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Predicting the future of forests in the Mediterranean under climate change, with niche- and process-based models: CO2 matters!

Journal

GLOBAL CHANGE BIOLOGY
Volume 17, Issue 1, Pages 565-579

Publisher

WILEY
DOI: 10.1111/j.1365-2486.2010.02254.x

Keywords

BIOMOD; CO2 fertilization; drought; GOTILWA; niche-based models; Pinus halepensis; Pinus sylvestris; Quercus ilex; species distribution

Funding

  1. Consolider Montes project [CSD2008-00040]
  2. Spanish 'Ministerio de Ciencia y Tecnologia' [CGL2006-01293/BOS, CGL2009-08191]
  3. CCTAME [FP7 212535]
  4. ALARM [FP5 GOCECT-2003-506675]
  5. ATEAM [FP5 EVK2-2000-00075]

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Assessing the potential future of current forest stands is a key to design conservation strategies and understanding potential future impacts to ecosystem service supplies. This is particularly true in the Mediterranean basin, where important future climatic changes are expected. Here, we assess and compare two commonly used modeling approaches (niche- and process-based models) to project the future of current stands of three forest species with contrasting distributions, using regionalized climate for continental Spain. Results highlight variability in model ability to estimate current distributions, and the inherent large uncertainty involved in making projections into the future. CO2 fertilization through projected increased atmospheric CO2 concentrations is shown to increase forest productivity in the mechanistic process-based model (despite increased drought stress) by up to three times that of the non-CO2 fertilization scenario by the period 2050-2080, which is in stark contrast to projections of reduced habitat suitability from the niche-based models by the same period. This highlights the importance of introducing aspects of plant biogeochemistry into current niche-based models for a realistic projection of future species distributions. We conclude that the future of current Mediterranean forest stands is highly uncertain and suggest that a new synergy between niche- and process-based models is urgently needed in order to improve our predictive ability.

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