Primates adjust movement strategies due to changing food availability

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Abstract

Animals are hypothesized to search their environments in predictable ways depending on the distribution of resources. Evenly distributed foods are thought to be best exploited with random Brownian movements; while foods that are patchy or unevenly distributed require non-Brownian strategies, such as Lévy walks. Thus, when food distribution changes due to seasonal variation, animals should show concomitant changes in their search strategies. We examined this issue in 6 monkey species from Africa and Mexico: 3 frugivores and 3 folivores. We hypothesized that the more patchily distributed fruit would result in frugivores showing more levy-like patterns of motion, while folivores, with their more homogenous food supply, would show Brownian patterns of motion. At least 3 and up to 5 of 6 species conformed to the overall movement pattern predicted by their primary dietary item. For folivorous black howler monkeys (Alouatta pigra), ursine colobus (Colobus vellerosus), and red colobus (Procolobus rufomitratus), Brownian movement was supported or could not be ruled-out. Two frugivores (spider monkeys, Ateles geoffroyi yucatanensis, and gray-cheeked mangabeys, Lophocebus albigena) showed Lévy walks, as predicted, but frugivorous vervet monkeys (Chlorocebus pygerythrus) showed a Brownian walk. Additionally, we test whether seasonal variation in the spatial availability of food support environmentally driven changes in movement patterns. Four of 5 species tested for seasonal variation showed adjustments in their search strategies between the rainy and dry seasons. This study provides support for the notion that food distribution determines search strategies and that animal movement patterns are flexible, mirroring changes in the environment.

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Reyna-Hurtado, R., Teichroeb, J. A., Bonnell, T. R., Hernández-Sarabia, R. U., Vickers, S. M., Serio-Silva, J. C., … Chapman, C. A. (2018). Primates adjust movement strategies due to changing food availability. Behavioral Ecology, 29(2), 368–376. https://doi.org/10.1093/beheco/arx176

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