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Conserving Southeast Asian trees requires mitigating both climate and land-use change

Nature Sustainability volume 7, pages 1313–1323 (2024)Cite this article

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Abstract

The forests of Southeast Asia harbour high levels of species diversity, providing a plethora of ecosystem services. However, this biodiversity is threatened by both climate and land-use change, the impacts of which are poorly understood. We modelled 1,498 tree species distributions under four shared socioeconomic pathways (SSPs) with varying global change intensities, and classified species into 11 spatially associated groups. We found both global changes to cause severe losses in tree distributions. Only under SSP1–2.6, the sustainable pathway with low intensities in both global changes, were distribution losses mitigated. Unexpectedly, losses were overall greatest under intermediate climate change pathways SSP2–4.5 and SSP3–7.0 rather than under the most extreme pathway, SSP5–8.5. This was because, although climate-driven losses were greatest under SSP5–8.5, land-use-driven losses were much more extensive under SSP2–4.5 and SSP3–7.0 than under SSP5–8.5. Crucially, other than SSP1–2.6, each SSP led to worst-case scenarios for several species groups. Our findings highlight that to most effectively conserve Southeast Asian tree distributions, policymakers need to prioritize a sustainable pathway that mitigates both global changes.

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Fig. 1: The weighted mean changes in tree species distributions (n = 1,498) due to future global changes from 2019 to 2090 under four separate SSPs.
Fig. 2: The projected distribution of weighted tree species richness in 2090 and the loss of, and gain in, weighted richness from 2019 to 2090 due to future climate and land-use changes under four separate SSPs.
Fig. 3: The mean net changes (gain or loss) in tree species distributions (bars and points) among the identified 11 species groups due to future global changes from 2019 to 2090 under four separate SSPs.
Fig. 4: Threatened species outcomes due to changes in tree species distributions following the IUCN Red List criteria.

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Data availability

All data used in this study are publicly available, except for occurrence data from SING Herbarium, Singapore Botanical Gardens. Occurrence data were obtained from the Global Biodiversity Information Facility (https://www.gbif.org) and the SING Herbarium. Past and future climate variables were obtained from the WorldClim database (https://www.worldclim.org/). Past land-use variables were obtained from the HILDA+ Global Land Use Change database (https://doi.org/10.1594/PANGAEA.921846) and future land-use variables from the Land Use Harmonised dataset (https://luh.umd.edu/) and the GCAM–Demeter land-use dataset by Chen et al.23 (https://data.pnnl.gov/group/nodes/dataset/13192). Static soil variables were obtained from the SoilGrids database (https://soilgrids.org/). The results data used to make Figs. 1–4 are available via Figshare (https://doi.org/10.6084/m9.figshare.22547140) (ref. 51).

Code availability

The computer code used in the analyses is available on request from the corresponding author. The code used to make Figs. 1–4 is available via Figshare (https://doi.org/10.6084/m9.figshare.22547140) (ref. 51).

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Acknowledgements

We thank J. M. T. Miyajima for her feedback on the writing and figures, and Y. Zeng for his comments on the manuscript. We also thank L. R. Carrasco and J. Y. Lim for their comments on an earlier draft of this manuscript. We thank SING Herbarium, Singapore Botanical Gardens, for providing access to their plant occurrence records.

Author information

Author notes

  1. Sean E. H. Pang

    Present address: Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), Department of Biology, Aarhus University, Aarhus, Denmark

Authors and Affiliations

  1. Department of Biological Sciences, National University of Singapore, Singapore, Singapore

    Sean E. H. Pang, Ryan A. Chisholm & Edward L. Webb

  2. Environmental and Life Sciences, Faculty of Science, Universiti Brunei Darussalam, Gadong, Brunei Darussalam

    J. W. Ferry Slik

  3. Viikki Tropical Resources Institute, Department of Forest Sciences, University of Helsinki, Helsinki, Finland

    Edward L. Webb

  4. Helsinki Institute of Sustainability Science (HELSUS), Helsinki, Finland

    Edward L. Webb

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Contributions

S.E.H.P. and E.L.W. conceived the study. S.E.H.P. designed the methodology and carried out the data preparation, modelling of tree distributions and analyses of distribution outcomes. S.E.H.P. visualized the results and prepared all figures and tables with inputs from J.W.F.S, R.A.C. and E.L.W. All authors contributed to the interpretation of the results and writing of the final manuscript.

Corresponding authors

Correspondence to Sean E. H. Pang or Edward L. Webb.

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Nature Sustainability thanks Jiban Deb, Zhenzhong Zeng, Pieter Zuidema 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 Distribution of tree species richness across Southeast Asia for all 1498 species, 11 species groups, and for all species across our study period.

(a) The projected distribution of weighted tree species richness across SEA based on a dataset of 1498 species. Weighted richness was calculated by weighting the presence of each species based on the size of their respective species group—each species group contributed equally—before min-max scaling the values (1 represents the presence of all 1498 species). Lower data availability in Sundaland meant a smaller fraction of known tree species from that region could be modelled, as compared to Indo–Burma and subtropical East Asia. (b) The projected distribution of tree species richness for each group, which was scaled to 0-1 for consistency in values across panels. A total of 11 spatially distinct species groups were identified. Species groups were labelled based on expert knowledge and quantitative examinations of underlying environmental conditions. (a, b) Projections were under climate conditions for the year 1960 and an undisturbed land use scenario. (c) The projected distribution of weighted tree species richness in 1960 and 2019 and the loss of, and gain in, weighted richness from 1960 to 2019, due to past climate and land use changes. Projected gains followed a realistic migration rate. Within richness maps, blacked out areas indicated non-forested areas that persisted from 1960 to 2090, across SSPs. Colour gradients for weighted richness, and the loss of, and gain in, weighted richness, were scaled to 0–0.34 for clarity, with 0.34 representing the highest weighted richness value observed across grid cells, scenarios, and time periods. Climate-related changes in weighted richness was less distinct because climate change impacts among species (groups) were more spatially variable; for more detailed spatial trends see group-specific maps (Supplementary Figs. 7–17). Maps follow a transverse cylindrical equal-area projection with grid cells of 5 km by 5 km.

Extended Data Fig. 2 The mean net changes in tree species distributions due to past global changes from 1960 to 2019 for all 1498 tree species and among the identified 11 species groups.

The mean net changes in tree species distributions (bars and points) due to past global changes from 1960 to 2019 for (a) all 1498 tree species (weighted mean) and (b) among the identified 11 species groups: SEA tropical deciduous lowland (n = 51), Mainland SEA deciduous lowland (n = 210), Central Bornean tropical submontane (n = 56), Sundaic tropical coastal & lowland (n = 185), SEA coastal & mangrove (n = 183), Sundaic tropical lowland (n = 160), Indo–Burmese deciduous lowland & submontane (n = 177), East Asian subtropical submontane (n = 85), SEA temperate & tropical montane (n = 112), Southern Tibetan plateau (n = 150), and Sundaic tropical montane (n = 129). Error bars indicate the 95% confidence intervals.

Extended Data Fig. 3 The gross changes in tree species distributions due to combined global changes, climate change only, or land-use change only from 1960 to 2019 for all 1498 tree species and among the identified 11 species groups.

The gross changes—as gains (blue) and losses (yellow)—in tree species distributions due to combined global changes (bar), climate change only (circle), or land-use change only (asterisk) from 1960 to 2019 for (a) all 1498 tree species (weighted mean) and (b) among the identified 11 species groups (mean).

Extended Data Fig. 4 The mean gross changes in tree species distributions among the identified 11 species groups due to future global changes from 2019 to 2090 under four separate SSPs.

The mean gross changes—as gain (blue) or loss (yellow)—in tree species distributions among the identified 11 species groups due to future global changes from 2019 to 2090 under four separate SSPs: combined (bar), climate change only (circle), or land-use change only (asterisk).

Extended Data Fig. 5 The environmental conditions that characterized the representative distribution of each species group.

The environmental conditions that characterised the representative distribution (as seen in Extended Data Fig. 1b) of each species group. For each representative distribution, environmental conditions from the 95th percentile of pixels were extracted and further weighted based on the pixel’s value (that is, proportion of member species present) to derive the boxplots shown. Hence, the environmental characteristics described here represent the conditions of only the most suitable of sites (or where more of each cluster’s member species is present). The box plots show the median (central line) and interquartile range (IQR) (box), with whiskers extending to ±1.5 × IQR. Values here were used to inform the labels of each species group. Species groups were ordered based on their distribution changes across pathways (as is in the main text): C10 = SEA tropical & deciduous lowland (pixels = 10648); C11 = Mainland SEA deciduous lowland (pixels = 10695); C1 = Central Bornean tropical submontane (pixels = 10342); C4 = Sundaic tropical coastal & lowland (pixels = 10176); C3 = SEA coastal & mangrove (pixels = 10420); C5 = Sundaic tropical lowland (pixels = 10223); C9 = Indo–Burmese deciduous lowland & submontane (pixels = 11511); C7 = East Asian subtropical submontane (pixels = 10943); C8 = SEA temperate & tropical montane (pixels = 10470); C6 = Southern Tibetan Plateau (pixels = 10432); and C2 = Sundaic tropical montane (pixels = 3537). The number of pixels used to quantify the environmental conditions characterising representative distributions differed among clusters because of differences in the number of pixels with tied valued and/or a narrower representative distribution (that is, C2).

Extended Data Table 1 Paraphrased description of each SSP and an expanded interpretation of its outcomes for climate and land-use change within SEA
Full size table
Extended Data Table 2 Net changes in tree distributions for the identified 11 species groups under past global changes from 1960 to 2019
Full size table
Extended Data Table 3 Net changes in tree distributions for the identified 11 species groups under past global changes from 2019 to 2090
Full size table

Supplementary information

Supplementary Information

Supplementary Figs. 1–41, Tables 1 and 2, Sections 1–7 and references.

Reporting Summary

Supplementary Data 1

Species list of the final 1,498 tree species that were used in this study. The list includes information on their taxonomy, vernacular name, IUCN status, sample sizes, specific model performances and whether selected for the reanalysis using a subset of species with more sampling data and better model performing.

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Pang, S.E.H., Slik, J.W.F., Chisholm, R.A. et al. Conserving Southeast Asian trees requires mitigating both climate and land-use change. Nat Sustain 7, 1313–1323 (2024). https://doi.org/10.1038/s41893-024-01417-4

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