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A model-driven approach to quantify migration patterns: individual, regional and yearly differences

Nils Bunnefeld, Luca Borger Orcid Logo, Bram van Moorter, Christer M Rolandsen, Holger Dettki, Erling Johan Solberg, Göran Ericsson

Journal of Animal Ecology, Volume: 80, Issue: 2, Pages: 466 - 476

Swansea University Author: Luca Borger Orcid Logo

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DOI (Published version): 10.1111/j.1365-2656.2010.01776.x

Abstract

1. Animal migration has long intrigued scientists and wildlife managers alike, yet migratory species face increasing challenges because of habitat fragmentation, climate change and over-exploitation. Central to the understanding migratory species is the objective discrimination between migratory and...

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Published in: Journal of Animal Ecology
Published: 2011
URI: https://cronfa.swan.ac.uk/Record/cronfa16622
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spelling 2021-07-16T14:57:07.0377560 v2 16622 2013-12-14 A model-driven approach to quantify migration patterns: individual, regional and yearly differences 8416d0ffc3cccdad6e6d67a455e7c4a2 0000-0001-8763-5997 Luca Borger Luca Borger true false 2013-12-14 SBI 1. Animal migration has long intrigued scientists and wildlife managers alike, yet migratory species face increasing challenges because of habitat fragmentation, climate change and over-exploitation. Central to the understanding migratory species is the objective discrimination between migratory and nonmigratory individuals in a given population, quantifying the timing, duration and distance of migration and the ability to predict migratory movements. 2. Here, we propose a uniform statistical framework to (i) separate migration from other movement behaviours, (ii) quantify migration parameters without the need for arbitrary cut-off criteria and (iii) test predictability across individuals, time and space. 3. We first validated our novel approach by simulating data based on established theoretical movement patterns. We then formulated the expected shapes of squared displacement patterns as nonlinear models for a suite of movement behaviours to test the ability of our method to distinguish between migratory movement and other movement types. 4. We then tested our approached empirically using 108 wild Global Positioning System (GPS)-collared moose Alces alces in Scandinavia as a study system because they exhibit a wide range of movement behaviours, including resident, migrating and dispersing individuals, within the same population. Applying our approach showed that 87% and 67% of our Swedish and Norwegian subpopulations, respectively, can be classified as migratory. 5. Using nonlinear mixed effects models for all migratory individuals we showed that the distance, timing and duration of migration differed between the sexes and between years, with additional individual differences accounting for a large part of the variation in the distance of migration but not in the timing or duration. Overall, the model explained most of the variation (92%) and also had high predictive power for the same individuals over time (69%) as well as between study populations (74%). 6. The high predictive ability of the approach suggests that it can help increase our understanding of the drivers of migration and could provide key quantitative information for understanding and managing a broad range of migratory species. Journal Article Journal of Animal Ecology 80 2 466 476 animal movement, moose, net squared displacement, nonlinear mixed models,spatialecology 31 12 2011 2011-12-31 10.1111/j.1365-2656.2010.01776.x COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University 2021-07-16T14:57:07.0377560 2013-12-14T01:18:03.6188898 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Nils Bunnefeld 1 Luca Borger 0000-0001-8763-5997 2 Bram van Moorter 3 Christer M Rolandsen 4 Holger Dettki 5 Erling Johan Solberg 6 Göran Ericsson 7
title A model-driven approach to quantify migration patterns: individual, regional and yearly differences
spellingShingle A model-driven approach to quantify migration patterns: individual, regional and yearly differences
Luca Borger
title_short A model-driven approach to quantify migration patterns: individual, regional and yearly differences
title_full A model-driven approach to quantify migration patterns: individual, regional and yearly differences
title_fullStr A model-driven approach to quantify migration patterns: individual, regional and yearly differences
title_full_unstemmed A model-driven approach to quantify migration patterns: individual, regional and yearly differences
title_sort A model-driven approach to quantify migration patterns: individual, regional and yearly differences
author_id_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2
author_id_fullname_str_mv 8416d0ffc3cccdad6e6d67a455e7c4a2_***_Luca Borger
author Luca Borger
author2 Nils Bunnefeld
Luca Borger
Bram van Moorter
Christer M Rolandsen
Holger Dettki
Erling Johan Solberg
Göran Ericsson
format Journal article
container_title Journal of Animal Ecology
container_volume 80
container_issue 2
container_start_page 466
publishDate 2011
institution Swansea University
doi_str_mv 10.1111/j.1365-2656.2010.01776.x
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
document_store_str 0
active_str 0
description 1. Animal migration has long intrigued scientists and wildlife managers alike, yet migratory species face increasing challenges because of habitat fragmentation, climate change and over-exploitation. Central to the understanding migratory species is the objective discrimination between migratory and nonmigratory individuals in a given population, quantifying the timing, duration and distance of migration and the ability to predict migratory movements. 2. Here, we propose a uniform statistical framework to (i) separate migration from other movement behaviours, (ii) quantify migration parameters without the need for arbitrary cut-off criteria and (iii) test predictability across individuals, time and space. 3. We first validated our novel approach by simulating data based on established theoretical movement patterns. We then formulated the expected shapes of squared displacement patterns as nonlinear models for a suite of movement behaviours to test the ability of our method to distinguish between migratory movement and other movement types. 4. We then tested our approached empirically using 108 wild Global Positioning System (GPS)-collared moose Alces alces in Scandinavia as a study system because they exhibit a wide range of movement behaviours, including resident, migrating and dispersing individuals, within the same population. Applying our approach showed that 87% and 67% of our Swedish and Norwegian subpopulations, respectively, can be classified as migratory. 5. Using nonlinear mixed effects models for all migratory individuals we showed that the distance, timing and duration of migration differed between the sexes and between years, with additional individual differences accounting for a large part of the variation in the distance of migration but not in the timing or duration. Overall, the model explained most of the variation (92%) and also had high predictive power for the same individuals over time (69%) as well as between study populations (74%). 6. The high predictive ability of the approach suggests that it can help increase our understanding of the drivers of migration and could provide key quantitative information for understanding and managing a broad range of migratory species.
published_date 2011-12-31T03:19:00Z
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score 10.9979105

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