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Climate change and biodiversity : Developing tools for assessing impacts and their implications for conservation. Guy Midgley, Mike Rutherford, Greg Hughes National Botanical Institute, Cape Town. With acknowledgements: Paul Williams, London Nat Hist Mus. Conservation investment.
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Climate change and biodiversity: Developing tools for assessing impacts and their implications for conservation Guy Midgley, Mike Rutherford, Greg Hughes National Botanical Institute, Cape Town With acknowledgements: Paul Williams, London Nat Hist Mus
Conservation investment Conserve species under natural conditions Conserve ecosystems and their natural processes Conserve habitats for maintaining biodiversity Maintain key processes (eg water yield) Support tourism and ecotourism Support livelihoods (eg wildflower, medicinal) Support commercial agri-business
Conservation investment Protected Areas often selected ad hoc, developed before good species data were available, on land not wanted or less valuable Biodiversity no longer static, but dynamic!
We address two main problems • How to predict climate change impacts on ecosystems and species (biodiversity) • How to assess ability of conservation strategies (current PA network) to cope with these impacts
Species distribution 36o 34o Environmental variables Bioclimatic modeling method
Species distribution Max temp envelope #records 36o 34o Environmental variables 36o 34o Maximum temperature Bioclimatic modeling method
Future projectionrisk assessment Automated methods Access Species data Grads Climate data Arcview Data matching SPlus Statistical model
Protea Atlas database (NBI) 330 species (Proteaceae), ~ 40 000 localities
Overall threat of climate change to Proteaceae diversity
Fynbos Biome distribution: current and future Lowland species Montane species
Leucospermumtomentosumdistribution: current and ~2050 (HadCM2excluding sulphates)
Protea lacticolordistribution: current and future (HadCM2excluding sulphates) 20 km
contract (highest risk) persist (safe) colonize (high risk) Displacement risk = 1 – persist/current
contract (highest risk) persist (safe) colonize (high risk) Extinction risk proportional to range loss (with and without dispersal)
Uncertainties Climate scenarios Spatial climate data (historic, current) Species distribution data Bioclimatic modelling approach Human land use Dispersal and establishment
Wind-dispersal Ant-dispersal Knowledge about dispersal syndromes is critical
Range size changes (HadCM2) (~250 Proteaceae, 2000 to ~2050)
Automated methods Access Species data Dynamic range modelling method Climate data Grads Arcview Data matching SPlus Statistical model Protected area risk analysis Future projectionrisk assessment Simple range shift assumptions
Time-slice models 2000 2010 2020 2030 2040 2050 (se scop)
Richness of dispersal pathways for the 18 species that are committed to migration
Human land use, intensity of transformation Dispersal pathways
Automated methods Access Species data Dynamic range modelling method Climate data Grads Arcview Data matching SPlus Statistical model Protected area risk analysis Future projectionrisk assessment Simple range shift assumptions