We found differences in three main behaviors between urban and rural burrowing owls, namely exploration, aggression toward predators and risk taking, in particular fear of humans. Our results show that rural birds were, in general, less fearless when facing humans, less explorative and less aggressive toward predators than their urban counterparts. Moreover, these behavioral traits did not vary independently of each other among these birds, as shown by the significant and positive correlations between them. Thus, rural individuals with larger flight initiation distances when facing a human also showed longer latencies in approaching a predator and a new food item. However, when the same relationships were investigated among urban birds, we found a significant covariance between fear of humans and exploration and between latency to approach a predator and a new feeding source, suggesting that selection pressures acting during urban invasion and while living in the city may be dismantling the behavioral correlations existent among individuals occupying more natural environments.

Two main hypotheses have been proposed to explain the existence of behavioral correlations. The constraint hypothesis assumes that a shared proximate link between personality traits, e.g., physiological or genetic factors (Ketterson and Nolan, 1999; van Oers et al., 2005), is responsible for the behavioral correlations. Conversely, the adaptive hypothesis states that correlations between personality traits emerge when selection favors correlated behaviors in particular environments, this correlation being adaptive itself (Bell, 2005). Here, we show how behavioral syndromes detected among rural birds changed when considering their urban counterparts, supporting the adaptive nature of these behavioral correlations (Scales et al., 2011; Bókony et al., 2012).

In the most natural scenario, burrowing owls that tolerate humans at closer distances were also more reactive toward predators, and approached new feeding sources sooner, the last two behavior remaining independent. However, in urban environments, burrowing owls face different ecological pressures compared to those present in rural ones. On the one hand, they are constantly interacting with people (Carrete and Tella, 2011), which may represent an important selective factor. Indeed, differences in the fear of humans between urban and rural populations seem to arise as a consequence of selective pressures precluding frightened individuals from colonizing urban areas and/or favoring their emigration/mortality in urbanized areas (Evans et al., 2010; Møller, 2010; Carrete and Tella, 2011; Atwell et al., 2012). At the same time, predation pressure also differs between urban and rural areas, with predators being much less abundant and diverse in the former than in the latter (Rebolo-Ifrán et al., in press). Avoidance of predation is an important determinant of fitness in many animals (Godin, 1997; Ruxton et al., 2004). However, when isolated from predators, costly or no longer functional antipredator behaviors can be selected against or their effectiveness cannot be selected any more, as occurs in species occupying islands (Blumstein and Daniel, 2002) or individuals bred in captivity (Carrete and Tella, 2015). Thus, urban individuals living in a predator-free area where humans are constantly present can lose their antipredator behavior while breaking its links to the fear of humans. Moreover, urban birds can take advantage of the predator release effect by gaining higher breeding success than their rural counterparts (Rebolo-Ifrán et al., in press), such that the heritabilities of the fear of humans (Carrete et al., 2016) and antipredator behavior (Bize et al., 2012) can subsequently reinforce the behavioral differences between urban and rural populations initially resulting from selection. Whatever the mechanism causing the decoupling between fear of humans and antipredator behaviors in urban individuals, our results—in line with those obtained by Myers and Hyman (2016) challenge previous interpretations of flight initiation distances (FID). While FID has been frequently used as an experimental measure of the response of individuals facing a predator (Díaz et al., 2013), our results suggest that FID actually measures their response toward humans which, in some but not in all cases (i.e., urban populations), correlates with responses to predators. Therefore, FID could be interpreted as a measure of fear of humans rather than a broader measure of response when facing predators.

Exploration positively correlated with fear of humans, more strongly among rural than among urban birds. However, exploration and antipredatory behavior were positively related among urban birds, but not among their rural counterparts. Exploration and fear of humans have been shown to be positively correlated in domestic red junglefowls (Agnvall et al., 2012), although studies relating exploration and antipredator behavior yielded inconclusive results (Jones and Godin, 2010; Couchoux and Cresswell, 2011). Explorative behaviors can be advantageous when invading novel habitats such as urban areas (Martin and Fitzgerald, 2005), neophilic/explorative phenotypes having advantages in exploiting novel food resources or food available in novel contexts (Tryjanowski et al., 2016).

#### REFERENCES

Agnvall, B., Jöngren, M., Strandberg, E., and Jensen, P. (2012). Heritability and genetic correlations of fear-related behaviour in red junglefowl– possible implications for early domestication. PLoS ONE 7:e35162. doi: 10.1371/journal.pone.0035162

Recently, it has been shown that exploration is repeatable and heritable (Dingemanse et al., 2002; Mazué et al., 2015), with permanent environmental (maternal) effects explaining most of the resemblance between parents and offspring (Schuett et al., 2013). Thus, bold and explorative individuals that successfully raise chicks in both urban and rural sites may transmit these behaviors to their progeny, even increasing the correlation between both behavioral traits in urban areas.

A major complication of assessing the consequences of human disturbance on wildlife is that those consequences are not always directly visible. For instance, even if seemingly unaffected (i.e., behaviorally calm), animals might undergo profound physiological changes in response to anthropogenic disturbances, or even to the mere presence of human observers. Our results show that fear of humans, a key behavior during urban invasion, correlated with other behaviors (antipredatory and exploratory behaviors) in birds living in natural environments. However, changes in selection pressures faced by urban individuals change these relationships, maintaining only those that are adaptive themselves (Bell, 2005). These results support the idea that differences among populations in the strength or direction of a behavioral correlation imply that these correlations can change during the evolutionary divergence of populations and are mainly due to the adaptive nature of each behavioral trait involved, demonstrating how human disturbances have the potential to contribute to population differentiation.

#### ETHICS STATEMENT

This study was carried out in accordance with the recommendations of Argentinean wildlife agencies and the owners of private properties. The protocol was approved by the Ethic committee of CSIC.

#### AUTHOR CONTRIBUTIONS

MC and JT conceived the idea. JT and MC conducted field work. MC analyzed the data. JT and MC wrote the paper and discussed the results and commented on the manuscript.

#### ACKNOWLEDGMENTS

M. Santillán helped with field work. Field work was conducted under permits from Argentinean wildlife agencies and the owners of private properties, and was funded by Canal Sur TV, Fundación Repsol and Projects RYC-2009-04860, CGL2012-31888 and CGL2015-71378-P from MINECO (Spain). D. Blumstein and an reviewer greatly helped to improve the paper.

a novel urban environment suggest rapid correlated evolutionary adaptation. Behav. Ecol. 23, 960–969. doi: 10.1093/beheco/ars059

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Carrete and Tella. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# An Approach to Distinguish between Plasticity and Non-random Distributions of Behavioral Types Along Urban Gradients in a Wild Passerine Bird

Philipp Sprau1 \* and Niels J. Dingemanse1, 2

*1 Behavioral Ecology, Department of Biology, Ludwig-Maximilians University of Munich, Munich, Germany, 2 Research Group Evolutionary Ecology of Variation, Max Planck Institute for Ornithology, Seewiesen, Germany*

The impact of urbanization has been widely studied in the context of species diversity and life history evolution. Behavioral adaptation, by contrast, remains poorly understood because empirical studies rarely investigate the relative importance of two key mechanisms: plastic responses vs. non-random distributions of behavioral types. We propose here an approach that enables the simultaneous estimation of the respective roles of these distinct mechanisms. We investigated why risky behaviors are often associated with urbanization, using an urban nest box population of great tits (*Parus major*) as a study system. We simultaneously and repeatedly quantified individual behavior (aggression and flight initiation distance) as well as environmental factors characterizing level of urbanization (numbers of pedestrians, cars and cyclists). This enabled us to statistically distinguish plastic responses from patterns of non-random distributions of behavioral types. Data analyses revealed that individuals did not plastically adjust their behavior to the level of urbanization. Behavioral types were instead non-randomly distributed: bold birds occurred more frequently in areas with more cars and fewer pedestrians while shy individuals were predominantly found in areas with fewer cars and more pedestrians. These novel findings imply a major role for behavioral types in the evolutionary ecology of urban environments and call for the full integration of among- and within-individual variation in urban ecological studies.

#### Edited by:

*Caroline Isaksson, Lund University, Sweden*

#### Reviewed by:

*Wayne C. Zipperer, USDA Forest Service, United States Geoffrey M. While, University of Tasmania, Australia*

> \*Correspondence: *Philipp Sprau<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Urban Ecology, a section of the journal Frontiers in Ecology and Evolution*

> Received: *30 March 2017* Accepted: *24 July 2017* Published: *08 August 2017*

#### Citation:

*Sprau P and Dingemanse NJ (2017) An Approach to Distinguish between Plasticity and Non-random Distributions of Behavioral Types Along Urban Gradients in a Wild Passerine Bird. Front. Ecol. Evol. 5:92. doi: 10.3389/fevo.2017.00092* Keywords: among-individual variation, phenotypic plasticity, personality, urbanization, great tit, within-individual variation

## INTRODUCTION

Urbanization represents one of the fastest environmental changes of our times (United Nations, Department of Economic and Social Affairs, and Population Division, 2017) and poses novel challenges to wildlife (Sih et al., 2011). Urban habitats are characterized by a loss of natural resources (e.g., food) and increased anthropogenic levels of disturbance (e.g., pedestrian and vehicular traffic, and industrial noise). There is a growing body of literature describing how animal populations are affected by urbanization (Marzluff, 2008). In birds, for instance, breeding densities are often higher in urban compared to natural habitats (Beissinger and Osborne, 1982). Furthermore, several studies have reported advanced lay dates, smaller clutch sizes and decreased offspring production in urban compared to rural habitats (reviewed in Chamberlain et al., 2009). More recently, research has focussed on behavioral modifications in urban wildlife (Lowry et al., 2013). Yet, the mechanisms responsible for associations between behavior and level of urbanization remain largely unknown.

Phenotypic and environmental variation are often associated (Stearns, 1989). Relationships between phenotypes and environments can be caused by various distinct mechanisms such as phenotypic plasticity, differential settlement, and/or the selective (dis)appearance of distinct types of individuals (Dingemanse et al., 2010). Several studies have demonstrated behavioral plasticity in response to urbanization (Lefebvre, 1995; Seferta et al., 2001; Bouchard et al., 2007; Levey et al., 2009). Song birds such as nightingales (Luscinia megarhynchos) and great tits (Parus major) have been shown to adjust song amplitude (Brumm, 2004) and frequency (Slabbekoorn and den Boer-Visser, 2006) to background noise. Phenotypes, however, also vary among individuals. Repeatable differences among individuals have been demonstrated for a variety of behaviors such as aggressiveness, boldness and exploration (Bell et al., 2009; Garamszegi et al., 2012; Holtmann et al., 2017). These particular types of behavioral traits have recently also been explicitly linked to urbanization. For instance, urban male song sparrows (Melospiza melodia) are bolder and more territorial than their rural conspecifics (Evans et al., 2010). Boldness reflects the tendency of an individual to take risks (Wilson et al., 1994; Réale et al., 2007). Accordingly, bold individuals are often considered to cope better with risky and novel situations and may therefore be favored in highly disturbed areas such as cities.

Importantly, behavioral traits vary simultaneously among and within individuals, due to individual variation (aka "personality") and plasticity (Dingemanse et al., 2010). Therefore, associations between behavior and urbanization can similarly originate from multiple processes. This can be usefully illustrated by viewing behavior in terms of reaction norms (Nussey et al., 2007; Dingemanse et al., 2010), where each individual is characterized by a reaction norm intercept (representing its average behavior in the average environmental condition, or behavioral "type"), and a reaction norm slope, representing its level of response (plasticity) to changes in urbanization (**Figure 1**). Viewing behavior in this way clarifies that the relationship between behavior and urbanization is the result of the combined effects of phenotypic plasticity and non-random distributions of behavioral types over urban gradients. Previous studies have not distinguished between these two mechanisms in

FIGURE 1 | Behavioral reaction norm plots, providing examples of possible relationships between anti-predator boldness (an exemplary behavioral phenotype) and an environmental gradient (e.g., amount of disturbance) among- vs. within-individuals. Each dot represents the average phenotype of an individual and each solid blue line represents its behavioral reaction norm over the range of environmental conditions (x-axis) that it occurred observationally. Among-individual variation in intercepts represents "personality variation" (*sensu* Dingemanse et al., 2010). Non-zero slopes represent within-individual plasticity (i.e., the observed relationship between y and x within the individual: βW, blue lines). In contrast, blue dashed lines represent the hypothesized phenotype of each individual outside the range of environments in which it occurred observationally.  Plots (A–D) show different scenarios for how behavior and the environment might be associated within and between individuals as detailed in the Introduction.

empirical demonstrations of relationships between behavior and urbanization.

We conceive four scenarios for how behavior and level of urbanization may be associated, though we recognize that further scenarios could be proposed. In the first scenario, individuals are non-randomly distributed over an urban gradient with respect to their reaction norm intercept (i.e., average behavior, or behavioral type) while they simultaneously do not respond plastically to changes in urbanization. This scenario is illustrated in **Figure 1A**, where individuals with high intercept values for boldness are more likely to occur in more disturbed areas, leading to a positive among-individual effect of disturbance on behavior (i.e., βA > 0, A for "among") and a zero within-individual effect (i.e., βW = 0, W for "within"). In the second scenario, individuals are instead randomly distributed over an urban gradient with respect to their intercept (i.e., behavioral type) while simultaneously not responding plastically to changes in urbanization. This scenario is illustrated in **Figure 1B**, showing a zero among- (βA = 0) and a zero within-individual effect (βW = 0). In the third scenario, individuals are both non-randomly distributed over an urban gradient with respect to their intercept while also responding plastically to changes in urbanization. This scenario is illustrated in **Figure 1C**, where individuals with high intercept values for boldness (i.e., bold behavioral types) are more likely to occur in more disturbed areas, leading to a nonzero among-individual effect of disturbance on behavior (i.e., βA > 0), while individuals also alter their phenotype in response to changes in level of disturbance, leading to a nonzero within-individual effect (i.e., βW > 0). In both scenarios 1 and 3, importantly, the effect of non-random distributions of behavioral types causes a difference between the among- and within-individual effect of the environmental gradient (van de Pol and Verhulst, 2006) (specifically, βW < βA in both examples). In the fourth scenario, individuals do not differ in intercept (i.e., all have the same behavioral type) but they do respond plastically to changes in urbanization. This scenario is illustrated in **Figure 1D**, where individuals up-regulate their boldness with increasing levels of disturbance (i.e., βW > 0), and where amongindividual relationships between boldness and disturbance exist solely because each individual happens to experience a narrow range of environmental conditions, causing among-individual variance in the average level of disturbance experienced (i.e., βA > 0). As a single mechanism causes variation at both levels, environmental effects on behavior do not differ between hierarchical levels (van de Pol and Verhulst, 2006) (i.e., βA = βw in **Figure 1D**). In summary, our sketch of alternative scenarios clarifies that conclusions regarding non-random distributions of behavioral phenotypes over urban gradients warrants repeated observations of both behavior and environmental factors such that level-specific effects of urbanization on behavior can be statistically teased apart (**Figure 1**).

We investigated the mechanisms causing relationships between urbanization and behavior in great tits.  That is, we repeatedly exposed the same individual to territorial intrusion experiments to measure aggressiveness (Araya-Ajoy and Dingemanse, 2014) and to flight initiation distance experiments to measure boldness (Blumstein, 2003), while quantifying aspects of urbanization during each test (numbers of pedestrians, cars and cyclists). This enabled us to statistically distinguish within-individual plasticity from patterns of non-random distributions of behavioral types (**Figure 1**). Human activity is known to alter behavioral phenotypes in animals (Fernandez-Juricic and Schroeder, 2003; Slabbekoorn and Peet, 2003). Based on previous literature (Brumm, 2004; Evans et al., 2010), we expected that birds would plastically up-regulate aggressiveness and down-regulate flight initiation distance with increasing levels of disturbance, and that more aggressive and bolder individuals would occur predominantly in areas with higher levels of disturbance (**Figure 1C**).

## METHODS

#### Study Site

We studied an urban nest box population of great tits consisting of 157 nest boxes along urban-to-rural gradients in the city of Munich, Germany (48◦ 8 ′ 6.45" N 11◦ 34′ 55.132" E) during the breeding seasons of the years 2014 and 2015 (Sprau et al., 2016). Nest boxes were located in the entire city area of Munich (20 × 27 km2 ) and covered a large range of human disturbance from highly disturbed habitats in the city center to relatively undisturbed habitats in sub-urban areas. All nest boxes were checked at least once per week from mid-March onwards and key fitness components quantified (e.g., lay date, clutch size, brood size, and number of fledged offspring). When the nestlings were 7–9 days old, parents were caught with a spring trap in the nest box, measured, and ringed if not previously captured.

#### Experimental Protocol

We quantified two behaviors both of which were assayed repeatedly for the same set of breeders: aggressiveness and boldness (measured as flight initiation distance; FID). Simulated territorial intrusions (i.e., aggression tests) were performed for all first broods found in our nest boxes by simultaneously presenting the male owner with a visual stimulus (a taxidermic mount of a male great tit) and an acoustic stimulus (a playback song) (as detailed in reference Araya-Ajoy and Dingemanse, 2014). In each year, each male was subjected to three aggression tests (between 7.30 and 15.00 h) when its mate was in the egg-laying phase (1, 3, and 5 days after its first egg was observed). The taxidermic mount was presented 1 m away from the subject's nest-box at 1.2 m height. We subsequently recorded the behavior of the subject for a period of 3 min after it had entered a 15 m radius around the nest box. Details of the experimental set up, and assayed behaviors, are provided in reference Araya-Ajoy and Dingemanse (2014). In short, an aggressive response was characterized by intensive alarm calling, approach to the stimulus, and, in the most extreme case, jumping and pecking of the cage that protected the mount. Here and elsewhere (Araya-Ajoy et al., 2016; Araya-Ajoy and Dingemanse, 2017) we used the subject's minimum approach distance to the mount as a measure of aggressiveness because previous work implied that this behavior represents a reliable predictor of the intensity of aggression. Subjects that did not arrive within 10 min were scored

as non-responsive, and those data were thus not taken forward for analyses (Araya-Ajoy and Dingemanse, 2014, 2017). We used six mounts and 11 playback song stimuli (recorded from German great tits populations; sampling frequency: 44.1 kHz; resolution: 16 bit). One mount and one song stimulus (broadcasted with a Ligno Xtatic V2 Digital Soundsystem) were randomly allocated to each test (following Araya-Ajoy and Dingemanse, 2014). Songs were played back at 85 dB (measured at one meter from the sound source (Brumm, 2004). One of nine observers performed the experiment at a distance of 15 m. We performed 333 aggression tests with 107 unique males.

The occupants of each nest box were also subjected to three FID-tests during the nestling phase of their first brood (10, 12 and 14 days after the nestlings had hatched). FID-tests were conducted between 8:00 and 16:00 h. After identifying (by color ring combination) the focal individual as the male or female parent, FID was measured by walking at a constant speed toward a bird from a starting distance of 15 m away from the nest box (Blumstein, 2003). We used a laser distance meter (Bosch PLR 25) to quantify distance to the mount during the aggression tests, and start and flight initiation distance. Overall, we performed 308 flight initiation tests, on 59 females and 54 males. Note that because of nest failure prior to the onset of the FID-tests, the number of FID-tests is lower than the number of the aggression tests. Five and four individuals were assayed, respectively, for aggressiveness and flight initiation distance in both years of study. This study was carried out in accordance with the ethical guidelines of the Tierschutzgesetz (TierSchG, German animal protection law), and approved by the Regierung Oberbayern (55.2-1-54-2532.2-7-07).

The level of urbanization at each nest box was quantified by measuring human activity (the number of pedestrians, cyclists and cars, Table S1). Human activity was measured within a range of 15 m from the nest box for 2 min following each behavioral assay (detailed above).

#### Statistical Analyses

We performed a principal component analysis (PCA) with varimax rotation ("prcomp" function of Package "stats" version 3.1.27 of R version 3.1.2) to ask whether our indexes of human activity (number of pedestrians, bikes and cars) could be summarized into a single axis (principal component) representing an urban gradient (Table S1).

We fitted univariate mixed-effect models to simultaneously estimate sources of variation in behavior within and among individuals (Dingemanse and Dochtermann, 2013) ("lmer" function of Package "lme4"version 1.1-10 of R). We investigated sources of variation in each of the two focal behaviors (aggressiveness and flight initiation distance) separately. Random intercepts were included for subject and observer identity, enabling us to partition the total variance into variance attributable to individual, observer, and within-individualwithin-observer residual. Start distance (covariate: only for flight initiation distance; meter), year (factor: 2014 vs. 2015), time of day (factor: morning vs. afternoon trial), and test sequence (covariate; within-individual test-day number; first vs. second vs. third test day) were fitted as fixed effects. The PCA resulted in two components (PC1 and PC2) describing two orthogonal axes of human activity. As detailed in the Introduction (**Figure 1**), we considered that environmental effects on behavior could vary within and among individuals (van de Pol and Wright, 2009). Specifically, a within-individual effect of the environment on behavior represents evidence for within-individual phenotypic plasticity, while the difference between among- and withinindividual effects represents statistical evidence for non-random distributions of behavioral types over environments (van de Pol and Wright, 2009; Dingemanse and Dochtermann, 2013). We thus calculated (1) each individual's average value (x¯j) for each of the two environmental variables (PC1 and PC2) as well as (2) each observation's deviation of these individual average values (xij − ¯xj) effects, and fitted both as part of the statistical model detailed above (van de Pol and Verhulst, 2006; van de Pol and Wright, 2009). We then reformulated the model to test whether the effect of the focal environmental axis differed between the within- and among-individual levels. Therefore, instead of fitting x¯j and (xij − ¯xj) and each individual's average value (x¯j) instead, such that the former estimated the within-individual effect (βW) (see formulae 3 in van de Pol and Wright, 2009). This enabled us to statistically assess the evidence for non-random distributions of behavioral types over environments (which would be the case provided that (βA − βW) 6= 0). We note that the within-subject centering approach used here has been criticized because values of x¯j are estimated with error, which causes estimates of the among-individual slope (βA) to be biased toward the withinindividual slope (βW) in datasets with a low numbers of repeats per individual (Ludtke et al., 2008), such as ours. This means that differences (1) between among- and within-individual effects (i.e., βA − βW), as well as associated levels of significance, represent conservative estimates. We assumed a Gaussian error distribution for aggression and boldness, which was confirmed by visual inspection of model residuals. All covariates were further centered on their mean value (Kreft et al., 1995). For each specified relationship, we calculated the parameter estimate with its associated 95% credible interval (calculated using the function "quantile"; Package "stats"version 3.1.2 in R). Credible intervals not including zero indicate statistical significance (i.e., p < 0.05) in the frequentist's sense.

#### RESULTS

## Axes of Environmental Variation

PCA applied to summarize variation in the number of pedestrians, cyclists, and cars, resulted in two significant principle components (PCs) that jointly explained 81% of the variance (Table S2). PC1 (Eigenvalue: 1.40; explained variance: 47%) loaded negatively on the number of bikes, cars, and pedestrians (Table S1); high values of PC1 were thus indicative of lower levels of human activity in general. PC2 (Eigenvalue: 1.04; explained variance: 35%) loaded negatively on the number of cars (−0.76) but positively on the number of pedestrians (0.65), and thus seemed to differentiate between streets differing in the primary means of transportation (e.g., larger streets suitable for cars vs. smaller streets suitable for pedestrians).

### Phenotypic Plasticity and Non-random Distributions of Behavioral Types

Our analyses of the sources of variation in behavior, which focussed on the simultaneous estimation of within-individual (βW) and among-individual (βA) effects of environmental variables related to urbanization (**Figure 1**), demonstrated that individuals did not plastically adjust their aggressiveness nor their flight initiation distance (FID) in response to withinindividual-among-day variation in PC1 or PC2 (Table 1). All models controlled for variation induced by aspects of the experimental design (starting distance, test sequence, time of day, and year), which were generally not of major importance (**Table 1**).

As a next step, we re-parameterised our models to directly estimate the difference (1) between the among- and withinindividual effects (βA − βW) of each focal gradient as a test for non-random distributions of behavioral types (van de Pol and Verhulst, 2006; van de Pol and Wright, 2009). This analysis produced strong evidence for non-random distributions of behavioral types with respect to FID because the difference (1) in effect of PC2 among- vs. within-individuals was associated with 95% CIs that did not overlap zero (mode: 0.28; 95% CIs: 0.03, 0.56; **Table 1**). The analysis implied that individuals that allowed observers to approach closer (i.e., "bolder" birds) were overrepresented in areas with more cars and fewer pedestrians, whereas "shyer" birds were more likely found in areas with fewer cars and more pedestrians (**Figure 2**). By contrast, there was no strong evidence for nonrandom distributions of aggressiveness types as the 95% CIs overlapped zero for all tested differences (1) between the within- and among-individual levels for aggressive behavior (**Table 1**). Notably, there was some support for non-random distributions of aggressiveness types with respect to PC1 as the CIs associated with the difference (1) between levels for this gradient slightly overlapped zero (95% CIs: −0.001, 0.38) (**Table 1**), suggesting that aggressive types were perhaps overrepresented in areas with more cyclists, cars, and pedestrians (**Table 1**).

#### DISCUSSION

Our study revealed that behavioral types, with respect to flight initiation distance, were non-randomly distributed over an urban gradient while individuals did not plastically adjust their behavior in response to changes in urban gradients experienced across repeated observations (days). Bolder birds (i.e., birds that could be approached by humans closely) were overrepresented in areas with more cars and fewer pedestrians, whereas shyer birds were more likely found in areas with fewer cars and more pedestrians (**Figure 2**). These findings imply that associations between behavior and urban gradients vary across hierarchical levels, in this case within and among individuals, and that meaningful conclusions regarding non-random distributions of "personality" types over urban environments thus require repeated measures study designs and variance partitioning approaches, as applied in this study.

TABLE 1 | Sources of variation in boldness and aggressiveness.

*We test here for within- and among individual effects of cyclists, cars, and pedestrians (summarized in PC1 and PC2, see Table S1). All models control for variation induced by various aspects of the experimental design (starting distance, test sequence, time of day, and year) and included random intercepts for subject individual and observer identity. We also present the difference (*∆*) between among and within-individual effects derived from the same statistical model reformulated following reference (van de Pol and Wright, 2009). Parameter estimates and are provided with 95% credible intervals (CIs).*

Human activity is known to affect behavior and abundance of animals (Gill et al., 1996; Fernandez-Juricic, 2000); there is considerable evidence that birds living in highly disturbed areas are more tolerant of humans than their conspecifics living in less disturbed areas (Moller, 2008; Evans et al., 2010; Scales et al., 2011; Clucas and Marzluff, 2012). In a similar vein, recent studies have shown that birds in more disturbed environments may display higher levels of territorial and defensive behaviors (Cilento and Jones, 1999; Evans et al., 2010; Fokidis et al., 2011; Scales et al., 2011). Most of these studies, however, have solely focused on differences in behavioral phenotypes at the population level. Researchers have only recently begun to assess individual variation in the context of urban ecology (Miranda et al., 2013). In this study we shed new light on relationships between urbanization and behavioral phenotypes by partitioning variation in behavioral phenotypes into within- and amongindividual components; this enabled us to investigate the relative roles of distinct mechanisms causing such associations. The applied approach allowed us to simultaneously assess whether individuals responded plastically to urban gradients and whether behavioral types were non-randomly distributed over urban gradients (**Figure 1**). Our findings reveal that behavioral types were indeed non-randomly distributed along a key axis of

and lower loadings more cars). As individuals do not plastically respond to changes in PC2 (Table 1), the relationship implies a non-random distribution of individual-level behavioral type along this urban gradient. The thick black line presents the regression line between FID and PC2 based on the fitted GLMM (Table 1). Shown are average scores ± SE.

urbanization (**Figure 2**): bolder individuals, i.e., birds that could be approached by humans closely, were predominantly found in areas with more cars, whereas shyer individuals were found more often in areas with more pedestrians. The documented effect might be explained by sensory constraints caused by traffic noise. We tested this post-hoc explanation by analyzing noise measurements that were taken during each test (detailed in the Supplementary Material), which demonstrated that noise neither affected FID nor aggression (Table S3).

Non-random distributions of behavioral types along environmental gradients have previously been documented in eastern chipmunks (Tamias striatus) where more explorative and docile individuals occupy habitats that experience the highest rates of human disturbance (Martin and Réale, 2008). Accordingly, bold animals might have an innately higher disturbance tolerance level than shy individuals. In urban environments, cars usually impose high risks as evident from high numbers of road kills (Spellerberg, 1998; Benitez-Lopez et al., 2010). In contrast, pedestrians may impose lower disturbance levels because birds quickly habituate to humans. Because bold individuals are often considered to cope better with risky situations (Smith and Blumstein, 2008), bold phenotypes in our study may be selected for in high-traffic environments because bolder individuals are more successful in colonizing such environments. Alternatively, bold phenotypes might be outcompeted by shy phenotypes for preferred types of territories and hence settle in these types of areas. It is also possible that prolonged exposure to particular environments permanently affects an individual's behavioral type, resulting in birds becoming bolder in high-traffic environments by means of developmental or other forms of plasticity with permanent effects. Experimental tests are therefore now required to address whether the non-random distributions of behavioral phenotypes documented in our study were caused by nonrandom settlement, habitat- and type-specific survival, or irreversible plasticity in response to long-term exposure to urban environmental effects. Similarly, we studied a very specific component of the urban environment, focusing on human traffic. Whether the relationships between behavior and aspects of urbanization shown in this paper apply generally to other components of urbanization remains to be evaluated by future studies.

Surprisingly, great tits did not show any sign of a plastic response to day-to-day variation in urban environmental gradients. It is possible that exposure to high levels of human disturbance for prolonged periods triggers habituation (see above) and consequently reduces short-term plastic responses. Such effects may suggest that birds experience only minor fluctuations in environmental conditions, and that each bird's characteristic level of urbanization is relatively stable. In urban environments, such a scenario seems unlikely as the numbers of cars, pedestrians and cyclists, in fact, varied substantially, for instance, between workdays and weekends. Temporal variation between workdays and weekends has in fact previously been shown to cause plastic adjustments in other behaviors (Brumm, 2004). We therefore conclude that phenotypic adjustments to day-to-day variation in human disturbance might well differ between behavioral traits, perhaps because the costs or limits associated with phenotypic plasticity are trait-specific (DeWitt et al., 1998; Auld et al., 2010). Our recent studies on aggressiveness, for example, demonstrated that this particular behavior (which birds did not plastically adjust to changes in human disturbance; **Table 1**) is also not plastically adjusted to population density (Araya-Ajoy and Dingemanse, 2017) or perceived predation risk (Abbey-Lee et al., 2016). Overall, the lack of evidence for within-individual plasticity suggests that its role in urban ecology may be more modest than previously anticipated (Lowry et al., 2013). At the same time, urbanization seems to drive non-random distributions of behavioral types via mechanisms yet to be revealed. Our study thereby demonstrates the importance of partitioning behavioral variation across hierarchical levels (Han et al., 2016; Moirón et al., 2016; Nicolaus et al., 2016), both in urban and other behavioral ecological studies, and the novel insights that may be gained by doing so.

In conclusion, we showed for great tits breeding in the city that behavioral types were non-randomly distributed over an urban environmental gradient. Based on these findings, future research should investigate whether non-random distribution of types is caused by selective appearance (i.e., differential settlement), selective disappearance (i.e., natural selection) or urbanizationrelated behavioral modification (i.e., developmental or other forms of irreversible plasticity).

## AUTHOR CONTRIBUTIONS

PS and ND conceived and designed the study, conceived the statistical approach, and wrote the manuscript

#### ACKNOWLEDGMENTS

We thank past and current members, as well as field assistants and students of the Behavioral Ecology Group (Ludwig-Maximilians University Munich) and the research group Evolutionary Ecology of Variation (Max Planck Institute for Ornithology), as well as all citizen scientists for input, discussion,

#### REFERENCES

and/or help with data collection. Werner Schimmel and Dietfried Molter are acknowledged for technical support and Petri Niemelä for stimulating discussion. PS was funded by the Deutsche Forschungsgemeinschaft (SP 1450/3-1) and ND by the Max Planck Society.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fevo. 2017.00092/full#supplementary-material

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Sprau and Dingemanse. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# First to Flush: The Effects of Ambient Noise on Songbird Flight Initiation Distances and Implications for Human Experiences with Nature

Alissa R. Petrelli 1 \*, Mitchell J. Levenhagen2 , Ryan Wardle1 , Jesse R. Barber 2 and Clinton D. Francis 1 \*

*1 Biological Sciences, California Polytechnic State University, San Luis Obispo, CA, United States, 2 Biological Sciences, Boise State University, Boise, ID, United States*

#### Edited by:

*Caroline Isaksson, Lund University, Sweden*

#### Reviewed by:

*Alejandro Ariel Rios-Chelen, Centro Tlaxcala de Biologia de la Conducta, Universidad Autonoma de Tlaxcala, Mexico Graeme Shannon, Bangor University, United Kingdom*

\*Correspondence:

*Alissa R. Petrelli<EMAIL_ADDRESS>Clinton D. Francis<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Behavioral and Evolutionary Ecology, a section of the journal Frontiers in Ecology and Evolution*

> Received: *20 January 2017* Accepted: *06 June 2017* Published: *21 June 2017*

#### Citation:

*Petrelli AR, Levenhagen MJ, Wardle R, Barber JR and Francis CD (2017) First to Flush: The Effects of Ambient Noise on Songbird Flight Initiation Distances and Implications for Human Experiences with Nature. Front. Ecol. Evol. 5:67. doi: 10.3389/fevo.2017.00067* Throughout the world, birds represent the primary type of wildlife that people experience on a daily basis. However, a growing body of evidence suggests that alterations to the acoustic environment can negatively affect birds as well as humans in a variety of ways, and altered acoustics from noise pollution has the potential to influence human interactions with wild birds. Birds respond to approaching humans in a manner analogous to approaching predators, but the context of the interaction can also greatly influence the distance at which a bird initiates flight or escape behavior (i.e., flight initiation distance or FID). Here, we hypothesized that reliance on different sensory modalities to balance foraging and threat detection can influence how birds respond to approaching threats in the presence of background noise. We surveyed 12 songbird species in California and Wyoming and categorized each species into one of three foraging guilds: ground foragers, canopy gleaners, and hawking flycatchers and predicted FIDs to decrease, remain the same and increase with noise exposure, respectively. Contrary to expectations, the canopy gleaning and flycatching guilds exhibited mixed responses, with some species exhibiting unchanged FIDs with noise while others exhibited increased FIDs with noise. However, FIDs of all ground foraging species and one canopy gleaner decreased with noise levels. Additionally, we found no evidence of phylogenetic structure among species' mean FID responses and only weak phylogenetic structure for the relationship between FIDs and noise levels. Although our results provide mixed support for foraging strategy as a predictor of bird response to noise, our finding that most of the species we surveyed have shorter FIDs with increases in noise levels suggest that human observers may be able to approach ground foraging species more closely under noisy conditions. From an ecological perspective, however, it remains unclear whether these mixed responses translate into lost foraging opportunity for hypervigilant birds that flee a threat too soon or greater predation risk due to impaired surveillance for those that only respond once approaching threats are near.

Keywords: anthropogenic noise, birdwatching, escape behavior, predation risk, vigilance, masking, threat detection

## INTRODUCTION

Anthropogenic noise encroaches on many natural landscapes (Barber et al., 2011; Francis et al., 2011; Lynch et al., 2011; Mennitt et al., 2013). A growing body of research indicates such noise can detrimentally affect wildlife (Barber et al., 2010; Francis and Barber, 2013; Shannon et al., 2016b) and might create a negative feedback process that degrades humans' experiences of nature (Francis et al., in review). In the context of birdwatching, an increasingly popular recreational activity (La Rouch, 2003; Carver, 2013), the ability to approach birds in the wild is a valued human experience that may be threatened by anthropogenic noise. Although there is typically a limit to how close a human can approach a wild animal before the animal initiates an escape, the distance at which flight is initiated varies across taxa and could depend on the animal's acoustic environment.

The acoustic environment serves as a critical medium through which many species, including humans, interact with their surroundings. Humans are motivated by natural sounds, such as bird songs and sounds, to seek out and experience natural places (Haas and Wakefield, 1998; Marin et al., 2011). Recent work has also demonstrated that listening to birdsong has the potential to enhance personal experiences with nature (Newman et al., 2013), improve stress recovery (Ratcliffe et al., 2013), and renew cognitive abilities after mental exertion (Abbott et al., 2016). In addition to these benefits to casual nature-seekers, birdsong is a valued tool for birdwatchers, who can acoustically localize wild birds for viewing or identification. However, in areas of elevated background sound, an observer's ability to detect birds is constrained by masking, the process through which background noise interferes with the perception of an acoustic signal (Pacifici et al., 2008; Ortega and Francis, 2012). Like humans, masking by noise can interfere with birds' abilities to detect and discriminate biologically relevant cues. For example, elevated ambient noise may influence how birds detect and respond to the threat of an approaching predator or human observer, which they perceive in the same manner (Frid and Dill, 2002). Thus, from the human perspective, where the quality of seeing and hearing a bird can depend upon the proximity of an approach, changes to the acoustic environment could indirectly influence the quality of the human experience with birds through changes in bird behavior.

Broadly speaking, anthropogenic noise can affect wildlife in many ways beyond threat detection (Francis and Barber, 2013). Previous research has demonstrated that increased noise can lead to decreased reproductive success (Halfwerk et al., 2011; Kight et al., 2012; Mulholland, 2016), impact community structure and ecological interactions (Francis et al., 2009), and degrade habitat quality (Francis et al., 2009; McClure et al., 2013; Ware et al., 2015). Most relevant to this study are the many ways that noise affects avian behavior (Shannon et al., 2016b), especially aspects of risk assessment and antipredator behavior. Karp and Root (2009) found that free-living hoatzins (Opisthocomus hoazin) increased alertness and flush more quickly when tourists approach while conversing loudly compared to silent approaches. Samia and Blumstein (2015) suggested that escape behavior in birds is largely explained by the flush early and avoid the rush (FEAR) hypothesis, which posits that prey will flee soon after predator detection to avoid costs associated with monitoring the predator. Considering this finding, it might be expected that birds should flush quickly unless threat detection is delayed or otherwise impaired due to interfering circumstances.

Flushing behavior is commonly measured as the distance between an observer and an animal when it flushes (termed "flight initiation distance," hereafter FID) and is often used as a proxy for a species' tolerance of predators as well as the presence of humans. Previous studies have determined that species identity, starting distance, individual body size, and vegetation cover are all important predictors of FID (Fernández-Juricic et al., 2002; Blumstein, 2003; Blumstein et al., 2003). The influence of noise on FID has been investigated in two nonavian systems. Chan et al. (2010a,b) demonstrated that Caribbean hermit crabs (Coenobita clypeatus) are slower to respond to an intruder when noise is played during the approach. Based on this finding, Chan et al. (2010a) proposed the distracted prey hypothesis, which suggests that animals have finite attention and become distracted from ecologically relevant cues when a stimulus such as background noise occupies some of that attention. Shannon et al. (2016a) found the opposite outcome; black-tailed prairie dogs (Cynomys ludovicianus) flushed more quickly when a human intruder approached under higher sound levels generated from speakers broadcasting roadway noise. The prairie dogs in this study were hypervigilant, committing more time to detecting potential threats through visual surveillance when background sound levels were high (Shannon et al., 2016a). Birds are also known to increase visual alertness when their auditory abilities are impaired by ambient noise. In a study of how noise influences reproductive success, Meillère et al. (2015) found that free-living house sparrows (Passer domesticus) flee their nests more quickly (i.e., increased FID) in disturbed, noisy areas compared to quiet, control areas, possibly due to increased visual vigilance. Quinn et al. (2006) demonstrated that chaffinches (Fringilla coelebs) in a laboratory setting spent more time visually scanning for predators than actively foraging during playbacks of white noise, a stimulus not found in nature, when compared to quiet conditions. Ware et al. (2015) confirmed these findings in a study of white-crowned sparrows (Zonotrichia leucophrys) foraging during playbacks of traffic recordings, a stimulus many free-living birds experience. However, these studies introduced an acute, high intensity noise stressor to measure short-term changes in vigilance and, therefore, provide limited insight to how chronic ambient noise influences the daily lives of free-living birds. Furthermore, to our knowledge, there are no previous studies that have examined how hypervigilance exhibited in captive birds translates to detection of and response to approaching threats in nature. Thus, we sought to test whether study of bird FIDs can provide evidence to clarify the contradicting hypotheses of distraction and hypervigilance. Furthermore, we sought to examine FID responses in light of the different avian feeding ecologies that have the potential to influence detection of approaching threats via auditory and visual surveillance.

Here, we categorize our study species into three foraging guilds based on foraging behavior: ground foragers, canopy gleaners, and hawking flycatchers. We then use existing literature on the sensory ecology linked to each foraging strategy to formulate predictions of how ambient noise might influence FIDs for our foraging guilds.

Most birds rely on vision for both foraging and vigilance and can maintain vigilance while searching for food through peripheral vision and frequent movement of the head and eyes to maximize the visual field (Lima and Bednekoff, 1999; Fernández-Juricic et al., 2008). These behaviors, as well as adapted visual fields, are thought to be primarily determined by feeding ecology (Martin, 2007). Ground foraging birds generally have wide lateral visual fields and engage in frequent head movements to compensate for time spent head-down looking for food (Fernández-Juricic et al., 2008, 2010, 2011). Thus, we hypothesize that ground foraging species rely heavily on acoustic cues for threat detection while foraging and predict that they are more susceptible to the effects of masking in noisy conditions and will exhibit decreasing FIDs as noise increases (**Figure 1**).

To our knowledge, no previous study has specifically investigated the sensory ecology of species that glean arthropods and fruit in the canopy. However, like the ground foragers, canopy gleaners also rely heavily on vision to both forage and scan for predators. Other potential mechanisms of predator detection, such as con- or hetero-specific alarm calls, might also be influenced by background noise (e.g., Templeton et al., 2016) and could also contribute to slower responses of these species. Yet, because these species often forage high in the canopy rather than on the ground, we predict either a weak negative influence of noise or no change in FID in response to humans approaching at the ground level (**Figure 1**).

In contrast, flycatching species that sally out from a perch to catch flying insects on the wing visually scan for prey. Gall and Fernández-Juricic (2009) determined that the vision of the flycatching black phoebe (Sayornis nigricans) is primed for tracking active prey in three dimensions, but, to our knowledge, no studies have examined audition of any flycatching species in the context of foraging. With their constant visual vigilance for seeking prey, we hypothesize that these species may also be able to detect approaching threats as an epiphenomenon of scanning for volant prey. Based on this foraging strategy and the FEAR hypothesis (Samia and Blumstein, 2015), we predict flycatching species exhibit unchanged FIDs because they would flush upon first visual detection of an approaching threat while visually scanning for prey regardless of noise level (**Figure 1**). Alternatively, if flycatching species also compensate for reduced auditory surveillance for threats with increased visual vigilance, FIDs may increase with noise levels because more frequent visual scans for threats would lead to earlier detections (**Figure 1**). It is important to note that our expectation of varied responses for different foraging guilds is speculative due to limited literature on the visual fields and sensory ecology of species outside the ground foraging guild. However, we ultimately focus on the implications of ambient noise altering avian behavior in the context of humanwildlife interactions. Importantly, changes in FID for common songbird species in response to noise might influence how close human observers can approach wild birds, and thus, alter the quality of an experience with wildlife and nature.

## gleaners to have shorter or unchanged FIDs (dashed or dotted lines), and flycatchers to have unchanged or longer FIDs in noise (dotted or solid lines).

## MATERIALS AND METHODS

We studied avian FIDs in relationship to background noise in urban parks and protected natural spaces throughout San Luis Obispo County, Muir Woods National Monument, California, and Grand Teton National Park, Wyoming. Our sites represented a range of background sound levels to capture natural variation in ambient noise and we did not manipulate the acoustic environment during our observations. Following previously described methods (e.g., Blumstein et al., 2003), a single observer located an individual bird and recorded the species, time, and initial distance between the observer and the target bird (Starting Distance) using an optical range finder (Nikon Aculon, Nikon Vision Co., Japan; TruPulse 360 R, Laser Technology, Inc., Colorado, USA). We targeted birds that were foraging, preening, or otherwise undisturbed by the intruder at the starting distance and not interacting with con- or hetero-specifics. While looking directly at the bird, the observer walked at a standard rate of 0.5 m/s along a straight path toward the target bird and dropped a marker at the distance where the bird flew away from the intruder (Flight Initiation Distance, measured as the distance between this marker and the bird's last perch). Immediately following the bird's escape, the observer measured time-averaged sound levels (Leq; A-weighted Leq, fast response, re. 20 µPa) for at least 1 min at the bird's last perch with a Larson Davis 824 or 831 sound pressure meter or a MicWi436 (MicW Audio, China) microphone paired with the SPLnFFT Sound Meter v6.2 iPhone application (FL's Audio Apps, France), a measurement kit equivalent to a type two sound level meter (Kardous and Shaw, 2014). We also measured wind speed with a Kestrel 4,000 weather meter (Kestrel Meters, USA). During the sound pressure level measurement, the observer scanned the surroundings and counted any pedestrians and their distances to the bird's last perch. We utilized pedestrian activity and whether each site was predominately human-modified (e.g., presence of buildings and other built surfaces, heavily-managed urban parks) to categorize

each site as either a predominantly developed or natural area. Because almost all trial locations were within 0.5 km of roadways, sound levels were primarily from anthropogenic sources and did not systematically differ between developed [49.35 ± 4.70 SD dB(A)] and natural areas [50.26 ± 9.55 SD dB(A); Welch's two sample t-test, t = 0.87, df = 155.66, p > 0.38]. The observer then used a surveyor's tape or laser range finder to measure the FID, the distance between the bird's last perch to the dropped marker. If the bird was perched above the ground, we measured the Euclidean distance as the square root of the sum of the squared horizontal distance and squared perch height (Møller et al., 2015). Finally, the observer categorized surrounding vegetation as open, medium, or dense. To avoid sampling the same individual more than once, the observer moved at least 250 m from the first survey before locating a subsequent individual. We also visited sites throughout each location only once to avoid resampling individuals.

#### Data Analysis

We used a log10 transformation on all distance data to normalize their distributions. We assigned each of the surveyed species to one of three foraging guilds based on foraging behavior: ground foragers, canopy gleaners, and hawking flycatchers (Ehrlich et al., 1988). Because we were most interested in the influence of noise on FIDs, using the entire dataset we first calculated adjusted FIDs as the residuals of a linear regression model where raw FIDs were explained by vegetation category and starting distance, two variables known to strongly influence FID (e.g., Fernández-Juricic et al., 2002; Blumstein, 2003; Blumstein et al., 2005). We then used linear mixed effect models using the lme4 package (Bates et al., 2014) in R with vegetation and starting distancecorrected FID (henceforth adjusted FID) for each foraging guild as a response variable and background sound level, wind speed, Julian date, time of day, developed vs. natural habitat, species and an interaction between sound level and species as fixed effects. Developed vs. natural habitat was used as a covariate to capture exposure to human activity and account for the possibility of habituation to human approach. Models for canopy gleaners did not include developed vs. natural habitat because all individuals were sampled in areas categorized as natural. In all models, we also treated location as a random intercept. We used Akaike information criterion corrected for small sample size (AICc) in model selection. Because of recent criticisms of model-averaging (Cade, 2015), we considered all models with 1AICc < 2 to be equivalent (Boersma et al., 2016). To gauge the influence of individual predictors, for each model with 1AICc < 2, we concluded that a predictor variable had a strong effect on adjusted FID when its 95% confidence interval (95% CIs) did not overlap zero.

Finally, foraging guilds may also reflect shared evolutionary histories that could influence variation in FID values among guilds. For example, all species categorized as hawking flycatchers are suboscines in family Tyrannidae. Thus, we tested for phylogenetic structure in mean FID values among species and species-specific model-estimated effect sizes for the influence of background sound levels on adjusted FIDs, including standard errors, using the phylosig function in the R package phytools (Revell, 2012), which incorporates the method from Ives et al. (2007) to account for sampling error. For our phylogenetic hypotheses, we used phylogenies from Jetz et al. (2012) and available from Birdtree.org. However, due to phylogenetic uncertainty among the Jetz et al. set, we used 100 randomly selected trees to calculate mean values for two common metrics for phylogenetic signal: Pagel's λ (Pagel, 1999) and Blomberg's K (Blomberg et al., 2003). Pagel's λ values vary from zero to one. High λ values indicate that closely related species have very similar trait values (i.e., high phylogentic structure). Low λ values indicate that trait values are unrelated to phylogeny. Blomberg's K-values > 1 suggest strong phylogenetic signal and values from zero to 1 suggest no phylogenetic signal to weak phylogenetic signal.

### RESULTS

We surveyed a total of 197 individuals of 12 species of songbird across 27 locations from January to July 2016. Due to wind speed exceeding Category 3 on the Beaufort scale, we excluded one observation from the dataset to prevent potential bias introduced to sound measurements (Francis et al., 2011). Of the resulting 196 individuals, 105 were ground foragers, 52 canopy gleaners, and 39 flycatchers (**Table 1**). These individuals were sampled across 46 developed and 150 natural sites. We conducted trials throughout the day (0,600–1,630); however, we conducted most our observations (179 of 196) between 0,600 and 1,200 h. Wind speed ranged from 0 m/s to 5.5 m/s, with an average of 0.68 ± 0.93 m/s. Background sound levels ranged from 17.0 to 77.4 dB(A) with an average of 50.04 ± 8.68 dB(A). Starting and flight initiation distances averaged 26.76 ± 15.01 and 10.77 ± 7.32 m, respectively. We found no evidence for phylogenetic structure for mean FID (Pagel's λ = 0.01, sd = 0.1; Blomberg's K = 0.41,

TABLE 1 | We observed 196 individuals from 12 songbird species and grouped the species into three foraging guilds.

sd = 0.03), but evidence for a weak to moderate phylogenetic signal for the effect of noise on adjusted FID (Pagel's λ = 0.50, sd = 0.30; Blomberg's K = 0.77, sd = 0.07).

Among the top models for ground foraging species, the parameters background noise, diet, and species had strong effects, where 95% confidence intervals did not overlap zero (**Table 2**). The model that only included the random effect of location (i.e., null) was 2.00 1AICc from the top model. We visualized our results using the most parsimonious top model, which demonstrates an overall negative influence of background noise level on FIDs for all ground foraging species (**Figure 2B**). However, several ground foraging species differed in their overall response distances (i.e., different intercepts per species; **Figure 2A**, **Table 3**). Our results indicate that the individuals from the ground foraging guild were generally slower to respond to an observer's approach with elevated background sound and that omnivorous species flush at farther distances than granivorous species.

Among the top models for canopy gleaning species, the parameters with strong effects, included background noise, species, and an interaction between the two (**Table 2**). The null model (location as random intercept only) was 38.42 1AICc lower than the top model. Wilson's warbler (Cardellina pusilla) adjusted FIDs were positively influenced by background noise, Pacific wren (Troglodytes pacificus) adjusted FIDs were negatively affected by noise and yellow warblers (Setophaga petechia) appear uninfluenced by noise (**Figures 2C–E**, **Table 4**).

In the flycatching guild, the best model contained species, background noise, and an interaction between the two as fixed effects (**Table 2**). It was 29.6 1AICc better than the null model

*All models include location as a random effect. K indicates the number of parameters in the model. Model parameters include background sound level (dB), diet (e.g., omnivorous), habitat (either developed or natural), species, wind speed, and an interaction between background sound and species (dB*\**Species). Bolded variable names indicate predictors with 95% confidence intervals that do not overlap zero.*

that only included the random effect of location and there were no other models with 1AICc < 2. Background noise levels had a strong positive influence on black phoebe (S. nigricans) adjusted FIDs (**Figure 2F**), but adjusted FIDs for both the Pacificslope flycatcher (Empidonax difficilis) and dusky flycatcher (Empidonax oberholseri) were negatively affected by noise, albeit weakly (**Figures 2G,H**, **Table 5**).

#### DISCUSSION

To our knowledge, this report is the first to specifically address changes in songbird FIDs in response to noise, with implications for how noise influences predation risk and missed foraging opportunity as well as bird behavior relevant to casual natureseekers and birdwatchers. Through our observations of wild songbird FIDs in varying acoustic conditions, we found evidence that noise influences bird FIDs in a variety of ways. However, we found no evidence that level of habitat development influenced FIDs of ground foraging and flycatching birds, suggesting that habituation to human presence may not strongly influence FID or the relationship between FID and background sound level. We also found no evidence that mean FID values were influenced by relatedness of species in our sample. However, the effect of noise on adjusted FIDs had some phylogenetic signal, likely reflecting that model estimates found identical relationships between noise and adjusted FID among all Emberizid species and because the congener flycatchers demonstrated similar adjusted FID responses with increasing noise. In general, however, our results indicate mixed responses across songbirds that can be species-specific and that might be explained in part by foraging behavior and, possibly, height of perch in the canopy.

All species within the ground foraging guild exhibited shorter FIDs in noise, as we predicted, such that the observer could approach closer to the target bird before it flushed. Shorter FID responses in noisy conditions may be explained by the distracted prey hypothesis, which posits that background noise occupies the target bird's finite attention and thus distracts the individual from other potentially important stimuli (Chan et al., 2010a). Although the distracted prey hypothesis may explain shortened FIDs in noise for some of these bird species, it is impossible to uncouple distraction from the effects of energetic masking. The high background sound in some areas might have masked the sounds of an approaching observer, leading to a slower response from the target bird. Of course, both mechanisms could operate simultaneously. Regardless, the result is that noise reduces an individual's ability to detect approaching threats and likely elevates an individual's risk of predation (Krause and Godin, 1996; Simpson et al., 2015); failure to detect predators at a sufficient distance could be lethal.

Although our results suggest ground foraging birds may be more at risk to predation in noisy environments, the effects of masking and noise on predator abundance and hunting ability must also be considered. Opportunistic avian nest predators avoid noisy areas in a natural gas extraction field (Francis et al., 2009, 2011) and owls, which are specialized acoustic predators, have trouble localizing or foraging in noisy conditions found in

the influence of ambient noise on vegetation and start distance-corrected FID values for all ground foraging species (B), canopy gleaning species Pacific wren (C), Wilson's warbler (D), yellow warbler (E), and flycatching species black phoebe (F), dusky flycatcher (G), and Pacific-slope flycatcher (H). For plotting purposes, we utilized the top-ranking model (lowest AICc and fewest parameters) for each of the foraging guilds (Table 2).

gas fields and near roadways (Mason et al., 2016; Senzaki et al., 2016). In laboratory settings, both Quinn et al. (2006) and Ware et al. (2015) demonstrated that ground foraging species spend significantly more time visually scanning for predators when ambient noise is high compared to quiet conditions. If ground foraging birds in our study also increase visual vigilance with

TABLE 3 | The influence of fixed effects on adjusted FID for ground foraging species.

*Presented are effect sizes, standard error, 95% confidence intervals from the top-ranking model (i.e., lowest AICc value and/or fewest parameters). The species included in this group are: American crow (Intercept), California towhee (CALT), dark-eyed junco (DEJU), golden-crowned sparrow (GCSP), white-crowned sparrow (WCSP), and California scrub jay (CASJ). Lower and upper confidence intervals represent 95% confidence. Bolded parameters have confidence intervals that do not overlap zero, indicating a strong effect.*

TABLE 4 | The output for a linear mixed effect model of the canopy gleaning guild.

*The species included in this guild are: Pacific wren (Intercept), Wilson's warbler (WIWA), and yellow warbler (YEWA). The variables include background sound (dB), species, and interactions between background sound and species. Lower and upper confidence intervals represent 95% confidence. Bolded parameters have confidence intervals that do not overlap zero, indicating a strong effect.*

*The species included in this guild are: black phoebe (Intercept), dusky flycatcher (DUFL), and Pacific-slope flycatcher (PSFL). The variables include background sound (dB), species, and an interaction between the two. Lower and upper confidence intervals represent 95% confidence. Bolded parameters have confidence intervals that do not overlap zero, indicating a strong effect.*

noise, our results suggest that they were still unable to detect and respond to approaching threats as quickly as in quiet conditions, despite compensating with visual scans. Notwithstanding the potential ultimate costs of failing to detect predators at an appropriate distance, for human observers seeking to experience wild birds at close range, species that flee at shorter distances in noisy conditions would be easier to approach, view and hear. Finally, diet was also a significant predictor of FID for ground foraging species; species with omnivorous diets exhibited longer FIDs than species with granivorous diets irrespective of noise levels. Although limited evidence can help explain this trend, Francis (2015) demonstrated that the abundance of most avian species decline in noisy areas, species with plant-based diets appear to be less sensitive than those with animal-based diets. It is also possible that other traits unique to the two omnivorous species, which are both corvids, could explain their longer FIDs relative to granivorous species. For example, various measurements of brain size are positively associated with FIDs (Symonds et al., 2016) and corvids are known to have relatively large brains (Emery and Clayton, 2004). Greater cognitive capacity could also potentially mitigate distraction by noise and other stimuli (i.e., distracted prey hypothesis) by permitting individuals to process multiple streams of sensory information and respond to approaching threats appropriately. Future work should explore the relative contributions of cognitive abilities and foraging modalities to explain FIDs or sensitivities to changes in background acoustics in general.

For the canopy gleaning guild, we predicted that FIDs would decrease as a result of distraction or masking due to increased ambient noise. However, we suspected that because these species often utilize high tree perches, they might only exhibit weakly decreased or unchanged FIDs to a human approaching on the ground due to vertical relief from the threat. Although these predictions were not supported for all canopy gleaning species, we observed a trend of decreased FIDs with increasing noise for Pacific wrens (T. pacificus) and a pattern of unchanged FIDs for yellow warblers (S. petechia). Similar to the ground foraging species, Pacific wrens may be more susceptible to the effects of masking or distraction due to frequent sensory modality shifts as they remain acoustically vigilant while visually foraging. Pacific wrens are known to forage in the low canopy and, thus, experience little vertical relief from ground-level threats; however, yellow warblers frequently utilize high perches, which may explain the lack of FID response for the species. Additionally, although our robust sampling of yellow warblers indicates that this species exhibits consistent FID behavior across medium to high sound levels [46.9–77.4 dB(A)], we were unable to conduct any approaches under relatively low ambient sound conditions [<40 dB(A)]. Future work should focus on observing yellow warblers in environments with less ambient noise, as even acoustic cues at these relatively quiet levels can elicit responses in other taxa (Shannon et al., 2016b) and perhaps there is a threshold below which noise might influence FID in yellow warblers. In contrast to our predictions for the canopy gleaning guild, Wilson's warblers (C. pusilla) exhibited a trend of increased FIDs with increasing noise. Wilson's warblers primarily forage by gleaning insects in the canopy, but they are also known to hawk for flying insect prey (Ehrlich et al., 1988) and, thus, might utilize sensory modalities differently when compared to other canopy gleaning species. However, yellow warblers are also occasional flycatchers (Ehrlich et al., 1988) and, yet, do not exhibit the same increased FIDs of Wilson's warblers. Thus, engaging in flycatching behavior cannot solely explain the pattern consistent with hypervigilance in noise among Wilson's warblers. Finally, although two of the canopy gleaning species studied here fit our predictions of decreased FIDs or no response, future research should explicitly include individual perch height as a potential predictor of variation in FIDs both within and across species.

Our observed results for Wilson's warblers follow our prediction for species of the flycatching guild such that these species would exhibit unchanged or increased FIDs in noise due to their foraging strategy of constant visual and aural vigilance that might allow these species to scan for predators and prey simultaneously. However, only one species of the flycatching guild, the black phoebe (S. nigricans), provided support for this prediction with increasing FIDs with increases in noise levels. The other two species of this guild, dusky flycatchers (E. oberholseri) and Pacific-slope flycatchers (E. difficilis), exhibited a weak trend of decreased FIDs with increasing noise. This muted response might be explained by our small sample size or perhaps like the canopy gleaning yellow warblers, by these flycatchers' shared tendency to utilize high perches, thereby relieving the threat of a ground-level approaching human. In the case of the black phoebe, however, our robust sampling in a wide range of ambient noise conditions indicates that this species is generally more likely to flush sooner if background noise is high. Such increased FIDs in noise might be explained by heightened vigilance in noisy conditions, which was demonstrated in laboratory settings with chaffinches (Quinn et al., 2006) and white-crowned sparrows (Ware et al., 2015) as well as in a field experiment with free-ranging prairie dogs (Shannon et al., 2016a). Furthermore, flushing more quickly in noise coincides with FEAR hypothesis, which suggests that birds are likely to flush quickly after detection in order to avoid the need to monitor an approaching threat (Blumstein, 2010). This response might allow for more foraging time if the bird flushes to an area with available food; however, fleeing from a beneficial foraging site may result in lost foraging opportunity and could incur the cost of increased energy expenditure.

In the context of human experience of wildlife, our results indicate that background noise may influence the quality of a birdwatching experience. Due to their hawking behaviors, both Wilson's warblers and black phoebes can be exciting birds to observe. Both species exhibited longer FIDs in noise, indicating that birdwatchers may experience difficulty when seeking these species and perhaps other hawking species in noisy

#### REFERENCES

conditions. However, most of the species in our sample trended toward shorter FIDs in noise, which would allow birders to approach closer. This may lead to a surprisingly positive outcome of anthropogenic noise pollution, under which birdwatching experiences are improved through field observations at closer range in noisier conditions. Particularly for new birdwatchers, this close viewing of wild birds may foster a personal and lasting connection with wildlife and lead to increased support for wildlife conservation. Of course, this potential benefit to nature-seekers of visually experiencing birds at a closer range must be viewed in the context of the quickly growing body of literature reporting the negative effects of noise and human disturbance to wild animals (Ellison et al., 2012; Francis and Barber, 2013; Shannon et al., 2016b). Nevertheless, our conclusions offer new perspective on the coexistence of humans and wildlife in an increasingly noisy world.

## ETHICS STATEMENT

This study was carried out in accordance with the guidelines of the Committee of the Care and Use of Laboratory Animals Resources, National Research Council, and the research protocol was approved by the California Polytechnic State University, Institutional Animal Care and Use Committee (#1519).

### AUTHOR CONTRIBUTIONS

AP, ML, and RW conducted the fieldwork. JB and CF conceived of the project. All authors contributed to analysis and interpretation of the data. AP was primarily responsible with writing the manuscript added by CF and all other authors contributed to writing, editing and, ultimately, approving of the manuscript for publication.

## ACKNOWLEDGMENTS

We thank Gita Kolluru, Crow White and two reviewers for constructive feedback on earlier versions of this paper. This project was funded by National Science Foundation Grant (CNH-1414171) to JB and CF.

Ware, H. E., Mcclure, C. J. W., Carlisle, J. D., and Barber, J. R. (2015). A phantom road experiment reveals traffic noise is an invisible source of habitat degradation. Proc. Natl. Acad. Sci. U.S.A. 112, 12105–12109. doi: 10.1073/pnas.1504710112

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Petrelli, Levenhagen, Wardle, Barber and Francis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Urban Great Tits (Parus major) Show Higher Distress Calling and Pecking Rates than Rural Birds across Europe

Juan Carlos Senar 1 \*, Laszlo Z. Garamszegi 2 , Vallo Tilgar 3 , Clotilde Biard4 , Gregorio Moreno-Rueda5 , Pablo Salmón6 , J. M. Rivas 5 , Philipp Sprau7 , Niels J. Dingemanse7 , Anne Charmantier 8, 9, Virginie Demeyrier 4, 8, Helena Navalpotro1 and Caroline Isaksson6

#### Edited by:

Patrick S. Fitze, Museo Nacional de Ciencias Naturales (CSIC), Spain

#### Reviewed by:

Deseada Parejo, Universidad de Extremadura, Spain Gonçalo C. Cardoso, Universidade do Porto, Portugal

> \*Correspondence: Juan Carlos Senar<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Behavioral and Evolutionary Ecology, a section of the journal Frontiers in Ecology and Evolution

> Received: 05 June 2017 Accepted: 29 November 2017 Published: 18 December 2017

#### Citation:

Senar JC, Garamszegi LZ, Tilgar V, Biard C, Moreno-Rueda G, Salmón P, Rivas JM, Sprau P, Dingemanse NJ, Charmantier A, Demeyrier V, Navalpotro H and Isaksson C (2017) Urban Great Tits (Parus major) Show Higher Distress Calling and Pecking Rates than Rural Birds across Europe. Front. Ecol. Evol. 5:163. doi: 10.3389/fevo.2017.00163 1 Unit of Evolutionary and Behavioural Ecology, Natural History Museum of Barcelona, Barcelona, Spain, 2 Department of Evolutionary Ecology, Estación Biológica de Doñana, Sevilla, Spain, 3 Department of Zoology, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia, 4 Institut d'Ecologie et des Sciences de l'Environnement, Sorbonne Universités, UPMC Univ Paris 06, UPEC, Paris 7, Centre National de la Recherche Scientifique, INRA, IRD, Paris, France, 5 Departamento de Zoología, Universidad de Granada, Granada, Spain, 6 Evolutionary Ecology Unit, Department of Biology, Lund University, Lund, Sweden, 7 Department of Biology, Ludwig Maximilians University Munich, Munich, Germany, 8 Centre d'Ecologie Fonctionnelle et Evolutive, Montpellier, France, 9 Département des Sciences Biologiques, Université du Québec à Montréal, Montreal, QC, Canada

Environmental change associated with urbanization is considered one of the major threats to biodiversity. Some species nevertheless seem to thrive in the urban areas, probably associated with selection for phenotypes that match urban habitats. Previous research defined different "copying styles" in distress behavior during the handling of birds. These behaviors vary along a continuum from "proactive" to "reactive" copers. By studying avian distress behaviors we aimed to broaden our understanding of the relationship between coping styles and urbanization. Using a large-scale comparative study of seven paired rural and urban sites across Europe, we assayed distress behaviors during handling of urban and rural-dwelling populations of the great tit Parus major. We detected no consistent pairwise differences in breath rate between urban and rural habitats. However, urban great tits displayed more distress calling (fear screams) and higher pecking rate (handling aggression) than rural birds. These findings suggest that urban great tits have a more proactive coping strategy when dealing with stressful conditions. This finding is in line with previous studies implying that urban great tits are more explorative, less neophobic, and display shorter flight distances than their rural counterparts, representing further aspects of the same "proactive," coping strategy. Future research should investigate whether reported differences in distress behavior are due to local adaption caused by natural selection or due to phenotypic plasticity.

Keywords: urbanization, distress behavior, handling, coping strategy, distress calling, pecking rate, local adaptation, great tit

## INTRODUCTION

Urban environments are expanding worldwide with an unprecedented speed (United Nations, 2014); environmental change associated with urbanization is being considered as one of the main current threats to biodiversity (Turner et al., 2004). However, urbanization also represents a potential source of selection and a new avenue to study evolutionary processes (Donihue and Lambert, 2014; Isaksson, 2015; Alberti et al., 2017; Hendry et al., 2017).

For wildlife, urban habitats differ in many respects from rural habitats, including microclimate, food abundance, pollution, abundance of exotic species, and predation risk (Luniak and Pisarski, 1982; Marzluff et al., 2001; Gaston, 2010; Gil and Brumm, 2014). Many of these factors induce stress, defined in a broad sense as changes away from an organisms' physiological homeostasis that emerge in response to a change in the environment (sensu Badyaev, 2005; Tuomainen and Candolin, 2012). This is especially the case for factors that represent, or are associated with increased perceived predation risk, such as elevated disturbance due to the presence of humans, pet mammals, and cars (Buchanan and Partecke, 2012). The obvious hypothesis based on this known disturbance or perceived threat is that urban animals would have an enhanced stress response than rural-dwelling animals. However, the results are mixed. For example, some studies reveal higher corticosterone levels (avian stress hormones) in urban compared to rural bird populations (Schoech et al., 2007; Fokidis et al., 2009; Zhang et al., 2011), whereas other studies found the opposite pattern or no significant differences (Partecke et al., 2006; French et al., 2008; Abolins-Abols et al., 2016).

Nevertheless, whatever the direction of the effects, they are likely to influence aspects of behavior, physiology, and life history of urban-dwellers in their coping response to the urban environment (Tuomainen and Candolin, 2012; Sprau and Dingemanse, 2017). Consequently, stress-responses, i.e., the behavioral and physiological means by which animals cope with environmental stress, play an important role in local adaptation (Badyaev, 2005). Hence, comparing stress-responses between urban- and rural-dwelling populations can shed light on the mechanisms underpinning adaptation to urban habitats. For example, predatory attack is most likely one of the highest stresses that a prey animal can experience, and this is irrespective of whether the animal is habituated to the urban environment. Behavioral responses to such threats are commonly referred to as distress behaviors (Møller and Ibáñez-Álamo, 2012). Previous research has shown that distress behaviors displayed during handling are parts of the same phenotypic syndrome that encapsulates an entire suite of behavioral and physiological traits that individual display in order to adaptively respond to stressful situations (Koolhaas et al., 1999, 2010; Carere and van Oers, 2004; Coppens et al., 2010; Carvalho et al., 2013; Class et al., 2014; Kluen et al., 2014). Responses can be categorized into different "coping styles," varying along a continuum from "proactive" to "reactive" copers, and they relate to the way organisms deal with stressors.

Accordingly, birds displaying a high frequency of distress behaviors represent proactive individuals. We focus in this paper on three distress behaviors easily recorded during handling: distress calling, handling aggression, and breath rate. Distress calling rate has previously been related to proactivity: in blackcapped chickadees Poecile atricapillus, distress calling rate was positively related to exploration rate, which is one of the main characteristics of the proactive coping style (Guillette and Sturdy, 2011). In siskins Carduelis spinus, individuals uttering more often distress calls, displayed also bolder behaviors in front of a novel object (Mateos-González and Senar, 2012; Pascual and Senar, 2014). In the Japanese quail Coturnix japonica, distress calling was positively related to the excretion of corticosterone metabolites, implying that high levels of distress calling are related to proactivity (Niall Daisley et al., 2005). Distress calling has therefore been used as a proxy of proactivity in several studies (Andersen, 2012; Pascual and Senar, 2014; Thorsteinsen, 2015; Richardson et al., 2016). Aggression has also been regarded as a typical response of proactive copers to stressful situations (Koolhaas et al., 1999; Carere et al., 2010), and pecking rate during handling is classically used to compare level of proactivity among individuals (Brommer and Kluen, 2012; van den Brink et al., 2012a,b; Class et al., 2014; Kluen et al., 2014; Dubuc-Messier et al., 2017). Breath rate has also been suggested as an indicator of coping style, with breath rates being higher in proactive individuals than in reactive ones (Carere and van Oers, 2004; van Oers and Carere, 2007; Torné-Noguera et al., 2014; Charmantier et al., 2017). Additionally, breath rate has been found to correlate positively to exploration rate, which as previously stated, is one of the main characteristics of the proactive coping style (Charmantier et al., 2017). The three behaviors have also been found to be heritable (Koenig et al., 1991; Brommer and Kluen, 2012). Therefore, these three distress behaviors can be used as an accurate proxy of coping style and their study may allow a broader understanding of the role of coping style mediating processes of urbanization in birds.

Recent work has found that distress behaviors during handling differ between urban and rural population of birds. For example, an interspecific comparison revealed that urban birds showed higher frequencies of distress calling when handled than their rural counterparts (Møller and Ibáñez-Álamo, 2012). Aggression, another typical response to stressful situations (Koolhaas et al., 1999), has also been found to differ in inter-specific comparisons between urban and rural individuals during handling (Møller and Ibáñez-Álamo, 2012). At the intra-specific level, urban birds have also been shown to display different breath rates compared to their rural counterparts during handling (Torné-Noguera et al., 2014; Abolins-Abols et al., 2016; Charmantier et al., 2017). Yet, this relationship may differ between species. To date, studies comparing distress behaviors between urban and rural bird populations (Møller and Ibáñez-Álamo, 2012; Torné-Noguera et al., 2014) have lacked sufficient replication to draw firm conclusions regarding the relationship between urbanization and avian distress behavior (Evans et al., 2009).

The main aim of the present paper was to investigate whether urban and rural populations of the great tits (Parus major) differ consistently in their behavioral response to an acute perceived threat, assayed during capturing and handling (Møller and Nielsen, 2010; Markó et al., 2013; Class et al., 2014; Kluen et al., 2014; Dubuc-Messier et al., 2017). To acquire a robust insight into the relationship between urbanization and distress behavior, we assayed three different distress behaviors (distress calling rate, pecking rate, and breath rate) in seven replicate pairs of urban and rural/forest populations across Europe.   Based on this growing body of literature, which suggests that a proactive coping strategy fits urban conditions, we predict that urban great tit populations show a higher distress calling rate, pecking rate and breath rate compared to their rural conspecifics.

#### MATERIALS AND METHODS

#### Study Areas and Sampling

Great tits were caught in seven distinct cities paired with a close rural site: Granada (Spain), Barcelona (Spain), Montpellier (France), Munich (Germany), Paris (France), Malmö (Sweden), and Tartu (Estonia; **Table 1**). Paired sites within localities were separated with distances ranging from 5 to 106 km (**Table 1**). Natural habitats were either forest or rural areas, but we refer to them as rural habitats for simplicity. To quantify the degree of urbanization in each locality we used aerial images from Google Maps following the methods previously described to assess the effect of the degree of urbanization on wild bird populations (Seress et al., 2014; Vincze et al., 2017). Briefly, each locality was represented by a 1 × 1 km2 rectangular area around the birds' capture site. The content in each rectangle was evaluated by dividing the image in 100 × 100 m2 cells and considering three land-cover characteristics in each: proportion of buildings, vegetation (including cultivated fields), and paved surfaces. The different land-cover measures obtained per site fitted into a principal component analysis (PCA) to estimate an urbanization score (PC1) per site (**Table 1**). The PC1 values were multiplied with −1 to obtain more positive values in the more urbanized localities.

Great tits were captured using spring traps, mist nets or funnel traps (Senar et al., 1997). Though trapping methods differed among localities, they were identical in six out of seven urban/rural pair sites (only Granada differed). Sampling was conducted during the main breeding season at each site (April–June), except for the Barcelona population, which was sampled from January to April. Overall, we quantified distress behaviors of 1,539 individual great tits. All individuals represent independent data points as re-captures were not included in our analyses comparing distress behavior between habitats (see below). However, behavioral information of individuals recorded in multiple years was used to estimate repeatability (68 birds for breath rate and 22 for pecking and distress calling rates, see below). All birds were sexed and aged (yearling vs. adult) according to plumage characteristics (Svensson, 1992).

#### Behavioral Tests

We performed measurements of distress behavior within 5 min after capture. We recorded three behaviors known to reflect coping style and stress responsiveness and/or fighting propensity during handling (Carere and van Oers, 2004; Fucikova et al., 2009; Laiolo et al., 2009; Markó et al., 2013; Torné-Noguera et al., 2014). Breath rate, distress call rate, and pecking rate were recorded in the same consecutive order. The target traits were not

#### TABLE 1 | Sampling localities of urban and rural great tits (Parus major) across Europe.

We print the name of each site, year of sampling, locations, sample size (n), urbanization index (PC1), and the distance between the rural and the urban site. The lower the urbanization index, the more urbanized the population's habitat; Munich being the most and Tartu the least urbanized habitats among the cities.

recorded in all localities: in Montpellier and Munich only breath rate was recorded.

Breath rate was recorded by counting the number of breast respiratory movements within 30 s, while holding the wings fixed and also ensuring reduced disturbance by visual and sound cues (Markó et al., 2013; Torné-Noguera et al., 2014). Pecking rate and distress calling rate were subsequently quantified by counting the number of pecks and distress calls that were emitted during the next 15 s of handling (Fucikova et al., 2009; Laiolo et al., 2009). Pecking rate refers to the number of pecks against a straight finger positioned at 1–2 cm from the beak of the focal bird while holding its legs (Markó et al., 2013). Distress calling rate was defined as the number of vocalized distress calls recorded during the same pecking rate trial (Markó et al., 2013). Breath rate was measured by multiple observers in Montpellier (n = 4) and Munich (n = 14). In the other localities, a single observer recorded all data in both the rural and urban site.

#### Statistical Analyses

To test for general differences in distress behavior between the urban and rural habitats across the seven localities, data were analyzed using univariate linear mixed-effects models (LMM), in which breath rate, pecking rate, or distress calling rate were sequentially included as the response variable. As fixed effects, we included habitat (rural vs. urban), sex and age (yearling vs. adult). Locality (n = 7 levels) and population (n = 2 populations∗ 7 localities = 14 populations) were included as random effects. LRT refers to a likelihood ratio test. As breath rate was measured by multiple observers in two of the localities (see above), we also included observer as random effect (23 levels, nested within locality) in models explaining variation in breath rate. Since the seven localities represented a wide range of different habitats, a habitat-specific residual variance was fitted to account for unequal residual variances between the two types (rural vs. urban). Breath rate was analyzed assuming a Gaussian error distribution, while pecking rate and distress call were analyzed assuming a Poisson error distribution.

Bivariate LMM were used to estimate among-population and among-individual-within-population covariances (and correlations) between each unique combination of two behaviors, where the two focal behaviors were entered as response variables (following procedures detailed by Dingemanse and Dochtermann, 2013).

Only a priori considered combinations of predictor variables were entered into the statistical models, which was based on biological thinking. This approach to the explicit test of a priori formulated hypotheses has been suggested to allow more general inferences than more traditional exploratory analyses in which various possible interactions are tested sequentially or based on statistical algorithm (Dochtermann, 2010). Accordingly, we were interested in the interaction between habitat and locality, because the difference between urban and rural habitats might differ among localities (see also below). We also included the interaction between habitat and sex, because previous analyses revealed sex differences in the expression of some of the focal behaviors (Markó et al., 2013). We also included an interaction term between habitat and age to test for age-specific differences between urban and rural populations (Markó et al., 2013). In cases where the interaction between sex and habitat was significant, we additionally modeled the sexes separately, in order to understand for which sex the differences between habitats were statistically supported.

We assumed that previous "capture history" did not affect distress behavior, an assumption that was confirmed for the Barcelona site, where the number of preceding captures had no effect on distress behaviors measured during the present study [distress calling rate: F(1, 119) = 1.4, p = 0.32; pecking rate: F(1, 119) = 0.6, p = 0.52; breath rate: F(1, 119) = 0.2, p = 0.73]. Furthermore, we can infer that "capture history" should not be an important confounder, as the data used in our analyses were largely based on first captures.

Adjusted repeatability was calculated following Nakagawa and Schielzeth (2010) using series of univariate LMMs where individual identity and population identity were fitted as a random effects, and where sex, age, and habitat as fixed effects. We repeated these analyses for rural and urban birds separately to calculate repeatability within each habitat type (urban vs. rural).

All statistical analyses were carried out using the package lme4, version 1.1-12 (Bates et al., 2011) and MCMCglmm version 2.23 (Hadfield, 2010) within the R (version 3.3.1) computing environment (R Development Core Team, 2017). For the modeling based on MCMCglmm, we ran Markov chains up to 230,000 iterations with 30,000 iterations of burn in and with 500 iterations of thinning interval (longer runs, such as 1,300,000 iterations, did not improve the results). For prior definition, we followed a technique called parameter expansion as was suggested by Hadfield (2010), and set V = 1, nu = 0.02, and alpha.V = 1,000. We repeated each run for each model 3–4 times to check the stability of results. After each run, the trace and distribution of all estimated parameters were checked visually, as well as autocorrelation between iterations. Furthermore, mixing and convergence were checked with Gelman-Rubin statistics (Gelman and Rubin, 1992).

## RESULTS

The selected urban areas had significantly higher urbanization scores than the selected rural areas [F(1, 13) = 96.08, p < 0.0001] based on the satellite data, confirming that our assignments were appropriate.

Distress calling rate showed a low and non-significant between-year repeatability within individuals (**Table 2**). However, repeatabilities of pecking and breath rates were of higher magnitude, and were similar between rural and urban populations (**Table 2**). Distress calling rate and pecking rate were positively correlated within populations (**Table 3**) but distress calling and pecking rates did not correlate significantly with breath rate (**Table 3**), suggesting that breath rate was independent from the two other traits. None of the between-population correlations were statistically significant (**Table 3**).

Distress calling rate was higher in birds from urban compared to rural habitats (**Figure 1**, **Table 4**). There was a non-significant TABLE 2 | Within-individual and between-year repeatability of distress calling, pecking, and breath rates.

Univariate linear mixed-effects models were performed fitting bird identity as a random effect. First, we present models where effects of sex, age, habitat, locality, and population, were not included (i.e., estimates represent the unadjusted repeatability). Second, we present models where the mentioned effects were controlled for (i.e., estimates represent adjusted repeatability; sensu Nakagawa and Schielzeth, 2010). We also present repeatability estimates separately for the urban and rural habitat (though for distress calling rate, variance and the sample size was too small to analyze the two habitats separately). Repeatability (r), standard error (SE), lower and upper confidence interval (CI), and significance (p-value) is provided. Sample size for breath rate: nrural = 32, nurban = 104 individuals measured twice in different years, for pecking and distress calling rates: nrural = 12, nurban = 32 individuals measured twice in different years.

trend toward an interaction between sex and habitat on distress calling rate (**Table 4**). Though the difference in distress calling rate between urban and rural areas tended to be stronger in females compared to males, urban great tits of both sexes had higher distress calling rates compared to their rural conspecifics. The interaction between locality and habitat could not be tested because models incorporating this fixed effect did not converge.

In the full model for pecking rate, there was no significant effect of habitat, age or their interaction (habitat: LRT = 2.75, p = 0.10; age LRT = 0.00, p = 0.98; habitat × age: LRT = 0.29, p = 0.59). Both locality and population were significantly related with great tit pecking rate (locality: LRT = 7.24, p = 0.01; population: LRT = 2.41, p < 0.01). In addition, males displayed higher pecking rates than females (sex: LRT = 4.63, p = 0.03). However, we also found a non-significant trend for an interaction between sex and habitat on pecking rate (sex × habitat: TABLE 3 | Correlations coefficients at different levels among distress calling, pecking, and breath rates.

We print the correlation coefficient (r) and the credible intervals (CI) betweenpopulation (the correlation of population means of the individual traits across different populations) and within-population (the correlation of individual-specific measurements within populations). When the upper and lower CI's does not cross over zero the correlation can be considered as significant (high-lighted in bold face).

LRT = 3.34, p = 0.07). A post-hoc analysis separating the sexes, revealed that there was an effect of habitat on pecking rate in males (LRT = 4.79, p = 0.03, **Figure 2**, **Table 5**) but not in females (LRT = 0.01, p = 0.95). The interaction between locality and habitat was non-significant (LRT: 0.34, p = 0.95) in the model for males indicating that the higher pecking rate of urban than rural males was consistent across localities (**Figure 2**).

Breath rate did not differ between urban and rural habitats when controlling for sex, age, locality, population, and observer effects (**Table 6**). The interaction between locality and habitat was also non-significant (LRT < 0.001, p = 0.99), indicating that this lack of difference was consistent across localities.

#### DISCUSSION

The present study reveals consistent differences in distress calling rate between urban and rural populations of great tits, across

The table shows the results of LMMs, in which the significance of terms was evaluated by likelihood-ratio test (LRT). Means of the parameter estimates (ß), variance components and their respective 95%CI intervals were obtained from the posterior distribution of the corresponding MCMCglmm. In square bracket we indicate to which factor level the parameter estimate corresponds.

multiple European localities, with urban birds having, in general, a higher distress calling rate than their rural conspecifics. In line with this finding, pecking rate showed similar tendencies, but only for males. By contrast, we did not find a general difference in breath rate between urban and rural birds.

Distress behaviors displayed during handling have been hypothesized to reflect the coping style of the different individuals in the presence of an acute stressful situation (Brommer and Kluen, 2012; Class et al., 2014; Kluen et al., 2014; Class and Brommer, 2016). Among the three measured distress behaviors, only distress calling rate showed a clear habitat effect. The higher distress calling rate of urban great TABLE 5 | Statistical summary of the analysis of pecking rate for male great tits between rural and urban habitats.

The table shows the results of LMMs, in which the significance of terms was evaluated by likelihood-ratio test (LRT). Means of the parameter estimates (ß), variance components, and their respective 95%CI intervals were obtained from the posterior distribution of the corresponding MCMCglmm. In square bracket we indicate to which factor level the parameter estimate corresponds.

TABLE 6 | Statistical summary of the analysis of breath rate between rural and urban habitats.

The table shows the results of LMMs, in which the significance of terms was evaluated by likelihood-ratio test (LRT). Means of the parameter estimates (ß), variance components and their respective 95%CI intervals were obtained from the posterior distribution of the corresponding MCMCglmm. In square bracket we indicate to which factor level the parameter estimate corresponds.

tits compared to their rural counterparts is consistent with findings of previous studies that compared bird species (Møller and Ibáñez-Álamo, 2012). Given that high distress calling rates have been related to a proactive coping style (Guillette and Sturdy, 2011; Pascual and Senar, 2014), our results support the view that urban birds are more proactive than rural birds.

Because distress calling and pecking rates were correlated, we expected similar habitat differences for the two traits. Indeed, pecking rate was related to urbanization, but only for males. This sexual difference has previously been observed (Markó et al.,

number of peckings during 15 s.

2013), and is congruent with the general observation that males are more aggressive than females. Under this general view, the higher aggression of urban male great tits could be boosted by intense intra-specific competition for a limited number of territories and a potentially higher breeding density in urban areas (Chamberlain et al., 2009). In line with this finding, elevated levels of male territorial aggression have been found in urban song sparrows Melospiza melodia, compared to rural male conspecifics. However, this pattern is not necessarily supported in other species (Newman et al., 2006; Bókony et al., 2010; Atwell et al., 2014; Hasegawa et al., 2014). Furthermore, bird species living in urban landscapes have been shown to peck in lower frequencies in response to handling than those inhabiting rural habitats (Møller and Ibáñez-Álamo, 2012). Yet, Møller and Ibáñez-Álamo (2012) did not discriminate between males and females, which hampers a straightforward comparison with our findings. Taken together, these inconsistent results do not allow assessing whether pecking rate is more related to aggression and the latter trait that differ between habitat types, or if it is per se a distress behavior that is subjected to selection.  Accordingly, our data on great tit pecking rate suggests that urban males are more proactive than rural males. The interaction between habitat and locality was not significant, indicating that the pecking rate differences among habitats was consistent across localities.

In songbirds, breath rate has been proposed as an indicator of acute stress resulting from a predatory attack, and more generally, as an indicator of stress sensitivity and coping style (Carere and van Oers, 2004; van Oers and Carere, 2007; Torné-Noguera et al., 2014). Indeed, some studies found higher breath rates in urban great tits compared to their rural counterparts (Torné-Noguera et al., 2014; Charmantier et al., 2017). Our study suggests that this finding does not represent a general pattern. In other species, such as dark-eyed juncos Junco hyemalis, urban birds even had a lower breath rate than their rural conspecifics (Abolins-Abols et al., 2016). Our study further reveals that breath rate did not correlate with any of the other behaviors in great tits. This is surprising, since a positive association between pecking rate and breath rate has previously been documented in the closely related blue tit Cyanistes caeruleus (Brommer and Kluen, 2012). However, breath rate has been shown to be affected by seasonal, annual and other ecological factors, hence it may be especially prone to micro-climatic and habitat variation which may mask links to both general urbanization and other behaviors (Torné-Noguera et al., 2014; Charmantier et al., 2017).

The relatively low repeatability of distress calling, potentially due to the low number of repeats per individual (Dingemanse and Dochtermann, 2013; Markó et al., 2013), points to a conservative measure of differences among habitats. Despite the low repeatability, there existed strong differences in distress calling among rural and urban individuals, what points to important differences among habitats, suggesting that distress calling may have evolved in response to urban-induced selection pressures. The between-year repeatability estimates for pecking and breath rate that we recorded, were generally higher than repeatabilities for other behaviors reported in literature (Bell et al., 2009). Given that male pecking rate was different across the habitats and that the repeatability was high, this particular trait seems part of the "urban behavioral phenotype." This finding is also supported by a recent study indicating a non-random distribution of behavioral types along urban gradients, but little behavioral plasticity in response to withinindividual variation to urbanization (Sprau and Dingemanse, 2017).

The combined datasets from different European projects across the distribution of great tits enabled us to conduct a cross-population comparison over a large geographic scale. It entailed study-specific differences in capture time, trapping methods, and observer effects. Nevertheless, we were able to detect consistent differences between habitats, implying that effects reported should be conservative. Altogether, our data suggest that urban great tits, especially males, have a more proactive coping strategy when dealing with stressful conditions than rural birds. This finding is in line with studies showing that urban great tits are more explorative and less neophobic than their rural counterparts (Tryjanowski et al., 2016; Charmantier et al., 2017; Riyahi et al., 2017), and display shorter flight initiation distances (Møller, 2008, 2012), which represent other aspects of the proactive coping strategy. Findings on the great tit are also in line with data from other bird species, for which urban populations have also been found to display proactive behaviors (Evans et al., 2010; Carrete and Tella, 2017), but these studies compared few populations (Miranda et al., 2013). Our study included 14 populations from a large geographic area and revealed consistent results across localities, allowing to generalize the impact of urbanization on great tit coping styles.

This proactive strategy of urban great tits could be advantageous in the urban environment. Proactive individuals, for instance, more rapidly discover and use new food resources (van Overveld and Matthysen, 2010), and are less afraid of novel objects or environments than reactive birds (Tryjanowski et al., 2016; Charmantier et al., 2017; Riyahi et al., 2017), which could help urban dwellers to take profit of the new feeding opportunities that a city can provide (e.g., Fisher and Hinde, 1949). Our study therefore implies that urban birds may adapt to human disturbance by a process of local adaptation, as suggested previously (Partecke et al., 2006; Sol et al., 2013). Future research should investigate whether the differences in distress behaviors reported here have an underlying genetic base, hence are due to local adaption caused by divergent natural selection, or originate from phenotypic plasticity.

## ETHICS STATEMENT

Behavioral data was recorded during ringing operations. Capture and ringing was performed under individual ringing permits delivered by the ringing office within each country.

## AUTHOR CONTRIBUTIONS

The study was conceived by JCS, LG, VT, CB, GM-R, and CI. The data were collected by VT, CB, GM-R, PaS, JR, PhS, ND, AC, VD, and HN. The statistical analyses were conducted by JCS and LG. The manuscript was written by JCS and LG with input from all other co-authors. Contributed funding and materials: JCS, VT, CB, GM-R, PhS, ND, AC, CI.

#### FUNDING

This work was supported by funds from the Ministry of Economy and Competitivity, Spanish Research Council (CGL-2016-79568-C3-3-P; to JCS. CGL2015-70639-P; to LG. CGL2014-55969-P; to GM-R), from the National Research, Development and Innovation Office (Hungary) (K-115970; to LG), from the Estonian Ministry of Education and Research (institutional research funding IUT no. 34-8; to VT), from the European Research Council (ERC-2013-StG-337365-SHE; to AC), from the OSU-OREME (to AC), from the Deutsche Forschungsgemeinschaft (DFG) (SP 1450/3-1; to PhS), from the European Union a Marie Curie Re-Integration grant (CIG 322217; to CI) and from the Swedish Research council (C0361301; to CI).

## ACKNOWLEDGMENTS

We thank the Institute of Parks and Gardens of Barcelona (Parcs i Jardins), the mairie de Montpellier and the cities of Paris and Rueil-Malmaison, for allowing us to sample birds in their city parks. We also thank the National Forest Office (ONF) for allowing us to survey tit populations breeding in state-owned forest, the late Leopoldo Gil, who always supported our work, for allowing us to sample birds in Can Catà forest area, and Hermanitas de la Asunción for allowing us to sample birds in the Sarria area. We also thank Lluïsa Arroyo, Arnaud Grégoire, Marcel Lambrechts, Mar Comas, Nuria Mora, Abelardo Requena, Alexia Mouchet, Maria Moiron, Robin Abbey-Lee, Natalia Perez Ruiz, Mattia Bessone, Rui Machado, Lucila Fernandez, Alexander Hutfluss, Diogo Barros, Gülperi Stenhouse, Josefa Bleu, Simon Agostini, Beatriz Decencière, Erika Beaugeard, Jeanne Dupuy, Eva Du Tien Hat, Laura Grosvalet, Lucie Mathieu, Juliette Rabdeau, Marine Ramirez, Baptiste Vancostenoble, Antonin Waterschoot, Audrey Fournier, Estelle Garrouste, Marilou Keck, Alice Le Priol, Cécilia Mondet, Amélie Pinel, and Quentin Pionneaud for help during field work. Anders P. Møller also provided useful advice during the early stages of the work.

## REFERENCES

phenotypic differences between urban and rural great tit chicks (Parus major). Front. Ecol. Evol. 5:79. doi: 10.3389/fevo.2017.00079

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Senar, Garamszegi, Tilgar, Biard, Moreno-Rueda, Salmón, Rivas, Sprau, Dingemanse, Charmantier, Demeyrier, Navalpotro and Isaksson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Garden Bird Feeding: Insights and Prospects from a North-South Comparison of This Global Urban Phenomenon

#### S. James Reynolds 1 \*, Josie A. Galbraith2 , Jennifer A. Smith3 and Darryl N. Jones 4

*1 Centre for Ornithology, School of Biosciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham, UK, 2 Centre for Biodiversity and Biosecurity, School of Biological Sciences, University of Auckland, Auckland, New Zealand, 3 Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA, 4 Environmental Futures Research Institute, Griffith University, Nathan, QLD, Australia*

Intentional feeding of wild birds in gardens or backyards is one of the most popular forms of human–wildlife interactions in the developed world, especially in urban environments. The scale and intensity of bird feeding are enormous with mainly birdseed consumed daily by a range of species. This represents a subsidy to natural diets of birds attracted to the feeders and typically involves novel dietary components. Yet, relatively little is known about how it influences the behavior and ecology of the species visiting feeders. In part, research has been hampered by logistical difficulties of working in urban areas but studies have demonstrated powerful influences on behavior and phenology of avian breeding, the spread of disease, and the structure of avian communities. Here, we compare bird feeding between Northern and Southern Hemispheres as a means of exploring how similarities and differences in avian responses might inform knowledge of this global urban phenomenon. We start by tracing its origins to north-western Europe and how its expansion has occurred before considering how geographical differences in feeding practices and attitudes map onto bird feeding "on the ground." We explore some of the major emerging themes of recent interest, including why citizens are motivated to feed birds, whether birds become fully dependent on food supplements, the role of feeding in avian disease transmission, and how feeding changes urban bird communities. By proposing that scientists work in collaboration with the public providing food to birds, we pose key research questions that need to be answered urgently and suggest accompanying experimental approaches to do so. These approaches are essential if we are to improve our understanding of how bird feeding shapes the behavior, ecology, movements, and community structure of urban birds. Our hope is that through such citizen science we will be able to provide advice as to location-relevant practices that should maximize benefits to both urban biodiversity and human well-being, and minimize potential adverse impacts. We demonstrate that bird feeding is important for urban biodiversity conservation, community engagement, and in establishing personal connections with nature and their associated benefits.

Keywords: carry-over effects, citizen science, community structure, dependency, disease transmission, food supplements, human well-being, source-sink dynamics

#### Edited by:

*Caroline Isaksson, Lund University, Sweden*

#### Reviewed by:

*Jose I. Aguirre, Complutense University of Madrid, Spain Jack Henry DeLap, Cornish College of the Arts, USA*

> \*Correspondence: *S. James Reynolds<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Behavioral and Evolutionary Ecology, a section of the journal Frontiers in Ecology and Evolution*

> Received: *31 January 2017* Accepted: *21 March 2017* Published: *07 April 2017*

#### Citation:

*Reynolds SJ, Galbraith JA, Smith JA and Jones DN (2017) Garden Bird Feeding: Insights and Prospects from a North-South Comparison of This Global Urban Phenomenon. Front. Ecol. Evol. 5:24. doi: 10.3389/fevo.2017.00024*

## INTRODUCTION

The world is urbanizing rapidly (United Nations, Department of Economic and Social Affairs, Population Division, 2014) and, as a result, human–wildlife interactions will become ever more commonplace. One of the most popular and globally common of such interactions is the feeding of wild birds in residential gardens or backyards (hereafter referred to as "garden bird feeding") that is widespread across many parts of the developed world (Jones, 2017). This pastime is increasingly becoming the subject of scientific and societal scrutiny (Jones and Reynolds, 2008; Robb et al., 2008a). Bird feeding is variously advocated as an essential conservation activity, a simple way for people to connect with nature in an urbanizing world, and a means for enhancing environmental awareness and psychological wellbeing (Schoech et al., 2008; Davies et al., 2012; Cox and Gaston, 2016). However, it has also been implicated in the spread of catastrophic avian diseases (e.g., Dhondt et al., 2007; Robinson et al., 2010), altering ecosystem structure (Galbraith et al., 2015), benefiting invasive species (Galbraith et al., 2015), changing predator-prey dynamics (Malpass et al., 2017), and even contributing to rapid evolutionary change (Bearhop et al., 2005). Furthermore, the possibility that birds may become dependent on anthropogenic food is a primary concern of both opponents to, and proponents of, bird feeding (e.g., Howard and Jones, 2004; Jones, 2011). These issues constitute a far from exhaustive list and clearly a more complete understanding of this interaction between birds and humans should be a significant research priority. This is especially pertinent within the context of urban ecology (Robb et al., 2008a) as it is becoming clear that many animal and plant populations exhibit "phenotypic signatures" associated with the urban areas in which they live (Alberti et al., 2017). We have previously described bird feeding as a supplementary feeding experiment on a global scale but without due consideration of its effects on the behavioral, community, and population ecology of the birds consuming supplements (Jones and Reynolds, 2008). However, despite the above concerns, scientific investigations into most aspects of the practice are fragmentary and geographically limited.

Here, we employ a comparison between Northern and Southern Hemispheres to compare and contrast the impacts of bird feeding on the biology of birds, and on avian populations and communities. We identify gaps in knowledge that require bridging as a matter of urgency because bird feeding appears to be growing in popularity (Jones, 2017). Ultimately, we will identify key research priorities that should be targeted by researchers and citizen scientists with the ultimate aim of promoting avian conservation efforts globally.

## DEFINITIONS, SCALE, AND GEOGRAPHY

Our first task is to define what we mean by garden or backyard "bird feeding." The focus of this review is the intentional feeding (i.e., food supplementation) through the provision of food to free-living birds (**Figure 1**). It does not include the incidental feeding of birds congregating at locations where food sources are available as a result of disposal (e.g., landfill, fish discards from fishing vessels) or feeding of non-avian companion taxa such as dogs Canis lupus familiaris and cats Felis catus silvestris. We considered the growing interest in bird feeding in the scientific literature by performing a literature search (**Figure 2**) of the ISI Web of Science in January 2017 entering "food supplement∗ AND bird∗ " as a search term. We defined the start of this literature search as being the year when the first major review was published in the primary scientific literature by Boutin (1990) who considered the impacts of feeding on free-living terrestrial vertebrates, including birds. The search included articles, proceedings papers, reviews, notes, and editorial materials, and was restricted to the "Ornithology" Web of Science category; this search yielded 206 items. **Figure 2** reveals a slow but steady increase in the scientific outputs related to bird feeding, demonstrating that the subject is of increasing interest.

The scale of bird feeding by a range of different criteria is remarkable. In the United Kingdom (UK), various studies have found that more than 60% of households regularly feed birds in their gardens, spending US\$188–226 million on 60,000 tons of birdseed annually (Fuller et al., 2008). Robb et al. (2008a) estimated that sufficient seed was distributed for blue tits Cyanistes caeruleus alone to support five times the national population of the species. In the United States (US), the 2011 National Survey of Fishing, Hunting, and Wildlife-Associated Recreation reported that 52.8 million households practiced some form of bird feeding, giving a countrywide rate of 73% (U.S. Fish Wildlife Service and U.S. Census Bureau, 2011) spending a total of US\$4 billion on birdseed and an additional US\$10 billion on related hardware and peripherals, annually. The annual amount of bird food supplied in the US is ∼500,000 tons, enough to feed 300 million chickadees Poecile spp. if they consumed nothing else (Robb et al., 2008a).