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Climate-driven connectivity loss impedes species adaptation to warming in the deep ocean

Nature Climate Change volume 15, pages 315–320 (2025)Cite this article

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A Publisher Correction to this article was published on 14 March 2025

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Abstract

Marine life are expected to have fewer thermal barriers restricting their movement to adjacent habitats than terrestrial species do. However, it remains unknown how this warming-induced connectivity loss varies in different ocean strata, limiting the predictability of warming impacts on biodiversity in the whole ocean. Here, we developed a climate connectivity framework across seascape strata under different climate change scenarios, which combines thermal gradient, human impacts and species tolerance thresholds. We show that warming may lead to connectivity loss, with its magnitude increasing with depth. Connectivity loss is projected to increase rapidly in 2050, particularly in deep strata, and may impair the movement capacity of deep-sea phyla in adapting to warming. With the compression of habitat ranges, over one-quarter of deep-sea species inhabit areas that may experience disrupted connectivity, threatening the maintenance of deep-sea biodiversity. Our results highlight the challenges that climate change poses to biodiversity conservation through disruption of deep-sea connectivity.

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Fig. 1: Exacerbation of climate connectivity loss through four strata under projected climate change (2020–2100).
Fig. 2: Offset capacity to warming threat in different strata.
Fig. 3: Overlapping areas of irreversible connectivity disruption and species distribution.

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Data availability

All input datasets for the models are publicly available data. The GSHHG global coastline dataset is available at https://www.soest.hawaii.edu/pwessel/gshhg/, the GEBCO global bathymetry dataset at https://doi.org/10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f, AquaMaps biodiversity dataset at https://www.aquamaps.org, the CMIP6 projected climate change dataset at https://esgf-node.llnl.gov/search/cmip6/, the contemporary temperature datasets at https://doi.org/10.1126/sciadv.1601545 (ref. 52) and https://www.ncei.noaa.gov/products/world-ocean-atlas and the global historical (2003–2013) stressors and habitat dataset at https://doi.org/10.1038/s41598-019-47201-9 (ref. 43). The output dataset of climate connectivity in year 2100 is available via Figshare at https://doi.org/10.6084/m9.figshare.27060730 (ref. 53).

Code availability

The codes for preprocessing and climate connectivity modelling used in this study are available via Zenodo at https://doi.org/10.5281/zenodo.14271879 (ref. 54). The codes for data preprocessing cannot be directly re-run for verification due to copyright issues with the raw data. We customized a demo dataset that can be used to run the climate connectivity model, which is accessible via Figshare at https://doi.org/10.6084/m9.figshare.27061555.v2 (ref. 55).

Change history

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Acknowledgements

This research was funded by the National Natural Science Foundation of China (no. 72394404 to Y. Li and Y. Lu, 42122044 to X. Liu. and 41701205 to Y. Li), National Key R&D Program of China Grant (no. 2022YFF0803100 to Y. Li and 2022YFC3105302 to X. Lin.), Xiamen Natural Science Foundation (no. 3502Z20227011 to Y. Li) and the Fundamental Research Funds for the Central Universities (no. 20720240093 to Y. Li and 20720240091 to Y.C.). Thanks also go to J.-G. Du, W.-L. Wang and W.-J. Cai for their valuable insights and suggestions on refining the structure of the article. We would like to thank Z. Xiao, J.-W. Lin and H. Hong from the Information Technology Department of Xiamen University Library for their technical support.

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Authors and Affiliations

  1. Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, National Observation and Research Station for the Taiwan Strait Marine Ecosystem, and Key Laboratory of Coastal and Wetland Ecosystems (Ministry of Education), College of the Environment and Ecology, Xiamen University, Xiamen, China

    Yuxuan Lin, Yuxin Chen, Xin Liu, Zhiyuan Xiang, Xinyi Zhang, Zhixue Chen, Yi Li & Yonglong Lu

  2. Department of Ocean Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China

    Yuxuan Lin

  3. State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China

    Xin Liu, Xin Lin & Yonglong Lu

  4. College of the Coast & Environment, Louisiana State University, Baton Rouge, LA, USA

    Edward A. Laws

  5. School of Oceanography, Shanghai Jiao Tong University, Shanghai, China

    Yuntao Zhou

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Contributions

Y. Lin, Y.C., E.A.L., X. Liu, X. Lin, Z.C., Y. Li and Y. Lu conceived the study. Y. Lin, Y.C., Z.X., X.Z., Z.C., E.A.L., Y.Z. and Y. Li contributed to the formal analysis of the quantitative and/or qualitative data and performed data visualization. Y. Lin and Y. Li wrote an initial draft and all authors reviewed and revised the paper.

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Correspondence to Yi Li.

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Nature Climate Change thanks Kristine Buenafe, Angus Mitchell and Anthony Richardson for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Climate refugia overlapped with unconnected areas under different scenarios.

Climate refugia were defined as regions with low climate velocity (<33% tercile) and high species richness (>67% tercile). The refugia regions with positive (successful) and negative (disrupted) connectivity were annotated in purple and red, respectively (data from ref. 33). Basemap from the GSHHG (https://www.soest.hawaii.edu/pwessel/gshhg).

Extended Data Fig. 2 Reversibility of climate connectivity disruption and biodiversity in different strata before 2100.

a, The bivariate map of the reversible/irreversible zone versus their overlaps with species richness. The reversibility is defined based on the connectivity under low-to-high scenarios. Cells of species richness were split into tercile for each stratum (data from ref. 33). b, The percentage of species richness in each bivariate category. Basemap in a from the GSHHG (https://www.soest.hawaii.edu/pwessel/gshhg).

Extended Data Fig. 3 Proportion of threatened species in climate refugia.

This figure illustrates the distribution of threatened species across different overlapping levels with climate refugia. A species was considered threatened when its experienced temperature (equal to local temperature plus mean warming stress) exceeded its thermal tolerance limits. Threatened species are categorized based on the proportion of their distribution range that overlaps with these refugia: (1) Poorly refuged species (<2% of distribution in refugia); (2) Moderately refuged species (2–10% of distribution in refugia); (3) Well refuged species (>10% of distribution in refugia).

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Supplementary Information

Supplementary Figs. 1–7, Tables 1–9 and Text 1–6.

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Lin, Y., Chen, Y., Liu, X. et al. Climate-driven connectivity loss impedes species adaptation to warming in the deep ocean. Nat. Clim. Chang. 15, 315–320 (2025). https://doi.org/10.1038/s41558-025-02256-7

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