<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Nowadays, the large-scale disturbance and subsequent temporary deforestation of mountain forests are widely discussed phenomena. In this study, we built both a logistic regression model (LRM) and a generalised additive model (GAM), in order to understand the drivers of deforestation after the Elisabeth windstorm (2004) in the Central Low Tatras, Slovakia. A set of topographic and biotic characteristics was selected as explanatory variables, while the presence of deforestation was a response variable. The results show that the most prone to windstorm-driven damage are forests growing at a high elevation, in the ridge’s surroundings, and on gentle slopes exposed to the wind during the disturbance. Moreover, the stands with a high proportion of Norway spruce and with medium-diameter trees, which are under forest management, were identified as more vulnerable. Additionally, both models were used to identify those stands, which would be most susceptible to damage by future windstorms. According to its explanatory power and building efficiency, we propose using of LRM rather than GAM in similar large-scale studies. The addressed methods can be used in local forest management, as scientifically based decision-making appears to be crucial for maintaining mountain forests resistant to gusty winds, as well as other disturbing agents.</p>
</abstract>

Nowadays, the large-scale disturbance and subsequent temporary deforestation of mountain forests are widely discussed phenomena. In this study, we built both a logistic regression model (LRM) and a generalised additive model (GAM), in order to understand the drivers of deforestation after the Elisabeth windstorm (2004) in the Central Low Tatras, Slovakia. A set of topographic and biotic characteristics was selected as explanatory variables, while the presence of deforestation was a response variable. The results show that the most prone to windstorm-driven damage are forests growing at a high elevation, in the ridge’s surroundings, and on gentle slopes exposed to the wind during the disturbance. Moreover, the stands with a high proportion of Norway spruce and with medium-diameter trees, which are under forest management, were identified as more vulnerable. Additionally, both models were used to identify those stands, which would be most susceptible to damage by future windstorms. According to its explanatory power and building efficiency, we propose using of LRM rather than GAM in similar large-scale studies. The addressed methods can be used in local forest management, as scientifically based decision-making appears to be crucial for maintaining mountain forests resistant to gusty winds, as well as other disturbing agents.

Assessing and forecasting the influence of environmental controls on windstorm disturbances in the Central Low Tatras, through regression models

Department of Physical Geography and Geoinformatics, Faculty of Natural Sciences

Department of Natural Environment, Faculty of Forestry

Moravian Geographical Reports

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Nowadays, the large-scale disturbance and subsequent temporary deforestation of mountain forests are widely discussed phenomena. In this study, we built both a...

Assessing and forecasting the influence of environmental controls on windstorm disturbances in the Central Low Tatras, through regression models

Published Online: Dec 26, 2023

Page range: 214 - 222

Received: Jul 25, 2023

Accepted: Dec 08, 2023

DOI: https://doi.org/10.2478/mgr-2023-0020

Keywords
driving factors, forest management, mountain spruce forests, wind disturbance, regression models, Slovakia

© 2023 Vladimír Šagát et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Assessing and forecasting the influence of environmental controls on windstorm disturbances in the Central Low Tatras, through regression models

Published Online: Dec 26, 2023

Page range: 214 - 222

Received: Jul 25, 2023

Accepted: Dec 08, 2023

DOI: https://doi.org/10.2478/mgr-2023-0020

Keywordsdriving factors, forest management, mountain spruce forests, wind disturbance, regression models, Slovakia

© 2023 Vladimír Šagát et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
driving factors, forest management, mountain spruce forests, wind disturbance, regression models, Slovakia