Species subjected to more variable environments should have greater phenotypic plasticity than those that are more restricted to specific habitat types leading to the expectation that migratory birds should be relatively more plastic than resident birds. We tested this comparatively by studying variation in flight initiation distance (FID), a well-studied antipredator behaviour. We predicted that variation in FID would be greater for migratory species because they encountered a variety of locations during their lives and therefore had less predictable assessments of risk compared to more sedentary species. Contrary to our prediction, we found that non-migratory species (sedentary) had greater variation in FID than migratory ones. Migratory and partially migratory birds had greater average FIDs than sedentary birds, suggesting that they were generally more wary. These results suggest that the predictability associated with not migrating permits more nuanced risk assessment which was seen in the greater variation in FID of sedentary bird species.

Department of Ecology and Evolutionary Biology University of California Los Angeles CA USA

Faculty of Environmental Sciences Czech University of Life Sciences Prague Kamýcká 129 165 00 Prague 6 Czech Republic

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Hall MJ, Burns AL, Martin JM, Hochuli DF. Flight initiation distance changes across landscapes and habitats in a successful urban coloniser. Urban Ecosyst. 2020 doi: 10.1007/s11252-020-00969-5. DOI

Møller AP, Samia DSM, Weston MA, Guay PJ, Blumstein DT. Flight initiation distances in relation to sexual dichromatism and body size in birds from three continents. Biol. J. Linn. Soc. 2016;117:823–831.

Morelli F, et al. Contagious fear: Escape behavior increases with flock size in European gregarious birds. Ecol. Evol. 2019;9:6096–6104. PubMed PMC

Samia DSM, et al. Rural-urban differences in escape behavior of European birds across a latitudinal gradient. Front. Ecol. Evol. 2017;5:66.

Blumstein DT. Developing an evolutionary ecology of fear: How life history and natural history traits affect disturbance tolerance in birds. Anim. Behav. 2006;71:389–399.

McFarland, D.

Stankowich T, Blumstein DT. Fear in animals: A meta-analysis and review of risk assessment. Proc. R. Soc. B Biol. Sci. 2005;272:2627–2634. PubMed PMC

Lima SL. Maximizing feeding efficiency and minimizing time exposed to predators: a trade-off in the black-capped chickadee. Oecologia. 1985;66:60–67. PubMed

Sol D, et al. Risk-taking behavior, urbanization and the pace of life in birds. Behav. Ecol. Sociobiol. 2018;72:59.

Lockwood R, Swaddle JP, Rayner JMV. Avian Wingtip Shape Reconsidered: Wingtip Shape Indices and Morphological Adaptations to Migration. J. Avian Biol. 1998;29:273–292.

Møller, A. P. Birds. in

Møller AP. Flight distance of urban birds, predation and selection for urban life. Behav. Ecol. Sociobiol. 2008;63:63–75.

Fernández-Juricic E, et al. Relationships of anti-predator escape and post-escape responses with body mass and morphology: a comparative avian study. Evol. Ecol. Res. 2006;8:731–752.

Weston MA, Mcleod EM, Blumstein DT, Guay PJ. A review of flight-initiation distances and their application to managing disturbance to Australian birds. Emu. 2012;112:269–286.

Hemmingsen A. The relation of shyness (flushing distance) to body size. Spolia Zool Musei Hauniensis. 1951;11:74–76.

Blumstein DT. Flight-initiation distance in birds is dependent on intruder starting distance. J. Wildl. Manage. 2013;67:852–857.

Glover HK, Weston MA, Maguire GS, Miller KK, Christie BA. Towards ecologically meaningful and socially acceptable buffers: Response distances of shorebirds in Victoria, Australia, to human disturbance. Landsc. Urban Plan. 2011;103:326–334.

Geist C, Liao J, Libby S, Blumstein DT. Does intruder group size and orientation affect flight initiation distance in birds? Anim. Biodivers. Conserv. 2001;28:69–73.

Mikula P. Pedestrian density influences flight distances of urban birds. Ardea. 2014;102:53–60.

Piratelli, A. J., Favoretto, G. R. & de Almeida Maximiano, M. F. Factors affecting escape distance in birds.

Burger J, Gochfeld M. Human activity influence and diurnal and nocturnal foraging of Sanderlings (Calidris alba) Condor. 1991;93:259–265.

Møller AP, Garamszegi LZ. Between individual variation in risk-taking behavior and its life history consequences. Behav. Ecol. 2012;23:843–853.

Ferguson SM, Gilson LN, Bateman PW. Look at the time: diel variation in the flight initiation distance of a nectarivorous bird. Behav. Ecol. Sociobiol. 2019;73:147.

Garamszegi LZ, Møller AP. Partitioning within-species variance in behaviour to within- and between-population components for understanding evolution. Ecol. Lett. 2017;20:599–608. PubMed

Bauer S, Hoye BJ. Migratory animals couple biodiversity and ecosystem functioning worldwide. Science. 2014;344:1242552. PubMed

Dufour P, et al. Reconstructing the geographic and climatic origins of long-distance bird migrations. J. Biogeogr. 2020;47:155–166.

Sol D, et al. Evolutionary divergence in brain size between migratory and resident birds. PLoS ONE. 2010;5:e9617. PubMed PMC

Bonnet-Lebrun AS, Somveille M, Rodrigues ASL, Manica A. Exploring intraspecific variation in migratory destinations to investigate the drivers of migration. Oikos. 2021;130:187–196.

Zurell D, Gallien L, Graham CH, Zimmermann NE. Do long-distance migratory birds track their niche through seasons? J. Biogeogr. 2018;45:1459–1468.

Samia DSM, Nakagawa S, Nomura F, Rangel TF, Blumstein DT. Increased tolerance to humans among disturbed wildlife. Nat. Commun. 2015;6:8877. PubMed PMC

Ydenberg RC, Dill LM. The economics of fleeing from predators. Adv. Study Behav. 1986;16:229–249.

Cooper, W. E. J. & Blumstein, D. T. Escape behavior: importance, scope, and variables. in

Sayol F, Sol D, Pigot AL. Brain size and life history interact to predict urban tolerance in birds. Front. Ecol. Evol. 2020;8:58.

Sayol F, Downing PA, Iwaniuk AN, Maspons J, Sol D. Predictable evolution towards larger brains in birds colonizing oceanic islands. Nat. Commun. 2018;9:2820. PubMed PMC

Tobias JA, Pigot AL. Integrating behaviour and ecology into global biodiversity conservation strategies. Philos. Trans. R. Soc. B Biol. Sci. 2019;374:20190012. PubMed PMC

Wilman H, et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology. 2014;95:2027.

Kamilar JM, Cooper N. Phylogenetic signal in primate behaviour, ecology and life history. Philos. Trans. R. Soc. B Biol. Sci. 2013;368:20120341–22012034. PubMed PMC

Machado JP, Antunes A, Borges R, Gomes C, Rocha AP. Measuring phylogenetic signal between categorical traits and phylogenies. Bioinformatics. 2018 doi: 10.1093/bioinformatics/bty800. PubMed DOI

Ericson PGP, et al. Diversification of Neoaves: integration of molecular sequence data and fossils. Biol. Lett. 2006;2:543–547. PubMed PMC

Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20:289–290. PubMed

Schliep KP. phangorn: phylogenetic analysis in R. Bioinformatics. 2011;27:592–593. PubMed PMC

Revell, L. J. & Chamberlain, S. A. Rphylip: An R interface for PHYLIP R package. (2014).

Blomberg SP, Garland T. Tempo and mode in evolution: phylogenetic inertia, adaptation and comparative methods. J. Evol. Biol. 2003;15:899–910.

Keck F, Rimet F, Bouchez A, Franc A. Phylosignal: An R package to measure, test, and explore the phylogenetic signal. Ecol. Evol. 2016;6:2774–2780. PubMed PMC

Münkemüller T, et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 2012;3:743–756.

Kot M. Adaptation: Statistics and a null model for estimating phylogenetic effects. Syst. Zool. 1990;39:227–241.

Blomberg SP, Garland TJ, Ives AR. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution (N. Y.) 2003;57:717–745. PubMed

Pagel M. Inferring the historical patterns of biological evolution. Nature. 1999;401:877–884. PubMed

Burnham KP, Anderson DR. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer; 2002.

McCullagh, P. & Nelder, J. A.

Pinheiro, J.

Nakazawa, M. ‘fmsb’ Functions for Medical Statistics Book with some Demographic Data - R package version 0.6.1. (2017).

R Development Core Team. R: A language and environment for statistical computing. (2021).

Venables, W. N. & Ripley, B. D.

Zobrazit více v PubMed

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