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      Efficient spatial multi‐state capture–recapture model to study natal dispersal: An application to the Alpine marmot

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          Abstract

          Studying natal dispersal in natural populations using capture-recapture data is challenging as an unknown proportion of individuals leaves the study area when dispersing and are never recaptured. Most dispersal (and survival) estimates from capture-recapture studies are thus biased and only reflect what happens within the study area, not the population. Here, we elaborate on recent methodological advances to build a spatially explicit multi-state capture-recapture model to study natal dispersal in a territorial mammal while accounting for imperfect detection and movement in and out of the study area. We validate our model using a simulation study where we compare it to a non-spatial multi-state capture-recapture model. We then apply it to a long-term individual-based dataset on Alpine marmot Marmota marmota. Our model was able to accurately estimate natal dispersal and survival probabilities, as well as mean dispersal distance for a large range of dispersal patterns. By contrast, the non-spatial multi-state estimates underestimated both survival and natal dispersal even for short dispersal distances relative to the study area size. We discuss the application of our approach to other species and monitoring setups. We estimated higher inheritance probabilities of female Alpine marmots, which suggests higher levels of philopatry, although the probability to become dominant after dispersal did not differ between sexes. Nonetheless, the lower survival of young adult males suggests higher costs of dispersal for males. We further discuss the implications of our findings in light of the life history of the species.

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          Inference from Iterative Simulation Using Multiple Sequences

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            Modeling Survival and Testing Biological Hypotheses Using Marked Animals: A Unified Approach with Case Studies

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              Spatially explicit maximum likelihood methods for capture-recapture studies.

              Live-trapping capture-recapture studies of animal populations with fixed trap locations inevitably have a spatial component: animals close to traps are more likely to be caught than those far away. This is not addressed in conventional closed-population estimates of abundance and without the spatial component, rigorous estimates of density cannot be obtained. We propose new, flexible capture-recapture models that use the capture locations to estimate animal locations and spatially referenced capture probability. The models are likelihood-based and hence allow use of Akaike's information criterion or other likelihood-based methods of model selection. Density is an explicit parameter, and the evaluation of its dependence on spatial or temporal covariates is therefore straightforward. Additional (nonspatial) variation in capture probability may be modeled as in conventional capture-recapture. The method is tested by simulation, using a model in which capture probability depends only on location relative to traps. Point estimators are found to be unbiased and standard error estimators almost unbiased. The method is used to estimate the density of Red-eyed Vireos (Vireo olivaceus) from mist-netting data from the Patuxent Research Refuge, Maryland, U.S.A. Estimates agree well with those from an existing spatially explicit method based on inverse prediction. A variety of additional spatially explicit models are fitted; these include models with temporal stratification, behavioral response, and heterogeneous animal home ranges.
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                Author and article information

                Contributors
                Journal
                Journal of Animal Ecology
                Journal of Animal Ecology
                Wiley
                0021-8790
                1365-2656
                January 2022
                November 19 2021
                January 2022
                : 91
                : 1
                : 266-278
                Affiliations
                [1 ]Université de Lyon Université Lyon 1 CNRS UMR 5558 Laboratoire de Biométrie et Biologie Évolutive Villeurbanne France
                [2 ]Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life Sciences Ås Norway
                [3 ]CREAF Cerdanyola del Vallès Catalonia Spain
                [4 ]BABVE Universitat Autònoma de Barcelona Cerdanyola del Vallès Catalonia Spain
                [5 ]CEFE CNRS University of Montpellier University of Paul Valéry Montpellier 3 EPHE IRD Montpellier France
                Article
                10.1111/1365-2656.13629
                34743354
                e3c692c1-b775-4a85-8bc7-3414d1dac364
                © 2022

                http://creativecommons.org/licenses/by/4.0/

                http://doi.wiley.com/10.1002/tdm_license_1.1

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