Abstract

AbstractOccupancy‐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.

Highlights

  • IntroductionThere is a need for affordable multi-species monitoring (Simberloff 1998)

  • With limited conservation funding, there is a need for affordable multi-species monitoring (Simberloff 1998)

  • Do non-target detections represent a wealth of information collected at little-to-no additional cost, but these data can be used to improve detection probability modeling for rare species (Iknayan et al 2014)

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Summary

Introduction

There is a need for affordable multi-species monitoring (Simberloff 1998). Many occupancy data collection methods, for example, using remote cameras, acoustic surveys, eDNA, or snow tracking, are multi-species by nature, detecting many non-target species even when directed at a focal species. Capitalizing on these non-target data presents an opportunity to maximize the effectiveness and efficiency of multi-species monitoring methods. Do non-target detections represent a wealth of information collected at little-to-no additional cost, but these data can be used to improve detection probability modeling for rare (i.e., low occupancy) species (Iknayan et al 2014). With multi-species monitoring, there will be differential trade-offs when monitoring trends of target versus non-target species

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