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Running head: complementarity and species coexistence
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Title. Complementarity as a mechanism of coexistence between functional groups of
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grasses
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N. Gross1,2*, K.N. Suding3λ, S. Lavorel1,2Φ, C. Roumet4Ω
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Laboratoire d’ECologie Alpine (LECA), UMR 5553 CNRS – Université Joseph Fourier, BP
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53, F- 38041 Grenoble, France.
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Station Alpine Joseph Fourier (SAJF), UMS 2579 CNRS – Université Joseph Fourier BP 53,
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F-38041 Grenoble, France.
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CA, 92697-2525, USA.
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Mende, 34293 Montpellier, Cedex 5, France.
Department of Ecology and Evolutionary Biology – University of California Irvine, Irvine,
Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), UMR 5175, CNRS – 1919, Route de
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*Correspondence author: Nicolas Gross (tel. +33-4 76 63 54 38, fax. +33-4 76 51 46 73, e-
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mail nicolas.gross@ujf-grenoble.fr )
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λ
e-mail: ksuding@uci.edu
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Ω
e-mail: sandra.lavorel@ujf-grenoble.fr
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Φ
e-mail: catherine.roumet@cefe.cnrs.fr
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ABSTRACT
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Increasing functional diversity often leads to an increase in ecosystem productivity in the
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form of overyielding. While the mechanisms (i.e. complementarity or facilitation) that
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underlie overyielding provide strong insights into species coexistence and community
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assembly, they are rarely tested. In subalpine grasslands, traditional management through
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manuring and hay-making results in intermediate productivity that is associated with high
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functional diversity. This functional diversity results from the coexistence between
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conservative plant species (with slow growth rates, low specific leaf area) and exploitative
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species (with fast growth rates, high specific leaf area). We hypothesized that overyielding
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occurs among these two functional groups and tested whether complementarity or facilitation
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can explain overyielding. Using three perennial grass species per functional group, we
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compared single- and mixed functional group mesocosms at low and intermediate level of
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fertility by manipulating fertilization to test the occurrence of overyielding. Additionally we
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measured the outcomes of biotic interactions among these two functional groups by
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manipulating plant density. After two growing seasons, we found evidence of overyielding
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under intermediate level of fertility. Overyielding was associated with a reduction of
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competition intensity when both functional groups were grown together. We suggest that
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complementarity, as evidenced by a decrease in competition intensity, rather than facilitation
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explains the observed overyielding. Indeed, we found evidence for complementary for light
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and modification of nutrient use as possible mechanisms for the overyielding. These results
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suggest that complementarity between functional groups might be an important mechanism
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enhancing functional diversity, particularly in harsh environments at intermediate rather than
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low fertility.
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Key words: overyielding, functional groups, biotic interactions, complementarity,
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fertilization, grasses, dominant species, subalpine grasslands
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INTRODUCTION
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In the past decade, many experiments have shown that increasing functional diversity
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can lead to an increase in ecosystem productivity, usually termed overyielding (Tilman et al.
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1997; Hector et al. 1999; see Hooper et al. 2005 for review). In addition to sampling effects
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(Huston 1997; Loreau 1998), overyielding can be caused by increasing functional
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complementarity and/or facilitation (Hooper et al. 2005). If species are able to use different
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resources, or if they can use the same resource but at different times or in different locations,
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then complementarity can increase overall resource utilization (Berendse 1982; Sala et al.
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1989; Naeem et al. 1994). Similarly, if some species ameliorate harsh conditions and increase
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resource availability for other groups of species, then facilitation can enhance ecosystem
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productivity (Mulder et al. 2001; Hooper & Dukes 2004).
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While biodiversity experiments have allowed rapid progress in our understanding on
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the role of functional diversity in community structure (Fargione et al. 2003) and ecosystem
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functioning (Tilman et al. 1997), results from these experiments have been strongly debated
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(Huston 1997; Loreau et al. 2001; Hooper et al. 2005). Most cases of overyielding have been
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related to effects of one particular plant functional group, nitrogen-fixers (e.g. Tilman et al.
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1997; Hooper 1998; Hector et al. 1999). It is unclear whether overyielding may also occur
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between other functional groups (but see van Ruijven & Berendse 2003; 2005). Furthermore,
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although facilitation and complementarity are the most likely mechanisms contributing to
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overyielding, the design of most biodiversity experiments is not suited to properly test which
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of these mechanisms is the primary driver of overyielding (Huston 1997; Hooper et al. 2005).
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Finally, biodiversity experiments are not designed to test how biodiversity effects change
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along fertility gradients. For instance, it is unclear how nutrient availability affects
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mechanisms of overyielding (Fridley 2002; 2003).
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Plant diversity often shows a hump-backed response to productivity (Mittelbach et al.
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2001) with decreased diversity associated with increased productivity in benign environments
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(Rajaniemi 2003) and an opposite relation in harsh environments (Gross et al. 2000; Suding et
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al. 2005). This typically is the case in European subalpine grasslands characterized by an
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intermediate productivity where traditional management combining fertilization and mowing
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has increased productivity and species or functional diversity (Tasser & Tappeiner 2002;
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Quétier et al. 2007). This high functional diversity which characterized fertilized subalpine
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hay meadows results from the coexistence between conservative species (characterized by a
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slow growth rate and low specific leaf area; Diaz et al. 2004; Wright et al. 2004) and
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exploitative species (characterized by a fast growth rate and high specific leaf area) (Quétier
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et al. 2007).
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Although conceptual competition models predict that conservative species are
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excluded from more fertile sites by competition with exploitative species (Grime 1977; Wedin
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& Tilman 1993), fertilization may promote coexistence of these two functional groups by two
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distinct mechanisms in subalpine grasslands. Fertilization may increase the facilitative effect
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of vegetation by increasing the size of plants (Mulder et al. 2001; Callaway et al. 2002).
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Additionally, fertilization may limit competition for soil resources (Wedin & Tilman 1993)
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and promote complementarity for light (Fridley 2002; 2003; Kahmen et al. 2006).
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In this study, we used a pot experiment to test whether overyielding occurred between
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conservative and exploitive grasses that coexist at intermediate level of fertility in fertilized
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subalpine hay meadows. We hypothesize that increasing fertility promotes overyielding in
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this system by (1) increasing facilitation through increased biomass or by (2) promoting
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complementarity in resource use.
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METHODS
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Study site - The experiment was located at the experimental garden of the Station
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Alpine Joseph Fourier, Lautaret Pass, central French Alps (Villar d’Arêne, 45.04°N, 6.34°E,
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elevation 2100 m). The climate is subalpine with a pronounced continental influence. Mean
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annual precipitation is 956 mm and the mean monthly temperatures range between -7.4°C in
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February and 19.5°C in July. The growing season starts after snowmelt, between mid-April
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and early May, and finishes at the end of September.
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Species and functional group definition - We studied two functional groups,
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conservative and exploitative species. In fertilized hay meadows at the study site conservative
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species represent 45 ± 12% and exploitative species represent 55 ±17% of total biomass. In
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unfertilized grasslands conservative species dominate, with 80±8% total biomass (data from
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Quétier et al. 2007). In this study we focused on grass species which make up from 50 % to
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80 % of total cover of subalpine grasslands at our study site (Gross et al. 2007). We selected
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three conservative and three exploitative perennial grass species, on the basis of their specific
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leaf area (SLA) and relative growth rate (RGR) measured under optimal (no resource
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limitation) conditions (see Gross et al. 2007) (Appendix S2). Exploitative species, Dactylis
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glomerata L., Agrostis capillaris (L.) P. De Beauvois and Poa alpina L. are characterized by a
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high relative growth rate and SLA. Conservative species Festuca paniculata (L.) Schinz et
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Thellung, Sesleria caerulea (L.) Arduino and Bromus erectus (L.) are characterized by a low
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growth rate and SLA. Subalpine grasslands are exclusively dominated by perennials
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vegetation where recruitment events are rare (Zeiter et al. 2006). For this reason, we chose to
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focus on the adult stage and used tiller collected from the field for the experiment.
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Experimental design – We conducted a two-year pot experiment where neighbor
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interactions and fertilization were manipulated in a factorial design (Fig. 1). The overall
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design consisted in nine planting schemes crossed with 2 levels of fertility replicated 8 times
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in a randomized block design, for a total of 144 pots.
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Species were grown in pots either at low or high density. In the low density treatment, the six
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species were grown individually (6 planting schemes). In the high density treatment, 6
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individuals were planted in a circle with 3 cm space between each individual. Two types of
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mixtures in which functional groups were grown either alone (intra-functional mixture) or
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together (inter-functional mixture) are realized. Intra-functional mixtures were composed by a
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single functional group of either exploitative or conservative species (2 planting schemes);
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two individuals per species were used (Fig. 1), totaling six individuals per pot. Inter-
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functional mixtures included species from the two functional groups (1 planting scheme). The
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six species were planted in a random pattern with the constraint that species groups alternated
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with one another to promote inter-functional interactions.
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The experiment was established between June 28th and July 5th 2003 by planting field-
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collected tillers of each species. Before planting, each tiller and roots were cut (3 cm and 5cm
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length respectively). Homogeneous tillers were planted following the design described in Fig.
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1, in 15 L pots (33 cm diameter, 26.3 cm deep), filled with a soil composed of 2/3 of sand, 1/4
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of calcinated clay and 1/12 commercial potting compost (Fertiligène®). The pH of the soil
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used in the experiment was similar to soil pH measured in adjacent fields (pot pH: 7.2 ± 0.5;
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field pH = 7.0 ± 0.3, p> 0.05). Pots were placed outside in the experimental garden; they were
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moved regularly within and between blocks throughout the course of the experiment, thereby
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making the spatial design fully random. Although water limitation may occurs in the field
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during the summer (unpublished data), we chose to water pots daily with an automatic system
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to avoid confounding effect and tested only the effect of fertilization in our experiment. We
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assumed that pots were not water-limited. During winter, they were buried to protect roots
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from frost.
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Half of the pots were fertilized by adding 15g/year of a commercial slow release
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fertilizer per pot (12% N, 12% P, 17% K, 2% Mg) which mimicked intermediate levels of
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fertility in fertilized grasslands. Previous studies have shown that in fertilized hay meadows, P
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is not limiting and nitrogen availability is on average equal to 78 mg of mineral N per kg of
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soil (Tosca et al. 1986; Quétier et al. 2007; Robson et al. 2007). The nitrogen availability in
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the unfertilized pots (6.1 mg of mineral N per kg of soil) corresponded to the lowest level of
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nutrients measured in unfertilized grasslands at the study site (Quétier et al. 2007; Robson et
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al. 2007).
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Harvest and Measurements – At the peak of biomass production during the second
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growing season (July 30th 2004), plant height and light interception were measured for each
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species in each pot. Light interception was quantified at 2 cm aboveground with a LI-190 (LI-
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COR®) under full sun between 11 a.m. and 2 p.m. for 20 random points per pot. We found no
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statistical differences in light interception between the fertilized mixture (71 ± 3 %) and in the
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fertilized meadows at peak biomass (71 ± 1%)(p > 0.05), measured in a previous study
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(Quétier et al. 2007). Five leaves per individual were randomly selected on the top of the
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plant canopy for chemical analysis. Leaves were dried at 60°C for 72h, ground and analyzed
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with a CHN microanalyser (Carlo Erba 1500) to determine leaf nitrogen concentration for
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each species in each treatment.
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In August 2004, at the end of the second growing season, all pots were harvested.
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Shoots and roots were washed carefully under water. Shoots were collected for each species
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whereas belowground biomass was taken without separating roots by species. Shoots and
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roots were dried for 48 h at 60°C and weighed. Root density in fertilized mixture pots (851 ±
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70 g/m²) was comparable to that measured in the field (913 ± 137 g/m²) (p > 0.05) (Robson et
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al., unpublished data). In inter-functional mixtures, root biomass of exploitative functional
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group was determined using Near Infra-Red Spectrometry (NIRS) method following Roumet
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et al. (2006) (See Appendix S3 for protocol and results of NIRS analysis). Root biomass of
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the conservative functional group was calculated by difference between total root biomass and
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predicted NIRS belowground biomass of the exploitative functional group.
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The total amount of nitrogen in leaf biomass for each species was determined by
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multiplying leaf nitrogen concentration (LNC) with total leaf biomass (Van Ruijven &
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Berendse 2005). We then determined the amount of leaf biomass produced per unit of
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nitrogen as proposed by van Ruijven & Berendse (2005) as an estimation of leaf nutrient use
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efficiency (LNUE).
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Overyielding calculations – Overyielding was assessed by comparing biomass of
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inter-functional mixtures with biomass of intra-functional mixtures (Fig. 1). Two indices were
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used to address different aspects of overyielding. The first index, relative yield total (RYT),
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was calculated as follows:
F
RYT   RYi , where F is the total number of functional groups and RYi  0i / Mi ,
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1 i
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where Oi is the biomass of functional group i in inter-functional mixture (3 plants) and Mi is
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the intra-functional biomass of i (6 plants). RYT > 1 indicates overyielding. It is one of the
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most common metrics for assessing overyielding (Hooper 1998, Hooper et al. 2004). While
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this index characterizes overyielding at mixture level, it does not address the specific response
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of functional groups and does not allow the rejection of the sampling effect (Hector 2006).
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The second index Di estimated the proportional deviation of the observed biomass in
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inter-functional mixtures from its expected value in intra-functional mixtures for each
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functional group (Loreau 1998):
Di = (Oi – piMi) / piMi,
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where piMi is the expected biomass of one functional group in intra-functional mixture,
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where pi the proportion of functional group i in inter-functional mixture (pi = 0.5 in this
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study).
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Because different functional groups can be affected differently by changes in functional
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composition, Di quantifies the response of each functional group. When Di > 0 for all
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functional groups, there is transgressive overyielding (Loreau 1998), i.e each functional group
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produces more biomass when grown with the other functional group than when grown alone.
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This provides a sufficient condition to unambigously reject sampling effects (Loreau 1998).
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We calculated these indices with aboveground, belowground and total biomass for each
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functional group.
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Outcomes of biotic interactions - Biotic interactions were quantified by comparing
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species of the different functional groups grown individually with species grown at high
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density in intra- or inter-functional mixtures (Fig. 1). We used a common competition index,
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the natural log response ratio for functional groups (LNRR). Because facilitation and
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competition operate simultaneously (Oksanen 2006), this index measures the net outcome of
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biotic interactions (Suding et al. 2003):
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LNRR interaction(i) = LN [(BM(i)with competition in mixture λ / BM(i )estimated without competition],
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where BM(i) with competition is the biomass of the functional group i (i.e. exploitative
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or conservative) and λ is the mixture type (intra- and inter-functional). In intra-functional
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mixture BM(i) with competition is piMi. In interfunctional mixture BM(i) with competition is
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Oi. BM(i) estimated without competition is the sum of species biomass from functional group
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i produced in low density pots (Fig. 1). When LNRR interaction < 0, net effects of neighbors are
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negative (interactions are dominated by competition) and when LNRR interaction > 0, net effects
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of neighbors are positive (interactions are dominated by facilitation).
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We also calculated indices at the species level to test whether species responses within a
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particular functional group were consistent with the group's aggregate response. This
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comparison was based only on responses of aboveground biomass since belowground
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biomass could not be estimated for individual species.
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Statistical analysis - Statistical analyses were conducted using the software JMP 5.0.1.
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(SAS institute Inc., Cary, NC, USA). First, analyses were performed at the functional group
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level. To test experimental treatment effects on biomass production per pot, light incidence,
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leaf nitrogen biomass and nutrient use efficiency, we conducted a set of full factorial ANOVA
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type III testing for combined Functional Mixture (Mixt.), Fertilization (Nut.) and Functional
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Groups (FG.) effects. We did not include in this analysis the low density treatment. ‘Mixture’
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compared biomass production of different functional groups in intra- versus inter-functional
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mixture. The assessment of overyielding was made by testing if the RYT value differed from 1
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and/or DT and Di differed from 0 using a Student t-test. We conducted one-way ANOVAs on
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LNRR interaction, LNUE and leaf nitrogen biomass to test the effect of the different
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functional mixtures on LNRR at each nutrient level.
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In a second set of analyses at the species level, we used the same set of ANOVA type
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III as for functional groups to test whether species within functional groups (FG) had similar
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responses to experimental treatments (Mixt. and Nut.) in terms of aboveground biomass
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production, total leaves nitrogen biomass and nutrient use efficiency.
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RESULTS
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Biomass production and overyielding – The inter-functional mixture was dominated
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by the exploitative functional group, comprising over 60% of the aboveground biomass in
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unfertilized conditions (low fertility treatment) (significantly difference across FG, p<0.05,)
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and over 80% with added nutrients (intermediate fertility treatment) (significantly difference
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across FG, p<0.0001, Fig. 2B). With fertilization, exploitative species comprised 65% of total
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root biomass per pot (FG significantly different p<0.05), whereas without fertilization it made
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up only 52% of the total root biomass (FG was not significantly different).
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The effect of functional mixture on biomass production was highly dependent on
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fertilization (Table 1, Fig. 2A, B). In low fertility treatment (no fertilization), functional group
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biomass was not affected by the type of functional mixture (i.e., whether there was a single or
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two functional groups) (Fig. 2A), despite intra-functional mixtures biomass was always
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greater than inter-functional mixtures biomass. At intermediate level of fertility (fertilization),
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the type of functional mixture affected biomass production (Table 1, Fig. 2B). Fertilization
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increased the biomass of both exploitative and conservative functional groups but this
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increase was greater in inter-functional mixture. Fertilization affected allocation patterns of
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the two functional groups differentially (Table 1). The exploitative functional group increased
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aboveground biomass (p<0.01) in the inter-functional mixture, whereas belowground biomass
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was unaffected. In contrast, the conservative functional group increased belowground biomass
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(p<0.01) while aboveground biomass was not affected in the inter-functional mixture.
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The RYT values of unfertilized plants were below 1 suggesting no overyielding (Table
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2). Consistent with this result, Di values for the exploitative functional group were not
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different from zero. Di value for the conservative functional group was slightly negative.
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With fertilization, total biomass was significantly lower in intra- than in interspecific mixtures
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(Table 1, Fig. 2B). We observed a high positive value of RYT (p<0.0001) (Table 2). The two
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functional groups showed positive Di values with fertilization, indicating the occurrence of
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overyielding for these two groups. These results were often, but not always, consistent with
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the response of belowground or aboveground biomass examined individually (Table 2).
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Positive Di for the conservative functional group was mainly driven by an increase in root
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biomass in inter-functional mixture. In contrast, the positive Di value for total biomass of the
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exploitative group was explained by an increase in shoot biomass. Significant positive Di
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values for both functional groups in fertilized mixture indicated that the sampling effect can
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be rejected.
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Effects of functional mixture on biotic interactions – The net outcomes of biotic
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interactions were negative in this experiment (Fig. 3) indicating the prevalence of competition
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rather than facilitation. Indeed, individuals from the low density treatment (individuals grown
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alone) always produced more biomass than plants in high density treatments. Without
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fertilization, the type of mixture had no effect on the outcomes of biotic interactions (Fig. 3
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A). With fertilization, outcomes were less negative in the inter-functional mixture for both
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functional groups than in their respective intra-functional mixtures (Fig. 3B).
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Effects of functional mixture on nutrient use efficiency - Whether functional groups
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grew alone or in the presence of the other functional group had a strong effect on the total
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amount of nitrogen in leaf biomass and on leaf nutrient use efficiency (LNUE) (Fig. 2).
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Without fertilization, both functional groups had lower leaf nitrogen biomass when grown
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together as compared to when grown alone, but this difference was stronger for the
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conservative functional group than for the exploitative functional group (Fig. 2C). Despite
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this difference, the LNUE was not significantly affected by the type of functional mixture
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(Fig. 2E). With fertilization, total leaf nitrogen biomass decreased for the conservative
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functional group, but increased for the exploitative functional group, when grown together as
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compared to they were grown alone (Table 1, Fig. 2D). The type of functional mixture
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affected the nutrient use efficiency (LNUE) in opposite ways for the two functional groups
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(Table 1; Fig. 2F). The conservative functional group showed increased LNUE in inter-
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functional mixture, whereas the exploitative functional group had decreased LNUE in the
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presence of the conservative group as compared to when grown alone.
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Light interception and species height - Light availability was strongly modified by the
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type of functional mixture as well as fertilization (Mixture: F2,39 = 15.44, p <0.0001, Nutrient:
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F1,39 = 122.52, p= 0.0001; Mixture*Nutrient: F2.39 = 7.51, p<0.001). As expected, fertilization
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decreased light availability (p<0.0001) (Appendix S1). Without fertilization the light
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availability was not strongly affected by the type of mixtures. At the opposite, functional
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groups had contrasting effects on light availability when fertilized (Appendix S1). The
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exploitative functional group had a strong effect on light, intercepting more than 80% of PAR
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when grown alone. The conservative functional group had a weaker effect on light levels,
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with less than 40% of light interception when grown alone. When both groups were grown in
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mixture, the canopy intercepted over 80% of PAR.
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Species grown in inter or intra-functional mixtures with fertilization strongly differed
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in plant height (Appendix S1), but these differences did not correspond to functional group
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designations. The exploitative D. glomerata, and the two conservative B. erectus and F.
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paniculata were significantly taller (ca. 20 cm height) than P. alpina and A. capillaris
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(exploitative) and S. caerulea (conservative) (ca. 7 cm) (p<0.0001). There was no significant
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difference in plant height between intra and inter-functional mixtures (Mixture, p value not
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significant for any species), indicating no plant elongation in the inter-functional mixture.
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Analysis at species level - Similar patterns of responses were observed at the species
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and functional group levels (Appendix S2). Consistent with analyses at the functional group
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level using aboveground biomass data, species from the two functional groups responded
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differently to nutrient addition and to the type of functional mixture. We observed non-
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significant responses without fertilization when comparing competition intensity in intra-
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versus inter-functional competition (Table S2). With fertilization, exploitative species
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experienced decreased competition intensity in inter- vs intra-functional mixture (p<0.05 for
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D. glomerata and P. alpina, non significant effect for A. capillaris, p = 0.15 (Table S2)). In
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contrast, the aboveground biomass of conservative species was not affected by inter-
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functional competition, with the exception of S. caerulea for which competition intensity
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significantly increased in inter-functional mixture (p<0.01, Table S2). Species responses were
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also strongly consistent with their functional groups response for leaf nitrogen biomass and
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leaf nutrient use efficiency (Appendix S2).
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DISCUSSION
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In this study, we showed that fertilization leading to intermediate level of fertility
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promotes transgressive overyielding between conservative and exploitative grass functional
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groups, supporting the idea that overyielding can occur without the presence of legumes (van
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Ruijven & Berendse 2003; 2005). Although grass species are usually considered as a same
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functional group when classifications are based on life-form (Hooper et al. 2005), they differ
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considerably in their traits and their responses to environmental factors (Diaz et al. 2004; Al
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Haj Khaled et al. 2005; Gross et al. 2007). Classification based on functional traits rather than
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simple growth form is critical when examining species coexistence or ecosystem processes
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(Diaz et al. 2004; Wright et al. 2006; Shipley et al. 2006; McGill et al. 2006).
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Complementarity as a mechanism of overyielding between grass species
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Consistent with previous studies conducted at similar altitudes (e.g. Choler et al. 2001;
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Callaway et al. 2002), facilitation was not detected in this study and the outcomes of biotic
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interactions were primarily negative (Fig. 3). Functional composition affected biomass
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productions only with fertilization (Fig. 2B), with interactions becoming less negative in
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inter-functional mixtures for both exploitative and conservative functional groups (Fig. 3 B).
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This result apparently contrasts with competition models (e.g. Grime 1977; Wedin & Tilman
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1993) that predict exclusion of conservative species by exploitative species in high fertility
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conditions. However, fertilization led to intermediate level of fertility in subalpine grasslands
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allowing the coexistence between the two functional groups (Grime 1977) (Quétier et al.
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2007). This result is also supported by field observations in harsh environments where
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diversity does not decrease with fertilization (Gross et al. 2000; Suding et al. 2005).
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The reduction of negative interactions in inter-functional mixture could be explained
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both by an increase of facilitation or a decrease of competition (Hooper et al. 2005).
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However, in our study biomass of plants growing alone (low density treatments) showed
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always a greater biomass production than plants in mixtures (high density treatments),
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indicating the overall importance of competitive interactions for the growth of established
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individuals. Additionally, two mechanisms of complementarity (for light and nitrogen) may
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act to promote a decrease in competition intensity between the two functional groups. For
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these reasons, overyielding in our experiment is most parsimoniously interpreted as a
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consequence of a complementarity effects between functional groups rather than facilitation.
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Two mechanisms of complementarity likely caused the overyielding between the two
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functional groups. First, differences in height among species in fertilized inter-functional
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mixtures might promote light partitioning (Fig. 1S B) (Naeem et al. 1994; Fridley 2002;
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2003). In a previous study (Gross et al. 2007), we found that grass species with different
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heights differed in their shade tolerance. Growth of short species like A. capillaris, P. alpina
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and S. caerulea were not affected by shade whereas tall species like D. glomerata and F.
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paniculata were shade intolerant (Gross et al. 2007). In our experiment, shade intolerant
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species overtopped short shade tolerant species. Thus, complementarity for light, promoted by
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aboveground space partitioning and differences in shade tolerance, may occur between
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functional groups within grasslands as it does within forests. Secondly, the decrease in leaf
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nitrogen biomass for conservative species was compensated by an increase in leaf production
367
per gram of nitrogen (LNUE) (Fig. 2F). This result confirms a previous study (van Ruijven &
368
Berendse 2005) where an increase in leaf nutrient use efficiency (LNUE) was observed as
369
functional diversity increased. The decrease in total nitrogen in leaf biomass and the increase
15
370
of LNUE for the conservative functional group might be due to its larger allocation to root
371
biomass in inter-functional as compared with intra-functional mixture (Fig. 2 B).
372
Without fertilization, we found no evidence that conservative species are better
373
competitors than exploitative species (Ryser & Lambers 1995). Competition was likely due to
374
belowground interactions as no light depletion was detected. Additionally, nitrogen tissue
375
content for the two functional groups decreased when grown together (Fig. 2D). Mechanisms
376
that explain dominance patterns at unproductive sites may require longer periods than two
377
growing seasons to be expressed. It is indeed not rare to find a shift of productivity and
378
species abundance in long-term experiments (van Ruijven & Berendse 2005). Conservative
379
species could ultimately dominate at low fertility sites because exploitative species are not
380
nutrient stress-tolerant (Grime 1977). Alternatively, conservative species could build a high
381
stature through time due to nutrient conservation (Aerts & Vanderpeijl 1993) and exclude
382
exploitative species by competition for space (Elberse & Berendse 1993).
383
384
Relevance of the functional group approach
385
In this study, responses at the functional group level were consistent with responses of
386
species within their own group (Appendix S1), confirming the existence of two distinct
387
functional strategies among the six grass species (Gross et al. 2007). Consistency between the
388
species and functional group levels was even stronger for responses to inter-functional
389
mixture of total nitrogen in leaf biomass and LNUE. Responses of conservative and
390
exploitative species tended to be opposite, highlighting the contrasting nutrient economies for
391
conservative and exploitative species (Aerts & Vanderpeijl 1993). Our study supported the
392
relevance of the functional groups approach to understand species interactions and
393
coexistence (Suding et al. 2003; McGill et al. 2006; Lavorel et al. 2007).
16
394
Idiosyncratic behavior of a species within its functional group is in no way
395
contradictory with the functional group approach, but rather provides additional insight into
396
coexistence mechanisms. Particular species behaviors within functional groups may inform us
397
on the existence of other trade-offs linked to other sets of traits (Suding et al. 2003; Ackerly
398
2004; Grime 2006). For instance, the short-stature species S. caerulea showed an original
399
response in inter-functional mixture with fertilization within the conservative group.
400
Differences in plant height may reflect differences in competitive ability within this group
401
(Gross et al. 2007). Within exploitative species, plant heights were linked with different shade
402
tolerances (Gross et al. 2007) and may lead to light partitioning within the group.
403
404
Conclusion
405
This study showed overyielding between conservative and exploitative grasses from
406
subalpine grasslands at intermediate rather than low level of fertility. Our results suggest that
407
complementarity, resulting in a reduction in competitive intensity, is likely to explain this
408
overyielding. Different mechanisms of complementarity may have occurred simultaneously in
409
this study. Our results suggest that both light partitioning (Fridley 2002; 2003) and
410
modification of leaf nutrient use efficiency (van Ruijven & Berendse 2005) may explain
411
overyielding, species coexistence and resulting high functional diversity in fertilized
412
subalpine grasslands. Although overyielding among grass species is likely to explain high
413
functional richness of subalpine grasslands other mechanisms linked with water use strategy
414
(unpublished data) or acting at the regeneration stage (Quétier et al. 2007) may also play
415
important roles in subalpine grasslands. Future field studies are needed to quantify and
416
understand the ecological role of complementarity especially in harsh environments with
417
intermediate fertility where diversity does not decrease with fertilization (Gross et al. 2000;
418
Rajaniemi 2003; Suding et al. 2005; Quétier et al. 2007).
17
419
Acknowledgements
420
This study was supported by the GEOTRAITS project of the French ACI-ECCO programme
421
and CNRS GDR 2574 Utiliterres. We thank M. Chausson, M. Enjalbal and C. Poillot for
422
technical assistance during the experiment; G. Girard for chemical analysis; R. Hurstel, R.
423
Douzet, S. Aubert and all the staff of the SAJF; F. Quétier and F. Grassein for light
424
interception data and T.M. Robson for roots data in the field; M.L. Navas and A. Bouasria for
425
discussions; I. Ashton, S. Harpole, P. Choler P. Liancourt, H. Cornelissen and the two
426
anonymous reviewers for their valuable comments during the preparation of the manuscript.
427
428
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23
567
Table 1. Effect of experimental treatments analyzed at the functional groups level for biomass
568
production, light incidence, amount of nitrogen in leaf biomass and Nutrient Use Efficiency
569
using full factorial ANOVA.
Total BM
Shoot BM
Root BM
N Leaves
LNUE
Effect
df
F ratio
p
F ratio
p
F ratio
p
F ratio
p
F ratio
p
FG
Mixt
FG*Mixt
Nut
FG*Nut
Mixt*Nut
FG*Mixt.*Nut
error
1
1
1
1
1
1
1
61
100.96
6.07
1.10
240.60
72.73
9.99
0.90
***
*
ns
***
***
**
ns
120.97
5.49
9.02
174.84
93.94
9.10
10.12
***
*
**
***
***
**
**
15.59
2.26
5.70
138.13
7.92
3.64
8.14
**
ns
*
***
**
ns
**
23.08
305.40
102.50
638.21
248.36
45.73
93.69
***
***
***
***
***
***
***
0.00
2.61
0.00
19.73
2.52
0.61
7.12
ns
ns
ns
***
ns
ns
*
570
571
FG., Functional Group, Mixt., functional mixture, Nut., Fertilization. We indicated degrees of
572
freedom (df) and Fisher ratio (F ratio); ns, non significant effect, *, p< 0.05, **, p<0.01,
573
***p<0.0001.
574
575
576
577
578
579
580
581
582
583
584
585
586
24
587
Table 2. Indices for assessing the degree of overyielding calculated for aboveground,
588
belowground and total biomass with (1) and without fertilization (0).
Total biomass
Aboveground biomass
Fertilization
Belowground biomass
Fertilization
p
p
Fertilization
p
p
p
p
0 **
0.81 ± 0.12
**
0*
0.78 ± 0.12
ns
0*
0.87 ± 0.12
ns
1
1.51 ± 0.07
***
1
1.25 ± 0.10
**
1
1.61 ± 0.14
***
-0.24 ± 0.10
*
0 ns
-0.18 ± 0.15
ns
0 ***
0.55 ± 0.13
*
1
-0.15 ± 0.20
ns
-0.13 ± 0.16
ns
0*
-0.24 ± 0.16
0.34 ± 0.11
*
1
0.45 ± 0.17
RYT
0 ***
-0.29 ± 0.12
*
1
1.06 ± 0.31
**
ns
0 ns
0.04 ± 0.18
ns
*
1
-0.03 ± 0.10
ns
D cons.
1
0*
D expl.
1
589
590
Overyielding occurs when RYT > 1 and Di > 0 for conservative (Cons.) and exploitative
591
(Expl.) species. We conducted one-way ANOVA to test for significant effects of fertilization
592
on overyielding. Fertilization column: 0 no fertilization, 1 fertilization. An asterisk in the
593
Fertilization column indicates whether the fertilization treatment significantly changed RYT
594
and Di value. Additionally we conducted a Student t-test to compare RYT values to 1 and Di
595
to 0: ns, p > 0.05, *, p<0.05, **, p<0.001, *** p<0.0001.
596
597
25
Biotic Interactions
Overyielding
Low density
(One plant per pot)
Intra
Intra
Conservative
Exploitative
Inter
598
599
Figure 1
600
601
602
603
26
10
ns
Root
Shoot
(A)
80
(B)
Root
Shoot
8
*
ns
6
Biomass (g)
Biomass (g)
60
4
40
*
20
2
0
INTRA
INTER
Cons.
INTRA
0
INTER
INTRA
Expl.
604
0,07
*
INTER
***
Aboveground N (g)
Aboveground N (g)
***
0,04
0,03
0,02
INTER
Expl.
0,8
(C)
0,06
0,05
INTRA
Cons.
(D)
0,6
0,4
**
0,2
0,01
0,00
INTRA
INTER
Cons.
605
(E)
INTER
INTRA
*
*
ns
50
40
(F)
60
ns
60
INTER
Expl.
80
LNUE (g/g)
LNUE (g/g)
INTRA
Cons.
Expl.
80
70
0,0
INTRA INTER
40
20
30
20
INTER
Cons.
606
607
INTRA
INTRA
INTER
Expl.
0
INTRA
INTER
Cons.
INTRA
INTER
Expl.
Figure 2
608
609
610
611
27
Cons.
Expl.
-0,2
-0,2
-0,4
-0,4
-0,6
-0,6
-0,8
-1,0
ns
-1,2
-1,8
Expl.
-0,8
-1,0
*
-1,2
ns
-1,4
-1,6
Cons.
0,0
LNRR
LNRR
0,0
-1,4
(A)
Intra
Inter
-1,6
-1,8
(B)
*
Intra
Inter
612
613
Figure 3
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
28
632
Figure captions
633
Figure 1. Experimental design, density was varied to assess the importance of positive and
634
negative interactions: In low density treatments, one plant per pot was grown, and in high
635
density treatments six individuals were grown. At high density, there were three types of
636
mixtures: “intra” indicates intra-functional mixture where each of the three species of a single
637
functional group were grown with two individuals; “inter” indicates inter-functional mixture
638
where functional groups were grown together with one individual per species. Symbols show
639
individual species and their position in mixtures. Dark symbols are species from the
640
conservative functional group and clear symbols indicate species from the exploitative
641
functional group. Arrows highlight the statistical comparisons conducted in this study:
642
comparison among high density treatments tested for overyielding; comparison between low
643
and high density treatments estimated the intensity and direction of plant-plant interactions
644
(LNRR, see methods for details). All treatments were repeated under fertilized and
645
unfertilized conditions.
646
Figure 2. (A, B) Root and shoot biomass, (C, D) aboveground nitrogen biomass and (E,F) leaf
647
nitrogen use efficiency (LNUE) for each conservative and exploitative functional groups
648
grown in INTER and INTRA functional mixtures, and in unfertilized (A, C, E) or fertilized
649
(B, D, F) conditions. INTRA is a species mixture composed of 3 conservative species (Cons.)
650
or 3 exploitative species (Expl.); INTER is a species mixture composed of 6 species both
651
conservative and exploitative species. One-way ANOVA post-hoc test was used to compare
652
mixture effects on total biomass for each group in each nutrient treatment. Abbreviations: ns,
653
not significant, *, p<0.05, **, p<0.001, *** p<0.0001.
654
Figure 3. Competition indices for conservative (Cons.) and exploitative (Expl.) groups using
655
natural log response ratio (LNRR) for intra-functional (INTRA) and inter-functional
656
competition (INTER) mixtures in (A) unfertilized and (B) fertilized conditions We used one-
29
657
way ANOVA post-hoc test to test the effect of mixtures on competition intensities for each
658
group in each nutrient treatment. Abbreviations: ns, differences between INTRA and INTER
659
are not significant, *, p<0.05.
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
30
682
SUPPLEMENTARY MATERIAL
683
Appendix S1 Mixture characteristics
684
Figure S1. Light interception and plant height
685
Appendix S2 Data per species
686
Table S1 analyze of variance for species
687
Table S2 competition intensity per species
688
Appendix S3 determination of root biomass
689
Table S3. NIRS calibration
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
31
707
Appendix S1. Mixture characteristics
708
100
a
% of light incidence
a
ab
80
No fertilization
Fertilization
b
60
c
40
c
20
0
Cons.
Expl.
Mix.
INTER
INTRA
709
710
Figure S1. Light incidence through plant canopy for conservative (Cons.) and exploitative
711
(Expl.) in intra- and inter-functional (Mix.) mixtures with and without fertilization. Letter
712
compared bar among treatment (student test).
40
intra
inter
Expl.
30
Cons.
Height (cm²)
A
AB
B
20
C
C
C
10
0
ta
ra
me
is
a
.c
A
lar
pil
glo
D.
n
lpi
ta
tus
a
a
P.
ula
nic
rec
e
B.
F
a
.p
lea
S.
eru
ca
713
714
Figure S2. Height of conservative (Cons.) and exploitative (Expl.) species in intra- and inter-
715
functional mixtures with fertilization. Letters compared between species in intra and inter-
716
functional mixture (Student test). There is no significant effect of different functional mixture
717
on height for each species
718
719
32
720
Appendix S2. Data per species
721
722
Table S1. Effect of experimental treatment analyzed at species level for biomass production,
723
light incidence, amount of nitrogen in leaf biomass and Nutrient Use Efficiency using full
724
factorial ANOVAs
725
Shoot BM
Effect
df
FG
Mixt
FG*Mixt
Nut
FG*Nut
Mixt*Nut
FG*Mixt.*Nut
error
1
1
1
1
1
1
1
183
N Leaves
F ratio p
51.43
1.87
3.89
81.78
40.00
2.92
4.57
***
ns
*
***
***
ns
*
LNUE
F ratio p
88.80
4.96
22.84
185.59
70.96
9.13
18.94
***
*
***
***
***
**
***
F ratio p
0.47
0.86
8.59
0.48
1.29
0.13
2.60
726
727
FG., Functional Group, Mixt., functional mixture, Nut., Fertilization, ns, non significant
728
effect, *, p< 0.05, **, p<0.01, ***p<0.0001.
729
730
731
732
733
734
735
736
737
738
33
ns
ns
**
ns
ns
ns
ns
739
Table S2. Data per species, biomass data, competition intensity measured with the natural log
740
ratio (LNRR), LNUE and amount of nitrogen in aboveground biomass in intra-functional
741
(intra) and inter-functional mixtures (inter) with (1) and without (0) fertilization.
SLA
(m²/kg)
Biomass
RGR
LNRR (interaction)
Above. N
LNUE
g
%
0
Fertilization
1
0
1
0
1
0
1
B. erectus
15.6
4.7
Intra
Inter
p
0.47 ns
0.21
p
3.84 ns
3.93
p
-1.82 ns
-1.95
p
-1.33 ns
-1.08
p
0.007 **
0.003
p
0.092 *
0.052
p
57 ns
66
p
45 *
61
F. paniculata
9.5
5.4
Intra
Inter
1.67 ns
1.41
2.35 ns
2.21
-0.07 ns
-0.73
-1.54 ns
-1.51
0.034 *** 0.039 *
0.018
0.029
61 ns
67
62 *
70
S. caerulea
13.9
2.4
Intra
Inter
0.38 ns
0.44
1.72 *
0.59
-0.39 ns
-0.41
-0.44 *
-1.25
0.007 *
0.004
0.042 ***
0.008
50 ns
59
36 **
61
D. glomerata
21.2
26.9
Intra
Inter
3.19 ns
2.11
14.85 *
24.29
-0.73 ns
-0.95
-1.17 *
-0.62
0.042 *
0.032
0.151 ***
0.330
49 ns
44
73 *
58
P. alpina
22.2
11.7
Intra
Inter
0.86 ns
0.67
5.35 *
7.56
-1.53 ns
-1.30
-1.28 *
-0.87
0.011 **
0.008
0.088 ***
0.151
59 ns
64
49 *
43
A. capillaris
22.7
16.0
Intra
Inter
0.76 ns
0.84
7.95 ns
9.07
-0.72 ns
-0.47
-1.86 ns
-1.37
0.008 ns
0.006
0.080 *
0.119
68 ns
68
76 *
61
742
743
We indicated SLA and RGR measured in non-limiting conditions measured in Gross et al.
744
(2007). We tested for each species if competition intra-functional (intra) is equal or not to
745
competition in inter-functional (inter) mixtures. ns, p >0.05 non significant , *, p<0.05, **,
746
p<0.001, *** p<0.0001.
747
748
749
750
751
752
753
754
755
756
34
757
Appendix S3. Root biomass determination in inter-functional mixtures
758
759
The fraction of each species in the root biomass of inter-functional mixtures was determined
760
using Near Infrared Reflectance Spectroscopy (NIRS) technology. Briefly, NIRS spectral data
761
of artificial mixtures are combined with their known botanical composition using a predictive
762
statistical model. This model is then used to predict the composition of unknown mixtures
763
(for more information see Roumet et al. 2006 and references therein). Sixty-two artificial 6-
764
species mixtures were prepared by mixing known root dry weight of the 6 species grown as
765
isolated plants. The root proportion of each species in mixtures ranged from 0 to 56%. Each
766
sample was packed in a quartz-glass cell and scanned using a NIRS systems 6500
767
spectrophotometer (NIRSystems Inc., Silver Spring, MD, USA). The absorbance was
768
recorded at 2 nm intervals from 400 to 2500 nm, to produce a spectrum with 1050 data-points
769
per sample. NIRS calibration was performed for each species by partial least squares (PLS)
770
regression analysis using ISI software system (Shenk et Westerhaus, 1991). The PLS models
771
were validated using internal cross-validation which helps to estimate the optimal number of
772
terms without causing over fitting. There are two stages of cross-validation. The first stage is
773
achieved by selecting four subsets of the data (25% of the samples) and excluding these to the
774
modelling process so that these excluded groups can be predicted to give an indication of
775
what the performance might be in an external validation test. In the second stage, internal
776
cross-validation gives a value for the standard error of cross-validation (SECV) of each set of
777
training data (75% of the samples). The model giving the lowest SECV with the fewest
778
number of factors based on internal cross-validation is finally re-fitted on the entire data set to
779
obtain the standard error of calibration (SEC). The calibration equations obtained (Table S3)
780
were accurate for the six species (r² > 0. 955) and the SECV ranged between 2.27 to 3.63%.
781
The calibration equations were then used to predict the composition of unknown conservative
35
782
and exploitative species in inter-functional root mixtures. Since calibrations were more
783
accurate for exploitative than for conservative species, the root biomass of conservative
784
species was calculated as the difference between total root biomass in mixture and root
785
biomass of exploitative species.
786
787
Table S3. Statistics of NIRS calibration for prediction of root proportion in artificial mixtures
788
of six species
Constituent
n
Conservative species
B. erectus
61
F. paniculata
57
S. caerulea
55
Exploitative species
A. capillaris
60
D. glomerata
60
P. alpina
60
Terms
Mean
SD
SEC
r²
SECV
5
5
5
21.54
16.96
13.98
13.2
10.8
10.9
2.50
1.58
2.31
0.964
0.979
0.955
3.42
2.27
3.47
5
5
5
16.34
19.74
15.81
11.0
11.6
12.2
1.92
1.79
1.92
0.970
0.976
0.975
3.63
2.94
3.11
789
790
n: number of samples used for calibration; Terms: number of terms used in the PLS
791
calibration model; SD: standard deviation of data set; SEC: standard error of calibration; r2:
792
standard coefficient of determination between measured and calculated values; SECV:
793
standard error of cross-validation.
794
795
36