Climatic extremes improve predictions of spatial patterns of tree species
Author(s) -
Niklaus E. Zimmermann,
Nigel G. Yoccoz,
Thomas C. Edwards,
Eliane S. Meier,
Wilfried Thuiller,
Antoine Guisan,
Dirk R. Schmatz,
Peter B. Pearman
Publication year - 2009
Publication title -
proceedings of the national academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.0901643106
Subject(s) - range (aeronautics) , niche , climate change , environmental science , environmental niche modelling , statistic , spatial ecology , species distribution , ecology , climatology , ecological niche , geography , statistics , biology , mathematics , habitat , materials science , composite material , geology
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.
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