The ability to directly monitor animal populations across time and space is a key element of wildlife conservation and management, but logistically difficult to achieve. Photographic capture rates from camera trap surveys can provide relative abundance indices (RAIs) for a wide variety of medium- to large-bodied wildlife species. RAIs are less complex than other estimation methods and are commonly used when true abundance is difficult or costly to measure. However, this method is controversial as it does not account for potential bias arising from imperfect detection. Here, we evaluate the reliability and precision of RAI estimates drawn from a large-scale camera trap survey for ten African herbivore species by comparing them against preexisting aerial survey data. RAIs correlated strongly with independent estimates, particularly when indices were derived from counts of all photographed animals. RAIs were most reliable for species that were nonmigratory, occurred in open habitats and had high rates of daily movement. Increasing survey coverage and duration both had strong but comparable effects on improving RAI precision. Our results suggest that RAIs from camera traps hold substantial promise as a tool for monitoring herbivore relative abundances, and we provide guidelines on the utility of this approach for ecological inference.
Bibliographical noteFunding Information:
University of Minnesota, Twin Cities, Grant/ Award Number: Alexander & Lydia Anderson Grant, Ecology, Evolution, and Behavior Research Award, Ecology, Evolution, and Behavior Travel Award, James W. Wilkie Fund for Natural History Fellowshi and Thesis Research Grant; National Science Foundation, Grant/Award Number: DEB-1020479 and GRFP-00039202; National Geographic Society, Grant/Award Number: Explorer Grant
Research clearance was provided by the Tanzania Wildlife Research Institute and Tanzania National Parks. We thank members of the Serengeti Lion Project, particularly D. Rosengren and N. I. Munuo, the Zooniverse staff, and the >70,000 volunteers who contributed to Snapshot Serengeti classifications (https://www.snapshot-serengeti.org/#/authors). The authors would like to acknowledge the Minnesota Supercomputing Institute (https://www.msi.umn.edu) for providing resources that contributed to data storage, processing and analysis. This work was supported by NSF grant DEB-1020479 to C. Packer for maintenance of the Serengeti Lion Project, National Geographic explorer grants, NSF GRFP Grant #00039202, the James W. Wilkie Fund for Natural History Fellowship, the University of Minnesota Thesis Research Grant, the Alexander & Lydia Anderson Grant, and the University of Minnesota Ecology, Evolution, and Behavior Department Research and Travel awards.
- abundance estimation
- camera traps
- count data
- relative abundance index
- unmarked individuals