Estimating animal abundance at multiple scales by spatially explicit capture–recapture

Abstract

Information about how animal abundance varies across landscapes is needed to inform management action but is costly and time-consuming to obtain; surveys of a single population distributed over a large area can take years to complete. Surveys employing small, spatially replicated sampling units improve efficiency, but statistical estimators rely on assumptions that constrain survey design or become less reasonable as larger areas are sampled. Efficient methods that avoid assumptions about similarity of detectability or density among replicates are therefore appealing. Using simulations and data from >3500 black bears sampled on 73 independent study areas in Ontario, Canada, we (1) quantified bias induced by unmodeled spatial heterogeneity in detectability and density; (2) evaluated novel, design-based estimators of average density across replicate study areas; and (3) evaluated two estimators of the variance of average density across study areas: an analytic estimator that assumed an underlying homogeneous spatial Poisson point process for the distribution of animals' activity centers, and an empirical estimator of variance across study areas. In simulations where detectability varied in space, assuming spatially constant detectability yielded density estimates that were negatively biased by 20% to 30%; estimating local detectability and density from local data and treating study areas as independent, equal replicates when estimating average density across study areas using the design-based estimator yielded unbiased estimates at local and landscape scales. Similarly, detectability of black bears varied among study areas and estimates of bear density at landscape scales were higher when no information was shared across study areas when estimating detectability. This approach also maximized precision (relative SEs of estimates of average black bear density ranged from 7% to 18%) and computational efficiency. In simulations, the analytic variance estimator was robust to threefold variation in local densities but the empirical estimator performed poorly. Conducting multiple, similar SECR surveys and treating them as independent replicates during analyses allowed us to efficiently

Experimental test of the efficacy of hunting for controlling human–wildlife conflict

“…Despite these 2 jurisdictions sharing a border, there are several differences between the populations of bears in Ontario and those in Minnesota. First, ongoing population monitoring in Ontario indicates that bear populations have been mostly stable since the 2000s (Obbard et al 2010;Howe et al 2013Howe et al , 2022. In Minnesota, increasing conflicts correlated strongly with a near doubling of the bear population, followed by declining conflicts with a strongly declining bear population.…”

“…During spring or summer, subadult males disperse (Lee and Vaughan 2003); therefore, harvest of older bears could create space for territories for mobile young bears (Rogers 1987). Areas selected for the spring season after 2014 were mostly more productive for bears, and more densely populated by humans (Howe et al 2013(Howe et al , 2022. Therefore, seasonal movements towards food (e.g., red oak [Quercus rubra] stands), and incidentally towards humans, during hyperphagia could contribute to perennially high conflicts in districts overlapping the WMUs selected for the pilot.…”

“…Ontario has one of the largest populations of black bears of any jurisdiction with between 85,000 and 105,000 bears across the province (E. Howe, Ontario Ministry of Natural Resources and Forestry, unpublished data). In contrast to black bear populations in much of the United States, the bear population in Ontario has been stable over the last century with relatively limited change in bear harvest since the early 2000s (Howe et al 2013(Howe et al , 2022. Human-black bear conflict is a major management issue in some parts of the province (Poulin et al 2003, Obbard et al 2014.…”

Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects

“…Adequately accounting for spatial heterogeneity and autocorrelation in detection probability is essential for obtaining reliable statistical inference in SCR analyses (Moqanaki et al, 2021; Howe et al, 2022). In the absence of known covariates, the effect of detector-specific variation in detection probability can be modelled by using a function that explains the true pattern of heterogeneity.…”

An evaluation of spatial capture‐recapture models applied to ungulate non‐invasive genetic sampling data

“…Spatial capturerecapture, often in conjunction with non-invasive genetic sampling (NGS) methods, is routinely used for the monitoring and status assessment of wildlife species of conservation and management interest (Borchers and Efford 2008, Royle and Young 2008, Lamb et al 2019. The SCR models can use NGS data resulting from various sampling methods, such as area or transect searches for sources of DNA left behind by animals (e.g., scats, urine, hair, feathers; Waits and Paetkau 2005, Beja-Pereira et al 2009, Lamb et al 2019) and genetic material obtained at fixed detectors (e.g., hair snares; Kéry et al 2011, Howe et al 2022. The prerequisite for using these data for SCR is that they allow for multiple detections (recaptures) of the same uniquely identifiable individuals at multiple sites.…”