Shifts in tree species distributions caused by climatic change are expected to cause severe losses in the economic value of European forestland. However, this projection disregards potential adaptation options such as tree species conversion, shorter production periods, or establishment of mixed species forests. The effect of tree species mixture has, as yet, not been quantitatively investigated for its potential to mitigate future increases in production risks. For the first time, we use survival time analysis to assess the effects of climate, species mixture and soil condition on survival probabilities for Norway spruce and European beech. Accelerated Failure Time (AFT) models based on an extensive dataset of almost 65,000 trees from the European Forest Damage Survey (FDS)--part of the European-wide Level I monitoring network--predicted a 24% decrease in survival probability for Norway spruce in pure stands at age 120 when unfavorable changes in climate conditions were assumed. Increasing species admixture greatly reduced the negative effects of unfavorable climate conditions, resulting in a decline in survival probabilities of only 7%. We conclude that future studies of forest management under climate change as well as forest policy measures need to take this, as yet unconsidered, strongly advantageous effect of tree species mixture into account.
Question: What are the main drivers for tree species distribution in the Bavarian Alps? What are the species-specific habitat requirements? Are predictions in accordance with expert knowledge?Location: Bavarian Alps (Southern Germany).Methods: To describe tree species-environment relationships, we established species distribution models for the 14 most common tree species of the region. We combined tree species occurrence data from forest inventories and a vegetation database with environmental data from a digital elevation model, climate maps and soil maps. For modelling, we used generalized additive models (GAM) combined with techniques to account for spatial autocorrelation and uneven coverage of environmental gradients. We developed parsimonious models to judge whether statistical models correspond to models based on expert knowledge.Results: Conceptual models were generally in accordance with expectations.Variables based on average temperatures were the most important predictors in most models. Proxies for soil properties such as water and nutrient availability were statistically significant and generally plausible, but appeared largely redundant for model performance. Altitudinal limits of tree species were generally well represented by models. Most species responded differently to summer and January temperatures, indicating that temperature variables are proxies not only for energy balance, but also for frost damage and drought. Although model building benefits considerably from collation with expert knowledge, there are limitations.Conclusions: Meaningful species distribution models can be obtained from noisy data sets covering only a small fraction of species ranges. Models calibrated with such data sets benefit from hypothesis-driven model building rather than strict data-driven model building. Hence, misleading explanations and predictions can be avoided and uncertainties identified. Nevertheless, projections based on climate scenarios can be substantially improved only with models calibrated on a wider data set. Ideally, environmental gradients should cover the whole niche space of a species, or at least include regions with analogous climate.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.