Spatially explicit power analysis for detecting occupancy trends for multiple species

Abstract

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.

Bringing back the endangered bridled nail-tailed wallaby at Taunton National Park (Scientific) through effective predator control

“…Other studies, such as those of Robley et al (2010) and Southwell et al (2019), have found results similar to those of our study and concluded that cameras provided more robust detection than did sand pads. Several studies have used activity indices as a measure of abundance with mixed success (Allen et al 1996;Burrows et al 2003;Hopkins and Kennedy 2004;Robley et al 2010;Doherty and Algar 2015;Fancourt 2016;Fancourt et al 2021b).…”

“…At Taunton, radiotracking studies have shown that cats spend only ,4% of their time on tracks (Fancourt et al 2021) and dogs avoid roads altogether during winter (Allen and Byrne 2008), making track-based activity indices extremely unreliable at times. The placement of sand pads and cameras and the effect it can have on detection and estimating population change have been discussed in earlier publications (Southwell et al 2019;Geyle et al 2020). At Taunton, cameras were placed to improve the detection of dogs, yet this may have under-sampled cat activity.…”

Experimenting With the Past to Improve Environmental Monitoring

“…With an appropriate model, simulations can then be run for a variety of scenarios, including changing the number of samples taken per year, altering the number of sites sampled, and sampling for different lengths of time (Rhodes and Jonzen, 2011;Barry et al, 2017;Christie et al, 2019;Weiser et al, 2019;White, 2019). Simulations can also be useful in deciding which streams of data to use (Weiser et al, 2020) or the effect of changing sampling methodology during the course of a study (Southwell et al, 2019). A lot of prior work has also used simulations to better understand optimal sampling schemes for invasive species (Chades et al, 2011;Holden and Ellner, 2016).…”

Umbrella effect of monitoring protocols for mammals in the Northeast US

“…Previous work on developing monitoring protocols through occupancy modelling focused both on single species 19 , 25 or multiple species 26 , 27 , underscoring the importance of conducting rigorous power analyses. We advanced our knowledge on the topic of optimal monitoring by empirically testing for an umbrella effect, contextually showing the extent to which the approach is applicable to multi-species monitoring of the mammalian community.…”