Model Development and Data Sources

Modeling

We aggregated to monthly averages on GEE, and extracted different seasonal/annual averages in R depending on the taxa of interest. We assembled models based on physical characteristics (elevation + soils + climate) as well as these characteristics based from different remote sensing platforms/products.

NASA Remote Sensing

  • SMOS: NASA-USDA Global Soil Moisture Data (Mladenova et al 2017; Bolten and Crow 2012; Bolten et al 2010)
  • MODIS NDVI: MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250m product
  • MODIS LST: MOD11A2.006 Terra Land Surface Temperature and Emissivity 8-day Global 1km product
  • TRMM: 3B43: Monthly Multi-Satellite Precipitation Estimates (Huffman 2012)
  • MODIS ET: MOD16A2.006: Terra Net Evapotranspiration 8-Day Global 500m product (Mu et al 2014)

We use NASA satellites to understand how temperature, soil moisture, and the productivity of trees and other planets drives the structure and composition of communities.

The Wonder of life

Biodiversity drives the natural world and ecosystem services

We use data collected by field assistants and research technicians from the National Ecological Observatory Network to develop our joint attribute models, and estimate where and when species will occur in the future under climate change.

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Observations and Interpolations

Elevation, Soils, and Climate

  • Land cover from the National Land Cover Database (Homer et al 2015).
  • Elevation, slope, aspect from the SRTM Digital Elevation Data 30m product (Farr et al 2007)
  • 250-m global soil metrics from soilgrids.org (Hengl et al 2017)
  • Climate data, University of Idaho Gridded Surface Meteorological Dataset (GRIDMET) (Abatzoglou 2012; Abatzoglou & Brown, 2012)

Climate Projections

  • 4-km downscaled climate projections from 20 global climate models (GCMs) and 2 representative concentration pathway (RCP) scenarios, from 2040 to 2099 from the Multivariate Adaptive Constructed Analogs: MACAv2-METDATA (Abatzoglou & Brown, 2012)