Estimating species richness with camera traps: modeling the effects of delay period, deployment length, number of sites, and interference imagery

Abstract

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.