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Global-scale parameters for ecological models.

Gianpaolo Coro1, Pasquale Bove2, Kathleen Kesner-Reyes3

  • 1Institute of Information Science and Technologies, Italian National Research Council, Pisa, 56124, Italy. gianpaolo.coro@cnr.it.

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|January 4, 2023
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Summary
This summary is machine-generated.

This study introduces a comprehensive marine dataset for ecological modeling, featuring 2132 raster files across 58 parameters. The data supports ecological niche models with global and regional scales, including future climate change projections.

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Area of Science:

  • Marine ecology
  • Geophysics
  • Environmental science

Background:

  • Ecological and niche modeling require diverse, high-resolution environmental data.
  • Existing marine datasets are often fragmented across repositories with varying formats and resolutions.

Purpose of the Study:

  • To compile a unified, extensive collection of marine-related environmental and geophysical data.
  • To provide standardized raster data suitable for ecological and ecological-niche modeling at multiple scales.
  • To include future climate change scenario data for predictive ecological studies.

Main Methods:

  • Data compilation from open-access repositories and new data generation.
  • Standardization of 2132 raster files for 58 parameters into ESRI-GRID ASCII format.
  • Spatial and temporal aggregation of data to regional and global scales, including monthly and annual resolutions.
  • Inclusion of future climate projections based on Representative Concentration Pathways (RCPs) and IPCC A2 scenario.

Main Results:

  • A dataset of 2132 raster files covering 58 parameters at regional and global scales.
  • Data includes global coverage (565 files), high-resolution grids (0.5° and 0.1°), and temporal aggregations (annual and monthly).
  • Forecast data for future climate scenarios (RCP 2.6, 4.5, 8.5, and A2) are integrated.

Conclusions:

  • The presented data collection offers a valuable, integrated resource for marine ecological and niche modeling.
  • Standardized data format and scale facilitate broader application in marine science research.
  • Inclusion of future climate scenarios enables predictive modeling of marine ecosystem responses to climate change.