Global 250 m coral reef prioritization layer (binary selection layer), v1.0 and supplementary tables

Description

Technical info

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 used for model training and validation. Each dataset is summarised by the number of observations, availability of life history data, temporal coverage (first and last observation year), and full citation. These datasets provide the empirical basis for modelling coral cover and life-history composition across global reef systems.

Table S2. Life-history trait assignments for coral taxa. Coral genera and species were classified into competitive, stress-tolerant, and weedy life-history strategies, with some taxa allocated proportionally across groups to reflect variation in morphology and ecological behaviour (e.g. branching vs. massive forms). These proportional assignments were used to derive life-history composition for all modelling analyses.

Table S3. Predictor variables (n = 42) used in coral-cover and life-history models. For each environmental and anthropogenic variable, the table reports a brief description, units, spatial and temporal resolution, and source dataset.

Table S4. Training and testing performance of machine-learning models predicting total coral cover and life-history group cover. Metrics include mean R² and mean absolute error (MAE) across 100 model runs (± SD). High training R² values indicate strong within-sample fits, while lower testing R² and higher MAE values reflect the challenge of predicting fine-scale coral cover patterns under real-world variability. Life-history models generally show lower predictive skill than total coral cover, consistent with greater noise and fewer observations in taxonomically resolved datasets.

Table S5. Regional model performance and selection criteria for coral-cover predictions. For each coral province, the table reports skill metrics (mean absolute error skill and median absolute deviation skill), their variability (SD), and one-sided permutation test p-values assessing improvement over a null model. Columns also show the proportion of models from the global pool meeting predefined MAE and MAD thresholds, and the percentage meeting both criteria. Models passing both thresholds were retained for regional ensemble predictions of 2020 and 2050 coral cover.

Table S6. Spatial optimization outcomes by countries, territories and jurisdictions under SSP3-7.0. For each coral reef area, we estimate total reef extent and the area selected as climate-resilient refugia under the 50 Reefs plus optimisation, including the proportion of newly identified priority area and the overlap with the original Beyer et al. (2018) prioritisation. Values for areas selected only by Beyer et al., as well as reef habitat not selected by either approach, are provided in km² and as percentages of total reef extent.

Table S7. Spatial optimization outcomes by countries, territories and jurisdictions under the higher-emissions SSP5-8.5 scenario, presented as a supplementary sensitivity analysis to Table S6. Columns and units are as in Table S6. Minor differences in total extent values between SSP3-7.0 and SSP5-8.5 (<0.01% of prioritised area) reflect gridding artefacts from the prioritisation step and do not affect interpretation.

Appendix S1. Gurobi solver equations and configurations. This appendix outlines the mathematical formulation of the spatial optimization problem and the Gurobi-specific settings used to obtain stable, spatially coherent solutions.