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

Remote-sensing correlates of biological diversity Catherine Graham Stony Brook University. NASA Funded: Tom Smith Sassan Saatchi Chris Schneider Robert Wayne. Graham lab: Jorge Velasquez Natalia Silva Pablo Menendez Other: Robert Hijmans Luis Coloma Santiago Ron.

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

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  1. Remote-sensing correlates of biological diversityCatherine GrahamStony Brook University NASA Funded: Tom Smith Sassan Saatchi Chris Schneider Robert Wayne Graham lab: Jorge Velasquez Natalia Silva Pablo Menendez Other: Robert Hijmans Luis Coloma Santiago Ron

  2. 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

  3. Species distribution modeling 1) Extract environmental data for point localities; 2) Make statistical model describing distribution in environmental space; 3) Project this model in geographic space to create a map. Elevation Annual Rainfall

  4. Free download 16 modeling methods Presence-only training data Independent presence-absence test data 6 geographic regions/series of taxonomic groups 250 species

  5. 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

  6. RS data in species distribution modeling: Virtual species experiment Points in currently-forested areas only Points in Climate Original distribution (climate-only) Current distribution (climate & RS)

  7. RS data in species distribution modeling: Virtual species experiment Points partitioned by RS & climate Points in RS-forest only Points in RS-forest & climate Sample size of 100 points *note correlations are between a binary and continuous prediction

  8. 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

  9. Myadestes Ralloides (Andean Solitare) Exp 1: climate Exp 2-4: climate and RS without data splitting Exp 5-7: climate and RS with data splitting Exp 1 Exp 7

  10. 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

  11. 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

  12. 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

  13. 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)

  14. Testing Chytrid-thermal-optimum hypothesis in Ecuador Temp 17- 25o C Temperature range does not correspond with declining frog distribution in Ecuador

  15. Ecological Niche Hypothesis Chytrid distribution model Maxent Climatic & RS variables

  16. high PC II low PC I low high Primarily temperatures during coldest and driest seasons PCA of environmental space of chytrid and frogs labeled by IUCN categories Primarily mean diurnal temperature range and precipitation AtelopusColostethusEleutherodactylus 70% of variation explained

  17. 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

  18. Remote-sensing correlates to biological diversity: training C U R S O – T A L L E RMétodos de modelamiento de distribución de especies y sus aplicacionesJulio 10 al 15 de 2006

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