High-resolution monthly precipitation and temperature time series from 2006 to 2100.

Predicting future climatic conditions at high spatial resolution is essential for many applications and impact studies in science. Here, we present monthly time series data on precipitation, minimum- and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (the CHELSA algorithm) to calculate mean monthly maximum and minimum temperatures, as well as monthly precipitation at ~5 km spatial resolution globally for the years 2006-2100. We validated the performance of the downscaling algorithm by comparing model output with the observed climate of the historical period 1950-1969.

Alpine glacial relict species losing out to climate change: The case of the fragmented mountain hare population (Lepus timidus) in the Alps.

Alpine and Arctic species are considered to be particularly vulnerable to climate change, which is expected to cause habitat loss, fragmentation and-ultimately-extinction of cold-adapted species. However, the impact of climate change on glacial relict populations is not well understood, and specific recommendations for adaptive conservation management are lacking. We focused on the mountain hare (Lepus timidus) as a model species and modelled species distribution in combination with patch and landscape-based connectivity metrics. They were derived from graph-theory models to quantify changes in species distribution and to estimate the current and future importance of habitat patches for overall population connectivity. Models were calibrated based on 1,046 locations of species presence distributed across three biogeographic regions in the Swiss Alps and extrapolated according to two IPCC scenarios of climate change (RCP 4.5 & 8.5), each represented by three downscaled global climate models. The models predicted an average habitat loss of 35% (22%-55%) by 2100, mainly due to an increase in temperature during the reproductive season. An increase in habitat fragmentation was reflected in a 43% decrease in patch size, a 17% increase in the number of habitat patches and a 34% increase in inter-patch distance. However, the predicted changes in habitat availability and connectivity varied considerably between biogeographic regions: Whereas the greatest habitat losses with an increase in inter-patch distance were predicted at the southern and northern edges of the species' Alpine distribution, the greatest increase in patch number and decrease in patch size is expected in the central Swiss Alps. Finally, both the number of isolated habitat patches and the number of patches crucial for maintaining the habitat network increased under the different variants of climate change. Focusing conservation action on the central Swiss Alps may help mitigate the predicted effects of climate change on population connectivity.

Risk of genetic maladaptation due to climate change in three major European tree species.

Tree populations usually show adaptations to their local environments as a result of natural selection. As climates change, populations can become locally maladapted and decline in fitness. Evaluating the expected degree of genetic maladaptation due to climate change will allow forest managers to assess forest vulnerability, and develop strategies to preserve forest health and productivity. We studied potential genetic maladaptation to future climates in three major European tree species, Norway spruce (Picea abies), silver fir (Abies alba), and European beech (Fagus sylvatica). A common garden experiment was conducted to evaluate the quantitative genetic variation in growth and phenology of seedlings from 77 to 92 native populations of each species from across Switzerland. We used multivariate genecological models to associate population variation with past seed source climates, and to estimate relative risk of maladaptation to current and future climates based on key phenotypic traits and three regional climate projections within the A1B scenario. Current risks from climate change were similar to average risks from current seed transfer practices. For all three climate models, future risks increased in spruce and beech until the end of the century, but remained low in fir. Largest average risks associated with climate projections for the period 2061-2090 were found for spruce seedling height (0.64), and for beech bud break and leaf senescence (0.52 and 0.46). Future risks for spruce were high across Switzerland. However, areas of high risk were also found in drought-prone regions for beech and in the southern Alps for fir. Genetic maladaptation to future climates is likely to become a problem for spruce and beech by the end of this century, but probably not for fir. Consequently, forest management strategies should be adjusted in the study area for spruce and beech to maintain productive and healthy forests in the future.

Climatic extremes improve predictions of spatial patterns of tree species.

Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D(2), +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.