Biology subjects

Peltoniemi, M.

Publications and source records attributed to Peltoniemi, M..

5 recordsLinked to original sources

bioRxiv2026

Loss of competitive strength in European conifer species under climate change

Climate change is expected to alter species assemblages by affecting the outcome of competition between species. Investigating processes of competition remains challenging particularly in tree communities, as they unfold over extensive spatio-temporal scales. Here, we developed a deep-learning approach to leverage a novel database of 135 million simulated local-scale tree responses to climate across continental Europe to investigate changes in the competitiveness of nine major tree species under different scenarios of climate change. Specifically, we trained a Deep Neural Network on local process model projections to investigate climate change effects on indicators of competitive strength and species dominance. We found decreasing competitive strength for all investigated evergreen coniferous species across their distribution, while major deciduous broadleaved species such as Quercus robur and Fagus sylvatica increased in competitiveness. Changes in tree species competition with climate differed locally, but most investigated species lost competitive strength at their warm range edges. As a consequence of these changes, up to 19% of Europes forests could experience a change in the dominant tree species until the end of the 21st century. Our results suggest a profound climate-induced reassembly of Europes forests and identify areas that may require specific attention in forest policy and management.

ecology↗

bioRxiv2025

Rot or not? Uncovering the spatial patterns and drivers of Norway spruce root rot with harvester data

Root rot is a major problem for forestry, leading to reduced timber quality, growth losses, and increased disturbance risks. Harvester data provides a promising source of information for improving the knowledge on the root rot distribution. Here, we used harvester data (1) to map the risk of spruce root rot in southern and central Finland, and (2) to understand the drivers of the spatial patterns in rot occurrence. First, we built a statistical model predicting the percentage of stems affected by root rot on stand-level. To train the model, we used an extensive set of harvester data, containing 10,402 clear-cut forest stands, where the presence of root rot was recorded for each cut tree using an algorithm based on bucking patterns (i.e., cutting of the stem into different log assortments) recorded by the harvester. The model consisted of two parts, a fixed component describing the effects of different drivers of root rot, and a spatial random component describing the spatial patterns not explained by the fixed part of the model. The fixed part included forest and site attributes, landscape characteristics and proxies of forest-use legacies. The model was then used to map root rot risk, by predicting the probability of root rot occurrence using spatial data sets of the variables in the fixed part of the model, and the known rot status of locations in the data set for the random part of the model. Finally, the map was tested with an independent validation data, verifying its ability to identify the high-risk areas. Proxies of forest-use legacies, tree size and site fertility were found to drive the percentage of rot-affected stems in stands. The results quantify the root rot risk in Finland in higher detail than before and demonstrate the large potential of harvester data in informing about the risk of root rot in boreal forests.

ecology↗

bioRxiv2022

Spatial patterns of biomass change across Finland in 2009 - 2015

Forest characteristics vary largely at the regional level and in smaller geographic areas in Finland. The amount of greenhouse gas emissions is related to changes in biomass and the soil type (e.g. upland soils vs. peatlands). Spatially accurate map data of forests and biomass changes could improve the ability to suggest optimal management alternatives for any patch of land, e.g. in terms of climate change mitigation. In our study, estimating and explaining spatial patterns of biomass change across Finland was the main interest. We analysed biomass changes on different soil and site types on forested land using the Finnish Multi-Source National Forest Inventory (MS-NFI) map layers of the 2009 and 2015 datasets. Silvicultural management and treatment units larger than individual pixels were created by automatic segmentation of the thematic maps. The segmented MS-NFI biomass maps of growing stocks, including above and below ground biomass resulted an average estimate of 77.7 tons ha-1 compared to the national forest inventory estimate of 76.5 tons ha-1 in 2015 for Finland. Forest soil type had a similar effect on average biomass in segmented MS-NFI and NFI data. Despite good regional and country-level results, at the segment level the biomass distributions were narrowed and averaging of biomass estimates was observed. Hence, biomass changes on segments can be considered only approximate values; also, those small differences in average biomass may accumulate when map layers from more than one time point are compared. MS-NFI classification results depend on the satellite images and field data used, causing variation in successive inventories. In addition, to avoid false biomass change observations due to the low growth rate of boreal forests, a six-year study period was set. A kappa of 0.44 was achieved for precision when comparing undisturbed and disturbed forest stands in the Global Forest Cover layer and MS-NFI segmented map, indicating the low ability of the global forest map to identify land cover changes for Finland. The segmented biomass maps provide a useful tool for forest owners to analyse carbon stock changes in their forests and how to affect the amount of carbon by forest management.

ecology↗

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↗