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Remote-sensing correlates of biological diversity

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					Remote-sensing correlates of
    biological diversity
            Catherine Graham
          Stony Brook University

 Graham lab:
                     NASA Funded:
 Jorge Velasquez
                     Tom Smith
 Natalia Silva       Sassan Saatchi
 Pablo Menendez      Chris Schneider
                     Robert Wayne
 Other:
 Robert Hijmans
 Luis Coloma
 Santiago Ron
      Remote-sensing correlates to
          biological diversity
Effectively use RS data in species distribution modeling and
     decision support
•    Virtual species experiment
•    Modeling Andean bird species
•    Conservation planning

Evaluate hypotheses explaining variability in species richness
•   Correlates of mammal richness across spatial scale
•   Effects of disease on amphibian richness pattern

Simulate the impacts of and climate change on species
    distributions (in press, Global Change Biology)

Train ecologists, evolutionary biologists and conservation
     biologists
           Species distribution modeling
1) Extract environmental       2) Make statistical model
  data for point localities;     describing distribution
                                 in environmental space;

                               3) Project this model in
                                 geographic space to
                                 create a map.
               Elevation




                 Annual
                 Rainfall
Free download




16 modeling methods
Presence-only training data
Independent presence-absence test data
6 geographic regions/series of taxonomic groups
250 species
  Effectively use RS data in species
        distribution modeling

• Problem: age and spatial accuracy of
  point locality data in relation to RS data.
• Solution: partition data in modeling
  – Use all point locality data with climate
    surfaces
  – Use only “accurate/recent” point locality
    data with remote-sensing layers
  RS data in species distribution
modeling: Virtual species experiment




                                  Points in
            Points in
                                  currently-
            Climate
                                  forested
                                  areas only

   Original distribution   Current distribution
   (climate-only)          (climate & RS)
  RS data in species distribution
modeling: Virtual species experiment


                            Points in Points in             Points
                            RS-forest RS-forest             partitioned
                            & climate only                  by RS &
                                                            climate

Climate+RS                   0.331          0.632            0.462


Sample size of 100 points
*note correlations are between a binary and continuous prediction
  RS data in species distribution modeling:
           Modeling Andean birds
Treatments:
Exp 1: climate only
Exp 2-4, climate and remote sensing layers without data
splitting.
- Exp 2: sampling from 1 km RS layers
- Exp 3: sampling from 10 km RS layers
- Exp 4: sampling from a neighborhood within a radius of 5km
Exp 5-7, climate and remote sensing layers with data splitting.
- Exp 5: sampling from 1 km RS layers
- Exp 6: sampling from 10 km RS layers
- Exp 7: sampling from a neighborhood within a radius of 5km
Myadestes
Ralloides
(Andean
Solitare)



                      Exp 1: climate
                      Exp 2-4: climate
                      and RS without
                      data splitting
                      Exp 5-7: climate
      Exp 1   Exp 7   and RS with data
                      splitting
 RS data in species
    distribution
     modeling:
   conservation
     planning



In collaboration with CI &
ProAves, we are redoing the
analyses with all ~300
species and models built with
both RS and climate data
                                Preliminary conservation assessment
                                with threatened parrots
  Conservation planning


                             Cerulean
                             warbler listed
                             as vulnerable
                             by IUCN

New
protected
area, 2005    Developing direct
              interactions with local
              conservation practitioners.
              - Courses
              - Data sharing
              - Decision support
      Remote-sensing correlates to
          biological diversity
Effectively use RS data in species distribution modeling and
     decision support
•    Virtual species experiment
•    Modeling Andean bird species
•    Conservation planning

Evaluate hypotheses explaining variability in species richness
•   Correlates of mammal richness across spatial scale
•   Effects of disease on amphibian richness pattern

Simulate the impacts of climate change on species
    distributions (in press, Global Change Biology)

Train ecologists, evolutionary biologists and conservation
     biologists
   Variability in species richness:
   Effects of disease on amphibian
          richness patterns
Chytrid-thermal-optimum
       hypothesis


                          Grey shading: estimated
                          percentage of species lost
                          from each altitudinal zone

                          Optimum temperatures for
                          chytrid: 17C – 25C

                                    Pounds et al. (2006)
     Testing Chytrid-thermal-optimum
           hypothesis in Ecuador




              Temp
              17- 25o C


Temperature range does not correspond with declining
frog distribution in Ecuador
Ecological Niche Hypothesis
    Chytrid distribution model




                             Maxent
                             Climatic & RS variables
                                 PCA of environmental space of chytrid and
                                     frogs labeled by IUCN categories
Primarily mean diurnal temperature


                                      high
      range and precipitation


                                             PC II




                                                                                                 Atelopus
                                                                                   70% of
                                                                                   variation
                                                                                                 Colostethus
                                                                                   explained     Eleutherodactylus
                                     low




                                                                         PC I
                                                        low                               high
                                                Primarily temperatures during coldest and driest seasons
       Forecasting Future
       Amphibian Declines
• Tracking with RS data: rainfall of the
  driest quarter is highly correlated with
  the mean leaf area index of the dry
  season

• Forecasting: Use GCMs to investigate
  future changes in precipitation
Remote-sensing correlates to
biological diversity: training




          CURSO–TALLER
Métodos de modelamiento de distribución
      de especies y sus aplicaciones
         Julio 10 al 15 de 2006

				
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