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Climate legacies drive the distribution and future restoration potential of dryland forests

Nature Plants volume 8, pages 879–886 (2022)Cite this article

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Abstract

Knowing the extent and environmental drivers of forests is key to successfully restore degraded ecosystems, and to mitigate climate change and desertification impacts using tree planting. Water availability is the main limiting factor for the development of forests in drylands, yet the importance of groundwater resources and palaeoclimate as drivers of their current distribution has been neglected. Here we report that mid-Holocene climates and aquifer trends are key predictors of the distribution of dryland forests worldwide. We also updated the global extent of dryland forests to 1,283 million hectares and showed that failing to consider past climates and aquifers has resulted in ignoring or misplacing up to 130 million hectares of forests in drylands. Our findings highlight the importance of a wetter past and well-preserved aquifers to explain the current distribution of dryland forests, and can guide restoration actions by avoiding unsuitable areas for tree establishment in a drier world.

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Fig. 1: Relative contribution of past climate (mid-Holocene, 6,000 years before the present), current climate and other environmental variables as drivers of the current distribution of dryland forests worldwide.
Fig. 2: The distribution of forests across global drylands considering climate legacies (changes in climate over the past 6,000 years) and aquifer trends.
Fig. 3: Comparison between the extent of forests calculated with and without considering the effects of climatic legacies and aquifer trends.
Fig. 4: Comparison between the calculated forest extent considering climate legacies and aquifers and the potential areas for restoring trees.

Data availability

All data generated or analysed during this study, which support the maps within this paper and other findings of this study, are available from Figshare at https://doi.org/10.6084/m9.figshare.13635212.

Code availability

The CNN-based code for the classification of forest/non-forest described in the Methods is freely available at https://github.com/EGuirado/CNN-Forest-Drylands

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Acknowledgements

We thank M. Berdugo for advice on the alternative stable states and hysteresis section, and B. M. Benito for advise on the biogeographical analysis of the paleopollen databases. This research was funded by the European Research Council (ERC Grant agreement 647038 (BIODESERT)) and Generalitat Valenciana (CIDEGENT/2018/041). E.G. was supported by the Consellería de Educación, Cultura y Deporte de la Generalitat Valenciana, and the European Social Fund (APOSTD/2021/188). D.A-S. was partially supported by DETECTOR (grant no. A-RNM-256-UGR18, Universidad de Granada/FEDER), LifeWatch SmartEcoMountains (grant no. LifeWatch-2019-10-UGR-01, Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), RESISTE (grant no. P18-RT-1927, Consejería de Economía, Conocimiento y Universidad from the Junta de Andalucía/FEDER) and EBV–ScaleUp project (funded by Google Earth Engine and the Group on Earth Observations). M.D-B. acknowledges support from the Spanish Ministry of Science and Innovation (for the I+D+i project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033), and from a project of the Fondo Europeo de Desarrollo Regional (FEDER) and the Consejería de Transformación Económica, Industria, Conocimiento y Universidades of the Junta de Andalucía (FEDER Andalucía 2014-2020 Objetivo temático ‘01 - Refuerzo de la investigación, el desarrollo tecnológico y la innovación’) associated with the research project P20_00879 (ANDABIOMA). S.T. was supported by DeepL-ISCO (grant no. A-TIC-458-UGR18, Ministerio de Ciencia e Innovación/FEDER), BigDDL-CET (grant no. P18-FR-4961, Proyectos I+D+i Junta de Andalucia 2018) and LifeWatch SmartEcoMountains.

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Authors and Affiliations

  1. Instituto Multidisciplinar para el Estudio del Medio ‘Ramón Margalef’, Universidad de Alicante, Alicante, Spain

    Emilio Guirado, Jaime Martínez-Valderrama & Fernando T. Maestre

  2. Laboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Sevilla, Spain

    Manuel Delgado-Baquerizo

  3. Unidad Asociada CSIC-UPO (BioFun), Universidad Pablo de Olavide, Sevilla, Spain

    Manuel Delgado-Baquerizo

  4. Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Siham Tabik

  5. iecolab. Inter-University Institute for Earth System Research, University of Granada, Granada, Spain

    Domingo Alcaraz-Segura

  6. Department of Botany, Faculty of Science, University of Granada, Granada, Spain

    Domingo Alcaraz-Segura

  7. Andalusian Center for the Assessment and Monitoring of Global Change -CAESCG-, University of Almeria, Almeria, Spain

    Domingo Alcaraz-Segura

  8. Departamento de Ecología, Universidad de Alicante, Alicante, Spain

    Fernando T. Maestre

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  1. Emilio Guirado
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Contributions

E.G., M.D.-B. and F.T.M. developed the original idea of the analyses presented in the manuscript. E.G and D.A.-S. developed the global survey. Artificial intelligence and remote sensing analyses were done by E.G. and S.T. Statistical modelling, mapping and data interpretations were done by E.G. and M.D-B. The manuscript was written by E.G., F.T.M. and M.D.-B., with contributions from all co-authors.

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Correspondence to Emilio Guirado.

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Nature Plants thanks Jean-François Bastin 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 Legacies in annual precipitation across global drylands.

The bar graphs show the sum of the annual precipitation legacy (precipitation from 6000 years before present minus current precipitation) in dam3 for each aridity level.

Extended Data Fig. 2

Trends (2002-2017) of aquifers located beneath forest areas in drylands.

Extended Data Fig. 3 Uncertainty map of the current extent of dryland forests.

Data based on the standard deviation of predictions obtained from the 5-fold cross-validation (see Methods section).

Extended Data Fig. 4

Comparison of our estimates of forest areas in drylands with recent estimates from the literature15,36.

Extended Data Fig. 5 Results of the analyses of pollen samples of mid-Holocene tree species from the LegacyPollen v.1 database42.

a) Spatial distribution of the studied mid-Holocene pollen samples (n=1121 at 119 sites) overlapping with current dryland forest areas (Fig. 2). b) Frequency histogram of the percentage of tree species pollen and its median represented as a dashed vertical line found in the samples per zone. c) Percentage of arboreal pollen found in the samples above 5% in pie charts (top) and Spearman-based correlation of the percentage of arboreal pollen with the aridity index (bottom). Significant in Europe (p-value < 2.2e-16), North America East (p-value = 2.3e-5) and North America West (p-value = 9.6e-12). Shades surrounding the lines represent 95% confidence interval.

Extended Data Fig. 6 Future changes (potential gains and losses) in dryland forest extent.

Data for the 2081-2100 time period according to Shared Socio-economic Pathways (SSP) and representative concentration pathways (RCP) scenarios 1–2.6, 3–7.0 and 5–8.5. Results represented in the map are from the SSP5-RCP8.5 scenarios (see Methods for details).

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Supplementary information

Supplementary Figs. 1–3, Tables 1–4 and Data 1 and 2.

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Guirado, E., Delgado-Baquerizo, M., Martínez-Valderrama, J. et al. Climate legacies drive the distribution and future restoration potential of dryland forests. Nat. Plants 8, 879–886 (2022). https://doi.org/10.1038/s41477-022-01198-8

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