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      Energyscapes and prey fields shape a North Atlantic seabird wintering hotspot under climate change

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          Abstract

          There is an urgent need for a better understanding of animal migratory ecology under the influence of climate change. Most current analyses require long-term monitoring of populations on the move, and shorter-term approaches are needed. Here, we analysed the ecological drivers of seabird migration within the framework of the energyscape concept, which we defined as the variations in the energy requirements of an organism across geographical space as a function of environmental conditions. We compared the winter location of seabirds with their modelled energy requirements and prey fields throughout the North Atlantic. Across six winters, we tracked the migration of 94 little auks ( Alle alle), a key sentinel Arctic species, between their East Greenland breeding site and wintering areas off Newfoundland. Winter energyscapes were modelled with Niche Mapper™, a mechanistic tool which takes into account local climate and bird ecophysiology. Subsequently, we used a resource selection function to explain seabird distributions through modelled energyscapes and winter surface distribution of one of their main prey, Calanus finmarchicus. Finally, future energyscapes were calculated according to IPCC climate change scenarios. We found that little auks targeted areas with high prey densities and moderately elevated energyscapes. Predicted energyscapes for 2050 and 2095 showed a decrease in winter energy requirements under the high emission scenario, which may be beneficial if prey availability is maintained. Overall, our study demonstrates the great potential of the energyscape concept for the study of animal spatial ecology, in particular in the context of global change.

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          Most cited references72

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          Ecological and Evolutionary Responses to Recent Climate Change

          Ecological changes in the phenology and distribution of plants and animals are occurring in all well-studied marine, freshwater, and terrestrial groups. These observed changes are heavily biased in the directions predicted from global warming and have been linked to local or regional climate change through correlations between climate and biological variation, field and laboratory experiments, and physiological research. Range-restricted species, particularly polar and mountaintop species, show severe range contractions and have been the first groups in which entire species have gone extinct due to recent climate change. Tropical coral reefs and amphibians have been most negatively affected. Predator-prey and plant-insect interactions have been disrupted when interacting species have responded differently to warming. Evolutionary adaptations to warmer conditions have occurred in the interiors of species' ranges, and resource use and dispersal have evolved rapidly at expanding range margins. Observed genetic shifts modulate local effects of climate change, but there is little evidence that they will mitigate negative effects at the species level.
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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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              Application of random effects to the study of resource selection by animals.

              1. Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence. 2. Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability. 3. Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed. 4. Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects. 5. Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection. 6. We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions limiting their generality. This approach will allow researchers to appropriately estimate marginal (population) and conditional (individual) responses, and account for complex grouping, unbalanced sample designs and autocorrelation.
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                Author and article information

                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society Publishing
                2054-5703
                January 2018
                17 January 2018
                17 January 2018
                : 5
                : 1
                : 171883
                Affiliations
                [1 ]CEFE UMR 5175, CNRS – Université de Montpellier – Université Paul-Valéry Montpellier – EPHE , Montpellier, France
                [2 ]Littoral Environnement et Sociétés (LIENSs), UMR 7266 CNRS-Université de La Rochelle , La Rochelle, France
                [3 ]Department of Integrative Biology, University of Wisconsin , Madison, WI, USA
                [4 ]Department of Mathematics and Statistics, University of Strathclyde , Livingstone Tower, 26 Richmond Street, Glasgow G1 1XQ, Scotland, UK
                [5 ]Percy FitzPatrick Institute, DST/NRF Centre of Excellence, University of Cape Town , Rondebosch, South Africa
                Author notes
                Author for correspondence: F. Amélineau e-mail: francoise.amelineau@ 123456gmail.com

                Electronic supplementary material is available online at https://dx.doi.org/10.6084/m9.figshare.c.3965346.

                Author information
                http://orcid.org/0000-0001-9723-5858
                http://orcid.org/0000-0002-6428-5012
                http://orcid.org/0000-0002-7711-9398
                Article
                rsos171883
                10.1098/rsos.171883
                5792952
                29410875
                8e3914be-0a98-49ab-aebe-821485353b57
                © 2018 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.

                History
                : 14 November 2017
                : 30 November 2017
                Funding
                Funded by: European commission Marie Curie CIG;
                Award ID: 631203
                Funded by: Institut Polaire Français Paul Emile Victor, http://dx.doi.org/10.13039/501100004796;
                Award ID: 388 Adaclim
                Categories
                1001
                60
                1004
                69
                202
                Biology (Whole Organism)
                Research Article
                Custom metadata
                January, 2018

                bioenergetics,biologging,habitat modelling,little auk (alle alle),migration,spatial ecology

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