Capture–recapture methods 417
mimic field situations. To ensure our simulations mirrored field conditions as closely as possible we based simulated deposition and removal rates on previous work (Woodruff et al., 2015). True capture probabilities were very high (0.36–0.76; Woodruff et al., 2016b), and in simulations were 0.29–
1.00.ConsistentwithLukacs&Burnham(2005), high cap- ture probabilities ($0.58)resultedinlowbias (#4%), although CI coverage was poor (mean = 0.18, range = 0.00–0.73). The simulations indicated highly precise estimates (i.e. low CV; meanCVcapwire = 0.7;meanCVMARK= 0.2), yet this can be deceptive, as a low CV can still have high bias (Arnason et al., 1991; Supplementary Table 1). As expected, and consistent with other studies (Miller et al., 2005; Conn et al., 2006; Stenglein et al., 2010; Rees et al., 2014; Roy et al., 2014), increasing the sample size and number of ses- sions improved both the bias and precision of the estimates. Other studies comparing MARK and capwire have pro-
duced conflicting results. Robinson et al. (2009) and Harris et al. (2010) found lower and more precise capwire estimates compared toMARK. The oppositewas true in this study and others (Gray et al., 2011, 2014; Lampa et al., 2015), with lower, more precise abundance estimates in MARK compared to capwire. An alternative method to consider is collecting samples during multiple sessions and collapsing them into a single session model (e.g. capwire). Although this is a less preferred method, it is useful when there are not enough re- captures for MARK to effectively estimate abundance (Robinson et al., 2009). Because capwire is designed for use with small popula-
tion size (,100; Miller et al., 2005), it is not surprising that as true abundance increased, the performance of cap- wire weakened. Given the input for our simulations (mean male deposition rates twice that of females), using the TIRM in capwire makes intuitive sense, and was supported by like- lihood ratio results. Nevertheless, 100%of capwire estimates were high. This is common for the TIRM model when cap- ture probabilities are equal (Miller et al., 2005), but that was not the case in our dataset. In the presence of heterogeneity, models that assume equal capture probability (e.g. Lincoln- Peterson, ECM in capwire) have been shown to underesti- mate populations (Seber, 1982; Miller et al., 2005; Petit & Valiere, 2006; Puechmaille & Petit, 2007). To evaluate bias under this model, we ran several simulations post factum using the even capturability model (data not shown). Although per cent CI coverage did improve in some cases, as expected, abundance was always underestimated with these models. When using empirical data (SPW, unpubl. data), we observed conditions in capwire when the lower CI was higher than the estimate, which is indicative of the model not capturing the distribution of the data sufficiently (M.W. Pennell, University of Idaho, pers. comm.). Based on our previous research with a higher number of
male compared to female samples (Woodruff et al., 2015, 2016b), remote camera data indicating no difference in use
of drinkers by males and females, (S. Doerries, pers. comm.), and a 0.66 male : 1 female ratio reported by Arizona Game and Fish Department in aerial survey results, we assumed a higher deposition rate for males in our study design. However, we recognize there could be other explanations for the skewed sex ratio in our samples. There may be a be- havioural difference in drinker visitation between males and females, especially females with fawns, which are seen less frequently on cameras at drinkers (D. Christianson, University of Arizona, pers. comm.) or a potential (un- detected) bias in males visiting drinkers as a result of the skewed sex ratio (2 males : 1 female) of food and water con- ditioned captive-released pronghorn in this population (USFWS, 2015). Another comparable method to consider for estimating
abundance is spatial capture–recapture that incorporates the inherent individual spatial heterogeneity within the model (Royle et al., 2013). By design, spatial capture– recapture assumes recaptures of the same individual at multiple locations. In our study system, there was little movement between sites and most of our individuals (92%) were detected at a single location both within and across years. However, in other systems this could be a potentially useful and robust method for estimating abundance.
Cost comparison
Determining the appropriate monitoring method depends on the data needed for management (e.g. abundance, sur- vival, genetic diversity), yet resources are often limited, and effective management should employ efficient monitor- ing methods to ensure the costs do not outweigh the benefits (Possingham et al., 1993). An often-voiced concern regard- ing genetic capture–recapture methods is the high cost. High costs can be associated with development of primers, and optimizing multiplexes (Schwartz & Monfort, 2008; Beja-Pereira et al., 2009), travel to sampling sites (Harris et al., 2010), collection method (e.g. scat detection dogs; Arandjelovic et al., 2015), or increased use of personnel (Poole et al., 2011). Although developing a new faecal DNA protocol can require a substantial initial investment, costs are reduced in subsequent years. Genetic capture– recapture costs increase with increasing population size whereas flight costs are constant. Therefore, genetic cap- ture–recapture can be more cost efficient at small popula- tion sizes and flights become more cost efficient at large population sizes. Several studies have found genetic capture–recapture
methods were more cost-effective compared to other field- based methods (e.g. radio-collaring and aerial telemetry; Solberg et al., 2006; DeBarba et al., 2010b), yet a direct com- parison of costs is difficult given the different tasks and in- formation acquired with each method. In 2014 our results
Oryx, 2020, 54(3), 412–420 © 2018 Fauna & Flora International doi:10.1017/S003060531800011X
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