HR: 0800h
AN: GC41A-0117    [Abstracts]
TI: Providing more informative projections of climate change impact on plant distribution in a mountain environment
AU: * Randin, C
EM: Christophe.Randin@unil.ch
AF: Dept. of Ecology & Evolution University of Lausanne, Building "Biophore", Lausanne, CH-1015, Switzerland
AU: Engler, R
EM: Robin.Engler@unil.ch
AF: Dept. of Ecology & Evolution University of Lausanne, Building "Biophore", Lausanne, CH-1015, Switzerland
AU: Pearman, P
EM: Peter.Pearman@unil.ch
AF: Dept. of Ecology & Evolution University of Lausanne, Building "Biophore", Lausanne, CH-1015, Switzerland
AU: Vittoz, P
EM: Pascal.Vittoz@unil.ch
AF: Faculty of Geosciences GSE University of Lausanne, Building Amphipôle, Lausanne, CH-1015, Switzerland
AU: Guisan, A
EM: Antoine.Guisan@unil.ch
AF: Dept. of Ecology & Evolution University of Lausanne, Building "Biophore", Lausanne, CH-1015, Switzerland
AB: Due to their conic shape and the reduction of area with increasing elevation, mountain ecosystems were early identified as potentially very sensitive to global warming. Moreover, mountain systems may experience unprecedented rates of warming during the next century, two or three times higher than that records of the 20th century. In this context, species distribution models (SDM) have become important tools for rapid assessment of the impact of accelerated land use and climate change on the distribution plant species. In this study, we developed and tested new predictor variables for species distribution models (SDM), specific to current and future geographic projections of plant species in a mountain system, using the Western Swiss Alps as model region. Since meso- and micro-topography are relevant to explain geographic patterns of plant species in mountain environments, we assessed the effect of scale on predictor variables and geographic projections of SDM. We also developed a methodological framework of space-for-time evaluation to test the robustness of SDM when projected in a future changing climate. Finally, we used a cellular automaton to run dynamic simulations of plant migration under climate change in a mountain landscape, including realistic distance of seed dispersal. Results of future projections for the 21st century were also discussed in perspective of vegetation changes monitored during the 20th century. Overall, we showed in this study that, based on the most severe A1 climate change scenario and realistic dispersal simulations of plant dispersal, species extinctions in the Western Swiss Alps could affect nearly one third (28.5%) of the 284 species modeled by 2100. With the less severe B1 scenario, only 4.6% of species are predicted to become extinct. However, even with B1, 54% (153 species) may still loose more than 80% of their initial surface. Results of monitoring of past vegetation changes suggested that plant species can react quickly to the warmer conditions as far as competition is low However, in subalpine grasslands, competition of already present species is probably important and limit establishment of newly arrived species. Results from future simulations also showed that heavy extinctions of alpine plants may start already in 2040, but the latest in 2080. Our study also highlighted the importance of fine scale and regional assessments of climate change impact on mountain vegetation, using more direct predictor variables. Indeed, predictions at the continental scale may fail to predict local refugees or local extinctions, as well as loss of connectivity between local populations. On the other hand, migrations of low-elevation species to higher altitude may be difficult to predict at the local scale.
DE: 0410 Biodiversity
DE: 0430 Computational methods and data processing
DE: 0466 Modeling
DE: 0476 Plant ecology (1851)
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting