DOI: 10.6084/m9.figshare.27719091.v4
title: Supplementary materials and datasets for the article："How Does Climate Change Influence the Regional Ecological–Social Risks of Harmful Dinoflagellates? A Predictive Study of China's Coastal Waters"

description: <b>The datasets here are for the outputs of the paper published in Global Change Biology</b><b>, </b><b>Su</b><b> et al. (2025)</b><b>Paper abstract: </b>Harmful dinoflagellates are widely distributed in coastal waters worldwide, posing multiple ecological and socioeconomic threats. Climate change may alter the biogeography of these species; however, few studies have linked shifts in harmful dinoflagellates' ecological distribution to their socioeconomic impacts. This study developed a framework to assess the spatiotemporal ecological–social risks posed by harmful dinoflagellates, identifying these algae as risk sources and considering mariculture and coastal populations as the primary risk receptors. China is the world's largest mariculture producer, with approximately 600 million residents living in coastal areas. Focusing on 14 key harmful dinoflagellate species in Chinese coastal waters, we evaluated ecological–social risks under present conditions and two projected climate scenarios for 2100. Our findings indicate that climate change may lead to reductions in suitable habitats for harmful dinoflagellates in tropical and subtropical regions, while habitats in higher-latitude areas are likely to remain stable or expand. Risk area expansion is projected for four species, and increased average risk intensity for three, with two species experiencing both. Nationally, total risk area is projected to remain stable, while cumulative risk intensity may decline by 16.64%. Regionally, risk intensity is expected to rise in northern provinces (up to 30.46%) and decline across most southern provinces. Importantly, we reveal a potential spatial “decoupling” of risk sources and receptors along the coast of China in the future. This decoupling demonstrates a reduced overlap between harmful dinoflagellate distributions and areas with dense mariculture or populations. Our findings suggest that, contrary to the common assumption that climate change universally exacerbates harmful algal impacts, these effects may vary across regions and species, highlighting the importance of localized adaptation strategies in risk assessment. This study provides a robust tool for understanding harmful dinoflagellate risks under climate change, thereby supporting the sustainable management of coastal ecosystems.<b>Datasets:</b><b>1</b><b>: </b><b>Appendices_and_original_data</b><b>I. Appendix_I_Method_for_calculating_the_TC_Index</b><b>: </b>Supplementary information on the methodology for calculating the TC index<b>II. Appendix_II_Cleaned_occurrence_data</b><b>: </b>Species occurrence data for modeling<b>III. Appendix_III_Supplementary_information_for_pseudo-absence_points_creating and Appendix_III_Species_pseudo_absence_data</b><b>: </b>This appendix describes how to generate pseudo absence points, and the pseudo absence points data<b>IV.</b><b>Appendix_IV_Supplementary_information_for_the_environmental_variable_selection_process_and_environmental_niches</b><b>: </b>This appendix contains the following supplementary information for environmental variable selection process of the manuscript, and environmental niches of the 14 species<b>V.</b><b> </b><b>Appendix_V_ODMAP_protocols</b><b>VI</b><b>. </b><b>Appendix_VI_Supplementary_information_for_SDM_tuning</b><b>: </b>This appendix contains the following supplementary information for the tune of species distribution model parameters of the manuscript<br><b>2</b><b>: </b><b>Processed_data</b>I. <b>Table_data_for_Figures 4_6_and_7</b><b>:</b>The files “Distribution_trend_of_species_suitable_habitats_along_latitude.xlsx” and “Species_diversity_cal_highest value.xlsx” relate to Figures 4b and 4c.The files “risk_areas_and_area_changes_in_each_province.xlsx” and “risk_areas_for_each_species.xlsx”relate to Figures 6a and 6b.The files “risk_intensity_and_intensity_changes_in_each_province.xlsx” and “risk_intensity_for_each_species.xlsx” relate to Figures 7a and 7b.II. <b>Raster_data_for_Figure_5</b><b>:</b>The files “Mariculture_production_value.tif”,“Population_present.tif”,“Population_SSP1-2.6.tif”, and “Population_SSP5-8.5.tif” relate to Figures 5c and 5d.- Coordinate system: GCS_WGS_1984- Spatial resolution: 0.08333333 degree (about 10 km)III. <b>Raster_data_for_Figure_S1_to_Figure S15</b><b>:</b>The raster data of Figure S1 is “Spatial_distribution_of_mariculture_ production_output_value.tif”.- Coordinate system: GCS_WGS_1984- Spatial resolution: 0.08333333 degree (about 10 km)The raster data of Figures S2 to S15 are the distribution change data of 14 harmful dinoflagellate species under SSP1-2.6 and SSP5-8.5 scenario, includes:- SP.1 -：<i>Akashiwo sanguinea</i>- SP.2 -：<i>Alexandrium minutum</i>- SP.3 -：<i>Alexandrium ostenfeldii</i>- SP.4 -：<i>Coolia monotis</i>- SP.5 -：<i>Dinophysis acuminata</i>- SP.6 -：<i>Gonyaulax spinifera</i>- SP.7 -：<i>Gonyaulax verior</i>- SP.8 -：<i>Gymnodinium catenatum</i>- SP.9 -：<i>Karenia mikimotoi</i>- SP.10 -：<i>Karlodinium veneficum</i>- SP.11 -：<i>Margalefidinium polykrikoides</i>- SP.12 -：<i>Noctiluca scintillans</i>- SP.13 -：<i>Prorocentrum lima</i>- SP.14 -：<i>Protoceratium reticulatum</i>- Coordinate system: GCS_WGS_1984- Spatial resolution: 0.08333333 degree (about 10 km)<br>IV. <b>Table_data_for_Figures_S16_and_S17</b><b>:</b>The files “risk_area_size_across_coastal_provinces.xlsx” relates to Figure S16.The files “risk_intensity_across_coastal_provinces.xlsx” relates to Figure S17.<br><b>3: R_code</b><b>I</b><b>. </b><b>Get_occ_data.R</b><b> </b>This code is used to retrieve occurrence data for the target species from public databases GBIF and OBIS,and to perform data cleaning and deduplication.<b>II. </b><b>Create_pseudo_absence_points.R</b><b>: </b>The code generates pseudo-absence points for the species based on the Spatial Restriction and Environmental Subsampling methods.<b>III.</b><b>SDM_tuning.R</b><b> </b>The code uses a brute-force (grid search) approach for parameter tuning of the machine learning models MaxEnt and Artificial Neural Network (ANN).