Spatial capture-recapture can improve environmental impact assessments for large carnivores

1. Environmental Impact Assessments (EIAs) for large carnivores frequently rely on summary statistics or relative abundance indices to evaluate the effects of infrastructure development on predator populations. However, these approaches often fail to account for imperfect detection and observer bias, which can result in misleading conclusions about population status and trends. This leads to potentially unreliable conclusions and suboptimal conservation decisions.

2. This study addresses these limitations by demonstrating the benefits of spatial capture–recapture (SCR), particularly its capacity to account for detection probability and spatial heterogeneity, using the wolf (Canis lupus) as a model species in a 3-year monitoring programme within a windfarm-impacted landscape in Portugal.

3. We show that, aside from the addition of georeferenced individual identification and survey effort data, implementing SCR requires only minimal adjustments to existing survey designs while yielding substantial analytical benefits. SCR generates unbiased estimates of density, abundance and space use—critical metrics for assessing population status—and allows the integration of spatial covariates (such as proximity to infrastructure) to enable explicit, model-based inference about potential impacts.

4. Practical implications. Incorporating SCR into EIA monitoring can replace relative abundance indices with robust ecological metrics, enhancing transparency and reliability for stakeholders. This approach supports adaptive management procedures and the development of effective mitigation and compensation measures across project phases, ultimately promoting the long-term resilience of large carnivore populations.