ArticleEcology2026
Spatially explicit capture-recapture models for relative density.
Article in Ecology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Evaluating synthetic substitutes to reduce illegal harvesting and support species recovery.Conservation biology : the journal of the Society for Conservation Biology · 2026Article
- Article
- Article
- Evaluating the use of non-invasive hair sampling and ddRAD to characterize populations of endangered species: Application to a peripheral population of the European mink.Ecology and evolution · 2023Article
- Assessing small-mammal trapping design using spatially explicit capture recapture (SECR) modeling on long-term monitoring data.PloS one · 2022Article
- Pushing the limits: ship rat (Biological invasions · 2022Article
- Spatial variation in population-density of snow leopards in a multiple use landscape in Spiti Valley, Trans-Himalaya.PloS one · 2021Article
- An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou.Ecology and evolution · 2020Article
- Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatially explicit capture-recapture (SECR) methods are used widely to estimate animal population density and related parameters. Maximum likelihood has been applied to two flavors of the SECR model-a full model that includes absolute density as a spatially varying parameter, and a model conditional on the number of detected individuals that assumes uniform density. We show how the conditional model may be extended to estimate relative density as a function of habitat and other spatial variables. Unlike absolute density, coefficients of relative density are robust to bias from heterogeneous individual detection. The conditional model has the further advantage of seamlessly including individual covariates of detection, and it is faster to fit than the full model. The spatial absolute density surface may be derived from the conditional model fit. Population growth rate (relative density with respect to time) may also be estimated from a conditional model, given temporal data. These properties are demonstrated by examples and simulation. It is suggested that conditional likelihood models, including those for relative density, play a central role in SECR.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.