The Eurasian lynx is a large carnivore widely distributed across Eurasia. However, our understanding of population status is heterogeneous across their range, with some populations isolated that are at risk of reduced genetic variation and a complete lack of information about others. In many European countries, Eurasian lynx are monitored through demographic studies crucial for their conservation and management. Even so, there are only rough and fragmented population assessments from Ukraine and Belarus, despite strict protection in both countries and their importance for lynx connectivity across Europe. We monitored lynx from October 2020 to March 2021 and used camera-trapping in combination with spatial capture-recapture (SCR) methods in a Bayesian Framework to provide the first SCR density estimation of three lynx populations across Ukraine and Belarus, including the Ukrainian Chornobyl Exclusion Zone, Southern Belarus, and the Ukrainian Carpathians. Our density estimates varied within our study areas ranging from 0.45 to 1.54 individuals/100 km². This work provides a substantial scientific component to the overall understanding of lynx conservation for a region where only broad information is available and opens the doors for further large-scale monitoring and trend assessments. The crucial information we provide can greatly enhance the range-wide assessments of the status of this protected species. We also discuss the implications for Eurasian lynx conservation, despite the geopolitical realities impacting species monitoring in the region. Our work serves as a baseline, not only for future conservation interventions but also to evaluate the effects of disturbance and threats to these protected populations.

Spatial capture-recapture (SCR) density estimation of Eurasian lynx from camera traps in three study areas in Europe (Ukrainian Chornobyl Exclusion Zone, Ukraine; Skolivski Beskydy National Park, Ukraine; Belarusian Pripyat-Polesia, Belarus). Camera traps were set up in a paired design to photograph the unique coat patterns of individuals during the winter period 2020-2021.

Individual animals were then identified and capture histories were processed by Bayesian SCR models. Abundance and density were estimated for the state space area and the minimum convex polygon of camera traps.

Details for the R code are provided in the README file.

R data software is required to re-run the analysis.

Code included for the analysis:

1) Code for density estimation (Code for density estimation.R) which runs the analysis in the R software and follow the comments (#) guiding you through the code. 

Files included for each study area:

1) capture file of Eurasian lynx events (edf.csv)

2) a list object for that study area (e.g. BPPlist.RData) that contains the number of individuals, occasions, and traps used to create the capture history object ("capthist") used by the secr package

3) two camera trap information files (tdf.csv and traps.txt), the .txt version of the file in required for the secr package in R to access the information on the camera traps to create the capture history

Sharing/access Information:

This code and data was generated through the hard work of park rangers and scientific staff in Ukraine and Belarus, and as part of student-led projects. Accreditation of the authors and data owners is greatly appreciated.