Survival and cause-specific mortality of European wildcat (Felis silvestris) across Europe

Humans have transformed most landscapes across the globe, forcing other species to adapt in order to persist in increasingly anthropogenic landscapes. Wide-ranging solitary species, such as wild felids, struggle particularly in such landscapes. Conservation planning and management for their long-term persistence critically depends on understanding what determine survival and what are the main mortality risks. We carried out the first study on annual survival and cause-specific mortality of the European wildcat with a large and unique dataset of 211 tracked individuals from 22 study areas across Europe. Furthermore, we tested the effect of environmental and human disturbance variables on the survival probability. Our results show that mortalities were mainly human-caused, with roadkill and poaching representing 57% and 22% of the total annual mortality, respectively. The annual survival probability of wildcat was 0.92 (95% CI = 0.87–0.98) for females and 0.84 (95% CI = 0.75–0.94) for males. Road density strongly impacted wildcat annual survival, whereby an increase in the road density of motorways and primary roads by 1 km/km2 in wildcat home-ranges increased mortality risk ninefold. Low-traffic roads, such as secondary and tertiary roads, did not significantly affect wildcat's annual survival. Our results deliver key input parameters for population viability analyses, provide planning-relevant information to maintain subcritical road densities in key wildcat habitats, and identify conditions under which wildcat-proof fences and wildlife crossing structures should be installed to decrease wildcat mortality.This research was funded by: the German Federal Ministry of Transport and Digital Infrastructure (BMVI) as part of the mFund project “WilDa—Dynamic Wildlife–Vehicle Collision warning, using heterogeneous traffic, accident and environmental data as well as big data concepts” grant number 19F2014B; the Deutscher Akademischer Austauschdienst (DAAD) Research Grants, Short-Term Grants, 2020 (57507441); the Deutsche Wildtier Stiftung (DeWiSt). The data from Cabañeros National Park were collected in the frame of the project OAPN 352/2011 funded by Organismo Autónomo Parques Nacionales. MM was supported by a research contract Ramón y Cajal from the MINECO (RYC-2015-19231). FDR was supported by a postdoctoral contract funded by the University of Málaga through the grants program “Ayudas para la Incorporación de Doctores del I Plan Propio de Investigación de la Universidad de Málaga (Call 2019)”. PM was supported by UIDB/50027/2020 with funding from FCT/MCTES through national funds.Peer reviewe

Comparison of Landsat-8 and Sentinel-2 Data for Estimation of Leaf Area Index in Temperate Forests

With the launch of the Sentinel-2 satellites, a European capacity has been created to ensure continuity of Landsat and SPOT observations. In contrast to previous sensors, Sentinel-2′s multispectral imager (MSI) incorporates three additional spectral bands in the red-edge (RE) region, which are expected to improve the mapping of vegetation traits. The objective of this study was to compare Sentinel-2 MSI and Landsat-8 OLI data for the estimation of leaf area index (LAI) in temperate, deciduous broadleaf forests. We used hemispherical photography to estimate effective LAI at 36 field plots. We then built and compared simple and multiple linear regression models between field-based LAI and spectral bands and vegetation indices derived from Landsat-8 and Sentinel-2, respectively. Our main findings are that Sentinel-2 predicts LAI with comparable accuracy to Landsat-8. The best Landsat-8 models predicted LAI with a root-mean-square error (RMSE) of 0.877, and the best Sentinel-2 model achieved an RMSE of 0.879. In addition, Sentinel-2′s RE bands and RE-based indices did not improve LAI prediction. Thirdly, LAI models showed a high sensitivity to understory vegetation when tree cover was sparse. According to our findings, Sentinel-2 is capable of delivering data continuity at high temporal resolution.Peer Reviewe