Climate is one of the major drivers of tree distribution, while soil variables or interspecific competition are often considered to be primary drivers of their local abundance, with the latter still debated. The debate on climate change impacts on ecosystems is of great relevance to trees, as these take many years to reach maturity and fecundity, and given their sessile growth strategy they are especially vulnerable to rapid changes in climatic conditions. Also, forest management plans usually span many decades, some extending to the end of the 21st century, highlighting the challenges to managing such organisms successfully for such long planning horizons. This calls for careful and adaptive management strategies and for a good understanding of the uncertainties related to the expected changes and their impacts on trees and forest ecosystems.
Many approaches exist to project the impact of climate change on trees and forests. Yet, most of the approaches either can be applied only to comparably small regions, to few species only, or they need to be run at very coarse spatial resolutions in order to enable coverage of larger spatial extents. Here we have used a species distribution model (SDM) to project how species might respond in their habitat preference at the scale of the European Alps as a result of projected climate change.
Species distribution models (SDMs) are simple but very efficient statistics-based methods to map the spatial range of species and to project climate change impacts on species ranges. The method is based on calibrating statistical relationships between the observed spatial distribution and climate and other spatial predictor variables. Sampling design is therefore very important, and the fitted spatial patterns represent the realized, not the fundamental niche. The method does not include processes or details on transient responses following change; neither does it provide structural information.
This method simply – but efficiently – provides an assessment of the suitability of any region for a species under current or projected future climate, and under the assumption that roughly the same species are available as potential competitors. SDMs are thus often used to assess whether a given species has a future in a specific region or not, while the question of whether it can reach a certain region and how long it would take a species to get there are not handled by this method. SDMs, therefore, are best suited to assess habitat suitability and whether certain management options concerning species preference/selection are likely sustainable in the long run.
In the following paragraphs, we present the SDM simulations performed, often also termed climate envelope models (CEM), for major tree species of the European Alps in order to assess the consequences of climate change on the habitat suitability of these tree species. We used presence/absence information from forest inventories of France (Alps only), Northern Italy, Austria, Southern Germany Slovenia and Switzerland in order to build a database of tree species presences and absences across the Alps. We compiled data for ca. 50 tree species for a total of >80 000 inventory plots, although some countries did not distinguish all species at the same taxonomic level (i.e. some countries did not distinguish between different oak and maple species).
We then compiled a series of climate maps under current and potential future climate from downscaled RCM models for future climates. Additionally, we compiled some topographic variables that may influence the spatial patterns of trees. Finally, we used the following variables as predictors of species distribution in our models: (1) degree days with a 5.56°C threshold, (2) temperature seasonality (standard deviation of monthly values), (3) summer precipitation (sum of April to September monthly values), (4) winter precipitation (October to March), (5) potential yearly global radiation, (6) slope angle (in degree), (7) topographic position (difference between the average elevation in a circular moving window applied to a 100m digital elevation model and the centre cell of the window, (8) aspect value (ranging from 0(south) to 100(north), and (9) distance to flowing water.
Potential future climate was taken from six different RCMs providing a range of potential climate futures. The use of several RCM models is meant to provide the mean trend that can be expected from climate change impacts on trees balanced with some measure of uncertainty associated with the projection of these trends. Several statistical models were used, since the choice of a statistical model has been shown to significantly contribute to uncertainty in projections. Using six statistical models along with six future climate model runs, we modelled 36 different possible futures per species and time slice reducing uncertainty by including both the variability in climate models and the variability originating from the choice of statistical methods.
We optimized each statistical model following procedures and where feasible. We then produced one presence/absence map per climate model/statistical model combination available. Following, we built ensembles of these model projections and classified them as follows: (1) a species is unlikely to find a suitable habitat if less than 30% of the projections indicated presence of a species; (2) a species is moderately likely, associated with high uncertainty, if 30-60% of the projections suggested that the species is there; (3) a species is most likely present, with rather low uncertainty, under projected climates if in >60% of the 36 model projections presence of a species is simulated. This simple classification avoids an over-interpretation of the results from the simple model approach used.
Figure 1 illustrates the potential future range shift in two species Fagus sylvatica L. (European beech) and Picea abies (L.) Karst. (Norway spruce) in eight panels, indicating the areas that are suitable for the two species under current and future climate conditions in three different time steps towards the end of the 21st century.
Both species are expected to lose much terrain at low altitudes, and will retract to higher altitudes following climate change. Currently, Norway spruce is planted at lower altitudes than it occurs naturally. These lower altitudes are still within the fundamental niche of the species, which is taken into consideration in the simulated maps; the maps also capture the extended range of the species to lower altitudes under both current and future climates. However, compared to beech, it extends to higher altitudes, reaching treeline in many parts of the Alps. This is specifically visible for the simulations for the 2051-2080 time period where occurrences of Norway spruce are projected to be at visibly higher elevations than those suitable for beech. Larger areas at lower altitudes become unsuitable for both species in the future, while the habitat suitability in large areas in Southern Germany is projected to be highly uncertain for both species. This uncertainty arises from highly contradicting projections on the part of both climate and SDM model combinations.
More than 50 tree species possibility of occurrence have been simulated for the Alpine region, while only two species are displayed here. A more complete set of species data can be viewed at the following LINK. From our results, it becomes evident that species with a higher drought tolerance, such as Quercus petraea and Quercus pubescens can be expected to become more abundant at lower altitudes throughout the Alps, while other species such as Acer pseudoplatanus, Tilia spp., Ulmus spp. or Abies alba are likely to further reduce their ranges in a manner similar to beech and spruce. Species from (Sub-) Mediterranean regions such as Quercus ilex, Ostrya carpinifolia or Q. suber are expected to extend their ranges to the North, but these species will not reach the areas formerly suitable for beech by the end of the 21st century. Several pine species are also expected to extend their ranges quite considerably. However, they will be unlikely to extend their ranges to very fertile soils either, and some of the species such as P. sylvestris could face indirect threats through insects and other pests, rather than direct threats from climate change alone.
None of the models is capable of projecting the effective fate of the different tree populations. The maps simply illustrate the habitat potential at certain time periods in the future. Species may still survive for quite a while at locations considered unsuitable. They will eventually face one or both of the following two threats: (1) physiological stress from a climate that they cannot tolerate, and (2) stronger competition from other, more suitable species and/or threats from antagonists such as insects, fungi, etc. that may profit in turn from a changing climate, and that may spread to trees that are less vigorous because of a combination of the two causes (1) and (2). Forest management can usually deal more or less well with the second type of threat if it is primarily due to changes in tree species competition. Dealing with changes in antagonists in forest management is more difficult, as the example of the Scots pine dieback in the Alps illustrates. Here, a rapid dieback at the lowest altitudes of Scots pine distribution has been observed over the last 10 years, which is the area that is projected to eventually become increasingly unsuitable in the future based on ensemble species distribution models (SDMs) as well.