BACKGROUNDA major impact of invasive Myocastor coypus in their introduction range is the collapse of riverbanks and nearby infrastructure, such as railway lines, due to the species' burrowing activities. Because widespread implementation of preventive measures along watercourses is unfeasible, identifying susceptible areas is key to guide targeted management actions. This study used species-habitat models to: (i) identify local environmental features of the railway line/watercourse intersections (RLWIs) that make them particularly susceptible to coypu damage, and (ii) predict species occurrence probability over a wide lowland-hilly area of northern Italy (Lombardy) to identify priority areas for monitoring. RESULTSLocal-scale models identified that the RLWIs most susceptible to burrowing were those surrounded by arable land with interspersed hedgerows locally characterized by high herbaceous vegetation and clay soil. In urbanized areas and areas of intensive agriculture, coypu dens were generally located significantly closer to the railway, increasing the risk of collapse. A landscape-scale species distribution model showed that lowland areas along major rivers and lake shores, and also agricultural areas with a dense minor hydrographic network, particularly in the southeast of the study area, are more likely to be occupied by coypu. CONCLUSIONLocal-scale models showed that specific environmental characteristics increase the risk of burrowing near RLWIs. The landscape-scale model allowed us to predict which areas require thorough monitoring of RLWIs to search for such local characteristics to implement preventive management measures. The proposed model-based framework can be applied to any geographical context to predict and prevent coypu damage. (c) 2024 Society of Chemical Industry.

Tree fall onto railway lines puts passengers at risk and causes large economic losses due to disruption of train services and damage to infrastructure. Railway lines in Germany are vulnerable to tree fall because of the large number of trackside trees that exist in that country with approximately 70% of all railway lines being tree-lined. In this paper we first tested whether a hybrid-mechanistic tree wind damage model, ForestGALES, could identify the sections of the railway network affected by tree fall in two federal states of Germany, Northrhine-Westphalia (NRW) and Thuringia (TH). We secondly tested whether the model, in combination with meteorological forecast models, could predict where tree fall occurred during a damaging windstorm. We used information on tree characteristics derived from LiDAR and aerial photography along the railway line network in NRW and TH to calculate the critical wind speed (CWS) at which damage is expected to happen for every individual tree as a function of its size and species, and the underlying soil. The railway network was then divided into 500 m sections and the statistics of the CWS, tree height, and species composition (broadleaf/conifer mix) within each were calculated. Analysis of past tree fall events recorded by Deutsche Bahn AG (DB) showed that there was a significantly lower minimum CWS and significantly greater maximum tree height in sections that had recorded damage. In a second step we compared the calculated CWS values for all trees against downscaled wind speed assessments across the two federal states during Storm Friederike (named Storm David internationally) on 18 January 2018 and tested the ability of the model to discriminate sections with recorded damage during the storm. Excellent model discrimination was found with an AUC value of 0.82 and an overall model accuracy of 74.2%. The first test showed that the ForestGALES model with precise individual tree information can identify the sections of a railway network most vulnerable to tree fall. The second analysis showed, for the one storm tested, that the ForestGALES model when combined with predicted storm wind speeds can identify the most probable sections of the railway network to experience tree fall during an approaching damaging storm. Such information could be of value in firstly planning remedial work along railway lines, and secondly preparing the railway network ahead of a major storm.