Spatially Explicit Power Analyses for Occupancy‐Based Monitoring of Wolverine in the U.S. Rocky Mountains

Abstract

Conservation scientists and resource managers often have to design monitoring programs for species that are rare or patchily distributed across large landscapes. Such programs are frequently expensive and seldom can be conducted by one entity. It is essential that a prospective power analysis be undertaken to ensure stated monitoring goals are feasible. We developed a spatially based simulation program that accounts for natural history, habitat use, and sampling scheme to investigate the power of monitoring protocols to detect trends in population abundance over time with occupancy-based methods. We analyzed monitoring schemes with different sampling efforts for wolverine (Gulo gulo) populations in 2 areas of the U.S. Rocky Mountains. The relation between occupancy and abundance was nonlinear and depended on landscape, population size, and movement parameters. With current estimates for population size and detection probability in the northern U.S. Rockies, most sampling schemes were only able to detect large declines in abundance in the simulations (i.e., 50% decline over 10 years). For small populations reestablishing in the Southern Rockies, occupancy-based methods had enough power to detect population trends only when populations were increasing dramatically (e.g., doubling or tripling in 10 years), regardless of sampling effort. In general, increasing the number of cells sampled or the per-visit detection probability had a much greater effect on power than the number of visits conducted during a survey. Although our results are specific to wolverines, this approach could easily be adapted to other territorial species.

Recommended survey designs for occupancy modelling using motion-activated cameras: insights from empirical wildlife data

“…We emphasize the use of biological knowledge of the target species coupled with clearly defined a-priori objectives that link monitoring or research effort with defined ecological questions or conservation actions (Martin et al, 2009). Our study also illustrates the value of data simulation approaches for assessing methods and study design before embarking on empirical data collection (Zurell et al, 2010; Ellis, Ivan & Schwartz, 2013). However, it is not always feasible for practitioners to carry out their own simulation exercises.…”

“…However, values of detection probability are dependent upon the length of the sampling occasion and researchers will often employ sampling occasions that are measured in weeks rather than days (Ellis, Ivan & Schwartz, 2013). Thus, if the daily detection probability is 0.03, we can recalculate p using the p * formula ( p * = 1−(1− p ) s ) such that at 1 week p = 0.19, 2 weeks = 0.35 and so on ( s is the number of sampling occasions).…”

Female breeding dispersal in wolverines, a solitary carnivore with high territorial fidelity

“…This demonstration of the complexity in carnivore spatial behaviour provides a stronger foundation for science-based management of wolverine populations that are subject to strong conservation and management concerns in many parts of its distribution (Saether et al 2005;Persson et al 2009;Inman et al 2013). Our results have implications for the design and interpretation of population monitoring schemes (Ellis et al 2014;, harvest strategies (Saether et al 2005) and conflict mitigation .…”

The effects of urbanization on population density, occupancy, and detection probability of wild felids

“…Studies of presence-absence (Gaston et al 2000) and occupancy (MacKenzie and Nichols 2004, MacKenzie et al 2006, Temple and Gutie´rrez 2013 of animals have reported a positive relationship between abundance and occurrence. Further, even for large changes in population size, intensive sampling is necessary to observe changes in occupancy (Ellis et al 2014). For example, occupancy estimates will increase only if additional sites are used as population densities increases.…”