Cooperative Fish and Wildlife Research Units Program: all
Education, Research and Technical Assistance for Managing Our Natural Resources

Optimization of occupancy-based monitoring has focused on balancing the number of sites and surveys to minimize field efforts and costs. When survey techniques require post-field processing of samples to confirm species detections, there may be opportunities to further improve efficiency. We used scat-based noninvasive genetic sampling for kit foxes (Vulpes macrotis) in Utah, USA, as a model system to assess post-field data processing strategies, evaluate the impacts of these strategies on estimates of occupancy and associations between parameters and predictors, and identify the most cost-effective approach. We identified scats with three criteria that varied in costs and reliability: (i) field-based identification (expert opinion), (ii) statistical-based morphological identification, and (iii) genetic-based identification (mitochondrial DNA). We also considered four novel post-field sample processing strategies that integrated statistical and genetic identifications to reduce costly genetic procedures, including (iv) a combined statistical-genetic identification, (v) a genetic removal design, (vi) a within-survey conditional-replicate design, and (vii) a single-genetic-replicate with false-positive modeling design. We considered results based on genetic identification as the best approximation of truth and used this to evaluate the performance of alternatives. Field-based and statistical-based criteria prone to misidentification produced estimates of occupancy that were biased high (~1.8 and 2.1 times higher than estimates without misidentifications, respectively). These criteria failed to recover associations between parameters and predictors consistent with genetic identification. The genetic removal design performed poorly, with limited detections leading to estimates that were biased high with poor precision and patterns inconsistent with genetic identification. Both statistical-genetic identification and the conditional-replicate design produced occupancy estimates comparable to genetic identification, while recovering the same model structure and associations at cost reductions of 67% and 74%, respectively. The false-positive design had the lowest cost (88% reduction) and recovered patterns consistent with genetic identification but had occupancy estimates that were ~32% lower than estimated occupancy based on genetic identification. Our results demonstrate that careful consideration of detection criteria and post-field data processing can reduce costs without significantly altering resulting inferences. Combined with earlier guidance on sampling designs for occupancy modeling, these findings can aid managers in optimizing occupancy-based monitoring.