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Consequences of global climate change for geographic distributions of cerrado tree species

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TLDR
In this article, the authors applied techniques from the emerging field of ecological niche modeling to develop a first-pass assessment of likely effects of climate change on tree species' distributions in the Cerrado biome by relating known occurrence points to electronic maps summarizing ecological dimensions.
Abstract
The present study applies a series of new techniques to understand the conservation of Cerrado tree species in the face of climate change. We applied techniques from the emerging field of ecological niche modeling to develop a first-pass assessment of likely effects of climate change on tree species’ distributions in the Cerrado biome by relating known occurrence points to electronic maps summarizing ecological dimensions. Distributional data represent 15,657 records for 162 tree species occurring in Cerrado. By focusing on the trees of one important and highly endemic biome, rather than the biota of a political unit, we were able to focus on developing biome-wide projections. An important limitation of this study is that only those species with more than 30 unique occurrence records were used-hence, the study is limited to those species of relatively broad geographic distribution, and does not take into account those species with narrower geographic distributions. Global climate change scenarios considered were drawn from the general circulation models of HadCM2; we assessed both a conservative and a less conservative scenario of how climates could change over the next 50 year using the (Hadley HHGSDX50 and HHGGAX50 scenarios, respectively): HHGSDX50 assumes 0.5%/yr CO2 increase, whereas HHGGAX50 assumes a 1%/yr CO2 increase. Results of predictions of present and future distributions varied widely among species. Present distributional models predicted areas of 655,211-2,287,482 out of the 2,496,230 km2 core area of Cerrado in Brazil. All models used to represent species’ present geographic ranges were highly statistically significant based on independent test data sets of point localities. Most species were projected to decline seriously in potential distributional area, with both scenarios anticipating losses of >50% of potential distributional area for essentially all species. Indeed, out of 162 species examined, between the two climate change scenarios, 18 (HHGSDX50 scenario) - 56 (HHGGAX50 scenario) were predicted to end up without habitable areas in the Cerrado region, and 91 (HHGSDX50 scenario) - 123 (HHGGAX50 scenario) species were predicted to decline by more than 90% in potential distributional area in the Cerrado region. Bearing in mind the limitations of the method, and considering its explicit assumptions, these results nevertheless should be cause for ample concern regarding Cerrado biodiversity. Since only 2.25% of the Cerrado biome is presently protected, this future scenario presents a pessimistic forecast, which would likely include widespread species loss from the biome, as well as dramatic shifts to the south and east, further complicating conservation planning efforts.

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References
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Journal ArticleDOI

Biodiversity hotspots for conservation priorities

TL;DR: A ‘silver bullet’ strategy on the part of conservation planners, focusing on ‘biodiversity hotspots’ where exceptional concentrations of endemic species are undergoing exceptional loss of habitat, is proposed.
Journal ArticleDOI

Conservatism of Ecological Niches in Evolutionary Time

TL;DR: Reciprocal geographic predictions based on ecological niche models of sister taxon pairs of birds, mammals, and butterflies in southern Mexico indicate niche conservatism over several million years of independent evolution but little conservatism at the level of families.
Journal ArticleDOI

The GARP modelling system: problems and solutions to automated spatial prediction

TL;DR: The essence of the GMS is an underlying generic spatial modelling method which filters out potential sources of errors and is generally applicable however, as the statistical problems arising in arbitrary spatial data analysis potentially apply to any domain.
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