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Keywords:

  • biodiversity;
  • ecosystem management;
  • productivity;
  • grassland restoration;
  • recolonization

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • 1
    Understanding ecosystem responses to plant species loss is essential for the optimal management of grasslands. Recent studies have examined the effects of simulated random species loss in experimental plant communities but not those of realistic non-random species loss resulting from transient extinction pressures in semi-natural grasslands.
  • 2
    To investigate the potential effects of non-random species loss on grassland productivity, we established mesocosms with mixed communities comprising 15 plant species, and exposed them to 2 years of high-intensity management (an extinction phase) followed by 2 years of low-intensity management (a restoration phase) allowing recolonization from differentially managed neighbouring plots. In addition, monocultures of each component species were subject to the same extinction–restoration phases.
  • 3
    During the extinction phase, species with high monoculture biomass had lower extinction probabilities in the mixed community than species with low monoculture biomass, but there was also species-specific variation. The species that were most productive or most persistent during the extinction phase were not the same as those performing best in the restoration phase.
  • 4
    No consistent effects of spontaneous recolonization from neighbouring communities on species richness or productivity of the focal communities were observed during the restoration phase.
  • 5
    We estimated that extinction of all but the species with the lowest extinction risk reduced biomass productivity by 42–49%; loss of all but the four species with the lowest extinction risk reduced it by 2–35%. Identical calculations for a random extinction scenario yielded reductions of 52% and 26–54%, respectively.
  • 6
    Synthesis and applications. Prediction of the effects of species loss on plant production and on other aspects of ecosystem functioning in semi-natural grasslands must account for both specific non-random extinction processes and post-extinction conditions. For European mesic grasslands experiencing a shift from high-intensity to low-intensity management, our results suggest that recolonization by ‘missing’ species must be actively assisted if high production is a management objective.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Recognition that loss of species may negatively affect ecosystem processes such as primary production or nutrient cycling has led to a rapidly growing literature on the effects of diversity loss over the last decade. In particular, a large number of studies have examined the effects of diversity loss using experimental systems in which species diversity could easily be manipulated (Schläpfer & Schmid 1999; Loreau 2000; Schmid, Joshi & Schläpfer 2002; Symstad et al. 2003). These studies have been criticized because they were based on the simplifying assumption of random species loss (Aarssen 1997; Huston 1997; Wardle 1999; Huston et al. 2000; Schwartz et al. 2000; Schmid et al. 2002; Schmid, Joshi & Schläpfer 2002; Smith & Knapp 2003; Symstad et al. 2003). Species extinctions in a local habitat are often non-random because not all species are equally likely to become extinct (Vitousek et al. 1997; Grime 1998, 2002; Stöcklin & Fischer 1999; Petchey & Gaston 2002). In natural or managed ecosystems, species loss is mainly related to habitat destruction, eutrophication, invasive species, climate change and harvesting (Chapin et al. 1997; Vitousek et al. 1997), the effects of which are likely to be species specific. Even stochastic extinction factors (Pimm, Jones & Diamond 1988; Hubbell 2001), such as habitat fragmentation, leading to small, isolated and therefore extinction-prone populations, may affect species differentially (Fischer & Stöcklin 1997). A particularly well-known case of non-random extinction is represented by previously species-rich mesic European grasslands, where intensified management has led to the removal of many competitively inferior species, while a set of high-yielding species has persisted (Fuller 1986).

Experimentally assessing the effects of non-random species loss resulting from land management, drought or other factors involves two distinct steps. First, the extinction pressure of concern must be applied to determine the post-extinction species assemblage. Secondly, ecosystem processes driven by the post-extinction assemblage must be compared with those of the pre-extinction assemblage in a common (post-extinction) environment. The latter could be achieved by examining the effect of recolonization into the impoverished assemblages or by replanting the two assemblages in a new common environment.

This approach is different from many biodiversity experiments, in which random species loss is assumed to have occurred already as communities consisting of the reduced species sets are established (Gonzalez & Chaneton 2002). It also differs from experiments in which a species richness gradient is induced by management or other environmental factors but resulting assemblages are not thereafter compared in common post-extinction environments (McNaughton 1977; Tilman & Downing 1994).

Random deletion of species, as simulated in previous biodiversity experiments, may thus over- or underestimate the effect of species loss on ecosystem functioning, depending on the relationship between extinction proneness and post-extinction performance of the species in a given extinction scenario and environment. In particular, under the sampling mechanism, if there is no correlation between species persistence and species performance in a specific ecosystem context, random extinction experiments will on average yield valid predictions. If the correlation is positive, effects of species loss will be overestimated. If the correlation is negative, effects will be underestimated. The magnitude of the effects of species loss because of a particular environmental factor can only be judged when extinction scenarios with their specific extinction sequences are imposed by applying this environmental factor. Subsequently, variables of ecosystem functioning can be examined in the post-extinction communities and environments. Although several recent studies factorially crossed biodiversity with nutrient–environment manipulations (Reich et al. 2001; He, Bazzaz & Schmid 2002; Craine et al. 2003; Fridley 2003), no study has so far attempted to estimate the effects of species loss induced by a transient management-related factor.

As previous biodiversity experiments have often been carried out in grassland systems and measured productivity as the variable of ecosystem functioning (Tilman, Wedin & Knops 1996; Hector et al. 1999; Pfisterer et al. 2004), we used this model system. We imposed an actual extinction scenario by simulating intensified grassland management in mesocosms. This is also relevant from an agricultural perspective. After several decades of intensification of grassland management across much of Europe, resulting in considerable species loss at the local scale (Fuller 1986), there is now a trend towards lower-input grassland systems. Therefore, the conditions affecting depauperate grasslands will no longer correspond with those that exerted the extinction pressure, while recolonization from a regional species pool may be limited (Loreau & Mouquet 1999; Schmid 2002). In our model, we therefore followed extinction management with ‘restoration’ management, as is now prescribed by agri-environment schemes in Europe (Ovenden, Swash & Smallshire 1998; Kleijn & Sutherland 2003). We investigated how non-random extinction processes affect plant production and whether these effects are smaller than those described for similar ecosystems in experiments with simulated random extinction.

We asked the following questions. Regarding characterization of the extinction scenario (a) are there species that are particularly likely to become extinct; (b) is extinction dependent on management intensity; and (c) is there an interactive effect of species identity and management intensity on extinction probability? Is extinction probability negatively related to productivity in monoculture? Regarding species loss effects, how does the biomass of species mixtures surviving after non-random extinction compare with the biomass of random mixtures of the same species richness drawn from the same original species pool? How does the management intensity in neighbouring communities affect recolonization and productivity during the restoration phase?

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

experimental set-up

Each experimental unit consisted of one focus plot and two adjacent neighbouring plots and was separated from adjacent units by a fence preventing seed dispersal. Three such experimental units together constituted one sub-block. Three neighbouring plot treatments [increasing fertilizer application and cutting frequency in treatments L (low), M (medium) and H (high), corresponding to increasing management intensity, MI] were replicated at the sub-block level. Three sub-blocks and 15 monoculture (MC) plots (one of each species used in the mixtures; see below) together constituted one block. The experimental field, consisting of three blocks, was situated in the experimental garden of the Institute of Environmental Sciences, north of the Irchel campus of the University of Zurich, Zurich, Switzerland.

This experimental set-up was able to induce extinction, followed by restoration, through a transient high-intensity management phase on the focus plots. The resultant species composition and biomass under different recolonizations from neighbouring plots were monitored. The MC plots experiencing the same extinction–restoration management as the focus plots made it possible to examine whether any observed species extinction was random with respect to species monoculture biomass in the extinction environment, and also with respect to species monoculture biomass in a new restoration environment.

soil preparation

In March 2000, we filled 132 trays measuring 48 × 33 cm (c. 1/6 m2 area) to a depth of 7 cm with consecutive layers of 5 L of sand, 1 L of low-humus grassland soil, 2 L of sand and finally 0·5 L of garden soil (to obtain good germination and establishment of seedlings). The high proportion of sand allowed rapid manipulation of nutrient levels and the vertical structure facilitated resource partitioning between species by differential root foraging. The trays were placed into three garden beds that were surrounded by metal fences with a recurved profile to exclude snails and protected by a coarse metal mesh from subterranean attack by rodents. The trays (hereafter referred to as plots) were placed on plastic plates to prevent root growth through drainage holes into the soil below.

selection of study species

Fifteen grassland species were selected. Seeds were sown for each species based on their yield contribution in mesic grasslands of the Swiss Plateau (this vegetation type has been classified as Lolio perennisArrhenateretum elatioris by Dietl 1995). We selected one representative vegetation survey (no. 2 in Dietl 1995) from which we derived seed mass per plot for each species as follows: yield contribution in the vegetation survey of 1–2%, 0·02 g; 2–5%, 0·03 g; 5–9%, 0·04 g; > 9%, 0·05 g. The selected species (with seed mass in 1/100 g and approximate seed number per plot in parentheses) were: Alopecurus pratensis (L.) (5, 34), Arrhenaterum elatius (Presl.) (5, 17), Chrysanthemum leucanthemum (L.) (2, 53), Crepis biennis (L.) (2, 18), Dactylis glomerata (L.) (4, 50), Festuca pratensis (Hudson) s.l. (3, 16), Holcus lanatus (L.) (4, 212), Lolium multiflorum (Lam.) (2, 5), Lolium perenne (L.) (2, 6), Plantago lanceolata (L.) (2, 10) Poa trivialis (L.) s.l. (2, 77), Taraxacum officinale (Weber) s.l. (4, 44), Trifolium pratense (L.) s.l. (3, 19), Trifolium repens (L.) (2, 33) and Trisetum flavescens (P.B.) (4, 180), totalling 2·8 g m−2.

seed material

Local seed material was obtained from the Swiss Federal Research Station for Agroecology and Agriculture, Zürich-Reckenholz, Switzerland, and from a commercial provider of local wild varieties (UFA, Winterthur, Switzerland). For each of the 78 plots to be seeded with species mixtures we prepared two seed lots, one with the pooled grass seeds and one with the pooled forb seeds, to facilitate sowing.

sowing

On 5 April 2000, the 78 mixtures and 3 × 15 monocultures (with seed mass per square metre equal to that of the mixtures) were sown onto the soil substrate by hand, regularly spreading the seeds. A small tray was used to press seeds into the substrate. Nine plots were left without seeds (bare plots). Plots were sufficiently watered about 1 h after seeding and a molluscicide was applied to prevent herbivory. On 31 March 2001, plots were resown with seed material of the same species and in the same amounts as in the previous year to obtain a more balanced age structure of the biennial and perennial species. Each of the three blocks was covered with a coarse shade mesh, which also provided protection against birds. The mesh was removed once communities had become well established. Plots were watered during particularly dry periods throughout the experiment.

final arrangement of plots

On 21 June 2000, the plots were numbered and mixture plots were randomly assigned to positions in the blocks (garden beds). Monocultures were placed in groups within blocks and systematically assigned to the north, south and north row of block 1, 2 and 3, respectively. The nine unseeded bare plots were assigned to the focus plot positions of one sub-block in each block to observe colonization from neighbouring plots. All species germinated and established well. Neighbouring plot treatments were randomly assigned to experimental units within each of the three sub-blocks of each block. As there was no seed dispersal in 2000, the wire mesh fences (100 × 70 cm with a grid size of 0·8 mm; Bopp, Zurich, Switzerland) were put in place from March 2001 onwards to prevent seed exchange between experimental units. Because of shallow soil and limited nutrients and frequent cutting, only a few (grass) inflorescences exceeded 70 cm in height.

first-year fertilizer treatments

On 13 July 2000 (and again each following spring), 4 g of potassium (20% K2O, 6·5% MgO; Hauert & Co., Grossaffoltern, Switzerland) and 2 g of phosphorus (P2O5) fertilizer were applied to each plot, providing a non-limiting supply of K, Mg and P. Nitrogen fertilizer was applied as follows: 1·67 g of ammonium saltpetre (AS; 27·5% N; Geistlich Söhne AG, Schlieren, Switzerland) was applied on 13 July, 1 August and 24 August 2000 to all plots except the low-intensity (L) neighbouring plots, and after each harvest date to harvested plots except neighbouring plots L (Table 1). Furthermore, on 24 August 2000 a pulse of 2 × 1·67 g was applied to medium-intensity (M) neighbouring plots and a pulse of 4 × 1·67 g to the high-intensity (h) neighbouring plots and MC and focus plots to enhance initially the effect of intensive management.

Table 1.  Cutting (C) and nitrogen fertilization (N) schedule during the different phases of the experiment. Plot types: focus, focus plots (mixtures and bare plots); L, low-intensity, M, medium-intensity, H, high-intensity neighbouring plots; MC, monocultures
Plot typeExtinction phaseRestoration phase
August 00*October 00April 01June 01August 01October 01April 02June 02August 02October 02April 03June 03August 03October 03
  • *

    See text for initial fertilizer application.

FocusCNCNCNCNCNCN  C   C 
MCCNCNCNCNCNCN  C   C 
L C  C   C   C 
M CN CN CN CN CN CN CN
HCNCNCNCNCNCNCNCNCNCNCNCNCNCN

regular fertilizer and cutting management, extensification management

In consecutive years nitrogen fertilizer (1·67 g AS) was added after each harvest, with the exception of neighbouring plot L treatment (Table 1), according to agricultural recommendations (C. Stutz, Swiss Federal Research Station for Agroecology and Agriculture, personal communication). At the beginning of the third year (2002), MC and focus plots were switched to an extensification treatment and were no longer fertilized. However, regular AS applications in the neighbouring plot M and H treatments were tripled throughout 2002–03 to speed up the extinction process on these plots.

weeding

Established plants not belonging to the original set of 15 experimental species were removed by hand-weeding on each cutting date. Similarly, monocultures were kept free of any other species. Only a little weed had to be removed in each growth period. Patches of bryophytes were removed in June 2001 to maintain low bryophyte cover.

harvest and measurements

Swards of each plot were cut at a height of 5 cm above the ground and the entire biomass, including plant litter, was placed in paper bags. Plot biomass was determined after the plant material was dried to a constant mass at 80 °C. Species composition for each plot was recorded on 5 June 2001, 19 May 2002 and 4 June 2003. Information on inflorescence formation of the individual species was gathered before the harvests in June 2001, August 2002 and June 2003.

data analyses

Biomass yield and species richness

Mean biomass yield of focus plots and of neighbouring plot treatments L, M and H for the years 2000–03 was estimated from the total yearly biomass harvested on each of the 18 plots assigned to each treatment. Similarly, mean biomass of monocultures was estimated from the total yearly biomass harvested on each of the three MC plots per species. Corresponding with the switch from extinction to restoration phase on focus and MC plots, the estimates for 2001 and 2002–03 represent the post-extinction biomass yields in the extinction phase and restoration phase, respectively. The effects of management intensity and year (2001–03) on biomass and species richness were tested using the split plot approach to repeated measures, with effects of year partitioned into a linear contrast and deviation (Meyer & Schmid 1999). These and all following analyses were performed using the software package genstat (Payne et al. 1993).

Species extinction

The effects of species identity (SI) and management intensity (MI) on extinction probability in the neighbouring plots was analysed using presence/absence of species in summer 2003 (after 3·5 years of experimental treatment) as the dependent variable in a binary logistic regression, with subsequent analysis of deviance (McCullagh & Nelder 1989). To be precise, what we analysed was species extinction from the seed bank, as we did not measure initial establishment of species in each plot. The full model included the blocking factors block and sub-block to control for spatial variation within the experimental site (Table 2). We fitted MI (d.f. 2; tested against experimental unit with d.f. 16) and SI (total d.f. 14) and the SI × MI interaction (total d.f. 28). The SI effect was partitioned into the linear contrasts of species biomass in monoculture (d.f. 1) and number of seeds per plot (d.f. 1), and deviation (d.f. 13), to find out whether extinction probability was related to species productivity and number of seeds sown rather than to idiosyncratic differences between species. The linear contrast species productivity was fitted using the mean of biomass over 2001–03 harvests for each species in the monoculture plots (Table 2). All of these terms were tested against the SI × experimental unit (within sub-block and block) interaction (d.f. 337) which itself was tested against the residual (d.f. 404).

Table 2.  Effects of species identity (SI, decomposed into linear effect of species productivity in monoculture, seed number per plot and remaining SI effects) and management intensity (MI) on extinction probability (analysis of deviance based on logistic regression). Listed are degrees of freedom (d.f), deviance, mean deviance, deviance ratio and the approximate F-probability (P). This analysis used neighbouring plots for the estimation of extinction probability and MC plots for the estimation of species productivity
Source of variationd.f.DevianceMean devianceDeviance ratioP
Block  2  3·657 1·82910·45 0·011
Sub-block (block)  6  1·052 0·175 0·61 0·722
MI  2  9·130 4·56515·80 < 0·001
Unit (sub-block (block)) 16  4·622 0·289  
Total SI 14392·71228·05132·62 < 0·001
 Species productivity  1  9·712 9·71211·29 < 0·001
 Seed number  1 12·34812·34814·36 < 0·001
 Remaining SI 12370·65230·88835·92 < 0·001
SI × MI 28 83·72 2·887 3·36 < 0·001
 Species productivity × MI  2  8·387 4·194 4·88 0·008
 Seed number × MI  2  0·603 0·301 0·35 0·705
 Remaining SI × MI 24 74·73 3·114 3·62 < 0·001
SI × unit (sub-block (block))337288·053 0·860 1·38 0·001
Residual404252·314 0·625  
Simulated extinction sequences and predicted mixture performance

Empirical species extinction sequences and performance of the persisting species were used to predict diversity–productivity curves for specific non-random as well as random extinction scenarios. As our extinction treatments did not lead to the joint extinction of a large enough number of species to obtain low-diversity communities, we simulated these based on the measured extinction probabilities of the individual species. We contrasted these simulations with others in which species all had the same extinction probability, i.e. were allowed to go extinct randomly. Furthermore, we made simulations in which the extinction sequence was fully correlated with species productivity (biomass in MC plots). We simulated species losses at three different levels, to obtain communities consisting of one, four or seven surviving species. In all simulations we used two alternative assumptions of how mixture yield was related to the monoculture yields of the surviving species: (i) the mixture yield equals the biomass of the highest-yielding monoculture (dominance) and (ii) mixture yield equals the average biomass of the monoculture yields (mixing; Schmid et al. 2002). For these predictions of mixture yield we used the average monoculture yields under the high-intensity (1-year extinction phase) and low-intensity (2-year restoration phase) managements. The MC yields of the extinction phase were those of 2001, and the MC yield of the restoration phase the average of 2002 and 2003 (n= 3 MC plots for each species). The predicted yield of random mixtures was calculated as the average of 25 random draws each of one, four and seven species from the original species pool. All predicted yields of communities after species loss were expressed relative to the observed full yield of mixtures in the focus plots under the same management. The observed number of species in these mixtures had dropped below 15 at the time when these harvests were taken (Figs 3 and 4).

image

Figure 3. Predictions of the biomass (mean ± SE) of random and non-random species mixtures in high-intensity grassland (extinction environment, experimental year 2001) using four sets of assumptions: models of mixture productivity were dominance (a, c) and mixing (b, d); extinction sequences were based on persistence rank in mixture (a, b) and on biomass rank in monoculture (c, d) (see the Materials and methods). All biomass values are expressed relative to the mean mixture yield of focus plots (horizontal broken line). Mean species number on focus plots is indicated by the vertical broken line.

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image

Figure 4. Predictions of the biomass of random and non-random species mixtures in extensified grassland (restoration environment, experimental years 2002–03) using four sets of assumptions: models of mixture productivity were dominance (a, c) and mixing (b, d); extinction sequences were based on persistence rank in mixture (a, b) and on biomass rank in monoculture (c, d) (see the Materials and methods). All biomass values are expressed relative to the mean mixture yield in focus plots (horizontal broken line). Mean species number on focus plots is indicated by the vertical broken line.

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The simulation of mixture yields based on component monoculture yields disregards any potential species complementarity. We do not suggest that complementarity mechanisms are negligible in our study systems. Indeed, using only dominance and mixing in our simulations allowed us to conclude that any differences between simulated and observed mixture yields indicate species complementarity (see the Discussion). Our main question remained, however, to establish whether the loss of the least productive or least persistent species affects ecosystem functioning less than random species loss. This would be particularly likely if a sampling mechanism operates.

Effects of neighbouring plot treatments on recolonization of focus plots

The effects of neighbouring plot treatments on the 18 focus plots during the extensification phase of the experiment (2002–03) were analysed using species richness in summer 2003 and biomass in the year 2003 as the dependent variables. The full model included block and sub-block to control for spatial variation within the experimental site, and neighbourhood treatment as the treatment factor.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

biomass and species richness in neighbouring plots

Community biomass in the neighbouring plots with L, M and H management throughout the experiment followed the expected pattern except in the first year of the experiment, when biomass was not yet significantly higher in treatment M than L (Fig. 1; main effect of management and year P < 0·001, interaction P < 0·001). Biomass in the second year was higher than in the third year, mainly because of elevated levels in June and October. Management (tested against plot) and year (linear effect, tested against the linear effect of year × plot interaction) each affected species richness (both P < 0·001) but their interaction was not significant (P = 0·15). In 2001 mean observed species richness per plot was highest in L plots (10·8) and lowest in H plots (10·2). In 2003 mean species richness per plot was again highest in L plots (6·1), followed by M plots (4·7) and H plots (4·4).

image

Figure 1. Biomass of neighbouring plots with low-, medium-, and high-intensity management from 2000 to 2003 (mean ± SE).

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biomass of mixtures in focus plots and of monocultures

The mean biomass of the 15 species in monoculture ranged from 32·4 to 360·6 g m−2 during the high-intensity extinction phase (year 2001). During the low-intensity restoration phase the respective figures were 1·2–56·4 g m−2 (2002) and 0·9–49·6 g m−2 (2003). The mean biomass of the 18 focus plots was similar to the mean biomass of the single highest-yielding species in monoculture during the extinction phase (370·8 vs. 360·6 g m−2), as well as during the restoration phase (60·0 vs. 57·0 g m−2 in 2002 and 41·3 vs. 43·0 g m−2 in 2003; Fig. 2). This indicated that high mixture yield could have been caused mainly by the sampling mechanism. The biomass ranks of the monocultures differed strongly between the high-intensity and the low-intensity management phases, although two species (Festuca pratensis and Lolium multiflorum) were among the top four in both phases (Fig. 2). Mean monoculture yield was 168·6 g m−2 in 2001, 27·6 g m−2 in 2002 and 21·0 g m−2 in 2003, or about 50% of the mixture or the best monoculture yields.

image

Figure 2. Biomass (mean ± SE) of the focus plot mixtures (n = 18) and each component monoculture (n = 3) under (a) the 2001 high-intensity (extinction), (b) the 2002 low-intensity (restoration) and (c) the 2003 low-intensity (restoration) management. Monocultures are ranked according to their yield in 2001 along the x-axis. Numbers above bars indicate biomass ranks. Species abbreviations for monocultures: Lp, Lolium perenne; Lm, Lolium multiflorum; Fp, Festuca pratensis; Ae, Arrhenaterum elatius; Tp, Trifolium pratense; Dg, Dactylis glomerata; Cl, Chrysanthemum leucanthemum; Tf, Trisetum flavescens; Ap, Alopecurus pratensis; Hl, Holcus lanatus; Cb, Crepis biennis; To, Taraxacum officinale; Pl, Plantago lanceolata; Pt, Poa trivialis; Tr, Trifolium repens.

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extinction probabilities in neighbouring plots

The effects of both SI and MI, as well as their interaction on extinction in the experimental plots, were highly significant (Table 2). When the SI effect was partitioned into two linear contrasts for monoculture biomass and initial seed number, and deviation, all three components were significant, indicating that extinction probability from the seed bank was in part due to low species productivities and low seed numbers, and in part due to other differences between species that we could not identify (Table 2). Persistence ranks of species (derived from the proportion of neighbouring plots in which a species was present in June 2002 and in June 2003) differed between the H and L plots, although three species (Alopecurus pratensis, Dactylis glomerata and Trisetum flavescens) were among the top five in both managements in each year (Table 3).

Table 3.  Persistence rank of species in high- and low-intensity management in the neighbouring plots. Persistence is defined as the proportion of plots in which a species was present in June 2002 or June 2003, respectively (net persistence)
SpeciesHigh intensityLow intensity
2002200320022003
Alopecurus pratensis 2 2 1 2
Arrhenaterum elatius 7111011
Chrysanthemum leucanthemum10 91210
Crepis biennis15111412
Dactylis glomerata 3 1 2 5
Festuca pratensis12 81514
Holcus lanatus14111312
Lolium multiflorum 6 7 814
Lolium perenne 1 4 4 3
Plantago lanceolata 9 9 5 3
Poa trivialis 8 5 6 6
Taraxacum officinale 5 611 8
Trifolium pratense1311 7 6
Trifolium repens1111 9 8
Trisetum flavescens 4 2 3 1

diversity–productivity curves for random and non-random extinction scenarios

The biomass of mixtures in focus plots and the biomass of monocultures, which both experienced the same management, provided a basis to choose among a number of simple models to explain the biomass of mixtures. The available data suggested that, in the present system, the biomass of the highest-yielding monoculture represented a reasonable estimator for mixture biomass (see above and Fig. 2). Therefore we chose this model for predicting diversity–productivity curves based on the biomass of component species and observed non-random species extinction sequences.

Species persistence as well as species productivity (above-ground biomass) of the monocultures are summarized in Table 3 and Fig. 2. Using these data and four sets of assumptions on extinction sequences and dominance vs. mixing (see the Materials and methods), we obtained the simulated diversity–productivity relationships shown in Figs 3 and 4. For the environmental conditions of the extinction phase, yield predictions for communities with randomly reduced species richness (seven, four and one species) were under all assumptions lower than those predicted (seven and four species) or observed (one species) for non-randomly reduced communities (Fig. 3a–d). For the environmental conditions of the restoration phase, the assumptions dominance and extinction sequence based on persistence gave similar yield predictions for communities with randomly and non-randomly reduced species richness (Fig. 4a). Using the other three sets of assumptions again gave significantly higher yield predictions for communities with non-randomly reduced species richness (seven, four and one species) than for communities with randomly reduced species richness (Fig. 4b–d) (see the Discussion).

effects of neighbouring plot treatments on recolonization of focus communities

Although many of the species had set seed in the second and third years, few individuals established from these seeds on the bare plots. At the time scale of the experiment, no effects of neighbouring plot treatments on species richness of focus plots were observed (Table 4). The effect of neighbouring plot treatment on focus plot biomass was also non-significant (Table 4). In 2003 biomass was highest (43·2 g m−2) near the L and lowest (38·7 g m−2) near the M neighbouring plot treatment. The effects of composition and of the amount of seeds produced may thus have exceeded the effects of the observed species richness of the neighbouring plot treatments.

Table 4.  Results of anova examining the effect of the colonizing species pool treatment (low-, medium- and high-intensity management (MI) of neighbouring plots) on species richness and biomass in the focus plots containing species mixtures. Listed are degrees of freedom (d.f), mean squares (MS), F-ratios and the P-values for the treatment effects
Source of variationd.f.Species richnessBiomass
MSF-ratioPMSF-ratioP
Block 22·1671·30 0·7620·14 
Sub-block (block) 31·6670·66 5·5601·34 
MI of neighbouring plots 22·6671·050·3850·8940·220·81
Residual102·533  4·144  

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Transitory changes of land management in naturally assembling grassland ecosystems may lead to species extinctions, whereas recolonization may not always be effective at a short to medium time scale (Loreau & Mouquet 1999; Smith et al. 2000). This raises the question of whether the absence of any of the extinct species in a post-extinction environment affects ecosystem functioning, e.g. community biomass production. Frequently, the effect of species loss will depend on which of the original species disappear during the extinction phase.

Simulated extinction showed that management intensity increased extinction probability, such that species with greater biomass production had lower extinction probabilities. There was also species-specific variation in extinction probability, and there was a significant interaction between management intensity and species identity. This suggests that, after management-induced extinction, the remaining species are particularly productive ones, and this may reduce the impact of species extinction on ecosystem functioning. However, this argument would require a positive correlation between species persistence during the extinction treatment and species performance in the post-extinction environment, or at least an above-average biomass of a few highly persistent species, as we found (Fig. 5). Thus, because of a few important remaining species (Alopecurus pratensis and Trisetum flavescens; Fig. 5b), the simulated species loss would have been less damaging to ecosystem functioning in the restoration environment than in the random extinction scenarios. Based on the random extinction assumption, species loss from communities originally containing 15 species led to a reduction in predicted yield by 26–54% when four species remained, whereas in the non-random extinction scenario it only reduced predicted yield by 2–35% (the yield predictions for the case of one remaining species were 54% and 42–49%, respectively) (Fig. 4). These biodiversity–ecosystem functioning relationships had to be predicted, because the duration of our experiment was too short to actually assemble the post-extinction communities as derived from the estimates of extinction probabilities. What we could analyse within the duration of the experiment was the relationship between the observed monoculture yield and the observed yield for the total mixture of all 15 species (Fig. 2).

image

Figure 5. Persistence of the 15 species in the extinction environment (high-intensity neighbouring plots) and monoculture biomass (MC plots; relative to mixture) in (a) the extinction environment (correlation coefficient 0·169, P= 0·55) and (b) the restoration environment (correlation coefficient 0·312, P= 0·26; for species abbreviations see Fig. 2).

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How do the observed and simulated effects of non-random species loss relate to ecosystem management? We distinguish between purposefully sown, tilled grassland and permanent grasslands subject to natural community assembly and ‘disassembly’ (Ostfeld & LoGiudice 2003). From the perspective of tilled grassland management, the potential for a yield higher than that of the highest-yielding monoculture may determine the choice of species mixtures. In our experiment, mixtures established from 15 species transgressed the mean yield of the best monoculture, Festuca pratensis, by 2% (in the restoration phase). From the perspective of the management of permanent, naturally assembling grasslands with shifts in management, however, an additional relevant effect of species loss concerns the lack of best-performing species (or species combinations) after management-induced extinctions. Persistence in the face of an extinction-inducing factor may be closely linked with high monoculture biomass in the extinction environment, but less so to high monoculture biomass in a changed post-extinction environment (Lolium perenne in Fig. 5). After the projected management-induced (non-random) extinction of all but one species and a subsequent shift to restoration management, the remnant species, Dactylis glomerata, would have yielded 13·1 g m−2, 49% less biomass than the best extinct species, Festuca pratensis, would have done in the same post-extinction environment (Fig. 5b). In a context with a transient extinction pressure therefore, non-random species loss may still have quite severe consequences.

The present study demonstrates that, in the context of species limitation in naturally assembling communities, non-random ‘sampling’ of species through transient environmental factors may be highly biologically relevant. Similar conclusions are also emerging for the diversity–productivity relationship in microbial communities (Hodgson, Rainey & Buckling 2002) and for effects of aquatic plant diversity on ecosystem variables in a wetland system (Engelhardt & Ritchie 2002).

The choice of management regimes is critical for subsequent correlations between species traits, the non-random sequence of species loss and the relation between species richness and yield for any given species assemblage (Petchey & Gaston 2002). We are only able to report a particular biodiversity–productivity relationship for a grassland system where the extinction pressure is fertilizer application and frequent cutting, followed by restoration management. Establishment of recolonization treatments through differential management of neighbouring communities, however, proved to be difficult. In spite of shallow soils and major treatment differences, the response of species richness was relatively inert, remaining above four species under all managements. Our impression is that the reason for this was not uncontrolled dispersal between experimental units. On the contrary, minimal colonization on bare plots suggests that experimental time scales well beyond the 3 years of the present study would be required to allow measurable effects of colonization on focus plot biomass.

Because of the limitations of our small-scale and short-period experimental approach, we are cautious in offering conclusions for grassland management based on the persistence–productivity relationships found in the experimental plots. Implications concern the restoration of European species-rich grasslands in the context of agri-environment schemes (Smith et al. 2000, 2002, 2003; Pywell et al. 2002, 2003). Recent studies have found that removal of extinction pressures is not always sufficient to enhance biodiversity in these grasslands (Pywell et al. 2003; Smith et al. 2003). Our results indicate that active assistance in plant recolonization may also be necessary to enhance plant production after shifts in grassland management. When, as in our experiment, mesic grasslands of the Swiss Plateau classified as Lolio perennisArrhenateretum elatioris (Dietl 1995) are turned into species-poor communities through fertilization and frequent cutting, and later subjected to low-intensity management, Festuca pratensis is a key ‘missing’ species that needs to be reintroduced if maximum production is an objective. Similar conclusions may emerge for other ecosystems and species that are subject to different extinction pressures and shifts in management and environmental conditions.

The more general conclusion concerns the differences between random and non-random species-loss effects on ecosystem functioning. Our results demonstrate that biodiversity–productivity relationships critically depend on the specific extinction process. The negative effects of the present scenario, of management-induced, non-random species loss, were less severe than would be predicted from random species loss. However, we do not know how robust this result is regarding other extinction-inducing factors, such as drought and heavy grazing.

To be relevant for ecosystem management, future research on the biodiversity–ecosystem functioning relationship needs to address further specific real-world extinction scenarios, post-extinction environments and species limitation–recolonization contexts (Gonzalez & Chaneton 2002; Pywell et al. 2002; Schmid et al. 2002; Smith & Knapp 2003; Pfisterer et al. 2004). Experimental studies on the correlation of species persistence in a variety of extinction contexts and species contributions to ecosystem functioning in post-extinction environments should become an important research focus. These studies should clarify the links between biodiversity and ecosystem functioning in the context of managed, naturally assembling communities. Random species loss represents a useful null model against which to contrast more realistic non-random species loss scenarios in future experiments.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

We are grateful to Theres Zwimpfer for assistance in maintaining the experimental communities, and to W. Dietl and C. Stutz of the Swiss Federal Research Station for Agroecology and Agriculture for advice regarding species selection and fertilizer treatments. Valuable comments by Frank Berendse, Jason Fridley, Owen Petchey, David Wardle, two anonymous referees and Steve Ormerod are gratefully acknowledged. B. Schmid was supported by the Swiss National Science Foundation (grant no. 31-65224·01) and the German Science Foundation (grant no. FOR 456 - WE 2618/6-1 to W.W. Weisser).

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  • Aarssen, L.W. (1997) High productivity in grassland ecosystems: effected by species diversity or productive species? Oikos, 80, 183184.
  • Chapin, F.S., Walker, B.H., Hobbs, R.J., Hooper, D.U., Lawton, J.H., Sala, O.E. & Tilman, D. (1997) Biotic control over the functioning of ecosystems. Science, 277, 500504.
  • Craine, J.M., Reich, P.B., Tilman, G.D., Ellsworth, D., Fargione, J., Knops, J. & Naeem, S. (2003) The role of plant species in biomass production and response to elevated CO2 and N. Ecology Letters, 6, 623630.
  • Dietl, W. (1995) Wandel der Wiesenvegetation im Schweizer Mittelland. Zeitschrift für Ökologie und Naturschutz, 4, 239249.
  • Engelhardt, K.A.M. & Ritchie, M.E. (2002) The effect of aquatic plant species richness on wetland ecosystem processes. Ecology, 83, 29112924.
  • Fischer, M. & Stöcklin, J. (1997) Local extinctions of plants in remnants of extensively used calcareous grasslands 1950–1985. Conservation Biology, 11, 727737.
  • Fridley, J.D. (2003) Diversity effects on production in different light and fertility environments: an experiment with communities of annual plants. Journal of Ecology, 91, 396406.
  • Fuller, R.M. (1986) The changing conservation interest of lowland grasslands in England and Wales: a review of grassland surveys 1939–1984. Biological Conservation, 40, 281300.
  • Gonzalez, A. & Chaneton, E.J. (2002) Heterotroph species extinction, abundance and biomass dynamics in an experimentally fragmented microecosystem. Journal of Animal Ecology, 71, 594602.
  • Grime, J.P. (1998) Benefits of plant diversity to ecosystems: immediate, filter and founder effects. Journal of Ecology, 86, 902910.
  • Grime, J.P. (2002) Declining plant diversity: empty niches or functional shifts? Journal of Vegetation Science, 13, 457460.
  • He, J.S., Bazzaz, F.A. & Schmid, B. (2002) Interactive effects of diversity, nutrients and elevated CO2 on experimental plant communities. Oikos, 97, 337348.
  • Hector, A., Schmid, B., Beierkuhnlein, C., Caldeira, M.C., Diemer, M., Dimitrakopoulos, P.G., Finn, J.A., Freitas, H., Giller, P.S., Good, J., Harris, R., Hogberg, P., Huss-Danell, K., Joshi, J., Jumpponen, A., Körner, C., Leadley, P.W., Loreau, M., Minns, A., Mulder, C.P.H., O'Donovan, G., Otway, S.J., Pereira, J.S., Prinz, A., Read, D.J., Scherer-Lorenzen, M., Schulze, E.D., Siamantziouras, A.S.D., Spehn, E.M., Terry, A.C., Troumbis, A.Y., Woodward, F.I., Yachi, S. & Lawton, J.H. (1999) Plant diversity and productivity experiments in European grasslands. Science, 286, 11231127.
  • Hodgson, D.J., Rainey, P.B. & Buckling, A. (2002) Mechanisms linking diversity, productivity and invasibility in experimental bacterial communities. Proceedings of the Royal Society of London B, 269, 2272283.
  • Hubbell, S.P. (2001) The Unified Neutral Theory of Biodiversity and Biogeography. Princeton University Press, Princeton and Oxford.
  • Huston, M.A. (1997) Hidden treatments in ecological experiments: re-evaluating the ecosystem function of biodiversity. Oecologia, 110, 449460.
  • Huston, M.A., Aarssen, L.W., Austin, M.P., Cade, B.S., Fridley, J.D., Garnier, E., Grime, J.P., Hodgson, J., Lauenroth, W.K., Thompson, K., Vandermeer, J.H. & Wardle, D.A. (2000) No consistent effects of plant diversity on productivity. Science, 289, 1255.
  • Kleijn, D. & Sutherland, W.J. (2003) How effective are European agri-environment schemes in conserving and promoting biodiversity? Journal of Applied Ecology, 40, 947969.
  • Loreau, M. (2000) Biodiversity and ecosystem functioning: recent theoretical advances. Oikos, 91, 317.
  • Loreau, M. & Mouquet, N. (1999) Immigration and the maintenance of local species diversity. American Naturalist, 154, 427440.
  • McCullagh, P. & Nelder, J.A. (1989) Generalized Linear Models. Chapman and Hall, London, UK.
  • McNaughton, S.J. (1977) Diversity and stability of ecological communities: a comment on the role of empiricism in ecology. American Naturalist, 111, 515525.
  • Meyer, A.H. & Schmid, B. (1999) Experimental demography of the old-field perennial Solidago altissima: the dynamics of shoot population. Journal of Ecology, 87, 1727.
  • Ostfeld, R.S. & LoGiudice, K. (2003) Community disassembly, biodiversity loss, and the erosion of an ecosystem service. Ecology, 84, 14211427.
  • Ovenden, G.N., Swash, A.R.H. & Smallshire, D. (1998) Agri-environment schemes and their contribution to the conservation of biodiversity in England. Journal of Applied Ecology, 35, 955960.
  • Payne, R.W., Lane, P.W., Digby, P.G.N. et al. (1993) GENSTAT 5 Reference Manual. Clarendon Press, Oxford, UK.
  • Petchey, O.L. & Gaston, K.J. (2002) Extinction and the loss of functional diversity. Proceedings of the Royal Society of London B, 269, 17211727.
  • Pfisterer, A.B., Joshi, J., Schmid, B. & Fischer, M. (2004) Rapid decay of diversity–productivity relationships after invasion of experimental plant communities. Basic and Applied Ecology, 5, 514.
  • Pimm, S.L., Jones, H.L. & Diamond, J. (1988) On the risk of extinction. American Naturalist, 132, 757785.
  • Pywell, R.F., Bullock, J.B., Hopkins, A., Walker, K.J., Sparks, T.H., Burke, M.J.W. & Peel, S. (2002) Restoration of species-rich grassland on arable land: assessing the limiting processes using a multi-site experiment. Journal of Applied Ecology, 39, 294309.
  • Pywell, R.F., Bullock, J.M., Roy, D.B., Warman, L., Walker, K.J. & Rothery, P. (2003) Plant traits as predictors of performance in ecological restoration. Journal of Applied Ecology, 40, 6577.
  • Reich, R.B., Knops, J., Tilman, D., Craine, J., Ellsworth, D., Tjoelker, M., Lee, T., Wedin, D., Naeem, S., Bahauddin, D., Hendrey, G., Jose, S., Wrage, K., Goth, J. & Bengston, W. (2001) Plant diversity enhances ecosystem responses to elevated CO2 and nitrogen deposition. Nature, 410, 809812.
  • Schläpfer, F. & Schmid, B. (1999) Ecosystem effects of biodiversity: a classification of hypotheses and exploration of empirical results. Ecological Applications, 9, 893912.
  • Schmid, B. (2002) The species richness–productivity controversy. Trends in Ecology and Evolution, 17, 113114.
  • Schmid, B., Hector, A., Huston, M.A., Inchausti, P., Nijs, I., Leadley, P.W. & Tilman, D. (2002) The design and analysis of biodiversity experiments. Biodiversity and Ecosystem Functioning: Synthesis and Perspectives (eds M.Loreau, S.Naeem & P.Inchausti), pp. 6175. Oxford University Press, Oxford, UK.
  • Schmid, B., Joshi, J. & Schläpfer, F. (2002) Empirical evidence for biodiversity–ecosystem functioning relationships. The Functional Consequences of Biodiversity: Empirical Progress and Theoretical Extensions (eds A.P.Kinzig, S.W.Pacala & D.Tilman), pp. 120150. Monographs in Population Biology No. 33, Princeton University Press, Princeton, NJ.
  • Schwartz, M.W., Brigham, C.A., Hoeksema, J.D., Lyons, K.G., Mills, M.H. & Mantgem, P.J. (2000) Linking biodiversity to ecosystem function: implications for conservation biology. Oecologia, 122, 297305.
  • Smith, M.D. & Knapp, A.K. (2003) Dominant species maintain ecosystem function with non-random species loss. Ecology Letters, 6, 509517.
  • Smith, R.S., Shiel, R.S., Bardgett, R.D., Millward, D., Corkhill, P., Rolph, G., Hobbs, P.J. & Peacock, S. (2003) Soil microbial community, fertility, vegetation and diversity as targets in the restoration management of a meadow grassland. Journal of Applied Ecology, 40, 5164.
  • Smith, R.S., Shiel, R.S., Millward, D. & Corkhill, P. (2000) The interactive effects of management on the productivity and plant community structure of an upland meadow: an 8-year field trial. Journal of Applied Ecology, 37, 10291043.
  • Smith, R.S., Shiel, R.S., Millward, D., Corkhill, P. & Sanderson, R.A. (2002) Soil seed banks and the effects of meadow management on vegetation change in a 10-year meadow field trial. Journal of Applied Ecology, 39, 279293.
  • Stöcklin, J. & Fischer, M. (1999) Plants with longer-lived seeds have lower local extinction rates in grassland remnants 1950–1985. Oecologia, 120, 539543.
  • Symstad, A.J., Chapin, F.S. III, Wall, D.H., Gross, K.L., Huenneke, L.F., Mittelbach, G.G., Peters, D.P.C. & Tilman, D. (2003) Long-term and large-scale perspectives on the relationship between biodiversity and ecosystem functioning. Bioscience, 53, 8998.
  • Tilman, D. & Downing, J.A. (1994) Biodiversity and stability in grasslands. Nature, 367, 363365.
  • Tilman, D., Wedin, D. & Knops, J. (1996) Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature, 379, 718720.
  • Vitousek, P.M., Mooney, H.A., Lubchenco, J. & Melillo, J.M. (1997) Human domination of Earth's ecosystems. Science, 277, 494499.
  • Wardle, D.A. (1999) Is ‘sampling effect’ a problem for experiments investigating biodiversity–ecosystem function relationships? Oikos, 87, 403407.