OIKOS 94: 468–480. Copenhagen 2001

Productivity and complementarity in grassland microcosms ofvarying diversity

Jeffrey S. Dukes

Dukes, J. S. 2001. Productivity and complementarity in grassland microcosms ofvarying diversity. – Oikos 94: 468–480.

Several researchers have hypothesized that, through various mechanisms, loss ofspecies and functional group richness from a plant community will affect themagnitude and interannual variability of productivity. To test this hypothesis, Iconducted a microcosm study of California grassland communities that differed inspecies richness. I grew cohorts of microcosms that simulated undisturbed grassland(in one year) and gopher-disturbed grassland (in two consecutive years). As thenumber of species per functional group decreased from 4 to 1, biomass productionremained constant in all three cohorts. As species richness decreased from 16 to 1 (or8 to 1, in either case including a drop in functional group richness), productivitydeclined in one of the cohorts. In this cohort, productivity of one polyculturemarginally exceeded that of the most productive monoculture. Resource complemen-tarity and a type of selection effect may have each contributed to the observeddiversity-productivity relationships. Results suggest the existence of a selection effectthat involves species that are highly productive in mixtures, rather than in monocul-ture. Over two seasons, species and functional group richness did not affect theinterannual variability of biomass production. Comparisons of interannual changesin the productivity of monocultures and polycultures suggested that, in some polycul-tures, increased water availability might have relieved interspecific competition morethan intraspecific competition. Based on results from this experiment and othermanipulative experiments, I develop a framework to explain the relationship betweenspecies richness and productivity in terrestrial plant communities. The frameworkhighlights the importance of environmental variation in shaping the diversity/produc-tivity relationship.

J. S. Dukes, Dept of Biological Sciences, Stanford Uni�., Stanford, CA 94305, USA(present address: Dept of Biology, Uni�. of Utah, 257 South 1400 East, Salt Lake City,UT 84112-0840, USA [dukes@biology.utah.edu]).

The ongoing, rapid decline in the number of species(Pimm et al. 1995) and populations (Hughes et al. 1997)on Earth has stimulated interest in the nature of linksbetween biodiversity and ecosystem processes. Recentstudies have shed light on the relationship betweendiversity and the productivity of plant communities(e.g., Naeem et al. 1996, Tilman et al. 1996, Hooperand Vitousek 1997, Loreau 1998a, Hector et al. 1999,Tilman 1999). Many researchers have found that, onaverage, community productivity declines as species arelost (Naeem et al. 1996, Tilman et al. 1996, 1997,Symstad et al. 1998, Hector et al. 1999). Relatively few

experiments have tested the influence of environmentalfactors on the diversity/productivity relationship.

Several recent papers have asserted the importance ofdetermining which mechanisms cause correlations be-tween biodiversity and ecosystem processes (Aarssen1997, Tilman 1997, 1999, Hector 1998, Loreau 1998b,Wardle 1999). Two contrasting mechanisms are men-tioned most frequently: the sampling effect and re-source complementarity. The sampling effect can causea correlation between diversity and a process such asbiomass production if the probability of including ahighly productive species in a community increases as

Accepted 28 March 2001

Copyright © OIKOS 2001ISSN 0030-1299Printed in Ireland – all rights reserved

OIKOS 94:3 (2001)468

species richness increases (Aarssen 1997, Huston 1997,Tilman 1997). Loreau (2000) has recently suggestedusing the term ‘‘selection effect’’ to encompass thesampling effect and any other effect by which specieswith extreme values of a given trait are more likely tobe included in more diverse communities. Resourcecomplementarity, or resource partitioning, occurs whendifferent species obtain the same resource from differ-ent locations, at different times, or from chemicallydifferent sources. In theory, this resource partitioningleads to more complete use of the available resources inthe system, and maximizes productivity. In practice,complementary resource use can be difficult to detect,even among species that would be expected to havedifferent resource requirements (Hooper 1998). When itoccurs, complementary resource use does not alwayscause mixtures to outproduce all monocultures of theircomponent species (Hooper 1998). Reasons for thisseeming discrepancy are discussed below, and in Gar-nier et al. (1997), Hector (1998) and Loreau (1998b).

According to some models, loss of species from acommunity should increase variation in annual produc-tivity (Doak et al. 1998, Tilman et al. 1998, Tilman1999, Yachi and Loreau 1999). Relatively few studieshave tested this theory experimentally. Three intriguingstudies have suggested that productivity becomes lessstable as diversity decreases (Dodd et al. 1994, Tilmanand Downing 1994, Tilman 1996). In particular, two ofthese studies suggest that biomass production of diversecommunities is less likely to crash in drought years(Tilman and Downing 1994, Tilman 1996; but seeHuston 1997). The question of whether diverse commu-nities are also more likely to capitalize on unusuallyhigh resource availability in, for instance, a very wetyear, has not been addressed experimentally.

I constructed grassland community microcosms ofvarying species composition and diversity to examinethe relationships of biodiversity with productivity, re-source complementarity, and interannual variation inproductivity. I sought to address four specific ques-tions: 1) How do changes in environmental variablessuch as weather and soil disturbance affect the relation-ship between diversity and productivity? 2) How doesspecies diversity (independent of functional diversity)contribute to the primary productivity of a community?3) To what extent does resource complementarity con-tribute to the productivity of diverse communities? 4)Can diversity limit interannual variation in the produc-tivity of grassland communities? To test the first ques-tion, I grew three cohorts of microcosms, simulatinggopher-disturbed grassland (in two consecutive growingseasons) and undisturbed grassland (in the secondgrowing season). In each of these cohorts, I includedcommunities that contained one, two, or four speciesfrom each of four functional groups (to test question 2).To test the third question, I grew monocultures of theeight grassland species that were used in the 8- and

4-species communities. To test the fourth question, Icompared aboveground biomass production of the twocohorts that simulated gopher-disturbed grasslands.

Methods

Study site and infrastructure

This experiment took place on a small hilltop in theJasper Ridge Biological Preserve (JRBP; near PaloAlto, California, USA: 37°24� N, 122°14� W, 120 melevation), during the 1996–1997 and 1997–1998 grow-ing years. The experiment was conducted in micro-cosms, which consisted of upright sections of PVC pipe(0.95 m long by 0.2 m diameter) that were fitted with aPVC cap at the bottom. A 12 mm diameter hole in thecap allowed the microcosms to drain. Microcosms werefilled to 1 cm from the top with a 1:3 mixture of sandand pulverized soil from the surrounding grassland.The top 10 cm (approximately) of each microcosm wasfilled with soil that had been wetted and heated (75–80°C for 48 h) to kill weed seeds. All microcosms werekept outdoors in a large metal-frame storage box.Microcosms received ambient precipitation, which wascontinuously measured by a tipping-bucket rain gaugelocated within 10 m of the experimental setup.

Experimental design

The microcosms contained species chosen from a poolof native and naturalized California grassland species.This species pool consisted of four species from each offour functional groups: annual grasses, perennialgrasses, early-season annual forbs, and late-season an-nual forbs (Table 1). In the San Francisco Bay Area,grassland species germinate or resprout after the firstsignificant autumn rains, which usually fall in Octoberor November. Most of the grasslands are dominated byintroduced annual grasses, which set seed and die inMay or June. Perennial bunchgrasses, which are mostlynative, senesce aboveground in June or July. Early-sea-son annual forbs generally die in April or May, andlate-season annual forbs continue growing through thesummer, finally setting seed and dying between Augustand October. Within these phenologically defined func-tional groups, species are generally similar in morphol-ogy and physiology (Gulmon et al. 1983, Mooney et al.1986, Chiariello 1989, Armstrong 1991). Among thesegroups, species differ in seasonal and spatial patterns ofresource use (Hooper and Vitousek 1998).

I constructed one 16-species and one eight-speciescommunity, two different four-species communities(termed 4-1 and 4-2), and eight different monocultures(Fig. 1). All polycultures had an equal number ofspecies from each functional group. I employed a

OIKOS 94:3 (2001) 469

Table 1. Species and seeding densities.

Species† Seeding density (seeds/m2)§Native? Species code‡

A�ena barbata N 1847A1Bromus hordeaceus N 2197A2Vulpia microstachys 2548Y A3Lolium multiflorum N 1019A4Plantago erecta Y 8057E1Lasthenia californica Y 7643E2Erodium botrys 2547N E3Microseris douglasii Y 6115E4Hemizonia congesta ssp. luzulifolia 1847Y L1Lessingia hololeuca Y 2962L2Calycadenia multiglandulosa 5096Y L3Epilobium brachycarpum Y 3567L4Nassella pulchra Y P1 5701 (1911)Elymus multisetus Y P2 3057 (1019)Festuca pratensis N 4076P3Elymus glaucus ssp. glaucus Y P4 4076

† Nomenclature follows Hickman (1993). ‡ The first letter of the species code represents the functional group classification ofthe species. A=annual grass, E=early-season annual forb, L= late-season annual forb, P=perennial grass. § Values inparentheses indicate the density of seeds sown in established treatments, where different from new treatments.

nested design, such that the eight-species communityconsisted of all the species in the two four-speciescommunities, and these eight ‘‘core’’ species were alsogrown in monoculture (Fig. 1). In 1996–1997, I main-tained 10 replicates of each of the microcosm types. In1997–1998, I maintained five replicates of each of themicrocosm types from the first growing year (whichwere termed ‘‘established’’ microcosms in this secondyear), and created five new replicates of each micro-cosm type (‘‘new’’ microcosms). Thus, there were threecohorts of microcosms: new 1996–1997, new 1997–1998, and established 1997–1998. Each cohort differedeither in disturbance level or in the weather conditionsit experienced. Within each cohort, microcosms werearranged in a block design, with one replicate of eachcommunity type arranged randomly in each block.Microcosms from other related experiments were alsoincluded in each block.

New microcosms in this experiment were designed tosimulate gopher-disturbed patches of California grass-land (which are similar in size to the microcosms, andcomprise an annual average of 26% of a grassland atJRBP; Hobbs and Mooney 1991). Established micro-cosms simulated undisturbed patches of grassland,where a litter layer shades seedlings, the soil column ismore developed, and perennial grasses are more mature(see below).

Seeding density and plant containment

In monocultures, seeds were sown at densities estimatedto be higher than necessary to maximize abovegroundbiomass production and to ensure 100% cover (basedon density and size per individual of plants in sur-rounding grassland; Table 1). I used a substitutivedesign (Jollife 2000), in which seeding densities in poly-

cultures were reduced according to the number of spe-cies in the community (i.e., for a 4-species community,a given species would be sown at one-quarter its densityin monoculture). In 1997–1998, to compensate for sur-vival of approximately one-third of the previous year’sperennial grass seedlings, microcosms in the establishedcohort received fewer perennial grass seeds than micro-cosms in the new cohort; all other seeding densitieswere the same (Table 1). After germination, all micro-cosms were checked for weeds frequently and weededas necessary.

Litter from the 1996–1997 growing year was returnedto the surface of established microcosms after seeds forthe 1997–1998 year were sown. Sleeves made of alu-minum window screening were stapled around micro-cosms and, as plants grew taller, raised to approximately6 cm above the rims to prevent plants in adjacentmicrocosms from becoming entangled. In the first

Fig. 1. Illustration of the experimental design. Each circlerepresents one community type. The number of sections ineach circle corresponds with the number of species in thatcommunity. Arrows identify the species that were used in eachpolyculture. Species codes are explained in Table 1. The eightspecies shown at the bottom were only used in the 16-speciescommunity. Labels identify the two 4-species communities, 4-1and 4-2.

470 OIKOS 94:3 (2001)

4-24-1

A1 A2 E1 E2 L1 L2 P1 P2

A1 E1L1 P1

A2 E2L2 P2

A3 A4 E3 E4 L3 L4 P3 P4

Annualgrasses

Earlyforbs

Lateforbs

Perennialgrasses

1-sp.

4-spp.

8-spp.

16-spp.

growing year, screens were raised around all micro-cosms on 31 March 1997. These sleeves were raisedaround the established microcosms from the start of thesecond growing year to contain litter from the previousyear and to simulate shading by neighboring litter.Screens around new microcosms were raised on 7 May1998. The screens reduced the intensity of perpendicu-larly incoming direct radiation by 26% (LI-250 lightmeter, Li-Cor, Lincoln, NE).

Harvests and productivity measures

During both years of the experiment, reproductivebiomass of all species was harvested as it ripened, toprevent the escape of seeds. Non-reproductive above-ground biomass of all species was harvested, one blockat a time, after senescence of the majority of late-seasonforb individuals. In 1996–1997, aboveground biomasswas harvested from fully-senesced monocultures be-tween 10 and 22 August, and from the remainingmicrocosms between 22 August and 5 September 1997.In 1997–1998, aboveground biomass was harvestedfrom 19 August to 15 September 1998. All plant mate-rial was sorted by species as it was harvested. Biomasswas dried at 65°C for 48 h, and weighed. Hereafter, Iuse the term aboveground net primary productivity(ANPP) for the sum of reproductive and non-reproduc-tive aboveground biomass.

I calculated slopes from linear regressions of ANPPon species richness for each microcosm cohort. In thefirst regression, I excluded the monocultures, to testwhether decreasing the number of species per functionalgroup would affect productivity (this test excepted anyeffect of functional group richness, which decreasedonly between 4-species communities and monocultures).In the second, I analyzed 1-, 4-, and 8-species micro-cosms to discern trends created solely by combinationsof the eight core species. In the third, I included allmicrocosms.

Statistical analysis of the diversity-productivityrelationship

Because several of the datasets that I consider here havedecreasing variance with increasing diversity, and be-cause transformations of these datasets do not elimi-nate this characteristic, I did not use linear regression totest for significant effects of diversity. Instead, usingonly the mean values of each community type, I deter-mined whether the regression slope of a given set ofdata was more positive than would be expected if they-values in the dataset were assigned randomly to theset of x-values (i.e., the species richness levels). Toaccomplish this, I randomly assigned a y-value fromthe dataset to each x-value, recorded the regression

slope of this random arrangement, and repeated theprocess until I had generated 999 random slopes basedon the data. I then added the actual slope to this poolof random slopes. I counted the number of slopes equalto or greater than the actual slope (equal to or less thanthe actual slope in cases where the actual slope was lessthan the median slope), and multiplied this value bytwo to account for a two-tailed distribution. I dividedthis number by 1000 to arrive at a P-value that esti-mated the likelihood that the observed relationshipbetween species richness and the y-value arose bychance (for a discussion of similar statistical techniquessee Manly 1997).

Measures of complementarity

The Relative Yield (RY) of a species and Relative YieldTotal (RYT) of a mixture indirectly estimate resourcecomplementarity by comparing the biomass productionof species in mixtures with the productivity of thosespecies in monocultures (de Wit 1960, Trenbath 1974,Snaydon 1991). Other measures of the performance ofmixtures, such as DT, the proportional deviation of theproductivity of a mixture from its expected value(Wardle et al. 1997, Loreau 1998b), are also based onthe performance of the individual species inmonocultures.

Most resource complementarity studies have consid-ered only 2-species mixtures, and have calculated RYand RYT using equations designed specifically for thiscase (e.g., Harper 1977). I calculated RY and RYTusing slightly different equations that Hooper (1998)modified for use with mixtures containing more thantwo components:

RYijk=Yijk/(Yik/nj) (1)

RYTjk= �nj

i=1

RYijk/nj (2)

RYTj=mean RYTjk (3)

where RYijk=Relative Yield of species i in communityj in block k, Yijk=yield (aboveground biomass produc-tion) of species i in community j in block k, Yik=yieldof the monoculture of species i in block k, nj=numberof species in community j, and RYTjk=Relative YieldTotal of community j in block k. I calculated DT for themixtures, where DTj is the value of DT for community j,as:

DTj=mean DTjk (4)

DTjk= �nj

i=1

(Oijk−Eijk)/Eijk (5)

OIKOS 94:3 (2001) 471

where the observed productivity, Oijk= the yield ofspecies i in community j in block k, and the expectedproductivity, Eijk=Yik/nj, or the yield of species i inmonoculture in block k, divided by the number ofspecies in community j.

In a given mixture, if the RY of a species is �1, thenthe species performs better in that mixture than inmonoculture. If RY=1 for a species, then that speciesperforms no better or worse in the mixture than inmonoculture. RYs�1 indicate that growth of the spe-cies is limited more by interspecific competition in themixture than by intraspecific competition inmonoculture.

Relative Yield Totals�1 and DT�0 suggest comple-mentary resource use, i.e., resource partitioning amongspecies in a community (but can also indicate facilita-tion among species). Relative Yield Totals=1 andDT=0 suggest that competition within a community isno different than for monocultures of the componentspecies, or that there is no interaction among the spe-cies. Relative Yield Totals will also equal 1 if increasesin RYs of some species are mirrored by decreases in theRYs of other species. Offsetting species responses canalso lead to a DT of zero. However, a DT of 0 will notnecessarily correspond with RYTs of 1 because speciesare weighted differently for these measures. The RYTof a mixture weights the performance of each speciesequally, while DT weights the performance of eachspecies relative to its productivity in monoculture. Rela-tive Yield Totals�1 and DT�0 suggest that competi-tion for resources among individuals of different speciesin the community is greater than that among individu-als of the same species. Although RYT�1 and DT�0suggest that resource complementarity (and/or facilita-tion) is occurring, a more convincing sign is overyield-ing, i.e., when the mixture outproduces the mostproductive monoculture of its component species.

All of these measures describe the net outcome ofecological interactions in a community, ignoring theintricacies of these interactions. For instance, in caseswhere the most productive species in monoculture per-forms poorly in a community setting, tests for ov-eryielding may not detect complementary or positiveinteractions among other components of the commu-nity. For this reason, assessments of community inter-actions should ideally present more than onecomplementarity measure.

Relative Yields and replacement designs indiversity studies

Replacement designs have been criticized because spe-cies are planted at different densities in monoculturesand in mixtures, which can make results difficult tointerpret (e.g., Connolly 1986, Taylor and Aarssen1989, Snaydon 1991, Garnier et al. 1997). RYTs are

among the most resistant measures to the problemswith replacement mixtures, especially if they are evalu-ated on the basis of final yields (Connolly 1986,Cousens and O’Neill 1993; see Hooper 1998 for furtherdiscussion of this topic). In this experiment, the seedingdensity of each species decreased as species diversityincreased. I attempted to select a range of seedingdensities that 1) would result in the same productivityacross the entire range of densities if the species weregrown in monoculture, and 2) reasonably resembled therange of plant densities found in the surrounding grass-lands. If the biomass production of any of the specieswas limited by seeding density in the most diversemixtures, then the RYT of those mixtures should, be-cause it is calculated on a per area basis, be less likelyto exceed 1, and DT should be less likely to exceed 0.Thus, for this experiment, estimates of complementaritybecome increasingly conservative as diversity increases.For a given community type, comparisons of thesemeasures across cohorts should be robust.

Measures of interannual variability in biomassproduction

To determine whether constancy of ANPP differedamong the treatments, I calculated C, the ratio ofANPP in the new 1997–1998 cohort to ANPP in thenew 1996–1997 cohort, for each of the treatments.Treatments from the two cohorts were identical insubstrate, species composition, and seeding density. Iused only newly established cohorts, so for these analy-ses all species were grown from seed. Because therewere twice as many replicates of each community in1996–1997, I arbitrarily chose to use only microcosmsin odd-numbered blocks of this cohort to calculate C.

To test whether biomass production of mixtures wasmore stable than one would anticipate based on theperformance of individual species, I calculated an ‘‘Ex-pected Constancy,’’ EC, for each mixture. This term issimilar in concept to the Relative Yield Total, as it isbased on the performance of species grown in monocul-ture. I calculated EC for 1997–1998 as follows:

ECj=mean ECjk (6)

where ECjk is the Expected Constancy for treatment j inblock k, and:

ECjk= �nj

i=1

pijkCik (7)

where nj= the number of species in treatment j, pijk=the proportion of total biomass of treatment j in blockk made up by species i in the 1996–1997 season, andCik= the constancy of monocultures of species i inblock k. EC represents the expected response of com-

472 OIKOS 94:3 (2001)

munity productivity to a new set of environmentalconditions. This expected value is based on the re-sponses of monocultures and the proportion of eachspecies in the mixture. Thus, if the species in themixture respond to the new conditions exactly as do thespecies in monoculture, then EC=C. If the responsesof two or more species in the mixture differed fromthose in monoculture in a counteracting manner (onespecies responded more positively than in monoculture,one more poorly), this could also cause EC=C. If thenew environmental conditions cause interspecific com-petition to become more important relative to intraspe-cific competition, then one would expect species toincrease their production in monoculture more than inthe mixture (or have a smaller decrease in production inmonocultures), causing EC�C. Conversely, if the newconditions cause intraspecific competition to increaserelative to interspecific competition, then one wouldexpect productivity of the mixtures to increase more (ordecrease less) than predicted by changes in the mono-cultures, causing EC�C. While this measure providesan indication of the net result of shifts in inter- vsintraspecific competition in the entire mixture, it pro-vides no details about responses of the individual spe-cies in mixture, and cannot discriminate amonginterference, competition, and facilitation. This measurealso depends on the initial conditions (seed density,community age, etc.) being identical at the start of eachgrowth period.

For each block in the 1996–1997 and new 1997–1998 cohorts, I calculated EC for 4-1, 4-2, and 8 speciesmixtures. I compared observed values (C) with ex-pected values (EC) for each of these treatments. I usedthis technique twice: once to predict changes in commu-nity productivity in the 1997–1998 season based on theabundance of species in the 1996–1997 communities,and a second time in reverse, to predict values for the1996–1997 polycultures from species abundances in1997–1998. Because the proportion of communitybiomass made up by each species differed in the twoseasons, these two calculations were independent.

Results

Precipitation

The timing and total amount of rainfall differed strik-ingly in the two growing years (Fig. 2; in this study,growing years start in November). The beginning of thefirst year (1996–1997) was unusually wet. By 24 Janu-ary 1997, precipitation at JRBP had exceeded the 667mm annual average (the average is based on rainfallrecords from 1974–1975 to 1997–1998). After January,the first year was abnormally dry. Only 91 mm of rainfell after 31 January, 10 mm of which dropped in themonth of May. The second year (1997–1998), which

Fig. 2. Cumulative precipitation during 1996–1997 (thin line)and 1997–1998 (thick line). The first date on the x-axis is 6November.

occurred during an El Nino event, was wetter. Anunusually large amount of rain fell late in the year. In1997–1998, 653 mm of rain fell after 31 January, 82mm of which fell during May.

Community productivity

The number of species per functional group did notsignificantly affect ANPP of any of the cohorts (Fig. 3,analyses that exclude monocultures in Table 2). Onaverage, ANPP of all cohorts declined as the number ofspecies and functional groups decreased simultaneously(note slopes of regressions that include monocultures inTable 2). This decline was only significant in the new1997–1998 cohort (Fig. 3, Table 2). In this cohort, 4-1and 8 species communities overyielded monocultures oftheir most productive constituents (Fig. 3d), but thesedifferences were only marginally significant (one-wayANOVA, 8 vs Bromus : P=0.07) or nonsignificant (4-1vs A�ena : P=0.52). In the other two cohorts, polycul-tures were outproduced by the most productive mono-cultures (Fig. 3b, f).

An asymptotic curve (y=872.2x/x+0.72; R2=0.42,n=12; Fig. 3d) represented data from the 1997–1998cohort better than the linear regression (R2=0.26;Table 2, Fig. 3d). Because ANPP of the monocultures

Table 2. Slopes from linear regressions of productivity (gm−2 yr−1) on species richness, and P-values for those slopesbased on randomization analysis.

Species richness of included treatments SlopeP-value

New 1996–1997 cohort15.00.3621, 4, 8

9.01, 4, 8, 16 0.2544.94, 8, 16 0.672

New 1997–1998 cohort53.40.0041, 4, 8

1, 4, 8, 16 0.008 23.94, 8, 16 2.40.682Established 1997–1998 cohort1, 4, 8 0.730 6.51, 4, 8, 16 4.50.600

4.04, 8, 16 0.190

OIKOS 94:3 (2001) 473

Fig. 3. Biomass production andconstancy of the monocultures(left-hand panels) and all treatments(right-hand panels). From top tobottom, biomass production in1996–1997 (a, b), and in the new (c,d) and established treatments (e, f) in1997–1998. Panels g and h show theproportion of 1996–1997aboveground biomass produced innewly-established communities in1997–1998. Error bars showstandard errors, and have beenremoved from the 1-speciestreatments in the right-hand panelsfor clarity. In the left-hand panels,bars that do not share the sameletter are significantly different(�=0.05) by Student-Newman-Keulspost-hoc tests of one-way ANOVAs.Annual grasses are represented byblack bars, early-season annual forbsby white bars, late-season annualforbs by grey bars, and perennialgrasses by striped bars. In theright-hand panels, the numbers 1 and2 identify the 4-1 and 4-2 treatments,respectively. Solid lines are linearregressions of data from 1-, 4-, 8-,and 16-species communities. Thedashed curve in (d) is the best fit ofthe asymptotic functiony=a(x/x+b), which fit the databetter than a linear regression (seetext). Statistics from biomassregressions are shown in Table 2.Statistics from constancy regression(panel h) are shown in Table 4.

OIKOS 94:3 (2001)474

varied widely, no curve can perfectly describe the ob-served diversity/productivity relationship.

In monoculture, species of the same functional typegenerally produced similar amounts of biomass. In thenew cohorts, annual grasses were the most productivespecies. The next most productive functional groupvaried by year. While the early-season annual forbswere next most productive in 1996–1997, the late-sea-son annual forbs were second most productive in thewetter 1997–1998 season (Fig. 3a, c). The perennialgrasses were generally the least productive species in thenew cohorts, in which they were seedlings. In contrast,the perennial grass Nassella pulchra and the annualgrass A�ena barbata were the most productive species inthe established cohort. With the exception of the peren-nial grasses, the established microcosm cohort wasmuch less productive than the new cohort in 1997–1998(Fig. 3).

Species composition affected the productivity ofpolycultures in the new 1996–1997 cohort. The 4-1polyculture had significantly higher ANPP than the 4-2polyculture (ANOVA: F11=37.2, P=0.0001, followedby Student-Newman-Keuls test, �=0.05). Productivityof these communities did not differ in the other twocohorts.

Complementarity

I calculated RYT and DT of the 4- and 8-speciescommunities to indirectly test for complementary re-source use (these values could not be calculated for16-species mixtures, which contained species that werenot grown in monoculture). In both new cohorts, RYTwas significantly �1 and DT was significantly �0 forthe 4-1 and 8-species mixtures (Table 3). In thesemixtures, the annual grass A�ena and the late-seasonforbs (especially Hemizonia) produced more biomassthan would be expected based on their growth inmonoculture (Table 3). The performance of these spe-cies compensated for the poor production of perennialgrasses (and in most cases, early-season annual forbs),resulting in a positive RYT. Relative Yield Totals werehigher in the new 1997–1998 polycultures than in poly-cultures in the other two cohorts (Student-Newman-Keuls test of RYTs: P�0.05; two-way ANOVA,cohort effect F2=4.137, P=0.022; species effect F2=2.60, P=0.084; interaction F4=2.17, P=0.086; Table3).

In the established cohort, high relative yields of thelate-season forbs and the perennial grass Elymus multi-setus partially compensated for annual grass relativeyields, which were much lower than in new communi-ties (Table 3). However, none of the established com-munities had an RYT significantly �1 or a DT

significantly �0.

OIKOS 94:3 (2001) 475

Tab

le3.

Rel

ativ

eY

ield

s,R

elat

ive

Yie

ldT

otal

s(R

YT

s),

and

DT

for

the

spec

ies

mix

ture

s.

Tre

atm

ent

Rel

ativ

eyi

elds

RY

T*

DT

*

(Yea

r)A

�ena

Bro

mus

Pla

ntag

oL

asth

enia

Hem

izon

iaL

essi

ngia

Nas

sella

Ely

mus

m.

New

4-1

(199

6–7)

2.31

�0.

25–

0.79

�0.

09–

1.72

�0.

16–

0.05

�0.

01–

1.22

�0.

03**

*0.

38�

0.06

***

4-1

(199

7–8)

2.33

�0.

31–

0.74

�0.

04–

1.13

�0.

28–

0.63

�0.

23–

1.21

�0.

06**

0.38

�0.

07*

4-2

(199

6–7)

–1.

17�

0.12

–1.

14�

0.08

–1.

21�

0.16

–0.

26�

0.05

0.94

�0.

030.

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Table 4. Slopes from linear regressions of constancy ofbiomass production (C) on species richness, and P-values ofslopes based on randomization analysis.

P-value forSpecies richness of included Slopetreatments slope=0

0.061, 4 8 0.5281, 4, 8, 16 0.010.7264, 8, 16 0.638 −0.03

Discussion

Results of this experiment lead to five main conclu-sions. First, the number of species per functional groupdid not affect the productivity of grassland microcosms.Second, changes in environmental conditions causedthe ANPP of functionally diverse communities to shiftrelative to that of the monocultures. As a result, diver-sity and productivity were positively related in only oneof three sets of environmental conditions. Third, ararely noted selection effect can lead to correlationsbetween productivity and diversity based on the perfor-mance of species in mixtures rather than in monocul-tures. Fourth, in wetter years, interspecific competitionmay become weaker relative to intraspecific competi-tion. Finally, diversity did not dampen the interannualvariation in grassland productivity over the short dura-tion of this experiment.

The relationship between diversity andproductivity

By maintaining functional group diversity as speciesdiversity dropped from 16 to 4, this experimental designprobably maximized resource complementarity (see be-low) and productivity at each level of species richness.Given this design, it was not surprising that speciesdiversity alone did not affect productivity in any of themicrocosm cohorts. These results are consistent withthose of previous research, which suggest that the pro-ductivity of grasslands is more responsive to functionaldiversity and species composition than species diversity(Hooper and Vitousek 1997, Tilman et al. 1997, Sym-stad et al. 1998, Hector et al. 1999).

Consistent with the ‘‘redundancy’’ hypothesis ofWalker (1992) and the results of several previous stud-ies (Naeem et al. 1996, Tilman et al. 1996, 1997,Symstad et al. 1998), productivity appeared to be anincreasing, asymptotic function of species richness inthe new 1997–1998 cohort. In the other two cohorts,diversity and productivity were not significantly related.This difference among cohorts was caused by shifts inANPP of the polycultures relative to ANPP of themonocultures. Based on these results, I developed aconceptual framework that encompasses several hypo-thetical properties of the diversity/productivity relation-ship for terrestrial plants (Fig. 4). Here, I outline five ofthe properties that are incorporated in the framework,and I provide examples of manipulative studies whoseresults support some aspect of these properties: 1)Monocultures vary widely in their productivity (Naeemet al. 1996, Garnier et al. 1997, Hooper and Vitousek1997, Hector et al. 1999, this study). 2) The mostproductive species mixtures may or may not outpro-duce the most productive monocultures, depending onenvironmental conditions (Naeem et al. 1996, Garnier

Interannual variation in productivity

Because the new microcosm cohorts of 1996–1997 and1997–1998 were identical in substrate, species composi-tion, and seeding density, differences between them inproductivity can be attributed to interannual differ-ences in weather. Late-season rainfall in 1997–1998 ledto increased biomass production in all treatments, withsurprisingly similar increases among the annual grass,early-season forb, and perennial grass monocultures(Fig. 3g). Late-season forbs responded more stronglythan other species to the increase in water availability,and Hemizonia congesta responded more than Lessingiahololeuca. The response of communities to the increasedprecipitation fell between the response of Hemizoniaand the response of species in other functional groups(Fig. 3h). Randomization analysis of regression slopesindicated that there was no relationship between diver-sity and constancy of biomass production (Table 4, Fig.3h).

Interannual variation in the productivity of monocul-tures was a poor predictor of variation in the mixtures.Predictions of changes in 1997–1998 biomass wereaccurate for 4-1 polycultures, but were smaller thanobserved in the 4-2 and 8 species mixtures (EC�C :two-way ANOVA, prediction effect F1=8.98, P=0.007; treatment effect F2=3.43, P=0.05; interactionF2=4.73, P=0.02; Table 5). Reverse predictions (ofchanges in biomass from 1997–1998 to 1996–1997)consistently underestimated the decrease in communitybiomass (EC�C : two-way ANOVA, prediction effectF1=15.2, P=0.0009; treatment effect F2=1.89, P=0.18; interaction F2=2.77, P=0.09; Table 5).

Table 5. Observed vs expected constancy of biomass produc-tion.

Community type Expected value Observed value(C)(EC)*

1997–1998 communities1.83 (0.16)1.88 (0.11)4-1

1.80 (0.22) 2.84 (0.20)4-22.24 (0.17)8 1.91 (0.16)

1996–1997 communities (reverse prediction)0.55 (0.05)0.58 (0.03)4-10.36 (0.02)4-2 0.59 (0.07)0.43 (0.03)8 0.60 (0.04)

* Values listed are means of all blocks, with standard errors inparentheses.

476 OIKOS 94:3 (2001)

Fig. 4. Conceptual framework (a) to describe observed rela-tionships between species richness and productivity of terres-trial plant communities. Species grown in monoculture rangewidely in productivity, as represented by the vertical grey bar.Productivity of polycultures that partition resources and in-clude species that perform well in mixture is unlikely to varygreatly with increasing species richness (horizontal black bar).These complementary mixtures may or may not outproducethe most productive monoculture, depending largely on envi-ronmental conditions such as disturbance and water availabil-ity (arrows indicate that black bar can shift up or downrelative to vertical grey bar). In the most favorable environ-mental conditions, increases in species richness may lead toslight increases in productivity of highly complementary mix-tures (asymptotic limit line). Mixtures of sub-optimal speciescomposition (i.e., where little resource partitioning occurs andspecies that perform well in mixture are absent; light greytriangle) will have lower productivity than mixtures that maxi-mize complementarity. Within this sub-optimal zone, produc-tivity necessarily increases with increasing species richness, ascomplementarity strengthens and chances of including themost productive species increase. Diagrams at right showpossible distributions of productivity levels in (b) favorableenvironmental conditions and (c) unfavorable environmentalconditions. Note that in either set of conditions, variability ofproductivity decreases with increasing species richness.

Why might overyielding only occur when resourceavailability is high? In resource-rich conditions, domi-nant species might be unable to fully utilize availableresources, allowing other species to access residual re-sources. Conversely, in resource-poor conditions, plantsmight shift their architecture and allocation to bestcompete for pockets of a limiting resource, thus reduc-ing resource partitioning. To date, several studies havecompared plant competition across gradients of soilnutrients (e.g., Austin and Austin 1980, Austin et al.1988, Campbell and Grime 1992, Garnier et al. 1997),but few (if any) have clearly observed increasing com-plementarity with increasing resource availability.

Complementarity vs selection effects

Results of this experiment suggest the existence of ararely noted form of selection effect. The most com-monly cited selection effect, the sampling effect, leadsto a positive relationship between the diversity andproductivity of communities when highly productivespecies (in monoculture) are more likely to be includedin diverse communities (Aarssen 1997, Huston 1997,Tilman 1997). This definition assumes that the speciesthat are most productive in monoculture have the mostpositive effects on community productivity, which isoften, but not always the case. Loreau (1998a, 2000)recently described an ‘‘inverse sampling effect’’ that canunder some circumstances lead to decreases in produc-tivity with increasing diversity. I suggest that a thirdtype of selection effect contributes to a positive rela-tionship between productivity and diversity when spe-cies that perform disproportionately well in mixturesare more likely to be included in diverse communities. Iterm this effect context-specific sampling. Naeem et al.(1996) suggested that a similar mechanism might bestexplain the positive relationship they observed betweenproductivity and plant species richness.

In the 1997–1998 cohort, resource partitioning (and/or facilitation) occurred in the 4-1 and 8 species poly-cultures (Table 3), and these mixtures (nonsignificantly)overyielded the most productive monocultures. In mostcases, high values for RYT and DT were driven by a fewspecies that strongly overyielded in mixtures, and com-pensated for underyielding by other species (Table 3).This suggests that context-specific sampling affected theoutcome of the experiment: the presence of species thatconsistently overyielded (e.g., A�ena and Hemizonia)contributed to the increase in productivity at highdiversity. Notably, the most productive species inmonoculture, Bromus, did not consistently have thehighest relative yield in mixtures (this was most clearlythe case in the new 1996–1997 cohort). Thus, a selec-tion effect can manifest itself through species that per-form well in polyculture rather than through the mostproductive species in monocultures.

et al. 1997, Hooper and Vitousek 1997, Hector et al.1999, this study). 3) Mixtures that include species thatpartition resources effectively and species that performwell in mixtures are unlikely to dramatically increaseproductivity as more species are added (Hooper 1998,Dukes 2001, this study). 4) The least productive mix-tures necessarily become more productive as more spe-cies are added (Naeem et al. 1996, Hooper 1998, Hectoret al. 1999). 5) Because finite resources dictate anabsolute limit on plant productivity in a given year,productivity cannot exceed an asymptote (Naeem et al.1996, Garnier et al. 1997, Tilman et al. 1997, Symstadet al. 1998, this study).

This framework builds on similar models that havebeen put forward to date (e.g., Vitousek and Hooper1993, Lawton 1994, Johnson et al. 1996, Schwartz et al.2000) in two respects. First, the framework considersthe entire distribution of communities at each diversitylevel (it encompasses an area rather than describing acurve – see also Sala et al. 1996). Second, it recognizesthe role that environmental conditions can play inshaping the distribution.

OIKOS 94:3 (2001) 477

As would be expected, RYT and DT were greatest inthe new 1997–1998 cohort, when the productivity ofthe polycultures was highest relative to that of themonocultures. In the new cohorts, increases in therelative yields of Bromus and Nassella drove the in-crease in complementarity measures from 1996–1997 to1997–1998. Interspecific competition for water mayhave limited productivity of these species in mixtures in1996–1997. In monocultures, interannual differences inproductivity of Bromus and Nassella were very similarto those of the other species in their functional groups,A�ena and Elymus multisetus.

Diversity vs constancy of biomass production

Previous studies of grasslands have found a negativerelationship between diversity and the interannual vari-ability of biomass production (Dodd et al. 1994,Tilman and Downing 1994, Tilman 1996; but see Hus-ton 1997). In this experiment, the presence of late-sea-son annual forbs, which are highly responsive tolate-season water availability, could have led to a posi-tive relationship between diversity and interannual vari-ability of biomass production through context-specificsampling. However, over the short duration of thisexperiment, linear regressions showed no clear relation-ship between species richness and constancy of biomassproduction (Fig. 3h).

In two cases, diverse communities responded morestrongly to increased water availability than would havebeen expected based on the responses of monocultures.Taken together, this result and the observed increase inresource complementarity measures in the second yearof the experiment suggest that in this system, interspe-cific competition might become weaker relative to in-traspecific competition when soil moisture is availablelate in the year. The prediction of 1996–1997 biomassbased on changes in monocultures suggests the reversemight also be true: interspecific competition may be-come stronger relative to intraspecific competition whenwater availability declines. As discussed above, whengrown in mixtures, Bromus and Nassella derive greaterbenefit from increased water availability than otherspecies used in this study. These species may be lesseffective competitors for water (or for a resource withcovarying availability, such as nitrate) than other spe-cies in this study, and may experience a relaxation ofinterspecific competition for this resource in wetteryears. Although measures of both constancy and com-plementarity suggest a shift in the relative strengths ofinter- and intraspecific competition, ecological interac-tions among plants are complex, and other factorscould explain the observed pattern. For instance, theroles of interference and facilitation in this experimentare unclear.

Established vs new microcosms

In the 1997–1998 season, aboveground productivity ofnew microcosms averaged approximately twice that ofestablished microcosms of the same composition (withthe exception of perennial grass monocultures). Thereare several possible explanations for the disparity inproductivity: 1) soil fertility was lower in establishedmicrocosms because a substantial portion of the poten-tially available nutrients had been locked up in plantbiomass or lost to leaching during the previous year, 2)nutrient immobilization by litter from the previous yearfurther lowered nutrient availability to plants, 3) plantsin established microcosms received less light becauseseedlings were shaded by the previous year’s litter andthe cylinder of window screening, and 4) soil columnsin the established microcosms were much harder, andmore resistant to root penetration and water infiltra-tion. Perennial grass monocultures were much moreproductive in the established treatment, where manyshoots regrew from the previous year’s roots, than innew cohorts, where all plants were seedlings.

It is possible that low resource availability minimizedresource complementarity in the established micro-cosms, thus reducing the productivity of polyculturesrelative to the monocultures.

Other considerations

Conclusions from this experiment are drawn solelyfrom aboveground biomass production in the grasslandmicrocosms. Belowground productivity is unlikely to beperfectly correlated with aboveground productivity, be-cause grassland species differ in their root:shoot (R:S)ratios. These differences can be generalized by func-tional groupings; for instance, R:S ratios of perennialgrasses are much greater than those of species in theother three functional groups (Armstrong 1991, Hooper1998). I do not know how the lack of belowgrounddata affected conclusions from this experiment. How-ever, Hooper (1998) found that the inclusion of esti-mated belowground biomass production had onlyminor qualitative effects on RYs and RYTs calculatedfor mixtures of three different grassland functionalgroups.

Because I waited to harvest non-reproductive tissueuntil the majority of late-season forbs had senesced,this study underestimates ANPP of species thatsenesced earlier in the year. While some decompositionof these species (most notably early-season annuals)must have occurred, three lines of reasoning lead me tobelieve this mass loss had only minor effects on theresults of this study. First, a substantial proportion ofthe biomass of early-season annuals is made up ofreproductive biomass (30–60%: Dukes unpubl.;Mooney et al. 1986). Because I harvested reproductive

478 OIKOS 94:3 (2001)

parts as they ripened, this proportion of the biomasshad no opportunity to decompose. Second, very little ofthe non-reproductive biomass of these species fell to theground, and litter suspended above the soil surfacetends to decompose slowly (Dukes and Field 2000).Finally, the period between the senescence of thesespecies and the final harvest was quite dry (Fig. 2),limiting decomposition. Any decomposition that tookplace led to underestimation of productivity and ov-eryielding, but did not affect calculations of RYT or DT

(assuming that decomposition rates did not vary acrosscommunity types for a given species).

Legumes have strongly affected productivity patternsin other biodiversity studies (e.g., Hooper 1998, Sym-stad et al. 1998, Hector et al. 1999, Huston et al. 2000).Legumes are thought to increase the yield of mixturesby increasing soil nitrogen availability, rather thanthrough resource partitioning. By omitting nitrogen-fixing plant species from this experiment, I precludedthis type of positive interaction. I expect that the lackof nitrogen-fixers limited the productivity of mixtures inthis experiment.

The relatively small size of the microcosms could alsohave limited the yield of mixtures in this experiment.Results from a separate study of annual species suggestthat resource partitioning can increase with soil depth(Sheley and Larson 1995). In this study, confinement ofthe roots to a volume of 30 L might have suppressedsome variation among species in rooting architecture.Root growth may also have been altered by an abnor-mal soil temperature profile. Because microcosms werenot buried, soil in this study probably experiencedgreater daily temperature fluctuation than did soil inthe surrounding grassland.

Several researchers advocate the use of randomdraws to replicate diversity levels (Kareiva 1996, Hus-ton 1997). While experimental designs based on ran-dom draws have yielded many important results (e.g.,Hector et al. 1999, Tilman 1999), nested designs alsohave utility, especially for experiments on a smallerscale (Allison 1999). The design of this experimentfacilitated calculation of resource complementaritymeasures such as RYT and DT. Further, this designisolated species richness within functional groups, clari-fying its lack of importance for ANPP relative tofunctional richness and species composition.

Conclusions

The influence of any one species on the productivity ofa community depends on many variables. When aspecies is removed from a community, the trajectory ofANPP depends on the initial species composition of thecommunity, on the identity of the species removed, andon the environmental conditions that the system experi-ences. In this experiment, species in the same functional

group were largely ‘‘redundant’’ with respect to produc-tivity. Thus, removing a species of annual grass from acommunity with several annual grasses is likely to havelittle effect on ANPP in the following year. However,even within functional groups, differences among spe-cies can influence ecosystem functions under certainenvironmental conditions (for instance, productivitydiffered significantly between the two 4-species polycul-tures in the new 1996–1997 cohort, but not in the othercohorts). If a species that performs particularly well ina community is removed, productivity may suffer tosome extent. Note that the performance of a species ina community (and not in monoculture) should deter-mine whether it causes a selection effect. Removing thelast species of a functional group is likely to havenoticeable effects. If that functional group contributes alarge proportion of the mixture’s biomass, productivityof the community is likely to drop. The removal of afunctional group may actually increase productivityunder conditions that are unfavorable for that group,but may have no effect or a negative effect on produc-tivity under different conditions, or in a later stage ofcommunity development.

Acknowledgements – I thank R. Baalbaki, J. Brown, C. Buch-ner, E. Check, A. Desai, G. Dukes, J. N. Dukes, J. M. Josling,J. Holl, S. Kimberlin, K. Nicholas, H. Pai, N. Pinter, L.Robinson, A. Schick, D. Stahl, B. Stauffer, B. Thomas, N.Toyoda, A. Van Houtte, J. Verville, and T. G. Wong forassistance over the course of this experiment. I thank H.Mooney and C. Field for providing equipment, lab space, andfriendly advice. D. Ackerly, P. Fine, A. Hector, B. Helliker, D.Hooper, H. Mooney, S. Schwinning, and P. Vitousek madehelpful criticisms of the manuscript. This research was sup-ported by a NASA Earth System Sciences Fellowship and anA. W. Mellon/Stanford University Grant for Student Researchat Jasper Ridge Biological Preserve.

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