Improved Prediction of Eurasian Beaver Gnawing Preferences in Riparian Habitats: A Machine Learning Approach.

ABSTRACT: In this study, we investigated the impact of Eurasian beavers (Castor fiber Linnaeus, 1758) on riparian woodlands in Central Italy using Machine Learning (ML) techniques. Beavers are ecosystem engineers who may modify riverine ecosystems through dam building and foraging activities. Their gnawing activity can significantly alter the composition and structure of riparian forests. Traditionally, statistical models have been used to understand factors influencing beaver activity. Thus, this study explores the potential of ML algorithms for this purpose. We implemented three ML algorithms-Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forests (RF)-to analyze data collected from three Italian rivers. Data included in situ measurements of trees (diameter, dist

SUBMITTER: Trentanovi G 

PROVIDER: S-EPMC12711435 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature