Rayleigh step-selection functions and connections to continuous-time mechanistic movement models
Abstract Background The process known as ecological diffusion emerges from a first principles view of animal movement, but ecological diffusion and other partial differential equation models can be difficult to fit to data. Step-selection functions (SSFs), on the other hand, have emerged as powerful...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | Movement Ecology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40462-023-00442-w |
| _version_ | 1797272819966410752 |
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| author | Joseph M. Eisaguirre Perry J. Williams Mevin B. Hooten |
| author_facet | Joseph M. Eisaguirre Perry J. Williams Mevin B. Hooten |
| author_sort | Joseph M. Eisaguirre |
| collection | DOAJ |
| description | Abstract Background The process known as ecological diffusion emerges from a first principles view of animal movement, but ecological diffusion and other partial differential equation models can be difficult to fit to data. Step-selection functions (SSFs), on the other hand, have emerged as powerful practical tools for ecologists studying the movement and habitat selection of animals. Methods SSFs typically involve comparing resources between a set of used and available points at each step in a sequence of observed positions. We use change of variables to show that ecological diffusion implies certain distributions for available steps that are more flexible than others commonly used. We then demonstrate advantages of these distributions with SSF models fit to data collected for a mountain lion in Colorado, USA. Results We show that connections between ecological diffusion and SSFs imply a Rayleigh step-length distribution and uniform turning angle distribution, which can accommodate data collected at irregular time intervals. The results of fitting an SSF model with these distributions compared to a set of commonly used distributions revealed how precision and inference can vary between the two approaches. Conclusions Our new continuous-time step-length distribution can be integrated into various forms of SSFs, making them applicable to data sets with irregular time intervals between successive animal locations. |
| first_indexed | 2024-03-07T14:34:39Z |
| format | Article |
| id | doaj.art-57a3db7587334dd9bc27d85b2f999f45 |
| institution | Directory Open Access Journal |
| issn | 2051-3933 |
| language | English |
| last_indexed | 2024-03-07T14:34:39Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Movement Ecology |
| spelling | doaj.art-57a3db7587334dd9bc27d85b2f999f452024-03-05T20:43:11ZengBMCMovement Ecology2051-39332024-02-011211810.1186/s40462-023-00442-wRayleigh step-selection functions and connections to continuous-time mechanistic movement modelsJoseph M. Eisaguirre0Perry J. Williams1Mevin B. Hooten2U.S. Geological Survey, Alaska Science CenterDepartment of Natural Resources & Environmental Science, University of NevadaDepartment of Statistics and Data Sciences, The University of Texas at AustinAbstract Background The process known as ecological diffusion emerges from a first principles view of animal movement, but ecological diffusion and other partial differential equation models can be difficult to fit to data. Step-selection functions (SSFs), on the other hand, have emerged as powerful practical tools for ecologists studying the movement and habitat selection of animals. Methods SSFs typically involve comparing resources between a set of used and available points at each step in a sequence of observed positions. We use change of variables to show that ecological diffusion implies certain distributions for available steps that are more flexible than others commonly used. We then demonstrate advantages of these distributions with SSF models fit to data collected for a mountain lion in Colorado, USA. Results We show that connections between ecological diffusion and SSFs imply a Rayleigh step-length distribution and uniform turning angle distribution, which can accommodate data collected at irregular time intervals. The results of fitting an SSF model with these distributions compared to a set of commonly used distributions revealed how precision and inference can vary between the two approaches. Conclusions Our new continuous-time step-length distribution can be integrated into various forms of SSFs, making them applicable to data sets with irregular time intervals between successive animal locations.https://doi.org/10.1186/s40462-023-00442-wChange of variablesContinuous-timeEcological diffusionFirst principlesFokker-PlanckHabitat selection |
| spellingShingle | Joseph M. Eisaguirre Perry J. Williams Mevin B. Hooten Rayleigh step-selection functions and connections to continuous-time mechanistic movement models Movement Ecology Change of variables Continuous-time Ecological diffusion First principles Fokker-Planck Habitat selection |
| title | Rayleigh step-selection functions and connections to continuous-time mechanistic movement models |
| title_full | Rayleigh step-selection functions and connections to continuous-time mechanistic movement models |
| title_fullStr | Rayleigh step-selection functions and connections to continuous-time mechanistic movement models |
| title_full_unstemmed | Rayleigh step-selection functions and connections to continuous-time mechanistic movement models |
| title_short | Rayleigh step-selection functions and connections to continuous-time mechanistic movement models |
| title_sort | rayleigh step selection functions and connections to continuous time mechanistic movement models |
| topic | Change of variables Continuous-time Ecological diffusion First principles Fokker-Planck Habitat selection |
| url | https://doi.org/10.1186/s40462-023-00442-w |
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