Spatial capture–recapture with multiple noninvasive marks: An application to camera‐trapping data of the European wildcat (Felis silvestris) using R package multimark

Abstract

In Switzerland, the European wildcat (Felis silvestris), a native felid, is protected by national law. In recent decades, the wildcat has slowly returned to much of its original range and may have even expanded into new areas that were not known to be occupied before. For the implementation of efficient conservation actions, reliable information about the status and trend of population size and density is crucial. But so far, only one reliable estimate of density in Switzerland was produced in the northern Swiss Jura Mountains. Wildcats are relatively rare and elusive, but camera trapping has proven to be an effective method for monitoring felids. We developed and tested a monitoring protocol using camera trapping in the northern Jura Mountains (cantons of Bern and Jura) in an area of 100 km2. During 60 days, we obtained 105 pictures of phenotypical wildcats of which 98 were suitable for individual identification. We identified 13 individuals from both sides and, additionally, 5 single right‐sided flanks and 3 single left‐sided flanks that could not be matched to unique individuals. We analyzed the camera‐trap data using the R package multimark, which has been extended to include a novel spatial capture–recapture model for encounter histories that include multiple “noninvasive” marks, such as bilaterally asymmetrical left‐ and right‐sided flanks, that can be difficult (or impossible) to reliably match to individuals. Here, we present this model in detail for the first time. Based on a “semi‐complete” data likelihood, the model is less computationally demanding than Bayesian alternatives that rely on a data‐augmented complete data likelihood. The spatially explicit capture–recapture model estimated a wildcat density (95% credible interval) of 26 (17–36) per 100 km2 suitable habitat. Our integrated model produced higher abundance and density estimates with improved precision compared to single‐sided analyses, suggesting spatially explicit capture–recapture methods with multiple “noninvasive” marks can improve our ability to monitor wildcat population status.

Wildcat population density in NE Portugal: A regional stronghold for a nationally threatened felid

“…This was not possible in our study due to logistics constrains. Nevertheless, our unpaired camera design still allowed to reliably estimate European wildcat's density through combining the left and right flank datasets to provide a single density estimate (Maronde et al, 2020).…”

“…This legal protection has contributed to reducing and locally inverting some of the abovementioned threats (Streif, Kraft, Veith, Kohnen, & Suchant, 2012), leading to the recent recovery of a few wildcat populations across Europe (Nussberger et al, 2018; Steyer et al, 2016). This apparent turnover in European wildcats' population trends has led to the identification of locally dense populations in some European regions, where densities have been estimated to be as high as 0.29 and 0.26 ind/km 2 in Switzerland (Kéry, Gardner, Stoeckle, Weber, & Royle, 2011; Maronde, McClintock, Breitenmoser, & Zimmermann, 2020) or 0.28 to 1.36 ind/km 2 in Sicily (Anile, Amico, & Ragni, 2012; Anile, Ragni, Randi, Mattucci, & Rovero, 2014). However, this trend appears not to be occurring across much of the Iberian Peninsula where wildcat populations are suspected to continue declining (Cabral et al, 2005; Gil‐Sánchez et al, 2020; Sobrino, Acevedo, Escudero, Marco, & Gortázar, 2009).…”

An integrated path for spatial capture–recapture and animal movement modeling

“…There remains much work to be done, but the integration of movement and SCR modeling will be worth the effort. There are many other areas of potential development, and integrated SCR movement models could be expanded to include any of the many recent extensions from either field (e.g., Royle et al, 2013b;Borchers and Fewster, 2016;Hooten et al, 2017;Patterson et al, 2017), including open population models (e.g., Gardner et al, 2010;Schaub and Royle, 2014;Glennie et al, 2019;Efford and Schofield, 2020), multi-state models (e.g., Morales et al, 2004;Lebreton et al, 2009), physiological processes (Hooten et al, 2019b), group-dynamic movements (Langrock et al, 2014), landscape connectivity (e.g., Royle et al, 2018), and misclassification or partial identity models (e.g., Link et al, 2010;Bonner and Holmberg, 2013;McClintock et al, 2013;Augustine et al, 2018;Maronde et al, 2020).…”

Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation

“…In the short term we expect the fraction of studies dealing with unmarked populations to grow, with increasing reliance on recently developed methods by those seeking estimates of abundance. The guidance provided by Gilbert et al (2020), coupled with improved accessibility of software and continued methodological refinement including integrated likelihood models (e.g., Ngoprasert et al, 2019;Maronde et al, 2020), should result in improved decision making and possibly greater adoption by future CT studies focused on abundance estimation.…”