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      Animal learning may contribute to both problems and solutions for wildlife–train collisions

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          Transportation infrastructure can cause an ecological trap if it attracts wildlife for foraging and travel opportunities, while increasing the risk of mortality from collisions. This situation occurs for a vulnerable population of grizzly bears ( Ursus arctos) in Banff National Park, Canada, where train strikes have become a leading cause of mortality. We explored this problem with analyses of rail-associated food attractants, habitat use of GPS-collared bears and patterns of past mortality. Bears appeared to be attracted to grain spilled from rail cars, enhanced growth of adjacent vegetation and train-killed ungulates with rail use that increased in spring and autumn, and in areas where trains slowed, topography was rugged, and human density was low. However, areas with higher grain deposits or greater use by bears did not predict sites of past mortality. The onset of reported train strikes occurred amid several other interacting changes in this landscape, including the cessation of lethal bear management, changes in the distribution and abundance of ungulates, increasing human use and new anthropogenic features. We posit that rapid learning by bears is critical to their persistence in this landscape and that this capacity might be enhanced to prevent train strikes in future with simple warning devices, such as the one we invented, that signal approaching trains.

          This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.

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          Most cited references 79

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          Ecological implications of behavioural syndromes.

          Interspecific trait variation has long served as a conceptual foundation for our understanding of ecological patterns and dynamics. In particular, ecologists recognise the important role that animal behaviour plays in shaping ecological processes. An emerging area of interest in animal behaviour, the study of behavioural syndromes (animal personalities) considers how limited behavioural plasticity, as well as behavioural correlations affects an individual's fitness in diverse ecological contexts. In this article we explore how insights from the concept and study of behavioural syndromes provide fresh understanding of major issues in population ecology. We identify several general mechanisms for how population ecology phenomena can be influenced by a species or population's average behavioural type, by within-species variation in behavioural type, or by behavioural correlations across time or across ecological contexts. We note, in particular, the importance of behavioural type-dependent dispersal in spatial ecology. We then review recent literature and provide new syntheses for how these general mechanisms produce novel insights on five major issues in population ecology: (1) limits to species' distribution and abundance; (2) species interactions; (3) population dynamics; (4) relative responses to human-induced rapid environmental change; and (5) ecological invasions. © 2012 Blackwell Publishing Ltd/CNRS.
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            When Good Animals Love Bad Habitats: Ecological Traps and the Conservation of Animal Populations

             JAMES BATTIN (2004)
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              A framework for understanding ecological traps and an evaluation of existing evidence.

              When an animal settles preferentially in a habitat within which it does poorly relative to other available habitats, it is said to have been caught in an "ecological trap." Although the theoretical possibility that animals may be so trapped is widely recognized, the absence of a clear mechanistic understanding of what constitutes a trap means that much of the literature cited as support for the idea may be weak, at best. Here, we develop a conceptual model to explain how an ecological trap might work, outline the specific criteria that are necessary for demonstrating the existence of an ecological trap, and provide tools for researchers to use in detecting ecological traps. We then review the existing literature and summarize the state of empirical evidence for the existence of traps. Our conceptual model suggests that there are two basic kinds of ecological traps and three mechanisms by which traps may be created. To this point in time, there are still only a few solid empirical examples of ecological traps in the published literature (although those few examples suggest that both types of traps and all three of the predicted mechanisms do exist in nature). Therefore, ecological traps are either rare in nature, are difficult to detect, or both. An improved library of empirical studies will be essential if we are to develop a more synthetic understanding of the mechanisms that can trigger maladaptive behavior in general and the specific conditions under which ecological traps might occur.

                Author and article information

                Philos Trans R Soc Lond B Biol Sci
                Philos. Trans. R. Soc. Lond., B, Biol. Sci
                Philosophical Transactions of the Royal Society B: Biological Sciences
                The Royal Society
                16 September 2019
                29 July 2019
                29 July 2019
                : 374
                : 1781 , Theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’ compiled and edited by Jakob Bro-Jørgensen, Daniel W. Franks and Kristine Meise
                Department of Biological Sciences, University of Alberta , Edmonton, Canada T6G 2E9
                Author notes
                © 2019 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                Funded by: Joint Initiative for Grizzly Bear Conservation of Canadian Pacific and Parks Canada Agency;
                Funded by: Natural Science and Engineering Research Council of Canada, Collaborative Research and Development Program;
                Award ID: CRDPJ 441928 - 12
                Funded by: Natural Sciences and Engineering Research Council of Canada, http://dx.doi.org/10.13039/501100000038;
                Award ID: Discovery Grant 2017-05915
                Research Article
                Custom metadata
                September 16, 2019


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