Regional coyote densities estimated from camera trapping covary with deer productivity, anthropogenic land use, and human–coyote conflicts

Density estimates assist conservation and management decisions. Unfortunately, management of elusive mesocarnivores occurring at low densities has often relied on qualitative metrics or population indices. The proliferation of camera trap sampling of wildlife provides an opportunity to apply recently developed methods for estimating density of unmarked mesocarnivores to answer pertinent ecological questions for informing management. Our goals were to estimate coyote Canis latrans density with camera‐trap distance sampling (CTDS) within three large regions of Indiana, USA, and compare density estimates with contemporaneously estimated indexes of 1) white‐tailed deer Odocoileus virginianus productivity; 2) landcover devoted to human land use (i.e. agriculture and development); and 3) human–coyote conflicts. Based on previous research, we predicted lower regional coyote densities would correspond to regions with 1) higher indexes of deer productivity; 2) more land modifications for human land use; and 3) fewer human–coyote conflicts. From 2019 to 2021, we deployed > 1000 camera traps in three regions of Indiana, USA. Coyote density estimates ranged from 0.13 km −2 (95% confidence interval, CI = 0.06 to 0.18, coefficient of variation, CV = 0.24) to 0.33 km −2 (CI = 0.11 to 0.53, CV = 0.34). Pairwise differences in bootstrapped density estimates supported all three of our predictions. Namely, we detected lower coyote densities in regions with greater deer productivity (estimated via ratios of young:adult female deer), more agriculture and development, and fewer human–coyote conflicts. Despite our large sample size of camera traps, the precision of our coyote density estimates was mediocre to poor (CV range: 0.24 to 0.34), which reduced power to detect changes in density across all the regions. Future incorporation of spatial covariates holds promise for improved precision of camera‐based estimates of density. Managers should consider CTDS in situations where individual identity cannot be accurately determined but management actions based on estimates of density or abundance are desired.