Spatially explicit capture–recapture models for relative density
- 1Department of Mathematics and Statistics University of Otago Dunedin New Zealand
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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BibTeX
@article{efford2026spatially,
title = {{Spatially explicit capture–recapture models for relative density}},
author = {Murray G. Efford},
journal = {Ecology},
year = {2026},
month = jul,
doi = {10.1002/ecy.70455},
url = {https://doi.org/10.1002/ecy.70455},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Spatially explicit capture–recapture models for relative density
AU - Murray G. Efford
PY - 2026
DA - 2026/07/01
JO - Ecology
DO - 10.1002/ecy.70455
UR - https://doi.org/10.1002/ecy.70455
AB - 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.
SN - 0012-9658
SN - 1939-9170
ER -