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

Abstract

Non-invasive genetic sampling (NGS) methods are becoming a mainstay in wildlife monitoring and can be used with spatial capture-recapture (SCR) methods to estimate population density. Yet SCR based on NGS remains relatively underused for ungulate population monitoring, despite the importance of robust density estimates for this ecologically and economically important group of species. This may be in part attributed to biological characteristics of ungulate species and data collection methods that lead to violations of SCR model assumptions.We conducted a simulation study to evaluate the robustness of SCR methods to spatially heterogeneous density (i.e., configuration of individuals into groups of variable sizes and composition), individual heterogeneity in space-use patterns, and adaptive sampling (i.e., variation in detectability across space that correlates with density). We evaluated each violation separately and in combination. We parameterized our simulations based on published information and preliminary analyses of NGS data sets of 3 ungulate species: chamois (Rupicapra rupicapra), red deer (Cervus elaphus), and wild boar (Sus scrofa). While SCR estimates were robust to grouping and adaptive sampling, abundance estimates could be negatively biased (up to 10% in our simulations) in the presence of

The importance of independence in unmarked spatial capture–recapture analysis

“…Different population densities and spacings of sampling locations should be investigated, to better understand changes in bias patterns, and also as closer spacing than generally recommended under independence may help increase spatial variation among aggregated individuals and between members of loosely cohesive groups (Chandler and Royle 2014, Augustine et al 2019). Indeed, patterns of individual space‐use can significantly impact density estimation (Dupont et al 2023), and non‐independence of unmarked individuals likely adds further complications. Nevertheless, broadly, the increase in bias with increasing non‐independence is consistent with increasing uncertainty in individual identification due to overlapping space use.…”

“…Cohesion is the degree to which group individuals move together, so detections of individuals become increasingly coordinated as members become more cohesive. Developing sampling designs that meet the independence assumption for group‐living species, such as wolves Canis lupis , lions Panthera leo , and some ungulates can be difficult, so there is growing interest in models that account for non‐independence (Reich and Gardner 2014, Hickey and Sollmann 2018, Emmet et al 2021, Dupont et al 2023). Notably, Emmet et al (2021) developed an SCR model to estimate the size of cohesive groups as well as population abundance, while accounting for detection heterogeneity due to group size.…”

“…Due to the lack of SCR‐based models for individuals that are both unmarked and non‐independent, unmarked models are applied and non‐independence is left unaddressed. Unmodeled correlation among detections from non‐independence can compromise model inferences, but so far has only been investigated for fully marked SCR models (López‐Bao et al 2018, Bischof et al 2020, Moqanaki et al 2021, Dupont et al 2023). Simulations show SCR to be relatively robust to low‐to‐moderate levels of aggregation and cohesion, but that they can still lead to false inferences about population parameters because correlated detections inflate precision and thereby reduce coverage of the estimated variance around the true value.…”

A multidisciplinary approach to estimating wolf population size for long‐term conservation

“…The possibility of estimating the proportion of reproductively mature individuals in the population is also highly relevant because it is the basis for the International Union for the Conservation of Nature Red List assessments based on criterion D (IUCN Standards and Petitions Committee, 2022) and is generally used to evaluate wolf conservation status. Unaccounted individual heterogeneity in detection or space use that is linked with age class or sex could potentially lead to significant bias (Cubaynes et al., 2010; Dupont et al., 2023). However, thanks to the relatedness analysis, we were able to account for differences between sexes and among individual status in space use and detectability, therefore accounting for individual heterogeneity (Cubaynes et al., 2010).…”

“…How to cite this article: Marucco, F., Boiani, M. V., Dupont, P., Milleret, C., Avanzinelli, E., Pilgrim, K., Schwartz, M. K., von Hardenberg, A., Perrone, S., Friard, O., Menzano, A., Bisi, F., Fattori, U., Tomasella, M., Calderola, S., Carolfi, S., Ferrari, P., Chioso, C., Truc, F., … Bischof, R. (2023). A multidisciplinary approach to estimating wolf population size for long-term conservation.…”