Integrating distance sampling with minimum counts to improve monitoring

Minimum counts are commonly used to estimate population size and trend for wildlife conservation and management; however, the scope of inference based on such data is limited by untestable assumptions regarding the detection process. Alternative approaches, such as distance sampling, occupancy surveys, and repeated counts, can be employed to produce detection-corrected estimates of population parameters. Unfortunately, these approaches can be more complicated and costly to implement, potentially limiting their use. We explored a conceptual framework linking datasets collected at different spatial scales under different survey designs, with the goal of improving inference. Specifically, we link landscape-scale distance sampling surveys with local-scale minimum counts in an integrated modeling framework to estimate mountain goat (Oreamnos americanus) abundance at both the local and regional scale in south-central Alaska, USA, and provide an estimate of detection probability (i.e., sightability) for the minimum counts. Estimated sightability for the minimum count surveys was 0.67 (95% credible interval [CrI] = 0.52-0.83) and abundance for the entire area was 5,600 goats (CV = 9%), both in broad agreement with estimates from previous studies. Abundance estimates at the local scale (i.e., individual min. count unit) were reasonably precise (CV & x305; = 18%), suggesting the integrated approach can increase the amount of information produced at both spatial scales by linking minimum count approaches with more rigorous survey designs. We propose that our integrated approach may be implemented in the context of a modified split-panel monitoring design by altering survey protocols to include frequent minimum counts within local count units and intermittent but more rigorous survey designs with inference to the entire study area or population of interest. Doing so would provide estimates of abundance with appropriate measures of uncertainty at multiple spatial scales, thereby improving inference for population monitoring and management. (c) 2019 The Wildlife Society.