Integrating multiple datasets into spatially-explicit capture-recapture models to estimate the abundance of a locally scarce felid

Pablo Ferreras,Paulo Célio Alves,Pedro Monterroso,José Jiménez,Francisco Díaz-Ruiz,Jorge Tobajas

doi:10.1007/s10531-021-02309-1

Abstract

The conservation of animal populations often requires the estimation of population size. Low density and secretive behaviour usually determine scarce data sources and hampers precise abundance estimations of carnivore populations. However, joint analysis of independent scarce data sources in a common modeling framework allows unbiased and precise estimates of population parameters. We aimed to estimate the density of the European wildcat (Felis silvestris) in a protected area of Spain, by combining independent datasets in a spatially-explicit capture-recapture (SCR) framework. Data from live-capture with individual identification, camera-trapping without individual identification and radio-tracking concurrently obtained were integrated in a joint SCR and count data model. Ten live captures of five wildcats were obtained with an effort of 2034 trap-days, whereas seven wildcat independent events were recorded in camera traps with 3628 camera-days. Two wildcats were radio-tagged and telemetry information on their movements was obtained. The integration of the different data sources improved the precision obtained by the standard SCR model. The mean (± SD) density estimated with the integrated model (0.038 ± 0.017 wildcats/km2, 95% highest posterior density 0.013–0.082) is among the lowest values ever reported for this species, despite corresponding to a highly protected area. Among the likely causes of such low density, low prey availability could have triggered an extinction vortex process. We postulate that the estimated low density could represent a common situation of wildcat populations in the southern Iberia, highlighting the need for further studies and urgent conservation actions in the furthermost southwestern range of this species in Europe.

Highlights

Accurate estimation of population size is often required to take decisions for the conservation of wildlife populations (Carbone et al 2001)
The results of our models highlight the advantages of incorporating diverse data sources for low density populations, whereby an integrated approach provides the means to improve precision in spatially-explicit capture-recapture (SCR) density estimates by making use of available data that might otherwise be discarded (Velli et al 2015; Murphy et al 2018) with the recipe “same process, different observation model” (Kéry and Royle, 2020)
Other studies (Jiménez et al 2021) that use both known and unknown identity samples from a single observation model using a natural mechanistic dependence between samples, found that density estimates improvement adding non-ID data is higher in low density populations

Summary

Introduction

Accurate estimation of population size is often required to take decisions for the conservation of wildlife populations (Carbone et al 2001). Estimating population size of mammalian carnivore species is often difficult due to their expansive use of space, secretive nature and typical low population densities (Sollmann et al 2014; Brassine and Parker 2015). Scarce datasets are often discarded, and population estimates are not attained, with the loss of valuable information required for taking conservation measures. Recent advances in analytical approaches have opened doors to jointly analyze several sources of scarce data in a common modeling framework, allowing for the estimation of population parameters and adequately informing conservation measures (Anile et al 2014; Velli et al 2015; Murphy et al 2018)

Objectives

Methods

Results

Conclusion