Which community attributes govern ecosystem functioning in drylands? A global assessment

Fernando T. Maestre & EPES-BIOCOM network

Departamento de Biología y GeologíaUniversidad Rey Juan Carlos

Móstoles, SPAIN

EPES-BIOCOM network

Argentina: Juan Gaitán, Donaldo Bran, Aníbal Prina & Eduardo Pucheta Australia: David Eldridge, Matt Tighe & James ValBrazil: Roberto Romao & Abel Conceicao Chile: Julio Gutiérrez, Claudia Barraza, Susana Gómez& Cristian Torres China: Deli Wang Ecuador: Carlos Iván Espinosa & Omar CabreraIsrael: Eli Zaady & Bertrand BoekenIran: Mohammad Janku Kenya: Vicente Polo & José P. VeigaMexico: Elisabeth Huber-Sannwald & Tulio Arredondo Morocco: Mchich Derak Peru: Jorge Monerris & David A. Ramírez Spain: José L. Quero, Miguel García-Gómez, Manuel Delgado-Baquerizo, Victoria Ochoa, Adrián Escudero, Santiago Soliveres, Pablo García-Palacios, Cristina Escolar, Miguel Berdugo, Beatriz Gozalo & Enrique Valencia Tunisia:  Zouhaier Noumi, Wahida Guiloufi & Mohammed Chiaeb USA : Matt Bowker, Becky Mou & Maria Miriti Venezuela: Adriana Florentino, Julio Blones, Abelardo Ospina & Rosa Mary Hernández

I. Introduction

Controlled experiments have suggested that biodiversity can potentially enhance the ability of ecosystems to maintain multiple functions and services. However, the effect of biodiversity on ecosystem multifunctionality has never been assessed globally.

Arid, semi-arid and dry-subhumid ecosystems (“drylands”) are a key terrestrial biome, covering 41% of Earth’s land surface and supporting over 38% of the global human. Despite their global extent and socio-ecological importance, the relationship between biodiversity and ecosystem multifunctionality has seldom been studied in these ecosystems.

We evaluated the effects of species richness, climate, and a range of other abiotic factors as drivers of multifunctionality in 224 dryland ecosystems from all continents except Antarctica.

Study area

Experimental design

The richness of perennial plant species was assessed using 80 in 1.5 x 1.5 m quadrats per plot.

224 30 m x 30 m plots surveyed, covering a wide range of ecosystems and climatic/soil type/land use conditions found in drylands.

To quantify multifunctionality, we calculated Z-scores of 14 functions related to C (organic C, -glucosidase, hexoses, pentoses, aromatic compounds and phenols), N (total N, NO3

--N, NH4

+-N, aminoacids, proteins, potential N transformation rate) and P (available inorganic P and phosphatase) cycling

Five replicated soil samples per microsite (bare ground and vegetated) and plot were obtained. Over 2600 soil samples were processed

Results. Richness effects on multifunctionality

Species richness was significantly and positively related to multifunctionality

1 2 3 4 5 6 7 8

Mul

tifun

ctio

nalit

y in

dex

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

A) Multifunctionality

C) Nitrogen cycling

1 2 3 4 5 6 7 8

C c

yclin

g in

dex

-2.0

-1.5

-1.0

-0.5

0.0

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B) Carbon cycling

Species richness (sqrt-transformed)

1 2 3 4 5 6 7 8

Nitr

ogen

cyc

ling

inde

x

-2

-1

0

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2

1 2 3 4 5 6 7 8

P c

yclin

g in

dex

-2

-1

0

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3

D) Phosphorus cycling

OLS, R2 = 0.030, P = 0.009 SAR, R2 = 0.022, P = 0.027

OLS, R2 = 0.018, P = 0.044SAR, R2 = 0.014, P = 0.082

OLS, R2 = 0.032, P = 0.008 SAR, R2 = 0.016, P = 0.061

OLS, R2 = 0.029, P = 0.011SAR, R2 = 0.022, P = 0.027

Results. Summary of multi-modelling

Species richness

Abiotic Climatic Geographic R2 AICc ΔAICc AICc wi

SL SA A1 A2 A3 A4 LA LO EL0.564 282.750 0 0.217

0.559 283.226 0.475 0.171

0.554 283.595 0.845 0.143

0.558 283.862 1.111 0.125

0.565 284.502 1.751 0.091

0.556 284.637 1.887 0.085

0.561 284.677 1.927 0.083

0.560 285.035 2.285 0.069

Best model without species richness: R2 = 0.539, AICc = 293.236, and ΔAICc = 10.486

Most parsimonious model without species richness: R2 = 0.515, AICc = 300.078, and ΔAICc = 17.328

SA = sand content , SL = slope, A1 = axis 1 of climatic PCA (mean annual precipitation, r = 0.910), A2 = axis 2 of climatic PCA (mean temperature of the driest quarter, r = 0.901), A3 = axis 3 of climatic PCA (precipitation in the driest quarter, r = 0.946), A4 = axis 4 of climatic PCA (annual mean temperature [r = 0.682] and mean temperature of the wettest quarter, r = 0.884), LA = lattitude, LO = longitude, and EL = elevation.

Results. Importance of richness and abiotic factors

SR SL SA A1 A2 A3 A4 EL

Impo

rtan

ce

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SR SL SA A1 A2 A3 A4 EL

Impo

rtan

ce

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Dependent variable

SR SL SA A1 A2 A3 A4 EL

Impo

rtan

ce

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0.6

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1.0

SR SL SA A1 A2 A3 A4 EL

Impo

rtan

ce

0.0

0.2

0.4

0.6

0.8

1.0

A) Multifunctionality B) Carbon cycling

C) Nitrogen cycling D) Phosphorus cycling

Species richness was significantly and positively related to multifunctionality

Conclusions

In spite of the importance of climate to ecosystem functioning, we found consistent, important effects of perennial plant species richness on multifunctionality.

Diversity effects may be particularly relevant for maintaining ecosystem functions linked to C and N cycling.

Mean annual temperature was the single most important predictor of multifunctionality. Climate change models predict increases in average annual temperature up to 4ºC in drylands. Such an increase will substantially reduce their ability to perform multiple functions related to C, N and P cycling simultaneously.

Our findings have important implications for the management of drylands, as they suggest that the preservation of plant species richness can potentially buffer negative effects of climate change and desertification on ecosystem multifunctionality.

Acknowledgements

This research was funded by the European Research Council under the European Community's Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement 242658 (BIOCOM).

http://www.escet.urjc.es/biodiversos/espa/investigacion/biocom/