Specification of threshold values for cultivation of tree species facing climate change using marginal occurrences (MARGINS)

Project duration

October 2012 - September 2015


Ecological niche models identify thresholds for physiological existence (i.e., fundamental niche) or observability of species (i.e., realized niche). The model output are probabilities of occurence, whereas the highest probabilities are found in the niche center, with probabilities converging to zero towards the margins. Validation with real observations reveals that beyond certain thresholds no occurrences of the species in question can be ascertained. The specification of these threshold values for the commercially important tree species of Bavaria is the central scope of the MARGINS project.


Niche models applied to geographic areas result in species distribution models (SDM), where the probabilities of occurrence are mapped onto geographic zones. Occurrences just before the distributional margins represent populations under extreme climatic influence. Hence, symptoms induced by climatic change should become visible in these populations "at the rear edges" first. Studying these extreme populations is the core part of the MARGINS project, and SDM are used to identify interesting populations, and later to transfer the obtained results to future conditions in Bavaria. Aiming at the combination of the strengths of both statistical niche modeling and effect-oriented case studies in a space-for-time approach, the MARGINS project aims to:

  1. define the geographic zones where Bavaria's six most important tree species (Norway spruce, Scots pine, Silver fir, common beech, sessile oak and pedunculate oak) grow at or very near their distributional margins,

  2. localize populations in these zones,

  3. characterize the environmental conditions defining the boundaries of the distribution area,

  4. validate, correct, and specify state-of-the-art niche models in the most critical warmer part,

  5. derive thresholds for commercial use of the species,

  6. identify reactions when reaching or transgressing critical thresholds,

  7. and to apply the improved thresholds to diverse regional climate scenarios in Bavaria.