Use of biodiversity modelling in environmental conservation a case study

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Use of biodiversity modelling in environmental
conservation - a case study

• Marinez Ferreira de Siqueira
• Giselda Durigan
• Mauro Muñoz
• Fabrício Pavarin
• A. Townsend Peterson

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Use of biodiversity modelling in environmental
conservation - a case study

• CRIA is working on the development of predictive
  geographic distribution models of trees in areas
  where the original vegetation is very fragmented
  or has been completely destroyed.
• We are testing the use of modeling tools to help
  in biodiversity conservation studies.
• Environmental data with good resolution together
  with precise data on species occurrence is
  fundamental
• OBJECTIVE TO PRODUCE DETAILED AND ACCURATE
  PREDICTIVE MAPS OF GEOGRAPHIC DISTRIBUTIONS OF
  SPECIES OF INTEREST FOR CONSERVATION

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Modelling biodiversity for São Paulo State
Aspidosperma cylindrocarpon Müll. Arg.
(Apocynaceae)
Climate IPCC - Intergovernmental Panel on Climate
Change (1961-1990) http//www.ipcc.ch
CIAT (Jones, 1991 in http//www.floramap-ciat.org
/ing/climate-grid.htm) Resolution 0,17º (10).
Worldclim Version 1.1 Global Climate Surfaces
(obtained courtesy of Robert Hijmans)
Resolution 0,0083333o. (a square kilometer
grid)
Soil IAC 1500.000
Topography U.S. Geological Surveys Hydro-1K,
resolution 0.01º (http//edcdaac.usgs.gov/gtopo30
/hydro) Layers elevation, slope, aspect, and
topographic index (tendency to pool water)
source Dr Ingrid Koch
Data points come from Species Link
(http//splink.cria.org.br/simple_search) and the
geographic coordinates of points were obtained
using geoLoc (http//splink.cria.org.br/geoloc)
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Soil Map - Brazil
Scale Brazil 1 5.000.000 source LBA IBGE
1981
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Soil Map of Brazil, São Paulo State and Watershed
of Médio Paranapanema
Scale Brazil 1 5.000.000 source IBGE
1981 São Paulo 1500.000 source IAC
1999 Watershed of Médio Paranapanema 1250.000
source IAC 1996
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Use of biodiversity modelling in environmental
conservation - a case study
Xylopia aromatica (Annonaceae)
Environmental data soil, geology, temperature,
precipitation
Blue present points for Xylopia
Green probable absence points for Xylopia
Red absence points for Xylopia

Data completely available on SinBiota and
SpeciesLinK projects (CRIA Fapesp)
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Use of biodiversity modelling in environmental
conservation - a case study
Summary of species richness (19 species) Areas of
maximal species richness
Area with great probability of occurrence of all
species
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Use of biodiversity modelling in environmental
conservation - a case study
Map erosion risk
Areas with great risk of erosion
Intersection between areas with great risk of
erosion and greater probability of species
occurence
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Use of biodiversity modelling in environmental
conservation - a case study
Casearia sylvestris Copaifera langsdorffii Croton
floribundus Gochnatia polymorpha Luehea
grandiflora Machaerium acutifolium Machaerium
brasiliense Matayba elaeagnoides Ocotea
corymbosa Pera obovata Platypodium
elegans Siparuna guianensis Stryphnodendron
obovatum Syagrus romanzoffiana Tabebuia
ochracea Tabernamontana hystrix Tapirira
guianensis Terminalia glabrescens Vochysia
tucanorum
Species list based on the greater probability of
species occurence
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Use of biodiversity modelling in environmental
conservation - a case study
Xylopia aromatica (Annonaceae)
Environmental data soil, geology, temperature,
precipitation

Data completely available on SinBiota and
SpeciesLinK projects (CRIA Fapesp)
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Xylopia aromatica (Annonaceae)
Use of biodiversity modelling in environmental
conservation - a case study
Multitemporal NDVI (greenness) Source AVHRR
satellite Resolution 1Km 1 month composition
Blue present points for Xylopia
Yellow absence points for Xylopia
Source EROS Data Center
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Conclusions
Using these tools, a methodology is being
developed to help identify key areas representing
concentrations of native species, which can be
used in planning for habitat restoration.
To do that properly we need more and better
environmental data such as the remotely sensed
data shown earlier.
And we need to develop and automate these
methodologies to make broad application more
feasible.
THANK YOU!!
marinez_at_cria.org.br