ArticleEcology2023
A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data.
Article in Ecology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A non-Euclidean movement model to quantify landscape connectivity with open spatial capture-recapture models.Ecology · 2026Article
- Map of death: spatially explicit mortality of the grey wolf.Proceedings. Biological sciences · 2025Article
- First rangewide density estimate of the endemic and isolated Luangwa giraffe in Zambia.Scientific reports · 2025Article
- Environmental variability across space and time drives the recolonization pattern of a historically persecuted large carnivore.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- When enough is enough: Optimising monitoring effort for large-scale wolf population size estimation in the Italian Alps.Ecology and evolution · 2024Article
- Sexual segregation results in pronounced sex-specific density gradients in the mountain ungulate, Rupicapra rupicapra.Communications biology · 2023Article
- Spatial variation in red deer density in a transboundary forest ecosystem.Scientific reports · 2023Article
- Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models.Ecology · 2023Article
- A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data.Ecology · 2023Article
- GPS collars have an apparent positive effect on the survival of a large carnivore.Biology letters · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial capture-recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub-models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub-models can be expressed as point processes taking place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi-complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatially continuous detection process in SCR studies that use area searches. In addition, the SCDL approach is more efficient than data augmentation for simple SCR models while losing its advantages for more complicated models that account for spatial variation in either population density or detection. We present the model formulation, test it with simulations, quantify computational efficiency gains, and conclude with a real-life example using non-invasive genetic sampling data for an elusive large carnivore, the wolverine (Gulo gulo) in Norway.
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