1 Introduction

Robust abundance estimates are fundamental parameters for managing wildlife populations, and central to understanding extinction risk (Campbell et al., 2002; Lande, 1993; Shaffer, 1981). Monitoring and understanding variation in abundance is critical for recovery efforts of threatened and endangered populations; however, producing accurate population estimates remains a challenge for many species. This is particularly true for species that occur at low density or in low abundance, that are cryptic, or that exhibit elusive behaviours which make capture difficult (Kéry, Gardner, Stoeckle, Weber, & Royle, 2011; Pollock, Marsh, Lawler, & Alldredge, 2006). Non-spatial capture-recapture (CR) analyses have been the standard method used to estimate abundance of many vertebrate species, however, spatially-explicit capture-recapture (SCR) models are becoming the new standard because they are robust to small sample sizes, produce precise density estimates, and can accommodate low capture probabilities (Borchers & Efford, 2008; Efford, Borchers, & Byrom, 2009; Ivan, White, & Shenk, 2013; Royle, Chandler, Sollmann, & Gardner, 2013). By including spatial information of captured individuals directly into the analyses, SCR models resolve issues surrounding the effective trapping area and temporary migration and are robust to assumptions about geographic closure that are common issues in non-spatial CR studies (Efford & Fewster, 2013; Royle et al., 2013). Recapturing individuals at different locations also provides information on individual activity centers, which are used to estimate animal density within the study area (Borchers & Efford, 2008; Royle et al., 2013).
SCR models directly depend on adequate number of unique individuals captured and recaptured at multiple spatial locations (Efford & Boulanger, 2019; Sun, Fuller, & Royle, 2014). Simulations are recommended to enable the assessment of sampling design on SCR parameter estimates, to inform optimal sampling design (Royle et al., 2013). Such studies have primarily focused on large carnivores, such as black bears (Ursus americanus ; Clark, 2019; Sollmann et al., 2012; Sun et al., 2014; Wilton et al., 2014), and a few additional taxa (Kristensen & Kovach, 2018; Tobler & Powell, 2013), while limited work has been done on species occurring at low densities over large areas and with more limited home range sizes. Non-invasive genetic sampling approaches can be used to alleviate the challenges associated with surveying rare and elusive species, by constructing capture histories from DNA collected from feces, hair, or other non-invasively collected samples (Kristensen & Kovach, 2018; Lampa, Henle, Klenke, Hoehn, & Gruber, 2013; Waits & Paetkau, 2005). Non-invasive methods often result in higher capture rates and lower expense than traditional capture-recapture methods (Lampa et al., 2013; Prugh, Ritland, Arthur, & Krebs, 2005; Waits & Paetkau, 2005), and SCR is increasingly being used in combination with non-invasive methods (Royle et al., 2013; Kristensen & Kovach, 2018; Lamb et al., 2018). Knowledge of the target species’ home range size helps inform the spatial sampling design (Sollmann et al., 2012; Sun et al., 2014), but empirical studies are still necessary as detection probabilities may be influenced by other factors (e.g. variable habitat conditions (Efford & Fewster, 2013). Efford & Boulanger (2019) presented formulae to determine the precision of new study designs by computing intermediate variables, such as the number of detected individuals and expected number of recaptures, which strongly correlate with precision. However, these formulae require starting values for density and detection parameters (Efford, 2019b), which may not be available for less studied species.
Here, we developed a framework to assess results from empirical studies to inform sampling designs (Fig. 1). The framework consists of (1) determining the number of unique individuals captured and spatially recaptured from empirical data; (2) running SCR models under the assumption of homogeneous distribution to estimate the detection parameters g0 (detection probability) and \(\sigma\) (spatial extent of an individual’s use of the landscape) to assess the precision of the density estimates; (3) running simulations to assess the influence of the species’ behaviour on density estimates and relative bias; (4) using empirical data to assess different sampling designs and evaluate precision and relative bias of the estimates; and (5) making recommendations on study design based on the resulting precision and relative bias of the estimates. The framework is implemented in R (R Core Team, 2019), using maximum likelihood methods.
To collect empirical data, we completed aerial surveys across the ranges of seven boreal caribou populations in Alberta, Canada. These ranges varied in size, exhibited differences in estimated caribou population density, and contained different levels of natural and anthropogenic disturbances (Fig. 2; see Appendix 1 for details). For each caribou population we used an aerial transect survey design to conduct non-invasive genetic sampling, through the collection of caribou fecal pellets. While we studied boreal caribou, our approach for evaluating study design is applicable to other species and systems.