The biogeochemical cycles of carbon (C), nitrogen (N) and phosphorus (P) are interlinked by primary production, respiration and decomposition in terrestrial ecosystems1. It has been suggested that the C, N and P cycles could become uncoupled under rapid climate change because of the different degrees of control exerted on the supply of these elements by biological and geochemical processes1,2,3,4,5. Climatic controls on biogeochemical cycles are particularly relevant in arid, semi-arid and dry sub-humid ecosystems (drylands) because their biological activity is mainly driven by water availability6,7,8. The increase in aridity predicted for the twenty-first century in many drylands worldwide9,10,11 may therefore threaten the balance between these cycles, differentially affecting the availability of essential nutrients12,13,14. Here we evaluate how aridity affects the balance between C, N and P in soils collected from 224 dryland sites from all continents except Antarctica. We find a negative effect of aridity on the concentration of soil organic C and total N, but a positive effect on the concentration of inorganic P. Aridity is negatively related to plant cover, which may favour the dominance of physical processes such as rock weathering, a major source of P to ecosystems, over biological processes that provide more C and N, such as litter decomposition12,13,14. Our findings suggest that any predicted increase in aridity with climate change will probably reduce the concentrations of N and C in global drylands, but increase that of P. These changes would uncouple the C, N and P cycles in drylands and could negatively affect the provision of key services provided by these ecosystems.
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We thank M. Scheffer, N. J. Gotelli and R. Bardgett for comments on previous versions of the manuscript, and all the technicians and colleagues who helped with the field surveys and laboratory analyses. This research is supported by the European Research Council (ERC) under the European Community's Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement no. 242658 (BIOCOM), and by the Ministry of Science and Innovation of the Spanish Government, grant no. CGL2010-21381. CYTED funded networking activities (EPES, Acción 407AC0323). M.D.-B. was supported by a PhD fellowship from the Pablo de Olavide University.
The authors declare no competing financial interests.
Extended data figures and tables
Extended Data Figure 1 Relationships between aridity and the concentration of inorganic P and the ratios of total N to inorganic P and organic C to inorganic P at our study sites.
Inorganic P, sum of Olsen inorganic P and HCl-P. The solid and dashed lines represent the fitted quadratic regressions and their 95% confidence intervals, respectively.
Extended Data Figure 2 Relationships between aridity and the concentration of carbohydrates (C), available N, available P and their ratios at our study sites.
Available N, sum of dissolved inorganic N and amino acids; available P, Olsen inorganic P. The solid and dashed lines represent the fitted quadratic regressions and their 95% confidence intervals, respectively.
Extended Data Figure 3 Relationships between aridity and the concentration of HCl-P fraction at our study sites.
We included in this model aridity (Ar; composite variable formed from Ar and Ar2), percentage of plant cover (Plant), percentage of clay (Clay), spatial position (Spatial; composite variable formed from distance from Equator (De) and longitude (Lon)), activity of phosphatase, organic matter component (OMC; first component from a PCA conducted with organic C (OC) and total N (TN)) and total P. We built our structural equation model by taking into account all these relationship, as explained in Methods. There are some differences between the a-priori model and the final model structures owing to removal of paths with coefficients close to zero (Fig. 2). Hexagons are composite variables30. Squares are observable variables.
Extended Data Figure 5 Global structural equation model, depicting the effects of aridity, clay percentage, plant cover and site position on the organic matter component, the inorganic-P concentration and phosphatase activity.
Spatial coordinates of the study sites are expressed in terms of distance from Equator (De) and longitude (Lon). The organic matter component (OMC) is the first component from a PCA conducted with organic C and total N. The inorganic-P concentration is the sum of Olsen inorganic P and HCl-P. Numbers adjacent to arrows are standardized path coefficients, analogous to relative regression weights, and indicative of the effect size of the relationship. Continuous and dashed arrows indicate positive and negative relationships, respectively. The width of arrows is proportional to the strength of path coefficients. The proportion of variance explained (R2) appears above every response variable in the model. Goodness-of-fit statistics for each model are shown in the lower right corner. There are some differences between the a-priori model and the final model structures owing to removal of paths with coefficients close to zero (see the a-priori model in Extended Data Fig. 4). Hexagons are composite variables30. Squares are observable variables. *P < 0.05, **P < 0.01, ***P < 0.001.
Extended Data Figure 6 Standardized total effects (direct plus indirect effects) derived from the structural equation modelling.
These include the effects of aridity, percentage of clay, plant cover, distance from Equator (De) and longitude (Lon) on the organic matter component (OMC, first component from a PCA conducted with organic C and total N), inorganic P (sum of Olsen inorganic P and HCl-P) and phosphatase activity (PhA).
Extended Data Figure 7 Global structural equation model, depicting the effects of aridity, clay percentage, plant cover and site position on the labile organic matter component, available-P concentration and phosphatase activity.
The labile organic matter component (labile OMC) is the first component from a PCA conducted with soil carbohydrates and the ratio of available N to the sum of dissolved inorganic N and amino acids. Available P is the Olsen inorganic P. Numbers adjacent to arrows are standardized path coefficients, analogous to relative regression weights, and indicative of the effect size of the relationship. Continuous and dashed arrows indicate positive and negative relationships, respectively. The width of arrows is proportional to the strength of path coefficients. The proportion of variance explained (R2) appears above every response variable in the model. Goodness-of-fit statistics for each model are shown in the lower right corner. There are some differences between the a-priori model and the final model structures owing to removal of paths with coefficients close to zero (see the a-priori model in Extended Data Fig. 4). Hexagons are composite variables30. Squares are observable variables. *P < 0.05, **P < 0.01, ***P < 0.001.
Extended Data Figure 8 Standardized total effects (direct plus indirect effects) derived from the structural equation modelling.
These include the effects of aridity, percentage of clay, plant cover, distance from Equator (De) and longitude (Lon) on the labile organic matter component (LOMC, first component from a PCA conducted with carbohydrates and available N), available P (Olsen inorganic P) and phosphatase activity (PhA).
Extended Data Figure 9 Relationships between total N and the potential net nitrification (upper graph) and mineralization rates (lower graph) measured at our study sites.
Air-dried soil samples were re-wetted to reach 80% of field water-holding capacity and incubated in the laboratory for 14 days at 30 °C (ref. 28). Potential net nitrification and ammonification rates were estimated as the difference between initial and final nitrate and ammonium concentrations28. The solid line denotes the quadratic model fitted to the data (R2 and P values shown in each panel).
Extended Data Figure 10 Relationships between the total N and microbial biomass N in a subset of 50 of our 224 sites.
All air-dried soil samples were adjusted to 55% of their water-holding capacity previous to the analyses of microbial biomass N. Microbial biomass N was determined using the fumigation–extraction method. Non-incubated and incubated soil subsamples were fumigated with chloroform for five days. Non-fumigated replicates were used as controls. Fumigated and non-fumigated samples were extracted with K2SO4 0.5 M in the ratio 1:5 and filtered through a 0.45-μm Millipore filter. Concentration of microbial biomass N was estimated as the difference between total N of fumigated and non-fumigated digested extracts28 and then divided by 0.54 (that is, by Kn, the fraction of biomass N extracted after the CHC13 treatment). The solid line denotes the quadratic model fitted to the data (R2 and P values shown in the graph).
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Delgado-Baquerizo, M., Maestre, F., Gallardo, A. et al. Decoupling of soil nutrient cycles as a function of aridity in global drylands. Nature 502, 672–676 (2013) doi:10.1038/nature12670
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