Evaluation of a multiple-species approach to monitoring species at the ecoregional scale

Monitoring is required of land managers and conservation practitioners to assess the success of management actions. "Shortcuts" are sought to reduce monitoring costs, most often consisting of the selection of a small number of species that are closely monitored to represent the status of many associated species and environmental correlates. Assumptions associated with such shortcuts have been challenged, yet alternative approaches remain scant. We evaluated an approach that departs significantly from the approach of selecting a few representative species. We explored two primary assertions: (1) that a coordinated multiple-species monitoring effort that collects presence-absence data on a broad range of species is a robust alternative to a few intensive single-species efforts, and (2) that the vertebrate species expected to be detected using this approach are numerous and diverse enough to represent all vertebrate species. We simulated monitoring the vertebrate species pool on an existing sample grid across the 7 million ha of public lands in the Sierra Nevada (USA) ecoregion. Based on the use of eight standard presence-absence protocols, we estimated the number of vertebrate species (excluding fish) with an adequate number of sample points within their range to detect ≥20% relative change in the proportion of points with detections between two points in time. We estimated that adequate detections would be obtained for 76% of the 465 vertebrate species, including 83% of all birds, 76% of all mammals, 65% of all reptiles, and 44% of all amphibians. Detection adequacy varied among life-history and ecological groups, but >50% of the species were adequately detected in every group with the exception of three groups: rare species, endemic species, and species of concern (33%, 24%, and 47% of associated species adequately detected, respectively). A multiple-species monitoring approach represents an effective and feasible alternative to the challenges of large-scale monitoring needs by targeting the most basic of population data for a large number and breadth of species.