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https://repositorio.inpa.gov.br/handle/1/15983
Title: | A unifying framework for quantifying the nature of animal interactions |
Authors: | Potts, Jonathan R. Mokross, Karl S. Lewis, Mark Alun |
Keywords: | Birds Statistical Mechanics Animals Movement Collective Behaviour Step Selection Territoriality Theoretical Ecologies Ecology Advection Behavior, Animals Animals Interaction Avoidance Behavior Bird Diffusion Ecosystem Home Range Insectivore Nonhuman Organismal Interaction Rainforest Resource Partitioning Simulation Territoriality Vocalization Animals Behavior, Animals Biological Model Physiology Population Dynamics Animal Behavior, Animals Birds Models, Biological Population Dynamics Rainforest Territoriality |
Issue Date: | 2014 |
metadata.dc.publisher.journal: | Journal of the Royal Society Interface |
metadata.dc.relation.ispartof: | Volume 11, Número 96 |
Abstract: | Collective phenomena, whereby agent-agent interactions determine spatial patterns, are ubiquitous in the animal kingdom. On the other hand, movement and space use are also greatly influenced by the interactions between animals and their environment. Despite both types of interaction fundamentally influencing animal behaviour, there has hitherto been no unifying framework for the models proposed in both areas. Here, we construct a general method for inferring population-level spatial patterns from underlying individual movement and interaction processes, a key ingredient in building a statistical mechanics for ecological systems. We show that resource selection functions, as well as several examples of collective motion models, arise as special cases of our framework, thus bringing together resource selection analysis and collective animal behaviour into a single theory. In particular, we focus on combining the various mechanistic models of territorial interactions in the literature with step selection functions, by incorporating interactions into the step selection framework and demonstrating how to derive territorial patterns from the resulting models. We demonstrate the efficacy of our model by application to a population of insectivore birds in the Amazon rainforest. © 2014 The Author(s) Published by the Royal Society. All rights reserved. |
metadata.dc.identifier.doi: | 10.1098/rsif.2014.0333 |
Appears in Collections: | Artigos |
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