418 S. P. Woodruff et al.
showed genetic capture–recapture methods were twice as expensive as aerial methods (USD 20,000 vs 10,000, SPW, unpubl. data). Importantly, for Sonoran pronghorn these monitoring methods are conducted at different times of year: genetic capture–recapture in May–June, when prong- horn are congregated at drinkers, and aerial in December, when pronghorn are spread out across their c. 7,000 km2 range. The wider geographical sampling of the aerial survey leads to a trade-off of lower precision in both detection probability and population estimation compared to genetic capture–recapture. Because of our targeted sampling design in genetic capture–recapture, any inference from our esti- mates applies largely to the individuals using drinkers. However, if the proportion of individuals not using the drin- kers was known or could be reliably estimated, abundance estimates obtained using genetic capture–recapture could easily be extrapolated to the entire population. Analogous considerations are critical for other species monitored in a similar fashion, such as bighorn sheep Ovis canadensis (Schoenecker et al., 2015), or other species often congregated at watering holes or mineral licks (e.g. elephants Loxodonta spp., zebras Equus zebra). Although our simulations indicate increase in precision
with more samples, depending on desired accuracy, preci- sion and budget, a wide range of sampling designs is fea- sible. Our results also indicate that at the current estimated population size (c. 200), a CV of c. 21% can be obtained using genetic capture–recapture methods for an annual cost saving of .USD 4,000 over aerial costs and would provide monitoring data annually rather than biennially. Other cost-saving measures for genetic capture–recapture include collecting and analysing only the freshest samples (Lucchini et al., 2002) or decreasing the sampling interval with multiple sampling sessions (Marucco et al., 2009; Woodruff et al., 2015) for higher success rates (i.e. fewer failed samples). Additionally, the use of a simpler method such as capwire that can be easily implemented with min- imal quantitative skills and training, as opposed to MARK, which requires significant knowledge of the software and modelling skills to obtain a reliable estimate, would also re- sult in cost savings. Methods such as physical capture and aerial telemetry
and surveys may be undesirable because of concerns for human safety, impacts on wildlife and other natural re- sources, and logistical
complexity.Moreover, these methods lack the ability to provide genotypic information on genetic diversity, relatedness, and genetic structure, which can pro- vide valuable information on risk of inbreeding depression, population connectivity, effective population size, and par- entage. This type of genetic monitoring would incur ad- ditional analysis time and labour cost but, depending on the questions being asked, could be conducted annually (e.g. for parentage analysis; DeBarba et al., 2010a), but more typically only once per generation (see Schwartz
et al., 2007 and Stetz et al., 2011 for recommendations on de- signing a genetic monitoring programme). Nevertheless, although potentially more expensive, aerial
surveys allow the results to be available more quickly com- pared to genetic analysis. However, in areas with dense can- opy cover (e.g. tropical or temperate rainforests) where aerial surveys are ineffective, genetic capture–recapture methods may provide one of the only reliable methods for estimating population abundance (e.g. Brinkman et al., 2011). Researchers must be aware of potential challenges as- sociated with genetic analysis (e.g. difficulty obtaining per- mits to export samples, low success rates in some climates) or a lag time between data collection and results (i.e. shipping samples if required, sample processing time in laboratory, time for capture–recapture analysis). The results of genetic capture–recapture in our study sys-
tem presents a challenge for researchers given the very high capture probabilities that lead to results indicating high le- vels of precision, yet the estimates still exhibit bias. As far as we are aware our capture probabilities (0.42–0.83;Woodruff et al., 2016b) are up to twice as high as other published cap- ture probabilities for ungulates (0.38 ± SE 0.047; Poole et al., 2011) and some of the highest reported in any capture– recapture study. Our results also highlight the importance of comparing estimator performance and cost prior to designing and implementing capture–recapture studies.
Management recommendations
Our research provides useful guidelines for designing a prac- tical and cost-effective genetic capture–recapturemonitoring strategy to obtain acceptable levels of accuracy and precision. Thismethod can easily be adapted for use in areas where an- imals congregate, such as wintering areas, roosting sites, or along migration routes. Additionally, this method could be integrated with an occupancy approach to inexpensively document population expansion to new geographical areas. However, researchers should be aware that capture probabil- ities are rarely this high. In other systems, substantially more effort would probably be needed to obtain this level of pre- cision. If there is high cost associated with more sampling sessions (i.e. an increased number of visits to sampling loca- tions is more costly), it is feasible to collectmore samples in fewer sessions. Although this would probably result in a biased, less precise estimate, managers may deem this trade- off of cost and level of accuracy and precision acceptable.
Acknowledgements We thank Arizona Game and Fish Department, Cabeza Prieta National Wildlife Refuge and University of Arizona personnel for assistance with sample collection, J. Atkinson and J.J. Hervert for provision of time and expertise, M. Smith and K. Cobb for fieldwork, the Waits lab and D. Christianson for comments on the text, and J. Adams for laboratory assistance. This material is based upon work supported by, or in part
Oryx, 2020, 54(3), 412–420 © 2018 Fauna & Flora International doi:10.1017/S003060531800011X
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