Inwentaryzacja dużych ssaków z wykorzystaniem fotopułapek – przykład realizacji w trzech nadleśnictwach północno-zachodniej Polski

Camera traps are frequently used in wildlife research, including biodiversity assessment, behavioral studies, and abundance estimation. New statistical methods, such as the Random Encounter Model (REM) – which allows population density estimation without the need for individual recognition – offer an opportunity to integrate camera traps into wildlife management practice. In this study, we demonstrate how camera trapping combined with REM can be applied to survey large mammal populations, using case studies from three forest districts in north-western Poland: Drawsko (290 km 2 forest area, 81 camera traps), Złocieniec (170 km 2 forest area, 60 camera traps) and Lutówko (100 km2 forest area, 46 camera traps). The camera traps were placed randomly and operated for about a month. Over 4,000 video sequences of mammal encounters were recorded, mainly of ungulates and carnivores. The most frequently detected species were the red deer ( Cervus elaphus ), the roe deer ( Capreolus capreolus ) and the wild boar ( Sus scrofa ), which were recorded at 70–90% of the locations in each district. Population densities estimated using the REM ranged from 2.2 to 4.2 individuals/100 ha for red deer, 1.6 to 4.7 individuals/100 ha for roe deer and 1.0 to 4.9 individuals/100 ha for wild boar. The most precise estimates with a coefficient of variation of approximately 0.25 were obtained in the Drawsko district, where the highest number of camera traps was used. In the Złocieniec and Lutówko districts, the precision was lower, with a coefficient of variation between 0.28 and 0.44. The camera traps also documented the presence of protected carnivores, such as the gray wolf ( Canis lupus ) and the Eurasian lynx ( Lynx lynx ) as well as invasive species – the raccoon dog ( Nyctereutes procyonoides ) and the common raccoon ( Procyon lotor ). The quality and variety of data collected through camera trap monitoring show that this method is superior to conventional techniques such as drive counts or snow tracking. This approach not only improves decision-making in ungulate management but also supports the assessment of the carnivore status and distribution.