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Article

Driving Mechanisms and Changes in Dominant Forest Tree Taxa in Europe Under Climate Change

by
Jing Zhou
1,
Qianhong Tang
1,
Yanan Zhao
1,
Xiaokang Hu
1,
Tao Wang
1,* and
Bingru Wang
2,*
1
College of Agriculture and Biological Science, Co-Innovation Center for Cangshan Mountain and Erhai Lake Integrated Protection and Green Development of Yunnan Province, Dali University, Dali 671003, China
2
Department of Geosciences and Natural Resource Management, University of Copenhagen, 1958 Copenhagen, Denmark
*
Authors to whom correspondence should be addressed.
Forests 2025, 16(6), 900; https://doi.org/10.3390/f16060900
Submission received: 10 April 2025 / Revised: 21 May 2025 / Accepted: 26 May 2025 / Published: 27 May 2025
(This article belongs to the Section Forest Ecology and Management)
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Abstract

Forest ecosystems are crucial for terrestrial ecosystem stability, particularly in carbon sequestration, nutrient cycling, and water conservation. With climate change exacerbating, understanding changes in suitable habitats for the main European tree taxa [Norway spruce (Picea abies), pedunculate oak (Quercus robur), and European beech (Fagus sylvatica)] and their drivers is critical for forest conservation in Europe. Here, we analyzed the factors driving the tree taxa distribution and suitable habitats under the current and two future scenarios, namely, optimistic and pessimistic. Based on a species distribution model, climatic, land use, and topographic factors were introduced as variables. This study determined that the main factors driving the tree taxa distributions were temperature, followed by land use. Under the future optimistic scenario, the suitable habitats change for the three tree taxa. Suitable habitats emerge in high-latitude regions and the northern Mediterranean. Meanwhile, suitable habitats are decreasing in Central Europe. Under the pessimistic scenario, more significant changes occurred in these regions. The total suitable habitat area for the three tree taxa did not change consistently under both scenarios. The suitable habitat area for Norway spruce increased, whereas that for pedunculate oak decreased. However, both regions with increasing or decreasing suitable habitats face the potential for forest succession, which will also affect the stability of forest ecosystem functions and should be a key focus.

1. Introduction

Climate change is one of the biggest challenges facing ecosystems today, and species are being tested by this change as atmospheric CO2 concentrations and temperatures increase [1]. Global warming causes temperature isoclines to move, resulting in changes in the spatial location and shape of a species’ suitable habitat [2], which may spread to high-latitude or high-altitude areas [3] that face the risk of extinction in response to climate change [4,5,6]. Therefore, exploring the spread trends and mechanisms of key species under different climate change scenarios is crucial for analyzing the impact of climate change on ecosystems.
Forests are the most crucial components for maintaining ecological services in terrestrial ecosystems and play an important role in carbon sequestration, nutrient cycling, and water conservation regulation [7]. Therefore, people have long paid attention to forest protection. Economically developed regions have funds to invest in protecting the ecological environment and have earlier prioritization of forest conservation. The corresponding measures have been effective in these areas. Studying forest composition and distribution changes in these regions has provided us with complete datasets for analyzing the mechanisms of forest responses to climate change. This has helped us analyze the roles played by climate factors and human activities in forest change. As one of the most economically developed regions worldwide, Europe has a long history of forest conservation and afforestation. The Danish Forest Act of 1805 brought forth the notion of forest reserves. Organized forestry in Iceland began in 1899, and Norway began its national afforestation plan in the late 1930s [8]. In Europe, forests have been recovering since the early 20th century, and the average forest area has increased by 25%–30% since the 1950s [9,10]. These changes in forests are often influenced by multiple factors, including climate, topography, and the socioeconomic characteristics of specific regions [10,11,12]. The increase in the forest area may be due to regeneration after a disturbance or expansion to non-forested areas [13].
However, regardless of the reason, climatic factors and human disturbances play important roles. Global warming is accelerating the changes in the stability of European forest ecosystems. In past decades, research has shown that the suitable habitats for iconic tree taxa such as Norway spruce and European beech are shrinking [14]. Frequent extreme drought events have led to an increase in tree mortality rates in Central Europe [15], and forest management faces significant challenges from future climate change [16]. Although species distribution models (SDMs) are widely used to predict tree species responses [17], existing research remains predominantly constrained by early climate scenarios or overlooks critical non-climatic variables such as terrain complexity and land use types, resulting in insufficient integration of multidimensional adaptive strategies in conservation planning. Meanwhile, the current findings place greater emphasis on changes in net forest area and evaluate the effectiveness of forest protection measures in terms of both increases and decreases. In the context of climate change, certain taxa may find some regions unsuitable for their survival, whereas others may become more conducive [2]. In addition to considering changes in the net area, a more detailed analysis of specific areas experiencing increases or decreases is necessary to provide targeted solutions for forest protection. Therefore, investigating the primary factors influencing European forest distribution based on climate, topographical factors and land use, and examining specific habitat changes in different future climate scenarios can enhance our understanding of the current threats to the European forest ecosystem with greater precision.
Norway spruce (Picea abies), pedunculate oak (Quercus robur), and European beech (Fagus sylvatica) are pivotal tree taxa in European forest ecosystems and have played a key role in sustaining forest growth for nearly a century [7,18]. Due to their capacity for environmental adaptation and human cultivation, they have expanded from their indigenous regions to various areas worldwide, particularly in Europe. However, under today’s increasingly severe climate change scenario, the distribution of these tree taxa may change. In this study, we focused on three tree taxa commonly found in Europe. We used the latest integrated species distribution models to investigate the potential driving factors influencing their distribution in Europe and analyzed the change process of potentially suitable habitats for these three tree taxa based on the current and two future scenarios, that is, optimistic SSP126 and pessimistic SSP585. We aimed to address the following three issues: (1) the main driving factors influencing the distribution of the main tree taxa in European forests, (2) changes in the potential distribution of the main tree taxa in Europe under different future scenarios, and (3) the potential risks faced by the three tree taxa in different regions and countries in Europe.

2. Materials and Methods

2.1. Plant Taxa Occurrence Records

Occurrence records of Norway spruce, pedunculate oak, and European beech were collected from the Global Biodiversity Information Facility (GBIF, www.gbif.org). During the data retrieval from the GBIF database, search parameters were configured with the following criteria: (1) temporal scope restricted to records from 1970 to 2023; (2) exclusive inclusion of occurrence records containing precise latitude-longitude coordinates; (3) systematic exclusion of entries exhibiting either duplicate georeferenced coordinates or coordinate uncertainty exceeding 3 km. Subsequently, a geographic filter was applied to isolate data points situated within the European biogeographical region. Then, the occurrence records of Norway spruce (Picea abies), pedunculate oak (Quercus robur), and European beech (Fagus sylvatica) were obtained with 253,242, 389,110, and 374,833 records, respectively. To mitigate spatial autocorrelation effects, spatial thinning processing was performed on the obtained occurrence records. The minimum distance threshold for spatial autocorrelation was determined by 30 km, implemented in the ecospat 4.1.1 package in R 4.3.2. Subsequently, spatial thinning was applied to retain only one randomly selected occurrence record within each critical distance, ensuring statistical independence of the remaining observations [19,20]. A total of 2849, 2842, and 2158 occurrence records of Norway spruce, pedunculate oak, and European beech, respectively, were used to construct the species distribution models.

2.2. Climate, Land Use, and Topographical Factors

Considering the environmental variables that may affect the distribution of species, 19 climatic factors (annual mean temperature, mean diurnal range, isothermality, temperature seasonality, max temperature of warmest month, min temperature of coldest month, temperature annual range, mean temperature of wettest quarter, mean temperature of driest quarter, mean temperature of warmest quarter, mean temperature of coldest quarter, annual precipitation, precipitation of wettest month, precipitation of driest month, precipitation seasonality, precipitation of wettest quarter, precipitation of driest quarter, precipitation of warmest quarter and precipitation of coldest quarter), eight land use factors (cropland, urban, forested primary land, non-forested primary land, rangeland, managed pasture, potentially forested secondary land and potentially non-forested secondary land), and three topographical factors (elevation, slope and aspect) were selected to construct the species distribution models for this study (Table S1). The 19 climatic factors were obtained from the WorldClim database (www.worldclim.org). Elevation, slope, and aspect as the topographical factors were determined using a digital elevation model (DEM) from WorldClim (www.worldclim.org) [21]. Eight land-use factors were included in this study from the land-use harmonization strategy website (https://www.luh.umd.edu/index.shtml). Further details regarding these environmental variables are provided in Table S1. For future factors, we selected two scenarios, SSP126 and SSP585, which represent the most optimistic and pessimistic climate change scenarios, respectively. SSP126 is the updated scenario of RCP2.6, which uses a sustainable world that takes a green pathway, with SSP1 + RCP2.6, forcing a low level of greenhouse gas emissions. SSP585 is the updated scenario of RCP8.5, which uses a world with rapid fossil fuel evolution, with SSP5 + RCP8.5, forcing a high level of greenhouse gas emissions [22,23]. Nineteen future climate factors for the year 2100 (5 arc-minute resolution, ~10 km at the equator) under both the SSP126 and SSP585 scenarios were sourced from WorldClim (www.worldclim.org). The averages of two general circulation models (GCMs), FIO-ESM-2-0 and MPI-ESM1-2-HR, were used to demonstrate the high accuracy of projecting climate change information for future climate factors, which accuracy under SSP scenarios was rigorously evaluated [24]. Eight future land use factors for 2100 were obtained from the land use harmonization strategy website (https://www.luh.umd.edu/index.shtml) [25]. As topographic factors, namely, elevation, slope, and aspect, did not change over short timescales, we used the same topographic factors under current and future scenarios. Therefore, in this study, we examined three scenarios, that is, the current scenario and two future scenarios, SSP126 and SSP585.

2.3. Model Construction

Species distribution models (SDMs) were constructed using the biomod2 4.2-4 package in R 4.3.2. To mitigate single-model biases and improve prediction robustness, we employed an ensemble approach through the above package [26]. This framework integrates six widely validated algorithms—Generalized Linear Model (GLM), Generalized Boosting Model (GBM), Classification Tree Analysis (CTA), Flexible Discriminant Analysis (FDA), Random Forest (RF), and Maximum Entropy (MaxEnt). Species occurrence records (presence records with geographic coordinates) were imported. The response variable was converted to binary presence pseudo-absences data. To obtain pseudo-absences, we used a random selection procedure with three repetitions, the number of pseudo-absences retrieved was equal to the number of each tree taxa occurrence records [26]. Modeling algorithms (GLM, GBM, CTA, FDA, RF, and MaxEnt) were trained using a random 70:30 split of the records into training and testing subsets. Model performance was evaluated via cross-validation: three-fold validation and stratified sampling with three replicates. Model accuracy was assessed using the ROC curve (AUC) and true skill statistic (TSS) scores. Calibrated SDMs with AUC less than 0.8 and TSS less than 0.5 were deleted. We established a preliminary species distribution model and obtained importance values for all the variables (importance values were calculated using permutation importance analysis, which quantifies each variable’s contribution to model accuracy). Strong collinearity between the variables may lead to an overestimation of the model. Therefore, variables with a Pearson correlation coefficient greater than 0.7 were regarded as having a strong correlation. Less important factors with lower importance values were deleted [27,28]. We repeated this process to obtain the remaining variables without strong collinearity and constructed the final species distribution model. This approach effectively mitigated overfitting risks while maintaining model predictive accuracy and specifically avoids retaining collinear variables that might inflate model performance metrics, while ensuring retained predictors possess both statistical independence and ecological relevance. The MSS (maximum training sensitivity plus specificity) logistic threshold was obtained using the “Presence Absence” package in R. Suitable habitats were defined as grid cells with predicted occurrence probabilities exceeding the MSS threshold, so the habitat suitability areas were delineated using the MSS logistic threshold.

3. Results

3.1. Evaluation of Species Distribution Models

Species distribution models (SDMs) were constructed using 17 factors remaining after rigorous collinearity screening. Model performance was evaluated using the ROC curve (AUC) and true skill statistic (TSS). As presented in Table 1, all six independent models within the ensemble framework exhibited strong predictive performance, and the AUC values of the integrated model were 0.8765, 0.8285, and 0.9054, and TSS values were 0.5853, 0.5058, and 0.6819 in Norway spruce, pedunculate oak, and European beech, respectively (Table 1). These results collectively validate the ensemble approach’s capacity to reduce inter-model uncertainty while enhancing predictive reliability.

3.2. Driving Factors Affecting Taxa Distributions

Among the three tree taxa, temperature-related factors were the key factors determining their distribution (Figure 1). The three most important factors influencing the distribution of Norway spruce were the mean temperature of the coldest quarter (importance value = 0.242), the maximum temperature of the warmest month (importance value = 0.203), and the fraction of forested primary land (importance value = 0.143). The three most important factors influencing the distribution of pedunculated oak were annual mean temperature (importance value = 0.291), temperature seasonality (importance value = 0.243), and mean temperature of warmest quarter (importance value = 0.155). The three most important factors influencing the distribution of European beech were temperature seasonality (importance value = 0.726), mean temperature of warmest quarter (importance value = 0.102), and mean temperature of driest quarter (importance value = 0.034). Temperature-related influences were dominant in all three tree taxa. Meanwhile, land use factors also played an important role in the distribution of taxa, especially for Norway spruce and pedunculate oak (Figure 1 and Table S2).

3.3. Changes in Habitat Suitability Under Different Future Scenarios

These three tree taxa are the most common and widely distributed in Europe. Their current suitable habitats are mostly throughout Europe, with the highest suitability in the central and mid-western regions. Among them, suitable habitats for Norway spruce were more distributed in areas with higher latitudes (Figure 2A). Pedunculate oak was more suitable for the midwestern region of Europe. However, it also had suitable habitats in the central and eastern regions of Europe (Figure 2D). European beech is mostly located in the central and western regions of Europe, with almost no suitable habitat distribution in the eastern region (Figure 2G).
With global climate change, changes have occurred in the suitable habitats of the three tree taxa, and there are differences among the future scenarios. SSP126 represents the most optimistic climate change scenario, describing a sustainable world that follows a green pathway with low greenhouse gas emissions. SSP585 represents the most pessimistic scenario, characterized by rapid fossil fuel development and high greenhouse gas emissions. Overall, in the favorable future scenario SSP126, the suitable habitats of the three tree taxa expanded, especially in high-latitude regions (Figure 2B,E,H). In the unfavorable future scenario SSP585, the suitable habitats of the three tree taxa expanded, especially with larger-scale suitable habitats emerging in high-latitude areas (Figure 2C,F,I). Nevertheless, the suitability of the original habitats has declined for all three taxa, especially with a substantial reduction in the suitability of habitats in the central and western regions of Europe. The fragmentation of suitable habitats has increased.

3.4. Distribution and Changes in Suitable Habitats Under Different Future Scenarios

The MSS logistic threshold was obtained using biomod2 to divide the distribution area into suitable and unsuitable habitats (Table S3). The current distribution points for the three tree taxa covered almost all the currently suitable habitats (Figure 3A,D,G). However, there was a lack of suitable habitat areas for pedunculate oak in Central Europe (Figure 3D). Under SSP126, changes in the suitable habitats of the three tree taxa were small, and the current distribution points still include suitable living conditions (Figure 3B,E,H). Suitable habitats for pedunculate oak and European beech in high-latitude regions increased slightly (Figure 3E,H). Meanwhile, that of pedunculate oak in the central region of Europe decreased (Figure 3E). Under the SSP585 scenario, along with the adverse effects of global climate change, the original suitable habitats for the three tree taxa decreased, particularly in the Central European region of the original distribution points. However, many suitable habitat areas have emerged in high-latitude regions (Figure 3C,F,I).
In terms of suitable habitat areas, the current suitable habitat area for pedunculate oak (5,362,695 km2) is larger than that of Norway spruce (4,472,960 km2) and European beech (4,398,732 km2). However, the changing trends in the areas of suitable habitats for the three tree taxa in future scenarios were not consistent (Figure 3). Under future scenarios, the suitable habitat areas for each tree taxa under the SSP585 scenario are larger than those under the SSP126 scenario. However, the suitable habitat area for pedunculate oak decreased, whereas that for Norway spruce increased. The change in the suitable habitat area for Norway spruce under the SSP126 scenario was less prominent, but it increased significantly under the SSP585 scenario. Pedunculate oak showed a significant decrease in both future scenarios, and European beech showed a slight decrease under the SSP126 scenario and an increase under the SSP585 scenario (Figure 3).
By analyzing the increasing and decreasing trends in the area suitable for the three tree taxa in Europe, we could observe changes in the distribution of suitable habitats. Under the SSP126 scenario, suitable habitat for Norway spruce increased slightly in the northern Mediterranean region. However, the overall areas of decrease and increase were similar (Figure 4A). Pedunculated oak was the most affected in the SSP126 scenario. The suitable habitat of pedunculate oak decreased significantly from east to west in Central Europe. Meanwhile, only a slight increase occurred in northern Europe (Figure 4B). European beech was least affected in the SSP126 scenario. The suitable habitat decreased slightly in southern Europe and increased slightly in northern Europe (Figure 4C). Under the SSP585 scenario, owing to the further deterioration of global climate change, the changes in the areas of increase and decrease in suitable habitats for each tree taxa were greater than the changes under SSP126. The suitable habitat for Norway spruce in Central Europe has decreased considerably. Meanwhile, it has increased largely in high-latitude areas and the northern part of the Mediterranean (Figure 4B). The suitable habitat for pedunculate oak decreased substantially from east to west in Central Europe. Meanwhile, it increased in high-latitude areas and increased slightly in the northwestern Mediterranean (Figure 4D). The change in suitable habitat for European beech was less than that of the other two tree taxa. It decreased in Central Europe and slightly increased in high-latitude areas and in the northwestern Mediterranean (Figure 4F).

3.5. Changes in Suitable Future Habitats in European Countries for the Three Tree Taxa

Under future scenarios, all three tree taxa will undergo extensive reduction in Central European countries, whereas an increase will be seen in countries at high latitudes (Figure 5). Under the SSP126 scenario, the suitable habitat areas for the three tree taxa in Poland, Belarus, northern Ukraine, and western Russia will all decrease, and pedunculate oak will have the greatest reduction (Figure 5A). Central European countries, such as the Czech Republic, Slovakia, and Hungary, will also be affected. There were also influences in Italy and Spain in the northern Mediterranean, mainly the reduction in suitable habitats for European beech and pedunculate oak (Figure 5A). The suitable habitat area in northern Europe has significantly increased, mainly concentrated in Norway, Sweden, Finland, and northwestern Russia. Increases in the northern Mediterranean, mainly concentrated in Switzerland, Austria, and Croatia, were also noted (Figure 5A).
Under the SSP585 scenario, these effects are further expanded. Suitable habitats for the three tree taxa in Central Europe decreased severely, especially in Poland, the Czech Republic, Belarus, Lithuania, Hungary, Serbia, Bulgaria, northern Ukraine, and western Russia. Some areas of France, Germany, Spain, and Portugal in Western Europe will also be affected. Suitable habitat for Norway spruce in Finland has decreased over large areas. In central and western Europe, the main tree taxa with reduced suitable habitats were pedunculate oak in the midwest, European beech in the central region, and Norway spruce in the west (Figure 5B). Suitable habitats mainly increased in the high-latitude and northern Mediterranean regions under the SSP585 scenario. In high-latitude regions, suitable habitats increased mainly in Norway, Sweden, Finland, and Iceland. Meanwhile, in the northern Mediterranean, the increase mainly occurred in Spain, Italy, Slovenia, Croatia, Bosnia and Herzegovina, Montenegro, Albania, Macedonia, and Greece, and some areas of countries such as France, Austria, and Switzerland. In the high-latitude regions, the increase in European beech was mainly concentrated in high-latitude areas. Meanwhile, in the northern Mediterranean region, the tree taxa with increases in suitable habitats was mainly Norway spruce (Figure 5B).

4. Discussion

4.1. Driving Factors of Different Tree Taxa Distributions

Species distribution is limited by environmental adaptation, and climatic factors usually affect the distribution of terrestrial species. As plants are less capable of actively adapting to the environment than animals, environmental factors determine the distribution and spread of plants [29]. When studying how climate affects the distribution and diversity of plant communities on a global scale, precipitation and temperature have been the two most considered variables [30]. The correlation between temperature and plant traits is stronger than that of precipitation. Meanwhile, precipitation can still play a key role in shaping plant growth [29,31]. The traits of plants adapting to the environment determine their community distribution. However, many other factors may affect plant distribution. The temperature during the period when plants do not grow in a year may have less impact than the temperature during the growing season [32]. Local factors such as slope, microclimate, and altitude may also interfere with plant survival [29]. Land use is also the most important factor influencing the distribution and spread of plants [33,34], particularly for plants affected by cultivation and protection. Therefore, to examine the distribution of plants on a larger scale, it is essential to fully consider all the factors to obtain an objective evaluation.
In this study, we introduced a variety of climatic, topographic, and land use factors and examined the key factors driving the distribution of common tree taxa in Europe, namely, Norway spruce, pedunculate oak, and European beech. Temperature-related factors were the main factors limiting the distribution of these tree taxa. Norway spruce was mainly affected by the mean temperature of the coldest quarter. Pedunculate oak was mainly affected by the annual mean temperature, and European beech was mainly affected by temperature seasonality (Figure 1). All these factors reflect the key temperature factors for these tree taxa to complete their normal annual growth cycle. The distribution of tree taxa in Europe is affected by temperature-related factors [29]. Therefore, more attention should be paid to the distribution of these tree taxa under climate change. Our analysis reveals that tree taxa distributions remain primarily driven by climatic factors. However, existing studies indicate that certain species may respond differently to climatic conditions depending on bioclimatic and biogeographic contexts. Specifically, forests in arid regions show stronger correlations with maximum temperatures and drought, while those in humid areas exhibit weaker associations with these factors [35,36]. To address this variability, our study incorporates topographic factors and land-use types into the assessment model alongside climatic variables, which may improve the accuracy of forest distribution predictions. A substantial portion of the distribution of these tree taxa, which are prevalent in Europe, occurs because of afforestation driven by production requirements. Consequently, in this study, we focused on the impact of land use on their distribution. Land use had the greatest influence on distribution after the influence of temperature-related factors. The fractions of forested primary land had a significant influence on Norway spruce. Meanwhile, the fractions of urban areas also affected the distribution of pedunculate oak (Table S2). Therefore, the effects of land use should be emphasized in future protection and afforestation efforts.

4.2. Changing Forest Distribution Conditions Under Different Future Scenarios

The responses of forest trees to climate change vary, and they may adapt to their current habitats, migrate, or become extinct [4]. Therefore, it is vital for forest scientists and managers to help forests adapt to constantly changing environments [37,38]. At present, the afforestation and protection of existing forests are increasingly focused on responding to climate change because the forest ecosystem, as an important component of the terrestrial ecosystem, plays an important role in providing ecosystem services, such as carbon sequestration and water conservation [7]. Therefore, under climate change, the changing trends in the main tree taxa in forests directly indicate changes in future forest patterns. This study has shown that temperature-related factors are the key factors driving the distribution of Norway spruce, pedunculate oak, and European beech. Therefore, the impacts brought by climate change will also have a profound influence on the future distribution patterns of these three tree taxa. In Europe, forests, wetlands, and coastal ecosystems in the Mediterranean are affected by climate change [39,40]. The increase in temperature and precipitation will also have a considerable impact on the suitable habitats for organisms in high-latitude regions [41,42]. Plants situated at various latitudes have consistently shown discrepancies in their responses to climate [43]. This study showed that suitable habitat areas for Norway spruce, pedunculate oak, and European beech have increased in the Mediterranean and high-latitude areas. Meanwhile, they have decreased in Central Europe (Figure 3), and this change varies under different future climate scenarios. Under the worse-expectation SSP585 scenario (Figure 4A,C,E), these impacts were substantially greater than those under the better-expectation SSP126 scenario (Figure 4B,D,F). Related research on European forest distribution has primarily focused on climate-driven factors, demonstrating that certain taxa can establish viable populations in novel regions following habitat loss [44]. This study expands this paradigm by analyzing three representative European tree taxa through the latest climate scenarios (optimistic SSP126 and pessimistic SSP585). We systematically evaluate national/regional-scale habitat suitability shifts while quantifying inter-taxa habitat overlap dynamics. Such a multi-taxa spatial prioritization framework delivers actionable insights for adaptive forest management strategies (Figure 5).
There were differences in the responses of different tree taxa to climate change. Norway spruce and pedunculate oak responded most strongly, whereas European beech had the least response. Overall, suitable areas in the high latitudes and Mediterranean region increased, but there was a significant reduction in the central region (Figure 4). Whether benefiting from the promotion of forest protection policies or afforestation activities, the three existing tree taxa occupied most of the suitable habitat areas under the current scenario, except that pedunculate oak is not distributed in the Central European area, where it is suitable for survival. This may be caused by reasons such as species competition (Figure 3). Future climate change may lead to a substantial reduction in the suitable habitat area for taxa and pose a risk of species extinction. However, by analyzing the increase and decrease in suitable habitat areas in future scenarios, it was found that this conclusion was not consistent among the different tree taxa in this study. Under the future scenarios, the suitable area for Norway spruce will increase, whereas that for pedunculate oak will decrease. European beech decreased slightly under the SSP126 scenario but increased under the SSP585 scenario (Figure 3). Therefore, to cope with the impacts of future climate change, we should not only notice a reduction in the suitable habitat of tree taxa in specific regions but also focus on areas where suitable habitats have increased. Only in this manner can we formulate detailed and effective forest protection policies that can be used to adequately respond to future climate change.

4.3. Responses of Different European Countries to Future Changes in Suitable Habitat

Forest protection depends on the level of focus and the specific measures implemented by each country or region. Understanding the future changes in suitable tree habitats by region will allow the relevant regions to make adequate preparations. In Europe, people began protecting forest resources long ago and have always attached importance to the impact of climate change on forest management. In Europe, forest resource conservation began long ago, and the influence of climate change on forest management has been consistently emphasized [10,45]. In this study, we found that both the SSP126 and SSP585 climate scenarios affected changes in the suitable habitats of the three taxa. Under the favorable SSP126 scenario, areas with reduced suitable habitats were mainly concentrated in some countries in Central Europe. Meanwhile, areas with increased habitat suitability were mainly located in high-latitude countries and some parts of the countries on the northern side of the Mediterranean (Figure 5). Although people have effectively controlled the impacts of climate change through various efforts to develop it in the desired direction (SSP126), climate change will still have a profound impact on suitable habitats for Norway spruce, pedunculate oak, and European beech. Therefore, people in the relevant regions also need to take preparatory measures to maintain the balance and stability of forest ecosystems.
However, under the unfavorable SSP585 scenario, there is a substantial reduction in suitable habitat area in the central region of Europe. Meanwhile, there is a large increase in habitat area in the high-latitude countries and northern regions of the Mediterranean (Figure 4 and Figure 5). Although the suitable areas for these tree taxa may not undergo a large-scale reduction until extinction with the intensification of climate change, they may migrate and have an impact on local ecosystem service functions. As shown in Figure 5, in regions such as Greece and Serbia, there is a reduction in suitable habitat for pedunculate oak, but also an increase in suitable habitat for Norway spruce. This may eventually lead to the succession of tree taxa. Therefore, the relevant countries and regions still need to be prepared for these possible future phenomena, such as the succession of tree taxa. Therefore, it is especially important to assess the impacts of this change on current ecosystem service functions, which are particularly important for maintaining local ecosystem stability.

5. Conclusions

The most important factors driving the distributions of Norway spruce, pedunculate oak, and European beech were climatic factors related to temperature, followed by land use. Early studies on European forest distribution focused solely on climate-driven factors. However, subsequent research demonstrates that land-use patterns and topographic parameters significantly influence forest development trajectories. To address this multidimensional interplay, our modeling framework incorporates climatic variables alongside land-use dynamics and terrain characteristics. Currently, the three tree taxa are almost entirely distributed in their suitable habitats, except that there are relatively few pedunculate oaks in Central Europe. In the future, with the relatively optimistic SSP126 scenario, the suitable habitats for the three tree taxa will undergo slight changes, mainly with the emergence of suitable habitats in the high-latitude regions and the northern Mediterranean. Meanwhile, suitable habitats in the Central European region will decrease. Under the pessimistic SSP585 scenario, more serious changes occurred. Suitable habitats in high-latitude regions and the northern part of the Mediterranean increased significantly. Meanwhile, suitable habitats in the central part of Europe decreased significantly. However, the total suitable habitat area for the three tree taxa did not change uniformly. Under climate change, the area of suitable Norway spruce habitat increased, whereas that of suitable pedunculate oak habitat decreased. The suitable habitats of the three tree taxa increased in the high-latitude and northern Mediterranean regions of Europe under climate change conditions, whereas they decreased in Central Europe. However, both regions experiencing an increase or decrease in suitable habitats will have the potential for forest succession. Existing evidence indicates that future global change impacts on tree taxa’ suitable habitats exhibit non-unidirectional patterns. Given this complexity, this study quantifies interspecies habitat overlap dynamics (expansion-contraction dynamics) for representative European tree taxa to help managers better understand the future distribution trends of different tree taxa. This will also impact the stability of the original forest ecosystem functions and require greater focus.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16060900/s1, Table S1: Climate, land use, and topographical factors used for model construction; Table S2: Importance values for the residual variables affecting the tree taxa distributions; Table S3: The maximum training sensitivity plus specificity (MSS) logistic threshold for the tree taxa; Table S4: The source links for the occurrence records dataset of Norway spruce (Picea abies), pedunculate oak (Quercus robur), and European beech (Fagus sylvatica).

Author Contributions

Conceptualization, T.W. and B.W.; methodology, J.Z., Q.T. and Y.Z.; investigation, J.Z., Q.T. and Y.Z.; writing—original draft preparation, J.Z., B.W. and T.W.; writing—review and editing, T.W., B.W. and X.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China, grant number 32201280, Foundation of Yunnan Province Science and Technology Department, grant number 202305AM070003, and Scientific Research Foundation of Education Department of Yunnan Province, grant number 2024Y854.

Data Availability Statement

All data are presented in the manuscript and Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

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Forests 16 00900 g001
Figure 1. Importance values of driving factors for Norway spruce (Picea abies) (A), pedunculate oak (Quercus robur) (B), and European beech (Fagus sylvatica) (C). Pink indicates temperature-related factors, blue indicates precipitation-related factors, green indicates land use-related factors, and orange indicates topography-related factors.
Figure 1. Importance values of driving factors for Norway spruce (Picea abies) (A), pedunculate oak (Quercus robur) (B), and European beech (Fagus sylvatica) (C). Pink indicates temperature-related factors, blue indicates precipitation-related factors, green indicates land use-related factors, and orange indicates topography-related factors.
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Figure 2. Habitat suitability for Norway spruce (Picea abies) (A–C), pedunculate oak (Quercus robur) (D–F), and European beech (Fagus sylvatica) (G–I) in current scenarios (A,D,G), and optimistic SSP126 (B,E,H) and pessimistic SSP585 (C,F,I) future scenarios.
Figure 2. Habitat suitability for Norway spruce (Picea abies) (A–C), pedunculate oak (Quercus robur) (D–F), and European beech (Fagus sylvatica) (G–I) in current scenarios (A,D,G), and optimistic SSP126 (B,E,H) and pessimistic SSP585 (C,F,I) future scenarios.
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Figure 3. Distribution of suitable (yellow area) and unsuitable habitats (gray area) for Norway spruce (Picea abies) (A–C), pedunculate oak (Quercus robur) (D–F), and European beech (Fagus sylvatica) (G–I) under current scenarios (A,D,G), and of optimistic SSP126 (B,E,H) and pessimistic SSP585 future scenarios (C,F,I). The green points represent the current occurrence records for each tree taxa.
Figure 3. Distribution of suitable (yellow area) and unsuitable habitats (gray area) for Norway spruce (Picea abies) (A–C), pedunculate oak (Quercus robur) (D–F), and European beech (Fagus sylvatica) (G–I) under current scenarios (A,D,G), and of optimistic SSP126 (B,E,H) and pessimistic SSP585 future scenarios (C,F,I). The green points represent the current occurrence records for each tree taxa.
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Figure 4. Changes in the suitable habitat area for Norway spruce (Picea abies) (A,B), pedunculate oak (Quercus robur) (C,D), and European beech (Fagus sylvatica) (E,F) under optimistic SSP126 (A,C,E) and pessimistic SSP585 future scenarios (B,D,F). The changes in area were obtained by subtracting the current suitable habitat layer from the calculated suitable habitat layer under future scenarios. Blue represents the areas where the suitable habitat increased, while red represents the areas where the suitable habitat decreased.
Figure 4. Changes in the suitable habitat area for Norway spruce (Picea abies) (A,B), pedunculate oak (Quercus robur) (C,D), and European beech (Fagus sylvatica) (E,F) under optimistic SSP126 (A,C,E) and pessimistic SSP585 future scenarios (B,D,F). The changes in area were obtained by subtracting the current suitable habitat layer from the calculated suitable habitat layer under future scenarios. Blue represents the areas where the suitable habitat increased, while red represents the areas where the suitable habitat decreased.
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Figure 5. Areas of increase and decrease in suitable habitat area for the three tree taxa in different regions of Europe under optimistic SSP126 (A) and pessimistic SSP585 (B) future scenarios.
Figure 5. Areas of increase and decrease in suitable habitat area for the three tree taxa in different regions of Europe under optimistic SSP126 (A) and pessimistic SSP585 (B) future scenarios.
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Table 1. Area under the ROC curve (AUC) values and true skill statistic (TSS) values of Norway spruce (Picea abies), pedunculate oak (Quercus robur), and European beech (Fagus sylvatica). CTA is classification tree analysis model; FDA is flexible discriminant analysis model; GBM is generalized boosting model; GLM is generalized linear model; MAXNET is maximum entropy model; RF is random forest model.
Table 1. Area under the ROC curve (AUC) values and true skill statistic (TSS) values of Norway spruce (Picea abies), pedunculate oak (Quercus robur), and European beech (Fagus sylvatica). CTA is classification tree analysis model; FDA is flexible discriminant analysis model; GBM is generalized boosting model; GLM is generalized linear model; MAXNET is maximum entropy model; RF is random forest model.
Tree TaxaModelAUC ValueTSS Value
Norway spruceCTA0.80530.5397
FDA0.82200.5120
GBM0.85820.5633
GLM0.80600.4848
MAXNET0.83300.5293
RF0.99990.9952
Integrated model0.87650.5853
pedunculate oakCTA0.81450.5253
FDA0.80600.4567
GBM0.83950.5163
GLM0.79530.4287
MAXNET0.80550.4478
RF0.99990.9947
Integrated model0.82850.5058
European beechCTA0.84380.6623
FDA0.87280.6473
GBM0.91080.6800
GLM0.87050.6433
MAXNET0.87730.6435
RF0.99990.9960
Integrated model0.90540.6819
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Zhou, J.; Tang, Q.; Zhao, Y.; Hu, X.; Wang, T.; Wang, B. Driving Mechanisms and Changes in Dominant Forest Tree Taxa in Europe Under Climate Change. Forests 2025, 16, 900. https://doi.org/10.3390/f16060900

AMA Style

Zhou J, Tang Q, Zhao Y, Hu X, Wang T, Wang B. Driving Mechanisms and Changes in Dominant Forest Tree Taxa in Europe Under Climate Change. Forests. 2025; 16(6):900. https://doi.org/10.3390/f16060900

Chicago/Turabian Style

Zhou, Jing, Qianhong Tang, Yanan Zhao, Xiaokang Hu, Tao Wang, and Bingru Wang. 2025. "Driving Mechanisms and Changes in Dominant Forest Tree Taxa in Europe Under Climate Change" Forests 16, no. 6: 900. https://doi.org/10.3390/f16060900

APA Style

Zhou, J., Tang, Q., Zhao, Y., Hu, X., Wang, T., & Wang, B. (2025). Driving Mechanisms and Changes in Dominant Forest Tree Taxa in Europe Under Climate Change. Forests, 16(6), 900. https://doi.org/10.3390/f16060900

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