Estimating sex-specific abundance in fawning areas of a high-density Columbian black-tailed deer population using fecal DNA

Abstract

The recent development of fecal-genetic capture-mark-recapture (CMR) methods has increased the feasibility of estimating abundance of forest-dwelling ungulates that are difficult to survey using visual methods. Unless genetic markers differentiating sex are incorporated into such studies, however, genetic CMR approaches risk missing sex-specific differences in population trends. We developed a singlereaction genetic assay for sex and individual identification, including 10 microsatellites and an SRY marker, and applied it in the context of a post-fawning CMR study of Columbian black-tailed deer (Odocoileus hemionus columbianus) in forested habitat of coastal California during 2011 and 2012. We measured sexspecific abundance and sex ratios in high-quality summer habitats encompassing 4 distinct fawning areas. We detected a significant interaction between sex and year, indicating different trends in the abundance of males and females. We also detected a significant decline in abundance of females between years (P ¼ 0.045), which agreed with independent telemetry-based estimates, and significant differences in female abundance among fawning areas (P ¼ 0.020) but no significant differences in the abundance of males for either variable (F 1-3,20 < 0.710, P > 0.410). When sex was not considered in the analysis, we found no significant differences in abundance between the 2 years, suggesting that differing trends between the 2 sexes obscured the femalespecific patterns. We estimated average local (i.e., on the high-quality summer ranges) density (D) for females at 41.0 (AE 5.9) deer/km 2 in 2011 and 29.1 (AE 6.8) deer/km 2 in 2012, and local density of males at 15.7 (AE 3.0) deer/km 2 across the 2 study years. Accordingly, sex ratios differed between years (95% CI ¼ 3.0-4.2 F:M ratio in 2011, 2.0-2.3 F:M ratio in 2012). Incorporating sex and individual markers into a single assay provided a cost-effective means of applying CMR estimation based on fecal DNA to a high-density ungulate population in a forested ecosystem and emphasized the importance of explicitly modeling sex in abundance estimation. Ó

Effect of activity states on habitat selection by black‐tailed deer

“…For example, home ranges of black‐tailed deer in our study were extremely small (0.71 km 2 in summer, 0.99 km 2 in winter; Bose et al ) resulting in very short travel distances between habitats used for foraging versus resting. In addition, our study area supports among the highest densities of black‐tailed deer recorded across their range (Lounsberry et al ), indicating high habitat productivity and likely shorter foraging and walking bouts. Thus despite some limitation, our results highlight advantages examining differences in habitat selection and activity state.…”

Noninvasive genetic spatial capture‐recapture for estimating deer population abundance

“…effective sampling area (0.91 km 2 ) likewise needed to be 60% larger (i.e., $1.5 km 2 ) to arrive at density estimates similar to those from SCR. Therefore, we reiterate the recommendation by Lounsberry et al (2015) that application of CR estimates should be taken solely as approximate indices of abundance when sampling is not continuous over the population range, and in such cases, recommend that SCR be applied to obtain relatively unbiased estimates of density (Efford and Fewster 2013). An important caveat, however, is that we had no way of empirically verifying the accuracy of the SCR estimates.…”

Behavioral response of white‐tailed deer to coyote predation risk

“…We replicated all genotypes at least twice and considered high-quality samples (i.e., all loci represented in ≥1 replicate and ≥7 loci amplified in the other) complete. We replicated genotypes falling below the threshold up to five additional times and only included those with no missing loci in further analyses (Lounsberry et al 2015). We calculated the allelic dropout rate for each heterozygous locus and used GenAlEx 6.5 Smouse 2006, 2012) to calculate the probability of individual identity and probability of identity for siblings for each locus and increasing combinations of the 12 loci (Waits et al 2001).…”