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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.

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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

References

  1. Middleton, N., Stringer, L., Goudie, A., & Thomas, D. The Forgotten Billion: MDG Achievement in the Drylands (UNDP United Nations Convention to Combat Desertification, 2011).

  2. Soong, J. L., Phillips, C. L., Ledna, C., Koven, C. D. & Torn, M. S. CMIP5 models predict rapid and deep soil warming over the 21st century. J. Geophys. Res. 125, e2019JG005266 (2020).

    Google Scholar 

  3. Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).

    Article  Google Scholar 

  4. Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314–318 (2020).

    Article  CAS  PubMed  Google Scholar 

  5. Schlaepfer, D. et al. Climate change reduces extent of temperate drylands and intensifies drought in deep soils. Nat. Commun. 8, 14196 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Jiang, H. in The End of Desertification? (eds Behnke, R. & Mortimore, M.) 513–536 (Springer, 2016).

  7. Gadzama, N. M. Attenuation of the effects of desertification through sustainable development of Great Green Wall in the Sahel of Africa. World J. Sci. Technol. Sustain. Dev. 14, 279–289 (2017).

    Article  Google Scholar 

  8. United Nations Decade on Restoration (accessed January 2021); https://www.decadeonrestoration.org/

  9. Ellison, D. et al. Trees, forests and water: cool insights for a hot world. Glob. Environ. Change 43, 51–61 (2017).

    Article  Google Scholar 

  10. Feng, X. et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Change 6, 1019–1022 (2016).

    Article  Google Scholar 

  11. Megdal, S. B. Transboundary groundwater resources: sustainable management and conflict resolution. Groundwater 55, 701–702 (2017).

    Article  CAS  Google Scholar 

  12. Jarvis, W.T. in Advances in Groundwater Governance (eds Villholth, K. G. et al.) 177–192 (CRC Press, 2017).

  13. Bastin, J.-F. et al. The extent of forest in dryland biomes. Science 356, 635–638 (2017).

    Article  CAS  PubMed  Google Scholar 

  14. Brandt, M. et al. An unexpectedly large count of trees in the West African Sahara and Sahel. Nature 587, 78–82 (2020).

    Article  PubMed  CAS  Google Scholar 

  15. Mbow, C. The Great Green Wall in the Sahel. Oxf. Res. Encycl. Clim. Sci. https://doi.org/10.1093/acrefore/9780190228620.013.559 (2017).

  16. Petrie, M. D. et al. Climate change may restrict dryland forest regeneration in the 21st century. Ecology 98, 1548–1559 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Liu, S., Jiang, D. & Lang, X. Mid-Holocene drylands: a multi-model analysis using Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) simulations. Holocene 29, 1425–1438 (2019).

    Article  Google Scholar 

  18. Delgado-Baquerizo, M. et al. Palaeoclimate explains a unique proportion of the global variation in soil bacterial communities. Nat. Ecol. Evol. 1, 1339–1347 (2017).

    Article  PubMed  Google Scholar 

  19. Delgado-Baquerizo, M. et al. Effects of climate legacies on above- and belowground community assembly. Glob. Change Biol. 24, 4330–4339 (2018).

    Article  Google Scholar 

  20. Hoelzmann, P. et al. Mid-Holocene land-surface conditions in northern Africa and the Arabian Peninsula: a data set for the analysis of biogeophysical feedbacks in the climate system. Glob. Biogeochem. Cycles 12, 35–51 (1998).

    Article  CAS  Google Scholar 

  21. Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Smettem, K. R. J., Waring, R. H., Callow, J. N., Wilson, M. & Mu, Q. Satellite-derived estimates of forest leaf area index in southwest Western Australia are not tightly coupled to interannual variations in rainfall: implications for groundwater decline in a drying climate. Glob. Change Biol. 19, 2401–2412 (2013).

    Article  Google Scholar 

  23. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Article  Google Scholar 

  24. Schmidt, R. et al. GRACE observations of changes in continental water storage. Glob. Planet. Change 50, 112–126 (2006).

    Article  Google Scholar 

  25. Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Friedl, M. A. et al. ISLSCP II MODIS (Collection 4) IGBP Land Cover, 2000–2001 (ORNL DAAC, Oak Ridge, TN, USA, 2010); https://doi.org/10.3334/ORNLDAAC/968

  27. Chen, M. et al. Global land use for 2015–2100 at 0.05° resolution under diverse socioeconomic and climate scenarios. Sci. Data 7, 320 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  28. National Centre for Earth Observation & Los, S.O. Global Vegetation Height Frequency Distributions from the ICESAT GLAS instrument produced as part of the National Centre for Earth Observation (NCEO) (NERC Earth Observation Data Centre, accessed 10 December 2020); http://catalogue.ceda.ac.uk/uuid/85e7d70a74244c73b71446940e05cde6

  29. Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).

    Article  CAS  PubMed  Google Scholar 

  30. Cherlet, M. et al. World Atlas of Desertification: Rethinking Land Degradation and Sustainable Land Management (Publications Office of the European Union, 2018).

  31. Ganopolski, A., Kubatzki, C., Claussen, M., Brovkin, V. & Petoukhov, V. The influence of vegetation-atmosphere-ocean interaction on climate during the mid-holocene. Science 280, 1916–1919 (1998).

    Article  CAS  PubMed  Google Scholar 

  32. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    Article  CAS  PubMed  Google Scholar 

  33. Scheffer, M. Tipping Points (Princeton Univ. Press, 2009).

  34. Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).

    Article  CAS  PubMed  Google Scholar 

  35. Runyan, C. W. & D’Odorico, P. Global Deforestation (Cambridge Univ. Press, 2016).

  36. Herzschuh, U. et al. Global taxonomically harmonized pollen data set for Late Quaternary with revised chronologies (LegacyPollen 1.0). PANGAEA https://doi.org/10.1594/PANGAEA.929773 (2021).

  37. Staal, A. et al. Hysteresis of tropical forests in the 21st century. Nat. Commun. 11, 4978 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Belsky, A. J. et al. The effects of trees on their physical, chemical and biological environments in a semi-arid savanna in Kenya. J. Appl. Ecol. 26, 1005–1024 (1989).

    Article  Google Scholar 

  39. Li, C. et al. Drivers and impacts of changes in China’s drylands. Nat. Rev. Earth Environ. 2, 858–873 (2021).

    Article  Google Scholar 

  40. Tatebe, H. et al. Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geosci. Model Dev. 12, 2727–2765 (2019).

    Article  CAS  Google Scholar 

  41. Trees, Forests and Land Use in Drylands: the First Global Assessment. Full Report (FAO, 2019).

  42. Diallo, H. A. in The Future of Drylands (eds Lee, C. & Schaaf, T.) 13–16 (Springer, 2008).

  43. A Spatial Analysis Approach to the Global Delineation of Dryland Areas of Relevance to the CBD Programme of Work on Dry and Subhumid Lands (UNEP-WCMC, 2014).

  44. Abatzoglou, J. et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Tachikawa, T., Hato, M., Kaku, M. & Iwasaki, A. Characteristics of ASTER GDEM version 2. IEEE Int. Geosci. Remote Sens. Symp. Proc. https://doi.org/10.1109/igarss.2011.6050017 (2011).

  46. Alibakhshi, S., Crowther, T. W. & Naimi, B. Land surface black-sky albedo at a fixed solar zenith angle and its relation to forest structure during peak growing season based on remote sensing data. Data Brief. 31, 105720 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Hamazaki, T. Advanced land observation satellite (ALOS). 5 Outline of ALOS satellite system. J. Jpn Soc. Photogramm. Remote Sens. 38, 25–26 (1999).

    Google Scholar 

  48. Mu, Q., Zhao, M., & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).https://doi.org/10.1016/j.rse.2011.02.019

  49. Zlotnicki, V., Bettadpur, S., Landerer, F. W. & Watkins, M. M. in Encyclopedia of Sustainability Science and Technology (ed. Meyers, R. A.) 4563–4584 (Springer, 2012).https://doi.org/10.1007/978-1-4419-0851-3_745

  50. Schepaschenko, D. et al. Comment on ‘The extent of forest in dryland biomes’. Science 358, 6362 (2017).

    Article  CAS  Google Scholar 

  51. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  CAS  PubMed  Google Scholar 

  52. Cheng, G., Han, J. & Lu, X. Remote sensing image scene classification: benchmark and state of the art. Proc. IEEE 105, 1865–1883 (2017).

    Article  Google Scholar 

  53. Xia, X., Xu, C. & Nan, B. Inception-v3 for flower classification. In Proc. 2nd International Conference on Image, Vision and Computing (ICIVC) 783–787 (IEEE, 2017).

  54. Fei-Fei, L., Deng, J. & Li, K. ImageNet: constructing a large-scale image database. J. Vis. 9, 1037 (2010).

    Article  Google Scholar 

  55. Guirado, E. et al. Tree cover estimation in global drylands from space using deep learning. Remote Sens. 12, 343 (2020).

    Article  Google Scholar 

  56. Legendre, P., Borcard, D. & Roberts, D. W. Variation partitioning involving orthogonal spatial eigenfunction submodels. Ecology 93, 1234–1240 (2012).

    Article  PubMed  Google Scholar 

  57. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).

    Article  Google Scholar 

  58. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  59. Lahouar, A. & Slama, J. B. H. Day-ahead load forecast using random forest and expert input selection. Energy Convers. Manage. 103, 1040–1051 (2015).

    Article  Google Scholar 

  60. Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14, 1137–1145 (1995).

    Google Scholar 

  61. Piñeiro, G., Perelman, S., Guerschman, J. P. & Paruelo, J. M. How to evaluate models: observed vs. predicted or predicted vs. observed? Ecol. Model. 216, 316–322 (2008).

    Article  Google Scholar 

  62. Friedl, M. & Sulla-Menashe, D. MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2019); https://doi.org/10.5067/MODIS/MCD12Q1.006

  63. The CMIP6 landscape. Nat. Clim. Change 9, 727 (2019).

  64. Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109.1, 213–241 (2011).

    Article  CAS  Google Scholar 

  65. Cao, X. et al. A taxonomically harmonized and temporally standardized fossil pollen dataset from Siberia covering the last 40 kyr. Earth Syst. Sci. Data 12, 119–135 (2020).

    Article  Google Scholar 

  66. Cao, X. et al. A late Quaternary pollen dataset from eastern continental Asia for vegetation and climate reconstructions: set up and evaluation. Rev. Palaeobot. Palynol. 194, 21–37 (2013).

    Article  Google Scholar 

  67. Li, C. et al. Harmonized chronologies of a global late Quaternary pollen dataset (LegacyAge 1.0). PANGAEA https://doi.org/10.1594/PANGAEA.933132 (2021).

  68. GlobalTreeSearch Online Database (Botanic Gardens Conservation International, UK, accessed 20 January 2022); https://tools.bgci.org/global_tree_search.php

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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 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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