Spatial Capture-Recapture Modeling in Wildlife Population Studies

Spatial capture-recapture (SCR) modelling is an individual-based analytical framework that uses the spatial coordinates of animal detections—derived from camera traps, genetic sampling or sensor arrays—to estimate population density, abundance and space use while explicitly accounting for variation in detection probability due to animal movement and landscape structure. By coupling encounter histories with the spatial configuration of detectors, SCR models resolve the longstanding challenge of defining an effective sampling area and mitigate edge effects inherent in traditional capture–recapture methods. Contemporary implementations adopt Bayesian or likelihood-based inference to integrate habitat covariates, resource selection functions and behavioural processes, thereby elucidating how environmental heterogeneity and connectivity influence population patterns. Applications range from wide-ranging carnivores to low-density ungulates and inform conservation across national and urban boundaries. Recent methodological advances centre on optimising detector placement, enhancing detection functions with machine learning and combining multiple data streams to bolster estimator precision and reduce bias.

Research from Nature Portfolio

Recent studies have applied Bayesian SCR techniques to non-invasive genetic and camera data to reveal fine-scale density gradients driven by behaviour and habitat change. In upland ungulates, SCR models accounting for sex-specific detection probabilities demonstrated how terrain ruggedness and forest cover produce distinct spatial segregation of males and females, enabling the mapping of sex-biased density surfaces that guide targeted management. In a transboundary forest ecosystem, extensive non-invasive DNA sampling combined with SCR yielded jurisdiction-specific estimates of red deer abundance, quantifying how insect-driven forest disturbance and contrasting harvesting regimes interact with elevation to shape density heterogeneity. These examples underscore the capacity of SCR to characterise spatial variation in activity centres and inform adaptive management across ecological and administrative landscapes.

Spatial Capture-Recapture Modeling in Wildlife Population Studies publication trend

The graph below shows the total number of articles in spatial capture-recapture modeling in wildlife population studies across all publications each year (not limited to Nature Index journals).

Technical terms

Encounter history: A record of spatially referenced detections for an identifiable individual across sampling occasions.

Detection function: A mathematical relationship describing how the probability of detecting an individual declines with distance from a detector or varies with covariates.

Activity centre: The latent home-range centre of an individual around which SCR models assume detections are probabilistically distributed.

Density surface modelling: The incorporation of spatial environmental covariates into SCR frameworks to predict variation in animal density across the landscape.

References

1. Spatiotemporal Bayesian Machine Learning for Estimation of an Empirical Lower Bound for Probability of Detection with Applications to Stationary Wildlife Photography. Computers (2024).
2. Sexual segregation results in pronounced sex-specific density gradients in the mountain ungulate, Rupicapra rupicapra. Communications Biology (2023).
3. A multidisciplinary approach to estimating wolf population size for long‐term conservation. Conservation Biology (2023).
4. Spatial variation in red deer density in a transboundary forest ecosystem. Scientific Reports (2023).
5. Unifying population and landscape ecology with spatial capture–recapture. Ecography (2017).
6. Trap Configuration and Spacing Influences Parameter Estimates in Spatial Capture-Recapture Models. PLOS ONE (2014).