Description
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. Proliferation of camera trap sampling of wildlife over the last 2 decades provides an opportunity to apply recently developed statistical methods for estimating density of unmarked animals including mesocarnivores. Our goals were to estimate coyote (Canis latrans) density with camera-trap distance sampling (CTDS) and compare it to an established method, genetic spatial capture-recapture (SCR), within three large regions of Indiana, USA. Based on previous research, we predicted smaller coyote densities in regions heavily modified by human land use (i.e., agriculture and development). From 2019 to 2021, we deployed >1000 camera traps and sampled > 900 km of transect for coyote scat. Using pairwise differences in bootstrapped density estimates from CTDS, we detected lower coyote densities in landscapes experiencing more agriculture and development, supporting our prediction. Coyote density estimates from CTDS in each RMU covaried inversely with contemporaneously estimated ratios of young white-tailed deer:adult female deer (Odocoileus virginianus). Despite our large sample size of camera traps, the precision of our coyote density estimates was mediocre to poor (coefficients of variation range: 0.24 to 0.34), which reduced power to detect changes in density across all the regions. Incorporation of spatial covariates holds promise for improved precision of camera-based estimates of density. Precision tended to be even worse for genetic SCR estimates (range: 0.18 to 0.48) and thus failed to reveal differences in regional density despite yielding point estimates with rankings identical to CTDS estimates. Future 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.

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