Download AND SPECIES RICHNESS

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 .
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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
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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
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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
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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
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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-
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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
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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
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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
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