Camera traps are widely used to collect information on the distribution and abundance of multiple species simultaneously. However, we still lack important guidance for designing camera-Trap surveys to monitor multiple species, and the consequences of species-specific responses to survey design strategies are often overlooked. Using camera-Trap data collected on ten medium-To-large North-American carnivores in northern Minnesota, USA, between 2016 and 2018 (23 337 active trap-days), we evaluated: 1) two different survey-design frameworks (random-versus road-based), 2) two different lure types (salmon oil versus fatty acid scent oil), 3) two different placement strategies (completely random versus randomly-selected sites with feature-based placement), 4) survey timing (spring versus fall) and 5) temporal trends in daily encounter probabilities. Using generalized linear mixed models, we found evidence of differential responses to all of these design strategies. For 9 out of 10 species, we found strong responses to survey design frameworks: red foxes Vulpes vulpes, coyotes Canis latrans, bobcats, Lynx rufus, striped skunks Mephitis mephitis, wolves C. lupus and gray foxes Urocyon cinereoargenteus, had estimated encounter frequencies that were 9-to 106-fold higher at unlured sites along secondary roads; black bears Ursus americanus, martens Martes americana and fishers Pekania pennanti had estimated encounter frequencies that were 15-to > 3600-fold higher at lured, randomly selected sites. For six species, salmon oil provided 2-to 4-fold more encounters than fatty acid scent oil, but feature-basedplacement only improved detections of fishers. Daily encounter probabilities differed between spring and fall for all species, and usually decreased slightly within each sampling period Our study confirms that even similar-sized or closely-related species respond differently to survey-design choices. To maximize encounter frequencies, we recommend that multi-species camera-Trap studies use a mix of survey-design strategies and include these design features during statistical analysis.
Bibliographical noteFunding Information:
Acknowledgements – We are grateful to Carolin Humpal and Barry Sampson for their help with data collection and species identification. The Minnesota Supercomputing Institute (MSI, <www.msi.umn.edu>) at the Univ. of Minnesota provided computational resources that contributed to our research. Funding – The project was funded by the Minnesota Department of Natural Resources and the Wildlife Restoration Program (Pittman-Robertson).
© 2021 The Authors.
- Camera trapping
- Detection survey
- Trail use