Strandström, M. | Search bioRxiv

bioRxiv2020

Mapping the probability of forest snowdisturbances in Finland

The changing forest disturbance regimes emphasize the need for improved damage risk information. Here, our aim was to (1) improve the current understanding of snow damage risks by assessing the importance of abiotic factors, particularly the modelled snow load on trees, versus forest properties in predicting the probability of snow damage, (2) produce a snow damage probability map for Finland. We also compared the results for winters with typical snow load conditions and a winter with exceptionally heavy snow loads. To do this, we used damage observations from the Finnish national forest inventory (NFI) to create a statistical snow damage occurrence model, spatial data layers from different sources to use the model to predict the damage probability for the whole country in 16 x 16 m resolution. Snow damage reports from forest owners were used for testing the final map. Our results showed that best results were obtained when both abiotic and forest variables were included in the model. However, in the case of the high snow load winter, the model with only abiotic predictors performed nearly as well as the full model and the ability of the models to identify the snow damaged stands was higher than in other years. The results showed patterns of forest adaptation to high snow loads, as spruce stands in the north were less susceptible to damage than in southern areas and long-term snow load reduced the damage probability. The model and the derived wall-to-wall map were able to discriminate damage from no-damage cases on a good level. The damage probability mapping approach identifies the drivers of snow disturbances across forest landscapes and can be used to spatially estimate the current and future disturbance risks in forests, informing practical forestry and decision-making and supporting the adaptation to the changing disturbance regimes.

ecology↗

bioRxiv2019

High-resolution mapping of forest vulnerability to wind for disturbance-aware forestry and climate change adaptation

Windstorms cause major disturbances in European forests and forest management can play a key role in making forests more persistent to disturbances. However, better information is needed to support decision making that effectively accounts for wind disturbances. Here we show how empirical probability models of wind damage, combined with existing spatial datasets, can be used to provide fine-scale spatial information about disturbance probability over large areas. First, we created stand-level damage probability models with predictors describing forest characteristics, recent forest management history and local wind, soil, site and climate conditions. We tested three different methods for creating the damage probability models - generalized linear models (GLM), generalized additive models (GAM) and boosted regression trees (BRT). Then, the damage probability maps were calculated by combining the models (GLM, GAM and BRT) with GIS data sets representing the model predictors. Finally, we demonstrated the predictive performance of the maps with a large, independent test data, which shows that the damage probability maps are able to identify vulnerable forests also in new wind damage events (AUC > 0.7). Use of the more complex methods (GAM and BRT) was not found to improve the predictive performance of the map compared to GLM, and therefore we would suggest using the more simple GLM method that can be more easily interpreted. The map allows identification of vulnerable forest areas in high spatial resolution (16 x 16 m2 raster resolution), making it useful in assessing the vulnerability of individual forest stands when making management decisions. The map is also a powerful tool for communicating disturbance risks to forest owners and managers and it has the potential to steer forest management practices to a more disturbance aware direction. Our study showed that in spite of the inherent stochasticity of the wind and damage phenomena at all spatial scales, it can be modelled with good accuracy across large spatial scales when existing ground and earth observation data sources are combined smartly. With improving data quality and availability, map-based risk assessments can be extended to other regions and other disturbance types.

ecology↗