titmice may suffer higher predation in flocks

(Contreras and Sieving, 2011). However, the same high Z alarm call could benefit both signaler and eavesdropper via byproduct mutualism if eavesdropping species respond in a coordinated fashion to the alarm call. Such a coordinated response is thought to reduce the ‘oddity effect’ (Landeau and Terborgh, 1986) of heterogeneous response and enhance the dilution (Foster and Treherne, 1981) and confusion (Neill and Cullen,

1974) effects of mass flight, thereby reducing predation risk to all group members

(Landeau and Terborgh, 1986; Bradbury and Vehrencamp, 2011; Magrath et al., 2014).

Coordinated flight can also help defray the costs of emitting the alarm call, which makes a group member more conspicuous to a predator and is a costly behavior (Alatalo and

Helle, 1990). For example, group response to an aerial predator alarm call of a ground squirrel reduced the predation risk to the signaling individual (Sherman, 1985).

All of these mechanisms remain untested however, and merit additional investigation. Titmouse alarm-calling behavior in the presence or absence of kin might help to unravel the fundamental fitness benefits to alarm call networks. Understanding the fitness benefits to both sentinel species and eavesdroppers is especially important because such eavesdropping networks are thought to be an important global phenomenon, with heterospecific sentinels in the family Paridae playing ‘community informant’ roles as purveyors of social information about predation risk in bird communities throughout the Holarctic (Schmidt et al., 2010; Contreras and Sieving,

2011; Szymkowiack, 2013; Magrath et al., 2014). Community-wide responses to parid mobbing calls have been widely reported in the literature (Hurd, 1996; Forsman and

Monkkonen, 2001; Sieving et al., 2004; Langham et al., 2006), to the point that they have been used to estimate forest passerine densities (Turcotte and Desrochers, 2002) and breeding success (Gunn et al., 2000; Doran et al., 2005). Our results suggest that responses to parid alarm calls mirror those to mobbing calls, and given the fact that anti-predator calls in this family are well conserved (Morse, 1970; Hailman, 1989;

Langham et al., 2006; Randler, 2012), they are likely similar in other Holarctic bird communities. Despite the ubiquity of parid information, however, the basic fitness benefits to the signaler are unknown and merit further investigation given the global importance of parid eavesdropping networks.

APPENDIX ADDITIONAL TABLES AND FIGURES

Table A-1. Principal coordinate axes of the microhabitat measures collected before playback. Principal coordinate axes were obtained by ordinating six microhabitat variables collected at the location of the focal individual before playback (Table A-4). We retained the first three axes (named) for further analyses based on cumulative variance explained and consultation of a scree plot. Coordinate Proportion of variance Eigenvalue Cumulative variance axis explained Edge-MH 6.036 0.359 0.359 Trunk-MH 4.199 0.249 0.608 Escape-MH 2.745 0.163 0.771 4 2.385 0.142 0.913 5 1.467 0.087 1.000

Table A-2. Principal coordinate axes from analysis of the foraging ecology data. We obtained 16 principal coordinate axes by ordinating 37 foraging variables (Table A-3) collected from field observations of foraging birds. We retained the first four axes (named) for further analyses based on cumulative variance explained, interpretability, and consultation of a scree plot. Coordinate Proportion of variance Eigenvalue Cumulative variance axis explained Trunk-F 0.152 0.264 0.264 Occlusion-F 0.098 0.171 0.434 Height-F 0.085 0.147 0.582 Aerial-F 0.059 0.102 0.684 5 0.046 0.079 0.764 6 0.029 0.051 0.814 7 0.023 0.040 0.855 8 0.021 0.037 0.891 9 0.019 0.033 0.925 10 0.015 0.026 0.950 11 0.010 0.017 0.967 12 0.008 0.014 0.981 13 0.005 0.009 0.990 14 0.004 0.007 0.996 15 0.002 0.003 1.000 16 0.000 0.000 1.000

Table A-3. Factor loadings for the foraging ecology principal coordinates used in the analysis. Variables shown consist of 37 measures of foraging behavior and microhabitat obtained from foraging observations of free-living birds. Named principal coordinate axes are, from left to right, the first four axes, explaining 68% of variance (Table A-2). Occlusion- Height- Category Variable Trunk-F Aerial-F F F Air -0.087 -0.054 -0.004 0.036 Branch -0.026 -0.027 -0.014 -0.004 Dead Branch 0.072 -0.016 -0.047 -0.012 Dead Leaf -0.018 0.088 -0.033 -0.018 Epiphyte -0.016 0.028 -0.048 -0.013 Fruiting Body 0.080 0.085 0.015 0.114 Foraging Substrate Ground 0.013 0.031 0.157 0.004 Live Leaf -0.086 -0.017 0.005 -0.002 Pine Cone -0.015 0.102 -0.073 -0.019 Pine Needles -0.039 0.041 -0.054 0.024 Trunk 0.099 -0.055 -0.011 -0.016 Vine -0.005 -0.031 -0.010 -0.043 Far -0.028 0.009 -0.007 0.016 Distance from Medium -0.012 -0.011 -0.016 -0.002 Trunk Near 0.032 -0.012 -0.013 -0.015 0 0.024 -0.048 0.036 0.004 1 0.047 -0.019 -0.007 0.012 Vegetation Density 2 -0.009 0.013 0.001 0.006 at Foraging Site 3 -0.048 0.014 -0.009 -0.011 4 -0.011 0.020 0.033 -0.038 Flake 0.047 0.012 -0.059 -0.018 Flush-pursue -0.089 -0.032 -0.017 0.007 Gape -0.019 0.145 -0.057 0.037 Glean -0.035 -0.017 -0.002 -0.016 Hammer 0.127 -0.055 -0.014 -0.024 Hang -0.041 0.009 -0.025 -0.030 Hang-down Probe 0.048 0.094 -0.014 0.078 Lunge 0.028 0.048 0.181 -0.003 Foraging Maneuver Peck 0.151 -0.046 -0.066 0.013 Probe 0.041 0.061 -0.028 0.012 Reach Down -0.005 0.024 0.117 -0.004 Reach-down Probe -0.018 0.128 -0.063 0.018 Reach-up -0.062 -0.003 0.001 -0.010 Sally -0.086 -0.079 0.010 0.073 Sally-hover -0.083 -0.058 -0.007 0.025 Sally-pounce -0.074 -0.078 -0.009 0.022 Average Foraging Foraging Height 0.015 -0.002 -0.019 0.009 Height

Table A-4. Average values for large and small principal coordinate scores of microhabitat data. Principal coordinate axes correspond to the first three axes and where those retained for analyses (Table A-1). We interpreted the principal coordinate axes by subsetting all data-points into extreme (small and large) values (Figure A-1). Values given are averages of continuous variables or counts in the case of categorical variables for the subset. See results section of chapter two for interpretation. Avg. Avg. # # Far Avg. Avg. Number Number Coordinate Distance Height Medium # Near Subset N from Vegetation Distance of Edge of Interior axis from from from trunk Trunk Density to Cover Sites Sites Speaker Ground Trunk Small 19 18.89 21.89 0 1 17 1.263 9 0 19 values Edge-MH Large 52 15.00 8.49 26 18 1 2.385 2.853 52 0 values Small 24 20.09 15.22 24 0 0 2.833 1.292 3 21 values Trunk-MH Large 41 18.23 12.82 0 0 40 1.439 5.305 30 11 values Small 44 14.70 9.05 0 36 4 2.795 1.655 18 26 Escape- values MH Large 38 21.39 15.93 32 0 5 1.368 5.263 16 22 values

Table A-5. Candidate model set of GLMs for overall response used in the model averaging. Candidate models selected were those with a ΔAICc <2.

Degrees Model McFadden's Log Akaike Variables of AICc ΔAICc rank R2 likelihood weight freedom Call relevance + Escape-MH + Trunk-F + Height-F 1 0.581 6 -37.80 88.02 0.00 0.026 + Aerial-F 2 Call relevance + Trunk-F + Height-F + Aerial-F 0.567 5 -38.89 88.08 0.06 0.026 Call relevance + Edge-MH + Trunk-F + Height-F + 3 0.580 6 -37.88 88.19 0.17 0.024 Aerial-F Call relevance + Edge-MH + Escape-MH + Trunk- 4 0.593 7 -36.89 88.34 0.33 0.022 F + Height-F + Aerial-F Call relevance + Escape-MH + Trunk-F + Height-F 5 0.591 7 -37.06 88.68 0.66 0.019 + Aerial-F + Sociality Call relevance + Trunk-MH + Escape-MH + Trunk- 6 0.591 7 -37.06 88.68 0.67 0.019 F + Height-F + Aerial-F Call relevance + Escape-MH + Trunk-F +Height-F 7 0.589 7 -37.21 88.97 0.95 0.016 + Aerial-F + Titmouse presence Call relevance + Edge-MH + Escape-MH + Trunk- 8 0.603 8 -36.13 88.98 0.96 0.016 F + Height-F + Aerial-F + Sociality Call Relevance + Trunk-MH + Trunk-F + Height-F 9 0.574 6 -38.34 89.09 1.08 0.015 + Aerial-F Call relevance + Trunk-F + Height-F + Aerial-F + 10 0.574 6 -38.36 89.14 1.12 0.015 Titmouse presence Call relevance + Edge-MH + Trunk-MH + Escape- 11 0.602 8 -36.21 89.14 1.13 0.015 MH + Trunk-F + Height-F + Aerial-F Call relevance + Edge-MH + Trunk-F + Height-F + 12 0.587 7 -37.37 89.31 1.29 0.014 Aerial-F + Sociality Call relevance + Trunk-F + Height-F + Aerial-F + 13 0.572 6 -38.46 89.33 1.31 0.014 Sociality Call relevance + Edge-MH + Trunk-MH + Trunk-F 14 0.586 7 -37.39 89.33 1.31 0.014 + Height-F + Aerial-F Call relevance + Escape-MH + Trunk-F + Height-F 15 0.599 8 -36.39 89.51 1.49 0.013 + Aerial-F + Sociality + Titmouse presence

Table A-5. Continued. Call relevance + Trunk-MH + Escape-MH + Trunk-F + 16 0.599 8 -36.46 89.63 1.61 0.012 Height-F + Aerial-F + Sociality Call relevance + Escape-MH + Trunk-F + Occlusion-F 17 0.584 7 -37.54 89.64 1.63 0.012 + Height-F + Aerial-F Call relevance + Trunk-F + Occlusion-F + Height-F + 18 0.570 6 -38.64 89.69 1.67 0.011 Aerial-F Call relevance + Edge-MH + Trunk-F + Occlusion-F + 19 0.584 7 -37.60 89.76 1.75 0.011 Height-F + Aerial-F Call relevance + Edge-MH + Trunk-F + Height-F + 20 0.584 7 -37.61 89.77 1.75 0.011 Aerial-F + Titmouse presence Call relevance + Edge-MH + Escape-MH + Trunk-F + 21 0.597 8 -36.58 89.87 1.86 0.010 Height-F + Aerial-F + Titmouse presence Call relevance + Edge-MH + Escape-MH + Trunk-F + 22 0.597 8 -36.58 89.88 1.87 0.010 Occlusion-F + Height-F + Aerial-F

Table A-6. Candidate set of best models to explain response type (dive versus freeze). Candidate models selected were those with a ΔAICc <2. Degrees Model McFadden's Log Akaike Variables of AICc ΔAICc Rank R2 likelihood weight freedom 1 Escape-MH + Trunk-F + Temperature 0.235 4 -78.91 166.04 0.00 0.021 2 Escape-MH + Trunk-F + Sociality + Temperature 0.249 5 -77.92 166.18 0.14 0.019 Escape-MH + Trunk-F + Aerial-F + Sociality + 3 0.263 6 -76.96 166.40 0.35 0.017 Temperature 4 Escape-MH + Trunk-F + Occlusion-F + Temperature 0.248 5 -78.05 166.43 0.39 0.017 Call relevance + Escape-MH + Trunk-F + Sociality + 5 0.259 6 -77.22 166.92 0.88 0.013 Temperature 6 Call relevance + Escape-MH + Trunk-F + Temperature 0.244 5 -78.33 166.99 0.94 0.013 Escape-MH + Trunk-F + Occlusion-F + Sociality + 7 0.259 6 -77.27 167.01 0.97 0.013 Temperature Escape-MH + Trunk-F + Aerial-F + Sociality + 8 0.242 5 -78.40 167.14 1.10 0.012 Temperature Call relevance + Escape-MH + Trunk-F + Aerial-F + 9 0.271 7 -76.37 167.38 1.33 0.011 Sociality + Temperature Escape-MH + Trunk-F + Occlusion-F + Aerial-F + 10 0.270 7 -76.49 167.62 1.57 0.009 Sociality + Temperature Call relevance + Escape-MH + Trunk-F + Height-F + 11 0.268 7 -76.60 167.84 1.79 0.008 Sociality + Temperature Escape-MH + Trunk-F + Occlusion-F + Aerial-F 12 0.253 6 -77.69 167.86 1.82 0.008 +Temperature 13 Edge-MH + Escape-MH + Trunk-F + Temperature 0.237 5 -78.78 167.90 1.86 0.008 Trunk-MH + Escape-MH + Trunk-F + Aerial-F + Sociality 14 0.267 7 -76.70 168.03 1.99 0.008 + Temperature 15 Trunk-MH + Escape-MH + Trunk-F + Temperature 0.236 5 -78.85 168.03 1.99 0.008 Call relevance + Escape-MH + Trunk-F + Occlusion-F + 16 0.251 6 -77.78 168.04 2.00 0.008 Temperature

Table A-7. Proportions of foraging maneuver and foraging microhabitat use in a winter bird community. All substrate and attack maneuvers are categorized after Remsen and Robinson’s (1990) schema. Substrate, vegetation density, height from ground, and distance from trunk were recorded for each attack maneuver. We categorized vegetation density on a 0-5 scale based on the amount of visible light in a 1-meter-diameter sphere around the focal individual at the time of the attack maneuver. Distance from trunk was binned into three categories (near, medium, and far from tree trunk).

Dead Dead Fruiting Live Pine Pine Species Air Branch Epiphyte Ground Trunk Vine Branch Leaf Body Leaf Cone Needles BAWW 0.0184 0.0859 0.0552 0.0061 0.0736 0.0000 0.0000 0.0000 0.0000 0.0000 0.7362 0.0245 BGGN 0.1789 0.2316 0.0000 0.0105 0.0842 0.0000 0.0000 0.4842 0.0000 0.0000 0.0105 0.0000 BHVI 0.0245 0.4878 0.0244 0.0000 0.0488 0.0000 0.0000 0.0976 0.0000 0.0000 0.1951 0.1220 CACH 0.0000 0.7381 0.0714 0.0238 0.0000 0.0238 0.0000 0.1190 0.0000 0.0000 0.0238 0.0000 DOWO 0.0000 0.2295 0.1803 0.0000 0.0656 0.0000 0.0000 0.0000 0.0000 0.0000 0.5246 0.0000 EAPH 0.2050 0.2941 0.0294 0.0882 0.0000 0.0000 0.0588 0.2059 0.0000 0.0294 0.0882 0.0000 ETTI 0.0000 0.3393 0.1607 0.2321 0.1250 0.0000 0.0000 0.0893 0.0000 0.0000 0.0000 0.0536 MYWA 0.0986 0.2817 0.0000 0.0704 0.0282 0.0423 0.0000 0.2676 0.0000 0.0000 0.1268 0.0845 NOCA 0.0000 0.2340 0.0000 0.0000 0.0000 0.0000 0.6170 0.1489 0.0000 0.0000 0.0000 0.0000 OCWA 0.0000 0.0153 0.0000 0.7538 0.0462 0.0462 0.0000 0.1385 0.0000 0.0000 0.0000 0.0000 PIWA 0.0278 0.1389 0.0000 0.1528 0.3472 0.0278 0.0278 0.0972 0.0139 0.0972 0.0694 0.0000 RBWO 0.0000 0.2400 0.2800 0.0000 0.0500 0.0000 0.0000 0.0100 0.0000 0.0000 0.3800 0.0000

RCKI 0.0272 0.1837 0.0000 0.0000 0.0000 0.0000 0.0000 0.7551 0.0000 0.0068 0.0204 0.0000 WEVI 0.0333 0.1667 0.0000 0.0000 0.1000 0.0000 0.0000 0.6333 0.0000 0.0000 0.0000 0.0667 YTWA 0.0145 0.0870 0.0290 0.3623 0.2174 0.0145 0.0000 0.0145 0.1159 0.0435 0.0580 0.0435 YBSA 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.9149 0.0851 OVEN 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.9615 0.0385 0.0000 0.0000 0.0000 0.0000 CAWR 0.0000 0.1111 0.0556 0.5556 0.0556 0.0000 0.0000 0.1111 0.0000 0.0000 0.0556 0.0556 AMGO 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Table A-7. Continued. Flush- Species Far Medium Near 0 1 2 3 4 Flake Gape Glean pursue BAWW 0.0798 0.1963 0.7239 0.1595 0.4540 0.3190 0.0675 0.0000 0.0184 0.0184 0.0000 0.7914 BGGN 0.4608 0.3039 0.2353 0.0297 0.1980 0.2970 0.4158 0.0594 0.0000 0.1667 0.0000 0.5000 BHVI 0.2857 0.3571 0.3571 0.0238 0.5952 0.2381 0.1429 0.0000 0.0000 0.0238 0.0000 0.4048 CACH 0.5882 0.2353 0.1765 0.0256 0.1538 0.2308 0.5128 0.0769 0.0000 0.0000 0.0000 0.3333 DOWO 0.2787 0.2295 0.4918 0.0492 0.7377 0.1967 0.0164 0.0000 0.0328 0.0000 0.0000 0.1311 EAPH 0.5600 0.2400 0.2000 0.1515 0.3333 0.3333 0.1818 0.0000 0.0000 0.0000 0.0000 0.0000 ETTI 0.4423 0.3462 0.2115 0.0000 0.0600 0.3800 0.4600 0.1000 0.0714 0.0000 0.0000 0.2679 MYWA 0.2951 0.3443 0.3607 0.0571 0.2286 0.2429 0.3429 0.1286 0.0000 0.0282 0.0000 0.5634 NOCA 0.5455 0.4545 0.0000 0.2979 0.2340 0.1702 0.2340 0.0638 0.0000 0.0000 0.0000 0.4468 OCWA 0.4426 0.3115 0.2459 0.0000 0.1875 0.3281 0.4688 0.0156 0.0154 0.0000 0.0154 0.0769 PIWA 0.4058 0.2464 0.3478 0.0286 0.2714 0.3714 0.3000 0.0286 0.0278 0.0278 0.0000 0.3750

RBWO 0.0900 0.3700 0.5400 0.0104 0.6771 0.1979 0.1146 0.0000 0.0400 0.0000 0.0000 0.0000

RCKI 0.5664 0.2308 0.2028 0.0200 0.0666 0.3733 0.4867 0.0533 0.0000 0.0250 0.0000 0.5063 WEVI 0.5000 0.2143 0.2857 0.0000 0.0670 0.2414 0.5862 0.1034 0.0000 0.0333 0.0000 0.5333 YTWA 0.4265 0.3382 0.2353 0.0000 0.2647 0.2794 0.3529 0.1029 0.0000 0.0145 0.0000 0.1449 YBSA 0.0000 0.1702 0.8298 0.2128 0.5957 0.0638 0.0000 0.1277 0.0000 0.0000 0.0000 0.0213 OVEN NA NA NA 0.0000 0.2692 0.3846 0.1538 0.1923 0.0000 0.0000 0.0000 0.0384 CAWR 0.2000 0.0000 0.8000 0.0000 0.1111 0.3889 0.2778 0.2222 0.0000 0.0000 0.0000 0.3333 AMGO 0.7692 0.0000 0.2308 0.0000 0.5385 0.4615 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Table A-7. Continued. Hang-down Reach-down Species Hammer Hang Lunge Peck Probe Pry Pull Reach probe probe BAWW 0.0000 0.0122 0.0061 0.0000 0.0000 0.1411 0.0000 0.0000 0.0000 0.0000 BGGN 0.0000 0.0098 0.0000 0.0000 0.0000 0.0098 0.0000 0.0000 0.0882 0.0000 BHVI 0.0000 0.0238 0.0000 0.0000 0.0000 0.0238 0.0000 0.0000 0.0714 0.0000 CACH 0.1905 0.2856 0.0000 0.0000 0.0000 0.0476 0.0000 0.0000 0.0476 0.0000 DOWO 0.6885 0.0164 0.0000 0.0000 0.0820 0.0492 0.0000 0.0000 0.0000 0.0000 EAPH 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0882 0.0000 ETTI 0.1071 0.1786 0.0000 0.0000 0.0000 0.2679 0.0000 0.0179 0.0536 0.0000 MYWA 0.0000 0.0000 0.0000 0.0000 0.0000 0.0845 0.0000 0.0000 0.1126 0.0000 NOCA 0.0000 0.0000 0.0000 0.0213 0.0000 0.0000 0.0000 0.0000 0.5319 0.0000 OCWA 0.0000 0.0154 0.0308 0.0000 0.0000 0.6769 0.0000 0.0000 0.0462 0.1231 PIWA 0.0000 0.0278 0.0139 0.0000 0.0000 0.4028 0.0000 0.0000 0.0834 0.0139

RBWO 0.1700 0.0000 0.0000 0.0000 0.1100 0.6000 0.0100 0.0000 0.0000 0.0000

RCKI 0.0000 0.0189 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1313 0.0000 WEVI 0.0000 0.1000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.1333 0.0000 YTWA 0.0000 0.0290 0.0145 0.0000 0.0000 0.6812 0.0000 0.0145 0.0435 0.0580 YBSA 0.7660 0.0000 0.0000 0.0000 0.0000 0.2128 0.0000 0.0000 0.0000 0.0000 OVEN 0.0000 0.0000 0.0000 0.0769 0.0000 0.0000 0.0000 0.0000 0.8846 0.0000 CAWR 0.0000 0.0000 0.0000 0.0000 0.0000 0.5556 0.0000 0.0000 0.1111 0.0000 AMGO 0.0000 0.0000 0.0769 0.0000 0.0000 0.9231 0.0000 0.0000 0.0000 0.0000

Table A-7. Continued. Species Sally Sally-hover Sally-pounce Average foraging height BAWW 0.0000 0.0061 0.0061 10.7761 BGGN 0.0196 0.1765 0.0294 12.5510 BHVI 0.0238 0.2857 0.1429 11.2262 CACH 0.0000 0.0000 0.0000 12.1750 DOWO 0.0000 0.0000 0.0000 16.0481 EAPH 0.5882 0.2059 0.1176 10.3240 ETTI 0.0000 0.0357 0.0000 11.1111 MYWA 0.1127 0.0845 0.0141 9.9203 NOCA 0.0000 0.0000 0.0000 4.2766 OCWA 0.0000 0.0000 0.0000 9.6563 PIWA 0.0000 0.0278 0.0000 14.5833 RBWO 0.0000 0.0000 0.0000 14.6083 RCKI 0.0000 0.3063 0.0063 8.3312 WEVI 0.0667 0.0333 0.1000 8.2500 YTWA 0.0000 0.0000 0.0000 15.2246 YBSA 0.0000 0.0000 0.0000 15.8298 OVEN 0.0000 0.0000 0.0000 0.0000 CAWR 0.0000 0.0000 0.0000 4.1667 AMGO 0.0000 0.0000 0.0000 21.3077

Figure A-1. Biplot of principal coordinate axes Edge-MH and Trunk-MH of the microhabitat variables. These axes represent the first and second axes obtained from principal coordinates analysis of 6 microhabitat variables (see Table A-4) recorded before each Z call playback. Points represent playback locations (samples) while blue lines indicate cut-off values used to define large and small sub-samples from which average values in Table A-4 were calculated.

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BIOGRAPHICAL SKETCH

Harrison Jones was born and raised in Indianapolis, Indiana. He obtained his

Bachelor of Science in 2012 from Haverford College (Haverford, PA), double majoring in biology and French literature. He then spent two years working as a field technician for a variety of research projects focusing on avian ecology and conservation in Peru,

Puerto Rico, and California before enrolling in a Master of Science program at the

University of Florida. His research interests include applied behavioral ecology

(conservation behavior) and avian ecology, especially in the Neotropics. In particular, he hopes to study the ecology and conservation of mixed-species foraging flocks of passerine birds in the tropical Andes for his Ph.D.