OIKOS 99: 231–240, 2002

Species richness, environmental fluctuations, and temporal changein total community biomass

Owen L. Petchey, Tim Casey, Lin Jiang, P. Timon McPhearson and Jennifer Price

Petchey, O. L., Casey, T., Jiang, L., McPhearson, P. T. and Price, J. 2002. Speciesrichness, environmental fluctuations, and temporal change in total communitybiomass. – Oikos 99: 231–240.

Theory and empirical results suggest that high biodiversity should often cause lowertemporal variability in aggregate community properties such as total communitybiomass. We assembled microbial communities containing 2 to 8 species of competi-tors in aquatic microcosms and found that the temporal change in total communitybiomass was positively but insignificantly associated with diversity in a constanttemperature environment. There was no evidence of any trend in variable tempera-ture environments. Three non-exclusive mechanisms might explain the lack of a netstabilising effect of species richness on temporal change. (1) A direct destabilisingeffect of diversity on population level variances caused some populations to varymore when embedded in more diverse communities. (2) Similar responses of thedifferent species to environmental variability might have limited any insurance effectof increased species richness. (3) Large differences in the population level variabilityof different species (i.e. unevenness) could weaken the relation between speciesrichness and community level stability. These three mechanisms may outweigh thestabilising effects of increases in total community biomass with diversity, statisticalaveraging, and slightly more negative covariance in more diverse communities. Ourexperiment and analyses advocate for further experimental investigations of diversity-variability relations.

O. L. Petchey, T. Casey, L. Jiang, P. T. McPhearson and J. Price, Dept of Ecology,E�olution, and Natural Resources, Cook College, Rutgers Uni�., New Brunswick, NJ08901, USA (current address of OLP, Dept of Animal and Plant Sciences, AlfredDenny Building, Western Bank, Sheffield S10 2TN, U.K. [o.petchey@sheffield.ac.uk]).

Recent theory describes how diversity affects the tem-poral variability of aggregate community properties(Micheli et al. 1999), such as total community biomass(for reviews see Tilman 1999 and Cottingham et al.2001). Empirical studies suggest lower temporal vari-ability in more diverse communities that may resultfrom a variety of mechanisms linking diversity andvariability (McNaughton 1985, Tilman 1996, Mc-Grady-Steed et al. 1997, Naeem and Li 1997). Here wepresent a novel experimental investigation of the rela-tions between species richness and the temporal vari-ability in total community biomass of aquaticmicrocosm communities in different environments. Weuse indirect evidence to distinguish, where possible,

between the mechanisms that may link species richnessand temporal variability.

The temporal variability of a community propertyhas been used as a measure of community stability(Tilman 1999). Mathematically, however, stability is anintrinsic property of a system, whereas observed levelsof temporal variability in population, community, orecosystem dynamics result from intrinsic and extrinsicfactors (Lewontin 1969, Ives 1995). Indeed, temporalvariability and stability (i.e. resilience) are monotoni-cally related in the absence of environmental variability,but this equitability breaks down when environmentalvariables fluctuate through time (Taylor 1992, Hor-wood 1993). We measured temporal variability because:

Accepted 21 May 2002

Copyright © OIKOS 2002ISSN 0030-1299

OIKOS 99:2 (2002) 231

(i) it is related to stability in a constant environment,(ii) diversity-variability relations are the focus of recenttheory, and (iii) observed variability may be of greaterpractical interest than intrinsic stability. The temporalchange in an aggregate community property, such astotal community biomass, can be measured by thecoefficient of variation in total biomass:

Temporal change=SD(Total biomass)

Total biomass

=

��s

i

Var(Bi)+�s

i

�s

i� j

Cov(Bi, Bj)

�s

j=1

B� i

(1)

Here B� i=�t=1n Bi,t/n and Bi,t is the biomass of the ith

(or jth) of s species during the tth of n samples throughtime (Lehman and Tilman 2000). Equation 1, in con-junction with the mean-variance relations of populationdensities, can be used as a null model (Gotelli andGraves 1996) of effects of species richness on temporalchange (Doak et al. 1998, Tilman et al. 1998). Equation1 also provides a tool for investigating which of thethree components of temporal change (total biomass,which is the sum of the biomass of each species; totalvariance, which is the sum of the temporal variance ofeach species; and total covariance, which is the sum ofthe temporal covariances between all pairs of species ina community) contribute stabilising and destabilisinginfluences. Increasing total biomass with increasing di-versity reduces temporal change; increasing total vari-ance or total covariance with increasing diversityincreases temporal change. The net effect of diversityon temporal change is determined by the relativestrength of these stabilising and destabilising contribu-tions. Equation 1 makes no attempt to and cannotidentify the mechanisms (Table 1) that cause changes intotal biomass, total variance, and total covariance as aresult of changes in diversity. Total covariance, forexample, can be negative due to inter-specific competi-tion even when species respond to environmental fluc-tuations in a correlated fashion. Other methods such asdirect experimental manipulation of the mechanism andmultivariate autoregressive models (Ives 1995) are moreappropriate for disentangling the mechanisms (e.g. spe-cies interactions and environmental variation) thatcause changes in total variances and covariances withdiversity.

We assembled microcosm communities that initiallycomprised two to eight competing ciliate and rotiferspecies to examine how diversity affects a range ofcommunity properties, including temporal change intotal community biomass. We limited our study tocompetitive communities because theory about effectsof species richness in competitive communities (Tilman

et al. 1997, Ives et al. 1999, Yachi and Loreau 1999,Hughes and Roughgarden 2000, Lehman and Tilman2000, Nijs and Roy 2000) is arguably better developedthan theory about effects of species richness in food-webs (May 1972, Naeem 1998, Rastetter et al. 1999,Ives et al. 2000). The generation times of the microbesin our experiment range from hours to days, so that oursix week experiment included twenty to fifty genera-tions and, therefore, long term population and commu-nity dynamics. Our data is appropriate for testingtheory about long-term effects of diversity on variabil-ity (Yachi and Loreau 1999, Hughes and Roughgarden2000). Microbial microcosms also allow direct manipu-lations of diversity and escape the interpretativedifficulties associated with indirect manipulations ofdiversity (Dodd et al. 1994, Tilman 1996) and observa-tional studies (Wardle et al. 1997). The different pat-terns of temperature fluctuations in our experiment,constant, fast, and slow fluctuations, tested whetherresults were affected by environmental variability. Thebiomass of each species in each community was esti-mated twice during the experiment. This allowed manymore individual communities than if we recordedbiomasses more often. Consequently we use the termtemporal change rather than temporal variability. Inaddition, we cannot separate temporal change causedby stochastic fluctuations from temporal change causedby directional changes (Gaston and McArdle 1994).

Regardless of the mechanisms that cause changes intotal biomass, total variance and total covariance, eq. 1provides a null model for comparison with our results.We determined how each of these three components oftemporal change (eq. 1) related to diversity in each ofthree environments. In the constant environment thetotal variance and covariance result only from inter-specific interactions, whereas in the variable environ-ments total variance and covariance result from bothinter-specific interactions and environmental fluctua-tions. We used additional analyses to identify, wherepossible, the mechanisms that might be responsible foreffects of diversity on each of these three componentsof temporal change (Table 1). The null model repre-sented by eq. 1 was unable to account for our positivetrend between temporal change and species richness inthe constant environment. A model of the experimentwas consistent with a direct destabilising effect of diver-sity on population level stability, that is absent from eq.1, causing the results in our experiment.

Materials and methods

Experimental design

We used a two-way factorial design with diversity(initially 2, 4, 6, or 8 species) and temperature regime(one constant temperature, two fast temperature fluctu-

232 OIKOS 99:2 (2002)

ations, and two slow temperature fluctuations) as treat-ments. There were 16 different communities; four ateach of the four diversity levels. Selection of speciescombinations followed established practice for manipu-lations of diversity within a single trophic level (Allison1999), except that we chose to have as many species aspossible represented across the two species communi-ties. We did not replicate identical communities in thesame environment because we were most interested inthe effects of diversity. The 16 different communitiesexperienced either of the five different temperatureregimes, making a total of 80 microcosms. Eight ofthese were contaminated by non-treatment species dur-ing the experiment and were excluded from all analyses.The constant temperature microcosms remained at22°C, while the fast (average rate of change, x�1

SD=6.9�4.7°C 12 h−1 and 6.7�5.0°C 12 h−1) andslow (1.7�1.3°C 12 h−1 and 2.1�1.9°C 12 h−1)patterns of temperature fluctuations varied between12°C and 32°C. This 20°C range of temperatures fallswithin the range seen in natural ecosystems in our areaof New Jersey, USA. The fast and slow fluctuationswere white and reddened time series of temperaturefluctuations (Halley 1996, Kaitala et al. 1997) verysimilar to the temperature series in Fig. 1 of Petchey(2000a).

Community assembly

We assembled and maintained each of the 80 microbialcommunities in a 250 ml Pyrex screw-capped bottle

Table 1. The mechanisms that can generate and/or modify relations between the three statistical components of temporalstability and diversity (eq. 1). References are for theoretical studies that show how that component can vary with diversity andthereby influence temporal stability.

Proposed mechanism by which species ReferencesComponent of Influence of increased speciesrichness on temporal stabilityrichness affects the componenttemporal

stability

Selection effects: total biomass is(a)Total(1) (Tilman et al. 1997, Tilman� Stabilising if large species1999, Loreau 2000, Loreaubiomass influenced by the greater dominate (total biomassand Hector 2001)increases with increasingprobability that any particular

species is included in a more diversity)diverse community. (This effect is � Destabilising if small species

dominate (total biomass de-more important when speciesdiffer more.) creases with increasing diver-

sity)� Stabilising (Chapin et al. 2000, PetcheyResource use complementarity:(b)

total biomass increases with 2000c, Loreau and Hector2001)increasing species richness because

of a greater range of resourcesexploited

(2) (Doak et al. 1998, Tilman et(a)Total � Stabilising if log(mean)-Statistical averaging (portfolioeffect): statistical effect of addingvariance al. 1998)log(variance) relation has slope

of greater than 1the biomass fluctuations of anumber of species together to get � Destabilising if log(mean)-variation in total biomass log(variance) relation has slope

of less than 1� Stabilising if species become (This study; Petchey 2000b)Changes in population level(b)

variability: individual populations less variable with increasingmay become more or less variable diversitywith increasing diversity � Destabilising if species

become more variable withindependent of the mean-variancerelation in 2(a) increasing diversity

(Cottingham et al. 2001)� Neutral for the average(c) Uneven population levelrelation, but adds scatter andvariability: a very variable speciesweakens any relationmight determine community level

variability regardless of all otherspecies’ presence or absence

Total (Lehman and Tilman 2000)(3) � Competitive interactions(a) Inter-specific interactions:(But see McCann et al. 1998,covariance contribute to asynchronousinteractions between populations

dynamics and reduce temporalcan affect whether biomasses Ives et al. 1999)fluctuate in synchrony or out of change in total biomasssynchrony

(McNaughton 1977, WalkerResponses to environmental(b) � Stabilising if different speciesfluctuations (insurance hypothesis): respond differently to 1995, Ives et al. 1999, Walker

et al. 1999, Yachi and Loreaua greater range of responses to environmental change (neutralotherwise)environmental change in more 1999)

diverse communities ensures futurefunctioning

OIKOS 99:2 (2002) 233

Fig. 1. Model frameworks for effects of diversity on popula-tion abundance and population variability. (a) Corresponds toDoak et al. (1998) and Tilman et al. (1998) where diversityaffects population variability via density compensation and themean-variance relation. (b) Corresponds to a frameworkwhich better fits the empirical data present here.

of the stock cultures to all the experimental microcosmsstandardised initial conditions. We removed 7 ml ofmedium each week and replaced this with 7 ml ofsoil-water medium with bacteria added within the pre-vious 24 h. Our design does not control initial commu-nity biomass across treatments, though this must havelittle effect on the results because of the many genera-tions of growth that occurred during the experiment(e.g. initial biomass was in the order of 1×10−4 mgper ml, whereas biomass during the experiment rangedfrom 10−2 to 100 mg per ml).

Community monitoring

We measured the population density of each speciesduring the 2nd and 6th week of the experiment. Thepopulation density of each species was estimated bycounting the number of individuals in appropriatelydiluted drops of media using a dissecting or compoundmicroscope. We calculated species biomass by multiply-ing the abundance of a species by the average cell massof ten individuals of a species. Cell mass was calculatedfrom the linear dimensions converted to mass usingequations that approximate cell shapes (Wetzel andLikens 1991).

Analyses

Statistical tests were analyses of covariance (ANCOVA)with mean realised species richness as a continuousexplanatory variable and environment (constant, fast,or slow fluctuating) as a categorical explanatory vari-able. Mean realised richness was the average number ofspecies observed over the two sampling points. Wegrouped both fast and both slow fluctuating treatmentsinto slow and fast fluctuating environments in accor-dance we previous analyses of such treatments (Petchey2000a). The response variable in the ANCOVAswas either: temporal change in total communitybiomass (see eq. 1); log of total community biomass

containing 100 ml of soil-water medium and six sterilewheat seeds. Soil-water medium was a sterilised mixtureof 0.55 g Carolina Biological Supply protozoan pellet,2.5 g of soil (from the Rutgers Display Garden pond,New Brunswick, NJ) and 1.5 l of well water. Sterilesoil-water nutrient medium was inoculated first withbacteria (Bacillus cereus, Bacillus subtilus and Serratiamarcescens) and then with different numbers and com-binations of species, chosen from a pool of ten bacteriv-orous ciliates and rotifers, four days before theexperiment started (Table 2). The ten bacterivorousprotists and rotifers were obtained by either isolatingthe species from the Rutgers University Display Gardenpond or obtaining the species from Carolina BiologicalSupply company (Burlington, NC, USA). The specieswere grown in soil-water medium stock cultures formany generations before the experiment. Adding amixed culture of microflagellates and bacteria from all

Table 2. The species compositions of the different communities and mean cell masses of the different species. Species sharingthe same initial diversity and letter (a, b, c, or d) were present in the same community.

Species Cell mass (mg) Initial diversity

8642

adcb4.4×10−2Tillina sp. abcdRotaria sp. 4.3×10−4 d ad bc acd

abcd abcd2.2×10−4 dMonostyla sp. acParamecium sp. 2.0×10−4 bcd ad abcd

abcdabdcabc2.4×10−5Colpoda sp.bba bd2.0×10−5Tetrahymena thermophilacd c1.3×10−5 cColpdium sp. d

Loxocephalus sp. 5.7×10−6 a cd abc abdabcdbdb7.2×10−7Uronema sp.

Loxodes sp. 4.9×10−7 b a ac abc

234 OIKOS 99:2 (2002)

(log10(�i=1s B� i)); log of total variance (log10(�i

s Var-(Bi)); total covariance (�i

s �i� js Cov(Bi, Bj)); mean pop-

ulation biomass (B� i); or population coefficient of varia-tion (CV(Bi)). We log-transformed total biomass andtotal variance to better meet ANCOVA assumptionsand used CV (as opposed to the generally more appro-priate SD(log(n))) to measure population stability be-cause of the zeros in the data (Gaston and McArdle1994). Spearman rank correlation was used to verify theconclusions of the ANCOVA for the relationships be-tween temporal change in total community biomassand realised species richness in each environment sepa-rately. The log(population mean, B� i)-log(populationvariance, Var(Bi)) relationship was fit using a separatelinear regression for each species.

We used a model of the experiment to investigatepotential causes of the diversity-variability trend in theconstant environment. Two models (Fig. 1) recreatedthe experimental results, either with a direct destabilis-ing effect of diversity on population level variabilityincluded or excluded. We limited the models to theconstant environment because environmental fluctua-tions would be difficult to incorporate.

In both models we estimated the direct effect ofdiversity on population biomass using the equationBi(s)=ai+bi×s where ai and bi are the estimates ofthe regression coefficients of the empirical relationsbetween the biomass of species i and species richness.Negative values of bi would indicate that the biomass ofa species decreases as species richness increases, that is,density compensation. This equation predicts thebiomass of each species in each environment at a givendiversity level quite well (85% of the variance in empir-ical species biomasses in the constant environment isexplained by this equation) mostly because it accountsfor the considerable differences between the meanbiomass of different species.

In the first model (Fig. 1a), we estimated the specieslevel variance as a function of only mean biomass:Var(Bi)=ci×Bi(s)zi. Here, ci and zi are the interceptand the slope, respectively, of the linearised relation,Log(Var(Bi))=Log(ci)+zi×Log(Bi(s)), for species i.In this first model, diversity only has an indirect effecton species variances which is mediated through thedirect effect of diversity on species biomasses.

In the second model (Fig. 1b), we included a directeffect of diversity on species variance by estimating thespecies variances using log(Var(Bi))= log(ci)+zi×log(Bi(s))+di× log(s). Here, di represents the directeffect of diversity on species variance and, when esti-mated from the data, is positive for 5 out of the 10species. We used these empirical relations to recreatethe biomass and stability of each species in each exper-imental community (matching the species combinationsin the actual communities) and used these values tocalculate the expected community stability using eq. 1.

Fig. 2. The relations between temporal change in total com-munity biomass and realised species richness. Filled circlesindicate communities in the constant environment, unfilledsquares represent communities in the fast fluctuating environ-ment, and unfilled triangles indicate communities in the slowfluctuating environment. The lines show the best linear fit byANCOVA (solid for constant temperature, dashed for fastenvironmental changes, and dotted for slow environmentalfluctuations.

Results

There was no strong nor general relation between thetemporal change in total community biomass and spe-cies richness. The temporal change in total communitybiomass was positively associated with diversity in theconstant environment, although the relationship wasnot significant at �=0.05 (Fig. 2; overall effect of meanrealised species richness in ANVOCA: F1,66=3.06, p�0.08; Spearman for constant environment rs=0.47, p�0.08; fast environment rs=0.22, p�0.25; slowenvironment rs=0.00, p�1.0). In the constant envi-ronment this indicates a weak destabilising effect ofspecies richness on temporal change in total communitybiomass. Lack of evidence of a strong stabilising effectof species richness on total community biomass requiresexplanation, as does the slightly destabilising effect ofspecies richness in the constant environment, becausetheory generally predicts a stabilising effect of increasedspecies richness.

More variable environmental conditions tended tocause greater change in total community biomass atlow level of species richness, though, again, the effectwas not significant at �=0.05 (effect of environment inANCOVA: F2,66=2.5, p�0.09; interaction in AN-COVA: F2,66=0.86, p=0.43). Total biomass changedmore in the slowly fluctuating environment, changedleast in the constant environment, and change inbiomass was intermediate in the fast fluctuating envi-ronment. The gradients of the relationships in the dif-ferent environments could result from measuringbiomass at just two time points and thereby constrain-ing the maximum value of temporal change to 141.2.

OIKOS 99:2 (2002) 235

Total biomass

The total community biomass increased with increasingdiversity (ANCOVA: F1,66=27.1, p�0.0001) and wasunaffected by the pattern of environmental fluctuations(Fig. 3a; ANCOVA: environment F2,66=1.2, p=0.32;interaction F2,66=0.7, p=0.52). Our data was insuffi-cient to apply tests that can distinguish between thedifferent mechanisms that cause positive biomass-diver-sity relations (Table 2; Hector 1998, Loreau 1998,Emmerson and Raffaelli 2000, Loreau and Hector2001). Five low diversity communities with conspicu-ously high total biomass (Fig. 3a), were, however, all ofthe same composition (Tillina+Loxodes ; and differedby the environment they experienced), indicating thatcomposition can explain some aspects of the relationbetween total biomass and diversity (Hooper and Vi-tousek 1997, Tilman 1997). Large differences amongspecies biomass (Fig. 4) suggest that selection effects(Table 1, mechanism 1a) might partially explain totalbiomass. Irrespective of the mechanism responsible,greater total biomass in more speciose communities willcontribute towards less temporal change in more spe-ciose communities in our experiment. Thereforechanges in total biomass cannot account for the slightlygreater temporal change in more diverse communitiesin the constant environment.

Total variance

The total variance increased with increasing speciesrichness (ANCOVA: F1,66=39.6, p�0.0001) and con-tributes towards greater temporal change in more di-verse communities (Fig. 3b). This effect was not,however, due to the absence of statistical averaging(Table 1, mechanism 2a). The slope of the mean-vari-ance relation was significantly greater than one for allspecies (Fig. 3d), the necessary condition for statisticalaveraging (Table 1). The slopes of the mean-variancerelationship in our experiment falls within the range forspatial variation found in natural populations (Down-ing 1986). There was also good evidence for densitycompensation: the density of every species tended todecrease with increasing species richness and this rela-tion was significant for five species (Table 3). Environ-mental variability did not affect total variance(ANCOVA: environment F2,66=2.7, p=0.07; interac-tion F2,66=1.2, p=0.30). These empirical patternsmatch the theoretical conditions necessary for statisticalaveraging to cause less temporal change in more diversecommunities (Doak et al. 1998, Tilman et al. 1998).

The population level variability of six of the ninespecies for which tests were possible showed a trendtowards greater variability in more diverse communi-ties, though only two trends were statistically significantat �=0.05 (Table 3). Whether these species-specific

Fig. 3. The relations between diversity and (a,b, and c) thethree components of temporal stability, and (d) the mean-vari-ance scaling relation. (d) The species-specific mean-variancerelationships had slopes ranging from 1.7 to 2.5. The unusualdistribution of points results from a constraint placed onlog(variance) when populations were present at one samplingtime, but not observed at the other (log(Var(0, x))�2 log(x)+constant). Different symbols and regression linesare for different species in (d) but need not be readily distin-guished. Values are plotted on log-scaled axis, rather thanplotting logged-values on a linear scale. Summed covariance is,by definition, zero in a community with only one species.

236 OIKOS 99:2 (2002)

Fig. 4. The mean population biomass of each species plottedagainst the mean population level variability (CV) of thespecies across the entire experiment. Error bars are �1 SE.

changes in competitive interactions along the speciesrichness gradient in the constant environment. Therewas no significant effect of environment or significantinteraction between realised richness and environment(ANCOVA: environment F2,66=0.1, p=0.89; interac-tion F2,66=0.4, p=0.66).

Negative covariance in variable environments couldresult from changes in the competitive interactions or agreater range of species responses to environmentalfluctuations in more diverse communities (Table 1,mechanism 3a). This highlights the limitations using thetotal covariance (and total variance) for examiningrelations between temporal change and species richnessin variable environments. Nevertheless, greater negativecovariance contributes toward lower temporal changein more diverse communities.

The model of dynamics in the constantenvironment

The model showed that excluding all possible directeffects of diversity on species variances results in anegative trend between diversity and the temporalchange in total biomass (slope= −1.31; p=0.63) andexplains 47% of the variance in the experimental changein total biomass. Including direct effects of diversity onpopulation level variability recreates the data moreaccurately (predicted slope=8.8; p=0.07; observedslope=8.4; p=0.07) and explains 76% of the observedvariance in change in total biomass.

Discussion

Here we present evidence for little or no relation be-tween the number of competitive species in microbialmicrocosm communities and the temporal change intotal community biomass. In constant environmentalconditions there was a trend towards greater change in

patterns amount to a net stabilising or destabilisingeffect of diversity on temporal change in total commu-nity biomass depends on the slopes and intercepts ofthe species-specific relationships and distribution of spe-cies among communities. For example, a species thatwas strongly destabilized by increased species richnessmay not influence the overall pattern if it has low meanvariability compared to all other species.

Mean population variability differed among species(Fig. 4). This unevenness in population level variabilitygenerally weakens relations between species richnessand temporal change by adding scatter to the relation,especially at low diversities (Table 1, mechanism 2c).

Total covariance

The total covariance became slightly more negative asdiversity increased (Fig. 3c) (ANCOVA: F1,66=5.8,p�0.05) and therefore contributed a weak stabilisinginfluence on temporal change in more diverse commu-nities. More negative covariance can only result from

Table 3. Effects of environmental variability and species richness on mean population density and population variability (CV)of each species. ANCOVA tested for effects of richness, environmental variation and their interaction. The symbols *, **, and*** indicate p�0.05, p�0.01, and p�0.001 respectively; ns indicates p�0.05.

Species Effect on mean population density Effect on population variability

Environment RichnessErrorInteractionRichnessEnvironment InteractionErrordfdf

Colpidium 13 * ** (negative) ns 5 * ns (negative) *ns (positive) *8 * ns (negative) *Colpoda 2 *** (positive) nsLoxocephalus 35 ns ns (negative) ns 34 nsns (positive) nsLoxodes 29 * *** (negative) ns 29 ns

nsns (negative)ns43ns** (negative)*43MonostylaParamecium ns34 ns ** (negative) ns 34 ns ns (positive)Rotaria 25 ns ns (negative) ns 25 ns * (negative) ns

ns3T. thremophila Insufficient data0nsns (negative)nsns (positive)ns13ns*** (negative)ns29Tillina

Uronema 23 ns ns (negative) ns 12 ns * (positive) ns

OIKOS 99:2 (2002) 237

total biomass in more diverse communities. Three non-exclusive explanations could account for this apparentcontradiction with frequent theoretical predictions of astabilising effect of diversity on aggregate communityproperties. First, a direct effect of diversity on popula-tion level variability (Table 1, mechanism 2b) is consis-tent with results in the constant environment. Thisdirect effect of diversity on population level stability isabsent from recent models (Doak et al. 1998, Tilman etal. 1998). Second, theory predicts stabilising effects ofdiversity only when species differ in their responses toenvironmental change (Ives et al. 1999). Our speciesmay not differ much in their response to environmentalchange, though we do not know their temperatureoptima. And third, the differences in population vari-ability that we observed among the species tend toweaken relations between diversity and the variabilityof aggregate properties. We also found evidence forcommunity wide density compensation and a strongpositive relation between species richness and totalcommunity biomass. Our results are not necessarily atodds with theory, in particular they may not contradictthe insurance hypothesis, and advocate for a moredetailed and mechanistically oriented investigation ofthe determinants of temporal change in microbial mi-crocosms and other communities.

Relating our result to other empirical results

Results from previous experimental manipulations ofdiversity (see Table 2 of Cottingham et al. (2001)) andobservational studies (McNaughton 1985) provideequivocal evidence for a general negative relation be-tween diversity and the temporal variability of aggre-gate community and ecosystem properties. Severaldifferences between studies might explain the contrast-ing results.

First, there are differences in how variability wasmeasured in different studies. Naeem and Li (1997) andMcGrady-Steed et al. (1997) included variability amongreplicates (spatial variability), and variability amongcommunities at each diversity level in their measure ofvariability, whereas we used a purely temporal measureof variability. Including variability among replicatescould be important if stabilising/destabilising mecha-nisms influence spatial and temporal variability differ-ently. Including variability among differentcommunities at each diversity level may influence diver-sity-variability relations because different high diversitycommunities share more species in common than dodifferent low diversity communities. This might predictlower biomass differences between high diversity com-munities than low diversity communities (Fukami et al.2001). McGrady-Steed et al. (1997) show, however, thatvariability decreases even when calculated for one com-munity at each diversity level.

Different variability metrics are used in differentstudies (Cottingham et al. 2001) and the metric ofchoice can affect the variability estimate (Gaston andMcArdle 1994). Furthermore, we only measuredbiomasses twice, whereas other studies measuredbiomass more frequently. It is possible that this couldaffect the exact form of the diversity-variability rela-tion, but it also seems unlikely that this alone couldcause opposing diversity-variability relations. Furtherexperiments and analyses of published experimentsshould assess potential differences between spatial andtemporal stability, employ a standard measure of vari-ability, and sample more than twice during anexperiment.

Second, the design of diversity manipulations caninfluence interpretation of results (Huston 1997, Wardle1998, Allison 1999). We selected species randomly,within some minor constraints, in order to experimen-tally remove effects of composition on variability. Wedid not replicate communities with the same speciescomposition because we were primarily interested inhow diversity affects community properties. Other ex-periments have used different diversity manipulationsranging from completely random selection of species(Symstad et al. 1998, Hector et al. 1999) to non-randomselections of species due to biological constraints (Mc-Grady-Steed et al. 1997). We reduced the potential biascaused by selecting a subset of all possible communities(we used 20 out of a possible 510) by assembling asmany different combinations as possible (at a cost offew samples through time). (In hindsight, we mighthave profited more by assembled many more communi-ties and at the expense of no environmental variabilitytreatment.) We cannot be sure, however, that a differ-ent subset of the 510 possible communities might haveproduced a completely different diversity-variability re-lation. At present we know of no method for predictingor measuring the potential for bias caused by selectinga subset of all possible communities.

Third, and we consider most likely, biological differ-ences between experiments might cause a direct desta-bilising effect of diversity on population level stabilityin our experiment that was absent, or much weaker instudies that found stabilising effects of species richness.We used communities containing only competitors andtheir resource, bacteria. In contrast, other studies havemanipulated diversity across several trophic levelssimultaneously (Naeem et al. 1994, McGrady-Steed etal. 1997, Naeem and Li 1997), manipulated diversityindirectly (Tilman 1996), and manipulated diversitywithin a pre-existing community (Tilman 1996). Includ-ing only competitors in communities may reduce thevariation in species responses to environmental variabil-ity and thereby weaken or prevent any insurance effect(Walker 1992, 1995, Ives et al. 1999). Including speciesthat range greatly in their population level variability(Fig. 4) would also weaken any diversity-variabilityrelation.

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Relating our result to theory

Equation 1 has been combined with mean-variancescaling relations to predict (Doak et al. 1998, Tilman etal. 1998, Tilman 1999) and investigate (Tilman 1996,1999) relations between diversity and the stability ofaggregate community properties. This is a phenomeno-logical and statistically based framework for under-standing and predicting diversity-stability relations.This model does not included direct effects of diversityon population level stability, rather, it assumes only anindirect effect of diversity on population stability that ismediated through effects of diversity on population size(Doak et al. 1998, Tilman et al. 1998, Tilman 1999).The mean-variance scaling relation modulates this indi-rect effect of diversity on population variance (Tilmanet al. 1998). This model therefore assumes that popula-tion level stability is determined only by populationdensity, and does not depend on the size or structure ofthe surrounding community. The direct effect of diver-sity on population variance (Fig. 1b) that we observedin our experiment allows community properties to af-fect population level variance directly and indepen-dently of population density. Including such a directeffect was sufficient to recreate the positive diversity-variability trend in the constant environment. We can-not identify the mechanism(s) that might be responsiblefor the apparent direct effect of diversity on populationlevel stability, though it seems unlikely that none occurin real ecological communities.

In general, eq. 1 and mean-variance scaling mayassume the absence of many other biological mecha-nisms that link diversity with the mean, variance, andcovariance. An alternate approach is to employ mecha-nistic and biologically based models to explain andpredict diversity-stability relations (Ives et al. 1999,Hughes and Roughgarden 2000, Ives et al. 2000). Suchmodels do not explicitly specify mean-variance relationsor mean-diversity relations, rather, these relations resultfrom inter-specific interactions between populationsand the pattern of species responses to environmentalchange. Explanations of empirical diversity-stability re-lations within this biologically based framework can beaccomplished using autoregressive models that isolateeffects of species interactions and responses to environ-mental fluctuations (Ives et al. 1999). The more mecha-nistic basis of biologically motivated models arepreferable if we seek a mechanistic explanation of di-versity-stability relations.

In conclusion, we found that diversity has stabilisingand destabilising influences on the temporal stability oftotal community biomass. Stabilising mechanisms werestatistical averaging, increasing total biomass with in-creasing diversity, and weakly decreasing negative co-variance with increasing diversity. A direct destabilisingeffect of diversity on species variability, however, wasstrong enough to overwhelm any stabilising influences

of diversity and result in slightly positive diversity-vari-ability relation in the constant environment.

Acknowledgements – Cheryl Inglis, Kevin Gaston, DaveHodgson, Jennifer Hughes, Tony Ives, Peter Morin, ShahidNaeem, and Phil Warren made comments on previous versionsthat improved this work. Yoko Kato helped collect data.Funding was provided by The Biodiversity Centre at CookCollege, Rutgers Univ., NSF USA grant 9806427 to PeterMorin, and a NERC Fellowship to OLP.

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