Making the most of hard‐won data: Incorporating commonly discarded information from unidentifiable samples improves precision and cost‐effectiveness in wolf population monitoring

Standard spatial capture–recapture (SCR) models routinely discard a large fraction of non‐invasive genetic detections that fail genotyping, wasting costly field effort and undermining the precision of population estimates. In environmental impact assessments (EIA), where conclusions must withstand regulatory scrutiny under fixed monitoring budgets, this data loss is a problem that can be solved.

We applied the random‐thinning SCR framework (RT‐SCR) to a three‐year Iberian wolf ( Canis lupus signatus ) EIA monitoring programme in northern Portugal. We treat genotyping failure as a stochastic thinning process within a multisession Bayesian model with covariates on density and detection. Of 173 confirmed wolf scats, 56 (32%) lacked individual identity, and rather than discarding them, we analysed them alongside 117 identified samples. We benchmarked RT‐SCR against standard SCR, evaluated model fit using posterior predictive checks and used simulation to test whether gains generalised beyond this specific dataset.

Incorporating unidentified detections improved precision for the habitat effect on density (15% CV improvement), estimates of space use (8% CV improvement) and detection parameter (up to 5% CV improvement). Wolves had stable year‐to‐year density (2.89–5.07 wolves/100 km ² ). Simulations showed that RT‐SCR yielded consistent precision gains and restored near‐nominal coverage for detection parameters, whereas excluding unidentified samples produced overconfident credible intervals under standard SCR (0.920 vs. 0.505; nominal 0.89). Posterior predictive checks found no strong evidence of systematic lack of fit, even for a pack‐living species.

Synthesis and applications . RT‐SCR provides a principled and cost‐effective way to recover information from samples that are usually discarded, improving both precision and the reliability of parameter estimates without extra field or laboratory investment. Gains were strongest for shared ecological parameters, including detection, space use and habitat associations, and are expected to be even greater where genotyping success is lower. This makes the framework especially valuable for EIA and other conservation monitoring programmes where every hard‐won sample matters.