# Hotspots of biogeochemical activity linked to aridity and plant traits across global drylands

David Eldridge, Jingyi Ding, Josh Dorrough, Manuel Delgado-Baquerizo, Osvaldo Sala, Nicolas Gross, Yoann Le Bagousse-Pinguet, Max Mallen-Cooper, Hugo Saiz, Sergio Asensio, et al.

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## To cite this version:

David Eldridge, Jingyi Ding, Josh Dorrough, Manuel Delgado-Baquerizo, Osvaldo Sala, et al. Hotspots of biogeochemical activity linked to aridity and plant traits across global drylands. Nature Plants, 2024, 10 (5), pp.760-770. 10.1038/s41477-024-01670-7. hal-04593756

HAL Id: hal-04593756

https://amu.hal.science/hal-04593756v1

Submitted on 18 Sep 2024

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1 **Hotspots of biogeochemical activity linked to aridity and plant traits across global drylands**
2

3

4 David J. Eldridge¹, Jingyi Ding², Josh Dorrough³,⁴, Manuel Delgado-Baquerizo⁵,
5 Osvaldo Sala⁶, Nicolas Gross⁷, Yoann Le Bagousse-Pinguet⁸, Max Mallen-Cooper⁹,
6 Hugo Saiz¹⁰, Sergio Asensio¹¹, Victoria Ochoa¹², Beatriz Gozalo¹¹, Emilio Guirado¹¹,
7 Miguel García-Gómez¹³, Enrique Valencia¹⁴, Jaime Martínez-Valderrama¹¹,¹⁵, César
8 Plaza¹², Mehdi Abedi¹⁶, Negar Ahmadian¹⁶, Rodrigo J. Ahumada¹⁷, Julio M.
9 Alcántara¹⁸, Fateh Amghar¹⁹, Luísa Azevedo²⁰, Farah Ben Salem²¹, Miguel
10 Berdugo¹⁴,²², Niels Blaum²³, Bazartseren Boldgiv²⁴, Matthew Bowker²⁵,²⁶, Donaldo
11 Bran²⁷, Chongfeng Bu²⁸,²⁹, Rafaella Canessa³⁰,³¹,³⁵, Andrea P. Castillo-Monroy³²,
12 Ignacio Castro³³, Patricio Castro-Quezada³⁴, Simone Cesarz³⁵,³⁶, Roukaya Chibani²¹,
13 Abel Augusto Conceição³⁷, Anthony Darrouzet-Nardi³⁸, Yvonne C. Davila³⁹, Balázs
14 Deák⁴⁰, Paloma Díaz-Martínez¹², David A. Donoso³², Andrew David Dougill⁴¹, Jorge
15 Durán⁴², Nico Eisenhauer³⁵,³⁶, Hamid Ejtehadi⁴³, Carlos Ivan Espinosa⁴⁴, Alex
16 Fajardo⁴⁵, Mohammad Farzam⁴⁶, Ana Foronda⁴⁷, Jorgelina Franzese⁴⁸, Lauchlan H.
17 Fraser⁴⁹, Juan Gaitán⁵⁰, Katja Geissler²³, Sofía Laura Gonzalez⁵¹, Elizabeth Gusman-
18 Montalvan⁴⁴, Rosa Mary Hernández³³, Norbert Hölzel⁵², Frederic Mendes Hughes³⁷,
19 Oswaldo Jadan³⁴, Anke Jentsch⁵³, Mengchen Ju²⁹, Kudzai F. Kaseke⁵⁴, Melanie
20 Köbel⁵⁵, Anika Lehmann⁵⁶, Pierre Liancourt³⁰, Anja Linstädter⁵⁷, Michelle A Louw⁵⁸,
21 Quanhui Ma⁵⁹, Mancha Mabaso⁶⁰, Gillian Maggs-Kölling⁶¹, Thulani P.
22 Makhalanyane⁶⁰, Oumarou Malam Issa⁶², Eugene Marais⁶¹, Mitchel McClaran⁶³,
23 Betty Mendoza⁶⁴, Vincent Mokoka⁶⁵, Juan P. Mora⁴⁵, Gerardo Moreno⁶⁶, Seth
24 Munson⁶⁷, Alice Nunes⁵⁵, Gabriel Oliva²⁷, Gastón R Oñatibia⁶⁸, Brooke Osborne⁶⁹,
25 Guadalupe Peter⁷⁰, Margerie Pierre⁷¹, Yolanda Pueyo⁷², R. Emiliano Quiroga¹⁷, Sasha
26 Reed⁷³, Ana Rey⁷⁴, Pedro Rey¹⁸, Víctor Manuel Reyes Gómez⁷⁵, Víctor Rolo⁶⁶,
27 Matthias C. Rillig⁷⁶, Peter C. le Roux⁵⁸, Jan Christian Ruppert³⁰, Ayman Salah⁷⁷,
28 Phokgedi Julius Sebei⁷⁸, Anarmaa Sharkhuu²⁴, Ilan Stavi⁷⁹, Colton Stephens⁴⁹,
29 Alberto L. Teixido¹⁴, Andrew David Thomas⁸⁰, Katja Tielbörger³⁰, Silvia Torres
30 Robles⁷⁰, Samantha Travers¹, Orsolya Valkó⁴⁰, Liesbeth van den Brink³⁰, Frederike
31 Velbert⁵², Andreas von Heßberg⁵³, Wanyoike Wamiti⁸¹, Deli Wang⁵⁹, Lixin Wang⁸²,
32 Glenda M. Wardle⁸³, Laura Yahdjian⁸⁴, Eli Zaady⁸⁵, Yuanming Zhang⁸⁶, Xiaobing
33 Zhou⁸⁶, Fernando T. Maestre⁸⁷

34 **Affiliations**

35 ¹Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, 2052, Australia

38 ²State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

40 ³Department of Planning and Environment, PO Box 656, Merimbula NSW 2548, Australia
42
43
⁴Fenner School of Environment & Society, Australian National University, Canberra,
2601, Australia

44
45
⁵Laboratorio de Biodiversidad y Funcionamiento Ecosistémico. Instituto de Recursos
Naturales y Agrobiología de Sevilla (IRNAS), CSIC, 41012, Sevilla, Spain

46
47
⁶Schools of Life Sciences, School of Sustainability, and Global Drylands Center,
Arizona State University, Tempe, AZ 85287

48
49
⁷Université Clermont Auvergne, INRAE, VetAgro Sup, Unité Mixte de Recherche
Ecosystème Prairial; Clermont-Ferrand, France

50
51
⁸Aix Marseille Univ, CNRS, Avignon Université, IRD, IMBE; Aix-en-Provence,
France

52
53
⁹Department of Forest Ecology and Management, Swedish University of Agricultural
Sciences (SLU), Umeå, Sweden

54
55
56
¹⁰ Departamento de Ciencias Agrarias y Medio Natural, Escuela Politécnica Superior,
Instituto Universitario de Investigación en Ciencias Ambientales de Aragón (IUCA),
Universidad de Zaragoza, Huesca, Spain.

57
58
¹¹Instituto Multidisciplinar para el Estudio del Medio “Ramón Margalef”, Universidad
de Alicante, Alicante, Spain

59
60
¹²Instituto de Ciencias Agrarias, Consejo Superior de Investigaciones Científicas,
Madrid, Spain

61
62
63
¹³Departamento de Ingeniería y Morfología del Terreno, Escuela Técnica Superior de
Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid,
Madrid, Spain

64
65
¹⁴Departmento de Biodiversidad, Ecología y Evolución, Facultad de Ciencias
Biológicas. Universidad Complutense de Madrid, 28040, Madrid, Spain

66
67
¹⁵Estación Experimental de Zonas Áridas (EEZA), CSIC, Campus UAL. Carretera de
Sacramento s/n 04120, La Cañada de San Urbano, Almería, Spain

68
69
¹⁶Department of Range Management, Faculty of Natural Resources and Marine
Sciences, Tarbiat Modares University, Noor, Iran

70
71
¹⁷Instituto Nacional de Tecnología Agropecuaria, Estación Experimental
Agropecuaria Catamarca, 4700 Catamarca, Argentina

72
73
¹⁸Instituto Interuniversitario de Investigación del Sistema Tierra de Andalucía,
Universidad de Jaén, E-23071 Jaén, Spain

74
75
76
¹⁹Laboratoire Biodiversité, Biotechnologie, Environnement et Développement
Durable (Biodev), Université M'hamed Bougara de Boumerdès, Avenue de
l'indépendance 35000 Boumerdès, Algérie

77
78
²⁰Departamento de Genética, Ecologia e Evolução, Universidade Federal de Minas
Gerais, Belo Horizonte, MG, 31270-901, Brazil
79

80

²¹Laboratory of Eremology and Combating Desertification (LR16IRA01), IRA,
Institut des Régions Arides Medenine, Tunisia

81

²²Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland.

82

83

²³Plant Ecology and Nature Conservation, University of Potsdam, Am Mühlenberg 3,
14476 Potsdam, Germany

84

85

86

²⁴Laboratory of Ecological and Evolutionary Synthesis, Department of Biology,
School of Arts and Sciences, National University of Mongolia, Ulaanbaatar 14201,
Mongolia.

87

²⁵School of Forestry, Northern Arizona University, Arizona, USA

88

89

²⁶Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff,
Arizona, USA

90

91

²⁷Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental
Agropecuaria Bariloche, Bariloche, Río Negro, Argentina

92

93

²⁸Institute of Soil and Water Conservation, Northwest A & F University, Yangling,
Shaanxi 712100, China

94

95

²⁹Institute of Soil and Water Conservation, Chinese Academy of Sciences and
Ministry of Water Resources, Yangling, Shaanxi, 712100, China

96

³⁰State Museum of Natural History Stuttgart, Rosenberg 1, 70191 Stuttgart, Germany

97

³¹Martin Luther University of Halle-Wittenberg, Halle (Saale), Germany

98

99

³²Grupo de Investigación en Ecología Evolutiva en los Trópicos-EETROP-
Universidad de las Américas, Quito, Ecuador

100

101

³³Universidad Simón Rodríguez. Instituto de Estudios Científicos y Tecnológicos
(IDECYT),

102

103

³⁴Grupo de Ecología Forestal y Agroecosistemas, Facultad de Ciencias
Agropecuarias, Carrera de Agronomía, Universidad de Cuenca, Ecuador

104

105

³⁵German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig;
Leipzig, Germany

106

³⁶Leipzig University, Institute of Biology; Leipzig, Germany

107

108

109

³⁷Departamento de Ciências Biológicas, Universidade Estadual de Feira de Santana,
Biológicas, Universidade Estadual de Feira de Santana, Feira de Santana, Bahia,
Brazil

110

³⁸Department of Biological Sciences, University of Texas at El Paso, Texas, USA

111

112

³⁹Faculty of Science, University of Technology Sydney, Sydney New South Wales
2007, Australia

113

114

⁴⁰HUN-REN 'Lendület' Seed Ecology Research Group, Institute of Ecology and
Botany, Centre for Ecological Research, Vácrátót, H-2163, Hungary
115 $^{41}$University of York, UK.

116 $^{42}$Misión Biológica de Galicia, Consejo Superior de Investigaciones Científicas,
117 Pontevedra, Spain

118 $^{43}$Faculty of Science, Ferdowsi University of Mashhad, Iran

119 $^{44}$Departamento de Ciencias Biológicas, Universidad Técnica Particular de Loja; Loja,
120 Ecuador.

121 $^{45}$Instituto de Investigación Interdisciplinaria (I3), Vicerrectoría Académica,
122 Universidad de Talca, Chile.

123 $^{46}$Department of Range and Watershed Management, Faculty of Natural Resources
124 and Environment, Ferdowsi University of Mashhad, Iran.

125 $^{47}$Veterinary Faculty, University of Zaragoza, Zaragoza, Spain

126 $^{48}$Investigaciones de Ecología en Ambientes Antropizados, Laboratorio Ecotono,
127 INIBIOMA (Universidad Nacional del Comahue, CONICET), Bariloche 8400, Río
128 Negro, Argentina.

129 $^{49}$Department of Natural Resource Science, Thompson Rivers University, Kamloops,
130 BC, V2C 0C8, Canada

131 $^{50}$Universidad Nacional de Luján-CONICET. Luján, Argentina.

132 $^{51}$Instituto de Investigaciones en Biodiversidad y Medioambiente (CONICET),
133 Universidad Nacional del Comahue, Argentina

134 $^{52}$Institute of Landscape Ecology, University of Münster, Heisenbergstr. 2, 48149
135 Münster, Germany

136 $^{53}$Disturbance Ecology and Vegetation Dynamics, Bayreuth Center of Ecology and
137 Environmental Research (BayCEER), University of Bayreuth, Universitaetsstrasse 30,
138 95440 Bayreuth, Germany

139 $^{54}$Earth Research Institute, University of California, Santa Barbara, CA 93106, USA

140 $^{55}$cE3c - Centre for Ecology, Evolution and Environmental Changes & CHANGE -
141 Global Change and Sustainability Institute, Faculdade de Ciências, Universidade de
142 Lisboa, 1749-016 Lisboa, Portugal

143 $^{56}$Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin,
144 Germany

145 $^{57}$University of Potsdam, Biodiversity Research / Systematic Botany, Potsdam,
146 Germany

147 $^{58}$Department of Plant and Soil Sciences, University of Pretoria, South Africa

148 $^{59}$Key Laboratory of Vegetation Ecology of the Ministry of Education, Jilin Songnen
149 Grassland Ecosystem National Observation and Research Station, Institute of
150 Grassland Science, Northeast Normal University, Changchun 130024, China
151  
152  
153  

$^{60}$Department of Biochemistry, Genetics and Microbiology, DSI/NRF SARChI in  
Marine Microbiomics, University of Pretoria, Hatfield, Lynnwood Road, Pretoria,  
South Africa, 0029

154  

$^{61}$Gobabeb - Namib Research Institute, Walvis Bay, Namibia

155  
156  

$^{62}$Institute of Ecology and Environmental Sciences of Paris,  
SU/IRD/CNRS/INRAE/UPEC, Bondy, France

157  
158  

$^{63}$School of Natural Resources and the Environment, University of Arizona, Tucson,  
AZ, USA

159  
160  

$^{64}$Departamento de Biología y Geología, Física y Química Inorgánica, Universidad  
Rey Juan Carlos; Móstoles, Spain

161  

$^{65}$Risk and Vulnerability Science Centre, University of Limpopo, South Africa

162  

$^{66}$INDEHESA, Forestry School, Universidad de Extremadura, Plasencia 10600, Spain

163  
164  

$^{67}$U.S. Geological Survey, Southwest Biological Science Center, Flagstaff, Arizona,  
USA

165  
166  
167  

$^{68}$Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura  
(IFEVA-CONICET), Cátedra de Ecología, Facultad de Agronomía, Universidad de  
Buenos Aires, C1418DSE Ciudad Autónoma de Buenos Aires, Argentina

168  

$^{69}$Department of Environment and Society, Utah State University, Moab, UT, USA

169  
170  
171  

$^{70}$Universidad Nacional de Río Negro, Sede Atlántica, Centro de Estudios  
Ambientales desde la NorPatagonia (CEANPa). Viedma, Río Negro, Argentina. -  
CONICET

172  

$^{71}$Normandie Universite, Unirouen, Inrae, Ecodiv, 76000 Rouen, France.

173  

$^{72}$Instituto Pirenaico de Ecología (IPE, CSIC), Zaragoza, Spain

174  

$^{73}$U.S. Geological Survey, Southwest Biological Science Center, Moab, Utah, USA

175  
176  

$^{74}$Museo Nacional de Ciencias Naturales, Consejo Superior de Investigaciones  
Científicas; Madrid, Spain

177  

$^{75}$Instituto de Ecología, A.C. Chihuahua, Chihuahua, Mexico

178  

$^{76}$Institute of Biology, Freie Universität Berlin, Berlin, Germany

179  

$^{77}$Al Quds University, Abu Dis, Palestine.

180  
181  

$^{78}$Mara Research Station, Limpopo Department of Agriculture and Rural  
Development, Makhado, 0920, South Africa

182  
183  

$^{79}$The Dead Sea and Arava Science Center, Yotvata, 88820, Israel, and Eilat Campus,  
Ben-Gurion University of the Negev, Eilat 88100, Israel

184  

$^{80}$Department of Geography and Earth Science, Aberystwyth University, Wales, UK.

185  
186  

$^{81}$Zoology Department, National Museums of Kenya, P.O. Box 40658-00100,  
Nairobi, Kenya.
187
188

⁸²Department of Earth Sciences, Indiana University Indianapolis (IUI), Indianapolis, Indiana 46202, USA

189
190

⁸³Desert Ecology Research Group, School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales 2006, Australia

191
192
193

⁸⁴Cátedra de Ecología, Facultad de Agronomía, Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA-CONICET); Ciudad Autónoma de Buenos Aires, Argentina

194
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196

⁸⁵Department of Natural Resources, Agricultural Research Organization, Institute of Plant Sciences, Gilat Research Center, Israel, and Kaye College of Education, Be'er Sheva, Israel

197
198

⁸⁶State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China.

199
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201

⁸⁷Environmental Sciences and Engineering, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia.
202 Abstract

Perennial plants create productive and biodiverse hotspots, known as fertile islands, beneath their canopies. These hotspots largely determine the structure and functioning of drylands worldwide. Despite their ubiquity, the factors controlling fertile islands under conditions of contrasting grazing by livestock, the most prevalent land use in drylands, remain virtually unknown. We evaluated the relative importance of grazing pressure and herbivore type, climate, and plant functional traits on 24 soil physical and chemical attributes that represent proxies of key ecosystem services related to decomposition, soil fertility, and soil and water conservation. To do this we conducted a standardized global survey of 288 plots at 88 sites in 25 countries worldwide. We show that aridity and plant traits are the major factors associated with the magnitude of plant effects on fertile islands in grazed drylands worldwide. Grazing pressure had little influence on the capacity of plants to support fertile islands. Taller and wider shrubs and grasses supported stronger island effects. Stable and functional soils tended to be linked to species-rich sites with taller plants. Together, our findings dispel the notion that grazing pressure or herbivore type are linked to the formation or intensification of fertile islands in drylands. Rather, our study suggests that changes in aridity, and processes that alter island identity and therefore plant traits, will have marked effects on how perennial plants support and maintain the functioning of drylands in a more arid and grazed world.

222

Keywords: carbon sequestration, drylands, decomposition, fertile patch, soil fertility, soil condition, soil health, soil stability
225 **Introduction**

226 Drylands are characterized by a sparse plant cover, with patches of perennial plants
227 nested within an ocean of unvegetated bare soil ¹,². These plant patches and the
228 enriched soil beneath their canopies, act as biogeochemical hotspots, critical for the
229 maintenance of plant and animal diversity, and essential functions and services related
230 to nutrient mineralisation and storage, and water regulation ¹,³,⁴. Dryland vegetation,
231 and the "fertile islands" they create, are predicted to be affected by livestock grazing,
232 the most pervasive land use in drylands ⁵. Overgrazing by livestock and wild (native)
233 herbivores is known to alter surface soils, suppress the infiltration of water, and
234 increase runoff water and sediment discharge ⁶,⁷, potentially intensifying the fertile
235 island effect by exacerbating the loss of resources from the interspaces and its
236 supplementation in nearby islands ⁸. Yet, there is little support for this notion, other
237 than studies showing that overgrazing leads to a greater relative effect of woody
238 island soils over interspace soils, but that severe overgrazing leads to total collapse ⁹.
239 Globally, there is little empirical support for the putative importance of grazing as a
240 causal agent of the fertile island effect ¹⁰,¹¹, particularly when considering the wide
241 range of plant types characterizing drylands, from grasses to shrubs and trees. This
242 makes it difficult to disentangle grazing effects from the inherent effects of those
243 plants that form the islands. This is an important knowledge gap, as predicted declines
244 in rainfall, changes in the structure of island plants, and forecasted increases of
245 grazing over the next century will likely place increasing pressure on drylands and
246 their perennial components, compromising their ability to sustain livestock, people,
247 and their cultures ¹².

248 Yet, despite the extensive body of knowledge dedicated to their study, the relative
249 importance of grazing, climate, and the traits of the focal island species on the
250 distribution and magnitude of fertile islands across global drylands remains virtually
251 unknown. To address this knowledge gap, we assess, for the first time, the relative
252 association between grazing, plant traits, climate and soil properties, and fertile
253 islands in grazed drylands worldwide. This improves our ability to predict the future
254 of dryland biodiversity and function, and can improve the management of perennial
255 vegetation, particularly as grasslands are likely to contract and woody dominated
256 systems increase in a drier and more heavily grazed world ⁸,¹³.

257 We examined the fertile island effect by comparing 24 soil physical, chemical
258 and functional attributes beneath the canopy of perennial vegetation compared with
259 their adjacent unvegetated interspaces across global drylands. The 24 attributes were
260 assembled into three synthetic functions that represent the capacity of soils to
261 mineralise organic matter (Decomposition), enhance fertility (Fertility), and conserve
262 water and maintain stability (Conservation, see Methods). We gathered data from 288
263 dryland sites across 25 countries on six continents (Fig. 1) to test the following two
264 contrasting hypotheses. First, we expected that the magnitude of the fertile island
265 effect would increase with increasing levels of both recent (standardised dung mass)
266 and long-term or historic (heuristic assessment; ungrazed to high) grazing pressure
267 (Hypothesis 1a). This prediction is based on the understanding that greater grazing
pressure will destabilise surface soils, mobilising sediment, seed, nutrients, and organic matter from unvegetated interspaces to plant patches, strengthening fertile islands ¹⁴,¹⁵. Additionally, livestock might be expected to have a greater effect than wild herbivores because they have not co-evolved with indigenous vegetation and therefore have more deleterious effects on both island plants and their soils ⁶, Hypothesis 1b). Alternatively, changes in climate and plant traits, factors that operate at much larger (regional and global) scales, could overwhelm the impacts of grazing, a factor that operates at the local scale, on fertile islands (Hypothesis 2a). More specifically, irrespective of grazing pressure, we would expect that plants would make a greater contribution to fertile islands in arid and hyper-arid ecosystems where soils are extremely bare and infertile compared with less arid ecosystems where the influence of plants would be relatively lower. For example, reduced rainfall and/or increased temperature would increase the harshness of the interspaces compared with the vegetated and more protected islands, thereby strengthening the fertile island effect. Plant effects might also be expected to vary among broad functional groups (tree vs shrub vs grass; Hypothesis 2b). These broad groups could have varying effects on soil biogeochemistry because of marked differences in shape, size, and structural complexity. Quantifying the contribution of grazing by different herbivores at different pressures, plant traits, climate, and soil properties on fertile islands allowed us to assess current and future impacts of grazing on ecosystem structure and functioning across global drylands, where woody vegetation is a predominant plant form ¹².

290

## 291 Results and Discussion

We found stronger associations among factors such as aridity and plant traits (Hypothesis 2) than factors such as grazing pressure (Hypothesis 1a) and herbivore identity (Hypothesis 1b) and the fertile island effect in drylands worldwide. This knowledge is key to contextualise the ecosystem consequences of increased livestock grazing pressure on the capacity of plants to create and maintain hotspots of biogeochemical activity.

Prior to exploring potential effects of grazing, plant traits, or environmental conditions, we examined the RII relationships of the 24 attributes distributed among the three synthetic functions. This exploration gives us a better understanding of how individual biogeochemical attributes and their three synthetic ecosystem functions might differ between islands and their interspaces (the fertile island effect). We found strong empirical evidence of a pervasive fertile island effect across all sites and continents and for 16 (67%) of the 24 attributes (Fig. 2). Our results are consistent with findings from empirical local studies revealing greater resource accumulation beneath perennial plant canopies for attributes as diverse as soil geochemistry ¹¹,¹³,¹⁶,¹⁷, soil physical properties ⁹, hydrology ¹⁸,¹⁹ and microbial community structure ⁴. Of all possible effects, the Decomposition function (which comprised C, N and P mineralisation), was the most strongly developed function within the islands (Fig. 2),
likely due to greater litter inputs<sup>4,20</sup>, microbial activity and plant biomass<sup>21</sup> beneath
perennial plant canopies<sup>22,23</sup>. The fertile island effect for the other functions was
mixed, with strong positive effects for C, and to a lesser extent P, but not for
micronutrients (Fig. 2). The fertile island effect for C and N was also greater in more
arid drylands. These findings reinforce the view that perennial plant patches are
hotspots of biological activity in drylands<sup>4</sup>, and this likely accounts for their potential
role as facilitators of protégé plant species through resource supplementation<sup>24</sup>.

We then sought to quantify the importance of potential associations among
measures of grazing and fertile islands. Using hierarchical linear mixed modelling
(see Methods) we found no consistent influence of grazing, either recent (standardized
grazing pressure) or long-term (ungrazed, low, medium, high) grazing pressure on the
mean (overall) fertile island effect (the average standardized value of all 24 attributes
shown in Table S1 in Supplementary Information). We also found a consistent, but
extremely weak negative effect of recent grazing pressure on Decomposition, contrary
to the results of global meta-analyses<sup>25</sup>. There were no significant effects of
increasing recent grazing pressure on either the Fertility or Conservation function
(Fig. 3a, Table S2). There were no significant effects of long-term (historic) grazing
pressure (ungrazed, low, medium, high) on any measures (Fig. S1, Table S2).

Of all effects, aridity was by far the strongest (Table S2), with a strong positive
effect on the Decomposition function, weak effects on the Fertility, but no effect on
the Conservation function (Fig. S3a, Table S2). Although the effects of island type
(tree, shrub, grass) were minor compared with the large aridity effect, we did identify
some trends. For example, there were consistent positive, though weak, fertile island
effects beneath shrubs, and to a lesser extent trees, irrespective of grazing pressure.
The only other noteworthy grazing-related effect was the negative interaction between
shrubs, and to a lesser extent trees, and mixed herbivores (Table S2).

Our results provide fresh insights into the links between grazing and fertile
islands, demonstrating that, across global drylands, grazing cannot be considered a
causal agent of the fertile island effect. Thus, placed in a global context, the local
influence of grazing on fertile islands is overshadowed by global environmental
variability. This result challenges the view of fertile islands and their formation,
which posits that islands are a biproduct of grazing<sup>11</sup>. This view has largely been
shaped by studies from the Chihuahuan Desert in the western United States where
increases in woody plant (generally shrub) density are linked to a dominance of
woody plant islands and ensuing desertification<sup>26</sup>. Undoubtedly, grazing-induced
disturbance can aggravate differences between perennial plants and their interspaces
in some situations by disturbing interspaces and intensifying the movement of
resources from interspace to island patches<sup>27</sup>. However, neither short- nor long-term
grazing pressure, nor herbivore type, were associated with the fertile island effect
under the conditions experienced across our extensive global dryland survey.

Given the importance of plant traits, a Random Forest algorithm was then used to
examine the degree to which a comprehensive suite of 15 functional traits of island
woody plant species explained differences in the fertile island effect for the three synthetic functions studied. These traits, which are related to plant size and structure, leaf characteristics, and the ability to respond to environmental stimuli (palatability, resprouting, deciduousness, allelopathy, see Methods) potentially influence the way nutrients are stored, mineralized, and made available to plants, and how soil and water are conserved beneath plant canopies <sup>28</sup>. Our trait data, which represent the most comprehensive dataset gathered to date across global drylands, were used to evaluate the relative importance of island plant structure. We used site-specific trait values rather than global averages, allowing us to account for potential differences in the morphology of island plants under different grazing pressure, herbivore type and environmental conditions. The extent to which different plant traits affected the three synthetic functions varied depending on the function considered (Fig. S4 in Supplementary Information). We found that the relative fertile effect for our three synthetic functions was generally greater when the islands were dominated by taller and wider plants, and to a lesser extent, by plants with larger leaves. Plant height was important for all functions, while the Decomposition function responded mostly to plant and leaf size, and the Fertility function was driven mostly by changes in plant size and leaf characteristics (Fig. S4 in Supplementary Information).

We then used Structural Equation Modelling <sup>29</sup> to explore potential associations among biotic and abiotic factors and the fertile island effect. Our *a priori* model (Fig. S5 in Supplementary Information) included environmental drivers (aridity, temperature, rainfall seasonality), soil (sand content, pH) and vegetation (perennial plant richness, relative cover of woody plants) properties, plant traits (the nine most important plant traits related to size, leaf characteristics, and inherent properties of woody plants such as the type of roots or whether they are allelopathic; identified using the Random Forest analyses, see Methods), and grazing (recent grazing, long-term grazing, and herbivore type). Grazing was included to test its potential indirect effects on the relative fertile island effect for the three soil functions evaluated. Our models revealed that decomposition was enhanced in areas of greater aridity (consistent with the hierarchical linear modelling, though not for carbon mineralisation, Fig. S2 Supplementary Information), more sandy soils, and where focal island species were more palatable (Fig. 4; Fig. S6 Supplementary Information). Fertility tended to be greater in sandy soils and with taller palatable species. Soils with larger values of the Conservation function (more stable, with greater water holding capacity) tended to be associated with taller island plants, potentially through mechanisms involving hydraulic lift <sup>30</sup>, and at plots supporting more perennial plant species (Fig. S6 Supplementary Information). A potential explanation for the link between the Conservation function, and both plant height and richness could relate to a greater leaf area <sup>31</sup> of larger island plants and therefore reduced surface evaporation <sup>32</sup>. After accounting for all direct and indirect pathways from both abiotic and biotic factors, our SEMs confirm that grazing had no effects on the three functions evaluated.
Among plant traits, plant size (height and canopy) was particularly important, with larger canopies associated with greater RII values of all three functions (but only for grasses), and taller grasses with greater RII values of the Decomposition function (Fig. S7 in Supplementary Information). Larger grasses are functionally more efficient at capturing resources<sup>33</sup> and enhancing hydrological functions<sup>34,35</sup> and may be a response to declining landscape productivity<sup>36</sup>. Larger plants may be avoided more by herbivores due to higher concentrations of tannins and secondary compounds<sup>37</sup>. Similarly, taller shrubs were associated with larger values of the Conservation and Fertility, but not Decomposition, functions (Fig. S7 in Supplementary Information). Taller shrubs would return more litter to the soil surface<sup>38</sup>, provide more varied habitat<sup>39</sup> and concentrate more resources excreted by canopy-resident invertebrates<sup>40</sup>, potentially accounting for greater fertility<sup>20</sup>. Finally, larger shrubs would support a greater density of understorey protégé species<sup>41</sup> and have a larger legacy effect on soils after death<sup>42</sup>. Interestingly, trees with larger canopies were associated with lower values of the Decomposition and Conservation functions (Fig. S7 in Supplementary Information). Large tree canopies are often preferred camping sites for herbivores<sup>39</sup>, leading to declines in soil structure<sup>43</sup>, and reductions in soil water holding capacity due to the proliferation of surface roots. Our results could suggest a waning of the fertile island effect under large trees.

Overall, our work provides solid evidence that factors such as climate and plant traits can overshadow the influence of factors such as grazing pressure on the capacity of plants to create fertile islands across global drylands. Our findings indicate that fertile islands will prevail in more arid environments regardless of grazing pressure and the composition of herbivores. In these environments, fertile islands sustain healthy and functional soils, moderate adverse environmental conditions, and provides refugia for plants and animals. Our results dispel the long-term assumption that increasing grazing pressure, either recent or longer term, or differences in herbivore type, can explain the magnitude of fertile island effects in drylands. Plant size, with taller and wider shrubs and grasses, supported stronger island effects. Stable and functional soils were also linked to species-rich sites with taller plants. The overwhelming importance of aridity and plant traits suggests that fertile islands may represent an autogenic response to drying and warming climates. These biogeochemical hotspots are likely to be more important as Earth's climate becomes hotter and drier.

428

## 429 Methods

### 430 Study area

We surveyed 288 plots at 88 sites in 25 countries on all continents except Antarctica (Algeria, Argentina, Australia, Botswana, Brazil, Canada, Chile, China, Ecuador, Hungary, Iran, Israel, Kazakhstan, Kenya, Mexico, Mongolia, Namibia, Niger, Palestine, Peru, Portugal, South Africa, Spain, Tunisia, and United States of America, Fig. 1). We used the sites described in ref. 12, but excluded 10 sites that did not have
sufficient trait data (see below). Site selection aimed to capture as much as possible of the wide variety of abiotic (climate, soil type, slope) and biotic (vegetation type, cover and species richness) features characterizing dryland ecosystems (e.g., grasslands, shrublands, savannas, open woodlands) found in drylands worldwide ¹²,⁴⁴. Elevation varied between 12 m and 2214 m a.s.l, and slope from 0º to 31.6º. The surveyed sites encompassed a wide variety of the representative vegetation physiognomies, including grasslands, shrublands, savannas and open woodlands (Fig. 1) found in drylands. Sites were surveyed between January 2016 and September 2019 ¹²,⁴⁴.

## Establishing and defining local grazing gradients

At each of the 88 sites, multiple 45 m x 45 m plots were sampled across a gradient in grazing pressure that was determined by local experts and compared with dung counts, livestock tracks, and livestock density data when available. Plots were selected from grazing gradients (distance to water measured using GIS) or specific paddocks that represented ungrazed, low, medium, or high levels of known grazing pressure. Thirty-five percent of sites had an ungrazed plot (e.g., an exclosure). All plots were established in areas representative of the vegetation and soil types found, so the impacts of grazing pressure could be assessed at each site without confounding factors associated with differences in climate, soil type or vegetation.

Field surveyors, who were all intimately associated with the long-term grazing history of these sites, characterised their plots using this four-scale heuristic category (ungrazed, low, moderate, high). Grazing pressure gradients were confirmed by measuring the mass of herbivore dung *in situ* ⁶. Dung production is known to be closely linked to animal activity, time spent grazing, and therefore grazing pressure ⁴⁵,⁴⁶, though more studies are needed in arid systems to validate these relationships. To measure dung, we collected the dung of different herbivores from within two 25 m² (where herbivores were large bodied, e.g., cattle, horses, large ungulates) or 1 m² (when herbivores were smaller bodied e.g., goats, sheep, rabbit, guanaco) quadrats ⁴⁴. Dung was oven dried and expressed as a mass per area. Where herbivores produced pellets, dung was counted from different herbivores, a subsample collected, and following oven drying, used to calculate the relationship between counts and oven-dry dung mass (Text S1 in Supplementary Information).

The mass of dung from each plot was then used to develop a continuous measure of grazing pressure. Dung mass represents the signature of grazing over periods of one to five years, depending on the presence of detritivores and litter decomposing invertebrates such as termites and dung beetles ⁴⁷. Dung decay rates will also likely vary across our sites due to differences in climatic conditions, the presence of exotic invertebrate decomposers, trampling and other factors ⁴⁸. Although these differences could potentially alter the amount of dung detected within a plot, this would have minimal impact on our measure of recent grazing pressure given the standardisation process we applied to dung mass across plots within a site.

For each plot, we standardised the value of the mass of dung of all herbivores within a plot by the maximum dung mass at that particular site (collection of plots).
478 Standardized values ranged from 0 to 1 (0.30 ± 0.01, mean ± SE) across the 88 sites.
479 A value of 1 for a particular plot indicates that this plot had the greatest grazing
480 pressure for that site and zero was ungrazed. This approach to standardising dung
481 mass within sites ensures the equivalence of sites that might have markedly different
482 levels of dung production, due to variation in site productivity, but have the same
483 level of grazing pressure (e.g., moderate grazing pressure). The method has also been
484 validated multiple times in grazing studies ⁴⁹,⁵⁰. Across our global study we recorded
485 29 different herbivore types, of which five were livestock (cattle, goat, sheep, donkey,
486 horse)¹².

487 Dung mass was a good proxy of grazing pressure using two approaches (see Text
488 S1 in Supplementary Information). First, there was a significant positive relationship
489 between dung mass and livestock density for a subset of sites in Iran, Australia, and
490 Argentina for which we had data on dung mass and animal density ¹². Second, we
491 performed a cluster analysis ⁵¹ to identify the optimum number of dung-based
492 clusters, based on dung mass, and found that this aligned well with the four heuristic
493 levels of grazing pressure ¹².

494 Third, we linked the four heuristic measures of long-term (decadal to multi-
495 decadal) grazing pressure to the presence of livestock tracks; semi-permanent features
496 created by livestock when they traverse the same path to and from water ⁵². The
497 density and size of these tracks is a useful indicator of the history of livestock grazing
498 ⁵³. We measured the width and depth of all livestock tracks crossing each of the 45 m
499 transects to derive a total cross-sectional area of tracks for each plot and expressed
500 this as the total track density and cross-sectional area per 100 m of transect (Fig. S8).
501 In summary, these three comprehensive measures of grazing intensity by herbivores
502 showed very similar trends, irrespective of whether we used dung mass as a measure
503 of recent grazing pressure, or the expert heuristic site classification as a measure of
504 long-term grazing pressure. This gives us a high degree of confidence that the
505 gradients we observed are true gradients in grazing pressure.
506

506 Vegetation and plant trait measurements

507 Field surveys followed a standardised sampling protocol ⁴⁴. Briefly, within each plot,
508 we located four 45 m transects oriented downslope, spaced 10 m apart across the
509 slope, for the vegetation surveys. Along each 45 m transect we assessed the cover of
510 perennial plants, by species, within 25 contiguous 1.5 m by 1.5 m quadrats. Perennial
511 plants were then recorded every 10 cm along this transect to obtain a measure of
512 perennial plant cover. Total plot-level plant richness was calculated as the total
513 number of unique perennial plant species found within at least one of the survey
514 methods (transects or quadrats) employed. In each site, we measured the height and
515 lateral spread of five randomly selected individuals of the dominant island plants.
516 Lateral spread (canopy width), a proxy of plant area, was assessed by measuring plant
517 diameter in two orthogonal directions through the plant centre. Fresh leaves were
518 collected from the same plants to assess an additional four plant traits in the
519 laboratory (leaf length, leaf area, and leaf carbon and nitrogen contents). These six
traits describe the size and leaf characteristic of the 162 perennial species in the vegetation patch that was dominated by trees, shrubs, or large perennial grasses, and which we assessed as potential fertile islands (see detailed measurements in Text S2 in Supplementary Information). Twenty-three percent of plots supported two co-dominant island species (i.e., two different tree, shrub, or grass species). For these plots, soil biogeochemical and plant trait data were weighted according to the mean cover of the co-dominant species within a plot.

We compiled information on eight additional plant traits (i.e., plant canopy shape, whether foliage reached the ground surface, N-fixation, deciduousness, allelopathy, palatability, resprouting, root type) using information from online plant trait databases such as BROT<sup>54</sup>, PLANTS<sup>55</sup>, Woody Plants Database (http://woodyplants.cals.cornell.edu) and TRY<sup>56</sup>. The eight categorical traits above were ranked numerically such that a larger value equated with greater function in terms of its own growth or its facilitatory effect on surrounding neighbours and conditions. This procedure is described in detail in Text S2 in Supplementary Information.

## Soil properties and sampling

Soils were sampled during the dry season. In each plot, five sampling points were randomly located in open areas devoid of perennial vascular plants (< 5% plant cover, hereafter 'open' microsite), and another five placed beneath the canopy of five randomly selected individuals of the dominant island plant (Text S3 in Supplementary Information). A composite sample of five 145 cm³ soil cores (0-7.5 cm depth) was collected from beneath each plant or bare area, bulked, and homogenized in the field. Soil samples were air-dried for 1 month, sieved (< 2 mm) and stored for physico-chemical analyses. The samples were then bulked to obtain one composite sample per plot for vegetated (island) and a separate composite sample for open areas. All analyses described here are for two composite samples per plot. We assessed soil pH (1:2.5 soil water suspension, sand content<sup>57</sup>, and the values of 24 soil ecological attributes that are linked to three ecosystem functions (Table S1 in Supplementary Information).

## Assessment of ecosystem functions

We calculated a relative interaction index (RII) and its 95% confidence interval<sup>58</sup> for the 24 ecological attributes as measures of the fertile island effect. A positive (or negative) value indicates a greater (or lesser) value of that attribute, respectively, in island soils. The RII is defined as the relative difference between attributes beneath the perennial plant islands and their open interspaces and was calculated as $RII = (X_i - X_o)/(X_i + X_o)$, where $X_i$ and $X_o$ represent the mean values of a given ecological attribute beneath a perennial plant patch (island) and in the open interspace, respectively. Values of the RII range from -1 to 1, with positive values indicating greater levels of a given attribute beneath the island and vice versa. Evidence of the fertile island effect (either positive or negative) is based on whether the 95%
561 confidence intervals (95% CIs), calculated using 'Rmisc' package in R⁵⁹ cross the
562 zero line.

563 We focussed on three proxies of function derived from the average RII of
564 different combination of the 24 soil attributes: 1) organic matter decomposition,
565 quantified using the activity of five soil extracellular enzymes related to the
566 degradation of organic matter [β-glucosidase, phosphatase, cellobiosidase, β-N-
567 acetylglucosaminidase and xylase], and measurements of soil carbon (hereafter
568 'Decomposition' (2) soil fertility, evaluated using multiple proxies of soil nutrient
569 availability and carbon (contents of dissolved organic and total N, NH₄⁺, NO₃⁻, total
570 P, Mn, K, Zn, Mg, Fe, Cu and soil C, hereafter 'Fertility'), and 3) resource
571 conservation (water regulation, using measures of soil water holding capacity, soil
572 porosity, stability of macro-aggregates >250 µm and mean weight diameter of soil
573 aggregates (hereafter 'Conservation'). Detailed measurements on these 24 soil
574 ecological attributes are described in Table S1 in Supplementary Information.

575 **Data compilation and statistical analysis**

576 Rainfall seasonality (coefficient of variation of 12 monthly rainfall totals) data were
577 extracted from the WorldClim Version 2.0 (http://www.worldclim.org/)⁶⁰ database,
578 which provides global climate data (0'30" × 0'30") for the 1970-2000 period. Aridity
579 was identified as precipitation/potential evapotranspiration and was derived from the
580 Global Aridity Index and Potential Evapotranspiration Climate Database v2 aridity
581 database (https://cgiarcsi.community/2019/01/24/global-aridity-index-and-potential-
582 evapotranspiration-climate-database-v2/)⁶¹, which includes global aridity data (0'30"
583 × 0'30") for the 1970-2000 period. Soil texture is a major determinant of water
584 holding capacity and pH is a major driver of plant and soil function in drylands⁶².
585 Sand content and pH data used in this study were obtained from samples taken from
586 the open areas (to ensure that their effects on the ecosystem functions measured are as
587 independent from those of organisms as possible). Relative woody cover was included
588 to account for different levels of woody plants so that this would not bias any results.
589 Standardized dung mass (dung mass in a plot/maximum dung mass within the site)
590 was used as a measure of recent grazing pressure.

591 **Statistical analyses**

592 We fitted a Bayesian hierarchical linear mixed model to evaluate whether the fertile
593 island effect differed (1) with increasing grazing pressure (continuous data:
594 standardized dung mass), 2) with long-term grazing pressure (categorical data:
595 ungrazed, low, moderate, high grazing), and 3) among herbivore types (categorical
596 data: sites dominated by either livestock, native, or mixed groups of native and
597 livestock). Our RII values were modelled with a Gaussian (normal) distribution, with
598 all individual ecosystem attributes (n = 24) estimated simultaneously in a single
599 model. Note that RII values are calculated at the plot level whereas grazing pressure is
600 calculated at the site level. The standardised response variable (RII) was modelled
601 hierarchically as a function of recent grazing pressure (standardised dung), long-term
602 grazing pressure (high, medium, low, ungrazed), herbivore type (livestock, native,
mixed), aridity, island type (tree, shrub, grass), and functional category (Decomposition, Fertility, Conservation). The model fitted individual ecosystem functional attributes as groups (random intercepts) with varying slopes associated with each of the main covariates (grazing and aridity). The model also included interactions between ecosystem function category and grazing, island type, and aridity to account for potential differences in the effects of each covariate within each ecosystem function category. We included site as a random intercept, accounting for the non-independence of data gathered from the same site.

We specified weakly informative normally distributed priors for the intercept and all regression coefficients (mean = 0 and scale = 2.5). Default priors were used for sigma (exponential, rate =1) and variance-covariance matrix of the varying intercepts and slope parameters (shape and scale of 1). Posterior simulations of model parameters were undertaken using the No-U-Turn Hamiltonian Monte Carlo sampler within Stan⁶³. Posterior distributions were estimated from four chains, each with 1000 iterations, after discarding the preliminary 1000 iterations. The convergence of models was assessed using visual diagnostics (autocorrelation, trace plots, and posterior predictive checks) and inspection of effective sample sizes (min. 1000) and r hat values (<1.01). Models were fitted using the package 'rstanarm'⁶⁴ within R⁵⁹. A hierarchical model provides several benefits over simple averaging of standardised indicators or multiple separate models⁶⁵: (i) simultaneous modelling of multiple attributes improves precision and estimates of uncertainty for each ecosystem function category; (ii) non-independence of multiple attributes within sites is explicitly accounted for; (iii) enables simultaneous estimation of overall fertile island effect for each ecosystem functional category and the individual soil attributes within these.

Structural Equation Modelling (SEM ²⁹) was employed to explore the direct and indirect impact of climate (aridity [ARID], rainfall seasonality [SEAS]), soil pH (pH), sand content (SAND), vegetation attributes (plot-level perennial plant cover [COV] and plant richness [RICH], plant height [HT], canopy width [WIDTH], shape [SHAPE], leaf length [LNGTH], leaf area [AREA], palatability [PALAT], resprouting [RESP], deciduousness [DECID], and allelopathy [ALLELO]), and grazing (standardised grazing pressure) on the fertile island effect (RII) after accounting for the effects of location (latitude, cosine longitude, sine longitude) across the globe. All explanatory variables were standardized (z-transformed) in the SEM analyses. The nine plant traits used in these analyses were selected from a potential pool of 15 potential traits using the significance of percentage increase in mean square error using Random Forest analyses (Fig. S3 in Supplementary Information). With these analyses we aimed to determine which traits are the most influential in describing the relative difference between islands and their interspaces (as measured with the RII) for each of the three synthetic functions (Decomposition, Fertility, Conservation). Random Forest is a robust approach when working with continuous and categorical variables. The 15 traits considered, which relate to plant size and structure, leaf characteristics, and ability to respond to environmental stimuli (palatability, resprouting, deciduousness, allelopathy) potentially influence: 1) how
646 nutrients are mineralized and made available to plants (Decomposition), 2) contribute
647 to soil nutrient (including carbon) pools (Fertility) and 3) how soil and water are
648 conserved (Conservation). Random forest analyses were conducted with the
649 rfPermute package 66.

650 Structural equation modelling allowed us to test hypothesized relationships
651 among predictors and the fertile island effect based on an *a priori* model that
652 constructs pathways among model terms based on *a priori* knowledge (Fig. S5 in
653 Supplementary Information). This model predicted that spatial location would affect
654 all the predictors such as climate, plant attributes (including site-level vegetation
655 attributes and plant traits), soil attributes and grazing. Climate would influence the
656 fertile island effect through its influence on soil properties, grazing, and plant
657 attributes. Grazing and soil properties would affect the fertile island effect directly, or
658 indirectly, by altering plant attributes. We ran the SEM on the RII of the three
659 functional categories (Decomposition, Fertility, Conservation, Fig. S4 in
660 Supplementary Information). To obtain the values for these three average functions,
661 we employed the concept of the multifunctionality index and averaged the values of
662 the RII for all individual attributes that comprised each function. Models with low $\chi^2$
663 and Root Mean Error of Approximation (RMSEA < 0.05), and high Goodness of Fit
664 Index (GFI) and $R^2$ were selected as the best fit model for our data. In addition, we
665 calculated the standardised total effects of each explanatory variable to show its total
666 effect. SEM analyses were performed using SPSS AMOS 22 (IBM, Chicago, IL,
667 USA) software.

668

669 **Data Availability**

670 The data used for this study will be made public within the Figshare repository upon
671 publication. https://doi.org/10.6084/m9 figshare.24873135.v1

672

673 **Acknowledgments**

674 Funding: This research was supported by the European Research Council [ERC grant
675 647038 (BIODESERT) awarded to F.T.M] and Generalitat Valenciana
676 (CIDEGENT/2018/041). D.E. was supported by the Hermon Slade Foundation
677 (HSF21040). J.DING was supported by the National Natural Science Foundation of
678 China Project (41991232) and the Fundamental Research Funds for the Central
679 Universities of China. M.D.B. acknowledges support from TED2021-130908B-
680 C41/AEI/10.13039/501100011033/Unión Europea Next Generation EU/PRTR and
681 the Spanish Ministry of Science and Innovation for the I + D + i project PID2020-
682 115813RA-I00 funded by MCIN/AEI/10.13039/501100011033. O.S. was supported
683 by US National Science Foundation (Grants DEB 1754106, 20-25166), and Y.L.B.-P.
684 by a Marie Sklodowska-Curie Actions Individual Fellowship (MSCA-1018 IF) within
685 the European Program Horizon 2020 (DRYFUN Project 656035). K.G. and N.B.
686 acknowledge support from the German Federal Ministry of Education and Research
687 (BMBF) SPACES projects OPTIMASS (FKZ: 01LL1302A) and ORYCS
688 (FKZ: FKZ01LL1804A). B.B. was supported by the Taylor Family-Asia Foundation
689 Endowed Chair in Ecology and Conservation Biology, and M.A.B. by funding from
690 the School of Forestry, Northern Arizona University. C.B. acknowledges funding
691 from the National Natural Science Foundation of China (41971131). D.B.
692 acknowledges support from the Hungarian Research, Development and Innovation
693 Office (NKFI KKP 144096) and A.F. support from ANID PIA/BASAL FB 210006
694 and the Millennium Science Initiative Program NCN2021-050. M.F. and H.E.
695 received funding from Ferdowsi University of Mashhad (Grant 39843). A.N. and
696 M.K. acknowledge support from FCT (CEECIND/02453/2018/CP1534/CT0001,
697 SFRH/BD/130274/2017, PTDC/ASP-SIL/7743/2020, UIDB/00329/2020), EEA
698 (10/CALL#5), AdaptForGrazing (PRR-C05-i03-I-000035) and LTsER Montado
699 platform (LTER_EU_PT_001) grants. O.V. acknowledges support from the
700 Hungarian Research, Development and Innovation Office (NKFI KKP 144096). L.W.
701 was supported by the US National Science Foundation (EAR 1554894). Z.Z. and X.Z.
702 were supported by the National Natural Science Foundation of China (U2003214).
703 H.S. is supported by a María Zambrano fellowship funded by the Ministry of
704 Universities and European Union-Next Generation plan. The use of any trade, firm, or
705 product names does not imply endorsement by any agency, institution or government.
706 Finally, we thank the many people who assisted with field work, and the landowners,
707 corporations and national bodies that allowed us access to their land.

708 Author contributions

709 F.T.M. designed and coordinated the field survey. D.J.E. and J.DING conceived the
710 study. J.D. undertook the Bayesian analyses, M.M-C. drafted the figures, and G.G.
711 produced the map. Laboratory analyses were performed by V.O., B.G., B.J.M., S.A.,
712 A.R., P.D.M., C.P., N.E., M.R., S.C. and M.D-B. The remaining authors collected and
713 managed field data collection. D.J.E. and J.DING wrote the draft manuscript in
714 collaboration with F.T.M. and O.S., and with contributions from all authors.

715 Competing interests

716 The authors declare no competing interests.

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<figure><img src="image_3.png" /><figcaption>**Figure 1.** Average function (mean relative interaction effect value across 24 soil attributes, see Methods) for the 288 plots at 88 sites across global drylands and examples of fertile islands at selected sites. The background map shows the distribution of aridity (1- [precipitation/potential evapotranspiration]) across global drylands.</figcaption></figure>

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<figure><img src="image_4.png" /><figcaption>**Figure 2.** The fertile island effect, as measured with the relative interaction effect (RII), beneath perennial dryland plants for the 24 soil attributes measured across three functions. Conserv = Conservation. Error bars are 95% CI and darker colours indicate significant positive effects.</figcaption></figure>

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<figure><img src="image_5.png" /><figcaption>**Figure 3.** Impacts of recent grazing and climate on the fertile island effect. (a) Relative interaction effect (RII) value surfaces for the three measures of ecosystem function (Decomposition, Fertility, Conservation) in relation to recent grazing pressure (standardized dung mass) and aridity, and mean (± 95% CI) predicted RII value for the three functions in relation to (b) long-term (historic) measure of herbivore grazing pressure (ungrazed, low, medium, high), and (c) herbivore type (livestock, native, mixed).</figcaption></figure>
<figure><img src="image_6.png" /></figure>

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**Figure 4.** Structural equation modelling assessing the direct and indirect effects of climate (aridity [ARID], rainfall seasonality [SEAS]), soil (pH and sand [SAND] content), plants (perennial cover [COV], perennial plant richness [RICH]), plant height [HT], plant shape [SHAPE], leaf area [AREA], leaf length [LNGTH], canopy width [WIDTH], palatability [PALAT], deciduousness [DECID], resprouting ability [RESP], and allelopathy [ALLEL]), and grazing (standardized grazing pressure) on the fertile island effect for soil decomposition (Decomposition), soil fertility (Fertility) and soil and water conservation (Conservation), after accounting for the effects of location (latitude, cosine longitude, sine longitude). Standardised path coefficients, adjacent to the arrows, are analogous to partial correlation coefficients, and indicative of the effect size of the relationship. Pathways are significantly negative (red unbroken line), significant positive (blue unbroken line) or mixed significant negative and significant positive (black unbroken lines). Non-significant pathways are not shown in the models. Model fit: (a) organic matter decomposition: $\chi^2 = 31.9$, df = 26, *P* = 0.20, *R*²=0.17, root mean error of approximation (RMSEA) < 0.001, Bollen-Stine = 0.40 (2000 bootstrap); (b) Fertility: $\chi^2 = 31.9$, df = 26, *P* = 0.20, *R*²=0.19, root mean error of approximation (RMSEA) < 0.001, Bollen-Stine = 0.40 (2000 bootstrap); (c) Conservation: $\chi^2 = 31.9$, df = 26, *P* = 0.20, *R*²=0.10, root mean error of approximation (RMSEA) < 0.001, Bollen-Stine = 0.40 (2000 bootstraps). N=288 for all analyses.