TY - JOUR
T1 - Estimating species richness with camera traps
T2 - modeling the effects of delay period, deployment length, number of sites, and interference imagery
AU - Mashintonio, Andrew F.
AU - Harris, Grant M.
AU - Stewart, David R.
AU - Butler, Matthew J.
AU - Sanderson, Jim
AU - Russell, Gareth
N1 - Funding Information:
We thank the U.S. Fish and Wildlife Service, United States Geological Survey and the U.S. Air Force, especially D. Derango, L. Johnson, K. Longshore and A. Alvidrez for data collection and permission, plus the individuals who performed image identification. The findings and conclusions in this article are those of the author(s) and do not necessarily represent the views of the U.S. Fish and Wildlife Service. We thank M. Byrne (Associate Editor), A. Knipps (Editorial Assistant), A. Tunstall (Copy Editor) and J. Levengood (Content Editor) and 2 anonymous reviewers for providing invaluable suggestions that improved our work. The use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Publisher Copyright:
© 2022 The Authors. Wildlife Society Bulletin published by Wiley Periodicals LLC on behalf of The Wildlife Society.
PY - 2022/9
Y1 - 2022/9
N2 - Biologists commonly use camera traps for estimating species richness to inform conservation actions, steer land protection, and reveal effects of climate change. Long-term studies using short delay periods (≤1 min) and numerous cameras produce voluminous amounts of redundant imagery. Thus, camera-trapping procedures maximizing richness estimates while minimizing data collection need development. We used imagery of mammals spanning 4 deserts in the United States to model the effects of delay, deployment length (i.e., study duration), number of sampling sites, and interference events on the proportion of known species richness detected (Rp). We also determined the proportion of subsamples containing each species (SR) under different sampling conditions to inform subsequent occupancy estimation. We generated contour plots describing the optimal configuration of sites and deployment length that minimized the image acquisition required to estimate Rp = 0.9. The optimal configuration was independent of delay (requiring ~50 sites and 13 months). The shortest delay (10 sec) generated ~8 times more images than the longest (3600 sec) without substantially improving Rp and rare species detection. The shortest duration to acquire Rp = 0.9 was 10 months but required ~70 sites. The fewest sites needed were 22 and 29, depending on camera placement, requiring approximately 50 months of deployment. Simulated short, one-month studies were only able to obtain Rp ~0.6 with 40–70 sites. Obtaining SR = 0.8 with a 3600 sec delay required between 1–12 months and 10 sites or 1–17 sites and 6 months for uncommon species. Adding interference imagery, even with long delays, produced SR ≥ 0.5 for rare species, generating data suitable for occupancy estimation. Overall, interference imagery had minimal effects on reducing SR estimates, unless the interference occurred continuously. Our guidance optimizes the number of sites, deployment length, and delay period while minimizing imagery acquisition to meet Rp and occupancy objectives with confidence.
AB - Biologists commonly use camera traps for estimating species richness to inform conservation actions, steer land protection, and reveal effects of climate change. Long-term studies using short delay periods (≤1 min) and numerous cameras produce voluminous amounts of redundant imagery. Thus, camera-trapping procedures maximizing richness estimates while minimizing data collection need development. We used imagery of mammals spanning 4 deserts in the United States to model the effects of delay, deployment length (i.e., study duration), number of sampling sites, and interference events on the proportion of known species richness detected (Rp). We also determined the proportion of subsamples containing each species (SR) under different sampling conditions to inform subsequent occupancy estimation. We generated contour plots describing the optimal configuration of sites and deployment length that minimized the image acquisition required to estimate Rp = 0.9. The optimal configuration was independent of delay (requiring ~50 sites and 13 months). The shortest delay (10 sec) generated ~8 times more images than the longest (3600 sec) without substantially improving Rp and rare species detection. The shortest duration to acquire Rp = 0.9 was 10 months but required ~70 sites. The fewest sites needed were 22 and 29, depending on camera placement, requiring approximately 50 months of deployment. Simulated short, one-month studies were only able to obtain Rp ~0.6 with 40–70 sites. Obtaining SR = 0.8 with a 3600 sec delay required between 1–12 months and 10 sites or 1–17 sites and 6 months for uncommon species. Adding interference imagery, even with long delays, produced SR ≥ 0.5 for rare species, generating data suitable for occupancy estimation. Overall, interference imagery had minimal effects on reducing SR estimates, unless the interference occurred continuously. Our guidance optimizes the number of sites, deployment length, and delay period while minimizing imagery acquisition to meet Rp and occupancy objectives with confidence.
KW - camera trap surveys
KW - community ecology
KW - delay period
KW - interference imagery
KW - mammals
KW - southwestern U.S.
KW - species richness
KW - study design
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U2 - 10.1002/wsb.1357
DO - 10.1002/wsb.1357
M3 - Article
AN - SCOPUS:85138215297
SN - 0091-7648
VL - 46
JO - Wildlife Society Bulletin
JF - Wildlife Society Bulletin
IS - 4
M1 - e1357
ER -