| 29 Jun 2026
A global long-term ecoregion-level dataset of biodiversity indicators for terrestrial vertebrates under climate and habitat change
Abstract. Climate and habitat change are reshaping global biodiversity, yet long-term, spatially explicit datasets that capture these dynamics across ecologically meaningful regions remain limited. Here, we present a global spatiotemporal dataset of ecoregion-level biodiversity indicators for terrestrial vertebrates derived from species-specific Area of Habitat (AOH). By integrating species occurrence records, expert-derived range maps, climate data, and temporally explicit habitat maps, we reconstructed distributions for 19146 species of amphibians, birds, mammals, and reptiles. We then derived four complementary biodiversity indicators—species richness, threatened species richness, species endemism, and AOH density—for global terrestrial ecoregions at five time points (1990, 2020, 2030, 2050, and 2100), with future projections under SSP245 and SSP585. Validation at both species and dataset levels demonstrated good performance. Predicted AOH consistently assigned higher suitability to independent occurrence records than to the broader range background, and estimated richness closely reproduced the major spatial patterns of occurrence-derived richness across ecoregions. In 2020, the four indicators showed distinct but broadly concordant spatial patterns, with the highest overall values concentrated in tropical ecoregions. Future projections revealed marked spatial heterogeneity, with declines in multiple indicators concentrated in many tropical ecoregions by 2100 and generally becoming stronger under SSP585. This dataset provides a globally consistent and ecologically meaningful resource for biodiversity monitoring, macroecological analysis, and conservation assessment under ongoing climate and habitat change. The dataset supporting this study is publicly available at https://doi.org/10.5281/zenodo.20119261.
Overall comments
• The manuscript is well written and easy to read
• The introduction is clear
• The ecoregion-scale focus is an advantage due to the rarity of ecoregion-level spatial data for biodiversity
• The data will be very useful for researchers interested in biodiversity change and related topics
Specific comments
Materials and methods
193-201. What is the justification for 500km? The rationale for the buffer-based screening is clear, but is there any support for 500 km compared to any other distance? The chosen distance will impact the occurrence records that are included and the resultant SDMs.
300-305. It is currently difficult to gauge how many individual occurrence records were in each effective occurrence record. Could you provide the average number? This is important because the number of individual species occurrence records can impact the accuracy of SDMs (https://doi.org/10.1111/ddi.13252).
The rationale for choosing 10 effective occurrence records for the threshold should also be provided, alongside acknowledgement of the potential implications of this approach for the accuracy of the SDMs.
389-392. It would be useful to add the year in which the IUCN Red List classifications were extracted, since they can change regularly.
Results
3.1 The accuracy assessment results appear a little abruptly as there is no description of the methods for accuracy assessment in the Materials and methods section. It would thus be good to move some of 3.1 to the Materials and methods section.
Discussion
It would be good to acknowledge the implications and limitations of the chosen AOH validation method, particularly for dataset-level validation. For example, it seems that the SDMs used for climatic suitability were not themselves validated; thus, individually they could have low accuracy. This can still be framed in light of the assessment being global scale, so accuracy cannot be guaranteed at all scales and for all individual components of the models, but some additional discussion of the more specific limitations would be good (if the word count allows).