Tomaž Skrbinšek, Matej Bartol, Barbara Boljte, Nuša Hrga, Tilen Hvala, Aleksandra Majić Skrbinšek, Elena Pazhenkova, Miha Predalič, Gregor Simčič, Marjeta Konec, Klemen Jerina

Genetic monitoring is for many bear populations the only approach that can provide robust, precise tracking of population abundance. But despite its many advantages, high costs, substantial field effort, and long time to results often make practical application challenging.
In Slovenia, we started with brown bear genetics in 2004, which evolved into national-scale monitoring in 2007. In 2015 we switched to genotyping-by-sequencing to improve cost effectiveness, increase reliability, and considerably shorten field-to-results time. We also set the periodicity of genetic sampling to every eight years. Sampling is conducted through citizen science, with results carefully communicated to all stakeholders and participants.
As management and conservation decisions require annual data, we developed a demographic model calibrated with genetic estimates and parameterized with the other demographic data. The model is re-run annually using new mortality data and rebuilt with each new genetic sampling.
During the third monitoring session in autumn 2023, we collected 2,864 noninvasive genetic samples. Most samples were collected by hunters and foresters. High-throughput sequencing enabled rapid and cost-effective analysis, with laboratory work completed within eight months. The entire study, including capture–mark–recapture modelling, was finalized within ten months. We obtained a population estimate with a narrow confidence interval (Autumn 2023: 737, 695–797; Spring 2023: 1001, 959–1061). The demographic model closely matched the genetic estimate, confirming its usefulness.
Over the last 20 years, genetic monitoring has evolved into robust, cost-effective cornerstone of brown bear management. In 2015 Slovenia partnered with Croatia to establish coordinated transboundary monitoring, and in 2026 we launched LIFE DinPin Bear to extend this across the Dinaric–Pindos population. We believe the knowledge gained and methods developed are applicable to other populations as well.