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      Elucidating the significance of spatial memory on movement decisions by African savannah elephants using state–space models

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          Abstract

          Spatial memory facilitates resource acquisition where resources are patchy, but how it influences movement behaviour of wide-ranging species remains to be resolved. We examined African elephant spatial memory reflected in movement decisions regarding access to perennial waterholes. State–space models of movement data revealed a rapid, highly directional movement behaviour almost exclusively associated with visiting perennial water. Behavioural change point (BCP) analyses demonstrated that these goal-oriented movements were initiated on average 4.59 km, and up to 49.97 km, from the visited waterhole, with the closest waterhole accessed 90% of the time. Distances of decision points increased when switching to different waterholes, during the dry season, or for female groups relative to males, while selection of the closest waterhole decreased when switching. Overall, our analyses indicated detailed spatial knowledge over large scales, enabling elephants to minimize travel distance through highly directional movement when accessing water. We discuss the likely cognitive and socioecological mechanisms driving these spatially precise movements that are most consistent with our findings. By applying modern analytic techniques to high-resolution movement data, this study illustrates emerging approaches for studying how cognition structures animal movement behaviour in different ecological and social contexts.

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          Most cited references38

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          Generalized linear mixed models: a practical guide for ecology and evolution.

          How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.
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            A tutorial on hidden Markov models and selected applications in speech recognition

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              State-space models of individual animal movement.

              Detailed observation of the movement of individual animals offers the potential to understand spatial population processes as the ultimate consequence of individual behaviour, physiological constraints and fine-scale environmental influences. However, movement data from individuals are intrinsically stochastic and often subject to severe observation error. Linking such complex data to dynamical models of movement is a major challenge for animal ecology. Here, we review a statistical approach, state-space modelling, which involves changing how we analyse movement data and draw inferences about the behaviours that shape it. The statistical robustness and predictive ability of state-space models make them the most promising avenue towards a new type of movement ecology that fuses insights from the study of animal behaviour, biogeography and spatial population dynamics.
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                Author and article information

                Journal
                Proceedings of the Royal Society B: Biological Sciences
                Proc. R. Soc. B.
                The Royal Society
                0962-8452
                1471-2954
                April 22 2015
                April 22 2015
                April 22 2015
                : 282
                : 1805
                : 20143042
                Affiliations
                [1 ]Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO 80523–1474, USA
                [2 ]Department of Anthropology, University of California Davis, 328 Young Hall, One Shields Avenue, Davis, CA 95616, USA
                [3 ]Etosha Ecological Institute, PO Box 6, Okaukuejo via Outjo, Namibia
                [4 ]Save the Elephants, PO Box 54667, Nairobi, Kenya
                Article
                10.1098/rspb.2014.3042
                4389615
                25808888
                839c43b1-9b7a-4d33-8ca0-59006f53fb43
                © 2015

                https://royalsociety.org/journals/ethics-policies/data-sharing-mining/

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