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Climate change has already triggered species distribution shifts in many parts of …

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- Climate change threats to plant diversity in Europe

Data Sources. Species' distribution data are available for 2,294 plants (20), comprising ≈20% of the total European flora, sampled between 1972 and 1996. Modeling was conducted by using available data for Europe on a 50 × 50 km grid. The mapped area comprises western, northern and southern Europe, but excludes most of the eastern European countries where recording effort was both less uniform and less intensive (21). After removing species with <20 records, we considered range responses of 1,350 plant species of Europe. We assume this sample can be taken as representative of the responses of European plant species to climate change because it includes most of the life forms and phytogeographic patterns found among plant species in Europe.

Climate data were obtained from the Climatic Research Unit ( and included mean annual, winter, and summer precipitation, mean annual temperature and minimum temperature of the coldest month (MTC), growing degree days (>5°) and an index of moisture availability (22). These variables were chosen because of their strong link with the physiology and growth of plant species (23, 24). For instance, MTC discriminates species based on their ability to assimilate soil water and nutrients, and continue cell division, differentiation and tissue growth at low temperatures (lower limit), and chilling requirements for processes such as bud break and seed germination (upper limit). The moisture index discriminates species through processes related to phenology, rooting strategy, leaf morphology, and xylem vulnerability to cavitation. However, because there is surprisingly little experimental work for any particular species to guide the choice of bioclimatically limiting variables, the variables are generic and represent a hypothetical minimum basic set for niche-based modeling. Climate data were averaged for the 1961-1990 period. The data were supplied on a 10-foot (1 ft = 0.3 m) grid covering Europe. They were aggregated by averaging to 50 × 50 km Universal Transverse Mercator (UTM) to match the species data grid. Niche-based models were calibrated on the 50 × 50 km UTM grid, and modeled species distributions were projected back onto the 10′ grid for current and future climate.

Future projections were derived by using climate model outputs made available through the Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre (‡‡ The modeled climate anomalies were scaled based on four scenarios proposed by the IPCC (12). The A1 scenario describes a globalized world with rapid economic growth and global population that peaks in mid-century and declines thereafter and assumes rapid introduction of new and more efficient technologies. Concentrations of CO2 increase from 380 ppm in 2000 to 800 ppm in 2080, and temperature rises by 3.6 K (12). The A2 scenario describes a heterogeneous world with regionally oriented economic development. Per capita economic growth and technological change are slower than in the other scenarios. Global concentrations of CO2 increase from 380 ppm in 2000 to 700 ppm in 2080, and temperature rises by 2.8 K. The B1 scenario describes a convergent world with global population that peaks in mid-century and declines thereafter, as in A1, but with a rapid change toward a service and information economy and the introduction of clean and resource-efficient technology. Concentrations of CO2 increase from 380 ppm in 2000 to 520 ppm in 2080, and temperature rises by 1.8 K. The B2 scenario describes a world in which the emphasis is on local solutions to socioeconomic and environmental sustainability. It is a world with continuously increasing global population (at a rate lower than A2), intermediate levels of economic development, and less rapid and more diverse technological change than in the B1 and A1 scenarios. Concentrations of CO2 increase from 380 ppm in 2000 to 550 ppm in 2080, and temperature rises by 2.1 K (12).

We did not assess the impacts of land-use change, even though this factor will potentially compound the effects of climate change on species distributions (25). However, given the spatial extent and resolution of our data and the magnitude of climate change in most projections, the effect of land use would be most likely overridden by climate (26, 27).

Niche-Based Models of Species Climatic Envelops. We used the biomod framework, which capitalizes on several widely used niche-based modeling techniques (generalized linear models, generalized additive models, classification tree analysis, and artificial neural networks) to provide alternative spatial projections (13). For each climate change scenario, models relating species distributions to the seven bioclimatic variables were fitted by using biomod and projected into the future. Then, a consensus principal component analysis was run to explore central tendencies in projections and select the niche-based model representing the greatest commonality among projections (8).

There is increasing evidence that model projections can be extremely variable, and there remains a need to test the accuracy of models and to reduce uncertainties (8, 28, 29). One recent analysis has however provided the first test of the predictive accuracy of such models by using bird observed species' range shifts and climate change in two periods of the recent past (30). This work provides validation of niche-based models under climate change and demonstrated how uncertainty can be reduced by selecting the most consensual projections, as done in this study. We are therefore confident that this strategy provides a robust and defensible approach to species range projections for the purposes of conservation planning and biodiversity management.

To evaluate species extinction risks, we summed the number of pixels lost, potentially gained (under universal migration), or stable by each species for the different climate-change scenarios. We assigned each species to an International Union for Conservation of Nature and Natural Resources (IUCN) threat category (IUCN 2001). Those that were not listed were classified as lower risk, depending on the projected reduction in range size from present to 2080. Present and future range sizes (area of occupancy) were estimated from the number of pixels where species occurred. Loss in range size was calculated by subtracting future potential range size from present potential range size. In line with IUCN Red List criterion A3(c), the following thresholds were then used to assign a species to a threat category (IUCN 2001). Extinct is a species with a projected range loss of 100% in 50 or 80 years, critically endangered has a projected range loss of >80%, endangered has a projected range loss of >50%, and vulnerable has a projected range loss of >30%. Although this Red Listing approach is simplistic and considers only the effects of climate change, it provides a synthetic overview of species-specific threats due to climate change.

To evaluate the percentage of extinctions for a given area, we summed the number of species lost (L) by pixel and related it to current species richness by pixel. The procedure was the same to assess the percentage of species gained (G) by pixel (under assumptions that species could reach a new suitable climate space). Percentage of species turnover by pixel, under the assumption of universal migration, is then given by T = 100 × (L + G)/(SR + G) where SR is the current species richness.

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