1. Integrating global species distributions, remote sensing and climate data to model change in species distributions

2. Walter Jetz (Yale U), Rob Guralnick (CU Boulder, Brian McGill (U Maine), Rama Nemani (NASA Ames), Forrest Melton (NASA Ames) Dr. Mao-Ning Tuanmu (Yale U, NASA-funded ), Dr. Adam Wilson (Yale U, YCEI-funded ), Dr. Benoit Parmentier (NCEAS, iPlant-funded ), Natalie Robinson (CU Boulder, NASA-funded ), George Cooper (U Maine, NASA-funded ) Dr. Jim Regetz (NCEAS), Dr. Mark Schildhauer (NCEAS), Martha Narro (iPlant), Dave Thau (Google), Jeremy Malczyk (Yale U) Postdocs, Students: PIs: Others: YCEI

3. Global 1km environmental layers Global spatial biodiversity data Models Quality Control Map of Life PredictionsInference Hierarchical Bayesian models Environment Topography: 90m global DEM Land cover type: Consensus Habitat Heterogeneity Net primary productivity Climate Temperature: in progress Cloud cover: close! Precipitation: in progress Bioclimatic variables Extreme events Change in: Species niches Species distributions 1972-92 vs. 1992-12

4. Amphibians Mammals GBIF species richness GBIF record count Expert species richness Meyer, Guralnick, Kreft & Jetz in prep.

5. Spatial biodiversity data Hurlbert and Jetz (PNAS 2007) Jetz et al. (Conservation Biology 2008)

6. Map of Life - An infrastructure for integrating and analyzing global species distribution knowledge Jetz et al. 2012, TREE mappinglife.org

7. Jetz et al. 2012, TREE Map of Life An online workbench and knowledgebase to dynamically document, integrate, validate, advance, analyze the disparate sources of global biodiversity distribution knowledge Tools and products: Aquatic and terrestrial global biodiversity layers Species lists for user- defined regions, on mobile devices Dynamically-updated threat assessments

8. ASTER GDEM V2 SRTM V4 1. Full global-extent 90m DEM Blended, void-filled, multi-scale smoothed For global derivation of terrain variables and distribution modeling Robinson et al (MS)

9. Limitations of Existing Products Classification errors Among-product disagreements IGBP DISCover, U of Maryland, GLC2000 and MODIS; Herold et al. 2008 2. Global consensus land cover

10. Classification errors Among-product disagreements Categorical data – False absences of minor land cover classes 2. Global consensus land cover Limitations of Existing Products

11. Goal Generate a harmonized set of 1-km resolution land cover product that provides scale-integrated and accuracy-weighted consensus land cover information on a continuous scale. Example use in biodiversity modeling: Minimize false absences and improve accuracy of species distribution models 2. Global consensus land cover

13. 1km Land Cover Prevalence 2. Global consensus land cover

14. Improvements to model accuracy Better 2. Global consensus land cover Tuanmu & Jetz (Global Ecology & Biogeography, in review)

15. Climate-aided interpolation Monthly climatologies (2000-2011) from MODIS and station means Interpolate daily station anomalies (including pre 2000) Goal: Develop daily 1km surfaces of tmax, tmin, and ppt with MODIS and climate station data (1970-2011). Satellite-Station Data Fusion 3. Global temp. & prec. layers

16. Satellite Weather Products Precipitation: MODIS Cloud Product (MOD06) Temperature: MODIS LST (MOD11A1) 3. Global temp. & prec. layers

17. CLIMATE INTERPOLATION WORKFLOW All the steps are implemented in Open Source GIS combining Linux Shell script, PostGres, R, Python, GRASS and GDAL. 3. Global temp. & prec. layers

18. cai_mod1TMax~ f(elev) caii_mod2TMax~ f(LST) cai_mod3TMax~ f(elev, LST) cai_mod4TMax~ f(lat) + f(lon) + f(elev) cai_mod5TMax~ f(lat, lon, elev) cai_mod6TMax~ f(lat, lon) + f(elev) + f(N_w, E_w) + f(LST) cai_mod7TMax~ f(lat, lon) + f(elev) + f(N_w, E_w) + f(LST) + f(LC1) cai_mod8TMax~ f(lat, lon) + f(elev) + f(N_w, E_w) + f(LST) + f(LC3) cai_mod9TMax~ f(x)+f(y) cai_krCAI_kr: y_var ~ tmax Max. temperature, 1 Sep. 2010 Climate aided interpolation Comparison of models Temperature (deg Celsius) 3. Global temp. & prec. layers

20. MOD35 Cloud Frequency (%) in February Venezuela (MODIS tile h11v08) 3. Global temp. & prec. layers

21. MOD35 Cloud Frequency (%) in February 3. Global temp. & prec. layers

22. Cloud data improves interpolation accuracy 3. Global temp. & prec. layers

23. Comparison of WorldClim and MOD35-informed mean monthly interpolation (February) Worldclim MOD35-Informed Mean monthly precipitation (mm) from WorldClim [lppt~s(y,x)+s(dem)] and MOD35-informed interpolation [lppt~s(y,x)+s(dem)+cld+cot+cer20 ] mm 3. Global temp. & prec. layers

24. Thanks!

25. Landcover Bias in Collection 5 (MOD35) Cloud Data MOD35 Collection 5 MOD35 Collection 6 Cloud Frequency (%) in March 0 40 80 100 60 20 The current (C5) MODIS Cloud mask has more frequent “cloudy” days over non-forest The updated (C6) mask less biased by land cover Non-Forest

26. Hartlaub’s Turaco: forest specialist, >1500m elevation MODIS Landcover 2001-2012 Expert range size: 180,000km 2 Suitable 2012 Expert range Range Refinement 180,000km 2 23,000km 2