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The Relationship between Productivity and Species Richness Author(s): R. B. Waide, M. R. Willig, C. F. Steiner, G. Mittelbach, L. Gough, S. I. Dodson, G. P. Juday, R. Parmenter Source: Annual Review of Ecology and Systematics, Vol. 30 (1999), pp. 257-300 Published by: Annual Reviews Stable URL: http://www.jstor.org/stable/221686 . Accessed: 29/06/2011 11:43 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at . http://www.jstor.org/action/showPublisher?publisherCode=annrevs. . Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. Annual Reviews is collaborating with JSTOR to digitize, preserve and extend access to Annual Review of Ecology and Systematics. http://www.jstor.org Annu. Rev. Ecol. Syst. 1999. 30:257-300 Copyright(? 1999 by AnnualReviews. All rightsreserved THE RELATIONSHIPBETWEEN PRODUCTIVITY ANDSPECIES RICHNESS R. B. Waide',M. R. Willig2,C. F.Steiner3,G. Mittelbach4, L. Gough5,S. I. Dodson6,G. P.Juday7,and R. Parmenter8 1LTERNetworkOffice,Departmentof Biology, Universityof New Mexico,Albuquerque, New Mexico 87131-1091; e-mail: [email protected];2Programin Ecology and ConservationBiology, Departmentof Biological Sciences & TheMuseum,TexasTech University,Lubbock,Texas79409-3131; e-mail: [email protected];3Kellogg Biological Stationand the Departmentof Zoology,MichiganState University,Hickory 4KelloggBiological Station Corners,Michigan49060; e-mail: [email protected]; and the Departmentof Zoology,Michigan State University,HickoryCorners,Michigan 5Departmentof Biological Sciences, University 49060; e-mail: [email protected]; of Alabama, Tuscaloosa,Alabama35487-0344; e-mail: [email protected]; 6Departmentof Zoology, Universityof Wisconsin,Madison, Wisconsin53706; e-mail: sidodson@facstaff:wisc.edu; 7ForestSciences Department,Universityof Alaska, Fairbanks,Alaska 99775-7200; e-mail: [email protected];8Departmentof Biology, Universityof New Mexico,Albuquerque,New Mexico 87131-1091; e-mail: [email protected] Key Words processes primary productivity, biodiversity, functional groups, ecosystem * Abstract Recent overviews have suggested that the relationship between species richness and productivity (rate of conversion of resources to biomass per unit area per unit time) is unimodal (hump-shaped). Most agree that productivity affects species richness at large scales, but unanimity is less regardingunderlying mechanisms. Recent studies have examined the possibility that variation in species richness within communities may influence productivity, leading to an exploration of the relative effect of alterations in species numberper se as contrasted to the addition of productive species. Reviews of the literatureconcerning deserts, boreal forests, tropical forests, lakes, and wetlands lead to the conclusion that extant data are insufficient to conclusively resolve the relationship between diversity and productivity, or that patterns are variable with mechanisms equally varied and complex. A more comprehensive survey of the ecological literatureuncovered approximately200 relationships, of which 30% were unimodal, 26% were positive linear, 12% were negative linear, and 32% were not significant. Categorization of studies with respect to geographic extent, ecological extent, taxonomic hierarchy, or energetic basis of productivity similarly yielded a heterogeneous distribution of relationships. Theoretical and empirical approaches increasingly suggest scale-dependence in the relationship between species richness and productivity; 0066-4162/99/1 120-0257$08.00 257 258 WAIDEETAL consequently,syntheticunderstanding maybe contingenton explicitconsiderationsof scale in analyticalstudiesof productivityanddiversity. INTRODUCTION The notion that productivity(rate of conversionof resourcesto biomass per unit areaper unit time) affects species richness can be tracedto at least the mid-1960s (45,106,113,153). Nonetheless, the causal mechanisms behind the patternsbetween productivityand species diversity,as well as the form of the relationship, have been in dispute for almost as long (53, 193). Indeed, studies of the relationship between productivityand diversity at large spatial scales have documented linear and unimodal patterns as well as no patterns at all (see review in GG Mittelbachet al, in litt. and SI Dodson et al, 5 la). Experimentalmanipulation of productivityvia fertilizationof small plots long has been known to decrease plantdiversity(reviewsin 48,65, 82). Importantly,boththeoreticalconsiderations (147; SM Scheiner et al, in litt.) and empiricalanalyses (KL Gross et al, in litt.) suggest thatpatternsarelikely scale dependent.Some of the disparityin perceived patternsmay be a consequenceof variationin the spatial scale of analyses. Efforts to determinethe relationshipbetween numberof species (or number of functional types, sensu 41) and the propertiesof ecosystems have increased as global loss of biodiversityand climate change have acceleratedover the past decade. One approachto this issue has been to examine the ways ecosystem processes influence species number, community composition, or trophic structure (e.g., 84, 167, 168, 191). A separateline of inquiryhas focused on the importance of the numberof species, the numberof functional groups, and the presence or absence of particularspecies (or groups) on ecosystem processes (e.g., 85, 102, 133, 134, 185, 190, 192). Field manipulationsandlaboratoryexperimentshave addressedthe role of these aspects of biodiversityin determiningratesof ecosystem processes (e.g., primaryproductivityand nutrientcycling). These two lines of inquiryhave been largely separatein the literaturedespite their conceptuallinkage. In this review,we synthesizeexisting knowledge of the relationshipbetween a commonly estimatedpropertyof ecosystems (primaryproductivity)and one aspect of biodiversity (species richness). Most theoretical studies use net primary productivity(NPP) as the drivingvariable,but empiricalstudiesoften use components or surrogatesof NPP.Ratherthanintroduceconfusing terminology,we use primaryproductivityin this paperas a generalterm to encompass componentsor surrogatesof NPP. We review the literatureand use case studies from terrestrial, aquatic,andwetlandbiomes for which detailedinformationis available.The work we reportis an extensionof researchinitiatedat the NationalCenterfor Ecological Analysis and Synthesis that focused on the influence of primaryproductivityon species richness.In addition,we considerhow species richnessmay affect ecosystem function(includingproductivity).This is a volatile andrapidlyexpandingarea of study (see 1,76, 79, 85, 107, 190). Unfortunately,the databasecurrentlyis too PRODUCTIVITY ANDSPECIESRICHNESS 259 small and conflictingto drawconclusions with certainty.Nonetheless, we suggest thatit is necessaryto bridgethese two approachesto achievea betterunderstanding of the relationshipbetween primaryproductivityandthe dynamicsof populations and communities.We do not addressthe related and importantissue of the relationship between diversity and stability (49,91, 189), nor do we discuss in detail otherpossible biotic and abiotic controlsof biodiversity. How Does ProductivityAffect SpeciesRichness? Most authorsagreethatproductivityaffectsdiversity(32,45, 106, 113, 162); moreover,a plethoraof mechanismshavebeen proposedto explainhow species richness respondsto variationin productivity(e.g., 84, 168, 167, 191). Nonetheless,no generalconsensusconcerningthe formof the patternhas emergedbasedon theoretical considerationsor empiricalfindings.Some factorsenhancerichnessas productivity increases,othersdiminishrichnessas productivityincreases,and some, in and of themselves, produce unimodal patterns(see below for details). Rather than any one mechanism having hegemony, it may be the cumulative or interactive effect of all such factorsthat determinesthe empiricalpatternwithin a particular study. Indeed, futureresearchshould identify the ecological context and spatial scale that predisposesystems to evince one patternratherthan another(154). Rosenzweig (167) provideda critical assessment of the mechanismsthought to affect patterns in the relationship between diversity and productivity.GG Mittelbach et al (in litt.) updated the summary and provided commentaryon the ecological scale at which mechanisms likely operate. Theories that predict a positive relationship between productivity and species richness include the species-energytheory (44,155,156,223) and theories invoking variousforms of interspecificcompetitionin heterogeneousenvironments(2). Mechanismsthought to diminish diversitywith increasingproductivityare more controversialand include evolutionaryimmaturity(especially with respect to anthropogenicemendations);habitathomogenization(sensu 187; 65,88); dynamicalinstabilitiesand system infeasibilities (125, 160, 162, 164, 165, 222; JC Moore & PC de Ruiter, submitted);and predator-preyratios (141, 142, 162, 163). Some mechanismspredict a unimodal patternin their own right. Relevanttheories include changes in environmentalheterogeneitywith productivity(87, 186), tradeoffsin competitive abilitiesandabilitiesto resist predation(105), effects of competitiveexclusion and environmentalstress (6,71,72), disturbanceand productivity(82), productivitydependentspecies-arearelations(147), and changingcompetitivestructure(167). Understandingthe productivity-diversityrelationshipwill requirethe imposition of orderon this apparentlychaotic arrayof possible explanations.This can be achieved,at least in part,by carefulattentionto the spatialand ecological scales at which patternsare detected (124, 167), and by equallyjudicious considerationof the spatialand temporalscales over which likely mechanismsoperate.JM Chase & MA Leibold (submitted)make significantheadwayin this regard.They develop simple conceptualmodels based on exploitativeresourcecompetitionor keystone 260 WAIDEETAL predationto show that unimodal relationshipsemerge at local scales, whereas monotonicallypositive relationshipsemerge at regional or global scales. Empirical data for benthic animals and vascularplants in Michigan ponds corroborate the expected scale dependencein diversity-productivityrelationships. The Effectof SpeciesDiversity on Productivity There is general agreementthat diversity of plant species is influenced by productivity (85, 192). The converseargument,thatthe numberand kinds of species influence productivity,has been the subject of a recent series of field and laboratoryexperiments(79,133,192). These experimentshave engendereda lively debate (85, 103,211) that has yet to reach resolution. Increasingthe numberof species in either field (192) or laboratory(133) experimentsmay have positive effects on productivityand other ecosystem processes. However,the debate concerns whetherthese effects are the result of increasedspecies richness per se, or the additionof differentfunctionalgroupsor particularspecies. Theoretical approachesto this issue occur in three categories that postulate either (a) a positive, linear relationship(54,112), (b) a positive, nonlinearrelationship (52,209), or (c) no relationshipbetween species richness and productivity (102). MacArthur(112) hypothesizedthat energy flow througha trophicweb would increase as the numberof species, and hence pathways for energy flow, increased. Elton's (54) reformulationof MacArthur'shypothesis suggested that the relationshipshouldbe linear.If, however,species have redundantfunctionsin ecosystems, the relationshipbetween species numberandecosystem functionmay be nonlinear(52,209). Ecosystemfunctionchangesrapidlyas species representing new functionalgroupsare added,but less rapidlywhen new species areredundant of existing functional groups. Lawton (102) proposed a model in which species may have strong,idiosyncraticeffects on ecosystems. If this is the case, thereis no predictableeffect of species richnessper se on ecosystem function.However,if the propertiesor functionaltraitsof individualspecies areknown,then we can predict which species will have strongeffects andwhich will not. Such hypothesespresent a useful frameworkfor the evaluationof observationsand experimentalresults. Some experimentalstudies with herbaceousplants have shown an increase in net primaryproductivitywith an increasein the diversityof species or functional groups (e.g., 132,133,185,192). However, as Huston (85) has argued (see also 1, 190), there are at least two mechanismsby which the productivityof a trophic level may increaseas the diversity(species, functionalgroups)of thattrophiclevel increases. 1. Increasingthe numberof species initially presentin a system increasesthe probabilityof encounteringan exceptionallyproductivespecies (e.g., a species that is proficientin convertingresourcesto biomass). Huston (85) has labeled this the "selectionprobabilityeffect."In this scenario,the productivityof the trophiclevel is determinedby the productivityof the most productivespecies. PRODUCTIVITY ANDSPECIESRICHNESS 261 2. Increasingthe numberof species initially presentin a system can resultin complementarityin resourceuse, if differentspecies use different resources.In this case, the productivityof an assemblageof species will be greaterthanthe productivityof any single species. Also, the total resource spectrumwill be more completely used in the more species-richsystem. Empiricalevidence from herbaceousplant communities(e.g., 79,80,85,132, 185, 192) supportshypothesis 1. No experimentalstudy supportshypothesis 2, despite its intuitiveappeal. [datafrom Tilmanet al (190) potentially supporthypothesis 2, but no informationis presentedon complementarityof resource use thatcould be used to test this mechanism].Complementarityin resourceuse is the functional basis behind intercroppingand polyculture (184,203). Although not all intercroppingschemes or polyculturesprovidehigher yields, many successful examples suggest that species complementaritymay be importantin determining ecosystem productivityand nutrientdynamics. The difficulty of designing and executing field experimentsto determinethe effect of changing species numberon productivityhas resulted in a scarcity of published studies. The clearest results include field experimentsconducted on communitiesdominatedby herbaceousvegetation(79, 192) and a microcosmexperimentconductedundercontrolledlaboratoryconditions (133). In the studyby Naeem et al (133), conductedin a controlledenvironment,a series of ecosystemprocesseswas measuredin high- andlow-diversitycommunities. The lower diversity systems were nested subsets of the higher diversitysystems. Estimatesof primaryproductivitywere greaterin microcosmswith higherspecies diversity. Tilman& Downing (188) examinedthe relationshipbetween plant species diversity and primaryproductivityin plots fertilized with N at the Cedar Creek Long-TermEcological Research site in Minnesota. They reportedthat the productivity of more diverse plots declined less and recoveredmore quickly after a severedroughtthandid the productivityof less diverseplots. They concludedthat the preservationof biodiversityis importantfor the maintenanceof productivityin fluctuatingenvironmentalconditions.This study is not a direct test of the effects of diversityon productivitybecause species richnesswas not manipulateddirectly butwas a productof changes in nitrogenaddition,andbecausethe studymeasured changes in productivityin responseto disturbance. In a differentexperiment,Tilmanet al (192) comparedproductivityandnutrientuse efficiency in grasslandplots seeded with differentnumbersof native species. Productivitywas greaterand soil mineralnitrogenwas utilized more completely in plots with greaterdiversity.Measurementsin nearbyunmanipulatedgrassland showed the same pattern.This study also concluded that the preservationof biodiversityis necessaryto sustainecosystem functioning. Huston(85) criticizedthe conclusionsof all threeof these studies,claimingthat each was taintedby the lack of rigoroustreatmentof cause and effect. In Huston's view, appropriatetests of the effect of species richnesson ecosystem processes do 262 WAIDEET AL not permitlargevariationin the size or functionof species. Hustonarguedthatone likely consequence of increasing species richness in an experimentinvolves the increasedprobabilityof introducinga productivespecies (the "selectionprobability effect"). If this happens,the effect of increasingspecies richness on productivity is attributedsimply to the increasedodds of encounteringspecies particularlywell adaptedto the environment.If variationin productivityexists among species used for an experiment,the effect of increasedspecies diversitycannotbe distinguished from the effect of increased functional diversity or mean plant size leading to differencesin total biomass among treatments. Huston'sposition, althoughadmirablefor its insistence on rigorouslydesigned experiments,requiresstudiesto be circumscribedto a limitedrangeof the variability thatexists in naturalecosystems. Experimentsthat incorporateonly species of similarsize and functionalstatus,while avoiding some of the pitfalls that Huston (85) described,may not advancesubstantiallyour understandingof naturalcommunities. Naturalcommunitiescomprise species that differ in size and function; as a result,the effect of the loss of diversityis interpretedmore easily throughexperimentsthat incorporatethat variability.Moreover,the questionof how similar species mustbe to achieveexperimentalrigorhas not been addressed.Loreau(107) and Hector (76) have recently suggested mechanisms to separatethe "selection probabilityeffect" from other effects resultingfrom experimentalmanipulations of biodiversity. Hooper (79) approachedthe question of complementaryresourceuse through an experimentaldesignthatvariedthe numberof functionalgroupsin experimental plots. Fourfunctionalgroups(early season annualforbs, late season annualforbs, perennialbunchgrasses,and nitrogen fixers) were planted in single-grouptreatments, as well as in two-, three-,andfour-waycombinations.In this experiment,no obvious relationshipexisted between functionaldiversityand productivityof the plots. The most productivetreatmentincluded only one functionalgroup,perennial bunchgrasses.The identity of the species in the treatmentswas as least as importantas the numberof species in affectingecosystem processes. Competition among some combinationsof functionalgroupsreducedproductivitycomparedto single-grouptreatments.These results corroborateLawton's (102) idiosyncratic model and Huston's (85) hypothesis 1. For practicalreasons, most experimentshave focused on structurallysimple ecosystems with relativelyfew species and have manipulatedonly a few species from each functional group. In general, results have shown a positive, asymptotic relationshipbetween ecosystem processes and species richness.These results suggest that once all functional groups are present, the additionof species with redundantfunctionshas little effect on ecosystem properties. The conclusion that diversityis importantfor maintainingecosystem function (188, 192), even if justifiablebased on the few studiesconductedto date, has been demonstratedonly for systems in which the range of richness is from 0 to about 30 species. Conclusionsaboutthe importanceof the additionor loss of species in PRODUCTIVITY ANDSPECIESRICHNESS 263 complex systemsrequirefurtherclarification(171). Structurallycomplex, speciesrich ecosystems, in which much of the loss of biodiversityworldwideis occurring, requirefurtherstudy. Questions of SpatialScale It generally is recognized that area and environmentalheterogeneityhave strong effects on diversity(84, 167). Equallyimportant,theireffects are intertwined(98) andproducescale-dependentrelationshipsbetweenproductivityanddiversity.For example,a unimodalpatternin the relationshipbetweendiversityandproductivity can be a consequence of a correlationbetween productivityand the parameters of the power function (S = CAZ,where S is species richness, A is area, and C and z are fittedconstantsequivalentto the interceptand slope, respectively,of the log-formof the relationship(7). In meadowcommunitiesdominatedby sedges and grasses, Pastoret al (147) documentthat C has a positive correlationwith area,z has a negativecorrelationwith area,andthis tradeoffproducesa unimodalformto the relationbetween diversityand productivity.In contrast,no scale dependence was detected in the relationshipbetween species richness and latitude for New Worldbats or marsupials,even though latitudeis often considereda broad-scale surrogatefor productivity(111). Moreover,two aspects of spatial scale-extent and focus-strongly affect the detection and form of the relationbetween richness and diversity (SM Scheiner et al, in litt.). Extent is the range of the independentvariable,which in this context is productivity,whereas focus defines the inference space to which variable estimatesapply (i.e., the areafrom which sampleswere obtainedto estimatepoint values for productivityand richness). In particular,a series of studies, each with restrictedextent along a gradientof productivity,may evince significant(positive and negative) linearrelationshipsas well as no relationshipbetween productivity and diversity,casting doubt on the hump-shapedpattern.If the slope of the relationshipdecreaseswith mean productivity,then a unimodalpatternemerges as a consequence of the accumulationof consecutive linear relationships(positive linear, decreasing to no relationship,decreasing to negative linear; patternaccumulationhypothesis). Guo & Berry (73) documentan emergenthump-shaped relationshipfrom a series of linearpatternsbased on an analysis of plant species richnessalong a grassland-shrubland transitionin Arizona. Nonetheless, a unimodalrelationshipmay emerge thatis not a consequenceof the accumulationof patternsat smaller extents. A series of fields dominatedby vascularplantsin the mid-westernUnited States exhibits a unimodalrelationship across grasslandsor across North America,but no or negativerelationshipswhen the extent of analyses were restrictedto be within communitytypes (KL Gross et al, in litt.). Moreover,the slopes within communitytypes were not correlated with mean productivityof the communities, suggesting that the patternaccumulation hypothesis was not in effect. In summary,relationshipsbetween diversity 264 WAIDEETAL and productivityhave been shown to be scale dependent, with the form of the scale-dependencevariablefrom studyto study,even in situationswhere unimodal patternsemerge at broadspatialscales. BIOME-SPECIFIC RELATIONSHIPS The relationshipbetween diversityand productivitycan be mediatedby different control mechanisms, depending on biome. As a consequence, patterns within biomes may differ, and emergent patternsacross biomes may not be the same as those within them. We explore the range of productivityand diversity in a numberof majoraquaticandterrestrialbiomes, andwe discuss the possible control mechanisms that lead to patternsbetween diversity and productivity.Although we have not attempteda comprehensivecoverage of all biomes, it is clear thatthe state of knowledge is quite variable among systems. Moreover,the form of the relationshipis not always clear, and understandingof the regulatorymechanisms is only rudimentaryin most cases. Aquatic Ecosystems Lakes Lakes are underutilizedbut optimal model systems for studying the relationship between species richness and primaryproductivity.They are well-delineated (bounded) communities in which species can be counted relatively easily, and in which primaryproductivityoften is measureddirectly using standardized14C uptakemethods (208). The 14Cmethodmeasuresproductivitybetween gross and net on a scale of minutes to hours. Annual levels of primaryproductivityare estimated by summing daily productivity.In lakes, the annual level of primary productivityrangesfrom about 1 to about 1300 g C m-2 yr- (5 la). Lacustrinespecies richness is influenced by lake primaryproductivity.Pure rainwaterhas a primaryproductivityof near zero because it lacks the nutrients necessaryto supportlife. Consequently,rainwaterin rockpools supportsfew or no species (50). The most productivelakes, such as sewage lagoons andtempletanks, are characterizedby extremeconditions, such as high temperatures,no oxygen at night,andlargediel shiftsin pH (e.g., 61). These conditionscan be enduredby only a few specialized species. Lakes between these extremesof primaryproductivity generally have the highest species richness. Lakes of intermediateproductivity, with sufficientnutrientsto supportphotosynthesisbutwithoutextremeconditions, supportthe most species in virtuallyall groupsof aquaticorganisms(5 la). The size of the body of waterinteractswith primaryproductivityto determine the numberof species in a lake (11, 35, 51, 81). An increase in lake area of ten ordersof magnitudeis associatedwith an increasein zooplanktonspecies richness of aboutone orderof magnitude(5 1). Indeed,over 50% of among-lakevariability ANDSPECIESRICHNESS PRODUCTIVITY 265 in richnessof crustaceanzooplanktonin NorthAmericanlakes is the consequence of lake size. Largerlakeshavemorezooplanktonspecies, regardlessof otherfactors includingprimaryproductivity. SI Dodson, SE Arnott,KL Cottingham(5 la) investigatedthe relationshipbetween the primaryproductivityof lake ecosystems and the numberof species of lacustrinephytoplankton,rotifers,cladocerans,copepods, macrophytesand fish. In a surveyof 33 well-studiedlakes, species richnessof all six taxashoweda significantunimodalresponseto annualprimaryproductivity(14Cestimate,g m-2 yr-) after lake area was taken into account. Moreover,the relationshipbetween richness and primaryproductivityfor phytoplanktonand fish was stronglydependent on lake area. The highest richness occurredin lakes with relatively low primary productivity(-100 g m-2 yr-1), such as those in the northerntemperatelakes areain the upperMidwest (United States) and in the ExperimentalLakes Area of Ontario,Canada.When temporaland spatial scales are considered,data for lake zooplanktonandmacrophytesprovidestrikingexamplesof unimodalrelationships between species richness and primaryproductivity.For small lakes (<10 ha), phytoplanktonspecies richness peaked in low-productivitylakes, whereas for largerlakes, phytoplanktonspecies richness merely declined in more productive lakes. Forlakes less than 1 ha, fish species richnesspeakedat low levels of primary productivity.For largerlakes, a peak was not evident, althoughmore productive lakes had more fish species. The relationshipbetween species richness and productivityhas been studied only for phytoplanktonand zooplanktonin a few otherlake and marinesituations. We summarizethe results of those studies below. Phytoplankton Agard et al (5) analyzed data on marinephytoplanktonspecies richnessandtheprimaryproductivityof 44 oceanographicstationsin the Caribbean to test predictionsof Huston's(84) dynamicequilibriummodel of species richness (maximumat intermediateproductivity).They arguedthat marinephytoplankton would likely exhibita relationshipbecauseof the relativelylargenumberof species andthe absenceof confoundingfactorssuchas spatialheterogeneity.Theyreported that species richness was correlatedpositively with primaryproductivity,except at high levels where the curve reached a plateau. Fishery statistics (see 84) for the region show that the diversity of harvested marine species of commercial importancemirrorsthe diversityof phytoplankton. In a 4-5 year study of three productivesurface mines in Pennsylvania,phytoplankton diversity was correlated inversely with primary productivity (30). These sites are within the range of productivities explored by SI Dodson, SE Arnott,KL Cottingham(5 la), and the resultsare consistentwith those of that study. Zooplankton Microcrustaceanspecies richness is correlatedwith degree days for a group of shallow Canadianand Alaskan tundraponds (75). These low- 266 WAIDEETAL productivityponds representvalues along the ascendingportionof the unimodal relationshipreportedby SI Dodson, SE Arnott, KL Cottingham(5 la). Patalas (148) reporteddatafor zooplanktonandJuly temperature(which is an indicatorof lake primaryproductivity)in Canadianlakes. Using these same data,Rosenzweig (167) found a unimodalrelationship. Wetlands Coastal and inland wetlands have been the subject of much of the research on the relationshipbetweenplantspecies diversityandproductivity.The mechanisms controlling species diversity along productivitygradientsin marshes may differ from those demonstratedfor terrestrialcommunities. In particular,selection for traits allowing survival in environmentswith high salinity and low soil oxygen may createlow-diversity,high-productivitycommunitieswithoutthe involvement of mechanismssuch as competition. In general,coastal marshesare some of the most productiveecosystems in the world. Tidal salt marshplant communitiesare structuredby salinity and flooding gradientscreatingdistinct zonationwith low plant species richness (20, 89). Primaryproductivityis high, up to 2500 g m-2 yr-1 (123). Mangrovesreplace tidal saltmarshesin the tropics,with low plantspecies richnessandhighly variableproductivitythatdependson tidalinfluences,runoff,andwaterchemistry(123). Tidal freshwatermarshes associated with rivers are more diverse because of monthly flooding and lower salinity levels (139) but are equivalentlyproductive.Inland freshwatermarshesand peatlandscan be highly diverse, dependentsomewhaton nutrientavailability.Productivityis high, often exceeding 1000 g m-2 yr-', and, if belowgroundestimatesare included,can exceed 6000 g m-2 yr-l (123). Plant species richnessin coastal marshesdecreasestowardthe coast along naturalsalinitygradients(4, 37, 101, 139) andmay be influencedby storm-drivensalt pulses (31, 57). Floodingandsoil anoxiaalso decreasespecies richness(66,68, 117, 123). The importanceof interspecificcompetition, disturbance,and stress tolerance in determiningspecies distributionshas been demonstratedexperimentally (18, 19,97,151,181). Disturbancebyherbivores(14, 58, 67, 138) andwave action (178) also may affect richness. Salt marshesmay be less productivethan fresh water marshesbecause of the metabolic cost of tolerance to salinity (70,139). Where freshwaterflows into a coastal area, bringing nutrientsor reducing salinity, productivitymay be higher than in areas without freshwaterinput (47, 123,225). Wave exposure also may restrictproductivity(97,220,221). In inlandandcoastalmarshes,soil characteristics such as pH, Ca, Mg, and anoxiamay correlatewith productivity(e.g., 17,62). In tidal salt marshes, sulfides ratherthan high salt concentrationsdecrease productivity (218). Mammalianand avian herbivoresrestrictabovegroundbiomass accumulationin some marshesby removingplantmaterial(9, 14,59,67, 138, 202), but they may have a stimulatoryeffect by adding nutrientsthroughfecal deposition (78). Based on the results of fertilizationexperimentsin marshes,vegetation frequentlyis limited by nitrogenor phosphorus(67, 126, 135). PRODUCTIVITYAND SPECIESRICHNESS 267 When plant species density (the numberof species per unit area) and productivity (estimatedby harvestsof peak standingcrop) are examined in concert for marshes, the relationshipbetween them depends on the scale of measurement and other factors. When productivityis increasedby fertilization,plant species richnessdecreases(136,204). Whenherbivoresareexcluded,productivityusually increases,accompaniedby a decreasein plantspecies richness(14, 58, 67). Certain abiotic variables(e.g., salinity) may have similareffects on plant species density and productivity.However,the relationshipbetween the two is not consistent in wetlands, although it is frequentlyunimodal (Table 1). In most cases, data are variable,with an outer envelope of points having a peak in species density at an intermediatelevel of standingcrop (114). In some cases, the relationshipreaches an asymptote,and plant species density does not decline over an extendedrange of standingcrops (e.g., 69,221). Moore & Keddy (124) demonstrateda unimodal relationshipacross communitytypes, althoughtherewas no relationshipbetween plant species density and standingcrop within communities. Approximatepeaks in species density are found at a range of standingcrop levels from 100 to 1500 g m-2 (Table 1). Once biomass reaches approximately 1000 g m-2, plant species density rarely exceeds 10 species per m2 and usually remainslow. However,the rangein plantspecies numbersis quitelargeat levels of biomass between 0 and 1000 g m-2, suggestingthatothervariablesaffect species densityat aparticularlevel of standingcrop.Stresstoleranceplays animportantrole in survivalin certainmarshhabitatsandmaycontrolspeciesrichnessindependently of otherfactorssuch as biomass (68, 69). The mechanismcausingconsistentlylow species numbersabove approximately1000 g m-2 standingcrop remainsunclear but is likely a combinationof abiotic stresses and biotic interactions. TerrestrialEcosystems ArcticTundra The arctic environmentrestrictsthe presence and productivityof vascularplant species (22,42). Because of the severityof the environmentand the common origins of the flora, approximately2200 vascular species are known in the entire arctic region (22). Many of the abiotic factors believed to control productivity also play a role in controlling diversity.In particular,low temperature,a short growing season, low rates of soil nutrientcycling, permafrost,wind exposure, and extremesof soil moisturemay constrainplantproductivity(reviewedin 177). Variousphysiological and morphologicaladaptations(e.g., cold hardiness,short stature,vegetativereproduction)allow arctictundraspecies to survivein such an environment(22,23). On a smaller scale, topographycan dramaticallyinfluence snow cover, exposure,soil drainage,and otherphysical propertiesof the substrate that may limit or enhance accumulationof plant biomass (175,176). Generally, the most productive arctic plant communities are those dominated by deciduous shrubsor graminoidsin areasof flowing water,where nutrientavailabilityis higher and few other vascularspecies are present (39,213). Nutrientavailability WAIDE ET AL 268 r X X | t mo t XbX?oE N I~~00 X CS CS O: ' IOOOO O Dm OzO O O b z o o) N o o Y~~~~~~~~~1 o o o o o m =~~~~~~~~7 s + e Pv O g m N xD ? c s b o o ? 0tt o Yn t 0 oQ s C) = N 00 OC) .sf)c N N 0 m m o C's o Cf) m o f m m mn 03 m t m N o. N m m~~~~~~~~~~~~c m N , N~~~~~~~~~~~~la N U o \_ Ot O O o O O w- i 7 75 'S; CD xDOmCS ttCS CS f) I ; I A o o A ? 4 I t | .E~~~~~~~~~~~~~u ucl O~~~~~~~~~ PRODUCTIVITY ANDSPECIESRICHNESS 269 consistently limits productivityin tundraecosystems, as demonstratedby many fertilizationstudies (40,77,94,161, 174). In general,the regionaland local species pools in the Arctic are limited by extremetemperature,shortgrowing season, low nutrientavailability,low soil moisture, and frost disturbance(21,27, 210,213). Local areas of enhancedresources (e.g., animalcarcasses)areoccupiedfrequentlyby plantsfoundin moreproductive sites, suggesting nutrientlimitationof species composition as well as of productivity (118). Herbivoryalso may be a factor affecting arctic plant communities (12, 90), but it has been studied insufficiently (for an exception, see 13). The relationshipbetween productivityand diversityrarelyhas been addressed specifically in arctic tundra(for an exception, see 60). We present a summary of mean abovegroundnet primaryproductivity[annualnet primaryproductivity (ANPP), g m-2 yr-1] and mean species richness (S) of vascularspecies for each community.In the arctic,the associationbetween S andANPP is weak (Figure 1). To gain insight, we dividedthe datainto two groups(High andLow Arctic) based on floristicand ecological considerations(25). A positive linear relationshipbetween species richness and ANPP is obvious in the High Arctic (AlexandraFiord, Polar Desert, Polar Semidesert,Devon Island, Russia, and Barrow;R2 = 0.45, p << 0.001), but no relationshipcharacterizes the Low Arctic (Figure 1). In the High Arctic where plant cover is sparse, light competitionis rarelyimportant.When stressfulconditions are ameliorated, more species inhabitmore favorableareas. This is exemplifiedby small sites of increasedmoistureor temperaturethat are more productiveand diverse than are drieror cooler sites (26,64, 127, 128). The lack of a relationshipin the Low Arctic likely is relatedto greaterplantcover, causing both light and nutrientcompetition to be importantin determiningspecies richness. Perhaps as conditions become more favorablefor higher plant productivityin the Low Arctic, light competition becomes more intense, countermandingthe effect of productivityon species richness. The clear relationshipbetween productivityand species richness in the extreme environmentof the High Arctic suggests similarregulationof these two parametersby abiotic factors. In the Low Arctic, the relationshipbecomes less clear,possibly suggesting the importanceof biotic regulationof species diversity in these communities. Hot Deserts Desert ecosystems are typically on the low end of the productivity gradient, rangingbetween 0 and 600 g m-2 yr-1 (Table2)eProductivityin desert ecosystems generallyis limited by moistureavailabilityand is highly variablein space and time (104,170,182,212,216). When rainfall is abundantfor extended periods, nutrientlimitation (particularlynitrogen) may regulate primaryproduction (55,74, 109, 129). Seasonal timing of precipitationdeterminesthe period and durationof primaryproduction,with some desertsexhibitingprimarilysingle season pulses of productivity(e.g., MojaveDesertin early spring,andChihuahuanDesert in mid- to late-summer),whereas other deserts have bimodal productivitypeaks 270 WAIDE ET AL _ N a co~~~ 7i N~~~~~~~~~~~~~~~~~~' CtN .CY) ~~~~~~~~~~~~~~~ U-~ o co~~co CN C~ ~~O ~ ~~~ O: m c 3: ~ o o 0 O CO o a a) V V o cd. cN E , t M -J 0~~~~ 0? > ~~~~~ 0 > m .b : 0 ~~~~~~o o > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~CO ssou43!S S@!3@dS 'S~~~~~~~~~~~~~~~II v ? t O bO 0 o Msuv. PRODUCTIVITY AND SPECIES RICHNESS TABLE 2 271 Productivity estimates of deserts Site United States Great Basin Desert Productivity/(g m-2 yr-1) Reference 125 150 Mojave Desert 0-62 13-44 15 10 Sonoran Desert 9-95 109 149 217 Chihuahuan Desert 50-100 43 Bajada: Alluvial fans 53-292 108 Bajada: Small arroyos 37-318 108 Bajada: Large arroyos 30-456 108 Basin: Slopes 51-179 108 Basin: Swales 292-592 108 52-258 108 Israel Negev Desert 4-43 144 India Rajastan 0-126 173 Mongolia Gobi Desert 0-155 96 22-155 56 Basin: Playa lake Tunisia Pre-Sahara Desert (e.g., the SonoranDesert in both springand late summer).Productivityin deserts is a consequenceof slowergrowthof woody shrubsandsucculentscombined,with highly variableflushes of annual and perennialgrasses and forbs. Comparisons of primaryproductionin deserts indicate that belowgroundproductivitymay be considerablygreaterthan aboveground(108 and referencestherein). Species diversityrangeswidely among deserts,dependingon geographiclocation, biogeographichistory,and extremesof moistureand temperature.However, many deserts supportrelatively large numbersof species (Table 3) and in many cases actually exceed the numbers of species found in other ecosystems with higherproductivity.At a continentalscale, the aridand semiaridecosystems of the American Southwest supportgreaternumbersof species of numeroustaxa than do otherecosystems of NorthAmerica (145, 146). Although the relationshipbetween productivityand species diversity in arid ecosystems has not been addressedspecifically,certainrelationshipsappearwhen comparing large-scale and small-scale patterns among and within deserts. Comparinglarge-scalepatternsthroughoutthe world, deserts that have near zero 272 WAIDE ET AL TABLE 3 Desert Great Basin Mojave Sonoran* Chihuahuan Numbers of species occurring in North American Deserts Mammals Birds 86 302 Reptiles 18 Amphibians Plants Reference 6 929 119 1,836 169 53 332 37 3 302 366 132 14 46 74 396 55 10 120 *Includesmany island endemic species from the Sea of Cortez. productivity (e.g., the polar desert of Antarctica, and various locations within continental deserts and dune fields) have concomitantly low species diversity, whereas deserts with hundreds of plant and thousands of animal species have comparably higher productivity. However, when comparing deserts of the same region (e.g., North America), the relationship between productivity and diversity is not as clear. For example, in North America (Table 2) the Mojave Desert has the lowest and the Chihuahuan Desert has the highest productivity, yet patterns of species numbers (Table 3) do not correspond to this pattern. At smaller scales, various sites within a desert also differ markedly in productivity and plant species diversity. Ludwig (108) showed variations among years for productivity in different habitat types within the Chihuahuan Desert of New Mexico and found that sites that benefited from additional "run-on" moisture were most productive (Table 2). These sites typically were dominated overwhelmingly by a single grass species (Hilaria mutica) and exhibited high productivity with low diversity as compared to more diverse, but less productive, upland bajada slopes. Tropical Forests Copious data on productivity and species richness of tropical forests exist in the literature. However, detecting patterns between productivity and species richness is hampered by the great variability in environmental conditions in tropical forests and the lack of standardized sampling techniques. Ecosystems ranging from savanna to cloud forest often are grouped under the rubric of tropical forest. Soil nutrient availability, precipitation, temperature, and solar radiation exhibit strong variation, even within relatively homogeneous groupings such as lowland tropical forest. Efforts to control for these variables by selecting sites with comparable conditions reduce sample sizes to the point where statistical power is jeopardized. A search of the literature encountered 168 reported values for some measure of productivity (NPP, litter fall, leaf fall) for tropical forest sites, but only 15 were associated with any kind of measure of species richness. Joint measures of net primary productivity and species richness at the same scale are infrequent and usually restricted to woody plant species. Reviews of studies of NPP in tropical forests have reported ranges of 6-16 t ha- I yr- 1 for tropical dry forest ( 13 1) and somewhat higher values for tropical evergreen forests [10.3-32.1 tha-' yr-1 (130); 11-21 tha-' yr-1 (34); 10.0-22.4tha-' yr-' PRODUCTIVITY AND SPECIESRICHNESS 273 (121)]. A searchof the recentliteraturedid not encounteranymoreextremevalues. Most, if not all, of the NPP measurementsreportedfor tropicalforests arebasedon incrementsof abovegroundbiomass and fail to include belowgroundproductivity or losses to consumptionof plant tissues. Partialmeasuresof NPP are often used for comparisonbetween sites. For example,thereis an extensivedatabaseon litter fall (157) with valuesrangingfrom 1.0-27.0 t ha- I yr- l. A recenteffortto estimate reasonableupperand lower boundsaroundthe totalNPP at 39 tropicalforest sites resultedin rangesfrom 3.4-6.0 and20.1-37.0 t ha- I yr- l, respectively(DA Clark, D Kicklighter,J Chambers,J Thomlinson,J Ni, E Holland, submitted). Cumulativelists of species of plantsandanimalsexist for diversekinds of tropical forests, but theirutility for comparisonis questionablebecause of differences in the effort expendedto constructsuch lists and the areaupon which the lists are based.For these reasons,it is difficultto comparevertebratespecies richnesseven in the best-studiedtropicalforests. Because a more standardizedapproachis used to count trees, it is possible to estimate the range in species richness for this group within tropicalforests. For example, Phillips et al (152) reporteda range of 56-283 tree species > 10 cm dbh for 1 ha plots in maturetropicalcontinental forests. Three studies in tropical rain forest reporteda positive relationshipbetween species richnessandrainfall(a surrogatefor productivity).Gentry(63) interpreted positive correlationsbetween tree species richness and annualprecipitation,seasonality, and soil richness as supportfor a positive relationshipbetween productivity and diversity.Huston (83) found tree-speciesrichnesspositively correlated with annualprecipitationand negativelycorrelatedwith soil fertility,and this was interpretedas a negativerelationshipbetween productivityand diversity.Phillips et al (152) reportedpositive relationshipsbetween tree species richness, climate (includingincreasingrainfall),anddisturbance.They suggestedthatmoredynamic systems have greaterproductivity,resultingin higher species richness. However, higher rainfallitself is related to increasedforest disturbanceand decreased soil nutrientconcentrationsbecauseof leaching(83), demonstratingthe tangleof cause and effect that can result when surrogatesof productivityform the bases of analyses. No studies relating diversity directly to forest productivityare availableto unravelthis tangle. BorealForests Boreal forests occur in the coldest environmentson earthin which trees survive and dominate vegetative cover. Nonetheless, a surprisingdiversity of climates and a wide range of ecosystem productivitiesare present in the boreal region. Because it controls rates of organic layer decompositionand thus the release of elements,soil temperatureis a pervasiveinfluenceon productivityof borealforests (194, 196, 197). Boreal forest productivitydecreaseswith decreasingsoil temperature,and increaseswith warmingsoil temperature(195, 197,200), providedthat moistureor otherfactorsdo not become limiting.Low soil temperaturesreducenutrientuptakeparticularlyin higher(vascular)plants(38). In turn,soil temperatures 274 WAIDEETAL are influencedby inherentsite factorssuch as slope, aspect, andtopographicposition with respectto cold air drainage(200) andby factorsthatchange duringsuccession (e.g., 206). Large-scalestand disturbanceswarm boreal soils, especially by removing or thinning the insulating soil organic mat (198). Advancing succession rebuildsthe organic layer, causing soil cooling and the build-upof high concentrationsof refractory,low-quality forest litter that depresses productivity, particularlyin conifer-dominatedforest types (201). On sites underlainby permafrostsoil, rootingdepthis restrictedto the annually thawed active layer at the surface, and groundlayer vegetation is dominatedby mosses. Mosses filterand sequesterincoming nutrients,restrictingnutrientavailability and productivityfor rooted vegetation (140). Sphagnummoss dominance on permafrostsites producesorganicsoils of such high acidity thatavailabilityof particularnutrientelementsis restrictedforrootedvegetation.On largerriverfloodplains, nitrogenadditionby alder (Alnus) shrubspromotes a substantialincrease in productivity(95, 199). On low elevationsites in semi-aridcentralAlaska, a soil water-balancemodel is well correlatedwith basal area growth in white spruce (224), demonstratingthat moisture can limit productivityon warm, dry south slopes as well. Belowgroundproductivityin the boreal region only recently has been measuredcarefully, and recent progress on methodological problems suggests that most of the previous literaturemay not be reliable, especially because the high turnoverof fine roots makes intervalmeasurementsof productivityproblematic (158). Fine root productionconstitutesa large partof total productionin boreal forests, accountingfor 32% and 49% of total productionin deciduous and coniferous stands,respectively,in centralAlaska (159). An often-citedvalue for averageborealforestabovegroundnet primaryproductionis 2,700 kg ha- Iyr- l (143). Productivityin the extensivelarchforestandsparse larchtaigaof Siberiatypicallyrangesfrom 2500 kg ha-l yr-l to 1400 kg ha-l yr-l respectively(95). Morerecentmultiyearmeasurementsofabovegroundproduction in boreal forests of centralAlaska include 9600 kg ha- I yr- I on a highly productive floodplain in peak alder/balsampoplar stage of development, and a range of 3600 kg ha-l yr-l to 4500 kg ha-l yr-1 in 200 year-old floodplain and upland white spruce forest, respectively (159). Upland birch/aspenforest averaged 8100 kg ha- yr-', and poorly productiveblack spruce on permafrostaveraged 680 kg ha-' yr-1 (158). Long-term studies reveal a high degree of interannual variabilityin primaryproduction. Ecological studies and floristic surveys provide estimates of overall species richnessin the borealforest (Table4). Totalregionalplant species richnessis correlated positively with productivity,increasing from the less productivemiddle or northernboreal region to the more productivesouthernboreal region and borealtemperatetransition(adjustingfor differencesin intensityof samplingeffort). Databasesadequatefor comparisonof diversityand productivitywith confidence at the local site and stand level do not exist, although some data are suggestive. Highly productivemature forests in the Bonanza Creek Long-TermEcological ResearchSite have lower plant species density (e.g., 205), perhapspartlybecause PRODUCTIVITYAND SPECIESRICHNESS 275 TABLE 4 List of studies in which estimatesare providedfor numberof species of plants in borealforests Location Vascular plants S. Ontario& Quebec 440 U.S. GreatLakes, Ontario 378 Alaska 375 Lichens 107 70 133 162 Total plants - Canada Ontario Mosses/ hepatics - - Finland 1,350 1,500 810 Sweden 2,000 2,100 1,000 552 - Number of plots Reference 197 36 103 8 103 110 60 100 228 172 3,660 National 137 5,100 National 137 - of more complete usurpationof resources. This suggests a negative and linear relationship. Succession plays a majorrole in the relationshipbetween diversityandproductivity as well. Most boreal forests are adaptedto a stand-replacementdisturbance regime, so they generallylack classic climax stages and an associatedspecialized complementof species. Totalplant species richnessincreasesduringborealforest succession, especially duringprimarysuccession (205), whereasproductivitydeclines in later successional stages (207), suggesting a hump-shapedrelationship through successional time. Many plant species of the forest understorypersist (albeit at low abundance)throughoutsecondary succession, during which time productivitydiffers greatly dependingon soil cooling and other influences of the stand (207).Species richness in late succession may be underestimatedsystematically in most of the literaturebecause the difficult-to-identifycryptogamscan constitute a large proportion,possibly even a majority,of the autotrophicplant species in boreal regions (Table 4). Vascularplant dominance is generally at a maximum during early succession, and cryptogamdiversity and abundanceare usually at a maximumlate in succession (207). Summary Integrationof results from the two aquaticand three terrestrialbiomes discussed above suggest thatthereexists no universalpatternin the relationshipbetween primaryproductivityandspecies richness.In most cases, patternsseem to changewith scale, but data within biomes are inadequatefor rigoroustests of this suggestion. Unimodal patternsare found in lakes, some wetlands, and throughsuccessional time in boreal forest. In terrestrialsystems, a positive relationshippertainsat regional or greaterscales. In no case, however,arethe dataadequateto examine the relationshipbetween primaryproductivityand species richness across scales and taxa. To addressthese issues, a broadersurveyof the literatureis necessary. 276 WAIDEETAL SURVEYOF PATTERNS Biome-specific considerationof studies leads to the conclusion that, one, extant dataareinsufficientto conclusivelyresolve the relationshipbetween diversityand productivity,and two, patternsare variable,with mechanismsequally varied and complex. This is in sharpcontrastto the broad claim that the unimodal pattern is among the few valid generalizationsin ecology (84, 86, 166, 167). Indeed, a unimodalpatternhas been heraldedas the "trueproductivitypattern"(166) and as the "ubiquitous"pattern(87). We surveyedthe publishedliteraturein ecology to assess such claims. Data Acquisition We conducteda literaturesearchto examine patternsin the relationshipbetween diversityand productivity.Using BIOSIS?, Biological Abstracts,and the search string: "species richness OR species diversityOR primaryproductivityOR production OR biomass OR rainfall OR precipitation,"we searched The American Naturalist, Oecologia, Oikos,Holarctic Ecology/Ecography,The Journal of Biogeography, The Journal of Ecology, and Vegetatiofor the years 1980-1997. We also manuallysearchedthesejournalsfor the years 1968-1979 (pre-database).We combinedresults of this searchwith those from GG Mittelbachet al (in litt.). The latterstudysearchedall issues of Ecology andEcological Monographsto 1993 using theJSTOR? database(plusa manualsearchof issues between 1994-1997), and electronicallysearcheda broad-spectrumof biological journalsfrom 1982-1997. In all cases, we included only studies with a sample size >4 that assessed a statisticalrelation(or presenteddata sufficientto calculate one) between species richnessand productivity(or its surrogates),regardlessof scale, taxon, or system. Agriculturaland intensively managed systems were excluded, as were systems subjectto severe anthropogenicdisturbance.Systems whose potentialproductivities were manipulatedexperimentallywere excluded as well. Relationshipsbetween species diversity and productivitywere classified into four types: linear positive, linear negative, unimodal, or no relationship.When possible, classifications were based on original published analyses. However, when proper statistics were not available, we used raw data to perform linear and quadraticregressions. Relationshipswere deemed significant if P < 0.10; regressionsin which the quadratictermwas significantlydifferentfrom zero were classifiedas unimodal.Two studiesproducedsignificant"U-shaped"relationships (i.e., positive quadraticin thepolynomialregressions).Because these relationships are rare,we have not included them in our figures;yet they were included when calculatingpercentages.Hence, histogramsat some scales do not sum to 100%. More than 200 relationshipsbetween diversity and productivitywere found in 154 articles.A tabulationof all studies surveyed,a summaryof statisticalresults, and informationon taxon, location, and measuresof productivityare availableon the WorldWide Web in the SupplementalMaterialssection of the main Annual Reviews site (http://www.annurev.org/sup/material.htm). ANDSPECIESRICHNESS PRODUCTIVITY 277 We exploredpatternsin the relationshipsbetween species richnessandproductivity via five schemes of classification.We classified studies using an ecological criterionof scale as withincommunity,acrosscommunity,or continental-to-global scales. We used a shift in the structureof vegetationor plantphysiognomy to define a change in communitytype (e.g., transitionsfrom desertto grasslandor from meadow to woodland).For most studies, we relied on descriptionsof sites by the authorsto generatethe classifications.In a few cases, we classified studies based on knowledgeof naturalhistory.Studieswhose sites were dispersedover distances greaterthan4000 km were classified as continental-to-global.All studies of noncontiguous lakes, ponds, streams,or rivers were classified as across-community or as continental-or-globalif the minimumdistancecriterionwas met. Patternsat differentecological scales were explored for animals and plants separately.Our second classification scheme was based on the greatest geographic distance between sites withina study.We recognizedfourgeographicscales: local (0-20 km), landscape (20-200 kmn),regional (200-4000 km), and continental-to-global (>4000 km). The thirdclassificationdistinguishedstudiesas terrestrialor aquatic, and furthersubdivided them into vertebrate,invertebrate,or plant. Our fourth method of classification focused on vertebratesand separatelyconsidered fish, mammals,amphibiansand reptiles,and birds.We also tallied patternsfor rodents separatelybecause the literaturereview generatednumerousstudies of rodentdiversity.The final classificationconsideredwhetherthe quantifiedmeasureof productivitywas based on energyavailableto a trophiclevel or the energyassimilated by the trophiclevel. The Patterns The relationshipbetween productivityand diversitydiffers with scale (Figure 2). Considering plants at the within-communityscale, unimodal relationships are about as common as positive relationships(24 and 22%, respectively);however, most studies reportedno patternat all (42%). Though the proportionof studies thatshow no significantrelationshipremainslarge at the across-communityscale, unimodal patternsare more than three times more prevalentthan positive relationships (about 39% of studies comparedto 11%).At the continental-to-global scale, the patternis dominatedby positive relationships(70% comparedto 10% for unimodalrelationships),and negativepatternsare absent. Foranimals,therewas a less dramaticshift in the prevalenceof unimodalversus positive relationshipsacross biotic scales. Unimodalrelationshipspredominateat the across-communityscale, whereaspositive relationshipsoccurmost commonly at the within-communityand continental-to-globalscales. As for plants, studies showing no relationshipare numerousat the within- and across-community scales, but negative relationships are a clear minority, regardless of scale of classification. Results of a geographicscale of classification(Figure 3) contrastedwith those basedon ecological scale. At the local scale (<20 km), studiesof plantcommunities exhibitedmostly unimodalrelationshipsor no relationship at all. The dominance Plants Withincommunitytypes 80 Animals 80 n=50 60 n-42 60 40 - 40 20 20.... UnimodalPositive Negative None . ...... UnimodalPositive Negative None Acrosscommunitytypes 80 80q 1 . n-36 60 40 4 C) 4- 0n=44 60 20 -2 U, 2 _ a) _ _ _ _ _ _ 7] 04 Unimodal Positive Negative None UnimodatPositive Negative None Continentalto global 80 - ..... 80 ......------.......................8- ..------------------ _ n=19 n610 60 60 40 40 20 201 0 UnimodalPositive Negative None .. L1 0 UnimodalPositive Negative None Productivity-DiversityPatterns Figure 2 Percentages of published studies exhibiting particularrelationships (positive linear, negative linear, unimodal, or no relationship) between species richness and productivity (or its surrogates) at each of three scales of ecological organization: within community types, among community types, and continental to global. Patternsare illustrated separately for plants and animals. Sample sizes refer to the number of analyzed data sets in each classification. Plants 80 80 n=10 604 60-60 40 Animals Local (<20km) _ -__n-12 n 60 , .. g.>eg-n=16X.. 201 ...... 40 20 Unimodat Positive Negative Unimodat Positive None Negative None Landscape (20-200 km) 80 80 n=20 60 4 n-13 60 40 40 WE.................... U) V20 20Il.& ___ 0 o UnimodatPositive Negative None Unimodal Positive Negative None 0 B<::. Regional (200-4000 km) :o 80 0) 0 60 80 n=28 n=28 60 0) Q..40 40 20 7 1 1 20 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ......m 0 0 Unimodat Positive Negative ~ ' ------ Unimodal Positive None --- ---Negative None Continental to Global (>4000 km) 80 80 n=10 0n=19 60 40 40 20 20 - 0 n1 60 0 Unimodal Positive Negative None Unimodal Positive Negative None Productivity-DiversityPatterns Figure 3 Percentages of published studies exhibiting particularrelationships (positive linear, negative linear, unimodal, or no relationship) between species richness and productivity (or its surrogates) at each of four scales of geographic organization: local (<20 km), landscape (20-200 km), regional (200-4000 km), and continental to global (>4000 km). Patterns are illustrated separately for plants and animals. Sample sizes refer to the number of analyzed data sets in each classification. 280 WAIDEETAL of unimodality,relativeto positive and negativerelationships,declinedfor studies whose extents are at the level of the landscape (20-200 km) or region (2004000 km). Again, studiesshowingno relationshipwere frequentat local to regional scales. Patternsat continentalto global scales were the same as for the biotic classification;positive relationshipspredominated. Studies of animals commonly exhibited no significant relationshipbetween productivityand diversity at local to landscape scales (67% and 46%, respectively). However, when patternsoccur, positive relationshipsbetween diversity and productivitywere most prevalentat all geographicscales. Our thirdmethod of classificationfocused on studies of vertebrates,examining productivity-diversitypatternsfor taxonomicgroupingsindependentof scale. Most striking was the dominance of positive relationshipsfor studies of birds and herpetofauna(Figure4). In contrast,patternswere not as distinct for fish or mammals. A hump-shapedrelationshipwas the most common patternfor fish. However,the proportionsof unimodaland positive relationshipswere similarfor fish and mammals.Most rodentdiversitystudiesproducedunimodalrelationships between productivityand diversity. When we divided studiesinto those concerningterrestrialand aquaticsystems, strikingdifferencesbecame apparent.Positive relationshipswere more numerous in studies of terrestrialvertebrates,whereas unimodal relationshipswere more common in studies of aquatic vertebrates(Figure 5). Positive relationshipspredominatedin studies of terrestrialinvertebratescomparedto a high percentage of unimodal relationshipsin studies of aquatic invertebrates.For both habitats, studies producingno relationshipswere numerousas well. Studies of plants in aquaticand terrestrialsystems generallydocumentedno relationshipbetween diversity andproductivity.For those studiesthatdid show a significantrelationship, a higherpercentageof unimodalityexists in aquaticsystems. Clearly,considerablevariationcharacterizesthe relationshipbetween productivity and diversity,even aftercontrollingfor aspects of ecological, geographic,or taxonomicscale. Partof this variabilitymay be a consequenceof the way in which productivitywas assessed for a particularsite (availableenergyversus assimilated energy) or the power of statistical tests used to assess relationships.To assess the degree to which these factors may have affected the patternor distributionof relationships(i.e., unimodal,positive linear,negative linear, no relationship),we conducteda hierarchicalG-test (183). In general, contrastswere orthogonaland based on a priori considerationsof energy, nested within habitat,nested within taxon (Figure 6). A final comparisonof the patternfor all studies versus only studies with sample sizes greaterthan 10 was conducted for heuristic purposes (shadedportionof dendrogramin Figure 6). With one exception (aquaticanimals based on all studies), the distributionof relationshipswas indistinguishablein contrastsbetween studies involving assimilatedversus availableenergy. In addition, no significantdifferencesin the distributionof relationshipswere detectedfor studies based on any othercontrastswith respect to habitat,taxon, or data.These PRODUCTIVITYAND SPECIESRICHNESS 281 Vertebrates 80 80 Fish 60 n=10 40 60 Mammals n=18 40 20 -2 0o 0 Unimodal Positive Negative None a) Unimodal Positive Negative None 80 --80 Amphibiansand reptiles 'V Rodents ~~~~~~n=12 n=8 60 -60- 40 40 4- 0 2 - 0 ) 0 Unimocal Positive 60 Birds Negative None Unimodal Positve Negative None n=11 40 20 - Unimodal Posftive Negative None Productivity-Diversity Patterns Figure 4 Percentages of published studies exhibiting particularrelationships (positive linear, negative linear, unimodal, or no relationship) between species richness and productivity (or its surrogates) for each of five groups of vertebrates: fish, amphibians and reptiles, birds, mammal, and rodents. Sample sizes refer to the number of analyzed data sets in each classification. 282 WAIDEET AL Aquatic Terrestrial 80 80 Vertebrates 60 n=31 60 40 - Vertebrates (fish) n=10 40 20 -20 0 - a) 1 80 V 4. Unimodal Positive Negafive 1 Invertebrates = C: n0A Unimodal Positive Negative None 8Q ll0 Invertebrates n=14 60 o0 40 40 - 20 None n=40 l 20 0 ~~J) ~~ Unimodal Positive Negative None 80- 80 Plants 60 n=68 60 Plants n=26 40 40 2: UnimodaelPositive Negative None - 2E.1 Unimodal Positive Negative None Unimodal Positive Negative None Patterns Productivity-Diversity Figure 5 Percentages of published studies exhibiting particularrelationships (positive linear, negative linear, unimodal, or no relationship) between species richness and productivity (or its surrogates) for each of three groups (vertebrates, invertebrates, and plants) in terrestrial and aquatic environments separately. Sample sizes refer to the number of analyzed data sets in each classification. PRODUCTIVITYAND SPECIESRICHNESS ENERGY Assimilated | I Available| HABITAT TAXON 283 DATA ~~~~Aquatic Animals Assimilated Terrestrial Available All Assimilated Aibevailable Plants Assimilated Terrestrial_ Available Figure 6 Dendrogram illustrating the results of a hierarchical G-test assessing differences in the distribution of relationships (positive linear, negative linear, unimodal, and no relationship) between studies classified in a nested fashion with respect to energy (available versus assimilated), habitat (aquatic versus terrestrial), taxon (animals versus plants), and data (all versus N > 10). Analyses conducted for heuristic purposes are shaded in gray. A statistically significant contrast (P < 0.05) is indicated by a black vertical bar. 284 WAIDEETAL results, especially when combined with those based on classificationsof studies based on ecological and geographicscale, suggest that no single relationshiphas hegemony,and that at best, the data are insufficientto corroboratethe priorityof any one relationshipbetween diversityand productivity. Regardless of the mannerof categorization,no single relationshipdescribed more than two thirdsof the studies. This is surprising,given that the relationship between species richnessandproductivityis often characterizedin the literatureas unimodal(e.g., 16, 87, 167, 191). This suggests thatno mechanismhas a dominant role in moldingpatterns,thatmultiplemechanismsmay operatesimultaneously,or thatconfoundingfactorsor methodologicallimitations(e.g., samplesize or extent) conspire to produce the apparentdiversityof relationshipsbetween productivity and diversity.We explore some of the factorsthatmay be involved in producinga varietyof patternsat differentscales. ConceptualIssues patternschangewith scale Determiningthe mannerin whichproductivity-diversity is an importantfirst step towardmore fully understandingthe applicability,limitations, and predictivepower of ecological theory (111). We have benefitedfrom several decades of experimentalstudy of the relationshipbetween system productivity and species richness, and a healthy body of theory complements this empiricaldata base. What has not been made clear is the applicabilityof theory to patternsat differentspatial scales. The ambiguityof the properscale of application is most obvious at the within-to-across-communityscale, or at the localto-landscape-to-regionalscale. Consideras an example the graphicaltheories of community structureproposed by Tilman (186). When combined with resource heterogeneity,Tilman's model predicts a unimodal species diversityresponse to increasingnutrientsupply (i.e., potentialprimaryproduction).What is not clear fromthe model is the scale at which the patternmanifests.In fact, the theoryitself can predictany type of patterndependingon rangeof productivity,location along a productivitygradient,heterogeneityof resources,and mannerin which resource heterogeneitycovaries with resourcesupply (2,3, 186). Ambiguities of the scale of operationand applicationalso may apply to alternativetheories proposed to explain productivity-diversityrelationships,such as the keystone predatormodel which also predicts hump-shapedrelationships(105; for reviews of additional theories see 3,167,191). Determininghow patternschange with scale in natural systems providesthe first step in understandingthe limitationsand properscales of applicationfor theoreticalframeworks. The scale of a studymay be an importantfactorto considerwhen predictingthe relationshipbetweendiversityandproductivity.Both geographic-andbiotic-based classificationsneed to be considered.Unimodalpatternsmay emerge at relatively small spatialscales. Almost half of the studies of plantcommunitiesthatreported distancesbetween study sites of less than 20 km (local scale) producedunimodal ANDSPECIESRICHNESS PRODUCTIVITY 285 patterns.When studies remain within a community,patternsare almost equally dividedbetween unimodalandpositive categoriesfor plants.This clearly changes when studies cross communities: Unimodal patternsbecome much more pronounced.At geographicscales greaterthan20 km (landscape-to-regionalscales), a largerproportionof positive patternsoccurs for plants. The range of productivitiesencompassedwithin a study may explain the decoupling of geographicand biotic classificationsof scale for plants.Althoughwe expect the two to covary(i.e., studiesthatare spreadover large geographicranges most likely have largerranges of productivity),the relationshipneed not always hold. Investigationsat small spatialscales may traverselargegradientsof potential productivity,as well as multiple communityboundaries,(e.g., elevationalgradients from woodland to tundra).Conversely, single community types may span huge distances and several geographic scales, yet may exhibit little variationin productivityor species richness. Proponentsof the unimodal patterncommonly arguethatpatternswithouthumps are a result of insufficientranges of productivity in the study (e.g., 167,168). Restrictingstudies to a single communitytype, from the outset,constrainsthemto a limitedrangeof species compositions.If unimodal relationshipsoccur primarilyacross communities,then within-community studies may be samplingonly portionsof the whole productivity-diversitycurve. Thereis evidence thatunimodalrelationshipsemergeonly when datafrom different communitiesalong the productivitygradientare accumulated(e.g., 124, 121; KL Gross et al, in litt.). Thus, futureresearchshould considerexplicitly the range of productivitysampled,which may be a majorfactordrivingthe change in plant communitytype. Despite the predominanceof unimodalpatternsat the across-communityscale, they can occur within communities(about24% of plant and animal studies), but at this scale nonsignificantrelationshipsare also numerous(42%). In additionto smallerproductivityrangesat this scale, a numberof ecological processes occurat smaller spatialand within-communityscales, which may result in nonsignificant relationships.For example, dispersal between patches of high species diversity and sink patches of low diversitymay mask productivity-diversitypatterns.Such mass or rescue effects (33, 179, 180) likely occur at smallerspatialscales in which patches are in close proximityand immigrationrates are high. Studies of animals showed no dramaticchanges in the frequencydistribution of relationshipsacross biotic or geographic scales. Positive relationshipsalways outnumberedunimodalrelationships,althoughthe differences were small at the within- and among-communityscales. Differences were more pronouncedacross geographicscales. Almost withoutexception, studies of animaldiversityfocused on subsetsof the animalcommunity(specific taxa such as rodentsor amphibians). Most theoretical explorations of productivity-diversityphenomena deal exclusively with the species richness of whole trophiclevels or guilds (e.g., 105, 186). Although models can be adaptedto deal with more restrictedtaxonomic groups, the predictionsmay be very different.This is especially so because most studiesof 286 WAIDEETAL animalsnot only deal with subsets of trophiclevels, but also consider organisms that have differentfeeding ecologies (e.g., studies of aquaticmacroinvertebrates can include primaryand secondaryconsumersas well as detritivores). Along gradientsof productivity,taxonomicturnovermay occur such thatfocal taxa within a trophiclevel drop out of the system, while other taxonomic groups (with the same feeding ecology) replacethem. Although overall species diversity of the trophiclevel may show one patternalong the gradient,the focal groupmay exhibit a completely differentone. Hence, the ability to assess the applicabilityof ecological theory and the influence of scale on productivity-diversitypatternsfor animalsis limited. The patternsand explanationspresentedthus far have dealt with studies at regional and smallerscales (<4000 km). Theoryat this spatialscale deals primarily with communitiesassembledfrom presumablyco-evolved regionalspecies pools. Although our cut-off point of 4000 km is arbitrary,we hoped to distinguishbetween these types of studies andthose whose communitiesmay deriveconstituent species fromdifferentregionalpools. This most likely occurs at the scale of whole continents or across continents (i.e., global scales). The results of our literature reviewindicatethatspecies richnessis primarilya positivefunctionof productivity at this largerscale, for both animalsand plants.Unimodalpatternswere abundant for animals, though due perhapsto previously mentioned factors. These studies often include sites along gradientsof latitude and can include species pools of differentages and evolutionaryhistories. Distinguishingthe ecological effects of availableenergy from the evolutionaryeffects is difficult. Despite the long history of interest in factors governing large-scale patterns of diversity,consensus remainselusive. Many potentialproblemsaccompanyany literaturereview that gathersdata from a wide variety of studies using disparate approaches,methods,andfoci. Manyof the caveatsandshortcomingsof ourreview provide guidance for future improvementsin assembling productivity-diversity patterns.First,most of the studieswe surveyeduse a correlateof productivity,often an indicatorof assimilatedenergy(such as standingcropbiomass) or available or soil nutrients).In general, energy (such as rainfall,latitude,evapotranspiration, we expect these variablesto be indicatorsof systemproductivity;nonetheless,correlationsmaybe poorfor some systemsor atcertaintimes of the year.Aboveground biomass is one of the most popularcorrelatesin plant studies, but simple models of trophic regulationcan predict complete decoupling of trophic-level biomass from productivitydependingon the trophicstructureof the system and the feeding efficiency of consumers(141). The studies included in our review use a great varietyof quadratandplot sizes, andrarelyare areaeffects explicitly addressedor controlled. Many different relationshipsbetween species diversity and productivitycan be generatedat a single biotic or geographic scale. Yet, the relative percentages of differentpatternschange with scale. Unimodal patternshave been described as textbook examples of productivity-diversityrelationships(16). Our review is noteworthyforthe lack of studiesevincinga significanthump(despiteourgenerous PRODUCTIVITY ANDSPECIESRICHNESS 287 criteriafor detecting one). This is especially true for animals, in which positive patternsdominateat almost all scales. Ourreview in no way discountsthe models and mechanismsthat predicthump-shapedrelationships,but it does attest to the potentialimportanceof scale when applyingsuchmodels andpredictions.Exciting future directions include investigatingwhy patternschange with scale; why in some systems unimodalpatternsare generatedat the within-communityand local scales, whereasin othersunimodalpatternsonly emergewhencrossingcommunity boundariesor large geographicaldistances. FUTURESTUDIES Twoimportantissues facingthe scientificcommunityarethe maintenanceof global biodiversityand the continuanceof the ecosystem services necessary to support humanlife. It is clear from numerousstudiesthatthese issues are inextricablyentwined (41). Modeling and empiricalstudiesdemonstratethat loss of biodiversity can influence key ecosystem characteristicssuch as primaryproductivity,predictability,and resistanceto invasionby exotics (41, 116). Theory and empirical studiesindicatethatchangesin primaryproductivityarerelatedto species richness at some scales but not at others. The goal of futureresearchmust be to provide mechanisticexplanationsfor observedpatternsin therelationshipbetweenprimary productivityand species richnessthroughwell-designed and carefullyinterpreted experiments(85) thatexplicitly considerspatialscale as well as local andregional mechanisms. A key strategyfor improvingour understandingof the interactionof biodiversity and productivity(or other ecosystem processes) considers the integrationof two common experimentalapproaches:the manipulationof productivityand the alterationof the numberof species or functionalgroups.A synthesis of ideas that have developedaroundthese two approachesis a prerequisitefor the advancement of a general theory that will direct the next generationof hypotheses and experiments. Conceptualmodels being developed by JB Grace (68a) and M Shackak (personalcommunication)foreshadowthis synthesis.These emergingmodels incorporatedisturbance,plant biomass (productivity),resourceheterogeneity,colonization, and the available species pool as primaryfactors controlling species density.Consequently,they emphasizethe importanceof multivariateapproaches to understandingpatternsof species density (Figure7). Central to understandingthe role that humans play in the present observed high extinction rate is the relationshipbetween anthropogenicdisturbanceand the naturaldisturbanceregime (219). The first attemptsto explain the control of species richnesshad an explicit appreciationfor the importanceof humanactivities (71, 72), which led to an integrationof disturbance,environmentalstress, and elements of productivityin an index of factorscontrollingspecies richness (6; 68a). Futurestudies need to refocus on the similaritiesand differences between natural and anthropogenicdisturbance.Incorporationof the unique natureof human 288 WAIDEET AL RescueEffect Assemblage Speciation E...~' iE .Extinction Respiration Export Respiration F'igure7 Conceptualmodel of a local ecosystem:the biotainteractingwith energy flow and nutrient cycling (unshaded shapes). For simplicity's sake, the model only considersthe plantcommunity.Shadedshapesrepresentlarger-scalespecies pools, nutrient pools, or energy inputs. Broad arrows represent the various system currencies of species (dashed),energy (dots) or nutrients(solid); narrowlines terminatingin a circle representregulatorsor filterswhich modifythe flow of species, nutrientsor energy.Hence,thecompositionof a localassemblageis derivedfroma regionalspecies pool via the action of five filters: available energy, nutrient availability, and the species of the productionand alreadyin the community,as well as structuralcharacteristics litter.Similarly,the productionof the ecosystemis affectedby availableenergy as processed by species assemblages and constrained by nutrient availability. Because energyflow andspecies flow areaffectedby some of the sameregulators,andin fact reciprocallyaffecteach other,the relationshipbetweenthemmaybe complex. activities into models of the relationship between biodiversity and ecosystem pro- cesses is necessary to merge the fields of evolutionaryecology and conservation biology. The time is appropriatefor the studyof therelationshipbetweenspecies richness and primaryproductivityto change focus from discerningpatternsto developing mechanisticexplanations,which can be tested throughmanipulativeor observational experiments.The availableevidence shows thatmultiplepatternsexist and change with scale. The implication is also clear that multiple causal factors exist for scales, habitats,and taxa. There is reasonablystrongevidence to demonstrate that productivity influences diversity at some scales, whereas functional or species diversity seems to influence productivity at other scales. Clever experiments and observationsbased on conceptualmodels of system dynamics (Figure7) will be needed to disentangle the web of cause and effect. With this in mind, we offer in PRODUCTIVITY ANDSPECIESRICHNESS 289 conclusion a few general ideas concerningthe characteristicsof futureresearch endeavors. * Investigatorsmust be carefulto matchthe scale upon which theory operatesto the scale of observation.In many cases this will requirethe collection of new data at the appropriatescale to test theory. o Some standardizationin operationaldefinitionsis necessaryfor meaningfulcomparison.In particular,the spatialand temporalframework for the measurementof species density and productivitymust be carefully controlled.Theory thatis based on net primaryproductivitycannotbe evaluatedusing partialmeasuresof NPP. Similarly,theorythatis based on guilds or communitiescannotbe evaluatedusing subsets of these communities. * Multivariateapproachesare needed to separatethe effects of co-varying causal factors.Investigatorsmust recognize thatdifferentspecies may respondto differentvariablesalong the same geographicgradientand that changes in total species richnessare the sum of these species-level responses. e Experimentsmust include severaltrophiclevels and multiple ecological scales. Withoutthis kind of experimentalapproach,results will be difficult to place in context. e Theory and experimentationneed to be extendedto high-diversitysystems. Microcosmsprovidea useful approachfor addressingbasic questions,but issues relatingto the loss of taxa from species-richsystems urgently requireattention. e More sophisticatedmanipulationsof productivityat multiple scales will be requiredto determinethe generalityof the patternbetween productivity and species richness.In particular,manipulationsof limiting resourcesthat increaseheterogeneityof resourceavailabilitywould providean interesting contrastto standardfertilizationexperiments. ACKNOWLEDGMENTS This workresultedfrom a workshopconductedat the NationalCenterfor Ecological Analysis and Synthesis (NCEAS), a Centerfunded by the National Science Foundation(Grant#DEB-94-21535), theUniversityof Californiaat SantaBarbara, and the State of California.MRW was supportedin part as a SabbaticalFellow on the same grantand received additionalsupportfrom a DevelopmentalLeave fromthe Officeof the Provost,TexasTechUniversity.We appreciatethe assistance providedby the staff at NCEAS, in particularJim Reichman, Matt Jones, Mark Schildhauer,and Marilyn Snowball. The Long-TermEcological Research Network Office of the Universityof New Mexico providedsupportfor RBW through CooperativeAgreementNo. DEB-9634135, as did grantDEB-9705814 from the 290 WAIDEETAL NationalScience Foundationto the Institutefor TropicalEcosystemsStudies,Universityof PuertoRico, andthe InternationalInstituteof TropicalForestryas partof the Long-TermEcological ResearchProgramin the LuquilloExperimentalForest. The following participantsin the workshopcontributedto the discussionleading to the manuscript:LindaBlum, Scott Collins, StephenCox, KatherineGross, Jeff Herrick,Michael Kaspari,ClarenceLehman,John Moore, Glenn Motzkin,Craig Osenberg,Michael Rosenzweig, Samuel Scheiner,Lee Turner,MariaVernet,and BruceWallace.The manuscriptwas substantiallyimprovedas a resultof comments by StevenChown,DeborahClark,StephenCox, JamesGrace,andMoshe Shackak. Louise Williams preparedthe tables, and Leida Rohena and Saioa de Urquiza helped with the bibliography. Visit the Annual Reviewshome page at http://www.AnnualReviews.org LITERATURE CITED 1. Aarssen LW. 1997. High productivity in grasslandecosystems: effected by species diversity or productivespecies? Oikos 80: 183-84 2. Abrams PA. 1988. Resource productivityconsumer species diversity: simple models of competition in spatially heterogeneous environments.Ecology 69:1418-33 3. AbramsPA. 1995. 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