Capture–recapture methods 413 The Sonoran pronghorn Antilocapra americana sonor-
iensis, endemic to the Sonoran Desert of the USA and Mexico, is categorized as Least Concern on the IUCN Red List (IUCN SSC Antelope Specialist Group, 2016) but was listed as endangered in 1967 under the U.S. Endangered Species Act (USFWS, 2015), and the Mexican population is listed on CITES Appendix 1. Abundance is currently esti- mated on a biennial basis using aerial counts corrected with sightability models (USFWS, 2015). Annual counts are not conducted because of the high cost. Aerial surveys indicate the US population increased from an estimated 21 (95%CI 18–33) individuals in 2002 to 202 (95%CI 171–334)in 2014 (USFWS, 2015). Estimated abundance from previous genetic capture–recapture research conducted on the portion of this population using developed water sites (an estimated 70%of the population; J.J. Hervert, Arizona Game & Fish Department, pers. comm.) was 121 (95%CI 112–132)in 2014 (Woodruff et al., 2016b). Although the genetic cap- ture–recapture estimate is known to be biased low, as not all pronghorn use the drinkers (168 individuals were seen during the multi-day aerial survey; J.J. Hervert, pers. comm.), the results suggest a significant proportion of the population was sampled using these methods (Woodruff et al., 2016b). We also cannot exclude the possibility that the aerial survey is biased high, as the same individual could potentially be observed and counted more than once during the multi-day survey. One concern with the current aerial surveymethod is the
low power to detect small, but potentially significant, changes in abundance as a result of large confidence inter- vals, whereas this is not a problem with the narrow confi- dence intervals (and resulting high precision) from the genetic capture–recapture estimate. Additionally, reduced sampling effort and the use of a simpler method (i.e. single session as opposed to multi-session analysis) provides man- agers the option to save both time and money (i.e. fewer samples collected and analysed and no need to contract an outside entity to conduct the analysis). In this study we used simulation modelling to evaluate
various genetic capture–recapture sampling designs for monitoring Sonoran pronghorn abundance on the Cabeza Prieta National Wildlife Refuge and the adjoining Barry M. Goldwater Range. The overall goal was to determine the optimal sample size required to yield precise abundance estimates and to evaluate the reliability of single and multi- session abundance estimators. Using data simulated with varying sampling intensity, we evaluated the differences in abundance estimates and associated precision using closed capture models implemented in MARK (White & Burnham, 1999) and single-session capture–recapture mod- els in capwire (Miller et al., 2005). We also performed a cost comparison between genetic capture–recapture methods compared to population estimation methods currently used for this species (i.e. aerial survey with sightability
correction) focusing on the cost of obtaining comparable data from both of these methods.
Methods
This study included simulated data generated based on a previously analysed data set from a multi-session closed population faecal DNA study of Sonoran pronghorn con- ducted on the Cabeza Prieta National Wildlife Refuge and Barry M. Goldwater Range, in south-western Arizona, USA. Single-session methods were not used in the previous analysis.Weused results of previously published data on de- position rates (Woodruff et al., 2015) and sex ratios (Woodruff et al., 2016b) to inform our simulation design. We provide relevant details of the previously collected data, but see Woodruff et al. (2016b) for a complete descrip- tion of the study.Within each of 2 years, 2013 and 2014,we collected pronghorn faecal samples at 11 drinkers (developed water sites) during 1–3 sampling sessions per drinker. We extracted and genotyped 494 and 692 faecal samples in 2013 and 2014, respectively, at 10 nuclearDNAmicrosatellite loci and one sex identification locus. We identified 91 and 100 unique individuals at drinkers in 2013 and 2014, respectively. We developed full likelihood parameterization models
(Otis et al., 1978)in MARK (White & Burnham, 1999). Because capture/recapture probability varied by group (sex and age), abundance was estimated separately for adult males, adult females, and fawns. Sex was determined via genetic techniques, and we determined age using size and morphology of faecal pellets (Woodruff et al., 2016a). Population estimates were summed, and we calculated 95% confidence intervals using the Delta method (Seber, 1982). To evaluate relative support for each model, we used Akaike’s information criterion corrected for small sample size (AICc). The top abundance estimation model included equal detection and redetection probabilities, both varying by time and group (i.e. males, females, fawns; Woodruff et al., 2016b). Abundance was estimated to be 116 (95%CI 101–132) at drinkers in 2013 and 121 (95% CI 112–132)in 2014 (Woodruff et al., 2016b).
Simulations
To estimate the optimal number of consensus genotypes needed for precise abundance estimates (CV#10–20%; Pollock et al., 1990), we simulated closed populations emulating our non-invasive genetic sampling framework. Because we assumed all samples achieved a consensus geno- type in simulations, number of samples collected refers to number of successful genotypes obtained (hereafter, sam- ples). Approximately 75% of field-collected faecal samples achieved consensus genotypes (Woodruff et al., 2016b)
Oryx, 2020, 54(3), 412–420 © 2018 Fauna & Flora International doi:10.1017/S003060531800011X
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