Terug naar overzicht

Combining habitat suitability with simulated movements for the predictive modelling of beaver distribution in Flanders

In Flanders, beavers have reappeared since 2000 and their numbers have expanded rapidly over the last decade, challenging impact management. To help identify areas with increased vulnerability to beaver damage we developed a species distribution model (SDM). In this model, we combined a MaxEnt-based approach to estimate habitat suitability and potential future spread with simulated dispersal using the SiMRiv R-package. We constructed our model in 2020 based on 2019 distribution data of beavers in Flanders and used it to predict population distribution probability up to 2022. To validate the combined model output, we compared the results for each 1 km² grid cell to the observed distribution of beavers in Flanders in 2020–2022. Our model predicted possible beaver occupancy in 4,361 out of 14,343 1 km² grid cells in Flanders. In 51.4% of these grid cells predicted probability of occupancy was accidental, in 29.3% it was low, in 13.0% medium and in 6.4% high. In 2020–2022 beavers were observed in 908 1 km² grid cells in Flanders. Comparison with the model output showed that the model performed well overall (AUC 0.825), although the negative predictive power was much higher than the positive predictive power since the model tends to overestimate actual occupancy over this three-year period. However, the accuracy rises with the projected level of occupancy probability. Although the overestimation fits the intended risk management purposes of the model, expanding it beyond the borders of Flanders and strengthening the backbone of the model with a more detailed population model could further improve the output and help improve the positive predictive power.

Details

Aantal pagina's 11
Volume 70
Type A1: Web of Science-artikel
Categorie Onderzoek
Tijdschrift European Journal of Wildlife Research|European journal of wildlife research online
Issns 1612-4642
Uitgeverij Springer
Taal Engels
Bibtex

@misc{5326e165-54c1-4318-888a-81cebef0263c,
title = "Combining habitat suitability with simulated movements for the predictive modelling of beaver distribution in Flanders",
abstract = "In Flanders, beavers have reappeared since 2000 and their numbers have expanded rapidly over the last decade, challenging impact management. To help identify areas with increased vulnerability to beaver damage we developed a species distribution model (SDM). In this model, we combined a MaxEnt-based approach to estimate habitat suitability and potential future spread with simulated dispersal using the SiMRiv R-package. We constructed our model in 2020 based on 2019 distribution data of beavers in Flanders and used it to predict population distribution probability up to 2022. To validate the combined model output, we compared the results for each 1 km² grid cell to the observed distribution of beavers in Flanders in 2020–2022. Our model predicted possible beaver occupancy in 4,361 out of 14,343 1 km² grid cells in Flanders. In 51.4% of these grid cells predicted probability of occupancy was accidental, in 29.3% it was low, in 13.0% medium and in 6.4% high. In 2020–2022 beavers were observed in 908 1 km² grid cells in Flanders. Comparison with the model output showed that the model performed well overall (AUC 0.825), although the negative predictive power was much higher than the positive predictive power since the model tends to overestimate actual occupancy over this three-year period. However, the accuracy rises with the projected level of occupancy probability. Although the overestimation fits the intended risk management purposes of the model, expanding it beyond the borders of Flanders and strengthening the backbone of the model with a more detailed population model could further improve the output and help improve the positive predictive power.",
author = "Frank Huysentruyt and Anneleen Rutten",
year = "2024",
month = jul,
day = "15",
doi = "https://doi.org/10.1007/s10344-024-01831-1",
language = "Nederlands",
publisher = "Springer",
address = "België,
type = "Other"
}