REFERENCES AND NOTES

1. J. H. Lawton and V. K. Brown, in Biodiversity andEcosystem Function, E.-D. Schulze and H. A.Mooney. Eds. (Springer-Verlag, Berlin, 1993), pp.255-270.

2. P. M. Vitousek and D. U. Hooper, ibid., pp. 3-14.3. B. H. Walker, Conserv. Biol. 6. 18 (1991).4. F. S. Chapin III, J. Lubchenco. H. L. Reynolds, in

Global Biodiversity Assessment, V. H. Heywood, Ed.(Cambridge Univ. Press, Cambridge. 1995), pp.289-301.

5. T. J. Givnish, Nature 371, 113 (1994).6. S. J. McNaughton, in (7) , pp. 361-383; S. Naeem,

L J. Thompson, S. P. Lawler, J. H. Lawton, R. M.Woodfin, Nature 375, 561 (1995).

7. D. Tilman, D. Wedin, J. Knops, Nature 379, 718(1996).

8. D. U. Hooper, Ecol. Monogr., in press.9. P. M. vitousek, Oikos 57, 7 (1990); F. S. Chapin III,

H. L Reynolds, C. D'Antonio, V. Eckhart, in GlobalChange in Terrestrial Ecosystems, B. Walker, Ed., inpress.

10. D. Tilman, C. L. Lehman, K. T. Thomson, Proc. Natl.Acad. Sci. U.S.A. 94, 1857 (1997).

11. To prepare for planting, a field at Cedar Creek Natural History Area, in Minnesota, was treated withherbicide and burned in August 1993, and had theupper 6 to 8 cm of soil removed to reduce the seedbank, was plov/ed and repeatedly harrowed, anddivided into 342 plots, each 13 m by 13 m (only theinner 11 m by 11 m was sampled). Plots were seeded in May 1994 and again in May 1995. To test foreffects of species diversity, we determined composition of each of 167 plots by random draws of 1, 2,4, 8, or 16 species from a core pool of 18 species(four species each of C3 grasses, C4 grasses, legumes, and forbs; two woody species), with 29 to 35replicates at each level of species diversity. To betterdistinguish between effects of species ana functionaldiversity, we assigned combinations of 1, 2, or 3functional groups containing 2,4, or 8 species to 76more plots, with compositions chosen by randomdraws of functional groups followed by species.When needed, we used a pool of 16 additional species (four in each of the nonwoody functionalgroups). Another 46 plots were created with 32 ofthese 34 species. Four plots were kept bare. These289 plots uncouple species diversity, functional diversity, and functional composition, but have a weakcorrelation between these and species composition.There is no such correlation in the 167-plot randomspecies subexperiment. The 289 plots have the following numbers of plots assigned to species andfunctional diversity classes:

Species per plot

0 1 2 4 8 16 32

0 4 -1 - 3 4 11 12 14 - -2 - - 33 13 14 - -3 - - 20 14 - -4 - - 10 18 1 165 - - - - 11 34 30

Functionalgroupsper plot

12. Unless noted otherwise, all analyses use treatmentspecies diversity, treatment functional diversity, andtreatment functional composition. In each plot weestimated the percent cover of each species in foursubplots (0.5 m by 1 m each). We measured peakaboveground living plant standing crop (an estimateof plant productivity) by clipping, drying, and weighing four 0.1 m by 3.0 m strips per plot. We measured% N in this aboveground biomass (plant % N), itstotal N (plant total N), soil NH„ and soil N03 extract-able in 0.01 KCI [four soil cores (2.5 cm by 20 cmdepth) per plot], and the proportion of incident light(PAR) that penetrated to the soil surface. In 1996,plots contained mature, flowering plants, but the relative abundances of species may still be changing.

13. Linear regressions for effects of species diversity:productivity, r = 0.20, P < 0.01, n = 289; plant % N,r = -0.24, P < 0.001, n = 286; plant total N, r =

0.10, P- 0.08, n = 286; soil NH4,r =-0.11, P =0.06, n = 289; soil N03, r = -0.18, P < 0.01. n =289, light penetration, r = -0.24, P < 0.001, n =288. For effects of functional diversity: productivity,r = 0.30, P < 0.001, n = 289; plant % N, r = -0.33,P < 0.001, n = 286; plant total N, r = 0.16. P <0.01, n = 286; soil NH4, r = -0.19, P = 0.01. n =289; soil N03, r = -0.29, P < 0.001, n = 289, lightpenetration, r = -0.34, P < 0.001. n = 288.

14. Regressions (as in 13), multiple regressions (asin Table 1), ANOVAs (as in Table 2), and MANOVAsthat used only the 167 plots of the random speciessubexperiment (7 7) had similar results and generally higher r2 values, indicating that results arenot caused by the weak correlation between diversity and species composition In the full 289-plotexperiment.

15. The 1995 average percent cover of each species orfunctional group in each plot was used to calculateits effective species or functional diversity as eH',where H' is the Shannon-Wiener diversity index forspecies or functional groups. Trends found usingtreatment diversity variables also occurred when using 1996 effective diversity.

16. There were 32 different combinations of five functional groups drawn 0,1,2,3,4 or 5 at a time. All 32combinations were represented in the experiment.For the nested ANOVAs, each plot with a given levelof functional diversity was further classified by whichof the 32 combinations it contained. Similar resultsoccurred when plots with bare soil or with 32 species

were excluded.17. In the MANOVA, P < 0.0001 for both funbtkw.

liversity and functional composition using w^r,Lamba, Pillai's Trace, Hotelling-Lawley TraceRoy's Greatest Root.

18. J. L. Harper, Population Biology ot Plants (Acade^Press, London, 1977); D. Tilman, Resource Comn?tition and Community Structure, Monograph^Population Biology (Princeton Univ. Press prinrJ't o n . N J , 1 9 8 2 ) . 2 s "

19. J. J. Ewel, M. J. Mazzarino, C. W. Berish, Ecol. Ao-,1 , 2 8 9 ( 1 9 9 1 ) . ^ *

20. R. T. Paine, Am. Nat. 100, 65 (1966); J. H. Br0VmD. W. Davidson, J. C. Munger, R. S. lnouye 2Community Ecology, J. Diamond and T. Case, Eds'(Harper and Row, New York, 1986). pp. 4-|_6l;S. R. Carpenter ef al., Ecology 68, 1863 (1987)- jPastor, J. D. Aber, C. A. McClaugherty, j. m (Jj2lillo, ibid. 65, 256 (1984); G. C. Daily, P. R. q"^N. M. Haddad, Proc. Natl. Acad. Sci. U.S.A 9q'592(1993).

21. P. M. Vitousek, L. R. Walker, L D. Whiteaker, oMueller-Dombois, P. A. Matson, Science 238, 802(1987).

22. We thank C. Lehman, C. Bristow, N. Larson, andct.research interns for assistance and C. Bristow, cLehman, C. Mitchell, S. Naeem, and A. Symstadfcccomments. Supported by NSF and the Andrew Mellon Foundation.

21 April 1997; accepted 16 July 1997

The Effects of Plant Composition and Diversityon Ecosystem Processes

David U. Hooper* and Peter M. Vitousek

The relative effects of plant richness (the number of plant functional groups) and composition (the identity of the plant functional groups) on primary productivity and soilnitrogen pools were tested experimentally. Differences in plant composition explainedmore of the variation in production and nitrogen dynamics than did the number offunctional groups present. Thus, it is possible to identify and differentiate among potential mechanisms underlying patterns of ecosystem response to variation in plantdiversity, with implications for resource management.

Recent experiments have shown increasing net primary productivity (NPP) andnutrient retention in ecosystems as thenumber of plant species increases (I, 2).Ecosystem response to plant richness couldoccur via complementary resource use ifplant species differ in the ways they harvestnutrients, light, and water (3, 4). Complementarity could happen in space, for example, because of differences in rootingdepths; in time, for example, because ofdifferences in phenology of plant resourcedemand; or in nutrient preference, for example, nitrate versus ammonium versus dissolved organic N. Greater plant diversitywould then allow access to a greater proportion of available resources, leading to in-

Department of Biological Sciences, Stanford University,Stanford, CA 94305-5020, USA.•To whom correspondence should be addressed at: Department of Integrative Biology, Room 3060, Valley Ufs SciencesBuilding, University of California, Berkeley, CA 94720-3140,USA. E-mail: hooper@socrates.berkeley.edu

creased total resource uptake by plane,lower nutrient losses from the ecosystem,and increased NPP, if the resources,inquestion are limiting growth. However,differences in plant composition (theidentity of the species present) may havelarge effects on ecosystem processes if th?traits of one or a few species dominate (5)-For example, if one species or group Ispecies reduces soil nutrients to a lowerlevel than do other species, then this species (or group) may dominate pools olavailable soil nutrients in mixtures (o);Such effects of composition could alsolead to lower soil nutrient pools and great>er nutrient retention as diversity increasebecause of an increasing probability .«including the dominant species at hig»erlevels of richness. In this case, however,increased ecosystem nutrient retention results from the presence of only one specifrather than from niche differentiation arwcomplementary resource use among m'apy-

Table'usedfc

0.72

0-75

•Composfuncticna•,P<0.(correcteca and b <treatment

Until rmechar

Weined hofunctioiin a serassessedtivity, rleach in.both thresponsfour funare potswere uslate se;bunchgthe Me.Franciscnate inwinterApril oidry seasthroughing autiMay anning ofPhenolcdueledcling.groupsv'ant tc'ncludiiW, cotQMraPlanted

1302 SCIENCE • VOL. 277 • 29 AUGUST 1997 • www.sciencemag.org

mma■Table 1. Statistics for productivity and inorganic N (inN) (73). Productivity data were natural log-transformed before ANOVA to improve normality. Modelsused for nonlinear regression are also shown.

ANOVA Regression by richness

Composition effects* Richness effectst R2 Linear Nonlinear

0.72 + E < -E""t+ P> -P**Ex L (0.053)LXP (0.039)

NS(1=2 = 3 = 4)

+E < -E*"+ N > - N(0.046)EX L (0.014)

B > 2, 3, 4*1 > 2, 3*1 >4@

Productivity0 . 1 3 A l l §

Intercept = 5.04**'Slope = 0.02 (NS)BLK (NS)

0 . 6 6 E o n l y | |Intercept = 4.408Slope = 0.216***BLK (NS)

Inorganic nitrogen pools0 . 2 9 A l b

Slope = 1.359***Intercept = -0.296***BLK (NS)

0 . 3 7 E o n l y *Slope - 0.43 (NS)Intercept = 0.002 (NS)BLK*

0.57

0.20

AllND<.

EonlyB = 92.13 + 66.72 * log(FG)

AllinN = x, + x2x, = -0.081x2 = 1.617x3 = -0.364

E onlyND

e(x3'FG)

'Composition effects: significant main effects and interactions from ANOVA. tRichness effects: differences among levels of functional group richness [B (bare), 1, 2, 3, or 4functional groups] without accounting for composition. .Significance for a priori ANOVA tests is denoted by the following: NS, not significant; @, Bonferroni family-wide P < 0.1;']P < 0.05; **, P < 0.01; and"", P < 0.001. Because the Bonferroni correction is conservative, when the uncorrected P value is lower than 0.10 but greater than the Bonferronicorrected P for family-wide confidence, the significance value is listed. § Regression including all treatments. Model is ln(B) = a + b'FG + BLK , where 3 is biomass in g/m2,a and b are the intercept and slope, respectively, FG is number of functional groups, and BLK is a categorical variable for block. Regression including only E-containingtreatments; see Fig. 1. Model is the same as for All. 1ND, analysis was not done because no trend was evident. "Regression model is inN = a + b'FG + BLK.

:om-soil

.inedsr ofipo-piant

Until now, a direct test to resolve thesemechanisms has not been reported.

We describe an experiment that examined how richness and composition of plantfunctional groups (7) affect nutrient cyclingin a serpentine grassland in California. Weassessed how plant diversity affects productivity, resource availability to plants, and Nleaching losses. The experiment focused onboth the plant and microbial mechanismsresponsible for such effects. Species fromfour functional groups defined by traits thata"e potentially relevant to nutrient cyclingwere used: early season annual forbs (E),kite season annual forbs (L), perennialbunchgrasses (P), and N-fixers (N) (8). Inj-he Mediterranean-type climate of the San''"ahcisco Bay region, annual plants germi-•^Ce in the fall after the first significant*'n.ter rains. E's set seed and senesce by

^pril or May, the beginning of the summer~"Y season. L's continue to grow and flower. °ugh the summer, senescing the follow-S autumn. P's senesce aboveground in late

' ay and resprout from roots at the begin-, 8 pf the following rainy season. N's are

Pnenologically similar to E's, but were in-,. d for their relevance to nitrogen cy-n8- In addition to phenology, theseups differ in other characteristics rele-

inT.t0 nutrient retention and turnover,:i0l\« tin 8 r°oting depth, root-to-shoot rasp.on

lant-S jstern. Ves in :.ever, t

(the jhave I:if the |

:e%]jup d \lowef £is sp^

rOlsff,s(6).,|d al* ■gre^

.we**.;*

.-j (J.C0I»petitive ability, size, and foliageEg •yio (9, 10). E's, L's, and P's werei. ec» in a factorial combination, and two

treatments containing N-fixers were alsoincluded: N's alone, and N's combined withall other groups (II). A disturbed serpentine grassland site was used, in which serpentine topsoil was layered over the preexisting subsoil to provide a common substrate on which to plant the experimentaltreatments.

Aboveground biomass, used here to estimate primary productivity, did not correlate with increasing functional group richness (Table 1) (12). However, there weresignificant differences among treatmentshaving the same number of functionalgroups (Fig. IA) (13). In general, composition (the identity of the functional groupspresent) explained much more variancethan did richness (the number of groupspresent) (Table 1)-. Complementarity maybe evident in some subsets of the treatments; for example, the E-containing treatments showed an increase in productivity asmore functional groups were included (E <EL, EP < ELP < ELPN; Fig. IA). However,mixture yields never approached the substantially higher biomass of the perennial-only treatment. Although these groups differ in both phenology and rooting depth,competitive interactions in mixture treatments had a strong effect on total plantbiomass. In mixtures, the smaller E's and L'sreduced the biomass of P's substantially below the levels expected on the basis ofplanting density and yields in single-grouptreatments (Fig. IB). Our results do not

address year-to-year variability in production in response to pests, disturbance, orclimatic variability (4, 14, 15). However,for NPP in this one year, traits of certainfunctional groups, such as competitivenessof E's and L's in mixture and large biomassof P's in monoculture, outweighed the effects of complementarity due to differencesin phenology and rooting depth.

If nutrient use among plants is complementary, the expectation is that functionalgroup mixtures will be able to reduce poolsof available N in soil to lower levels thanwill single functional group treatments. Onthe other hand, if one group is dominant,this group alone (and all mixtures containing it) should have the lowest soil N levels.We measured pool sizes of inorganic N inthe top 10 cm of soil in February during thewet mid-winter growing season (16). Increasing functional group richness was correlated with reduced soil inorganic N poolsin the experimental plots (Fig. 1C and Table 1). However, E's alone reduced inorganic N pools to the lowest level of any singlefunctional group treatment, and all morediverse treatments containing E's hadequally low pool sizes. This pattern is consistent with Tilman's R* hypothesis (6, 17),in which the most competitive species reduces resource pools to the lowest level.Because a greater proportion of the treatments contained the dominant E's as diversity increased, this led to lower average Npool sizes as well. As with productivity,

_ _ www.sciencemag.org • SCIENCE • VOL. 277 • 29 AUGUST 1997 1303

.composition explained substantially moreof the variance in the data than did functional group richness alone (Table 1).

To obtain an integrative measure of howplant composition and diversity affect Nlosses from the ecosystem, we added traceramounts of the stable isotope 15N and followed its fate over the course of a growingseason (18). Unlike the single time-point

400 r

300 -

<r200 -

100 -

A ." Pil I | .

- P "a M

2^ -J13"""

1 2 3Number of functional groups

* " L * < V ^ $ t > < A % & ATreatment

'V

3 r

f+ txz+O__

^ a r I ; —| C-^Tj

measurement of inorganic N, increasingfunctional group richness did not significantly affect 15N retention; total losses were similar for all treatments except for significantlylower retention in bare plots (Fig. 2 andTable 2). In all treatments, most 15N wasrecovered in soil. Other experiments lookingat ecosystem N retention have yielded similar results, implying that, in the short term,microbial immobilization is a more important pathway for N retention than plantuptake (19). However, the presence of microbes alone is not sufficient; microbial immobilization relies on C inputs from plants,resulting in low soil retention in bare plots inthis and other experiments (Fig. 2) (20).

Composition, but not richness, of plantfunctional groups affected the distributionof 15N between plants and soil (Fig. 2 andTable 2). If plant 15N uptake were complementary between all three groups, we wouldexpect to see a general increase in plant N

Table 2. ANOVA results for 15N retention. Regressions were not performed because no trendswere evident. Soil 15N data were natural log-transformed before ANOVA to improve normality.NS, @, *, **, and *** as in Table 1.

0 1 2 3 4Number of functional groups

Fig. 1. Response of (A) aboveground biomass tofunctional group richness (mean ±1 SE, n=6), (B)aboveground biomass in 1993 to functional groupcomposition, and (C) soil inorganic N (microgram ofN per gram of soil) in February 1993 to functionalgroup richness. Treatments are B = bare plots,E = early season annuals, L = late season annuals,P = perennial bunchgrasses, N = N-fixers, EL =earlies plus lates, EP = earlies plus perennials,LP = lates plus perennials, ELP = earlies plus latesplus perennials, and ELPN = earlies plus lates plusperennials plus N-fixers. In (A) and (C), points areoffset from whole numbers for clarity only. The solidline is the regression through all data points, andthe dashed line is the regression through only thosetreatments that contain early season annuals. SeeTable 1 for regression parameters. In (B), stackedbars show the average functional group composition of each treatment (n = 6, ±1 SE of the totalplot biomass). In (B) and (C), means within one levelof richness with the same nonlabel letter (a, b, c, x,and y) are not significantly different at Bonferroni-co r rec tedP<0 .10 .

Composition effectsRichness

effects

Plant 15N0 .50 LxP (0 .030 )

(E + ELP < EL + EP)Soil 15N

0 . 6 9 + E > - E ( 0 . 0 1 2 )+L > -L@+ P< -P (0.019)t

Total 15N recovery0 . 8 7 + E > - E * *

+ L > - L * *E x L ®EXP*L x P "(Due to low recovery in B)

tPost-hoc test including only vegetated treatments:3(P+EP+LP+ELP) = 4(E+L+EL).

NS

NS

NS

retention as diversity increased. Insrwhere differences among treatments -spcurred, they resulted from interaction/among certain combinations of groupswith productivity (Table 2). Complenwtarity among these functional groups apnarently had a smaller effect on ecosystem \jretention than did other attributes, such a?litter quality and root turnover, that affect_ed microbial immobilization.

In summary, we observed two pattern^for the response of ecosystem processes tochanges in plant functional group richness::and composition. For productivity and1||retention, there was no response to chano.es in functional group richness, although"within a given level of richness, treatments of different composition differedfrom each other. For inorganic N, we oh.served a decrease in soil pool sizes as plaritfunctional group richness increased. How.ever, the mechanism by which this p&|curred was not complementary nutrientuse resulting from functional group richness per se; rather, it resulted from the1,dominant effects of one functional group,the early season annuals, in all mixtures ofwhich it was a component.

These results point to two primary conclusions. First, differences in functionalgroup composition can have a larger effecton ecosystem processes than does function-;;al group richness alone. The effects of differences in composition are widely recognized in intercropping and agroforestry,where much time and expense are investedin finding species or genetic varieties thatcombine in more diverse agroecosystems.to'improve total yield (4, 14, 21). This suggests that the functional properties of par-,ticular species and combinations of specie,more than richness per se, control yield."and nutrient use (2, 22). Second, because/differences in species composition can be,correlated with differences in species richness, we need to look at all species or func-

6

o5E■O 40)

Plants Soil

a\ a b * *

Totala a a 4

>o1 2 diiiiuJ

_ _ _ _ _ _llllllll II8 £ I # Q Go (./> Qfi 8 & t # Q&OiGLfl 8 <? «* * $ «"*> ^ -"to'

rionalgr°uPdiverse comanismsofdPc ^ ^ d 'rityorfaahbid but so acounteract t

The impnessandcones cut both"managemeniterm maximcases less divform as wellinagricultuPproductiononly with tfertilizer, anterm, along •tal costs of shand, knowlteristics of csustainableintercroppin]experiment ;protect natumanage formeasured b\Wiener inde:sition. The ficomponent :likely to benumber of S]ecosystem pr

REFE

Treatment

Fig. 2. Recovery of 15N in plants (roots, shoots, and litter) and soil (soil organic matter, microbiomass, and inorganic nitrogen pools). "Total" is the sum of plant and soil recovery. Treatments are^in Fig. 1, except no treatments with N-fixers were used with this experiment. Bars are means ±1 Sij-= 3. Differences of means within levels of richness are designated as in Fig. 1. See Table 2 for addition*statistics.

1. S. Naeem sTrans. R. ScTilman, D. W

2. S. Naeem et3. B. R. Trenba4.1H. Vanden

Vandermeer,Ycrk, 1990),

5. F. S. ChaDii(1980); D. A433(1990);.'(1392).

6. D. Tilman, FFunction ofgraphs in Po[Ion Univ. Pr(D. Wedin, EcFunctional gion physiologyrelevant to cc

. sification willBPUon. Aithoclearcut, thewhich specie•o generalizehelp to simpli' Sly of specie

Biodiversity a"wj H. A. I.'993), pp. 3-Chapin III, ibi

'fi 1-17-140]-?• Early seasonLindley, Mien■Plantago en■ Hemizonia la.

1304 SCIENCE • VOL. 277 • 29 AUGUST 1997 • www.sciencemag.org

I-TOTOB

&>o>tip

mio

-,^1 aroups grown alone as well as in morejiverse combinations to understand mech-

rc^

-nisms of diversity effects on ecosystem pro--gsses. As diversity changes, complementa-zk or facilitation among species are possible, but so are many other effects that maycounteract these (23, 24).

The implications of the effects of richness and composition on ecosystem process-x cut both ways for conservation and landjjanagement. If the only goal is the short-fam maximization of production, in somecases less diverse cropping systems may perform as well as more diverse systems, as seenaiagriculture and forestry. However, higherproduction in monocultures often comesonly with the added expense of energy,fertilizer, and pesticides over the longerterm, along with the external environmental costs of such inputs (25). On the otherhand, knowledge of the functional characteristics of component species can aid in•ustainable management of low-diversityintercropping systems. The results of ourexperiment also indicate that in aiming toprotect natural ecosystems, we cannot justmanage for "species diversity" alone—asmeasured by richness or the Shannon-Wiener index, which ignore species composition. The functional characteristics of thecomponent species in any ecosystem arelikely to be at least as important as therurmber of species for maintaining criticalecosystem processes and services.

REFERENCES AND NOTES

;arec

?■ S. Naeem ef al., Nature 368, 734 (1994); Philos.Trans. R. Sec. London Ser. B 347, 249 (1995); D.TCrnan, D. Wedin, J. Knops, Nature 379, 718 (1996).

2--S. Naeem et al., Oikos 76, 259 (1996).^-B.R.Trenbath,/Wv. Agron. 26, 177 (1974).-• J- H. Vandermeer, in Agroecology, C. R. Carrol, J. H.

Vandermeer, P. M. Rosset, Eds. (McGraw-Hill, New2-Yok, 1990), pp. 481-516.sfc.S. Chapin III, Annu. Rev. Ecol. Syst. 11, 233.11980); D. A. Wedin and D. Tilman, Oecologia 84,J33 (1990); S. E. Hobbie, Trends Ecol. Evol. 7, 336(1992). -a n Vi~"- i'man, Plant Strategies and the Dynamics and■Function of Plant Communities, vol. 26 of Mono-

.; faphsin Population Biologv. R. M. May, Ed. (Prince-. wni Univ. Press, Princeton, NJ, 1988); andv. u. Wedin, Ecology 72, 1038 (1991).'-" ^c'tonal groups are groupings of species based

"^Physiology, morphology, life history, or other traitsaw"'t0 controls on an ecosystem process; clas-^ation will depend on the process under investi-paapn. Although functional groups are not always.j-^cut, they help to elucidate mechanisms by-"•tenspecies influence ecosystem processes, help

Generalize such mechanisms across species, and,-J3 to simplify studies in systems with a large diver-bL Species fp- M- Vitousek and D. U. Hooper, in

; -ZM^rsity and Ecosystem Function, E.-D. Schulzei°q-3v A' Moonev' Eels. (Springer-Verlag, Berlin,•q*JjJ. PP. 3-14; S. E. Hobbie, D. B. Jensen, F. S.- 117^ '"' ibid- PP. 385-408; C. Korner, ibid., pp.

.-- UnrjLSeason annuals: Lasthenia californica DC. ex■'•'' pL^-'•'Microseris douglasii (DC.) Schultz-Bip., and

•Ve a9° erecfa (Morris); Late season annuals:art.? , -—'i' v1"'^"3/! i-euc __.:--...:. ... i i..:_ ...I 0nia iuzulaefolia (DC.) ssp. mdis and Lessingia

micradenia E. Greene var. glabrata (Keck) Ferris; Perennial bunchgrasses: Stipa pulchra Hitchc. andSitanion jubatum Smith; N-fixers: Lotus subpinnatusLag. and Astragalus gambellianus Sheldon.

9. S. L Gulmon era/., Oecologia 58, 33 (1983).10. R. J. Hobbs and H. A. Mooney, ibid. 67,342 (1985);

H. A. Mooney, R. J. Hobbs, J. Gorham, K. Williams,ibid. 70, 555 (1986); N. R. Chiariello, in GrasslandStructure and Function: California Annual Grassland,L. F. Huenneke, H. A. Mooney, Eds. (Kluwer. Dordrecht, Netherlands, 1989). pp. 47-58; J. Armstrong, thesis, Stanford University, Stanford, CA(1991).

11. There were a total of 10 treatments at five levels ofplant diversity: B (bare plots); E, L, P, and N (1-group); EL, EP, and LP (2-group); ELP (3-group); andELPN (4-group) (species richness thus ranged from0 to 9 species). We aimed for a planting density of200 g/m2 in 1 -group treatments, based on previousmeasurements of aboveground biomass in undisturbed serpentine grassland [S. J. McNaughton,Ecology 49,962 (1968); S. N. Turitzin, Am. Midi. Nat.107, 95 (1982)]. In mixture treatments, 1-groupplanting densities were cut to one-half, one-third, orone-fourth to maintain constant overall planting density. Because of the small stature of most serpentinespecies, they attained self-supporting populationswithin relatively small plots (9). Plots were locatednear the Kirby Canyon Landfill (Waste ManagementInc.), on an area that was originally bare of topsoil.Serpentine topsoil was graded over the subsoil to adepth of approximately 30 cm. Treatments wereplanted in 1.5 m by 1.5 m plots (0.5 to 1 m bufferzone between plots) in a randomized complete blockdesign, with 10 treatments per block and six replicates of each treatment. Treatments were planted inthe winter of 1991-92, and measurements weremade in the following growing season (winter/spring1993).

12. We measured aboveground biomass by clippingannual species at the soil surface in five randomlyplaced 10 cm by 10 cm quadrats in each plot. ForP's, we measured diameter and height of randomindividuals within each plot, then determined regressions for these measurements, withaboveground biomass on randomly harvested individuals [D. U. Hooper, Ecology, in press]. Clippings were sorted by species, dried at 65°C, andweighed. Total aboveground biomass is the sum ofmeasurements made at peak biomass for eachfunctional group: April for E's and N's, May for P's,and September for L's.

13. Data were analyzed in two ways, using SYSTAT[SYSTAT Inc., SYSTAT for Windovjs: Statistics, Version 5 Edition (Evanston, IL. 1992)]. First, the effectsof functional group richness alone were tested, usinglinear and, where appropriate, nonlinear regressions(See Table 1). Second, the effects of compositionwere tested, using analysis of variance (ANOVA) withthe following model: var = CONST + E + L + P +ExL + ExP + LxP + ExLxP + N + ExLxPxN+ BLK, where var is the response variable of interest;CONST is a constant; E, L, P, N, and combinationsthereof are main effects and interactions of the functional groups; and BLK is a categorical variable representing the experimental blocks. In addition toANOVA main effects and interactions, the following apriori comparisons were tested: differences amonglevels of functional group richness and differencesamong treatments within a level of functional grouprichness for the 1 -group and 2-group levels. Probability levels were corrected by Bonferroni adjustment(see Table 1). For productivity and plant 15N, wherevalues for the bare plot are meaningless, we used ameans model coding for the ANOVA to avoid confounding the results-ln these cases, ANOVA maineffects and interactions were tested with balancedcomparisons among the remaining treatments [Fordetails, see D. U. Hooper and P. M. Vitousek, Ecol.Monogr., in press].

14. K. Steiner, Intercropping in Tropical Smallholder Agriculture with Special Reference to West Africa [German Agency for Technical Cooperation (GTZ), Es-chbom, Germany, 1982].

15. S. J. McNaughton, Ecol. Monogr. 55,259 (1985); D.Tilman and J. A. Downing, Nature 367, 363 (1994).

16. Three random soil samples 5 cm in diameter and 10cm deep were composited from each plot. A 20-gsubsample was extracted in 0.5 M K2S04, filteredafter 36 hours in refrigeration, and the filtrate wasmeasured colorimetrically for ammonium and nitrateon an Alpkem RFA/2 autoanalyzer (Perstorp Analytical, Silver Spring, MD).

17. D. Tilman and D. Wedin, Ecology 72, 685 (1991).18. Near the beginning of the growing season (January

1993), stainless steel cores (30 cm in diameter by 40cm long) were inserted to a depth of 30 cm intorandomly selected quadrats within experimentalplots in three of the six replicate blocks. No treatments that included N-fixers were used for this experiment. After 2 weeks, we added tracer amountsof 15N as ammonium sulfate (26.4 mg "SN, —65%enriched, total N addition = 5.6 kg N/ha) in solutionwith deionized water (a total of 18 ml). The solutionwas spread onto the soil surface then watered in byslowly adding ~ 1000 ml of deionized water. Coreswere harvested at the beginning of the dry season inApril 1993. Aboveground biomass was separated byspecies, dried (65°C for 3 days), weighed, ground,and analyzed for percent N, and percent ,5N on aEuropa Scientific Tracermass mass spectrometer (inthe laboratory of M. K. Firestone, University of California, Berkeley). Root biomass was estimated bywashing subsamples of soil (400 g) on a root elutria-tor (Gillison hydropneumatic elutriation system,model GVF1300, Benzonia, Ml) at the laboratory ofL. Jackson at the University of California Davis Agricultural Extension in Salinas, CA. Roots were notseparated by species; they were analyzed as abovefor N and 1SN content. Soil moisture was measuredgravimetrically. Dried soil samples were ground on aroller mill and analyzed for N and 15N as above. See[D. U. Hooper, thesis, Stanford University, Stanford,CA (1996)] for more details.

19. P. M. Vitousek and P. A. Matson, Science 225, 51(1984); S. C. Hart, M. K. Firestone, E. A. Paul, J. LSmith, Soil Biol. Biochem. 25, 431 (1993).

20. J. Martinez and G. Guiraud, J. Soil Sci. 41, 5 (1990);J. J. Ewel, M. J. Mazzarino, C. W. Berish, Ecol. Appl.1,289(1991).

21. M. G. R. Cannell, D. C. Malcolm, P. A. Robertson,Eds., The Ecology of Mixed Species Stands of Trees(Blackwell, Oxford, 1992).

22. F. S. Chapin, III, H. L. Reynolds, C. M. D'Antonio,V. M. Eckhart, in Global Change in Terrestrial Ecosystems, B. Walker and W. Steffen, Eds. (CambridgeUniv. Press, Cambridge, 1996), pp. 403-428.

23. D. Binkley. in The Ecology of Mixed-Species Standsof Trees, M. G. R. Cannell, D. C. Malcolm, P. A.Robertson, Eds. (Blackwell, Oxford, 1992), pp. 99-123; F. S. Chapin III, L. R. Walker, C. L. Fastie, L. C.Sharman, Ecol. Monogr. 64, 149 (1994).

24. M. D. Bertness and R. Callaway, Trends Ecol. Evol.9, 191 (1994).

25. C. B. Field, in Global Biodiversity Assessment, United Nations Environment Programme, Eds. (Cambridge Univ. Press, Cambridge, 1995), pp.402-406; R. Naylor, Annu. Rev. Energy Environ. 21,99(1996).

26. We thank Waste Management Inc. and the Center forConservation Biology, Stanford University, for accessto field sites. D. Turner, D. Herman, C. Chu, P.Brookes, M. Hanes, L. Jackson, M. A. Read, N. M.Holbrook, C. Benton, L. Chu, A. Cottrell, H. Far-rington, M. Jones, D. Mallery, B. Tibbie, M. Vander-marck, and E. Vela all provided valuable field andlaboratory assistance. T. Chapin and J. Neff gaveuseful comments on earlier drafts of this manuscript.Financial support was provided by grants to D.U.H.from NSF (Predoctoral Fellowship and Doctoral Dissertation Improvement Grant DEB-9212995), fromthe Morrison Institute for Population and ResourceStudies, and from the NASA/Stanford Program forGlobal Change. Additional support came from thePew Scholars Program in Conservation and Environment and from the A. W. Mellon Foundation.

26 March 1997; accepted 18 July 1997

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