High Resolution Global Environmental Stratification to Model Shifting Bioclimatic Conditions and Climate Change Impacts on Terrestrial Ecosystems

Background Understanding climate-driven ecological change benefits from frameworks capable of translating climate model outputs into ecologically interpretable spatial patterns. This study presents a global application of the Environmental Stratification (EnS) methodology, using Maximum Likelihood Classification, and integrating outputs from 22 CMIP6 Earth System Models (ESMs) across four Shared Socioeconomic Pathways (SSP: 1-2.6; 2-4.5; 3-7.0; 5-8.5), two historical baseline climate datasets (1960–1990; 1970–2000), and two future averaged time periods (2021–2040; 2041–2060). Both “Consensus” and “High-Risk” multimodel majority ensembles were selected and used in the global analysis. The resulting Future_Global_EnS Database (<ext-link xmlns:ns4="http://www.w3.org/1999/xlink" ext-link-type="uri" ns4:href="https://doi.org/10.5281/zenodo.15099763">https://doi.org/10.5281/zenodo.15099763</ext-link>), provides these highresolution EnS projections at 30 arc seconds resolution (~1km²). The global EnS analysis reveals extensive bioclimatic reorganization by mid-century. Cooler and mesic zones, particularly boreal, temperate, and montane systems, exhibit high zonal turnover, significant poleward latitudinal shifts, upslope migrations, and mountaintop extirpations. Tropical, arid, and extremely hot zones show significant spatial expansion and internal reorganization under high-emissions scenarios, including large-scale growth of drylands and intensification of extremely hot bioclimatic conditions. Results from the global EnS analyses align closely with empirical observations of bioclimatic and ecological shifts, providing spatially explicit, ecologically grounded metrics for anticipating where and how terrestrial ecosystems will change. By delineating statistically based coherent and ecologically meaningful geospatial strata, the EnS methodology provides a powerful tool for translating complex climate projections into interpretable ecological insights and enables rigorous analysis of climate impacts at decision-relevant scales. Both the EnS methodology and Future_Global_EnS Database provide a resource with immediate utility for biodiversity conservation, land-use policy, agricultural sustainability, natural resource management, and adaptation to rapid climate change, at global to regional and local scales.