Behavioural and Ecological Consequences of Urban Life in Birds 9782889454976

Table of contents :
Cover
Frontiers Copyright Statement
Behavioural and Ecological Consequences of Urban Life in Birds
Table of Contents
Editorial: Behavioural and Ecological Consequences of Urban Life in Birds
Introduction
Fear and Boldness
Behavioral and Fitness Consequences of Human-Provided Resources
Physiological and Behavioral Effects to Novel Abiotic Stressors
Reproduction and Life History
Biodiversity and Conservation in Urban Habitats
Conclusion
Author Contributions
Funding
Acknowledgments
References
Rural-Urban Differences in Escape Behavior of European Birds across a Latitudinal Gradient
Introduction
Materials and Methods
Field Data Collection
Additional Data
Calculating the Phi Index
Statistical Methods
Results
Discussion
Ethics Statement
Authors Contributions
Funding
Acknowledgments
Supplementary Material
References
Time Since Urbanization but Not Encephalisation Is Associated with Increased Tolerance of Human Proximity in Birds
Introduction
Materials and Methods
Data Collection
Comparative Analysis
Results
Discussion
Author Contributions
Funding
Supplementary Material
References
Behavioral Correlations Associated with Fear of Humans Differ between Rural and Urban Burrowing Owls
Introduction
Materials and Methods
Study Species, Area and Population Monitoring
Behavioral Experiments
Risk Taking
Exploration/Avoidance
Antipredatory Behavior
Analytical Procedures
Results
Discussion
Ethics Statement
Author Contributions
Acknowledgments
References
An Approach to Distinguish between Plasticity and Non-random Distributions of Behavioral Types Along Urban Gradients in a Wild Passerine Bird
Introduction
Methods
Study Site
Experimental Protocol
Statistical Analyses
Results
Axes of Environmental Variation
Phenotypic Plasticity and Non-random Distributions of Behavioral Types
Discussion
Author Contributions
Acknowledgments
Supplementary Material
References
First to Flush: The Effects of Ambient Noise on Songbird Flight Initiation Distances and Implications for Human Experiences with Nature
Introduction
Materials and Methods
Data Analysis
Results
Discussion
Ethics Statement
Author Contributions
Acknowledgments
References
Urban Great Tits (Parus major) Show Higher Distress Calling and Pecking Rates than Rural Birds across Europe
Introduction
Materials and Methods
Study Areas and Sampling
Behavioral Tests
Statistical Analyses
Results
Discussion
Ethics Statement
Author Contributions
Funding
Acknowledgments
References
Garden Bird Feeding: Insights and Prospects from a North-South Comparison of This Global Urban Phenomenon
Introduction
Definitions, Scale, and Geography
North vs. South
Human Motivations and Reasons for Feeding
Feeding and Dependency
Feeding and Disease Transmission
Feeding and Avian Community Structure
Directions for Future Research
What Contribution Do Food Supplements Make to a Bird'S Diet?
Proposal A
Do Individual Birds Over-Commit to Defence of Feeders?
Proposal B
Do Individual Birds Experience Carry-Over Effects from Supplementary Feeding?
Proposal C
Does Feeding Influence Mating Strategies and Gene Flow in Urban Bird Populations?
Proposal D
Does Feeding Create an Ecological Trap for Urban Bird Populations?
Proposal E
Are Feeders Implicated in the Spread of Disease?
Proposal F
Does Feeding Inevitably Change Urban Bird Community Structure?
Proposal G
Concluding Remarks: What Needs to Be Done to Answer these Research Questions?
Author Contributions
Funding
Acknowledgments
References
Urban Bird Feeders Dominated by a Few Species and Individuals
Introduction
Methods
Experimental Feeding Stations
Species Visitation and Association Data
Individual Visitation Data
Statistical Analyses
Species Visitation and Co-occurrence (Camera Trap Data)
Individual Visitation (PIT-Tag Data)
Results
Camera Trap Visitation Events
Structure of Bird Assemblage at Feeding Stations
Species Visitation Patterns
Species Associations
Individual Visitation Patterns
Discussion
Summary of Findings
Visitation Patterns
Species Associations
Underlying Foraging Mechanisms
Individual Variability
Conclusions
Author Contributions
Funding
Acknowledgments
Supplementary Material
References
Does Urbanization Affect Predation of Bird Nests? A Meta-Analysis
Introduction
Methods
Literature Screening and Data Collection
Scoring Urbanization
Data Extraction
Statistical Analyses
Meta-Analyses and Meta-Regressions
Sensitivity Analyses and Publication Bias
Results
General Results
Meta-Analyses and Meta-Regressions
Sensitivity Analyses and Publication Bias
Discussion
Code and Data Availability
Author Contributions
Funding
Acknowledgments
Supplementary Material
References
Switch to a Novel Breeding Resource Influences Coexistence of Two Passerine Birds
Introduction
Methods
Comparing Insulation Properties of Natural Cavities and Nest Boxes
Testing for Species-Specific Effects of Cold Snaps
Influence of Elevation on Rate of Species Replacement across Populations
Experimental Test of Nest Box Density on Dynamics of Species Replacement
Statistical Analysis
Results
Testing the Assumption That Natural Cavities and Nest Boxes Differ In Insulation Properties
Influence of Cold Snaps on Incubation Patterns and Nest Success
Influence of Elevation on Dynamics of Species Replacement
Influence of Experimental Manipulation of Nest Box Density on Settlement Patterns
Discussion
Ethics Statement
Author Contributions
Acknowledgments
Supplementary Material
References
Multi-Element Analysis of Blood Samples in a Passerine Species: Excesses and Deficiencies of Trace Elements in an Urbanization Study
Introduction
Materials and Methods
Study Sites
Reproductive Parameters and Blood Sampling
Haptoglobin Assay
NO2 Measurements
Multi-Element Analysis
Statistical Analysis
Results
Reproduction Parameters and Immune Marker
Relationships between NO2 and Biological Parameters
Relationships between Element Concentrations and Biological Parameters
Discussion
Conclusion
Ethics Statement
Author Contributions
Acknowledgments
Supplementary Material
References
Anthropogenic Nest Materials May Increase Breeding Costs for Urban Birds
Introduction
Methods
Study Species
Study Site and Blood Samples
Statistical Analyses
Results
Carpodacus Mexicanus
Passer Domesticus
Discussion
Ethics Statement
Author Contributions
Funding
Acknowledgments
Supplementary Material
References
The Influence of Urban Environments on Oxidative Stress Balance: A Case Study on the House Sparrow in the Iberian Peninsula
Introduction
Materials and Methods
Area Characterization
Bird Sampling
Oxidative Stress Biomarkers
Data Analysis
Results
Area Characterization
Body Condition and Oxidative Stress Biomarkers
Discussion
Author Contributions
Acknowledgments
References
Species-Dependent Effects of the Urban Environment on Fatty Acid Composition and Oxidative Stress in Birds
Introduction
Materials and Methods
Study Species
Field Sites and Sampling
Fatty Acid Extraction and Gas Chromatography/Mass Spectrometry (GC/MS) Analysis
Lipid Peroxidation Quantification
Data Handling and Statistical Analyses
Results
Habitat Differences in FA Profiles
Total SFA (totSFA) and MUFA
ω-3 PUFA
ω-6 PUFA
ω-9 PUFA
Species Differences in FA Profiles
Habitat Differences in Health Markers
ω-6/ω-3 PUFA Ratio
Peroxidation Index (PI)
Malondialdehyde (MDA)
Discussion
Habitat Differences in Fatty Acid Profiles
Species and Family Differences in Fatty Acid Profiles
Species and Habitat Differences in Health Markers
Conclusions
Ethics Statement
Author Contributions
Acknowledgments
Supplementary Material
References
Growing in Cities: An Urban Penalty for Wild Birds? A Study of Phenotypic Differences between Urban and Rural Great Tit Chicks (Parus major)
Introduction
Materials and Methods
Study Sites and Data Collection
Telomere Measurements
Feather Color Analysis
Statistical Analyses
Results
Discussion
Urbanization and Body Size in Nestling Great Tits
Urbanization and Plumage Characteristics
Urbanization and Telomere Length
Regional Differences in Nestlings' Morphology and Reproductive Output
Author Contributions
Acknowledgments
References
Elevated Immune Gene Expression Is Associated with Poor Reproductive Success of Urban Blue Tits
Introduction
Materials and Methods
Experimental Design and Field Protocol
Ethics Statement
Expression of Immune and Reference Genes
Primer Design
RNA Extraction, Reverse Transcription and RT-qPCR Protocol
Screening of Malaria Parasites
Statistical Analysis
Model Selection Approach
Immune Transcript Levels and Malaria Prevalence in Adult Blue Tits
Immune Transcript Levels and Malaria Prevalence in Cross-Fostered Nestlings
Reproductive Investment and Breeding Success of Urban and Forest Cross-Fostered Nests
Results
Immune Transcript Levels and Malaria Prevalence of Urban and Forest Adults
Immune Transcript Levels and Malaria Prevalence of Forest-Reared Nestlings
Breeding Success in Urban and Forest Cross-Fostered Nests
Discussion
Author Contributions
Funding
Acknowledgments
Supplementary Material
References
Artificial Light at Night Reduces Daily Energy Expenditure in Breeding Great Tits (Parus major)
Introduction
Methods
Experimental Set-Up
Daily Energy Expenditure
Activity Measurements: Visit Rates and Activity Timing
Caterpillar Availability
Statistical Analyses
Results
Daily Energy Expenditure
Activity Measurements: Visit Rates and Activity Timing
Caterpillar Availability
Discussion
Ethics Statements
Author contributions
Funding
Acknowledgments
Supplementary Material
References
Two Neural Measures Differ between Urban and Rural Song Sparrows after Conspecific Song Playback
Introduction
Materials and Methods
Subjects
Plasma Testosterone
Histology
Imaging and Quantification
Statistics
Results
Plasma Testosterone
FOS Immunoreactivity
AVT Immunoreactivity
Discussion
Habitat Differences in Neural Response to Conspecific Song
Habitat Differences in AVT-Like Immunoreactivity
Conclusions
Ethics Statement
Author Contributions
Funding
Acknowledgments
Supplementary Material
References
European Blackbirds Exposed to Aircraft Noise Advance Their Chorus, Modify Their Song and Spend More Time Singing
Introduction
Materials and Methods
Study Species and Study Site
Noise Measurements
High Quality Recordings and Song Analyses
Automatic Recordings and Dawn Chorus Analyses
Statistics
Results
Noise
Song Characteristics
Dawn Chorus
Discussion
Author Contributions
Funding
References
Reproductive Contributions of Cardinals Are Consistent with a Hypothesis of Relaxed Selection in Urban Landscapes
Introduction
Materials and Methods
Study System
Surveys of Avian Communities
Banding and Nest-Searching
Calculation of Reproductive Contributions of Individuals to Local Populations
Results
Discussion
Author Contributions
Funding
Acknowledgments
References
Urbanization Is Associated with Divergence in Pace-of-Life in Great Tits
Introduction
Materials and Methods
Study Area and Monitoring
Sites and Monitoring
Breeding Data Collection
Urbanization Measures
Behavioral Assays
Data Analysis
Results
Discussion
Phenotypic Divergence between Forest and City and along the Urbanization Gradient
Plastic vs. Genetic Origin and Adaptive Nature of Divergence
Perspectives on a Multivariate Long-Term Process
Ethics Statement
Author Contributions
Funding
Acknowledgments
Supplementary Material
References
Humans and Tits in the City: Quantifying the Effects of Human Presence on Great Tit and Blue Tit Reproductive Trait Variation
Introduction
Methods
Study Sites
Life-History Data
Ethics Statement
Quantifying Human Presence
Human Presence Protocol
Distances from Paths and Roads
Statistical Analyses
Repeatability
Statistical Analyses
Results
Repeatability of Human Presence on the Ground and With a GIS-Based Approach
Effects of Human Presence, Distance to Paths and Roads on Life-History Traits and Reproductive Success
Discussion
Author Contributions
Funding
Acknowledgments
Supplementary Material
References
Does Seasonal Decline in Breeding Performance Differ for an African Raptor across an Urbanization Gradient?
Introduction
Methods
Study Site and Species
Defining the Urban Gradient
Statistical Analysis
Ethical Note
Results
Urban Gradient
Timing of Breeding and Breeding Performance along the Urban Gradient
Discussion
Conclusion
Author Contributions
Funding
Acknowledgments
References
Mechanisms Associated with an Advance in the Timing of Seasonal Reproduction in an Urban Songbird
Introduction
Methods
Field Study
Capture and Morphological Measures
Blood Sample Collection—Field
Luteinizing Hormone Assay
Common Garden Study
Bird Capture and Housing
Blood Sample Collection—Captivity
Morphological Measurements
Gonadal Development
Corticosterone and Testosterone Assays
mRNA Extraction and qPCR
DNA Extraction
Feather isotopes
Genotyping-by-sequencing
Captive study classification
Field study classification
Statistical Analyses of Hormone and Physiology Data
Field study
Captive study
Results
Field Study
Luteinizing Hormone
Common Garden Study
Gonads
Cloacal Protuberance Volume
Testosterone
CORT
Fat
LHR and FSHR
GR and MR
Discussion
Conclusions
Author Contributions
Acknowledgments
Supplementary Material
References
Urbanization Alters the Influence of Weather and an Index of Forest Productivity on Avian Community Richness and Guild Abundance in the Seattle Metropolitan Area
Introduction
Methods
Study Region and Site Selection
Bird Surveys
Avian Richness and Relative Abundance
Vegetation Greenness Index
Annual Variation in Weather
Statistical Analyses
Results
Variation in NDVI and Weather among Years and Sites
Annual Variation in Bird Species Richness
Does the Relationship of Richness to NDVI and Weather Vary with Site Type as Hypothesized?
Variation in Abundance among Years and Sites
Does the Relationship of Abundance to NDVI and Weather Vary with Site Type as Hypothesized?
Discussion
Limitations and Future Directions
Ethics Statement
Author Contributions
Funding
Supplementary Material
References
Impacts of Urban Areas and Their Characteristics on Avian Functional Diversity
Introduction
Methods
Avian Assemblage Data
Avian Trait Data
Functional Diversity Metrics
City Characteristics
Statistical Analysis of Urbanization Effects
Results
Paired Analyses
Regression Trees
Discussion
Conclusions
Author contributions
Funding
Acknowledgments
Supplementary Material
References
Dispersal in the Urban Matrix: Assessing the Influence of Landscape Permeability on the Settlement Patterns of Breeding Songbirds
Introduction
Materials and Methods
Results
Discussion
Conclusion
Ethics Statement
Author Contributions
Funding
Acknowledgments
Supplementary Material
References
Changes over 26 Years in the Avifauna of the Bogotá Region, Colombia: Has Climate Change Become Important?
Introduction
Methods
Study Area
Audubon's Christmas Bird Count Methods and Circle
The CBC Circle
Analysis
Results
Land Cover and Temperature Changes
General Ornithological Results
Urban vs. Rural Areas
Long-Term Changes in Birds in the Bogotá Area
Discussion
Lack of Continuity in Observations in Particular Sites
Direct Human Actions
``Natural'' Changes in Some Habitats
Interactions with Other Species
Climate Change
A Look at the Past
A Glimpse into the Future
The Present Study in the Context of Urban Ornithology in Latin America
Author Contributions
Funding
Acknowledgments
Supplementary Material
References
Predicting Metapopulation Responses to Conservation in Human-Dominated Landscapes
Introduction
Materials and Methods
Study Area and Species
Demographic Data Collection
Demographic Parameters
Transition Matrix
Wood Thrush Occupancy and Abundance Estimates
Metapopulation Model Structure
Model Scenario Descriptions
Scenario (A) Null
Scenario (B) Reforest (Replace Impervious Surface)
Scenario (C) Cowbird Removal
Scenario (D) Reforest and Cowbird Removal
Results
Occupancy Model Results
Regression Model Results
Transition Matrix
Metapopulation Simulation Results
Discussion
Ethics statement
Author Contributions
Acknowledgments
Supplementary Material
References
The Degree of Urbanization of a Species Affects How Intensively It Is Studied: A Global Perspective
Introduction
Materials and Methods
Bird Assemblages
Research Effort Data, Urbanization and Species' Traits
Statistical Analyses
Results
Discussion
Author Contributions
Funding
Acknowledgments
Supplementary Material
References
Back Cover

Citation preview

BEHAVIOURAL AND ECOLOGICAL CONSEQUENCES OF URBAN LIFE IN BIRDS EDITED BY : Caroline Isaksson, Amanda D. Rodewald and Diego Gil PUBLISHED IN : Frontiers in Ecology and Evolution

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June 2018  |  Urbanization and Birds

BEHAVIOURAL AND ECOLOGICAL CONSEQUENCES OF URBAN LIFE IN BIRDS Topic Editors: Caroline Isaksson, Lund University, Sweden Amanda D. Rodewald, Cornell University, United States Diego Gil, Museo Nacional de Ciencias Naturales (MNCN), Spain

Urban jackdaw. Image: Amparo Herrera-Dueñas.

Urbanization is next to global warming the largest threat to biodiversity. Indeed, it is becoming increasingly evident that many bird species get locally extinct as a result of urban development. However, many bird species benefit from urbanization, especially through the abundance of human-provided resources, and increase in abundance and densities. These birds are intriguing to study in relation to its

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resilience and adaption to urban environments, but also in relation to its susceptibility and the potential costs of urban life. This Research Topic consisting of 30 articles (one review, two meta-analyzes and 27 original data papers) provides insights into species and population responses to urbanization through diverse lenses, including biogeography, community ecology, behaviour, life history evolution, and physiology. Citation: Isaksson, C., Rodewald, A. D., Gil, D., eds. (2018). Behavioural and Ecological Consequences of Urban Life in Birds. Lausanne: Frontiers Media. doi: 10.3389/978-2-88945-497-6

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Table of Contents 07

Editorial: Behavioural and Ecological Consequences of Urban Life in Birds Caroline Isaksson, Amanda D. Rodewald and Diego Gil

1. FEAR AND BOLDNESS 12 Rural-Urban Differences in Escape Behavior of European Birds Across a Latitudinal Gradient Diogo S. M. Samia, Daniel T. Blumstein, Mario Díaz, Tomas Grim, Juan Diego Ibáñez-Álamo, Jukka Jokimäki, Kunter Tätte, Gábor Markó, Piotr Tryjanowski and Anders Pape Møller 25 Time Since Urbanization but not Encephalisation is Associated With Increased Tolerance of Human Proximity in Birds Matthew R. E. Symonds, Michael A. Weston, Wouter F. D. van Dongen, Alan Lill, Randall W. Robinson and Patrick-Jean Guay 34 Behavioral Correlations Associated With Fear of Humans Differ Between Rural and Urban Burrowing Owls Martina Carrete and José L. Tella 43 An Approach to Distinguish Between Plasticity and Non-Random Distributions of Behavioral Types Along Urban Gradients in a Wild Passerine Bird Philipp Sprau and Niels J. Dingemanse 51 First to Flush: The Effects of Ambient Noise on Songbird Flight Initiation Distances and Implications for Human Experiences With Nature Alissa R. Petrelli, Mitchell J. Levenhagen, Ryan Wardle, Jesse R. Barber and Clinton D. Francis 61 Urban Great Tits (Parus Major) Show Higher Distress Calling and Pecking Rates Than Rural Birds Across Europe Juan Carlos Senar, Laszlo Z. Garamszegi, Vallo Tilgar, Clotilde Biard, Gregorio Moreno-Rueda, Pablo Salmón, J. M. Rivas, Philipp Sprau, Niels J. Dingemanse, Anne Charmantier, Virginie Demeyrier, Helena Navalpotro and Caroline Isaksson 2. BEHAVIOURAL AND FITNESS CONSEQUENCES OF HUMAN-PROVIDED RESOURCES 71 Garden Bird Feeding: Insights and Prospects From a North-South Comparison of This Global Urban Phenomenon S. James Reynolds, Josie A. Galbraith, Jennifer A. Smith and Darryl N. Jones 86 Urban Bird Feeders Dominated by a Few Species and Individuals Josie A. Galbraith, Darryl N. Jones, Jacqueline R. Beggs, Katharina Parry and Margaret C. Stanley 101 Does Urbanization Affect Predation of Bird Nests? A Meta-Analysis Ernő Vincze, Gábor Seress, Malgorzata Lagisz, Shinichi Nakagawa, Niels J. Dingemanse and Philipp Sprau 113 Switch to a Novel Breeding Resource Influences Coexistence of Two Passerine Birds Renée A. Duckworth, Kelly K. Hallinger, Nerissa Hall and Ahva L. Potticary Frontiers in Ecology and Evolution

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3. PHYSIOLOGICAL AND BEHAVIOURAL EFFECTS TO NOVEL ABIOTIC STRESSORS 124 Multi-Element Analysis of Blood Samples in a Passerine Species: Excesses and Deficiencies of Trace Elements in an Urbanization Study Juliette Bailly, Bruno Faivre, Nadine Bernard, Mickaël Sage, Nadia Crini, Vincent Driget, Stéphane Garnier, Dominique Rieffel and Renaud Scheifler 133 Anthropogenic Nest Materials May Increase Breeding Costs for Urban Birds Monserrat Suárez-Rodríguez, Regina D. Montero-Montoya and Constantino Macías Garcia 143 The Influence of Urban Environments on Oxidative Stress Balance: A Case Study on the House Sparrow in the Iberian Peninsula Amparo Herrera-Dueñas, Javier Pineda-Pampliega, María T. Antonio-García and José I. Aguirre 153 Species-Dependent Effects of the Urban Environment on Fatty Acid Composition and Oxidative Stress in Birds Caroline Isaksson, Martin N. Andersson, Andreas Nord†, Maria von Post and Hong-Lei Wang 166 Growing in Cities: An Urban Penalty for Wild Birds? A Study of Phenotypic Differences Between Urban and Rural Great Tit Chicks (Parus Major) Clotilde Biard, François Brischoux, Alizée Meillère, Bruno Michaud, Manon Nivière, Stéphanie Ruault, Marie Vaugoyeau and Frédéric Angelier 180 Elevated Immune Gene Expression is Associated With Poor Reproductive Success of Urban Blue Tits Pablo Capilla-Lasheras, Davide M. Dominoni, Simon A. Babayan, Peter J. O’Shaughnessy, Magdalena Mladenova, Luke Woodford, Christopher J. Pollock, Tom Barr, Francesco Baldini and Barbara Helm 193 Artificial Light at Night Reduces Daily Energy Expenditure in Breeding Great Tits (Parus Major) Anouk A. M. H. Welbers, Natalie E. van Dis, Anne M. Kolvoort, Jenny Ouyang, Marcel E. Visser, Kamiel Spoelstra and Davide M. Dominoni 203 Two Neural Measures Differ Between Urban and Rural Song Sparrows After Conspecific Song Playback Kendra B. Sewall and Scott Davies 214 European Blackbirds Exposed to Aircraft Noise Advance Their Chorus, Modify Their Song and Spend More Time Singing Javier Sierro, Elodie Schloesing, Ignacio Pavón and Diego Gil 4. REPRODUCTION AND LIFE-HISTORY 227 Reproductive Contributions of Cardinals are Consistent With a Hypothesis of Relaxed Selection in Urban Landscapes Amanda D. Rodewald and Peter Arcese 234 Urbanization is Associated With Divergence in Pace-of-Life in Great Tits Anne Charmantier, Virginie Demeyrier, Marcel Lambrechts, Samuel Perret and Arnaud Grégoire 247 Humans and Tits in the City: Quantifying the Effects of Human Presence on Great Tit and Blue Tit Reproductive Trait Variation Michela Corsini, Anna Dubiec, Pascal Marrot and Marta Szulkin

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259 Does Seasonal Decline in Breeding Performance Differ for an African Raptor Across an Urbanization Gradient? Sanjo Rose, Petra Sumasgutner, Ann Koeslag and Arjun Amar 268 Mechanisms Associated With an Advance in the Timing of Seasonal Reproduction in an Urban Songbird Adam M. Fudickar, Timothy J. Greives, Mikus Abolins-Abols, Jonathan W. Atwell, Simone L. Meddle, Guillermo Friis, Craig A. Stricker and Ellen D. Ketterson 5. BIODIVERSITY AND CONSERVATION IN URBAN HABITATS 281 Urbanization Alters the Influence of Weather and an Index of Forest Productivity on Avian Community Richness and Guild Abundance in the Seattle Metropolitan Area Benjamin Shryock, John M. Marzluff and L. Monika Moskal 295 Impacts of Urban Areas and Their Characteristics on Avian Functional Diversity Emily Oliveira Hagen, Oskar Hagen, Juan D. Ibáñez-Álamo, Owen L. Petchey and Karl L. Evans 310 Dispersal in the Urban Matrix: Assessing the Influence of Landscape Permeability on the Settlement Patterns of Breeding Songbirds Brian S. Evans, A. Marm Kilpatrick, Allen H. Hurlbert and Peter P. Marra 322 Changes Over 26 Years in the Avifauna of the Bogotá Region, Colombia: Has Climate Change Become Important? F. Gary Stiles, Loreta Rosselli and Susana De La Zerda 343 Predicting Metapopulation Responses to Conservation in Human-Dominated Landscapes Zachary S. Ladin, Vincent D’Amico, Jan M. Baetens, Roland R. Roth and W. Gregory Shriver 355 The Degree of Urbanization of a Species Affects How Intensively it is Studied: A Global Perspective Juan D. Ibáñez-Álamo, Enrique Rubio and Kwanye Bitrus Zira

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EDITORIAL published: 26 April 2018 doi: 10.3389/fevo.2018.00050

Editorial: Behavioural and Ecological Consequences of Urban Life in Birds Caroline Isaksson 1*, Amanda D. Rodewald 2 and Diego Gil 3 1

Department of Biology, Lund University, Lund, Sweden, 2 Cornell Lab of Ornithology and Department of Natural Resources, Cornell University, Ithaca, NY, United States, 3 Departamento de Ecología Evolutiva, Museo Nacional de Ciencias Naturales (CSIC), Madrid, Spain Keywords: biodiversity, environmental stress, pollution, species interactions, urbanization

Editorial on the research topic Behavioural and Ecological Consequences of Urban Life in Birds

INTRODUCTION

Edited and reviewed by: Jordi Figuerola, Estación Biológica de Doñana (EBD), Spain *Correspondence: Caroline Isaksson [email protected] Specialty section: This article was submitted to Behavioral and Evolutionary Ecology, a section of the journal Frontiers in Ecology and Evolution Received: 21 March 2018 Accepted: 10 April 2018 Published: 26 April 2018 Citation: Isaksson C, Rodewald AD and Gil D (2018) Editorial: Behavioural and Ecological Consequences of Urban Life in Birds. Front. Ecol. Evol. 6:50. doi: 10.3389/fevo.2018.00050

The footprint of urban areas continues to expand across the globe, with urban land cover expected to triple between 2000 and 2030 (United Nations, 2014). This dramatic transformation from natural or less-intensively used land to impervious surfaces and buildings has altered species composition and distributions, homogenized communities, and even prompted local extinctions (Marzluff and Ewing, 2001; McKinney, 2002; Shochat et al., 2010). Because the most intense urban development is projected to occur within biodiversity hotspots (Elmqvist et al., 2013), urbanization stands to be one of the greatest threats to species persistence in the future. Yet nearly 20% of the roughly 10,000 described bird species can still be found in cities (Aronson et al., 2014). Thus, understanding the enabling factors that allow species to persist within urbanized landscapes is as important as identifying the drivers of species loss. The present issue on “Behavioural and Ecological Consequences of Urban Life in Birds” showcases 30 articles that provide insights into species responses to urbanization through diverse lenses, including biogeography, community ecology, life history evolution, and physiology. Behavioral responses are a central theme across articles, in part because behavior often defines initial responses to urbanization and provide clues about how certain species cope with urban habitats, human presence, and novel resources (e.g., food and nest boxes) and conditions (e.g., pollution, noise, artificial light). As Topic Editors we aimed both to cover a wide range of topics and approaches and to highlight research around the world, especially those studies conducted outside of Europe and North America. We believe that it is important to cover research in the Southern Hemisphere, as this comparatively less studied area contains the highest levels of avian diversity in the world (Thomas et al., 2008). In total, 19 countries from 6 continents are represented in the articles within this issue, which collectively report on more than 700 bird species. The top-three species for urban/rural comparisons were, not surprisingly, the great tit (Parus major), the house sparrow (Passer domesticus) and the blackbird (Turdus merula). This pattern is consistent with the suggestion in the contributing paper by Ibáñez-Álamo et al., who show that urbanization not only affects the birds but also how we, researchers, study them. The more urban a species is the more frequently it is studied. Here we review and synthesize the key findings of this issue. The articles provide a nice overview of the general advances in the field of urban ecology, especially in relation to birds. In addition, these studies tackle many still unanswered questions at both macro- and micro-ecological levels, along with phenotypic and evolutionary responses to environmental change that urgently need to be addressed to maintain avian diversity on an urbanizing planet.

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Fear and Boldness

(Charmantier et al.), which is consistent with the exploratory behavior that Carrete and Tella report for urban burrowing owls. Great tit aggression was also assayed in the study by Sprau and Dingemanse using staged trials with a caged mount and playback of song. As for FID (see above)—boldness increased with traffic-load.

Millions of people around the world enjoy birds through bird-feeding or bird-watching, but the reality is that the experience is not always mutually pleasant from the perspective of birds. Whereas, humans often enjoy the interaction and entice birds to come closer, birds must evaluate the risk at every encounter. A common expectation is that urban birds should be less fearful of humans than rural counterparts. Several contributed papers address the potential fear experienced by birds by using flight initiation distance (FID) as a proxy for “fear” toward humans i.e., the shorter FID, the closer humans can approach and the more fearless is the bird. Indeed, two meta-analyses of 32 (Samia et al.) and 42 European species (Symonds et al.), respectively, report that the more urbanized a species is (i.e., longer time since colonization of cities), the shorter the FID and, presumably, the less fear is experienced by the birds. A field-intensive study of burrowing owls (Athene cunicularia) by Carrete and Tella also found that breeding pairs were more fearless toward humans in urban compared to rural habitats. But what drives those patterns? Symonds et al. explicitly tested the extent to which encephalization (brain complexity and size) affected tolerance level of urban birds, but found no significant association between brain size and FID in this set of 42 species. Interestingly, the authors of the two metaanalyses suggest contrasting conclusions to the shorter FIDs of urban birds—released selection via reduced predation (Samia et al.) or enhanced selection of birds with low responsiveness toward humans (Symonds et al.). Sprau and Dingemanse applied a statistical approach to distinguish plastic responses from patterns of non-random distributions of behavioral variation in FID and aggression in the great tit. The striking result was that the two axes of behavior were non-randomly distributed, such that fearless (bold) birds occurred more frequently in areas with more cars but fewer humans, while fearful (shy) individuals were predominantly found in areas with fewer cars and more humans (Sprau and Dingemanse). Potential explanations for this pattern include non-random settlement, habitat- and type-specific survival, or irreversible plasticity in response to long-term exposure to urban environmental stress. The unexpected finding of shorter FID with fewer humans might be explained by vehicle noise, as Petrelli et al. found that noisy urban environments facilitated closer human approaches, and thus shorter FIDs, than more quiet habitats. Other contributed papers explored behavior (often these assays are linked to so-called personalities or behavioral syndromes) using other approaches. Two studies examined coping responses of urban and rural great tits when handled, a widely used technique to score aggression. Senar et al. found that urban tits used a more pro-active coping strategy via more distress calls (fear screams) and higher pecking rates (aggression), whereas Charmantier et al. detected no such differences in great tits from Montepellier. Instead, the urban great tits from Montpellier were more explorative to a novel environment

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Behavioral and Fitness Consequences of Human-Provided Resources The novelty, availability, and predictability of resources, such as bird-feeders and nest boxes, are among the most striking ways that urban landscapes differ from non-urban areas. Yet the extent to which urban-dwelling species respond to and rely upon human-provided resources varies widely. The so-called “urban exploiters” depend on the anthropogenic resources and are often abundant within cities, whereas the “urban adapters” are more opportunistic (McKinney, 2002). While the extent to which anthropogenic resources positively or negatively affect birds varies among species, systems, and geographies, several papers in this issue provide new insights on this respect. Anthropogenic resources, such as birdseeds and invasive fruits, may create more predictable and homogenous environments that reduce selective pressures compared to natural environments. Rodewald and Arcese corroborate this hypothesis by demonstrating reduced variability in reproductive contributions within and among-female Northern cardinals (Cardinalis cardinalis) in urban than rural environments, despite comparable variation in body condition. In this way, urban females were relatively homogeneous in terms of performance, whereas rural females spanned a wider range of high to low performers. While bird-feeding is associated with many positive outcomes for birds, Reynolds et al. remind that community responses may differ between continents and hemispheres; for example, gregarious non-native species tend to dominate at feeding tables in the Southern hemisphere. Indeed, Galbraith et al. report that 10 of the 11 species recorded using urban bird feeders in New Zealand were non-native, including the two dominating species of the house sparrow P. domesticus and the spotted dove Streptopelia chinensis. This improved benefit for invading species may explain the patterns of abundance and spreading that characterize these species. Other articles highlight the tradeoffs between quantity and quality of urban-associated resources, particularly when urban resources are less diverse and of lower nutritional value. Isaksson et al. revealed differences in the nutritional fatty acid physiology of four common passerine species between the urban and rural habitats that were not attributed to specific foods. Rather, the fatty acid profile of urban tits (Paridae) and sparrows (Passeridae) suggest that the urban diet of these two families could affect the birds through two different pathways, inflammation and oxidative stress, respectively. Of course, food is not the only urban resource, and many birds are attracted to nest boxes, especially when natural holes are limited. The presence of nest boxes can allow a species to colonize areas that otherwise would have been inhospitable and may boost reproductive success of certain species. At the same

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time, nest boxes may increase risk of predation if they are more readily located than natural cavities. Consistent with this idea, a meta-analysis conducted by Vincze et al. showed that artificial nests (both hole and cup nests) in cities were more likely to be depredated than natural nests, but surprisingly, vice versa in less urbanized areas. Although differences might be attributed to shifts in predator composition, abundance, or behavior between rural and urban landscapes, no ecological drivers were identified. Apart from predation, another ecological factor that can influence breeding success in an artificial nest box is weather. Despite the careful attention given to the design and construction of nest boxes, it was surprising to learn from Duckworth et al. that nest boxes are typically less insulated than natural nest cavities and, consequently, are associated with lower survival of nestlings in inclement weather.

There is a growing literature of studies showing that noise pollution is an important factor affecting acoustic communication in birds, and that many bird species show modifications in song amplitude, frequency and temporal characteristics that increase the chances of song reaching receivers (Gil and Brumm, 2014). Two articles in this issue deal with acoustic communication in urban environments. The first of them, by Sewall and Davies, identifies differences between urban and rural populations in the brain expression of an early gene (FOS), a proxy for recent neural activity, indicating different neural responsiveness to stimuli that could underpin differences in behavior in the song sparrow (Melospiza melodia). Another study, by Sierro et al., shows that, in the particular noise environment of an airport, European blackbirds modify the composition of their song, but not their overall frequency characteristics, reducing the part of the song which has the lowest amplitude. In addition, birds near the airport advance the timing of their dawn chorus at the period of the season when overcraft activity overlaps most with their singing. In contrast, this advance disappears later in the season, when the natural timing of song is much earlier than the airport activity.

Physiological and Behavioral Effects to Novel Abiotic Stressors Urbanization is commonly associated with pollution, whether due to emissions in air, artificial light at night (ALAN) or noise (e.g., Salmón et al., 2018). Indeed, Bailly et al. demonstrate that although levels of nitrogen gas (NO2 ) were consistently higher in urban than in rural areas, most non-essential metals were undetectable in the blood of great tits (P. major) living in urban and rural environments. Passive exposure (e.g., oral intake and inhalation from ambient environment) is not the only form of pollution that can affect birds. In fact, some urban birds actively seek out toxic substances, such as cigarette butts, to include in their nest. Previously, it has been shown that this reduces ectoparasite load of nestlings (Suárez-Rodríguez et al., 2012). Here the contributed paper by Suárez-Rodriguez et al. show genotoxic effects of cigarette butts on the incubating and caring parent of house finch (Carpodacus mexicanus) and house sparrow (P. domesticus) from Mexico City that led to an interesting trade-off between parasite repellence and DNA-damage. A number of papers in this issue investigate other markers of cellular stress and damage, specifically oxidative damages to protein and lipids, telomere length and gene expression of inflammatory genes between urban and rural passerines (Herrera-Duenas et al.; Isaksson et al.; Biard et al.; Capilla-Lasheras et al.). Results provided mixed evidence across species; sparrows (Passeridae) for instance, show overall more damage in relation to urbanization compared to tits (Paridae) (see also Salmón et al., 2018). For all these correlative studies the exact causal driver of the stress and damage experienced in urban birds could not be identified. In contrast, ALAN was experimentally manipulated by Welbers et al., who exposed breeding great tits to either white, green, red LED light or left dark as a control and investigated its effect on daily energy expenditure (DEE). The DEE of great tits exposed to white and green light during chick feeding was significantly lower than those exposed to the control treatment—a pattern that the authors regarded as an indirect consequence of the positive association between lights and insect abundance. This is an intriguing positive effect of an urban pollution source that may lead to balance out some of the more negative effects.

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Reproduction and Life History Understanding the ecological and evolutionary consequences of urbanization requires, in part, that we identify how urbanassociated factors impact fitness across phenotypes, populations, and species. Several articles reported that cities reduced breeding success via a range of pathways including clutch size, fledgling success and chick quality (Biard et al.; Capilla-Lasheras et al.; Charmantier et al.; Corsini et al.), though others found equivalent breeding performance and condition among rural and urban populations (Rodewald and Arcese). Although reduced productivity is often attributed to predation, this pattern may also reflect a life-history strategy characterized by urban birds adopting a slower pace of life compared to rural individuals (Sepp et al., 2018). In this special issue, Charmantier et al. provide counterevidence to a slow life in the city by showing that urban great tits have behavioral phenotypes linked to a fast-pace of life, although they produce smaller clutches. Thus, urban great tits in this study seemed to be constrained during reproduction rather than these differences being the result of a proper life-history strategy. One constraint might be related to lower overall access of natural high quality foods or a mismatch between timing of breeding and available food. Phenological shifts can also influence reproduction in urban populations. Due to the higher temperatures in the cities (i.e., heat island effect), cities often show phenological advances in spring with earlier bud burst and insect emergence. If birds are not able to respond to this environmental change the consequences for breeding can be detrimental (Visser and Both, 2005). Here, however, a study on great tits showed that urban birds advance their breeding which is in line with the presumed earlier phenology of a city (Charmantier et al.). In contrast, the breeding performance of the black sparrowhawk (Accipiter melanoleucus), but not the timing of breeding per se, showed different seasonal patterns in urban and rural areas, declining

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across the season in cities but improving in rural habitats (Rose et al.). The black sparrowhawks have relatively recently colonized the urban habitats of Cape Town, and thus the seemingly selective advantages of earlier breeding in the city may have not arisen yet. In the contributed paper by Fudickar et al. the underlying mechanism of advancing the timing of breeding is investigated in male dark-eyed juncos (Junco hyemalis). By comparing sedentary urban birds with migratory individuals, they show that the earlier timing of breeding in the urban population is facilitated by earlier increase in upstream baseline activity of the hypothalamic-pituitary-gonadal (HPG) axis and an earlier release of gonodal suppression affecting testosterone production (Fudickar et al.).

urban-dwelling birds. In a first study by Stiles et al. it is shown that cowbirds together with humans and feral dogs have a direct negative effect on several native bird species. The second paper by Ladin et al. uses a modeling approach to test how the negative trend of cowbirds on the population growth of the forest umbrella species, the wood trush (Hylocichla mustelina) could be averted. Their models suggest that removing cowbirds along with reforestation could stop the decline of the wood thrush.

CONCLUSION This special issue highlights a few overarching themes among studies of urban birds, and the large number of articles reflect the great interest and timeliness of the topic. Many of the contributed studies involve a comparison of two single populations - one rural and one urban. Though comparative studies are certainly useful in identifying possible mechanisms and patterns, unreplicated designs provide limited inference about mechanisms and the variability in outcomes, as well as the underlying drivers of the effects and variabilities. Identifying drivers is challenging given that cities differ from rural habitats in a many respects, just as cities differ from each other. Although experiments are typically difficult to conduct, we would like to stress the need to envisage manipulations whenever possible to do so. Taken together, the articles in this special issue highlight the wide variety of responses—both positive and negative— to urbanization and the challenge that biologists face in trying to generalize. The differential resilience of species in the face of urbanization changes community composition and affects interspecific relationships, thus fuelling new feedback mechanisms that add complexity to the final picture. Thus, the field of urban avian ecology have many challenges ahead and intriguing new venues for future research.

Biodiversity and Conservation in Urban Habitats Urbanization stands out as one of the most important threats to biodiversity on our planet. Several articles in the special issue examined this threat explicitly, and most contributors at least alluded to the conservation implications of their findings. Focusing on the Seattle Metropolitan area in the US, Shryock et al. showed that relationships between avian species richness and vegetative productivity, as measured by the Normalized Difference Vegetation Index (NDVI), are mediated by urban development. Specifically, they found that species richness declined with NDVI within areas undergoing active development, but less so in areas of established housing development and forested reserves. Urbanization also may affect the functional diversity of avian communities in ways that might have ecosystem-level consequences. Oliveira Hagen et al. compared avian functional diversity of 25 urban areas using 27 traits from more than 500 species. Interestingly, they found that avian functional diversity is higher in cities than in seminatural habitats, a pattern attributed to higher functional habitat diversity in cities compared to single habitats within more natural landscapes. Both studies suggest that city planners can moderate the effect of urbanization on the avian community by maintaining or restoring diverse and heterogeneous native vegetation. Habitat management also may facilitate species movements and dispersal, which can be constrained in urban systems even for highly mobile organisms, as shown by Evans et al. The altered habitat structure and the simplified avifauna composition of urban habitats change the life conditions of many species and the interspecific interactions among them, something that can strongly benefit some species whereas other species will suffer. A species that has benefited from these modifications is the North American brown-headed cowbird (Molothrus ater). This is a brood parasitic species that has increased in population densities with deforestation. Two contributed papers raise the concern of the increased abundance of this species for other

AUTHOR CONTRIBUTIONS All three authors have made substantial work with the topic issue and with the present editorial piece.

FUNDING DG was funded while editing this special issue by a research grant from the Ministerio de Economía y Competitividad (CGL201455577R).

ACKNOWLEDGMENTS We wish to thank all the contributing authors for their efforts to make this issue a success. We also wish to thank the team working for Frontiers in Ecology & Evolution for their patience and their support.

REFERENCES

drivers. Proc. R. Soc. B 281:20133330. doi: 10.1098/rspb.2013 .3330 Elmqvist, T., Fragkias, M., Goodness, J., Güneralp, B., Marcotullio, P. J., McDonald, R. I., et al. (2013). Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities. New York, NY: Springer.

Aronson, M. F., La Sorte, F. A., Nilon, C. H., Katti, M., Goddard, M. A., Lepczyk, C. A., et al. (2014). A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic

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Gil, D., and Brumm, H. (2014). “Acoustic communication in the urban environment: patterns, mechanisms, and potential consequences of avian song adjustments,” in Avian Urban Ecology, eds D. Gil and H. Brumm (Oxford: Oxford University Press), 69–83. Marzluff, J. M., and Ewing, K. (2001). Restoration of fragmented landscapes for the conservation of birds: a general framework and specific recommendations for urbanizing landscapes. Restorat. Ecol. 9, 280–292. doi: 10.1046/j.1526-100x.2001.009003280.x McKinney, M. L. (2002). Urbanization, biodiversity, and conservation. Bioscience 52, 883–890. doi: 10.1641/0006-3568(2002)052[0883:UBAC]2.0.CO;2 Salmón, P., Stroh, E., Herrera-Dueñas, A., von Post, M., and Isaksson, C. (2018) Oxidative stress in birds along a NOx and urbanisation gradient: an interspecific approach. Sci. Total Environ. 622–623, 635–643. doi: 10.1016/j.scitotenv.2017.11.354 Sepp, T., McGraw, K. J., Kaasik, A., and Giraudeau, M. (2018). A review of urban impacts on avian life-history evolution: does city living lead to slower pace of life? Glob. Chang. Biol. 24, 1452–1469. doi: 10.1111/gcb.13969 Shochat, E., Lerman, S. B., Anderies, J. M., Warren, P. S., Faeth, S. H., and Nilon, C. H. (2010). Invasion, competition, and biodiversity loss in urban ecosystems. BioScience 60, 199–208. doi: 10.1525/bio.2010.60.3.6 Suárez-Rodríguez, M., López-Rull, I., and Garcia, C. M. (2012). Incorporation of cigarette butts into nests reduces nest ectoparasite load in urban birds: new ingredients for an old recipe? Biol. Lett. 9:20120931. doi: 10.1098/rsbl.2012.0931

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Thomas, G. H., Orme, C. D. L., Davies, R. G., Olson, V. A., Bennett, P. M., Gaston, K. J., et al. (2008). Regional variation in the historical components of global avian species richness. Glob. Ecol. Biogeogr. 17, 340–351. doi: 10.1111/j.1466-8238.2008.00384.x United Nations (2014). World Urbanization Prospects: The 2014 Revision (United Nations, Department of Economic and Social Affairs, Population Division, New York). Available online at: www.un.org/en/development/desa/publications/ 2014-revision-world-urbanization-prospects.html Visser, M. E., and Both, C. (2005). Shifts in phenology due to global climate change: the need for a yardstick. Proc. R. Soc. B 272, 2561–2569. doi: 10.1098/rspb.2005.3356 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 © 2018 Isaksson, Rodewald and Gil. 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) and the copyright owner 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.

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ORIGINAL RESEARCH published: 26 June 2017 doi: 10.3389/fevo.2017.00066

Rural-Urban Differences in Escape Behavior of European Birds across a Latitudinal Gradient Diogo S. M. Samia 1*, Daniel T. Blumstein 2 , Mario Díaz 3 , Tomas Grim 4 , Juan Diego Ibáñez-Álamo 5, 6 , Jukka Jokimäki 7 , Kunter Tätte 8 , Gábor Markó 9, 10, 11 , Piotr Tryjanowski 12 and Anders Pape Møller 13 1

Department of Ecology, Bioscience Institute, University of São Paulo, São Paulo, Brazil, 2 Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, United States, 3 Department of Biogeography and Global Change, BGC-Museo Nacional de Ciencias Naturales-CSIC, Madrid, Spain, 4 Department of Zoology and Laboratory of Ornithology, Palacky University, Olomouc, Czechia, 5 Behavioral and Physiological Ecology Group, Centre for Ecological and Evolutionary Studies, University of Groningen, Groningen, Netherlands, 6 Department of Wetland Ecology, Estación Biológica de Doñana, CSIC, Sevilla, Spain, 7 Nature Inventory and EIA-Services, Arctic Centre, University of Lapland, Rovaniemi, Finland, 8 Department of Zoology, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia, 9 Ecology Research Group, Hungarian Academy of Sciences, Hungarian Natural History Museum, Eötvös Loránd University, Budapest, Hungary, 10 Behavioural Ecology Group, Department of Systematics, Zoology and Ecology, Eötvös Loránd University, Budapest, Hungary, 11 Department of Plant Pathology, Szent István University, Budapest, Hungary, 12 Institute of Zoology, Poznan´ University of Life Sciences, Poznan, Poland, 13 Ecologie Systématique Evolution, Centre National de la Recherche Scientifique, Université Paris-Sud, AgroParisTech, Université Saclay, Orsay, France Edited by: Caroline Isaksson, Lund University, Sweden Reviewed by: Ximena J. Nelson, University of Canterbury, New Zealand Michael Reichert, University College Cork, Ireland *Correspondence: Diogo S. M. Samia [email protected] Specialty section: This article was submitted to Behavioral and Evolutionary Ecology, a section of the journal Frontiers in Ecology and Evolution Received: 17 November 2016 Accepted: 02 June 2017 Published: 26 June 2017 Citation: Samia DSM, Blumstein DT, Díaz M, Grim T, Ibáñez-Álamo JD, Jokimäki J, Tätte K, Markó G, Tryjanowski P and Møller AP (2017) Rural-Urban Differences in Escape Behavior of European Birds across a Latitudinal Gradient. Front. Ecol. Evol. 5:66. doi: 10.3389/fevo.2017.00066

Behavioral adjustment is a key factor that facilitates species’ coexistence with humans in a rapidly urbanizing world. Because urban animals often experience reduced predation risk compared to their rural counterparts, and because escape behavior is energetically costly, we expect that urban environments will select for increased tolerance to humans. Many studies have supported this expectation by demonstrating that urban birds have reduced flight initiation distance (FID = predator-prey distance when escape by the prey begins) than rural birds. Here, we advanced this approach and, for the first time, assessed how 32 species of birds, found in 92 paired urban-rural populations, along a 3,900 km latitudinal gradient across Europe, changed their predation risk assessment and escape strategy as a function of living in urban areas. We found that urban birds took longer than rural birds to be alerted to human approaches, and urban birds tolerated closer human approach than rural birds. While both rural and urban populations took longer to become aware of an approaching human as latitude increased, this behavioral change with latitude is more intense in urban birds (for a given unit of latitude, urban birds increased their pre-detection distance more than rural birds). We also found that as mean alert distance was shorter, urban birds escaped more quickly from approaching humans, but there was no such a relationship in rural populations. Although, both rural and urban populations tended to escape more quickly as latitude increased, urban birds delayed their escape more at low latitudes when compared with rural birds. These results suggest that urban birds in Europe live under lower predation risk than their rural counterparts. Furthermore, the

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patterns found in our study indicate that birds prioritize the reduction of on-going monitoring costs when predation risk is low. We conclude that splitting escape variables into constituent components may provide additional and complementary information on the underlying causes of escape. This new approach is essential for understanding, predicting, and managing wildlife in a rapidly urbanizing world. Keywords: alert distance, antipredator behavior, buffer distance, flight initiation distance, phi index, pre-detection distance, rural-urban difference, urbanization

INTRODUCTION

Alexander, 1974), birds in larger flocks might tolerate more human disturbance than birds alone or in smaller flocks (Dill and Ydenberg, 1987; Reimers et al., 2006), although current evidence does not show a substantial effect of flock size on tolerance to humans of urban birds (Møller, 2015; Samia et al., 2015c). Some studies also suggest that increased tolerance to humans is a function of disturbance frequency (Levey et al., 2009; Engelhardt and Weladji, 2011; McGiffin et al., 2013), which suggests that either the individual habituation process (i.e., phenotypic plasticity; Blumstein, 2016), differential sorting according to individual’s personalities (Carrete and Tella, 2013), or strong selection against fearful individuals (Carrete et al., 2016) may be involved in generating tolerance. Among many recognized correlates of human tolerance, latitude has recently been identified as a potentially important correlate of tolerance in rural and urban environments. Latitude is used as a proxy for predation risk since predator density tends to decrease with increasing latitude (Laurila et al., 2008; Schemske et al., 2009). In a study of 159 species of European birds, FID decreased as latitude increased, in parallel with a clinal reduction in raptor abundance (raptors are key avian predators; Díaz et al., 2013). Other interspecific studies of FID across latitudinal gradients—or among continents—have recently revealed novel patterns in escape behavior (Møller et al., 2014, 2016; Samia et al., 2015b). However, these large scale studies have not considered a main predictor of FID, the prey’s alert distance (AD; Figure 1A), which is the predator-prey distance when an individual prey becomes aware of and begins to monitor the predator (Cooper and Blumstein, 2015). Along with body mass, AD explains most of variation in FID in taxa studied to date (Samia et al., 2013; Cooper et al., 2015). A strong relationship between FID and AD implies that they both reflect the general level of fear that individuals experience while both scanning for predators and fleeing following a predator’s approach. Variation in the strength of this relationship will however reflect different escape tactics in response to varying levels of predation risk (Cooper and Blumstein, 2014). While a robust body of evidence has shown that FID is generally smaller in urban than rural places (review in Samia et al., 2015c), latitudinal studies of rural-urban differences in FID that account for variation in AD should permit us, for the first time, to assess how birds modify their predation risk assessment and escape strategy (in addition to modifying their FID) when living in cities. These anti-predatory decisions can be inferred from two variables. First, the prey’s pre-detection distance (Figure 1A), which is a measure of how long a prey takes to become aware of an approaching human [i.e., the

Urbanization is one of the main drivers of the current global biodiversity crisis (Foley et al., 2005; Grimm et al., 2008; McDonald, 2008). More than half of the world’s humans live in cities and this proportion is expected to increase over the next decades (United Nations, 2014). In addition, the urban areas are increasing at an even greater rate than the urban population (United Nations, 2014). Given a rapidly urbanizing world, we need to better understand the traits that permit species to coexist with humans, which are often perceived as predators by animals (Frid and Dill, 2002). In this sense, behavioral plasticity is one of the key elements that permits species to coexist with humans (Sol et al., 2013). For instance, because escape from nonthreatening humans is costly (Ydenberg and Dill, 1986; Cooper and Frederick, 2007; Samia et al., 2016), and because humans in cities rarely hunt or otherwise intentionally kill animals (Berger, 2007), urban prey are expected to respond to humans by reducing costly anti-predator behavior. In addition, humans in urban areas often displace predators (Møller, 2012), creating human shields where prey are relatively safe (Berger, 2007; Ibáñez-Álamo et al., 2012; Møller, 2012). If animals in urban areas experience reduced risk of predation by their native predators, this should select for reduced anti-predator responses. In line with these expectations, a growing body of literature has shown that animals generally have relaxed anti-predator behavior in urban environments (e.g., Møller and Ibáñez-Álamo, 2012). In particular, individuals in urban populations generally tolerate more human disturbance by reducing their flight initiation distances (FID = predator-prey distance when escape of the prey begins; Figure 1A) compared to rural populations (Samia et al., 2015c). Despite this apparently ubiquitous pattern, the drivers of the rural-urban difference in human tolerance have just begun to be explored. Recent studies indicate an important role of a species’ body size on urbanization with larger birds being more tolerant (Samia et al., 2015c) and having increased survivorship in cities (Brown and Graham, 2015). Brain size also plays a key role: successful urban invaders and exploiters are characterized by having larger brains (Sol et al., 2005, 2013; Maklakov et al., 2011) and several studies have shown a negative correlation between brain mass and FID (Møller and Erritzøe, 2014; Samia et al., 2015a; but see Carrete and Tella, 2011). Studies also suggest that big-brained species are more innovative and flexible in their ability to deal with the novel challenges imposed by urban environments, such as the chronic disturbance caused by human presence. Because predation risk is expected to decrease with group size (Hamilton, 1971; Pulliam, 1973;

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FIGURE 1 | (A) Illustration of the five anti-predator behaviors considered in the present study. Starting distance (SD) is measured as the distance at which an approaching human begins to approach a prey. Alert distance (AD) is measured as the distance at which prey responds by orienting toward the approaching human. Flight initiation distance (FID) is measured as the human-prey distance when the prey begins to flee. Pre-detection distance is a measure of how long a prey takes to become aware of an approaching human (i.e., the difference between SD and AD). Buffer distance is an estimate of tolerance calculated as the distance at which a prey fled after being alerted by an approaching human (i.e., the difference between AD and FID). (B) Some hypothetical relationships between SD, AD, and FID as a function of short, medium, or long pre-detection distances and short, medium, or long buffer distances.

distance between starting distance (SD) and AD; where SD is the distance to the bird when approach begins]. Second, the prey’s buffer distance (Figure 1A), which is a more rigorous estimate of tolerance estimated as the distance at which a prey fled “after being alerted by an approaching human” (i.e., the distance between AD and FID; Cooper and Blumstein, 2015). Using this approach, we can explore whether the previously identified reduction in urban birds’ FID is traded-off with an increase in

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other costly anti-predatory behaviors. For instance, a shorter FID associated with a larger AD might imply increased vigilance costs since the prey’s attention cannot simultaneously be focused on other fitness-enhancing activities, such as foraging. By contrast, a shorter FID associated with a shorter AD would result in a shorter buffer distance, implying that birds escape sooner after detecting an approaching human. If this reduction in tolerance is associated with a longer pre-detection distance, it may illustrate a strategy by

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most potentially confounding environmental characteristics (e.g., weather, altitude, soil), yet philopatry and assortative mating may decrease, or even prevent, movements between urban and rural study sites (Møller, 2009, 2015).

which birds reduce their monitoring costs. Figure 1B illustrates these and other hypothesized scenarios. We studied the difference in escape decisions of urban and adjacent rural populations of 32 bird species along a large latitudinal gradient across Europe (3,900 km). We investigated whether rural and urban birds differ in their awareness and tolerance along this gradient by estimating their pre-detection distance and buffer distance. We also studied the factors potentially related to rural-urban differences in these antipredatory indicators by exploring seven potential correlates of pre-detection distance and buffer distance: latitude, species’ body mass, species’ brain mass, number of humans living in the city, rural-urban difference in mean SD (hereafter, 1SD), ruralurban difference in mean AD (hereafter, 1AD), and ruralurban difference in mean flock size (hereafter, 1flock size). Our hypotheses concerning the effect of these predictor variables are summarized in Table 1.

Additional Data Information about latitude and human population size of the cities were extracted from Wikipedia (https://www.wikipedia. org). Body mass information was extracted from Cramp and Perrins (1977–1994). Brain mass information was extracted from Møller and Erritzøe (2014). As explained above, mean flock size was estimated by field observations of individual birds.

Calculating the Phi Index The relationship between SD, AD, and FID is constrained by an envelope. This means that AD can only assume values ≤ SD (according to methodology applied to study escape behavior, an individual is never experimentally approached if already alerted by an approaching observer; see Section Field Data Collection) and FID can only assume values ≤ AD (a prey individual cannot escape from a predator before it has detected it). The phi index (8) is a goodness-of-fit metric that was originally developed to provide estimates, unbiased by this envelope relationship, of how close FID is to AD, which corresponds to our definition of buffer distance (Samia and Blumstein, 2014). Formally,

MATERIALS AND METHODS Field Data Collection We collected data from April to September 2015 (breeding season) using a standard protocol (Blumstein, 2006). Data were collected in urban and adjacent rural areas of 10 cities from eight European countries: Czech Republic, Denmark, Estonia, Finland, France, Hungary, Poland, and Spain (data set available in Supplementary Material 1). Observers used binoculars to identify birds that were foraging or engaged in “relaxed behavior” (i.e., roosting or preening). Highly vigilant or obviously alarmed individuals, or individuals near their nests were not approached. Subsequently, each individual bird was approached in a straight line by a human walking at a constant speed (0.5 m/s). The distance at which the experimenter started to approach the focal bird was recorded as SD, while the distance at which the bird first oriented toward the approaching human and stopped its previous activity was recorded as AD, and the distance at which the animal began to flee was recorded as FID. The number of birds within a radius of 10 m around the focal individual was recorded as flock size. We avoided sampling the same individual twice by moving to another site immediately after samples were taken. If the same general area was visited, only individuals of different species, sex or age than those sampled before were tested. A modest degree of re-sampling subjects, however, has been shown to not influence the results of studies like this (Runyan and Blumstein, 2004). We used a paired study design with one urban and one rural area in each study location. The distance between each pair of urban and rural site varied between 1 and 20 km. All urban study sites included both urban centers, characterized by areas with multi-story buildings, as well as suburban areas, characterized by areas with single-family houses. Rural areas were dominated by open farmland with scattered houses. Definitions of urban (at least 50% of built-up area, building density >10 buildings/ha and, a residential human density >10 humans/ha) and rural habitats (5–20% built-up areas, a building density 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. We simultaneously and repeatedly quantified an individual’s behavior and environment. That is, we repeatedly exposed the same individual to territorial intrusion experiments to measure aggressiveness (Araya-Ajoy and Dingemanse, 2014) and to flight

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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 15m radius around the nest box. Details of the experimental set up, and assayed behaviors, are provided in reference ArayaAjoy 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 (ArayaAjoy 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

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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).

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 (¯xj ) for each of the two environmental variables (PC1 and PC2) as well as (2) each observation’s deviation of these individual average values (xij − x¯ j ) 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 − x¯ j ) and each individual’s average value (¯xj ) 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.

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

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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).

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Phenotypic Plasticity and Non-random Distributions of Behavioral Types

TABLE 1 | Sources of variation in boldness and aggressiveness.

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).

Fixed effects Intercept

Aggressiveness (Minimal approach distance)

Estimate

Estimate

95% CIs

−0.57

−0.93, −0.19

95% CIs

0.05

−0.22, 0.34

PC1 Within individuals

−0.14

−0.34, 0.06

−0.06

−0.23, 0.13

Among individuals

0.07

−0.08, 0.22

0.13

−0.02, 0.27

1 (Among—within)

0.19

−0.01, 0.42

0.19

−0.001, 0.38

PC2 Within individuals

−0.12

−0.41, 0.16

0.12

−0.23, 0.46

Among individuals

0.17

0.04, 0.30

−0.05

−0.22, 0.10

1 (Among - within)

0.28

0.03, 0.56

−0.17

−0.55, 0.17

Start distance

−0.11

−0.22, 0.01

n.a.

Year

−0.13

−0.57, 0.30

0.12

−0.15, 0.38

n.a.

SEQUENCE (FIRST TEST AS REFERENCE) Second test

0.25

−0.04, 0.54

−0.05

−0.37, 0.23

Third test

0.23

−0.07, 0.56

−0.08

−0.4, 0.21

−0.09

−0.26, 0.07

0.04

−0.09, 0.17

95% CIs

σ2

95% CIs

Time of day Random effects

σ2

Individual

0.013

0.01, 0.02

0.017

Observer

0.20

0.12, 0.42

0.04

0.02, 0.08

Residual

0.65

0.56, 0.84

0.91

0.80, 1.14

0.01, 0.02

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

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.

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Boldness (Flight initiation distance)

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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).

FIGURE 2 | Among-individual variation in flight initiation distance (FID) as a function of the compound environmental gradient PC2 that combines number of pedestrians and cars (more positive loadings represent more pedestrians 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

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AUTHOR CONTRIBUTIONS

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.

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 (LudwigMaximilians University Munich) and the research group Evolutionary Ecology of Variation (Max Planck Institute for Ornithology), as well as all citizen scientists for input, discussion,

SUPPLEMENTARY MATERIAL

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

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ORIGINAL RESEARCH published: 21 June 2017 doi: 10.3389/fevo.2017.00067

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. Levenhagen 2 , Ryan Wardle 1 , 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 protected] Clinton D. Francis [email protected] 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

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INTRODUCTION

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 (FernándezJuricic 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

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

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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; FernándezJuricic 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.

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FIGURE 1 | Predicted responses for three foraging guilds of songbirds. We expect ground foragers to have shorter FIDs in noise (dashed line), canopy 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

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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.

(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.

Data Analysis

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,

RESULTS

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., FernándezJuricic 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

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

Scientific name

Sample size

GROUND American Crow

Corvus brachyrhynchos

12

California Scrub Jay

Aphelocoma californica

13

California Towhee

Melozone crissalis

12

Dark-eyed Junco

Junco hyemalis

23

Golden-crowned Sparrow Zonotrichia atricapilla

7

White-crowned Sparrow Zonotrichia leuchophrys

38

Pacific Wren

Troglodytes pacificus

12

Wilson’s Warbler

Cardellina pusilla

9

Yellow Warbler

Setophaga petechia

31

Black Phoebe

Sayornis nigricans

19

Dusky Flycatcher

Empidonax oberholseri

15

Pacific-slope Flycatcher

Empidonax difficilis

5

CANOPY

FLYCATCHING

Total

54

12 species

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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

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

TABLE 2 | This model selection table reports all the models within 2.00 1AICc in addition to the null model (intercept only) for each foraging guild. Foraging Guild Model

K df AICc 1AICc weight

GROUND dB + Species

3 9

10.6

0.00

0.139

dB + Diet + Species

4 9

10.6

0.00

0.139

Diet

2 4

10.9

0.31

0.119

dB + Diet

3 5

11.4

0.77

0.095

dB + Habitat + Species

4 10 11.8

1.25

0.075

dB + Diet + Habitat + Species

5 10 11.8

1.25

0.075

Null (intercept only)

1 3

2.00

0.051

dB + Species + dB*Species

12.6

CANOPY 4 8 −37.5

0.00

0.389

dB + Species + Wind speed + 5 9 −36.5 dB*Species

1.06

0.229

Null (intercept only)

1 3

38.42

0.000

dB + Species + dB*Species

5 8 −37.9

0.00

0.443

Null (intercept only)

1 3 −29.6

8.28

0.007

0.9

FLYCATCHER

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.

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FIGURE 2 | (A) illustrates mean adjusted FID and standard error for each species in the ground foraging guild. Species depicted are American crow (AMCR), California towhee (CALT), California scrub jay (CASJ), dark-eyed junco (DEJU), golden-crowned sparrow (GCSP), and white-crowned sparrow (WCSP). (B–H) illustrate 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).

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

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

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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 [200 ha) forest or meadow, with little anthropogenic effects. 2. Rural area: Landscape dominated by anthropogenic effects, such as agriculture (pasture, crop field, orchard, farmland), or very intensive forestry (clear-cuts), with little housing (1 at each time step and dividing by the total number of sites. We estimated population trends (mean % annual change) for each model scenario using the equation: r = log

(4)

Nit+1 Nit

(5)

where Nit+1 and Nit are the total number of birds per site i (from Equation 4) at time step (t + 1) and t, respectively. To estimate metapopulation annual growth rate (λ) for each model scenario, we summed the number of birds across 21 sites at each time step (Equation 4), and divided the total number of birds in our study area at time step t + 1 (N t+1 ) by the total number of birds

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Nf No



×

100 T

(6)

where Nf is the number of birds at the final time step, No is the number of birds at the initial time step (to ), and T is the total number of years the model is run for. We estimated mean annual site occupancy by dividing the number of occupied sites per year (defined as having at least two individuals) by the number of years. After testing simulation-derived demographic data for departures from normality with a Shapiro–Wilk test, we used one-way analysis of variance (ANOVA) with Tukey’s post-hoc tests to test for differences in population trend, mean annual growth rate (λ), and a multiple pair-wise comparison test (Marascuilo, 1966) for differences in site occupancy among the four model scenarios.

We computed the annual growth rate lambda (λi ) within each site i with the equation: λi =



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MODEL SCENARIO DESCRIPTIONS

described in the Reforest scenario above). Under this model scenario, we used parameters estimated for adult survival, fecundity, and recruitment following the methods described above for the Cowbird removal scenario, and immigration probabilities were estimated as a function of reduced proportion of impervious surface (as stated above).

Scenario (A) Null Null scenario parameters at each time step were derived independently for each site by drawing randomly from uniform distributions constrained by upper and lower 95% CI around the means of IPM annual estimates from 2002 to 2012 in EW for adult survival, recruitment, and fecundity (i.e., number of fledglings per female). Similarly, we estimated the proportion of immigrants for the metapopulation at each time step by randomly drawing from a uniform distribution with a mean and SD taken from IPM estimates. To model current observed conditions within the study area, we limited breeding (i.e., set fecundity = 0), and constrained immigration rates (i.e., 1% of total birds per site) to occupy currently unoccupied sites at (to ), while allowing birds to immigrate at estimated rates into currently occupied sites and to reproduce at currently estimated levels of fecundity.

RESULTS Occupancy Model Results The impervious surface model had the most support (Akaike weight = 0.62) as indicated by AIC model comparison (Appendix S1). Site occupancy was negatively related to the proportion of impervious surface within a 500 m buffer around forest sites (Figure 2A), and differed from the null model according to the likelihood ratio test (Kent, 1982) (χ 2 = 11.6, df = 1, P < 0.01). From significant correlation between impervious surface within a 500 m buffer around sites and wood thrush site occupancy, we derived the equation:

Scenario (B) Reforest (Replace Impervious Surface)


logit y = −1.31x + 1.01

We modeled the effect of replacing the proportion of impervious surface with forest habitat on wood thrush metapopulation dynamics (based on results from predictive site occupancy models) by allowing 90% of total individuals at each time step to enter into and effectively breed (i.e., no reduction of fecundity) in previously unoccupied sites. These adjustments in the likelihood of site occupancy follow from concomitant reductions in impervious surface from ca. 0.8 (Null scenario) to around 0.05 using a predictive site occupancy model fit using the R package “unmarked” (Fiske and Chandler, 2011). Adult survival, recruitment, and fecundity were all estimated using the methods described above for the Null scenario.

representing the predicted probability of patch occupancy (y) given the proportion of impervious surface (x) within a 500 m buffer around a forest patch (Figure 2A).

Regression Model Results Adult wood thrush survival from 1974 to 2012 was negatively related to the proportion of parasitized nests (χ 2 = 80.1, df = 3, P < 0.01; Figure 2B). We inferred the following equation from the beta regression model of the negative relationship between adult wood thrush survival and the proportion of cowbird parasitized nests:

Scenario (C) Cowbird Removal


logit y = −0.24x + 0.067

We based our modeled reduction in cowbird parasitism pressure on beta regression models by increasing the probability of adult survival corresponding to a reduction in the proportion of cowbird nests parasitized from 0.65 (current observed parasitism rate) to range between (0.25 and 0.05). We increased estimated mean fecundity (i.e., number of fledglings per female) from 0.97 (current observed fecundity) to 1.19 using a linear model representing a predictive relationship between fecundity and the mean number of cowbird eggs laid per nest. We additionally increased mean recruitment based on the linear model relationship between the proportion of cowbird parasitized nests and annual number of recruited individuals. In this model scenario, immigration probabilities and fecundity in initially unoccupied sites were the same as in the Null scenario.

(8)

where y is annual adult survival and x is the proportion of cowbird parasitized wood thrush nests (Figure 2B), to estimate a mean adult survival (0.51 ± 0.01) corresponding to a simulated reduction in the proportion of parasitized nests by 62% (Table 2). Wood thrush annual fecundity was negatively related to mean cowbird eggs laid per nest (F = 9.72, df = 37, P < 0.01; Figure 2C). We used linear regression in the “stats” R package and found that wood thrush annual fecundity (i.e., number of offspring produced per female) was negatively related to mean cowbird eggs laid per nest (F = 9.72, df = 37, P < 0.01). With the governing linear regression equation: y = −0.25x + 1.25

Scenario (D) Reforest and Cowbird Removal

(9)

where y is predicted wood thrush fecundity and x is the mean cowbird eggs laid per nest (Figure 2C), we estimated a mean fecundity (1.19 ± 0.04) given the simulated reduction in mean number of cowbird eggs per nest from 1.18 to 0.5 (Table 2). The mean number of annually recruited wood thrushes was also negatively related to the proportion of parasitized nests (F =

We modeled the simultaneous effects of reducing impervious surface and cowbird parasitism pressure by using regressionbased models to estimate according increases in immigration probabilities to all sites, and allowed breeding in all sites (as

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(7)

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FIGURE 2 | Regression models showing relationships between (A) proportion of impervious surface (within 500 m buffer around sites) and site occupancy, (B) proportion of cowbird-parasitized nests and adult wood thrush survival, (C) mean cowbird eggs laid per nest and wood thrush fecundity (mean fledglings per female), and (D) proportion of cowbird parasitized nests and mean number of recruited individuals per year.

5.61, df = 37, P < 0.05), resulting in the following linear model equation: y = −1.37x + 1.57

TABLE 3 | Example transition matrix containing probabilities (Θ) of dispersal movement among 21 discrete forest sites, and functional emigration (ε) from the study area.

(10)

where y is the number of recruited birds and x is proportion of parasitized nests (Figure 2D). We simulated increases in annual wood thrush recruitment rates by randomly drawing values from a uniform distribution with mean and SD of 0.085 ± 0.01 (Table 2).

Transition Matrix We estimated transition probabilities among sites inferred from mean relatedness of birds among sites using microsatellite data from 177 genotyped adult wood thrushes sampled from 13 discrete sites (see Appendix S2). The final normalized transition matrix contained estimated transition probabilities from the 13 sampled sites, transition probabilities of 0.008 and 0.85 for leaving and remaining in a site, respectively, for the 8 initially unoccupied sites, and a 22nd site representing emigration out of the study area (see example transition matrix in Table 3).

1

2

3

1

Θ1, 1

Θ2, 1

Θ3, 1

···

Θ21, 1

ε1

2

Θ1, 2

Θ2, 2

Θ3, 2

···

Θ21, 2

ε2

3 . . .

Θ1,3 . . .

Θ2, 3 . . .

Θ3, 3 . . .

···

Θ21, 3 . . .

ε3 . . .

21

Θ1, 21

Θ2, 21

Θ3, 21

···

Θ21, 21

ε21

ε

ε1

ε2

ε3

···

ε21

ε22

···

..

.

21

ε

Reforest scenario (0.94 ± 0.01), and both were lower than the combined Reforest and Cowbird removal scenario (1.0 ± 0.01; Table 4, Figure 3). Annual growth rates per site (λi ) (mean ± SD) differed among model scenarios (F = 46.3, df = 3, P < 0.001). Mean growth rates per site for the Null scenario (0.92 ± 0.03) were lower than all other scenarios, and mean growth rate per site for the Cowbird removal scenario (0.99 ± 0.04) was lower than both the Reforest scenario (1.02 ± 0.06), and the combined Reforest and Cowbird removal scenario (1.05 ± 0.08; Table 4). Additionally, we found that the probability that site-level mean annual growth rates (λi ) were >1 over the 30-year simulation period was low under the Null scenario (0.06 ± 0.05), and higher probabilities were observed for Cowbird removal, Reforest, and combined

Metapopulation Simulation Results Metapopulation-wide annual growth rates (λ) (mean ± SD) differed among all model scenarios (F = 567.1, df = 3, P < 0.001; Table 4), and were lowest for the Null scenario (0.88 ± 0.01) after 30 years (Table 4, Figure 3). The annual growth rate for the Cowbird removal scenario (0.92 ± 0.02) was lower than in the

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Site

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TABLE 4 | Model-estimated means (SD) for metapopulation lambda (λ), lambda per site (λi ), probability λ ≥ 1, annual population trend (%), and the proportion of occupied sites for four model scenarios. Models Model derived estimates

Null

Reforest

Cowbird removal

Reforest and Cowbird removal

Metapopulation lambda (λ)

0.88 (0.01)

0.94 (0.01)

0.92 (0.02)

1.00 (0.01)

Lambda per site (λi )

0.92 (0.03)

1.02 (0.06)

0.99 (0.04)

1.05 (0.08)

Prob(λ ≥ 1)

0.06 (0.05)

0.52 (0.08)

0.33 (0.26)

0.89 (0.2)

Metapopulation trend

−12.8 (1.15)

−5.93 (0.30)

−7.92 (0.68)

−0.32 (0.21)

Population trend per site

−11.7 (1.84)

−3.79 (3.98)

−5.68 (2.96)

1.92 (3.90)

0.42 (0.36)

0.98 (0.17)

0.67 (0.36)

0.98 (0.17)

Proportion of occupied sites

FIGURE 3 | Wood thrush population size (N) estimates (mean and 95% CI) over a 30-year period under four model scenarios: (A) Null (light gray), (B) Reforest (dark gray), (C) Cowbird removal (gray), and (D) Reforest and Cowbird removal (black).

per site (λi ) is shown in Figure 4. Mean annual growth rate (λi ) was lowest ( 1) between 0.02 and 0.10 impervious surface over the range of cowbird index values (Figure 4).

Reforest and Cowbird removal scenarios (0.33 ± 0.26, 0.52 ± 0.08, and 0.89 ± 0.2, respectively; Table 4, Figure 3). Annual population trend estimates (mean ± SE) differed among the four model scenarios (F = 61.1, df = 3, P < 0.001; see Appendix S3). Trend estimates were lower for the Null scenario (−11.7 ± 0.40) compared to all other scenarios, and both Cowbird removal (−5.68 ± 0.65) and Reforest (−3.79 ± 0.87) scenarios were lower than the combined Reforest and Cowbird removal scenario (1.92 ± 0.85; Table 4). The proportion of occupied sites were similar under the Reforest scenario and the combined Reforest and Cowbird removal scenario (0.98 ± 0.17), and both were greater than the Null scenario (0.42 ± 0.36) and the Cowbird removal scenario (0.67 ± 0.36; χ 2 , df = 3, 7.815), which did not differ (Table 4). The relationship of combinations of proportions of impervious surface (ranging from 0 to 0.35) and cowbird index (ranging between 0 and 0.5) to mean annual growth rate

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DISCUSSION Simulated conservation scenarios had a positive effect on wood thrush population growth rates. Our results suggest that coincident replacement of impervious surface with forest habitat and reducing cowbird parasitism rates would have the greatest benefit for wood thrush populations. Although both conservation measures (Reforest scenario and Cowbird removal scenario) had positive effects on wood thrush population growth, they were

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which supports our comparison of population-trend estimates under modeled conservation scenarios. Given differences among growth rates throughout the breeding range (Sauer et al., 2012), conservation efforts for wood thrushes should address both regional and local factors that likely drive source-sink dynamics (Keagy et al., 2005). Negative effects of urbanization and impervious surface on wood thrushes and likely other forest-breeding birds may be a key driver of site occupancy (Longcore and Jones, 1969; Roth and Johnson, 1993, this study). Our findings indicate that reducing the proportion of impervious surface from 0.60 (in Null) to 0.05 (Scenarios B and D), would increase site occupancy by 57% and the probability of (λ ≥ 1) by 88.5%. We found an upper threshold of 0.17 for the proportion of impervious surface where mean growth rate per site was positive (λi ≥ 1). Despite known challenges regarding habitat loss and increasing rates of impervious surface in urban areas (Nowak and Greenfield, 2012), reforestation and “greening” of urbanized landscapes provide many plausible and beneficial opportunities for helping populations of forest-breeding migratory species (De Sousa, 2014; Haase et al., 2014). Existing strategies to mitigate current rates of habitat loss include tools and methods for guiding future sustainable design and development of urban areas (Le Roux et al., 2014; Scott and Lennon, 2016). However, we should not be limited in our creativity or by our ability to work within existing urban areas (Madre et al., 2014). Several examples of successful reforestation within urban areas exist including long-term (20-year) benefits of reforestation within New York City parks where reforestation efforts led to increases in tree species diversity, forest structure complexity, and native tree regeneration (Simmons et al, 2016). Additionally, initiatives for maintaining urban green spaces and keeping pace with rates of human development have become important priorities within existing city plans (Tan et al., 2013; Norton et al., 2015). Cowbird parasitism can also have negative effects on songbird species by reducing fecundity and can contribute to populationlevel declines (Mayfield, 1977; Kilpatrick, 2002). In addition, cowbird parasitism can have less obvious relationships to wood thrush population dynamics such as adult survival, perhaps due to adults incurring increased energetic costs related to provisioning cowbird nestlings (Ladin et al., 2016a). Simulations of cowbird removal showed that reducing the proportion of parasitized nests from 0.65 (Null) to between 0.25 and 0.0 (Cowbird removal), resulted in a 4.3% increase in metapopulation-wide annual growth rate (λ), a 7.1% increase in mean annual growth rates per site (λi ), and an 82% increase in the probability that λ ≥ 1. Additionally, we observed a 37% increase in site occupancy under the Cowbird removal scenario. Given the positive trend for cowbirds in the New England/midAtlantic (1.2; Sauer et al., 2012) and demonstrated efficacy of cowbird removal for benefiting host species (e.g., Kirtland’s warblers Dendroica kirtlandii; Siegle and Ahlers, 2004; McLeod and Koronkiewicz, 2014), we propose that cowbird removal be tested in the field to evaluate model predictions. While empirically-based studies on how populations will respond to potential conservation strategies are critical starting

FIGURE 4 | Contour plot showing the relationship of combinations of proportions of impervious surface and index of cowbird parasitism (i.e., proportion of cowbird parasitized nests × number of cowbird eggs per nest) to mean lambda per site (λi ).

insufficient to stabilize population growth when implemented individually. We think wood thrush conservation efforts in the New England/mid-Atlantic region could combine local and regional strategies to reduce current rates of regional population declines and stabilize the population within the urbanized midAtlantic USA. The implementation plan for the New England/mid-Atlantic Coast Bird Conservation Region 30 (BCR 30; characterized by a highly developed landscape), has identified habitat loss, degradation, and fragmentation as the greatest threat to bird species, and has designated wood thrushes as a species of “Highest Priority” with a regional population estimate 1/3 lower than objective goals (Atlantic Coast Joint Venture, 2012). We estimated that 23% of all forest habitat within BCR 30 (590,794 ha), is contained in forest fragments ≤20 ha that can provide valuable breeding and habitat for wood thrushes and other forest-breeding birds. Given observed differences in wood thrush occupancy across a range of patch sizes (Keller and Yahner, 2007; MacIntosh et al., 2011), we think that solely focusing conservation efforts on larger forest patches would be a mistake in attempting to achieve established population goals for wood thrushes in BCR 30. We compared population trends among model scenarios and 40 years of empirical data from EW (−2.79), which were similar to Breeding Bird Survey (BBS) route regression trend estimates (1966–2012) for the New England/mid-Atlantic Coast (−2.77) and the state of Delaware (−2.1) (Sauer et al., 2012). However, more recent trends (from 2002 to 2012) have decreased to −8.42 in EW, and to −3.73 and −10.52 for the New England/mid-Atlantic Coast and Delaware, respectively (Sauer et al., 2012). In silico population trends for 30 years into the future were similar for Null in EW (−11.5), and for the overall study area (−12.8). This confirmed continuity among regional BBS, locally-observed, and model-predicted population trends

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points aiding in the design and implementation of conservation efforts, there are several other factors that can influence (potentially in unpredictable ways) intended outcomes from the application of conservation strategies that should be considered. For instance, how populations are likely to respond to climate change both on breeding and wintering grounds within spatiallyexplicit areas must be considered in concert with other factors that are examined (Giannini et al., 2015; Regos et al., 2015). One broadly-applicable consideration would be how species distribution shifts, and variation within species among populations to climate change might influence responses to specific conservation actions within a given area (Hällfors et al., 2016). Given the centrality of latitude for the location of our study area within the breeding range of wood thrushes in the eastern United States, species distribution shifts are likely to have little to minor effects on the performance of our model scenarios. However, for situations where conservation actions are targeting populations located at the margins of species distribution ranges, this should be directly accounted for (Hampe and Petit, 2005). Also, due to weak migratory connectivity between breeding and wintering areas found for wood thrushes, it will be hard to predict how climate- and landscape-change on wintering grounds may influence (via carry-over effects) population-limiting responses during breeding (Rushing et al., 2016). We are aware of additional and difficult-to-predict population responses that our model does not account for that include likely increases in human population growth rates, as well as, synergistic effects among climate change, increased human population densities in urban areas, and continued resultant land cover change due to increased rates of urbanization. However, our model does indirectly capture some of the inherent variation in wood thrush population responses to these factors through our inclusion of “noise” from longterm empirical relationships that end up being propagated within model predictions of the conservation scenarios we compared. Our predicted population responses of wood thrushes to conservation scenarios could be used to estimate growth rates in other mid-Atlantic locations. However, estimated transition probabilities (which we held constant over time) do not account for other potentially contributing factors to wood thrush population dynamics, and ignore variation in habitat quality over time. Future models could improve these shortcomings by modifying transition probabilities and emigration rates through time according to predicted changes in habitat quality. Predicting

bird population responses to future land use change (Martinuzzi et al., 2015), and mitigating negative effects of urbanization using sustainable landscape planning tools (Mason et al., 2007; Lerman et al., 2014; Gagné et al., 2015) will aid the conservation of wood thrushes and other forest-obligates. In general, we advocate for future studies using integrated population models and other empirically-based model simulations to evaluate potential population responses to conservation efforts.

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ETHICS STATEMENT We followed recommended guidelines for ethical scientific research and publishing conduct provided by the University of Delaware’s Center for Science, Ethics and Public Policy.

AUTHOR CONTRIBUTIONS ZL was lead author on the study, and lead data collection and analyses. VD was involved in study design, study area establishment, and review of manuscript drafts. JB helped develop modeling framework for metapopulation models. RR was responsible for long-term data collection and review of manuscript drafts. WS was responsible for data management, budgetary management, study design, and review of manuscript drafts.

ACKNOWLEDGMENTS We thank the Northern Research Station of the US Forest Service, McIntire-Stennis Forestry Research Program, and the University of Delaware for funding. We thank cooperators from the University of Delaware, City of Newark Municipal parks, and White Clay Creek State Park for study site accessibility. We thank S. Adalsteinnson, J. Buler, A. Colavecchio, D. Delaney, D. Ecker, N. Hengst, S. Mkheidze, K. Pastirik, C. Rega, J. Richardson, K. Serno, and M. Walker for help with field work and data collection.

SUPPLEMENTARY MATERIAL The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fevo. 2016.00122

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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 © 2016 Ladin, D’Amico, Baetens, Roth and Shriver. 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.

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ORIGINAL RESEARCH published: 11 May 2017 doi: 10.3389/fevo.2017.00041

The Degree of Urbanization of a Species Affects How Intensively It Is Studied: A Global Perspective Juan D. Ibáñez-Álamo 1, 2*, Enrique Rubio 1 and Kwanye Bitrus Zira 3 1

Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, Netherlands, 2 Department of Wetland Ecology, Estación Biológica de Doñana, Sevilla, Spain, 3 A.P. Leventis Ornithological Research Institute, Jos, Nigeria

Edited by: Caroline Isaksson, Lund University, Sweden Reviewed by: Erno Vincze, University of Pannonia, Hungary Jose A. Masero, University of Extremadura, Spain *Correspondence: Juan D. Ibáñez-Álamo [email protected] Specialty section: This article was submitted to Behavioral and Evolutionary Ecology, a section of the journal Frontiers in Ecology and Evolution Received: 10 February 2017 Accepted: 20 April 2017 Published: 11 May 2017 Citation: Ibáñez-Álamo JD, Rubio E and Bitrus Zira K (2017) The Degree of Urbanization of a Species Affects How Intensively It Is Studied: A Global Perspective. Front. Ecol. Evol. 5:41. doi: 10.3389/fevo.2017.00041

The expansion of urban areas is currently one of the most important worldwide landscape changes. This process, termed urbanization, has important ecological effects and is known to alter many aspects of the biology of organisms (including birds). However, human-nature interactions can also be affected by this process. We hypothesized that urbanization can particularly affect how intensively we investigate birds. We predict that species living in close proximity to humans will be more easily or preferably studied, thus promoting a bias in research effort toward urban birds. In order to test this hypothesis we have collected a detailed database of urban and non-urban avian communities including information from five biogeographic realms and more than 750 bird species. We obtained four different indicators of research effort (two previously considered and two new ones) as well as information on different confounding factors that are known to affect research effort such as conservation status, body mass, distribution range and phylogeny, in addition to the previously unconsidered historical factor of year of description of the species. We found a positive and significant association between the degree of urbanization of a species and how frequently it is investigated. We also found the expected effect for biogeographic realm, body mass and distribution range, and year of description, but not for conservation status. In addition, we found a strong correlation among all research effort variables which support the use of Google Scholar as a reliable source for these kind of studies. Our findings suggest that urbanization is not only affecting the biology of organisms but also how we study them. These results might have important implications if this research bias is maintained in the long term. Future investigation should aim at exploring the ultimate reasons for this research bias toward urban birds and whether it is also happening for other groups of organisms. Keywords: birds, human-nature interactions, research effort, urbanization

INTRODUCTION The process of urbanization is dramatically changing the environment, modifying not only abiotic elements such as habitat structure or connectivity, but also biotic elements (Grimm et al., 2008; Gaston, 2010; Forman, 2014). There is mounting evidence suggesting that this anthropogenic landscape change modifies different components of biodiversity, including taxonomic, functional and evolutionary diversity (Devictor et al., 2008; McKinney, 2008;

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between the number of papers obtained from Web of Knowledge and those of the Birdlife International library catalog. It is also worth to mention that no previous study has used Google Scholar as a source to test research effort predictions. All previously used databases are, to some extent, difficult to get access to because they are not free or easily accessible. Thus, exploring the feasibility of this open-access scientific database to investigate research effort and its relationship with other commonly used databases is important. The aims of our study were: (i) to investigate whether the degree to which a species is urbanized (number of cities in which it is found) is related to the research effort it receives; (ii) to find other potential factors that can affect research effort in birds (including the previously unconsidered effect of year of description of a species); and (iii) to test whether there are correlations between research effort variables obtained from different sources (including Google Scholar and more traditional databases), in order to explore whether they can offer similar information. In order to do so, we used a global database of bird species found in urban and non-urban habitats and collected the number of papers published for each species in four different databases (Google Scholar, Web of Science, Zoological Records and the Handbook of the Birds of the World Alive). We decided to use birds as a model group because they are very well known in relation to urbanization (Marzluff et al., 2001; Lepczyk and Warren, 2012; Gil and Brumm, 2014) allowing us to compile a geographically wide database and extract general conclusions. This global coverage is important given the worldwide expansion of urban areas and will also allow us to identify differences in research effort allocation among regions.

Newbold et al., 2015; Ibáñez-Álamo et al., 2016; Knop, 2016; Morelli et al., 2016) and other aspects of the biology of organisms like animal behavior or life-history traits (Ibáñez-Álamo and Soler, 2010; Møller and Ibáñez-Álamo, 2012; Díaz et al., 2013; Gil and Brumm, 2014; Møller et al., 2015). This intensive alteration of the environment has attracted increasing attention by the scientific community (Marzluff, 2016; McDonnell and MacGregor-Fors, 2016) and has ultimately lead to recognize urbanization as a major global challenge (United Nations, 2016). Humans are intrinsically associated with the urbanization process (Forman, 2014). The urban habitat is created by us to meet our species-specific requirements and now the majority of the World’s human population is living in cities (United Nations, 2012). It is thus normal that many papers in the field of Urban Ecology have focused on investigating the interaction between organisms and humans. The majority of them focused on how humans can affect organisms, for example by altering animal’s escape behavior (Díaz et al., 2013, 2015; Samia et al., 2015), while others explored the opposite direction of the interaction, how urban nature can affect humans (Fuller and Irvine, 2010; Soga and Gaston, 2016). In relation to the latter, it is logical to think that scientists are not immune to this effect and the focus of their investigations might also be influenced by urban nature. For instance, researchers might be biased to study species that live in urban habitats more often, due to different reasons such as ease to study or preferences toward those species encountered more often by researchers or with scientifically attractive traits. These reasons have already been proposed to affect research effort (how intensely we study a topic or subject) and are used to explain for example why we tend to investigate more often larger species or those with broader distribution ranges (Brooke et al., 2014; Ducatez and Lefebvre, 2014; McKenzie and Robertson, 2015). Despite previous studies, it has been suggested that there is a need to explore alternative factors in order to explain research effort (Murray et al., 2015) and that urbanization might be one of these factors. The knowledge about how we carry out our research is very valuable as it can be used to re-orientate our effort toward those less studied topics or areas, it can help detect potential biases in our conclusions, or even better justify our management efforts (De Lima et al., 2011; Ibáñez-Álamo et al., 2012; McKenzie and Robertson, 2015). Another important and related issue regarding the study of research effort is methodological. Even though there are alternative ways to measure research effort (Murray et al., 2015), the most commonly used variable is the number of published papers (De Lima et al., 2011; Brooke et al., 2014; Ducatez and Lefebvre, 2014; McKenzie and Robertson, 2015). Previous studies on the topic have used different research databases to look for published papers, from Web of Science to Zoological Records, including those obtained from different research organizations (e.g., Birdlife International; De Lima et al., 2011; Ducatez and Lefebvre, 2014; McKenzie and Robertson, 2015). However, even if it has been highly recommended for this type of study (Pautasso, 2016), to our knowledge there has been only one attempt to investigate whether different databases might be offering similar conclusions in relation to research effort in birds (De Lima et al., 2011). In this case the authors found a positive correlation

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MATERIALS AND METHODS Bird Assemblages We used a global database of studies presenting information for 17 countries and four continents on urban and non-urban bird communities published recently (Ibáñez-Álamo et al., 2016). Basically, the database was created using an exhaustive literature search in different websites (i.e., Web of Science, Google Scholar, and SmartCat) and a careful selection of papers including complete bird assemblages from urban and non-urban habitats (defined according to Marzluff et al., 2001; e.g., urban areas characterized with >50% of the surface built and >10 buildings per ha). These assemblages were collected following the same field method, during the same period and by the same field observer, thus offering standardized information that avoids many potential confounding factors in these kind of comparative studies. From each study, we obtained: (i) urban bird assemblage, (ii) non-urban bird assemblage, and (iii) site. There is a more detailed description of data collection in Ibáñez-Álamo et al. (2016).

Research Effort Data, Urbanization and Species’ Traits Using the database described above, we created a new dataset including all the 767 species from the 28 paired study sites (Supplementary Material 1). To quantify research effort for

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et al., 2011; Murray et al., 2015); (iv) the biogeographic realm for that species as the research effort can vary depending on the development of the region (i.e., highly developed countries investing more in research and consequently having more probabilities to investigate their species) (De Lima et al., 2011; Ducatez and Lefebvre, 2014). In addition to these factors capturing information on geographic (biogeographic realms), biotic (distribution range and body mass) and human effects (conservation status and urbanization), we wanted to control also for historical factors. Therefore, we also collected (v) the year of description of the species obtained from the Handbook of the Birds of the World Alive (Del Hoyo et al., 2016), as those birds described more recently may have received a lower research effort.

each species, we collected information on the number of papers published in different databases. This variable has been commonly used in previous studies and has been suggested to reflect the research effort invested in a species better than alternative ones (McKenzie and Robertson, 2015). First, we used Web of Science, as it has been previously used in these kinds of studies (McKenzie and Robertson, 2015; Murray et al., 2015). Using quotes, we searched for each scientific name in all available databases within the search engine and without time restriction. A recent study showed that this is an effective method that is not affected by changes in scientific names with time (Ducatez and Lefebvre, 2014). Second, we extracted the number of papers in Zoological Records from a previously published compilation (Ducatez and Lefebvre, 2014). Third, we also looked for the number of references per species obtained with Google Scholar, using the scientific name in quotes and without the patents option activated, again without time limits. The literature search in Google Scholar and Web of Science were both done in January 2017. Finally, we extracted the number of papers included in the reference section of the Handbook of the Birds of the World Alive during August 2016 (Del Hoyo et al., 2016). This compilation of birds is known for its quality and up-to-date information among ornithologists and thus, the number of references used for each species should indicate the overall knowledge for that particular species. In order to investigate whether species that are found in urban areas are more studied, we calculated the number of cities for each species in which it was found in our database (max. 28 cities). This variable matches the definition of urbanization used by Croci et al. (2008), which considers a species to be urbanized if it is found in urban centers, but also accounts for the intensity of such effect by adding the number of cities in which the species can be found. Croci’s definition of urbanization is strongly correlated (sensu Cohen, 1988) to all other indexes of urbanization previously used in Urban Ecology, and thus can be considered a good proxy for the extent of urbanization of a species (Møller, 2014). Given that research effort in birds is multifaceted and can be affected by several different factors (Ducatez and Lefebvre, 2014; McKenzie and Robertson, 2015; Murray et al., 2015), we also collected information on different factors that might affect research effort according to previous studies, but we tried to avoid co-linearity of predictors (i.e., between distribution range and population size or body mass and clutch size Saether, 1987; Jetz et al., 2008). We obtained the following information for each species: (i) range of breeding distribution (square kilometers) according to Birdlife International (www.birdlife.org) given that species with large distribution are more likely to be studied than those occupying small areas (i.e., endemic) (Ducatez and Lefebvre, 2014; McKenzie and Robertson, 2015); (ii) Body mass collected from the Handbook of the Birds of the World Alive (Del Hoyo et al., 2016) as a proxy for size because large-sized species are more likely to be detected and manipulated and hence studied (Ducatez and Lefebvre, 2014; McKenzie and Robertson, 2015); (iii) Conservation status obtained from the International Union for Conservation of Nature (www.iucn.org) as threatened species might attract a greater attention by scientists (De Lima

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Statistical Analyses We first calculated a correlation matrix of the four research effort variables in order to detect whether they offer similar information. All research effort variables in addition to body mass and breeding distribution range were log-transformed to achieve normality. We chose a single research effort variable based on the results of these correlations to run the subsequent analyses (see Results). Given that a previous study (Ducatez and Lefebvre, 2014) found differences among avian taxa in research effort, species cannot be considered independent units in our context. Therefore, we estimated the phylogenetic relationships of the species in our database using the Mesquite environment (Maddison and Maddison, 2015) and calculating the consensus (i.e., majority rules consensus) tree of 1000 phylogenetic trees downloaded from birdtree.org (Jetz et al., 2012; Supplementary Material 2). Then, we used a stepwise backward model selection running phylogenetic generalized least square models (PGLS; i.e., Díaz et al., 2013, 2015). The best models were selected based on corrected Akaike Information Criteria (AICc ), using a threshold AICc value of 2. The full model included all single effects of the variables described above (Table 2) in addition to the interaction between biogeographic realm and the degree of urbanization as urban development vary geographically (Seto et al., 2012). We performed our analyses in R 3.3.2 (R Core Team, 2016) using the R libraries “ape” (Paradis et al., 2004), “MASS” (Venables and Ripley, 2002) and “mvtnorm” (Genz et al., 2016) as well as the function pglm3.3.r created by R. Freckleton which allows to run PGLS using an orthogonal (type III) fit of models. As a first step, we calculated the phylogenetic scaling parameter lambda (λ), which varies from 0 (phylogenetic independence) to 1 (variables completely covary according to their shared evolutionary history; Freckleton et al., 2002) and provides information on the variation explained by phylogeny. Secondly, we run the models correcting for the estimated λ and applied the backward procedure. Once the best model was found, we assessed the importance of each predictor regarding our (research effort) dependent variable based on their effect sizes calculated from P-values and t-tests (Díaz et al., 2015). We used Cohen’s criteria (Cohen, 1988) to quantify their importance explaining our dataset as small (r ≤ 0.10, explaining less than 1% of the variance), intermediate (r = 0.11–0.49, explaining between 2 and 24% of the variance), and large (r ≥ 0.50, explaining more than 25% of the variance).

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analyses using Croci’s definition of urbanization more strictly, thus using a binary variable instead of the urbanization index described above. Our results were the same as those obtained with the urbanization index except for the interaction between biogeographic realm and urbanization that was not retained in the best model (Supplementary Material 4), similarly to what happened with our sensitivity analysis. This additional analysis provides support for the robustness of our results and suggest caution regarding the mentioned interaction.

TABLE 1 | Correlations between all four variables of research effort. R2

Web of science

Handbook alive

Zoological records

Google scholar

0.94***

0.66***

0.92***

Web of science

0.62***

Handbook alive

0.94*** 0.64***

***P < 0.001.

RESULTS Our dataset contained information on 767 bird species, of which 49.0% were found in an urban area. The three most urbanized species in our database were the Rock pigeon (Columba livia), the House sparrow (Passer domesticus) and the Common starling (Sturnus vulgaris) (Supplementary Material 1). Given the extreme degree of urbanization of these three species, we decided to run a sensitivity analysis in order to check the importance of those species in our findings (Supplementary Material 3). The search in Google Scholar provided a higher number of references in comparison with the other databases (Supplementary Material 1), probably due to the use of different search engines and size of the database. Despite this, the results for our correlation analyses among research effort variables indicated that all of them are highly correlated (Table 1). The r-values indicated a strong and significant correlation among all four research effort variables, with Google Scholar showing the highest values with all others. Thus, we carried out our model selection procedure using Google Scholar as it is more easily accessible and correctly represents research effort. The minimum adequate model of Google Scholar research effort retained six variables, including urbanization index, and explained 73% of the variance of our database [PGLS; λ = 0.33, Adjusted R2 = 0.73, F (1, 754) = 477.87, P < 0.0001; Table 2]. The phylogenetic signal of this model was relatively small. All variables retained in the final model were statistically significant and had intermediate effect sizes indicating that they are important for explaining research effort. The only exception was the interaction between the biogeographic realm and urbanization index, which involved a small effect size (Table 2), and in fact was not retained in the best model if we exclude the three most urbanized species (Supplementary Material 3). This interaction indicated that the relationship between research effort and urbanization was stronger in the Palearctic. We found a positive association between the level of species urbanization and the attention received by researchers (Table 2, Figure 1). In addition, we found a positive effect of body mass and breeding distribution suggesting that larger and more widely distributed species have been studied more (estimate ± standard error of 0.11 ± 0.03 and 0.44 ± 0.03, respectively; Table 2). But a negative influence of year of description showing that fewer papers have been published for those species described more recently (Table 2; Figure 2). The biogeographic realm also had a significant and intermediate effect per se to explain research effort as those species from Neotropical and Oriental realms have been less studied than those of the other three realms represented in our database (Figure 3). We also run the

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DISCUSSION As hypothesized, we found that the level of urbanization of a species is an important predictor of the attention it receives from scientists (Figure 1). This is a clear example that urbanization is not only affecting the biology of organisms but also how intensively we research them, and consequently altering humannature interactions. Our results seem to point out that the urbanization effect on research effort is global and does not depend on the biogeographic realm given that the small effect size of the interaction between the degree of urbanization and the biogeographic realm disappeared in our sensitivity analysis (without the three most urbanized species) and in our analysis using a binary variable (Supplementary Materials 3, 4). Our results markedly contrast with those obtained in a recent study done with a group of mammals (Brooke et al., 2014). In that investigation, they found a significant negative relationship between the mean human population density of the distribution range of a species and its research effort. This effect disappeared when other factors where included in the model. The differences between the two studies could be due to group-specific effects regarding research effort or different methodological approximations (e.g., mean human population density being influenced by other factors in addition to urbanization). Future studies on avian research effort should investigate the effect of human population density in order to distinguish between these two options. Our findings highlight the importance of biases in research effort like the one described here, even though it does not necessarily involve re-orientating our scientific aims. It is possible that urban birds are significantly more studied because they show a particular set of traits (Kark et al., 2007; Croci et al., 2008; Møller, 2014; Sol et al., 2014) that might be of special scientific relevance (i.e., more complex social breeding; Kark et al., 2007) or because they are involved in particularly important economic or health issues (i.e., spread of some diseases; Kilpatrick, 2011). Alternatively, other reasons of more concern (i.e., logistic/monetary constrains or personal preferences) may be behind our results. The ease of study has been raised as an important determinant to explain research effort in birds (Ducatez and Lefebvre, 2014; McKenzie and Robertson, 2015; Murray et al., 2015) and mammals (e.g., Brooke et al., 2014). Some of the traits of urban birds can facilitate their investigation. Nesting in holes, for example, is a common trait of birds living in cities (Croci et al., 2008; Sol et al., 2014) which allows the use of nest-boxes by scientists, making their study easier.

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TABLE 2 | Full and minimum adequate models explaining avian research effort. Predictor

df

SS

F

p

Effect size

FULL MODEL (AICc = 1764.94) Number of cities

1

3.551

477.99