Spatially explicit power analysis for detecting occupancy trends for multiple species.

Assessing the statistical power to detect changes in wildlife populations is a crucial yet often overlooked step when designing and evaluating monitoring programs. Here, we developed a simulation framework to perform spatially explicit statistical power analysis of biological monitoring programs for detecting temporal trends in occupancy for multiple species. Using raster layers representing the spatial variation in current occupancy and species-level detectability for one or multiple observation methods, our framework simulates changes in occupancy over space and time, with the capacity to explicitly model stochastic disturbances at monitoring sites (i.e., dynamic landscapes). Once users specify the number and location of sites, the frequency and duration of surveys, and the type of detection method(s) for each species, our framework estimates power to detect occupancy trends, both across the landscape and/or within nested management units. As a case study, we evaluated the power of a long-term monitoring program to detect trends in occupancy for 136 species (83 birds, 33 reptiles, and 20 mammals) across and within Kakadu, Litchfield, and Nitmiluk National Parks in northern Australia. We assumed continuation of an original monitoring design implemented since 1996, with the addition of camera trapping. As expected, power to detect trends was sensitive to the direction and magnitude of the change in occupancy, detectability, initial occupancy levels, and the rarity of species. Our simulations suggest that monitoring has at least an 80% chance at detecting a 50% decline in occupancy for 22% of the modeled species across the three parks over the next 15 yr. Monitoring is more likely to detect increasing occupancy trends, with at least an 80% chance at detecting a 50% increase in 87% of species. The addition of camera-trapping increased average power to detect a 50% decline in mammals compared with using only live trapping by 63%. We provide a flexible tool that can help decision-makers design and evaluate monitoring programs for hundreds of species at a time in a range of ecological settings, while explicitly considering the distribution of species and alternative sampling methods.