Use of aerial distance sampling to estimate abundance of tule elk across a gradient of canopy cover and comparison to a concurrent fecal DNA spatial capture-recapture survey

Historically, aerial surveys have been used widely to monitor abundance of large mammals in the western United States. In California, such surveys have typically served as minimum count indices rather than true abundance estimates. Here, we evaluated the utility of aerial multiple covariate distance sampling (MCDS) to estimate abundance of three populations of tule elk (Cervus canadensis nannodes) in northern California. We also compared estimates and costs with published results from a concurrent fecal DNA spatial capture-recapture (SCR) survey. During December 2018 and 2019, we flew line transects for distance sampling of tule elk in Colusa and Lake counties. We modeled detection functions and evaluated effects of group size, canopy cover, and survey year. We averaged the top models comprising ≥0.95 of Akaike Model Weight and estimated abundance of both total and discrete populations. Detection probability increased with increasing group size and decreasing canopy cover. We estimated a two-year average total population size of N̂ = 674 elk (90% CI = 501–907) in our survey area which was similar to N̂ = 653 elk (90% CI = 573–745) from SCR estimates. Overall precision was greater (CV = 0.08; range = 0.11–0.30 by population) for SCR than for MCDS (CV = 0.18; range = 0.22–0.43 by population). Although estimates differed somewhat between methods for the individual populations, the combined estimate across the study region compared favorably. Total cost of SCR and MCDS surveys was $98,326 and $147,324, respectively. While SCR efforts were more precise and less expensive overall, our MCDS approach reduced staff time by 64% (587 person-hours) and the number of survey days by 87% (64 days). Our results suggest MCDS methods can produce reliable abundance estimates across a gradient of canopy cover, particularly when observations can be pooled across populations to decrease variance. We recommend future research to assess use of hybrid models, such as mark-recapture distance sampling or hierarchical distance sampling, to improve precision and estimation of detection probability.