Improved prediction of Eurasian beaver gnawing preferences in riparian habitats: a machine learning approach

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, distance from riverbank, species) and information on beaver damage (presence/severity). A two-step implementation has been proposed to predict whether a tree would be damaged by beavers and, if so, the severity of the damage (dead or alive tree). In the first step, three algorithms achieved high accuracy (up to 93% of damaged/undamaged trees correctly classified) and kept satisfactory performances even if when trained with small subsets of the data (85% accuracy when trained with 20% of the data). In the second stepthe algorithms reached accuracy (85%) comparable to step 1, despite the smaller subset available (159 samples out of 476 in the total dataset). This suggests that ML could significantly reduce the amount of field data collection needed to assess beaver impacts. Moreover, the following key factors influencing beaver gnawing activity were identified: tree diameter and distance from the riverbank were the most important predictors, while tree species and site location had less influence. Supplementary Material File (maintext.docx) - Download - 5.12 MB Information & Authors Information Version history Peer review timeline Published Ecology and Evolution Version of Record17 Dec 2025Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection

Authors Metrics & Citations Metrics Article Usage 257views 134downloads Citations Download citation Giovanni Trentanovi, EMANUELE SANTI, Emiliano Mori, et al. Improved prediction of Eurasian beaver gnawing preferences in riparian habitats: a machine learning approach. Authorea. 04 June 2025. DOI: https://doi.org/10.22541/au.174903339.98087175/v1 DOI: https://doi.org/10.22541/au.174903339.98087175/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.