Camera trapping monitoring for managing ungulate populations in central italy's beech forest
Date Issued
2023
Type
conferenceObject
Abstract
In response to the rising ungulate population and its impact on agriculture and forest ecosystems, the adoption of reliable and cost-effective wildlife monitoring techniques is mandatory. To ptimize forest management, we utilized phototrapping to assess wild ungulate presence and abundance n a 560-hectare non-hunting beech forest in central Italy, managed for timber production. Using a 00 m × 500 m grid and stratified sampling, we installed 9 camera traps (CTs) on trees working in continuous. From 2019 to 2021, camera-trapping sessions were conducted in both early summer nd autumn. Trapping rate (TR) was determined as the ratio of photographic events to the total survey effort (number of CTs * working hours/24 h) multiplied by 100. Density was estimated using the Random Encounter Model (REM). Descriptive and Kruskal-Wallis variance analyses were erformed, considering species, season, years, and forest cover as variables. TR was different (p<0.000) between wild boar and roe deer (71.5±5.2 and19.9±1.9 respectively), as well as REM 20.1±2.7 vs 4.8±0.7 animals/100 ha respectively). Overall, for wild boar, apart from 2021 (p < 0.0001), there was no significant annual variation of the TR for the two species.
Roe deer, a selective grazer, were more prevalent (p ≤ 0.0001) in summer (6.6 ± 1.2 animal/100 ha), while wild boars, consumers of acorns and beech nuts, were more abundant in autumn (25.0 ± 3.0 animal/100 ha). The monitoring method proved to be highly sensitive and easily replicable and show unexpected inverse seasonal variations determining phases of concentration in the periods of maximum sensitivity respect to each species.
Keywords: camera trapping; REM; roe deer; ungulate; wild boar
Conference(s)
4th INTERNATIONAL CONFERENCE on Agriculture and Life Sciences (ICOALS 4) Tirana 2023