Abstract

Photoautotrophic surface communities forming biological soil crusts (biocrusts) are crucial for soil stability as well as water, nutrient and trace gas cycling at regional and global scales. Quantitative information on their global coverage and the environmental factors driving their distribution patterns, however, are not readily available. We use observations and environmental modelling to estimate the global distribution of biocrusts and their response to global change using future projected scenarios. We find that biocrusts currently covering approximately 12% of Earth’s terrestrial surface will decrease by about 25–40% within 65 years due to anthropogenically caused climate change and land-use intensification, responding far more drastically than vascular plants. Our results illustrate that current biocrust occurrence is mainly driven by a combination of precipitation, temperature and land management, and future changes are expected to be affected by land-use and climate change in similar proportion. The predicted loss of biocrusts may substantially reduce the microbial contribution to nitrogen cycling and enhance the emissions of soil dust, which affects the functioning of ecosystems as well as human health and should be considered in the modelling, mitigation and management of global change.

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References

  1. 1.

    Weber, B., Büdel, B. & Belnap, J. (eds) Biological Soil Crusts: An Organizing Principle in Drylands Vol. 226 (Springer, 2016).

  2. 2.

    Chamizo, S., Cantón, Y., Rodríguez-Caballero, E. & Domingo, F. Biocrusts positively affect the soil water balance in semiarid ecosystems. Ecohydrology 9, 1208–1221 (2016).

  3. 3.

    Pointing, S. B. & Belnap, J. Microbial colonization and controls in dryland systems. Nat. Rev. Microbiol. 10, 551–562 (2012).

  4. 4.

    Elbert, W. et al. Contribution of cryptogamic covers to the global cycles of carbon and nitrogen. Nat. Geosci. 5, 459–462 (2012).

  5. 5.

    Lenhart, K. et al. Nitrous oxide and methane emissions from cryptogamic covers. Glob. Change Biol. 21, 3889–3900 (2015).

  6. 6.

    Porada, P., Pöschl, U., Kleidon, A., Beer, C. & Weber, B. Estimating global nitrous oxide emissions by lichens and bryophytes with a process-based productivity model. Biogeosciences 14, 1593–1602 (2017).

  7. 7.

    Barger, N. N., Belnap, J., Ojima, D. S. & Mosier, A. NO gas loss from biologically crusted soils in Canyonlands National Park, Utah. Biogeochemistry 75, 373–391 (2005).

  8. 8.

    Weber, B. et al. Biological soil crusts accelerate the nitrogen cycle through large NO and HONO emissions in drylands. Proc. Natl. Acad. Sci. USA 112, 15384–15389 (2015).

  9. 9.

    Andreae, M. O. & Crutzen, P. J. Atmospheric aerosols: biogeochemical sources and role in atmospheric chemistry. Science 276, 1052–1058 (1997).

  10. 10.

    Kleffmann, J. et al. Daytime formation of nitrous acid: a major source of OH radicals in a forest. Geophys. Res. Lett. 32, L05818 (2005).

  11. 11.

    Noffke, N., Christian, D., Wacey, D. & Hazen, R. M. Microbially induced sedimentary structures recording an ancient ecosystem in the ca. 3.48 Billion-year-old Dresser Formation, Pilbara, Western Australia. Astrobiology 13, 1103–1124 (2013).

  12. 12.

    Lenton, T. M. & Daines, S. J. Matworld — the biogeochemical effects of early life on land. New Phytol. 215, 531–537 (2016).

  13. 13.

    Porada, P. et al. High potential for weathering and climate effects of non-vascular vegetation in the Late Ordovician. Nat. Commun. 7, 12113 (2016).

  14. 14.

    Holland, H. D. The oxygenation of the atmosphere and oceans. Philos. Trans. Roy. Soc. B 361, 903–915 (2006).

  15. 15.

    Crutzen, P. J. Geology of mankind. Nature 415, 23–23 (2002).

  16. 16.

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, Cambridge, 2013).

  17. 17.

    Schlesinger, W. H. et al. Biological feedbacks in global desertification. Science 247, 1043–1048 (1990).

  18. 18.

    Concostrina-Zubiri, L. et al. Biological soil crusts across disturbance-recovery scenarios: effect of grazing regime on community dynamics. Ecol. Appl. 24, 1863–1877 (2014).

  19. 19.

    Garcia-Pichel, F., Loza, V., Marusenko, Y., Mateo, P. & Potrafka, R. M. Temperature drives the continental-scale distribution of key microbes in topsoil communities. Science 340, 1574–1577 (2013).

  20. 20.

    Maestre, F. T. et al. Changes in biocrust cover drive carbon cycle responses to climate change in drylands. Glob. Change Biol. 19, 3835–3847 (2013).

  21. 21.

    Reed, S. C. et al. Changes to dryland rainfall result in rapid moss mortality and altered soil fertility. Nat. Clim. Change 2, 752–755 (2012).

  22. 22.

    Weber, B. et al. A new approach for mapping of biological soil crusts in semidesert areas with hyperspectral imagery. Remote Sens. Environ. 112, 2187–2201 (2008).

  23. 23.

    Rozenstein, O. & Karnieli, A. Identification and characterization of biological soil crusts in a sand dune desert environment across Israel–Egypt border using LWIR emittance spectroscopy. J. Arid Environ. 112, 75–86 (2015).

  24. 24.

    Rodriguez-Caballero, E., Escribano, P. & Canton, Y. Advanced image processing methods as a tool to map and quantify different types of biological soil crust. ISPRS J. Photogramm. Remote Sens. 90, 59–67 (2014).

  25. 25.

    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).

  26. 26.

    Gallien, L., Douzet, R., Pratte, S., Zimmermann, N. E. & Thuiller, W. Invasive species distribution models — how violating the equilibrium assumption can create new insights. Glob. Ecol. Biogeogr. 21, 1126–1136 (2012).

  27. 27.

    Bowker, M. A., Belnap, J. & Miller, M. E. Spatial modeling of biological soil crusts to support rangeland assessment and monitoring. Rangel. Ecol. Manag. 59, 519–529 (2006).

  28. 28.

    Fischer, T. & Subbotina, M. Climatic and soil texture threshold values for cryptogamic cover development: a meta analysis. Biologia 69, 1520–1530 (2014).

  29. 29.

    Büdel, B. et al. Improved appreciation of the functioning and importance of biological soil crusts in Europe: the Soil Crust International Project (SCIN). Biodivers. Conserv. 23, 1639–1658 (2014).

  30. 30.

    Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441 (2015).

  31. 31.

    Fowler, D. et al. Effects of global change during the 21st century on the nitrogen cycle. Atmos. Chem. Phys. 15, 13849–13893 (2015).

  32. 32.

    Ouyang, H. & Hu, C. Insight into climate change from the carbon exchange of biocrusts utilizing non-rainfall water. Sci. Rep. 7, 2573 (2017).

  33. 33.

    Davies-Barnard, T., Valdes, P. J., Singarayer, J. S., Wiltshire, A. J. & Jones, C. D. Quantifying the relative importance of land cover change from climate and land use in the representative concentration pathways. Glob. Biogeochem. Cycles 29, 842–853 (2015).

  34. 34.

    Delgado-Baquerizo, M. et al. Decoupling of soil nutrient cycles as a function of aridity in global drylands. Nature 502, 672–676 (2013).

  35. 35.

    Couradeau, E. et al. Bacteria increase arid-land soil surface temperature through the production of sunscreens. Nat. Commun. 7, 10373 (2016).

  36. 36.

    Chamizo, S. et al. Discriminating soil crust type, development stage and degree of disturbance in semiarid environments from their spectral characteristics. Eur. J. Soil Sci. 63, 42–53 (2012).

  37. 37.

    Field, J. P. et al. The ecology of dust. Front. Ecol. Environ. 8, 423–430 (2010).

  38. 38.

    Neff, J. C. et al. Increasing eolian dust deposition in the western United States linked to human activity. Nat. Geosci. 1, 189–195 (2008).

  39. 39.

    Stanelle, T., Bey, I., Raddatz, T., Reick, C. & Tegen, I. Anthropogenically induced changes in twentieth century mineral dust burden and the associated impact on radiative forcing. J. Geophys. Res. Atmos. 119, 13526–13546 (2014).

  40. 40.

    Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. & Zhao, M. Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on modis deep blue aerosol products. Rev. Geophys. 50, RG3005 (2012).

  41. 41.

    Mulitza, S. et al. Increase in African dust flux at the onset of commercial agriculture in the Sahel region. Nature 466, 226–228 (2010).

  42. 42.

    Bishop, J. K. B., Davis, R. E. & Sherman, J. T. Robotic Observations of dust storm enhancement of carbon biomass in the North Pacific. Science 298, 817–821 (2002).

  43. 43.

    Evan, A. T., Flamant, C., Fiedler, S. & Doherty, O. An analysis of aeolian dust in climate models. Geophys. Res. Lett. 41, 5996–6001 (2014).

  44. 44.

    Fröhlich-Nowoisky, J. et al. Bioaerosols in the Earth system: climate, health, and ecosystem interactions. Atmos. Res. 182, 346–376 (2016).

  45. 45.

    Després, V. R. et al. Primary biological aerosol particles in the atmosphere: a review. Tellus B 64, 15598 (2012).

  46. 46.

    Goudie, A. S. Desert dust and human health disorders. Environ. Int. 63, 101–113 (2014).

  47. 47.

    Morris, C. E. et al. Bioprecipitation: a feedback cycle linking earth history, ecosystem dynamics and land use through biological ice nucleators in the atmosphere. Glob. Change Biol. 20, 341–351 (2014).

  48. 48.

    Williams, L. et al. Biological soil crusts of arctic Svalbard and of Livingston Island, Antarctica. Polar Biol. 40, 399–411 (2016).

  49. 49.

    Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

  50. 50.

    Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).

  51. 51.

    Weber, B., Bowker, M., Zhang, Y. & Belnap, J. in Biological Soil Crusts: An Organizing Principle in Drylands (eds Weber, B. et al.) 479–498 (Springer, 2016).

  52. 52.

    Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).

  53. 53.

    Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).

  54. 54.

    Requena-Mullor, J. M. et al. Modeling spatial distribution of European badger in arid landscapes: an ecosystem functioning approach. Landsc. Ecol. 29, 843–855 (2014).

  55. 55.

    Roura-Pascual, N. et al. Geographical potential of Argentine ants (Linepithema humile Mayr) in the face of global climate change. Proc. R. Soc. Lond. B 271, 2527–2534 (2004).

  56. 56.

    Deblauwe, V., Barbier, N., Couteron, P., Lejeune, O. & Bogaert, J. The global biogeography of semi-arid periodic vegetation patterns. Glob. Ecol. Biogeogr. 17, 715–723 (2008).

  57. 57.

    Verbruggen, H. et al. Macroecology meets macroevolution: evolutionary niche dynamics in the seaweed Halimeda. Glob. Ecol. Biogeogr. 18, 393–405 (2009).

  58. 58.

    Lara-Romero, C. et al. Habitat selection by European badgers in Mediterranean semi-arid ecosystems. J. Arid Environ. 76, 43–48 (2012).

  59. 59.

    Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).

  60. 60.

    Moreno-Amat, E. et al. Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data. Ecol. Model. 312, 308–317 (2015).

  61. 61.

    Warren, D. L. & Seifert, S. N. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342 (2011).

  62. 62.

    Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography 33, 607–611 (2010).

  63. 63.

    Cabra-Rivas, I., Saldaña, A., Castro-Díez, P. & Gallien, L. A multi-scale approach to identify invasion drivers and invaders’ future dynamics. Biol. Invasions 18, 411–426 (2016).

  64. 64.

    Liu, C., Berry, P. M., Dawson, T. P. & Pearson, R. G. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28, 385–393 (2005).

  65. 65.

    Fawcett, T. An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861–874 (2006).

  66. 66.

    Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).

  67. 67.

    Eisenhauer, J. G. Regression through the Origin. Teach. Stat. 25, 76–80 (2003).

  68. 68.

    Bowker, M. A. et al. in Biological Soil Crusts: An Organizing Principle in Drylands (eds Weber, B. et al.) 173–197 (Springer, 2016).

  69. 69.

    Maestre, F. T. et al. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc. Natl. Acad. Sci. USA 112, 15684–15689 (2015).

  70. 70.

    Zelikova, T. J., Housman, D. C., Grote, E. E., Neher, D. A. & Belnap, J. Warming and increased precipitation frequency on the Colorado Plateau: Implications for biological soil crusts and soil processes. Plant. Soil 355, 265–282 (2012).

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Acknowledgements

This work was supported by the Max Planck Society, the Paul Crutzen Nobel Laureate Fellowship, and the German Research Foundation (DFG-FOR 1525: INUIT; WE2393/2; BU666/11-17). J.B. is supported by USGS Climate and Land Use and Ecosystems programs. The authors want to thank J.M.R. Mullor for his help during spatial distribution modelling, C. Reick for his support during the modelling and data acquisition process, and J. Kesselmeier for his helpful internal review of our study. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government. We would like to dedicate this publication to Professor Otto L. Lange.

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Affiliations

  1. Multiphase Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany

    • Emilio Rodriguez-Caballero
    • , Ulrich Pöschl
    •  & Bettina Weber
  2. Departamento de Agronomía, Universidad de Almería, Carretera Sacramento s/n, Almería, Spain

    • Emilio Rodriguez-Caballero
  3. US Geological Survey, Southwest Biological Science Center, Moab, Utah, USA

    • Jayne Belnap
  4. Plant Ecology and Systematics, Department of Biology, University of Kaiserslautern, Kaiserslautern, Germany

    • Burkhard Büdel
  5. Atmospheric Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany

    • Paul J. Crutzen
  6. Biogeochemistry Department, Max Planck Institute for Chemistry, Mainz, Germany

    • Meinrat O. Andreae
  7. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    • Meinrat O. Andreae

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Contributions

E.R.-C. and B.W. designed the study and analysed the data; E.R.-C. developed the models. J.B., B.B., M.O.A., P.C., U.P., B.W. and E.R.-C. contributed to interpreting the data. E.R-C., B.W. and U.P. wrote the paper.

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The authors declare no competing interests.

Corresponding authors

Correspondence to Emilio Rodriguez-Caballero or Bettina Weber.

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https://doi.org/10.1038/s41561-018-0072-1

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