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Satellite-derived Ecosystem Functional Types (EFTs) are increasingly used in ecology and conservation to characterize ecosystem heterogeneity. The diversity of EFTs, also known as Ecosystem Functional Diversity (EFD), has been suggested both as a potential metric of ecosystem-level biodiversity and as a predictor for ecosystem functioning, ecosystem services, and resilience. However, the impact of key methodological choices on patterns of EFTs and EFD have not been formally assessed. Using Costa Rica as a study system, we compared EFTs and EFD, derived from MODIS and Landsat data using different methodological assumptions, at both national and local extents. Our results showed that the regional spatial patterns of EFTs and EFD derived from 250 m MODIS and 30 m Landsat are notably different. The selection of sensors for deriving EFTs and EFD is dependent on the study area, data quality, and the research objective. Given its finer spatial resolution, Landsat has greater capacity to differentiate more EFTs than MODIS, though MODIS could be a better choice in frequently cloudy areas due to its shorter revisiting time. We also found that the selection of spatial extent used to derive EFD is critical, as smaller extents (e.g., at a local rather than a national scale) can show much higher diversity. However, diversity levels derived at smaller extents appear to be nested within the diversity levels derived at larger extents. As EFTs and EFD continue to develop as a tool for ecosystem ecology, we highlight the important methodological choices to ensure that these metrics best fit research objectives.

Mongolia contains some of the largest intact grasslands in the world, but is vulnerable to future changes in climate and continued increases in the number of domestic livestock. As these are two major drivers of change, it is important to understand interactions between the impact of climate and grazing on productivity of Mongolia’s rangelands and the livelihoods they sustain. We use a gridded, spatially explicit model, the Rangeland Production Model (RPM), to explore the simultaneous and interacting effects of climate and management changes on Mongolia’s rangeland and future livestock production. Comparing the relative impact of temperature, precipitation, and grazing intensity, varied individually and in combination, we find that climatic factors dominate impacts on forage biomass and animal diet sufficiency. Site rainfall strongly mediates the impact of grazing on standing biomass, such that more productive or higher-rainfall sites are more vulnerable to increases in grazing pressure. Gridded simulations covering Mongolia’s Gobi-Steppe ecoregion show that while rangeland biomass is generally predicted to increase under future climate conditions, interactions among spatially varying drivers create strong heterogeneity in the magnitude of change.