ArticleScientific data2023
Eco-ISEA3H, a machine learning ready spatial database for ecometric and species distribution modeling.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Coastal Zone Information Model: A comprehensive architecture for coastal digital twin by integrating data, models, and knowledge.Fundamental research · 2026Review
- Machine learning for N-dimensional spatial reasoning tasks on the web.Frontiers in bioinformatics · 2026Article
- The species distribution model based on the random forest algorithm reveals the distribution patterns of Neophocaena asiaeorientalis.Scientific reports · 2025Article
- Colony Site Selection of Gray Heron (Ecology and evolution · 2025Article
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2 authors.
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Abstract
We present the Eco-ISEA3H database, a compilation of global spatial data characterizing climate, geology, land cover, physical and human geography, and the geographic ranges of nearly 900 large mammalian species. The data are tailored for machine learning (ML)-based ecological modeling, and are intended primarily for continental- to global-scale ecometric and species distribution modeling. Such models are trained on present-day data and applied to the geologic past, or to future scenarios of climatic and environmental change. Model training requires integrated global datasets, describing species' occurrence and environment via consistent observational units. The Eco-ISEA3H database incorporates data from 17 sources, and includes 3,033 variables. The database is built on the Icosahedral Snyder Equal Area (ISEA) aperture 3 hexagonal (3H) discrete global grid system (DGGS), which partitions the Earth's surface into equal-area hexagonal cells. Source data were incorporated at six nested ISEA3H resolutions, using scripts developed and made available here. We demonstrate the utility of the database in a case study analyzing the bioclimatic envelopes of ten large, widely distributed mammalian species.
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