Particle algorithms for animal movement modelling in receiver arrays
Abstract Particle filters and smoothers are sequential Monte Carlo algorithms used to fit non‐linear, non‐Gaussian state‐space models. These algorithms are well placed to fit process‐oriented models to animal‐tracking data, especially in receiver arrays, but to date they have received limited attent...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.70028 |
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| _version_ | 1849338091186159616 |
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| author | Edward Lavender Andreas Scheidegger Carlo Albert Stanisław W. Biber Janine Illian James Thorburn Sophie Smout Helen Moor |
| author_facet | Edward Lavender Andreas Scheidegger Carlo Albert Stanisław W. Biber Janine Illian James Thorburn Sophie Smout Helen Moor |
| author_sort | Edward Lavender |
| collection | DOAJ |
| description | Abstract Particle filters and smoothers are sequential Monte Carlo algorithms used to fit non‐linear, non‐Gaussian state‐space models. These algorithms are well placed to fit process‐oriented models to animal‐tracking data, especially in receiver arrays, but to date they have received limited attention in the ecological literature. We introduce a Bayesian filtering–smoothing algorithm that reconstructs individual movements and patterns of space use from animal‐tracking data, with a focus on passive acoustic telemetry systems. Within a sound probabilistic framework, the methodology integrates the movement process and the observation processes of disparate datasets, while correctly representing uncertainty. In a simulation‐based analysis, we compare the performance of our algorithm to the prevailing heuristic methods used to study movements and space use in passive acoustic telemetry systems and analyse algorithm sensitivity. We find the particle smoothing methodology outperforms heuristic methods across the board. Particle‐based maps represent simulated movements more accurately, even in dense receiver arrays, and are better suited to analyses of home ranges, residency and habitat preferences. This study sets a new state‐of‐the‐art for movement modelling in receiver arrays. Particle algorithms provide a robust, flexible and intuitive modelling framework with potential applications in many ecological settings. |
| format | Article |
| id | doaj-art-b8fb18dfdd6b43d4b3c30be83550ca49 |
| institution | Kabale University |
| issn | 2041-210X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj-art-b8fb18dfdd6b43d4b3c30be83550ca492025-08-20T03:44:31ZengWileyMethods in Ecology and Evolution2041-210X2025-08-011681808181910.1111/2041-210X.70028Particle algorithms for animal movement modelling in receiver arraysEdward Lavender0Andreas Scheidegger1Carlo Albert2Stanisław W. Biber3Janine Illian4James Thorburn5Sophie Smout6Helen Moor7Department of Systems Analysis, Integrated Assessment and Modelling Eawag Swiss Federal Institute of Aquatic Science and Technology Dübendorf SwitzerlandDepartment of Systems Analysis, Integrated Assessment and Modelling Eawag Swiss Federal Institute of Aquatic Science and Technology Dübendorf SwitzerlandDepartment of Systems Analysis, Integrated Assessment and Modelling Eawag Swiss Federal Institute of Aquatic Science and Technology Dübendorf SwitzerlandSchool of Engineering Mathematics and Technology University of Bristol Bristol UKSchool of Mathematics and Statistics University of Glasgow Glasgow UKSchool of Applied Sciences Edinburgh Napier University Edinburgh UKScottish Oceans Institute University of St Andrews St Andrews UKDepartment of Systems Analysis, Integrated Assessment and Modelling Eawag Swiss Federal Institute of Aquatic Science and Technology Dübendorf SwitzerlandAbstract Particle filters and smoothers are sequential Monte Carlo algorithms used to fit non‐linear, non‐Gaussian state‐space models. These algorithms are well placed to fit process‐oriented models to animal‐tracking data, especially in receiver arrays, but to date they have received limited attention in the ecological literature. We introduce a Bayesian filtering–smoothing algorithm that reconstructs individual movements and patterns of space use from animal‐tracking data, with a focus on passive acoustic telemetry systems. Within a sound probabilistic framework, the methodology integrates the movement process and the observation processes of disparate datasets, while correctly representing uncertainty. In a simulation‐based analysis, we compare the performance of our algorithm to the prevailing heuristic methods used to study movements and space use in passive acoustic telemetry systems and analyse algorithm sensitivity. We find the particle smoothing methodology outperforms heuristic methods across the board. Particle‐based maps represent simulated movements more accurately, even in dense receiver arrays, and are better suited to analyses of home ranges, residency and habitat preferences. This study sets a new state‐of‐the‐art for movement modelling in receiver arrays. Particle algorithms provide a robust, flexible and intuitive modelling framework with potential applications in many ecological settings.https://doi.org/10.1111/2041-210X.70028animal trackingmovement ecologypassive acoustic telemetrypatterstate‐space modelutilisation distribution |
| spellingShingle | Edward Lavender Andreas Scheidegger Carlo Albert Stanisław W. Biber Janine Illian James Thorburn Sophie Smout Helen Moor Particle algorithms for animal movement modelling in receiver arrays Methods in Ecology and Evolution animal tracking movement ecology passive acoustic telemetry patter state‐space model utilisation distribution |
| title | Particle algorithms for animal movement modelling in receiver arrays |
| title_full | Particle algorithms for animal movement modelling in receiver arrays |
| title_fullStr | Particle algorithms for animal movement modelling in receiver arrays |
| title_full_unstemmed | Particle algorithms for animal movement modelling in receiver arrays |
| title_short | Particle algorithms for animal movement modelling in receiver arrays |
| title_sort | particle algorithms for animal movement modelling in receiver arrays |
| topic | animal tracking movement ecology passive acoustic telemetry patter state‐space model utilisation distribution |
| url | https://doi.org/10.1111/2041-210X.70028 |
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