patter: Particle algorithms for animal tracking in R and Julia
Abstract State‐space models are a powerful modelling framework in movement ecology that represents individual movements and the processes connecting movements to observations. However, fitting state‐space models to animal‐tracking data can be difficult and computationally expensive. Here, we introdu...
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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.70029 |
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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 State‐space models are a powerful modelling framework in movement ecology that represents individual movements and the processes connecting movements to observations. However, fitting state‐space models to animal‐tracking data can be difficult and computationally expensive. Here, we introduce patter, a package that provides particle filtering and smoothing algorithms that fit Bayesian state‐space models to tracking data, with a focus on data from aquatic animals in receiver arrays. patter is written in R, with a performant Julia backend. Package functionality supports data simulation, preparation, filtering, smoothing and mapping. In two examples, we demonstrate how to implement patter to reconstruct the movements of a tagged animal in an acoustic telemetry system from acoustic detections and ancillary observations. With perfect information, the particle filter reconstructs the true (unobserved) movement path (Example One). More generally, particle algorithms represent an individual's possible location probabilistically as a weighted series of samples (‘particles’). In our illustration, we resolve an individual's (unobserved) location every 2 min during 1 month and use particles to visualise movements, map space use and quantify residency (Example Two). patter facilitates robust, flexible and efficient analyses of animal‐tracking data. The methods are widely applicable and enable refined analyses of space use, home ranges and residency. |
| format | Article |
| id | doaj-art-838bad77cee04ffaac4bf89a3c5239e2 |
| 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-838bad77cee04ffaac4bf89a3c5239e22025-08-20T03:44:31ZengWileyMethods in Ecology and Evolution2041-210X2025-08-011681609161610.1111/2041-210X.70029patter: Particle algorithms for animal tracking in R and JuliaEdward 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 State‐space models are a powerful modelling framework in movement ecology that represents individual movements and the processes connecting movements to observations. However, fitting state‐space models to animal‐tracking data can be difficult and computationally expensive. Here, we introduce patter, a package that provides particle filtering and smoothing algorithms that fit Bayesian state‐space models to tracking data, with a focus on data from aquatic animals in receiver arrays. patter is written in R, with a performant Julia backend. Package functionality supports data simulation, preparation, filtering, smoothing and mapping. In two examples, we demonstrate how to implement patter to reconstruct the movements of a tagged animal in an acoustic telemetry system from acoustic detections and ancillary observations. With perfect information, the particle filter reconstructs the true (unobserved) movement path (Example One). More generally, particle algorithms represent an individual's possible location probabilistically as a weighted series of samples (‘particles’). In our illustration, we resolve an individual's (unobserved) location every 2 min during 1 month and use particles to visualise movements, map space use and quantify residency (Example Two). patter facilitates robust, flexible and efficient analyses of animal‐tracking data. The methods are widely applicable and enable refined analyses of space use, home ranges and residency.https://doi.org/10.1111/2041-210X.70029Bayesian inferencemovement ecologypackageparticle filterpassive acoustic telemetrystate‐space model |
| spellingShingle | Edward Lavender Andreas Scheidegger Carlo Albert Stanisław W. Biber Janine Illian James Thorburn Sophie Smout Helen Moor patter: Particle algorithms for animal tracking in R and Julia Methods in Ecology and Evolution Bayesian inference movement ecology package particle filter passive acoustic telemetry state‐space model |
| title | patter: Particle algorithms for animal tracking in R and Julia |
| title_full | patter: Particle algorithms for animal tracking in R and Julia |
| title_fullStr | patter: Particle algorithms for animal tracking in R and Julia |
| title_full_unstemmed | patter: Particle algorithms for animal tracking in R and Julia |
| title_short | patter: Particle algorithms for animal tracking in R and Julia |
| title_sort | patter particle algorithms for animal tracking in r and julia |
| topic | Bayesian inference movement ecology package particle filter passive acoustic telemetry state‐space model |
| url | https://doi.org/10.1111/2041-210X.70029 |
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