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Biodiversity–productivity relationships are key to nature-based climate solutions

Nature Climate Change volume 11, pages 543–550 (2021)Cite this article

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Abstract

The global impacts of biodiversity loss and climate change are interlinked, but the feedbacks between them are rarely assessed. Areas with greater tree diversity tend to be more productive, providing a greater carbon sink, and biodiversity loss could reduce these natural carbon sinks. Here, we quantify how tree and shrub species richness could affect biomass production on biome, national and regional scales. We find that GHG mitigation could help maintain tree diversity and thereby avoid a 9–39% reduction in terrestrial primary productivity across different biomes, which could otherwise occur over the next 50 years. Countries that will incur the greatest economic damages from climate change stand to benefit the most from conservation of tree diversity and primary productivity, which contribute to climate change mitigation. Our results emphasize an opportunity for a triple win for climate, biodiversity and society, and highlight that these co-benefits should be the focus of reforestation programmes.

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Fig. 1: Schematic diagram of a possible pathway to biodiversity-based climate solutions.
Fig. 2: Biome-level projections in alleviating the loss of tree diversity from 2005 to the 2070s.
Fig. 3: Biome-level projections in the effect of a climate change mitigation to alleviate the loss of tree diversity-dependent ΔP from 2005 to the 2070s.
Fig. 4: Biome-level sums in the effect of a climate change mitigation to alleviate the loss of tree diversity-dependent ΔP from 2005 to the 2070s.
Fig. 5: Subregion-level projections in the effect of a climate change mitigation to alleviate the loss of tree diversity-dependent ΔP from 2005 to the 2070s.
Fig. 6: Country-level outcomes of a climate change mitigation to conserve tree diversity-dependent productivity.

Data availability

The source data underlying figures (Supplementary Data 1–6) are archived in the Dryad repository: https://doi.org/10.5061/dryad.vq83bk3s2.

Code availability

The code that supports the findings of this study is available from the corresponding author upon reasonable request.

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Acknowledgements

The paper was formed, analysed and written through workshops hosted by the National Center for Ecological Analysis and Synthesis (NCEAS), USA. F.I., J.C. and L.E.D. acknowledge support from the US National Science Foundation (NSF) Long-Term Ecological Research (LTER) Network Communications Office (DEB-1545288). A.S.M. and K.O. acknowledge support from the Ichimura New Technology Foundation. A.S.M., K.O., T.M. and H.O. were funded by the Environment Research and Technology Development Fund (ERTDF; JPMEERF15S11420) of the Environmental Restoration and Conservation Agency (ERCA) of Japan. A.S.M. was supported by the Grants-in-Aid for Scientific Research of the Japan Society for the Promotion of Science (JSPS; 15KK0022). F.I. acknowledges support from a US NSF CAREER award (DEB-1845334). A.G. was supported by the Liber Ero Chair in Biodiversity Conservation. M.L. was supported by the TULIP Laboratory of Excellence (ANR-10-LABX-41). T.M. and H.O. were funded by the ERTDF (JPMEERF20202002) of the ERCA. We thank Y. Kobayashi and R. Inoue (Yokohama National University) for help organizing the data. M. Maeda provided illustrations for the conceptual diagram.

Author information

Authors and Affiliations

  1. Faculty of Environment and Information Sciences, Yokohama National University, Hodogaya, Yokohama, Kanagawa, Japan

    Akira S. Mori & Kei-ichi Okada

  2. Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, CO, USA

    Laura E. Dee

  3. Department of Biology, Quebec Centre for Biodiversity Science, McGill University, Montreal, Quebec, Canada

    Andrew Gonzalez

  4. Department of Wildlife Biology, Forestry and Forest Products Research Institute, Forest Research and Management Organization, Tsukuba, Ibaraki, Japan

    Haruka Ohashi

  5. Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, MN, USA

    Jane Cowles & Forest Isbell

  6. California State University Los Angeles, Los Angeles, CA, USA

    Alexandra J. Wright

  7. Theoretical and Experimental Ecology Station, CNRS, Moulis, France

    Michel Loreau

  8. Ecology and Biodiversity Group, Department of Biology, Utrecht University, Utrecht, the Netherlands

    Yann Hautier

  9. Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK

    Tim Newbold

  10. Department of Forest Resources, University of Minnesota, St Paul, MN, USA

    Peter B. Reich

  11. Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia

    Peter B. Reich

  12. Center for International Partnerships and Research on Climate Change, Forestry and Forest Products Research Institute, Forest Research and Management Organization, Tsukuba, Ibaraki, Japan

    Tetsuya Matsui

  13. Institute of Industrial Science, The University of Tokyo, Meguro, Tokyo, Japan

    Wataru Takeuchi

  14. Faculty of Bioindustry, Tokyo University of Agriculture, Abashiri, Hokkaido, Japan

    Kei-ichi Okada

  15. School of Life Sciences, Technical University of Munich, Freising, Germany

    Rupert Seidl

  16. Berchtesgaden National Park, Berchtesgaden, Germany

    Rupert Seidl

Authors
  1. Akira S. Mori
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  2. Laura E. Dee
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Contributions

A.S.M. designed the study with critical inputs from F.I., R.S. and T.N. H.O. and T.M. analysed species distributions, and A.S.M. and K.O. contributed to the following analyses. J.C., A.G. and A.S.M. developed a conceptual diagram of biodiversity-dependent climate solutions. H.O. and T.N. contributed to developing the protocol of species distribution modelling. W.T. contributed to developing land-use data. A.S.M., L.E.D., A.G., R.S. and F.I. prepared drafts to have further discussions among all authors. A.J.W., J.C., Y.H., P.B.R. and M.L. provided substantial inputs on drafts and revisions of the paper.

Corresponding author

Correspondence to Akira S. Mori.

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The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Christian Ammer, Louis Iverson, Jacqueline Oehri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Maps showing the projected changes in tree diversity under the mitigation scenarios from 2005 to 2070s.

The proportional changes (%) in mean α-diversity (remaining species richness estimated at the fine grid-scale) are shown within each of the coarse grids (n = 32,670 grids). Results are shown for the five Shared Socioeconomic Pathways (SSPs) and the three Global Climate Models (GCMs).

Extended Data Fig. 2 Maps showing the projected changes in tree diversity under the baseline scenarios from 2005 to 2070s.

The proportional changes (%) in mean α-diversity (remaining species richness estimated at the fine grid-scale) are shown within each of the coarse grids (n = 32,670 grids). Results are shown for the five Shared Socioeconomic Pathways (SSPs) and the three Global Climate Models (GCMs).

Extended Data Fig. 3 Biome-level projections in the effects of a climate change mitigation to alleviate the loss of tree diversity (ΔSR) from 2005 to 2070s.

The effect sizes [inverse of log(mitigation/baseline)] of ΔSR were estimated based on mean α-diversity values within each of the coarse grids (the total number of the coarse grids = 32,670). The effect size is shown as a log ratio scale; zero corresponds to the true absence of the outcome. Positive and negative values of effect size indicate more and less effectiveness of mitigation policy, respectively (green and red arrow, respectively). The points and horizontal bars indicate means and their 95% confidence intervals, respectively. Results are shown for the five Shared Socioeconomic Pathways (SSPs: SSP1, sustainability; SSP2, middle-of-the-road; SSP3, regional rivalry; SSP4, inequality; SSP5, fossil-fuelled development) and the three Global Climate Models (GCMs). Results are also provided as Supplementary Data 2.

Extended Data Fig. 4 Biome-level projections in the effects of a climate change mitigation to alleviate the loss of tree diversity-dependent productivity (ΔP) from 2005 to 2070s.

The effect sizes [inverse of log(mitigation/baseline)] of ΔP were estimated at the local scale (at the 30 arcseconds; the total number of grids = ~ 115 million for each scenario). The effect size is shown as a log ratio scale; zero corresponds to the true absence of the outcome. Positive and negative values of effect size indicate more and less effectiveness of mitigation policy, respectively (green and red arrows, respectively). All points indicate mean effect size. Results are shown for the five Shared Socioeconomic Pathways (SSPs) and the three Global Climate Models (GCMs). See Supplementary Data 3 for the values of means and the associated 95% confidence intervals of the effect sizes.

Extended Data Fig. 5 Maps showing the effects of a climate change mitigation to alleviate the loss of tree diversity-dependent productivity (ΔP) from 2005 to 2070s.

The effect sizes [inverse of log(mitigation/baseline)] of ΔP were estimated at the local scale (at the 30 arcsec; the total number of fine grids ~ 115 million for each scenario). Positive and negative values of effect size indicate more and less effectiveness of mitigation policy, respectively. In these maps, means of the effect sizes within each of the coarse grids (n = 32,670 coarse grids) are shown. Results are shown for the five Shared Socioeconomic Pathways (SSPs) and the three Global Climate Models (GCMs). Files to produce these maps are provided as Supplementary Data 4.

Extended Data Fig. 6 Biome-level sums in alleviating the loss of tree diversity-dependent productivity (ΔP) from 2005 to 2070s.

Proportional reductions (%) in ΔP are summarised for each of 14 different biomes. Negative values indicate the relative magnitude of reduction in productivity loss by the implementation of additional climate mitigation policy compared to the estimates based on business-as-usual scenario. Results are shown for the five Shared Socioeconomic Pathways (SSPs) and the three Global Climate Models (GCMs).

Extended Data Fig. 7 Subregion-level projections in the effects of a climate change mitigation to alleviate the loss of tree diversity-dependent productivity (ΔP) from 2005 to 2070s.

The effect sizes [inverse of log(mitigation/baseline)] of ΔP were estimated at the local scale (at the 30 arcseconds; the total number of grids = ~ 115 million for each scenario). The effect size is shown as a log ratio scale; zero corresponds to the true absence of the outcome. Positive and negative values of effect size indicate more and less effectiveness of mitigation policy, respectively (green and red arrows, respectively). The points indicate means. Subregions are based on the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services; IPBES; https://www.ipbes.net/regional-assessments). Results are shown for the five Shared Socioeconomic Pathways (SSPs) and the three Global Climate Models (GCMs). See Supplementary Data 5 for the values of means and the associated 95% confidence intervals of the effect sizes.

Extended Data Fig. 8 The relationships between the country-level social cost of carbon (CSCC33) and the country-level conservation of tree diversity-dependent productivity.

The lines and shaded areas are the estimates based on a generalized additive mixed model and their 95% confidence intervals, respectively. Results are shown for the five Shared Socioeconomic Pathways (SSPs) and the three Global Climate Models (GCMs). See Supplementary Data 6 for the values of means and the associated 95% confidence intervals of the effect sizes [inverse of log(mitigation/baseline)] of climate change mitigation policy to alleviate the loss of tree diversity-dependent productivity for each country.

Extended Data Fig. 9 The relationships between the country-level social cost of carbon (CSCC33) and the country-level per-area conservation of tree diversity-dependent productivity.

The size of circles is proportional to the forested area of each country. The colors of circles correspond to the country-level sum of productivity conservation shown in Extended Data Figure 8 (see the color scale at the bottom right). Results are shown for the five Shared Socioeconomic Pathways (SSPs) and the three Global Climate Models (GCMs). Names of major and outlier countries are shown beside the symbols; ISO 3166-1 alpha-3 code is used to indicate countries.

Extended Data Fig. 10 Tree species analysed.

The maps showing the total number of tree species reported in each country75, and the number of the target species (those analysed in the present study) and the proportion (%) of these target species within the total number of species reported in each country75.

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Supplementary Methods, Tables 1 and 2, and Figs. 1–6.

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Mori, A.S., Dee, L.E., Gonzalez, A. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Chang. 11, 543–550 (2021). https://doi.org/10.1038/s41558-021-01062-1

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