Species‐specific differences in detection and occupancy probabilities help drive ability to detect trends in occupancy

Abstract

Occupancy‐based surveys are increasingly used to monitor wildlife populations because they can be more cost‐effective than abundance surveys and because they may track multiple species, simultaneously. The design of these multi‐species occupancy surveys affects statistical power to detect trends in occupancy because individual species vary in resource selection, detection probability, and rarity. We tested for differences in the ability of a large‐scale monitoring program to detect changes in single‐species occupancy of 13 medium–large mammal species captured on n = 183 cameras systematically placed across five national parks in the Canadian Rockies (~21,000 km2). We focus the interpretation of our findings on three species at risk: grizzly bear, wolverine, and caribou. We found that statistical power to monitor trends in occupancy depends not only on the established elements associated with power (sampling size, effect size, and variation in estimates), but also on species‐specific detection and occupancy probabilities. These two probabilities, however, affected power differently. For most species in our study, power is insensitive to detection probability. Increasing replicate‐specific detection probability only improved power when the cumulative detection probability was below 0.80. Therefore, efficient species monitoring must consider that power no longer improves by increasing sample size or the replicate‐specific detection probability once this threshold is reached. On the other hand, species with occupancy probabilities close to 0.5 had lower statistical power than those with higher or lower occupancy, that is, power was higher for both rare and very common species. This pattern is due to the heretofore‐underappreciated effect of the binomial variation in occupancy. The implications of these findings are species‐specific. Grizzly bears, for example, had high detection and occupancy probabilities, resulting in high power to detect a population change. Conversely, wolverines had low detection probability and the power to detect change could be improved if detection probability was increased using lure or complimentary survey techniques. Caribou, however, with both low detection and occupancy probabilities, were likely too rare on the landscape to rely on camera‐based occupancy for monitoring. Practitioners should be aware of these species‐specific trade‐offs and may need to tailor monitoring programs to prioritize particular species of conservation concern.

Landbird trends in protected areas using time‐to‐event occupancy models

“…Conversely, estimates of occupancy had higher variability when w equaled 0.5. This was due to the variance properties of the binomial distribution where variance is highest at 0.5 and lowest near zero and one (Steenweg et al 2019). We, like other studies, found that Fig.…”

“…Thus, we had high power to detect changes in occupancy for most species in our analysis (Steenweg et al 2019). The mean time to first detection for individual species ranged from 0.9 min (95% BCI = 0.8 to 1.0, P cum = 1.0) for house wrens (Troglodytes aedon) to 7.2 min (95% BCI = 4.6 to 14.1, P cum = 0.75) for downy woodpeckers (Dryobates pubescens).…”

“…The mean time to first detection for individual species ranged from 0.9 min (95% BCI = 0.8 to 1.0, P cum = 1.0) for house wrens (Troglodytes aedon) to 7.2 min (95% BCI = 4.6 to 14.1, P cum = 0.75) for downy woodpeckers (Dryobates pubescens). Thus, we had high power to detect changes in occupancy for most species in our analysis (Steenweg et al 2019).…”

“…Long-term monitoring programs across large spatial scales are required to understand how the direct and indirect effects of human activity affect wildlife populations. Occupancy models are frequently used to monitor changes in distribution (K ery et al 2013), relative abundance (Steenweg et al 2019), and species diversity (Sutherland et al 2016, Tenan et al 2017. They are also used to understand how factors such as climate change (Mor an-Ord oñez et al 2017), inter-specific competition (Wisz et al 2013, Staniczenko et al 2017, and industrial development affect wildlife populations (Johnson et al 2015, Rich et al 2017.…”

Multi‐species occupancy models: review, roadmap, and recommendations

“…The burgeoning interest in making inferences about communities has led to analytical approaches that utilize data on all observed species, including bycatch species that are obtained incidentally during the course of a study that was designed with one or more focal species in mind (Iknayan et al 2014, Guillera‐Arroita 2017, Ovaskainen et al 2017, Steenweg et al 2019). While the design is optimized for the focal species, there is often information about other species readily or potentially available for extrapolation.…”