Decoupling of soil nutrient cycles as a function of aridity in global drylands

ABSTRACT 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

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FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS LONG-TERM SOIL WARMING DECREASES MICROBIAL PHOSPHORUS UTILIZATION BY INCREASING ABIOTIC PHOSPHORUS SORPTION AND

PHOSPHORUS LOSSES Article Open access 16 February 2023 EXPANDING AGROFORESTRY CAN INCREASE NITRATE RETENTION AND MITIGATE THE GLOBAL IMPACT OF A LEAKY NITROGEN CYCLE IN CROPLANDS Article

Open access 28 December 2022 GLOBAL DISTRIBUTION AND DRIVERS OF RELATIVE CONTRIBUTIONS AMONG SOIL NITROGEN SOURCES TO TERRESTRIAL PLANTS Article Open access 30 July 2024 REFERENCES * Finzi,

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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. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Departamento de Sistemas Físicos, Químicos y Naturales, Universidad Pablo de Olavide, Carretera de Utrera,

kilómetro 1, 41013 Sevilla, Spain, Manuel Delgado-Baquerizo & Antonio Gallardo * Departamento de Biología y Geología, Area de Biodiversidad y Conservación, Escuela Superior de Ciencias

Experimentales y Tecnología, Universidad Rey Juan Carlos, Calle Tulipán Sin Número, 28933 Móstoles, Spain, Manuel Delgado-Baquerizo, Fernando T. Maestre, Jose Luis Quero, Victoria Ochoa, 

Beatriz Gozalo, Miguel García-Gómez, Santiago Soliveres, Miguel Berdugo, Enrique Valencia, Cristina Escolar, Adrián Escudero & Vicente Polo * School of Forestry, Northern Arizona

University, Flagstaff, 86011, Arizona, USA Matthew A. Bowker * Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, 80523, Colorado, USA Matthew D. Wallenstein &

 Pablo García-Palacios * Departamento de Ingeniería Forestal, Campus de Rabanales Universidad de Córdoba, Carretera de Madrid, kilómetro 396, 14071 Córdoba, Spain, Jose Luis Quero *

Department of Biology, Colorado State University, Fort Collins, 80523, Colorado, USA Pablo García-Palacios * División de Ciencias Ambientales, Instituto Potosino de Investigación Científica

y Tecnológica, San Luis Potosí, San Luis Potosí, 78210, Mexico , Tulio Arredondo & Elisabeth Huber-Sannwald * Departamento de Biología, Universidad de La Serena, La Serena 599, 1700000,

Chile, Claudia Barraza-Zepeda & Julio R. Gutiérrez * Instituto Nacional de Tecnología Agropecuaria, Estación Experimental San Carlos de Bariloche 277, Bariloche, Río Negro, 8400,

Argentina , Donaldo Bran & Juan Gaitán * Departamento de Biología Animal, Universidad de Jaen, Biología Vegetal y Ecología, 23071 Jaen, Spain, José Antonio Carreira * Université de Sfax,

Faculté des Sciences, Unité de Recherche Plant Diversity and Ecosystems in Arid Environments, Route de Sokra, kilomètre 3.5, Boîte Postale 802, 3018 Sfax, Tunisia , Mohamed Chaieb & 

Zouhaier Noumi * Departamento de Ciências Biológicas, Universidade Estadual de Feira de Santana, Avenida Transnordestina Sin Número, Bairro Novo Horizonte, Feira de Santana, 44036-900,

Brasil, Abel A. Conceição & Roberto Romão * Direction Régionale des Eaux et Forêts et de la Lutte Contre la Désertification du Rif, Avenue Mohamed 5, Boîte Postale 722, 93000 Tétouan,

Morocco , Mchich Derak * School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia , David J. Eldridge * Instituto de

Ecología, Universidad Técnica Particular de Loja, San Cayetano Alto, Marcelino Champagnat, Loja, 11-01-608, Ecuador , Carlos I. Espinosa & Elizabeth Guzman * Departamento de Biología,

Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de San Juan, Rivadavia, San Juan, J5402DCS, Argentina, M. Gabriel Gatica & Eduardo Pucheta * Departamento de

Ciencias Básicas, Laboratorio de Genómica y Biodiversidad, Universidad del Bío-Bío 447, Chillán, 3780000, Chile, Susana Gómez-González & Cristian Torres-Díaz * Instituto de Edafología,

Facultad de Agronomía, Universidad Central de Venezuela, Ciudad Universitaria, Caracas, 1051, Venezuela , Adriana Florentino * Facultad de Agronomía, Universidad Nacional de La Pampa,

Casilla de Correo 300, 6300 Santa Rosa, La Pampa, Argentina , Estela Hepper & Aníbal Prina * Laboratorio de Biogeoquímica, Centro de Agroecología Tropical, Universidad Experimental Simón

Rodríguez, Caracas, 47925, Venezuela , Rosa M. Hernández & Elizabeth Ramírez * Department of Range and Watershed Management, Faculty of Natural Resources and Environment, Ferdowsi

University of Mashhad, Azadi Square, Mashhad 91775–1363, Iran, Mohammad Jankju & Kamal Naseri * Institute of Grassland Science, Northeast Normal University and Key Laboratory of

Vegetation Ecology, Ministry of Education, Changchun, Jilin Province 130024, China , Jushan Liu & Deli Wang * Department of Biological Sciences, Northern Arizona University, PO Box 5640,

Flagstaff, Arizona 86011–5640, USA, Rebecca L. Mau * Department of Evolution, Ecology and Organismal Biology, Ohio State University, 318 West 12th Avenue, Columbus, Ohio 43210, USA, Maria

Miriti * Département des Sciences Biologiques, Université du Québec à Montréal Pavillon des Sciences Biologiques, 141 Président-Kennedy, Montréal, Québec H2X 3Y5, Canada, Jorge Monerris *

Production Systems and the Environment Sub-Program, International Potato Center. Apartado 1558, Lima 12, Peru , David A. Ramírez-Collantes * Department of Agronomy and Soil Science, School

of Environmental and Rural Science, University of New England, Armidale, New South Wales 2351, Australia, Matthew Tighe * Departamento de Química y Suelos, Decanato de Agronomía, Universidad

Centroccidental “Lisandro Alvarado”, Barquisimeto 3001, Venezuela, Duilio Torres * Department of Agronomy and Natural Resources, Institute of Plant Sciences, Agricultural Research

Organization, The Volcani Center, Bet Dagan 50250, Israel, Eugene D. Ungar * Office of Environment and Heritage, PO Box 363, Buronga, New South Wales 2739, Australia , James Val * Zoology

Department, National Museums of Kenya, Ngara Road, Nairobi, 78420-00500, Kenya, Wanyoike Wamiti * Department of Natural Resources and Agronomy, Agriculture Research Organization, Ministry of

Agriculture, Gilat Research Center, Mobile Post Negev 85280, Israel, Eli Zaady Authors * Manuel Delgado-Baquerizo View author publications You can also search for this author inPubMed 

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search for this author inPubMed Google Scholar CONTRIBUTIONS F.T.M., M.D.-B. and A.G. designed this study. F.T.M. coordinated all field and laboratory operations. Field data were collected

by all authors except A.E., A.G., B.G., E.V., M.B. and M.D.W. Laboratory analyses were done by V.O., A.G., M.B., M.D.-B., E.V. and B.G. Data analyses were done by M.D.-B. and M.A.B. The

paper was written by M.D.-B., F.T.M., M.D.W. and A.G., and the remaining authors contributed to the subsequent drafts. CORRESPONDING AUTHOR Correspondence to Manuel Delgado-Baquerizo. ETHICS

DECLARATIONS COMPETING INTERESTS 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. EXTENDED DATA FIGURE 4 A-PRIORI STRUCTURAL EQUATION MODEL USED IN THIS STUDY. 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 (_R_2) 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 (_R_2) 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 (_R_2 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 (_R_2 and _P_ values shown in the graph). POWERPOINT

SLIDES POWERPOINT SLIDE FOR FIG. 1 POWERPOINT SLIDE FOR FIG. 2 POWERPOINT SLIDE FOR FIG. 3 SOURCE DATA SOURCE DATA TO FIG. 1 SOURCE DATA TO FIG. 2 SOURCE DATA TO FIG. 3 RIGHTS AND

PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE 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). https://doi.org/10.1038/nature12670 Download citation * Received: 19 March 2013 * Accepted: 17 September 2013 * Published: 30

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