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Integrating animal movement with habitat suitability for estimating dynamic migratory connectivity
Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science. Max Planck Inst Ornithol, Germany;Univ Konstanz, Germany.ORCID iD: 0000-0002-2254-5779
Univ Zurich, Switzerland.
Food & Agr Org United Nations, Ghana.
US Geol Survey, USA.
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2018 (English)In: Landscape Ecology, ISSN 0921-2973, E-ISSN 1572-9761, Vol. 33, no 6, p. 879-893Article in journal (Refereed) Published
Abstract [en]

High-resolution animal movement data are becoming increasingly available, yet having a multitude of empirical trajectories alone does not allow us to easily predict animal movement. To answer ecological and evolutionary questions at a population level, quantitative estimates of a species' potential to link patches or populations are of importance. We introduce an approach that combines movement-informed simulated trajectories with an environment-informed estimate of the trajectories' plausibility to derive connectivity. Using the example of bar-headed geese we estimated migratory connectivity at a landscape level throughout the annual cycle in their native range. We used tracking data of bar-headed geese to develop a multi-state movement model and to estimate temporally explicit habitat suitability within the species' range. We simulated migratory movements between range fragments, and calculated a measure we called route viability. The results are compared to expectations derived from published literature. Simulated migrations matched empirical trajectories in key characteristics such as stopover duration. The viability of the simulated trajectories was similar to that of the empirical trajectories. We found that, overall, the migratory connectivity was higher within the breeding than in wintering areas, corroborating previous findings for this species. We show how empirical tracking data and environmental information can be fused for meaningful predictions of animal movements throughout the year and even outside the spatial range of the available data. Beyond predicting migratory connectivity, our framework will prove useful for modelling ecological processes facilitated by animal movement, such as seed dispersal or disease ecology.

Place, publisher, year, edition, pages
Springer, 2018. Vol. 33, no 6, p. 879-893
Keywords [en]
Anser indicus, Bar-headed goose, Empirical random trajectory generator, Migratory connectivity, Movement model, Stepping-stone migration model
National Category
Ecology
Research subject
Natural Science, Ecology
Identifiers
URN: urn:nbn:se:lnu:diva-76887DOI: 10.1007/s10980-018-0637-9ISI: 000434156200003Scopus ID: 2-s2.0-85046025678OAI: oai:DiVA.org:lnu-76887DiVA, id: diva2:1233299
Available from: 2018-07-17 Created: 2018-07-17 Last updated: 2019-08-29Bibliographically approved

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van Toor, Mariëlle L.

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  • apa
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