A statistical framework for modelling migration corridors
Abstract Management of animal populations requires spatially explicit knowledge of movement corridors, such as those used during seasonal migrations. Global Positioning System (GPS) tracking data allow for mapping of corridors from directly observed movements, but such tracking data are absent for m...
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| Format: | Article |
| Language: | English |
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Wiley
2022-11-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.13969 |
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| author | Tristan A. Nuñez Mark A. Hurley Tabitha A. Graves Anna C. Ortega Hall Sawyer Julien Fattebert Jerod A. Merkle Matthew J. Kauffman |
| author_facet | Tristan A. Nuñez Mark A. Hurley Tabitha A. Graves Anna C. Ortega Hall Sawyer Julien Fattebert Jerod A. Merkle Matthew J. Kauffman |
| author_sort | Tristan A. Nuñez |
| collection | DOAJ |
| description | Abstract Management of animal populations requires spatially explicit knowledge of movement corridors, such as those used during seasonal migrations. Global Positioning System (GPS) tracking data allow for mapping of corridors from directly observed movements, but such tracking data are absent for many populations. We developed a novel statistical corridor modelling approach that predicts movement corridors from cost‐distance models fit directly to migration tracking data. Unlike existing predictive approaches, this does not require the ad hoc transformation of habitat suitability surfaces into resistance surfaces. We tested the ability of the approach to recover parameters used to generate simulated movements. We then used GPS data from three migrating mule deer Odocoileus hemionus herds in Idaho and Wyoming to model corridors as a function of elevation, slope, aspect, percent shrub, date of peak green‐up, snow‐off date and human footprint. We assessed the predictive ability of the fitted models using validation tracks from the same herd as well as from the other herds. The approach reproduced parameters used to generate the simulated movements, predicted the corridors used by migratory populations, and described the direction, magnitude and confidence levels of the effects of environmental variables on corridors. Within‐herd validation indicated that fitted corridor models are more accurate at predicting migration corridors than null models, and cross‐herd validation indicated that fitted models for some herds accurately predicted the observed migrations of other herds. In addition to the practical benefit of mapping corridors for management, our statistical corridor modelling framework sets the stage for evaluating fundamental questions about the fitness trade‐offs, navigation, learning, fidelity and movement constraints that influence migratory and other corridor‐generating behaviour. Models of predictive corridors can inform management and planning for the conservation of migrations across taxa, including the potential restoration of corridors. Our corridor modelling approach is also readily applied to non‐migratory animal movements. |
| format | Article |
| id | doaj-art-629227c59d0042c2bd0343e8935c35ca |
| institution | Kabale University |
| issn | 2041-210X |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj-art-629227c59d0042c2bd0343e8935c35ca2025-08-20T03:45:07ZengWileyMethods in Ecology and Evolution2041-210X2022-11-0113112635264810.1111/2041-210X.13969A statistical framework for modelling migration corridorsTristan A. Nuñez0Mark A. Hurley1Tabitha A. Graves2Anna C. Ortega3Hall Sawyer4Julien Fattebert5Jerod A. Merkle6Matthew J. Kauffman7Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology University of Wyoming Laramie Wyoming USAIdaho Department of Fish and Game Boise Idaho USAU.S. Geological Survey, Northern Rocky Mountain Science Center West Glacier Montana USAWyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology University of Wyoming Laramie Wyoming USAWestern EcoSystems Technology (WEST), Inc. Laramie Wyoming USAWyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology University of Wyoming Laramie Wyoming USADepartment of Zoology and Physiology University of Wyoming Laramie Wyoming USAU.S. Geological Survey, Wyoming Cooperative Fish and Wildlife Research Unit, Zoology and Physiology Department University of Wyoming Laramie Wyoming USAAbstract Management of animal populations requires spatially explicit knowledge of movement corridors, such as those used during seasonal migrations. Global Positioning System (GPS) tracking data allow for mapping of corridors from directly observed movements, but such tracking data are absent for many populations. We developed a novel statistical corridor modelling approach that predicts movement corridors from cost‐distance models fit directly to migration tracking data. Unlike existing predictive approaches, this does not require the ad hoc transformation of habitat suitability surfaces into resistance surfaces. We tested the ability of the approach to recover parameters used to generate simulated movements. We then used GPS data from three migrating mule deer Odocoileus hemionus herds in Idaho and Wyoming to model corridors as a function of elevation, slope, aspect, percent shrub, date of peak green‐up, snow‐off date and human footprint. We assessed the predictive ability of the fitted models using validation tracks from the same herd as well as from the other herds. The approach reproduced parameters used to generate the simulated movements, predicted the corridors used by migratory populations, and described the direction, magnitude and confidence levels of the effects of environmental variables on corridors. Within‐herd validation indicated that fitted corridor models are more accurate at predicting migration corridors than null models, and cross‐herd validation indicated that fitted models for some herds accurately predicted the observed migrations of other herds. In addition to the practical benefit of mapping corridors for management, our statistical corridor modelling framework sets the stage for evaluating fundamental questions about the fitness trade‐offs, navigation, learning, fidelity and movement constraints that influence migratory and other corridor‐generating behaviour. Models of predictive corridors can inform management and planning for the conservation of migrations across taxa, including the potential restoration of corridors. Our corridor modelling approach is also readily applied to non‐migratory animal movements.https://doi.org/10.1111/2041-210X.13969corridor ecologycorridor predictioncost‐distance modellingmaximum likelihoodmigration ecologymovement ecology |
| spellingShingle | Tristan A. Nuñez Mark A. Hurley Tabitha A. Graves Anna C. Ortega Hall Sawyer Julien Fattebert Jerod A. Merkle Matthew J. Kauffman A statistical framework for modelling migration corridors Methods in Ecology and Evolution corridor ecology corridor prediction cost‐distance modelling maximum likelihood migration ecology movement ecology |
| title | A statistical framework for modelling migration corridors |
| title_full | A statistical framework for modelling migration corridors |
| title_fullStr | A statistical framework for modelling migration corridors |
| title_full_unstemmed | A statistical framework for modelling migration corridors |
| title_short | A statistical framework for modelling migration corridors |
| title_sort | statistical framework for modelling migration corridors |
| topic | corridor ecology corridor prediction cost‐distance modelling maximum likelihood migration ecology movement ecology |
| url | https://doi.org/10.1111/2041-210X.13969 |
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