Understanding step selection analysis through numerical integration
Abstract Step selection functions (SSFs) are flexible statistical models used to jointly describe animals' movement and habitat preferences. The popularity of SSFs has grown rapidly, and various extensions have been developed to increase their utility, including the ability to use multiple stat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.14248 |
| _version_ | 1797359847567523840 |
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| author | Théo Michelot Natasha J. Klappstein Jonathan R. Potts John Fieberg |
| author_facet | Théo Michelot Natasha J. Klappstein Jonathan R. Potts John Fieberg |
| author_sort | Théo Michelot |
| collection | DOAJ |
| description | Abstract Step selection functions (SSFs) are flexible statistical models used to jointly describe animals' movement and habitat preferences. The popularity of SSFs has grown rapidly, and various extensions have been developed to increase their utility, including the ability to use multiple statistical distributions to describe movement constraints, interactions to allow movements to depend on local environmental features, and random effects and latent states to account for within‐ and among‐individual variability. Although the SSF is a relatively simple statistical model, its presentation has not been consistent in the literature, leading to confusion about model flexibility and interpretation. We believe that part of the confusion has arisen from the conflation of the SSF model with the methods used for statistical inference, and in particular, parameter estimation. Notably, conditional logistic regression (CLR) can be used to fit SSFs in exponential form, and this model fitting approach is often presented interchangeably with the actual model (the SSF itself). However, reliance on CLR reduces model flexibility, and suggests a misleading interpretation of step selection analysis as being equivalent to a case–control study. In this review, we explicitly distinguish between model formulation and inference technique, presenting a coherent framework to fit SSFs based on numerical integration and maximum likelihood estimation. We provide an overview of common numerical integration techniques (including Monte Carlo integration, importance sampling and quadrature), and explain how they relate to popular methods used in step selection analyses. This general framework unifies different model fitting techniques for SSFs, and opens the way for improved inferential methods. In this approach, it is straightforward to model movement with distributions outside the exponential family, and to apply different SSF model formulations to the same data set and compare them with AIC. By separating the model formulation from the inference technique, we hope to clarify many important concepts in step selection analysis. |
| first_indexed | 2024-03-08T15:29:44Z |
| format | Article |
| id | doaj.art-a2ae8174917b41f9b3954c1c7d2f9144 |
| institution | Directory Open Access Journal |
| issn | 2041-210X |
| language | English |
| last_indexed | 2024-03-08T15:29:44Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj.art-a2ae8174917b41f9b3954c1c7d2f91442024-01-10T06:33:14ZengWileyMethods in Ecology and Evolution2041-210X2024-01-01151243510.1111/2041-210X.14248Understanding step selection analysis through numerical integrationThéo Michelot0Natasha J. Klappstein1Jonathan R. Potts2John Fieberg3Dalhousie University Halifax Nova Scotia CanadaDalhousie University Halifax Nova Scotia CanadaUniversity of Sheffield Sheffield UKUniversity of Minnesota Saint Paul Minnesota USAAbstract Step selection functions (SSFs) are flexible statistical models used to jointly describe animals' movement and habitat preferences. The popularity of SSFs has grown rapidly, and various extensions have been developed to increase their utility, including the ability to use multiple statistical distributions to describe movement constraints, interactions to allow movements to depend on local environmental features, and random effects and latent states to account for within‐ and among‐individual variability. Although the SSF is a relatively simple statistical model, its presentation has not been consistent in the literature, leading to confusion about model flexibility and interpretation. We believe that part of the confusion has arisen from the conflation of the SSF model with the methods used for statistical inference, and in particular, parameter estimation. Notably, conditional logistic regression (CLR) can be used to fit SSFs in exponential form, and this model fitting approach is often presented interchangeably with the actual model (the SSF itself). However, reliance on CLR reduces model flexibility, and suggests a misleading interpretation of step selection analysis as being equivalent to a case–control study. In this review, we explicitly distinguish between model formulation and inference technique, presenting a coherent framework to fit SSFs based on numerical integration and maximum likelihood estimation. We provide an overview of common numerical integration techniques (including Monte Carlo integration, importance sampling and quadrature), and explain how they relate to popular methods used in step selection analyses. This general framework unifies different model fitting techniques for SSFs, and opens the way for improved inferential methods. In this approach, it is straightforward to model movement with distributions outside the exponential family, and to apply different SSF model formulations to the same data set and compare them with AIC. By separating the model formulation from the inference technique, we hope to clarify many important concepts in step selection analysis.https://doi.org/10.1111/2041-210X.14248animal movementhabitat selectionMonte Carlo integrationstep selection function |
| spellingShingle | Théo Michelot Natasha J. Klappstein Jonathan R. Potts John Fieberg Understanding step selection analysis through numerical integration Methods in Ecology and Evolution animal movement habitat selection Monte Carlo integration step selection function |
| title | Understanding step selection analysis through numerical integration |
| title_full | Understanding step selection analysis through numerical integration |
| title_fullStr | Understanding step selection analysis through numerical integration |
| title_full_unstemmed | Understanding step selection analysis through numerical integration |
| title_short | Understanding step selection analysis through numerical integration |
| title_sort | understanding step selection analysis through numerical integration |
| topic | animal movement habitat selection Monte Carlo integration step selection function |
| url | https://doi.org/10.1111/2041-210X.14248 |
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