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Evol Appl. 2013 Sep;6(6):865-74. doi: 10.1111/eva.12072. Epub 2013 May 8.

Predicting bird song from space.

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Department of Ecology and Evolutionary Biology, University of California Los Angeles Los Angeles, CA, USA ; Center for Tropical Research, Institute of the Environment and Sustainability, University of California Los Angeles Los Angeles, CA, USA.


Environmentally imposed selection pressures are well known to shape animal signals. Changes in these signals can result in recognition mismatches between individuals living in different habitats, leading to reproductive divergence and speciation. For example, numerous studies have shown that differences in avian song may be a potent prezygotic isolating mechanism. Typically, however, detailed studies of environmental pressures on variation in animal behavior have been conducted only at small spatial scales. Here, we use remote-sensing data to predict animal behavior, in this case, bird song, across vast spatial scales. We use remotely sensed data to predict the song characteristics of the little greenbul (Andropadus virens), a widely distributed African passerine, found across secondary and mature rainforest habitats and the rainforest-savanna ecotone. Satellite data that captured ecosystem structure and function explained up to 66% of the variation in song characteristics. Song differences observed across habitats, including those between human-altered and mature rainforest, have the potential to lead to reproductive divergence, and highlight the impacts that both natural and anthropogenic change may have on natural populations. Our approach offers a novel means to examine the ecological correlates of animal behavior across large geographic areas with potential applications to both evolutionary and conservation biology.


anthropogenic effects; avian song; behavioral ecology; random forests; remote sensing; reproductive isolation; spatial heterogeneity

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