ArticleEcology and evolution2021
Spatial capture-recapture with random thinning for unidentified encounters.
Article in Ecology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Jaguar Density at the Northeastern Limit of Its Distribution in Mexico.Ecology and evolution · 2026Article
- Genomic Insights Into the Origin, Decline and Recovery of the Once Critically Endangered Iberian Lynx.Molecular ecology · 2025Review
- Genetic monogamy in high density populations of a threatened Mediterranean rodent.Scientific reports · 2025Article
- Low signs of territorial behavior in the Eurasian otter during low-water conditions in a Mediterranean river.Scientific reports · 2024Article
- Occupancy data improves parameter precision in spatial capture-recapture models.Ecology and evolution · 2022Article
- Estimating red fox density using non-invasive genetic sampling and spatial capture-recapture modelling.Oecologia · 2022Article
- Evaluating otter reintroduction outcomes using genetic spatial capture-recapture modified for dendritic networks.Ecology and evolution · 2021Article
- Spatial capture-recapture with random thinning for unidentified encounters.Ecology and evolution · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial capture-recapture (SCR) models have increasingly been used as a basis for combining capture-recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count model), where no individual identities are available and spatial mark-resight (SMR) where individual identities are available for only a marked subset of the population. Currently lacking, though, is a model that allows unidentified samples to be combined with identified samples when there are no separate classes of "marked" and "unmarked" individuals and when the two sample types cannot be considered as arising from two independent observation models. This is a common scenario when using noninvasive sampling methods, for example, when analyzing data on identified and unidentified photographs or scats from the same sites.Here we describe a "random thinning" SCR model that utilizes encounters of both known and unknown identity samples using a natural mechanistic dependence between samples arising from a single observation model. Our model was fitted in a Bayesian framework using NIMBLE.We investigate the improvement in parameter estimates by including the unknown identity samples, which was notable (up to 79% more precise) in low-density populations with a low rate of identified encounters. We then applied the random thinning SCR model to a noninvasive genetic sampling study of brown bear (
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Registered trials
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