DOI: 10.5281/zenodo.18686274
title: Global 250 m coral reef prioritization layer (binary selection layer), v1.0 and supplementary tables

description: DescriptionThis dataset contains the global 250 m resolution binary prioritization surface generated for the coral reef portfolio optimization analysis described in the associated manuscript. The raster identifies selected planning units resulting from the optimization framework designed to maximize climate resilience, life history representation, spatial cohesion, and risk minimization.

Each pixel represents a 250 m × 250 m grid cell (~0.003° × 0.003° at the equator). Pixel values are binary:0 = not selected1 = selected

The raster corresponds to the finalized optimization solution used in the main analysis (v1.0).

Fileprioritisations_250m.tifFormat: GeoTIFFFile size: 76 MBNumber of layers: 1

Spatial PropertiesCoordinate Reference System (CRS): WGS 84EPSG: 4326Units: degrees (longitude/latitude)Resolution: 0.003° × 0.003° (~250 m at the equator)Extent:xmin: -179.9907xmax: 179.9763ymin: -34.29605ymax: 32.51395

DimensionsRows: 22,270Columns: 119,989Total cells: 2,672,494,? (automatically defined by raster dimensions)

Data TypeSingle-band rasterValue range: 0–1Mean value: 0.3042389

InterpretationValues of 1 indicate 250 m pixels included in the optimized portfolio. Values of 0 indicate pixels not selected. The mean value reflects the proportion of reef pixels selected globally under the specified optimization constraints.

GenerationThe raster was produced using an integer programming framework and solved using Gurobi Optimizer. Input layers to the prioritization included 250 m global predictions of coral cover, life history groups, spatial cohesion, and model uncertainty. Full methodological details are provided in the associated manuscript.

Versionv1.0 – Corresponds to manuscript submission version.
description: This repository also contains the supplementary tables for a global analysis of climate-resilient coral reefs. These materials provide full documentation of the datasets, model inputs, performance metrics, and optimization methods used to generate global predictions of coral cover and life-history composition, as well as the spatial prioritization results.

Table S1. Coral reef observation datasets.Summary of all empirical datasets used for model training and validation, including number of observations, availability of life-history data, temporal coverage, and full citations. These datasets form the empirical foundation for modelling coral cover and life-history composition across global reef systems.

Table S2. Life-history trait assignments.Classification of coral genera and species into competitive, stress-tolerant, and weedy life-history strategies. Some taxa are allocated proportionally across groups to reflect variation in morphology and ecological behaviour. These assignments underpin all life-history composition estimates.

Table S3. Predictor variables.Details of the 42 environmental and anthropogenic predictors used in the coral-cover and life-history models. Variables include brief descriptions, units, spatial and temporal resolution, and data sources.

Table S4. Model training and testing performance.Performance metrics for machine-learning models predicting total coral cover and life-history group cover, including mean R² and mean absolute error (MAE) across 100 runs (± SD). Results highlight the relative predictive skill of cover versus life-history models under real-world variability.

Table S5. Regional model performance and selection criteria.Skill metrics for coral-cover predictions across coral provinces, including MAE skill, MAD skill, variability (SD), and permutation test p-values. The table also reports the proportion of global models meeting predefined thresholds and those retained for ensemble predictions of 2020 and 2050 coral cover.

Table S6. Spatial optimization outcomes.Country-, territory-, and jurisdiction-level results from the 50 Reefs Plus optimization at 250 m resolution. Includes reef extent, area selected as climate-resilient refugia, proportion of new priority area, overlap with Beyer et al. (2018), and areas selected by either or neither approach (km² and percentages).