Precision and bias of spatial capture–recapture estimates: A multi‐site, multi‐year Utah black bear case study

Abstract

Spatial capture-recapture (SCR) models are powerful analytical tools that have become the standard for estimating abundance and density of wild animal populations. When sampling populations to implement SCR, the number of unique individuals detected, total recaptures, and unique spatial relocations can be highly variable. These sample sizes influence the precision and accuracy of model parameter estimates. Testing the performance of SCR models with sparse empirical data sets typical of low-density, wide-ranging species can inform the threshold at which a more integrated modeling approach with additional data sources or additional years of monitoring may be required to achieve reliable, precise parameter estimates. Using a multi-site, multi-year Utah black bear (Ursus americanus) capture-recapture data set, we evaluated factors influencing the uncertainty of SCR structural parameter estimates, specifically density, detection, and the spatial scale parameter, sigma. We also provided some of the first SCR density estimates for Utah black bear populations, which ranged from 3.85 to 74.33 bears/100 km 2 . Increasing total detections decreased the uncertainty of density estimates, whereas an increasing number of total recaptures and individuals with recaptures decreased the uncertainty of detection and sigma estimates, respectively. In most cases, multiple years of data were required for precise density estimates (<0.2 coefficient of variation [CV]). Across study areas there was an average decline in CV of 0.07 with the addition of another year of data. One sampled population with very high estimated bear density had an atypically low number of spatial recaptures relative to total recaptures, apparently inflating density estimates. A complementary simulation study used to assess estimate bias suggested that when <30% of recaptured individuals were spatially recaptured, density estimates were unreliable and ranged widely, in some cases to >3 times the simulated density. Additional research could evaluate these requirements for other density scenarios. Large numbers of individuals detected, numbers of spatial

Density estimates reveal that fragmented landscapes provide important habitat for conserving an endangered mesopredator, the spotted-tailed quoll

“…However, these estimates were associated with large confidence intervals and fewer quoll detections and spatial re-detections. Similar results were found in Schmidt, et al 55 where overly high density estimates of black bears ( Ursus americanus ) were associated with fewer individuals and relatively low spatial re-captures, suggesting inflation of density estimates. Spatial re-detections are particularly important for reducing inflated estimates, and surveys which detected fewer individuals with spatial re-detections may result in less precise density estimates.…”

Accounting for heterogeneous density and detectability in spatially explicit capture–recapture studies of carnivores

“…Noticeably, these scenarios were not exhaustive of the entire parameter space. For SECR models, a minimum of 20 spatial recaptures is recommended (Efford et al, 2004(Efford et al, , 2009 as the precision of density estimates is influenced by the number of spatial recaptures in a sample (Schmidt et al, 2022;Sun et al, 2014). As this work aimed to assess the influence of spatial and sex-based heterogeneity on density estimates, irrespective of the influence of sample size, parameter values were selected to maintain a relatively similar and sufficient number of recaptures across sampling areas while still being biologically realistic for black bears.…”

“…This approach has been employed in several SECR studies of bears (Ursus spp. ; Azad et al, 2019;Howe et al, 2013;Schmidt et al, 2022). With sufficiently large datasets, covariates can be included to account for differences in density and detectability to better reflect the actual state of the population (Sollmann et al, 2012).…”

“…While aggregating data can enhance precision and power to detect and model some sources of variation in detectability or density (Azad et al, 2019; Howe et al, 2013; Schmidt et al, 2022), this approach may come at the expense of bias (MacKenzie et al, 2005). When carnivores are sampled across broad landscapes, the assumption that detection and density parameters remain constant across space is likely violated.…”

“…Identifying models that allow for aggregation of survey data to improve precision while still accounting for important sources of heterogeneity to reduce bias is thus an essential consideration for SECR studies on elusive species where pooling data across space or time is common (Howe et al, 2013(Howe et al, , 2022Schmidt et al, 2022). Yet, few studies have systematically examined the performance of model selection and consequences of misspecification of both the density and detectability submodels on SECR model performance under realistic levels of sex-based and spatial variation in carnivores.…”