Vegetation resistance to increasing aridity when crossing thresholds depends on local environmental conditions in global drylands

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Abstract

The crossing of aridity thresholds triggers abrupt changes in multiple functional and structural ecosystem attributes across global drylands. While we understand the consequences associated with aridity thresholds, the key factors influencing dryland vegetation resistance when crossing them remain unclear. Here, we used field observations from 58 dryland sites across five continents and satellite remote sensing data (2000-2022) to show that plant richness, soil moisture dynamics and texture, and bare soil fraction are important variables contributing to vegetation resistance. Additionally, drought history (frequency and magnitude of past droughts) is important in interaction with plant richness and soil texture. Interestingly, plant species richness was negatively related to vegetation resistance, except in areas with higher drought history and in grasslands. Our results highlight that vegetation resistance depends on local environmental conditions. Enhancing our understanding of the factors important for vegetation resistance is an important step towards dryland conservation efforts and sustainable management strategies.

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Most cited references 110

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A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index

Sergio Vicente-Serrano, Santiago Beguería, Juan-I Lopez-Moreno (2010)

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Quantifying the evidence for biodiversity effects on ecosystem functioning and services.

Patricia Balvanera, Andrea B Pfisterer, Nina Buchmann … (2006)

Concern is growing about the consequences of biodiversity loss for ecosystem functioning, for the provision of ecosystem services, and for human well being. Experimental evidence for a relationship between biodiversity and ecosystem process rates is compelling, but the issue remains contentious. Here, we present the first rigorous quantitative assessment of this relationship through meta-analysis of experimental work spanning 50 years to June 2004. We analysed 446 measures of biodiversity effects (252 in grasslands), 319 of which involved primary producer manipulations or measurements. Our analyses show that: biodiversity effects are weaker if biodiversity manipulations are less well controlled; effects of biodiversity change on processes are weaker at the ecosystem compared with the community level and are negative at the population level; productivity-related effects decline with increasing number of trophic links between those elements manipulated and those measured; biodiversity effects on stability measures ('insurance' effects) are not stronger than biodiversity effects on performance measures. For those ecosystem services which could be assessed here, there is clear evidence that biodiversity has positive effects on most. Whilst such patterns should be further confirmed, a precautionary approach to biodiversity management would seem prudent in the meantime.

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Is Open Access

SoilGrids250m: Global gridded soil information based on machine learning

Tomislav Hengl, Jorge Mendes de Jesus, Gerard B. M. Heuvelink … (2017)

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods—random forest and gradient boosting and/or multinomial logistic regression—as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10–fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

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Author and article information

Contributors

Christin Abel: (View ORCID Profile)

Fernando T. Maestre: (View ORCID Profile)

Miguel Berdugo: (View ORCID Profile)

Abdulhakim M. Abdi: (View ORCID Profile)

Stephanie Horion: (View ORCID Profile)

Rasmus Fensholt: (View ORCID Profile)

Journal

Title: Communications Earth & Environment

Abbreviated Title: Commun Earth Environ

Publisher: Springer Science and Business Media LLC

ISSN (Electronic): 2662-4435

Publication date Created: December 2024

Publication date (Electronic): July 16 2024

Volume: 5

Issue: 1

Article

DOI: 10.1038/s43247-024-01546-w

SO-VID: d12ed032-bfd2-456d-a827-1b4f9ad664a3

License:

https://creativecommons.org/licenses/by/4.0

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