The Relationship Between Productivity and Species Richness

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 regarding underlying mechanisms. Recent studies have examined the possibility that variation in species richness within communities may influence productivity, leading roan exploration of the relative effect of alterations in species number per se as contrasted to the addition of productive species. Reviews of the literature concerning 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 literature uncovered approximately 200 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; consequently, synthetic understanding may be contingent on explicit considerations of scale in analytical studies of productivity and diversity.

Full text

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?Annu. Rev. Ecol. Syst. 1999. 30:257–300
Copyright c
°1999 by Annual Reviews. All rights reserved
THE RELATIONSHIP BETWEEN PRODUCTIVITY
AND SPECIES RICHNESS
R. B. Waide1,M.R.Willig
2
, C. F. Steiner3, G. Mittelbach4,
L. Gough5,S.I.Dodson
6
,G.P.Juday
7
, and R. Parmenter8
1LTER Network Office, Department of Biology, University of New Mexico, Albuquerque,
New Mexico 87131-1091; e-mail: Rwaide@lternet.edu; 2Program in Ecology and
Conservation Biology, Department of Biological Sciences & The Museum, Texas Tech
University, Lubbock, Texas 79409-3131; e-mail: cmmrw@ttacs.ttu.edu; 3Kellogg
Biological Station and the Department of Zoology, Michigan State University, Hickory
Corners, Michigan 49060; e-mail: STEINER@kbs.msu.edu; 4Kellogg Biological Station
and the Department of Zoology, Michigan State University, Hickory Corners, Michigan
49060; e-mail: mittelbach@kbs.msu.edu; 5Department of Biological Sciences, University
of Alabama, Tuscaloosa, Alabama 35487-0344; e-mail: lgough@biology.as.ua.edu;
6Department of Zoology, University of Wisconsin, Madison, Wisconsin 53706;
e-mail: sidodson@facstaff.wisc.edu; 7Forest Sciences Department, University of Alaska,
Fairbanks, Alaska 99775-7200; e-mail: gjuday@mail.lter.alaska.edu; 8Department of
Biology, University of New Mexico, Albuquerque, New Mexico 87131-1091;
e-mail: parmentr@sevilleta.unm.edu
Key Words primary productivity, biodiversity, functional groups, ecosystem
processes
■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 regarding underlying mechanisms. Recent
studies have examined the possibility that variation in species richness within com-
munities may influence productivity, leading to an exploration of the relative effect of
alterations in species number per se as contrasted to the addition of productive species.
Reviews of the literature concerning 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
mechanismsequallyvariedandcomplex.Amorecomprehensivesurvey of the ecologi-
calliteratureuncoveredapproximately 200 relationships, ofwhich30%wereunimodal,
26% were positive linear, 12% were negative linear, and 32% were not significant.
Categorization of studies with respect to geographic extent, ecological extent, taxo-
nomic hierarchy, or energetic basis of productivity similarly yielded a heterogeneous
distribution of relationships. Theoretical and empirical approaches increasingly sug-
gest scale-dependence in the relationship between species richness and productivity;
0066-4162/99/1120-0257$08.00 257

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
258 WAIDE ET AL
consequently, synthetic understanding may be contingent on explicit considerations of
scale in analytical studies of productivity and diversity.
INTRODUCTION
The notion that productivity (rate of conversion of resources to biomass per unit
area per unit time) affects species richness can be traced to at least the mid-1960s
(45,106,113,153). Nonetheless, the causal mechanisms behind the patterns be-
tween productivity and species diversity, as well as the form of the relationship,
have been in dispute for almost as long (53,193). Indeed, studies of the relation-
ship between productivity and diversity at large spatial scales have documented
linear and unimodal patterns as well as no patterns at all (see review in GG
Mittelbach et al, in litt. and SI Dodson et al, 51a). Experimental manipulation
of productivity via fertilization of small plots long has been known to decrease
plant diversity (reviews in 48, 65, 82). Importantly, both theoretical considerations
(147; SM Scheiner et al, in litt.) and empirical analyses (KL Gross et al, in litt.)
suggest that patterns are likely scale dependent. Some of the disparity in perceived
patterns may be a consequence of variation in the spatial scale of analyses.
Efforts to determine the relationship between number of species (or number
of functional types, sensu 41) and the properties of ecosystems have increased
as global loss of biodiversity and climate change have accelerated over the past
decade. One approach to this issue has been to examine the ways ecosystem pro-
cesses influence species number, community composition, or trophic structure
(e.g., 84, 167,168,191). A separate line of inquiry has focused on the importance
of the number of species, the number of functional groups, and the presence or
absence of particular species (or groups) on ecosystem processes (e.g., 85, 102,
133, 134, 185,190,192). Field manipulations and laboratory experiments have ad-
dressed the role of these aspects of biodiversity in determining rates of ecosystem
processes (e.g., primary productivity and nutrient cycling). These two lines of in-
quiry have been largely separate in the literature despite their conceptual linkage.
In this review, we synthesize existing knowledge of the relationship between a
commonly estimated property of ecosystems (primary productivity) and one as-
pect of biodiversity (species richness). Most theoretical studies use net primary
productivity (NPP) as the driving variable, but empirical studies often use compo-
nents or surrogates of NPP. Rather than introduce confusing terminology, we use
primary productivity in this paper as a general term to encompass components or
surrogates of NPP. We review the literature and use case studies from terrestrial,
aquatic, and wetland biomes for which detailed information is available. The work
we report is an extension of research initiated at the National Center for Ecological
Analysis and Synthesis that focused on the influence of primary productivity on
species richness. In addition, we consider how species richness may affect ecosys-
tem function (including productivity). This is a volatile and rapidly expanding area
of study (see 1,76,79,85, 107, 190). Unfortunately, the database currently is too

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 259
small and conflicting to draw conclusions with certainty. Nonetheless, we suggest
thatit is necessary to bridge these twoapproachestoachieve abetterunderstanding
of the relationship between primary productivity and the dynamics of populations
and communities. We do not address the related and important issue of the rela-
tionship between diversity and stability (49,91,189), nor do we discuss in detail
other possible biotic and abiotic controls of biodiversity.
How Does Productivity Affect Species Richness?
Most authors agreethatproductivity affects diversity (32, 45, 106, 113, 162); more-
over,aplethoraofmechanismshavebeenproposedtoexplainhow species richness
responds to variation inproductivity(e.g., 84,168, 167,191).Nonetheless, no gen-
eralconsensus concerning the form ofthepatternhas emerged based on theoretical
considerations or empirical findings. Some factors enhance richness as productiv-
ity increases, others diminish richness as productivity increases, 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 factors that determines the empirical pattern within a particular
study. Indeed, future research should identify the ecological context and spatial
scale that predispose systems to evince one pattern rather than another (154).
Rosenzweig (167) provided a critical assessment of the mechanisms thought
to affect patterns in the relationship between diversity and productivity. GG
Mittelbach et al (in litt.) updated the summary and provided commentary on
the ecological scale at which mechanisms likely operate. Theories that predict
a positive relationship between productivity and species richness include the
species-energy theory (44,155,156,223) and theories invoking various forms of
interspecificcompetition in heterogeneous environments (2). Mechanisms thought
to diminish diversity with increasing productivity are more controversial and in-
clude evolutionary immaturity (especially with respect to anthropogenic emen-
dations); habitat homogenization (sensu 187; 65, 88); dynamical instabilities and
system infeasibilities (125,160,162,164, 165, 222; JC Moore & PC de Ruiter,
submitted); and predator-prey ratios (141,142,162,163). Some mechanisms pre-
dict a unimodal pattern in their own right. Relevant theories include changes in
environmental heterogeneity with productivity (87,186), tradeoffs in competitive
abilities and abilities to resist predation (105), effects of competitive exclusion and
environmental stress (6,71,72), disturbance and productivity (82), productivity-
dependent species-area relations (147), and changing competitive structure (167).
Understanding the productivity-diversity relationship will require the imposi-
tion of order on this apparently chaotic array of possible explanations. This can be
achieved, at least in part, by careful attention to the spatial and ecological scales at
which patterns are detected (124,167), and by equally judicious consideration of
the spatial and temporal scales over which likely mechanisms operate. JM Chase
& MA Leibold (submitted) make significant headway in this regard. They develop
simple conceptual models based on exploitative resource competition or keystone

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
260 WAIDE ET AL
predation to show that unimodal relationships emerge at local scales, whereas
monotonically positive relationships emerge at regional or global scales. Empir-
ical data for benthic animals and vascular plants in Michigan ponds corroborate
the expected scale dependence in diversity-productivity relationships.
The Effect of Species Diversity on Productivity
There is general agreement that diversity of plant species is influenced by pro-
ductivity (85,192). The converse argument, that the number and kinds of species
influence productivity, has been the subject of a recent series of field and lab-
oratory experiments (79,133,192). These experiments have engendered a lively
debate (85,103,211) that has yet to reach resolution. Increasing the number of
species in either field (192) or laboratory (133) experiments may have positive
effects on productivity and other ecosystem processes. However, the debate con-
cerns whether these effects are the result of increased species richness per se, or
the addition of different functional groups or particular species.
Theoretical approaches to this issue occur in three categories that postulate
either (a) a positive, linear relationship (54,112), (b) a positive, nonlinear rela-
tionship (52,209), or (c) no relationship between species richness and productiv-
ity (102). MacArthur (112) hypothesized that energy flow through a trophic web
would increase as the number of species, and hence pathways for energy flow,
increased. Elton’s (54) reformulation of MacArthur’s hypothesis suggested that
the relationship should be linear. If, however, species have redundant functions in
ecosystems, the relationship between species number and ecosystem function may
benonlinear(52,209).Ecosystemfunctionchangesrapidlyasspecies representing
new functional groups are added, but less rapidly when new species are redundant
of existing functional groups. Lawton (102) proposed a model in which species
may have strong, idiosyncratic effects on ecosystems. If this is the case, there is no
predictable effect of species richness per se on ecosystem function. However, if the
properties or functional traits of individual species are known, then we can predict
which species will have strong effects and which will not. Such hypotheses present
a useful framework for the evaluation of observations and experimental results.
Some experimental studies with herbaceous plants have shown an increase in
net primary productivity with an increase in the diversity of 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 mechanisms by which the productivity of a trophic
level may increase as the diversity (species, functional groups) of that trophic level
increases.
1. Increasing the number of species initially present in a system increases the
probability of encountering an exceptionally productive species (e.g., a
species that is proficient in converting resources to biomass). Huston (85)
has labeled this the “selection probability effect.” In this scenario, the
productivity of the trophic level is determined by the productivity of the
most productive species.

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 261
2. Increasing the number of species initially present in a system can result in
complementarity in resource use, if different species use different
resources. In this case, the productivity of an assemblage of species will be
greater than the productivity of any single species. Also, the total resource
spectrum will be more completely used in the more species-rich system.
Empirical evidence from herbaceous plant communities (e.g., 79,80,85,132,
185,192) supports hypothesis 1. No experimental study supports hypothesis 2,
despite its intuitive appeal. [data from Tilman et al (190) potentially support hy-
pothesis 2, but no information is presented on complementarity of resource use
that could be used to test this mechanism]. Complementarity in resource use is the
functional basis behind intercropping and polyculture (184,203). Although not
all intercropping schemes or polycultures provide higher yields, many successful
examples suggest that species complementarity may be important in determining
ecosystem productivity and nutrient dynamics.
The difficulty of designing and executing field experiments to determine the
effect of changing species number on productivity has resulted in a scarcity of
published studies. The clearest results include field experiments conducted on
communities dominated by herbaceous vegetation (79,192) and a microcosm ex-
periment conducted under controlled laboratory conditions (133).
In the study by Naeem et al (133), conducted in a controlled environment, a se-
riesof ecosystem processes was measured in high-andlow-diversity communities.
The lower diversity systems were nested subsets of the higher diversity systems.
Estimates of primary productivity were greater in microcosms with higher species
diversity.
Tilman & Downing (188) examined the relationship between plant species di-
versity and primary productivity in plots fertilized with N at the Cedar Creek
Long-Term Ecological Research site in Minnesota. They reported that the pro-
ductivity of more diverse plots declined less and recovered more quickly after a
severe drought than did the productivity of less diverse plots. They concluded that
the preservation of biodiversity is important for the maintenance of productivity in
fluctuating environmental conditions. This study is not a direct test of the effects
of diversity on productivity because species richness was not manipulated directly
but was a product of changes in nitrogen addition, and because the study measured
changes in productivity in response to disturbance.
Inadifferentexperiment, Tilmanetal(192)comparedproductivityand nutrient-
use efficiency in grassland plots seeded with different numbers of native species.
Productivity was greater and soil mineral nitrogen was utilized more completely
in plots with greater diversity. Measurements in nearby unmanipulated grassland
showed the same pattern. This study also concluded that the preservation of bio-
diversity is necessary to sustain ecosystem functioning.
Huston (85) criticized the conclusions of all three of these studies, claiming that
each was tainted by the lack of rigorous treatment of cause and effect. In Huston’s
view, appropriate tests of the effect of species richness on ecosystem processes do

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
262 WAIDE ET AL
not permit large variation in the size or function of species. Huston argued that one
likely consequence of increasing species richness in an experiment involves the
increasedprobabilityofintroducingaproductivespecies(the“selectionprobability
effect”). If this happens, the effect of increasing species richness on productivity
is attributed simply to the increased odds of encountering species particularly well
adapted to the environment. If variation in productivity exists among species used
for an experiment, the effect of increased species diversity cannot be distinguished
from the effect of increased functional diversity or mean plant size leading to
differences in total biomass among treatments.
Huston’s position, although admirable for its insistence on rigorously designed
experiments, requires studies to be circumscribedto a limitedrange of thevariabil-
ity that exists in natural ecosystems. Experiments that incorporate only species of
similar size and functional status, while avoiding some of the pitfalls that Huston
(85) described, may not advance substantially our understanding of natural com-
munities. Natural communities comprise species that differ in size and function;
as a result, the effect of the loss of diversity is interpreted more easily through ex-
periments that incorporate that variability. Moreover, the question of how similar
speciesmustbeto achieveexperimentalrigorhasnotbeenaddressed.Loreau(107)
and Hector (76) have recently suggested mechanisms to separate the “selection
probability effect” from other effects resulting from experimental manipulations
of biodiversity.
Hooper (79) approached the question of complementary resource use through
anexperimentaldesignthatvariedthenumberoffunctionalgroupsinexperimental
plots. Four functional groups (early season annual forbs, late season annual forbs,
perennial bunchgrasses, and nitrogen fixers) were planted in single-group treat-
ments,as well as in two-, three-, and four-way combinations. In this experiment, no
obvious relationship existed between functional diversity and productivity of the
plots. The most productive treatment included only one functional group, peren-
nial bunchgrasses. The identity of the species in the treatments was as least as
important as the number of species in affecting ecosystem processes. Competition
among some combinations of functional groups reduced productivity compared to
single-group treatments. These results corroborate Lawton’s (102) idiosyncratic
model and Huston’s (85) hypothesis 1.
For practical reasons, most experiments have focused on structurally simple
ecosystems with relatively few species and have manipulated only a few species
from each functional group. In general, results have shown a positive, asymp-
totic relationship between ecosystem processes and species richness. These results
suggest that once all functional groups are present, the addition of species with
redundant functions has little effect on ecosystem properties.
The conclusion that diversity is important for maintaining ecosystem function
(188, 192), even if justifiable based on the few studies conducted to date, has been
demonstrated only for systems in which the range of richness is from 0 to about
30 species. Conclusions about the importance of the addition or loss of species in

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 263
complex systems require further clarification(171). Structurallycomplex, species-
rich ecosystems, in which much of the loss of biodiversity worldwide is occurring,
require further study.
Questions of Spatial Scale
It generally is recognized that area and environmental heterogeneity have strong
effects on diversity (84,167). Equally important, their effects are intertwined (98)
and produce scale-dependent relationships between productivity and diversity. For
example, a unimodal pattern in the relationship between diversity and productivity
can be a consequence of a correlation between productivity and the parameters
of the power function (S=CAz, where Sis species richness, Ais area, and C
and zare fitted constants equivalent to the intercept and slope, respectively, of the
log-formof the relationship (7). Inmeadowcommunitiesdominatedbysedges and
grasses, Pastor et al (147) document that Chas a positive correlation with area, z
has a negative correlation with area, and this tradeoff produces a unimodal form to
the relation between diversity and productivity. In contrast, no scale dependence
was detected in the relationship between species richness and latitude for New
World bats or marsupials, even though latitude is often considered a broad-scale
surrogate for productivity (111).
Moreover, two aspects of spatial scale—extent and focus—strongly affect the
detection and form of the relation between richness and diversity (SM Scheiner
et al, in litt.). Extent is the range of the independent variable, which in this con-
text is productivity, whereas focus defines the inference space to which variable
estimates apply (i.e., the area from which samples were obtained to estimate point
values for productivity and richness). In particular, a series of studies, each with
restricted extent along a gradient of productivity, may evince significant (positive
and negative) linear relationships as well as no relationship between productivity
and diversity, casting doubt on the hump-shaped pattern. If the slope of the re-
lationship decreases with mean productivity, then a unimodal pattern emerges as
a consequence of the accumulation of consecutive linear relationships (positive
linear, decreasing to no relationship, decreasing to negative linear; pattern ac-
cumulation hypothesis). Guo & Berry (73) document an emergent hump-shaped
relationship from a series of linear patterns based on an analysis of plant species
richness along a grassland-shrubland transition in Arizona.
Nonetheless, a unimodal relationship may emerge that is not a consequence of
the accumulation of patterns at smaller extents. A series of fields dominated by
vascular plants in the mid-western United States exhibits a unimodal relationship
across grasslands or across North America, but no or negative relationships when
the extent of analyses were restricted to be within community types (KL Gross
et al, in litt.). Moreover, the slopes within community types were not correlated
with mean productivity of the communities, suggesting that the pattern accumu-
lation hypothesis was not in effect. In summary, relationships between diversity

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
264 WAIDE ET AL
and productivity have been shown to be scale dependent, with the form of the
scale-dependence variable from study to study, even in situations where unimodal
patterns emerge at broad spatial scales.
BIOME-SPECIFIC RELATIONSHIPS
The relationship between diversity and productivity can be mediated by different
control mechanisms, depending on biome. As a consequence, patterns within
biomes may differ, and emergent patterns across biomes may not be the same
as those within them. We explore the range of productivity and diversity in a
number of major aquatic andterrestrial biomes, andwediscuss thepossiblecontrol
mechanisms that lead to patterns between diversity and productivity. Although
we have not attempted a comprehensive coverage of all biomes, it is clear that the
state of knowledge is quite variable among systems. Moreover, the form of the
relationship is not always clear, and understanding of the regulatory mechanisms
is only rudimentary in most cases.
Aquatic Ecosystems
Lakes
Lakes are underutilized but optimal model systems for studying the relation-
ship between species richness and primary productivity. They are well-delineated
(bounded) communities in which species can be counted relatively easily, and
in which primary productivity often is measured directly using standardized 14C
uptake methods (208). The 14C method measures productivity between gross and
net on a scale of minutes to hours. Annual levels of primary productivity are
estimated by summing daily productivity. In lakes, the annual level of primary
productivity ranges from about 1 to about 1300gCm
−2yr−1(51a).
Lacustrine species richness is influenced by lake primary productivity. Pure
rainwater has a primary productivity of near zero because it lacks the nutrients
necessary to support life. Consequently, rainwater in rock pools supports few or no
species (50). The most productive lakes, such as sewage lagoons and temple tanks,
are characterized by extreme conditions, such as high temperatures, no oxygen at
night,and largedielshifts in pH (e.g., 61). These conditions can be endured byonly
a few specialized species. Lakes between these extremes of primary productivity
generally have the highest species richness. Lakes of intermediate productivity,
with sufficient nutrients to support photosynthesis but without extreme conditions,
support the most species in virtually all groups of aquatic organisms (51a).
The size of the body of water interacts with primary productivity to determine
the number of species in a lake (11,35,51,81). An increase in lake area of ten
orders of magnitude is associated with an increase in zooplankton species richness
of about one order of magnitude (51). Indeed, over 50% of among-lake variability

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 265
in richness of crustacean zooplankton in North American lakes is the consequence
oflakesize.Largerlakeshavemorezooplanktonspecies, regardless of otherfactors
including primary productivity.
SI Dodson, SE Arnott, KL Cottingham (51a) investigated the relationship be-
tween the primary productivity of lake ecosystems and the number of species of
lacustrine phytoplankton, rotifers, cladocerans, copepods, macrophytes and fish.
Inasurveyof 33 well-studied lakes,species richness of all six taxa showed a signif-
icant unimodal response to annual primary productivity (14C estimate, g m−2yr−1)
after lake area was taken into account. Moreover, the relationship between rich-
ness and primary productivity for phytoplankton and fish was strongly dependent
on lake area. The highest richness occurred in lakes with relatively low primary
productivity (∼100gm
−2yr−1), such as those in the northern temperate lakes
area in the upper Midwest (United States) and in the Experimental Lakes Area of
Ontario, Canada. When temporal and spatial scales are considered, data for lake
zooplanktonandmacrophytesprovidestrikingexamplesofunimodalrelationships
between species richness and primary productivity. For small lakes (<10 ha),
phytoplankton species richness peaked in low-productivity lakes, whereas for
larger lakes, phytoplankton species richness merely declined in more productive
lakes. For lakes less than 1 ha, fish species richness peaked at low levels of primary
productivity. For larger lakes, a peak was not evident, although more productive
lakes had more fish species.
The relationship between species richness and productivity has been studied
only for phytoplankton and zooplankton in a few other lake and marine situations.
We summarize the results of those studies below.
Phytoplankton Agard et al (5) analyzed data on marine phytoplankton species
richnessand the primaryproductivityof 44 oceanographicstationsin the Caribbean
to test predictions of Huston’s (84) dynamic equilibrium model of species richness
(maximum at intermediate productivity). They argued that marine phytoplankton
wouldlikelyexhibit a relationship becauseof the relativelylargenumber of species
andthe absence of confounding factors such asspatialheterogeneity.Theyreported
that species richness was correlated positively with primary productivity, 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
importance mirrors the diversity of phytoplankton.
In a 4–5 year study of three productive surface mines in Pennsylvania, phy-
toplankton diversity was correlated inversely with primary productivity (30).
These sites are within the range of productivities explored by SI Dodson,
SE Arnott, KL Cottingham (51a), and the results are consistent with those of that
study.
Zooplankton Microcrustacean species richness is correlated with degree days
for a group of shallow Canadian and Alaskan tundra ponds (75). These low-

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
266 WAIDE ET AL
productivity ponds represent values along the ascending portion of the unimodal
relationship reported by SI Dodson, SE Arnott, KL Cottingham (51a). Patalas
(148) reported data for zooplankton and July temperature (which is an indicator of
lake primary productivity) in Canadian lakes. Using these same data, Rosenzweig
(167) found a unimodal relationship.
Wetlands
Coastal and inland wetlands have been the subject of much of the research on
the relationship between plant species diversity and productivity. The mechanisms
controlling species diversity along productivity gradients in marshes may differ
from those demonstrated for terrestrial communities. In particular, selection for
traits allowing survival in environments with high salinity and low soil oxygen
may create low-diversity, high-productivity communities without the involvement
of mechanisms such as competition.
In general, coastal marshes are some of the most productive ecosystems in the
world. Tidal salt marsh plant communities are structured by salinity and flooding
gradients creating distinct zonation with low plant species richness (20, 89). Pri-
mary productivity is high, up to 2500 g m−2yr−1(123). Mangroves replace tidal
salt marshes in the tropics, with low plant species richness and highly variable pro-
ductivity that depends on tidal influences, runoff, and water chemistry (123). Tidal
freshwater marshes associated with rivers are more diverse because of monthly
flooding and lower salinity levels (139) but are equivalently productive. Inland
freshwater marshes and peatlands can be highly diverse, dependent somewhat on
nutrient availability. Productivity is high, often exceeding 1000 g m−2yr−1, and,
if belowground estimates are included, can exceed 6000 g m−2yr−1(123).
Plant species richness in coastal marshes decreases toward the coast along nat-
ural salinity gradients (4, 37, 101,139) and may be influenced by storm-driven salt
pulses(31, 57). Floodingandsoilanoxia alsodecreasespecies richness (66, 68, 117,
123). The importance of interspecific competition, disturbance, and stress toler-
ance in determining species distributions has been demonstrated experimentally
(18, 19, 97, 151,181). Disturbance by herbivores (14, 58,67, 138) and wave action
(178) also may affect richness.
Salt marshes may be less productive than fresh water marshes because of the
metabolic cost of tolerance to salinity (70,139). Where freshwater flows into a
coastal area, bringing nutrients or reducing salinity, productivity may be higher
than in areas without freshwater input (47,123,225). Wave exposure also may
restrict productivity (97, 220,221). In inland and coastal marshes, soil characteris-
tics such as pH, Ca, Mg, and anoxia may correlate with productivity (e.g., 17, 62).
In tidal salt marshes, sulfides rather than high salt concentrations decrease pro-
ductivity (218). Mammalian and avian herbivores restrict aboveground biomass
accumulationin some marshes by removing plant material (9, 14, 59, 67, 138, 202),
but they may have a stimulatory effect by adding nutrients through fecal deposi-
tion (78). Based on the results of fertilization experiments in marshes, vegetation
frequently is limited by nitrogen or phosphorus (67,126,135).

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 267
When plant species density (the number of species per unit area) and produc-
tivity (estimated by harvests of peak standing crop) are examined in concert for
marshes, the relationship between them depends on the scale of measurement
and other factors. When productivity is increased by fertilization, plant species
richness decreases (136, 204). When herbivores are excluded, productivity usually
increases, accompanied by a decrease inplant species richness (14, 58, 67).Certain
abiotic variables (e.g., salinity) may have similar effects on plant species density
and productivity. However, the relationship between the two is not consistent in
wetlands, although it is frequently unimodal (Table 1). In most cases, data are
variable, with an outer envelope of points having a peak in species density at an
intermediate level of standing crop (114). In some cases, the relationship reaches
an asymptote, and plant species density does not decline over an extended range
of standing crops (e.g., 69, 221). Moore & Keddy (124) demonstrated a unimodal
relationship across community types, although there was no relationship between
plant species density and standing crop within communities.
Approximate peaks in species density are found at a range of standing crop
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 m2and usually
remains low. However, the range in plant species numbers is quite large at levels of
biomass between 0 and 1000 g m−2, suggesting that other variables affect species
densityat aparticularlevelofstanding crop. Stresstoleranceplays animportantrole
insurvivalin certain marshhabitatsandmay controlspeciesrichnessindependently
of other factors such as biomass (68, 69). The mechanism causing consistently low
species numbers above approximately 1000 g m−2standing crop remains unclear
but is likely a combination of abiotic stresses and biotic interactions.
Terrestrial Ecosystems
Arctic Tundra
The arctic environment restricts the presence and productivity of vascular plant
species (22,42). Because of the severity of the environment and the common ori-
gins of the flora, approximately 2200 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 nutrient cycling, permafrost, wind exposure,
and extremes of soil moisture may constrain plant productivity (reviewed in 177).
Various physiological and morphological adaptations (e.g., cold hardiness, short
stature, vegetative reproduction) allow arctic tundra species to survive in such an
environment (22,23). On a smaller scale, topography can dramatically influence
snow cover, exposure, soil drainage, and other physical properties of the substrate
that may limit or enhance accumulation of plant biomass (175,176). Generally,
the most productive arctic plant communities are those dominated by decidu-
ous shrubs or graminoids in areas of flowing water, where nutrient availability is
higher and few other vascular species are present (39,213). Nutrient availability

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
268 WAIDE ET AL
TABLE 1 List of herbaceous wetlands in which the relationship between plant species density (D) and standing crop has been examined
Standing crop Plot size Peak
Wetland type Location range (g m 2) D range (m2)inD
a
N Relationship R2p-value Reference
Fen England 600–4800 3–26 0.25 1000 34 Negative —b— 214
Fen England, 300–4000 2–50 4 500 85 Negative 0.2 0.0001 215
Wales 3
Freshwater Nova Scotia 0.4–580.8 0–23 0.25 120 121 +, unimodal 0.4 <0.001 221
lakeshore 0
Freshwater Ontario 0.1–900 0–12 0.04 100 63 Unimodal 0.4 <0.01 220
lakeshore 8
Freshwater Ontario, 0–2600 2–24 0.25 400 224 Unimodal 0.3 <0.000 124
shoreline Quebec 4 1
Freshwater Quebec 12–1224 1–24 0.25 500 48 Unimodal 0.4 <0.05 178
shoreline 1
Freshwater Ottawa 10–1100 4–12 0.25 250 7cNone NS NS 47
shoreline
Coastal marsh Louisiana 100–3900 1–11 1 1500 36 Negative 0.0 0.01 68
2
Coastal marsh Louisiana 100–700 4–12 1 350 32 None NS NS 66
Coastal marsh Louisiana 0–700 1–17 1 150 180 +, unimodal 0.0 — 69
9
Salt marsh Spain 4–1280 1–25 0.25 400 50 Unimodal 0.2 <0.001 62
9
+Relationship was asymptotic.
aThe peak in D was estimated visually from figures and is approximate.
b—Indicates that regression statistics were not reported.
cOnly means were reported.
–

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 269
consistently limits productivity in tundra ecosystems, as demonstrated by many
fertilization studies (40,77,94, 161,174).
In general, the regional and local species pools in the Arctic are limited by ex-
treme temperature, short growing season, low nutrient availability, low soil mois-
ture, and frost disturbance (21, 27, 210, 213). Local areas of enhanced resources
(e.g., animal carcasses) are occupied frequentlyby plants found in more productive
sites, suggesting nutrient limitation of species composition as well as of produc-
tivity (118). Herbivory also may be a factor affecting arctic plant communities
(12,90), but it has been studied insufficiently (for an exception, see 13).
The relationship between productivity and diversity rarely has been addressed
specifically in arctic tundra (for an exception, see 60). We present a summary
of mean aboveground net primary productivity [annual net primary productivity
(ANPP), g m−2yr−1] and mean species richness (S) of vascular species for each
community. In the arctic, the association between S and ANPP is weak (Figure 1).
To gain insight, we divided the data into two groups (High and Low Arctic) based
on floristic and ecological considerations (25).
A positive linear relationship between species richness and ANPP is obvi-
ous in the High Arctic (Alexandra Fiord, Polar Desert, Polar Semidesert, Devon
Island, Russia, and Barrow; R2=0.45, p ¿0.001), but no relationship charac-
terizes the Low Arctic (Figure 1). In the High Arctic where plant cover is sparse,
light competition is rarely important. When stressful conditions are ameliorated,
more species inhabit more favorable areas. This is exemplified by small sites of
increased moisture or temperature that are more productive and diverse than are
drier or cooler sites (26, 64,127, 128). The lack of a relationship in the Low Arctic
likely is related to greater plant cover, causing both light and nutrient competition
to be important in determining species richness. Perhaps as conditions become
more favorable for higher plant productivity in the Low Arctic, light competi-
tion becomes more intense, countermanding the effect of productivity on species
richness. The clear relationship between productivity and species richness in the
extreme environment of the High Arctic suggests similar regulation of these two
parameters by abiotic factors. In the Low Arctic, the relationship becomes less
clear, possibly suggesting the importance of biotic regulation of species diversity
in these communities.
Hot Deserts
Desert ecosystems are typically on the low end of the productivity gradient,
ranging between 0 and 600 g m−2yr−1(Table 2). Productivity in desert ecosys-
tems generally is limited by moisture availability and is highly variable in space
and time (104,170,182,212, 216). When rainfall is abundant for extended peri-
ods, nutrient limitation (particularly nitrogen) may regulate primary production
(55,74,109, 129). Seasonal timing of precipitation determines the period and du-
ration of primary production, with some deserts exhibiting primarily single season
pulses of productivity (e.g., Mojave Desert in early spring, and Chihuahuan Desert
in mid- to late-summer), whereas other deserts have bimodal productivity peaks

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
270 WAIDE ET AL
Figure 1 Scatter plot of the relationship between mean species richness and mean aboveground net primary productivity
(ANPP) in arctic tundra. Site names in the insert are arranged by latitude, with Alexandra Fiord being farthest north. High Arctic
sites include Alexandra Fiord through Barrow; Low Arctic sites include Atkasook through Eagle Summit.

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 271
TABLE 2 Productivity estimates of deserts
Site Productivity/(g m−2yr−1) Reference
United States
Great Basin Desert 125 150
Mojave Desert 0–62 15
13–44 10
Sonoran Desert 9–95 149
109 217
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
Basin: Playa lake 52–258 108
Israel
Negev Desert 4–43 144
India
Rajastan 0–126 173
Mongolia
Gobi Desert 0–155 96
Tunisia
Pre-Sahara Desert 22–155 56
(e.g., the Sonoran Desert in both spring and late summer). Productivity in deserts
is a consequence of slower growth of woody shrubs and succulents combined, with
highly variable flushes of annual and perennial grasses and forbs. Comparisons
of primary production in deserts indicate that belowground productivity may be
considerably greater than aboveground (108 and references therein).
Species diversity ranges widely among deserts, depending on geographic loca-
tion, biogeographic history, and extremes of moisture and temperature. However,
many deserts support relatively large numbers of species (Table 3) and in many
cases actually exceed the numbers of species found in other ecosystems with
higher productivity. At a continental scale, the arid and semiarid ecosystems of the
American Southwest support greater numbers of species of numerous taxa than
do other ecosystems of North America (145,146).
Although the relationship between productivity and species diversity in arid
ecosystems has not been addressed specifically, certain relationships appear when
comparing large-scale and small-scale patterns among and within deserts.
Comparing large-scale patterns throughout the world, deserts that have near zero

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
272 WAIDE ET AL
TABLE 3 Numbers of species occurring in North American Deserts
Desert Mammals Birds Reptiles Amphibians Plants Reference
Great Basin 86 302 18 6 929 119
Mojave 53 332 37 3 1,836 169
Sonoran∗302 366 132 14 46
Chihuahuan 74 396 55 10 120
∗Includes many island endemic species from the Sea of Cortez.
productivity (e.g., the polar desert of Antarctica, and various locations within con-
tinental 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 produc-
tivity 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 sa-
vanna 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.
Reviewsof studies of NPP in tropical forests havereported ranges of 6–16 t ha−1
yr−1fortropicaldryforest (131) and somewhat higher valuesfortropical evergreen
forests [10.3–32.1 t ha−1yr−1(130); 11–21 t ha−1yr−1(34); 10.0–22.4 t ha−1yr−1

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 273
(121)].A search of the recentliterature did not encounterany more extreme values.
Most, if not all, of the NPP measurements reported for tropical forests are based on
increments of aboveground biomass and fail to include belowground productivity
or losses to consumption of plant tissues. Partial measures of NPP are often used
for comparison between sites. For example, there is an extensive database on litter
fall(157) with values ranging from 1.0–27.0t ha−1yr−1. Arecent effort to estimate
reasonable upper and lower bounds around the total NPP at 39 tropical forest sites
resulted in ranges from 3.4–6.0 and 20.1–37.0 t ha−1yr−1, respectively (DA Clark,
D Kicklighter, J Chambers, J Thomlinson, J Ni, E Holland, submitted).
Cumulative lists of species of plants and animals exist for diverse kinds of trop-
ical forests, but their utility for comparison is questionable because of differences
in the effort expended to construct such lists and the area upon which the lists are
based. For these reasons, it is difficult to compare vertebrate species richness even
in the best-studied tropical forests. Because a more standardized approach is used
to count trees, it is possible to estimate the range in species richness for this
group within tropical forests. For example, Phillips et al (152) reported a range
of 56–283 tree species >10 cm dbh for 1 ha plots in mature tropical continental
forests.
Three studies in tropical rain forest reported a positive relationship between
species richness and rainfall (a surrogate for productivity). Gentry (63) interpreted
positive correlations between tree species richness and annual precipitation, sea-
sonality, and soil richness as support for a positive relationship between produc-
tivity and diversity. Huston (83) found tree-species richness positively correlated
with annual precipitation and negatively correlated with soil fertility, and this was
interpreted as a negative relationship between productivity and diversity. Phillips
et al (152) reported positive relationships between tree species richness, climate
(includingincreasing rainfall), anddisturbance.Theysuggested that more dynamic
systems have greater productivity, resulting in higher species richness. However,
higher rainfall itself is related to increased forest disturbance and decreased soil
nutrient concentrations because of leaching(83), demonstrating the tangle of cause
and effect that can result when surrogates of productivity form the bases of anal-
yses. No studies relating diversity directly to forest productivity are available to
unravel this tangle.
Boreal Forests
Boreal forests occur in the coldest environments on earth in which trees survive
and dominate vegetative cover. Nonetheless, a surprising diversity of climates
and a wide range of ecosystem productivities are present in the boreal region.
Because it controls rates of organic layer decomposition and thus the release of
elements, soil temperature is a pervasive influence on productivity of borealforests
(194, 196,197). Boreal forest productivity decreases with decreasing soil temper-
ature, and increases with warming soil temperature (195,197,200), provided that
moistureor other factors do not become limiting.Lowsoil temperatures reduce nu-
trient uptake particularly in higher (vascular) plants (38). In turn, soil temperatures

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
274 WAIDE ET AL
are influenced by inherent site factors such as slope, aspect, and topographic posi-
tion with respect to cold air drainage (200) and by factors that change during suc-
cession (e.g., 206). Large-scale stand disturbances warm boreal soils, especially
by removing or thinning the insulating soil organic mat (198). Advancing suc-
cession rebuilds the organic layer, causing soil cooling and the build-up of high
concentrations of refractory, low-quality forest litter that depresses productivity,
particularly in conifer-dominated forest types (201).
On sites underlain by permafrost soil, rooting depth is restricted to the annually
thawed active layer at the surface, and ground layer vegetation is dominated by
mosses. Mosses filter and sequester incoming nutrients, restricting nutrient avail-
ability and productivity for rooted vegetation (140). Sphagnum moss dominance
on permafrost sites produces organic soils of such high acidity that availability of
particularnutrient elements is restrictedforrootedvegetation. Onlargerriverflood-
plains, nitrogen addition by alder (Alnus) shrubs promotes a substantial increase
in productivity (95,199). On low elevation sites in semi-arid central Alaska, a soil
water-balance model is well correlated with basal area growth in white spruce
(224), demonstrating that moisture can limit productivity on warm, dry south
slopes as well. Belowground productivity in the boreal region only recently has
been measured carefully, and recent progress on methodological problems sug-
gests that most of the previous literature may not be reliable, especially because
the high turnover of fine roots makes interval measurements of productivity prob-
lematic (158). Fine root production constitutes a large part of total production in
boreal forests, accounting for 32% and 49% of total production in deciduous and
coniferous stands, respectively, in central Alaska (159).
Anoften-cited value for average boreal forest aboveground net primaryproduc-
tionis 2,700 kg ha−1yr−1(143).Productivityin the extensivelarch forest andsparse
larch taiga of Siberia typically ranges from 2500 kg ha−1yr−1to 1400 kg ha−1yr−1
respectively(95). More recent multiyearmeasurements ofabovegroundproduction
in boreal forests of central Alaska include 9600 kg ha−1yr−1on a highly produc-
tive floodplain in peak alder/balsam poplar stage of development, and a range
of 3600 kg ha−1yr−1to 4500 kg ha−1yr−1in 200 year-old floodplain and up-
land white spruce forest, respectively (159). Upland birch/aspen forest averaged
8100 kg ha−1yr−1, and poorly productive black spruce on permafrost averaged
680 kg ha−1yr−1(158). Long-term studies reveal a high degree of interannual
variability in primary production.
Ecological studies and floristic surveys provide estimates of overall species
richness in the boreal forest (Table 4). Total regional plant species richness is cor-
related positively with productivity, increasing from the less productive middle
or northern boreal region to the more productive southern boreal region and bo-
real/temperate transition (adjusting for differences in intensity of sampling effort).
Databases adequate for comparison of diversity and productivity with confidence
at the local site and stand level do not exist, although some data are suggestive.
Highly productive mature forests in the Bonanza Creek Long-Term Ecological
Research Site have lower plant species density (e.g., 205), perhaps partly because

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 275
TABLE 4 List of studies in which estimates are provided for number of species of plants in
boreal forests
Vascular Mosses/ Total Number
Location plants Lichens hepatics plants of plots Reference
S. Ontario & Quebec 440 — — — 197 36
U.S. Great Lakes, 378 — — — 103 8
Ontario
Alaska 375 107 70 552 103 110
Canada — — 133 — 60 100
Ontario 162 — — — 228 172
Finland 1,350 1,500 810 3,660 National 137
Sweden 2,000 2,100 1,000 5,100 National 137
of more complete usurpation of resources. This suggests a negative and linear
relationship.
Succession plays a major role in the relationship between diversity and produc-
tivity as well. Most boreal forests are adapted to a stand-replacement disturbance
regime, so they generally lack classic climax stages and an associated specialized
complement of species. Total plant species richness increases during boreal forest
succession, especially during primary succession (205), whereas productivity de-
clines in later successional stages (207), suggesting a hump-shaped relationship
through successional time. Many plant species of the forest understory persist
(albeit at low abundance) throughout secondary succession, during which time
productivity differs greatly depending on soil cooling and other influences of the
stand (207).Species richness in late succession may be underestimated system-
atically in most of the literature because the difficult-to-identify cryptogams can
constitute a large proportion, possibly even a majority, of the autotrophic plant
species in boreal regions (Table 4). Vascular plant dominance is generally at a
maximum during early succession, and cryptogam diversity and abundance are
usually at a maximum late in succession (207).
Summary
Integration of results from the two aquatic and three terrestrial biomes discussed
above suggest that there exists no universal pattern in the relationship between pri-
maryproductivity and species richness. Inmostcases, patterns seemtochangewith
scale, but data within biomes are inadequate for rigorous tests of this suggestion.
Unimodal patterns are found in lakes, some wetlands, and through successional
time in boreal forest. In terrestrial systems, a positive relationship pertains at re-
gional or greater scales. In no case, however, are the data adequate to examine the
relationship between primary productivity and species richness across scales and
taxa. To address these issues, a broader survey of the literature is necessary.

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
276 WAIDE ET AL
SURVEY OF PATTERNS
Biome-specific consideration of studies leads to the conclusion that, one, extant
data are insufficient to conclusively resolve the relationship between diversity and
productivity, and two, patterns are variable, with mechanisms equally varied and
complex. This is in sharp contrast to the broad claim that the unimodal pattern
is among the few valid generalizations in ecology (84,86, 166, 167). Indeed, a
unimodal pattern has been heralded as the “true productivity pattern” (166) and as
the “ubiquitous” pattern (87). We surveyed the published literature in ecology to
assess such claims.
Data Acquisition
We conducted a literature search to examine patterns in the relationship between
diversity and productivity. Using BIOSIS®,Biological Abstracts, and the search
string: “species richness OR species diversity OR primary productivity OR pro-
duction OR biomass OR rainfall OR precipitation,” we searched The American
Naturalist, Oecologia, Oikos, Holarctic Ecology/Ecography, The Journal of Bio-
geography, The Journal of Ecology, and Vegetatio for the years 1980–1997. We
also manually searched these journals for the years 1968–1979 (pre-database). We
combined results of this search with those from GG Mittelbach et al (in litt.). The
latter study searched all issues of Ecology and Ecological Monographs to 1993 us-
ingthe JSTOR®database (plusamanualsearchof issues between 1994–1997), and
electronically searched a broad-spectrum of biological journals from 1982–1997.
In all cases, we included only studies with a sample size ≥4 that assessed a
statistical relation (or presented data sufficient to calculate one) between species
richness and productivity (or its surrogates), regardless of scale, taxon, or system.
Agricultural and intensively managed systems were excluded, as were systems
subject to severe anthropogenic disturbance. Systems whose potential productivi-
ties were manipulated experimentally were excluded as well.
Relationships between species diversity and productivity were 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 quadratic regressions. Relationships were deemed significant if P ≤0.10;
regressions in which the quadratic term was significantly different from zero were
classified as unimodal. Two studies produced significant “U-shaped” relationships
(i.e., positive quadratic inthe polynomial regressions). Because these relationships
are rare, we have not included them in our figures; yet they were included when
calculating percentages. Hence, histograms at some scales do not sum to 100%.
More than 200 relationships between diversity and productivity were found in
154 articles. A tabulation of all studies surveyed, a summary of statistical results,
and information on taxon, location, and measures of productivity are available on
the World Wide Web in the Supplemental Materials section of the main Annual
Reviews site (http://www.annurev.org/sup/material.htm).

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 277
We explored patterns in the relationships between species richness and produc-
tivity via five schemes of classification. We classified studies using an ecological
criterion of scale as within community, across community, orcontinental-to-global
scales. We used a shift in the structure of vegetation or plant physiognomy to de-
fine a change in community type (e.g., transitions from desert to grassland or from
meadow to woodland). For most studies, we relied on descriptions of sites by the
authors to generate the classifications. In a few cases, we classified studies based
on knowledge of natural history. Studies whose sites were dispersed over distances
greater than 4000 km were classified as continental-to-global. All studies of non-
contiguous lakes, ponds, streams, or rivers were classified as across-community
or as continental-or-global if the minimum distance criterion was met. Patterns at
different ecological scales were explored for animals and plants separately. Our
second classification scheme was based on the greatest geographic distance be-
tweensites within a study.Werecognized four geographic scales: local(0–20 km),
landscape (20–200 km), regional (200–4000 km), and continental-to-global
(>4000 km). The third classification distinguished studies as terrestrial or aquatic,
and further subdivided them into vertebrate, invertebrate, or plant. Our fourth
method of classification focused on vertebrates and separately considered fish,
mammals, amphibians and reptiles, and birds. We also tallied patterns for rodents
separately because the literature review generated numerous studies of rodent di-
versity. The final classification considered whether the quantified measure of pro-
ductivity was based on energy available to a trophic level or the energy assimilated
by the trophic level.
The Patterns
The relationship between productivity and diversity differs with scale (Figure 2).
Considering plants at the within-community scale, unimodal relationships are
about as common as positive relationships (24 and 22%, respectively); however,
most studies reported no pattern at all (42%). Though the proportion of studies
that show no significant relationship remains large at the across-community scale,
unimodal patterns are more than three times more prevalent than positive rela-
tionships (about 39% of studies compared to 11%). At the continental-to-global
scale, the pattern is dominated by positive relationships (70% compared to 10%
for unimodal relationships), and negative patterns are absent.
For animals, there was aless dramatic shift in the prevalence of unimodal versus
positive relationships across biotic scales. Unimodal relationships predominate at
the across-community scale, whereas positive relationships occur most commonly
at the within-community and continental-to-global scales. As for plants, stud-
ies showing no relationship are numerous at the within- and across-community
scales, but negative relationships are a clear minority, regardless of scale of
classification.
Results of a geographic scale of classification (Figure 3) contrasted with those
basedon ecologicalscale.At the localscale(<20 km), studies ofplantcommunities
exhibited mostly unimodal relationships or no relationship at all. The dominance

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
Figure 2 Percentages of published studies exhibiting particular relationships (positive lin-
ear, 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. Patterns are illustrated separately
for plants and animals. Sample sizes refer to the number of analyzed data sets in each
classification.

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
Figure3 Percentages of published studies exhibitingparticularrelationships(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.

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
280 WAIDE ET AL
of unimodality, relative to positive and negative relationships, declined for studies
whose extents are at the level of the landscape (20–200 km) or region (200–
4000km).Again, studies showingno relationship were frequent at local to regional
scales. Patterns at continental to global scales were the same as for the biotic
classification; positive relationships predominated.
Studies of animals commonly exhibited no significant relationship between
productivity and diversity at local to landscape scales (67% and 46%, respec-
tively). However, when patterns occur, positive relationships between diversity
and productivity were most prevalent at all geographic scales.
Our third method of classification focused on studies of vertebrates, examin-
ing productivity-diversity patterns for taxonomic groupings independent of scale.
Most striking was the dominance of positive relationships for studies of birds
and herpetofauna (Figure 4). In contrast, patterns were not as distinct for fish or
mammals. A hump-shaped relationship was the most common pattern for fish.
However, the proportions of unimodal and positive relationships were similar for
fish and mammals. Most rodent diversity studies produced unimodal relationships
between productivity and diversity.
When we divided studies into those concerning terrestrial and aquatic systems,
striking differences became apparent. Positive relationships were more numerous
in studies of terrestrial vertebrates, whereas unimodal relationships were more
common in studies of aquatic vertebrates (Figure 5). Positive relationships pre-
dominated in studies of terrestrial invertebrates compared to a high percentage
of unimodal relationships in studies of aquatic invertebrates. For both habitats,
studies producing no relationships were numerous as well. Studies of plants in
aquatic and terrestrial systems generally documented no relationship between di-
versity and productivity. For those studies that did show a significant relationship,
a higher percentage of unimodality exists in aquatic systems.
Clearly, considerable variation characterizes the relationship between produc-
tivity and diversity, even after controlling for aspects of ecological, geographic, or
taxonomic scale. Part of this variability may be a consequence of the way in which
productivity was assessed for a particular site (available energy versus assimilated
energy) or the power of statistical tests used to assess relationships. To assess
the degree to which these factors may have affected the pattern or distribution of
relationships (i.e., unimodal, positive linear, negative linear, no relationship), we
conducted a hierarchical G-test (183). In general, contrasts were orthogonal and
based on a priori considerations of energy, nested within habitat, nested within
taxon (Figure 6). A final comparison of the pattern for all studies versus only
studies with sample sizes greater than 10 was conducted for heuristic purposes
(shaded portion of dendrogram in Figure 6). With one exception (aquatic animals
based on all studies), the distribution of relationships was indistinguishable in
contrasts between studies involving assimilated versus available energy. In addi-
tion, no significant differences in the distribution of relationships were detected for
studies based on any other contrasts with respect to habitat, taxon, or data. These

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 281
Figure4 Percentages of published studies exhibitingparticularrelationships(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, mam-
mal, and rodents. Sample sizes refer to the number of analyzed data sets in each classification.

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
282 WAIDE ET AL
Figure5 Percentages ofpublishedstudies exhibiting particularrelationships(positivelinear,
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.

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 283
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 rela-
tionship) 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.

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
284 WAIDE ET AL
results, especially when combined with those based on classifications of studies
based on ecological and geographic scale, suggest that no single relationship has
hegemony, and that at best, the data are insufficient to corroborate the priority of
any one relationship between diversity and productivity.
Regardless of the manner of categorization, no single relationship described
more than two thirds of the studies. This is surprising, given that the relationship
between species richness and productivity is often characterized in the literature as
unimodal (e.g., 16, 87, 167,191). This suggests that no mechanism has a dominant
role in molding patterns, that multiple mechanismsmay operate simultaneously, or
that confounding factors or methodologicallimitations (e.g., sample size or extent)
conspire to produce the apparent diversity of relationships between productivity
and diversity. We explore some of the factors that may be involved in producing a
variety of patterns at different scales.
Conceptual Issues
Determiningthemannerinwhichproductivity-diversitypatterns change with scale
is an important first step toward more fully understanding the applicability, limi-
tations, and predictive power of ecological theory (111). We have benefited from
several decades of experimental study of the relationship between system pro-
ductivity and species richness, and a healthy body of theory complements this
empirical data base. What has not been made clear is the applicability of theory
to patterns at different spatial scales. The ambiguity of the proper scale of appli-
cation is most obvious at the within-to-across-community scale, or at the local-
to-landscape-to-regional scale. Consider as an example the graphical theories of
community structure proposed by Tilman (186). When combined with resource
heterogeneity, Tilman’s model predicts a unimodal species diversity response to
increasing nutrient supply (i.e., potential primary production). What is not clear
from the model is the scale at which the pattern manifests. In fact, the theory itself
can predict any type of pattern depending on range of productivity, location along
a productivity gradient, heterogeneity of resources, and manner in which resource
heterogeneity covaries with resource supply (2,3, 186). Ambiguities of the scale
of operation and application also may apply to alternative theories proposed to
explain productivity-diversity relationships, such as the keystone predator model
which also predicts hump-shaped relationships (105; for reviews of additional
theories see 3,167,191). Determining how patterns change with scale in natural
systems provides the first step in understanding the limitations and proper scales
of application for theoretical frameworks.
The scale of a study may be an important factor to consider when predicting the
relationshipbetweendiversityand productivity.Both geographic- andbiotic-based
classifications need to be considered. Unimodal patterns may emerge at relatively
small spatial scales. Almost half of the studies of plant communities that reported
distances between study sites of less than 20 km (local scale) produced unimodal

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 285
patterns. When studies remain within a community, patterns are almost equally
divided between unimodal and positive categories for plants. This clearly changes
when studies cross communities: Unimodal patterns become much more pro-
nounced. At geographic scales greater than 20 km (landscape-to-regional scales),
a larger proportion of positive patterns occurs for plants.
The range of productivities encompassed within a study may explain the de-
coupling of geographic and biotic classifications of scale for plants. Although we
expect the two to covary (i.e., studies that are spread over large geographic ranges
most likely have larger ranges of productivity), the relationship need not always
hold. Investigations at small spatial scales may traverse large gradients of potential
productivity, as well as multiple community boundaries, (e.g., elevational gradi-
ents from woodland to tundra). Conversely, single community types may span
huge distances and several geographic scales, yet may exhibit little variation in
productivity or species richness. Proponents of the unimodal pattern commonly
argue that patterns without humps are a result of insufficient ranges of productiv-
ity in the study (e.g., 167, 168). Restricting studies to a single community type,
from the outset, constrains them to a limited range of species compositions. If uni-
modal relationships occur primarily across communities, then within-community
studies may be sampling only portions of the whole productivity-diversity curve.
There is evidence that unimodal relationships emerge only when data from differ-
ent communities along the productivity gradient are accumulated (e.g., 124,121;
KL Gross et al, in litt.). Thus, future research should consider explicitly the range
of productivity sampled, which may be a major factor driving the change in plant
community type.
Despite the predominance of unimodal patterns at the across-community scale,
they can occur within communities (about 24% of plant and animal studies), but
at this scale nonsignificant relationships are also numerous (42%). In addition to
smaller productivity ranges at this scale, a number of ecological processes occur at
smaller spatial and within-community scales, which may result in nonsignificant
relationships. For example, dispersal between patches of high species diversity
and sink patches of low diversity may mask productivity-diversity patterns. Such
mass or rescue effects (33, 179,180) likely occur at smaller spatial scales in which
patches are in close proximity and immigration rates are high.
Studies of animals showed no dramatic changes in the frequency distribution
of relationships across biotic or geographic scales. Positive relationships always
outnumbered unimodal relationships, although the differences were small at the
within- and among-community scales. Differences were more pronounced across
geographic scales. Almost without exception, studies of animal diversity focused
on subsets of the animal community (specific taxa such as rodents or amphibians).
Most theoretical explorations of productivity-diversity phenomena deal exclu-
sively with the species richness of whole trophic levels or guilds (e.g., 105, 186).
Although models can be adapted to deal with more restricted taxonomic groups,
the predictions may be very different. This is especially so because most studies of

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
286 WAIDE ET AL
animals not only deal with subsets of trophic levels, but also consider organisms
that have different feeding ecologies (e.g., studies of aquatic macroinvertebrates
can include primary and secondary consumers as well as detritivores).
Along gradients of productivity, taxonomic turnover may occur such that focal
taxa within a trophic level drop out of the system, while other taxonomic groups
(with the same feeding ecology) replace them. Although overall species diversity
of the trophic level may show one pattern along the gradient, the focal group may
exhibit a completely different one. Hence, the ability to assess the applicability of
ecological theory and the influence of scale on productivity-diversity patterns for
animals is limited.
The patterns and explanations presented thus far have dealt with studies at re-
gional and smaller scales (<4000 km). Theory at this spatial scale deals primarily
with communities assembled from presumably co-evolved regional species pools.
Although our cut-off point of 4000 km is arbitrary, we hoped to distinguish be-
tween these types of studies and those whose communities may derive constituent
species from different regional pools. This most likely occurs at the scale of whole
continents or across continents (i.e., global scales). The results of our literature
reviewindicate that species richnessis primarily a positive function of productivity
at this larger scale, for both animals and plants. Unimodal patterns were abundant
for animals, though due perhaps to previously mentioned factors. These studies
often include sites along gradients of latitude and can include species pools of
different ages and evolutionary histories. Distinguishing the ecological effects of
available energy from the evolutionary effects is difficult.
Despite the long history of interest in factors governing large-scale patterns
of diversity, consensus remains elusive. Many potential problems accompany any
literature review that gathers data from a wide variety of studies using disparate
approaches,methods,andfoci.Many of thecaveatsand shortcomings ofourreview
provide guidance for future improvements in assembling productivity-diversity
patterns. First, most of the studies we surveyed use a correlate of productivity, of-
ten an indicator of assimilated energy (such as standing crop biomass) or available
energy (such as rainfall, latitude, evapotranspiration, or soil nutrients). In general,
we expect these variables to be indicators of system productivity; nonetheless, cor-
relationsmay be poor for some systems oratcertaintimesof the year.Aboveground
biomass is one of the most popular correlates in plant studies, but simple models
of trophic regulation can predict complete decoupling of trophic-level biomass
from productivity depending on the trophic structure of the system and the feed-
ing efficiency of consumers (141). The studies included in our review use a great
variety of quadrat and plot sizes, and rarely are area effects explicitly addressed or
controlled.
Many different relationships between species diversity and productivity can
be generated at a single biotic or geographic scale. Yet, the relative percentages
of different patterns change with scale. Unimodal patterns have been described
as textbook examples of productivity-diversity relationships (16). Our review is
noteworthyfor thelackofstudies evincing asignificanthump(despite our generous

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 287
criteria for detecting one). This is especially true for animals, in which positive
patterns dominate at almost all scales. Our review in no way discounts the models
and mechanisms that predict hump-shaped relationships, but it does attest to the
potentialimportanceofscalewhen applying such models and predictions. Exciting
future directions include investigating why patterns change with scale; why in
some systems unimodal patterns are generated at the within-community and local
scales,whereasinothersunimodal patterns only emerge whencrossingcommunity
boundaries or large geographical distances.
FUTURE STUDIES
Two important issues facingthescientificcommunity are the maintenance of global
biodiversity and the continuance of the ecosystem services necessary to support
human life. It is clear from numerous studies that these issues are inextricably en-
twined (41). Modeling and empirical studies demonstrate that loss of biodiversity
can influence key ecosystem characteristics such as primary productivity, pre-
dictability, and resistance to invasion by exotics (41,116). Theory and empirical
studies indicate that changes in primary productivity are related to species richness
at some scales but not at others. The goal of future research must be to provide
mechanisticexplanationsfor observed patterns in the relationship between primary
productivity and species richness through well-designed and carefully interpreted
experiments (85) that explicitly consider spatial scale as well as local and regional
mechanisms.
A key strategy for improving our understanding of the interaction of biodiver-
sity and productivity (or other ecosystem processes) considers the integration of
two common experimental approaches: the manipulation of productivity and the
alteration of the number of species or functional groups. A synthesis of ideas that
have developed around these two approaches is a prerequisite for the advancement
of a general theory that will direct the next generation of hypotheses and exper-
iments. Conceptual models being developed by JB Grace (68a) and M Shackak
(personal communication) foreshadow this synthesis. These emerging models in-
corporate disturbance, plant biomass (productivity), resource heterogeneity, col-
onization, and the available species pool as primary factors controlling species
density. Consequently, they emphasize the importance of multivariate approaches
to understanding patterns of species density (Figure 7).
Central to understanding the role that humans play in the present observed
high extinction rate is the relationship between anthropogenic disturbance and
the natural disturbance regime (219). The first attempts to explain the control of
speciesrichness had an explicit appreciation for the importanceof human activities
(71,72), which led to an integration of disturbance, environmental stress, and el-
ements of productivity in an index of factors controlling species richness (6; 68a).
Future studies need to refocus on the similarities and differences between natu-
ral and anthropogenic disturbance. Incorporation of the unique nature of human

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
288 WAIDE ET AL
Figure 7 Conceptual model of a local ecosystem: the biota interacting with energy
flow and nutrient cycling (unshaded shapes). For simplicity’s sake, the model only
considers the plant community. Shaded shapes represent larger-scale species pools,
nutrient pools, or energy inputs. Broad arrows represent the various system currencies
of species (dashed), energy (dots) or nutrients (solid); narrow lines terminating in
a circle represent regulators or filters which modify the flow of species, nutrients or
energy. Hence, the composition of a local assemblage is derived from a regional species
pool via the action of five filters: available energy, nutrient availability, and the species
already in the community, as well as structural characteristics of the production and
litter. Similarly, the production of the ecosystem is affected by available energy as
processed by species assemblages and constrained by nutrient availability. Because
energy flow and species flow are affected by some of the same regulators, and in fact
reciprocally affect each other, the relationship between them may be complex.
activities into models of the relationship between biodiversity and ecosystem pro-
cesses is necessary to merge the fields of evolutionary ecology and conservation
biology.
Thetime is appropriate for the studyoftherelationship between species richness
and primary productivity to change focus from discerning patterns to developing
mechanistic explanations, which can be tested through manipulative or observa-
tional experiments. The available evidence shows that multiple patterns exist and
change with scale. The implication is also clear that multiple causal factors exist
for scales, habitats, and taxa. There is reasonably strong evidence to demonstrate
that productivity influences diversity at some scales, whereas functional or species
diversity seems to influence productivity at other scales. Clever experiments and
observations based on conceptual models of system dynamics (Figure 7) will be
needed to disentangle the web of cause and effect. With this in mind, we offer in

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 289
conclusion a few general ideas concerning the characteristics of future research
endeavors.
•Investigators must be careful to match the scale upon which theory
operates to the scale of observation. In many cases this will require the
collection of new data at the appropriate scale to test theory.
•Some standardization in operational definitions is necessary for
meaningful comparison. In particular, the spatial and temporal framework
for the measurement of species density and productivity must be carefully
controlled. Theory that is based on net primary productivity cannot be
evaluated using partial measures of NPP. Similarly, theory that is based on
guilds or communities cannot be evaluated using subsets of these
communities.
•Multivariate approaches are needed to separate the effects of co-varying
causal factors. Investigators must recognize that different species may
respond to different variables along the same geographic gradient and that
changes in total species richness are the sum of these species-level
responses.
•Experiments must include several trophic levels and multiple ecological
scales. Without this kind of experimental approach, results will be difficult
to place in context.
•Theory and experimentation need to be extended to high-diversity systems.
Microcosms provide a useful approach for addressing basic questions, but
issues relating to the loss of taxa from species-rich systems urgently
require attention.
•More sophisticated manipulations of productivity at multiple scales will be
required to determine the generality of the pattern between productivity
and species richness. In particular, manipulations of limiting resources that
increase heterogeneity of resource availability would provide an interesting
contrast to standard fertilization experiments.
ACKNOWLEDGMENTS
This work resulted from a workshop conducted at the National Center for Ecolog-
ical Analysis and Synthesis (NCEAS), a Center funded by the National Science
Foundation(Grant #DEB-94-21535), the Universityof California at SantaBarbara,
and the State of California. MRW was supported in part as a Sabbatical Fellow
on the same grant and received additional support from a Developmental Leave
from the Office ofthe Provost, Texas Tech University. We appreciate the assistance
provided by the staff at NCEAS, in particular Jim Reichman, Matt Jones, Mark
Schildhauer, and Marilyn Snowball. The Long-Term Ecological Research Net-
work Office of the University of New Mexico provided support for RBW through
Cooperative Agreement No. DEB-9634135, as did grant DEB-9705814 from the

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
290 WAIDE ET AL
National Science Foundation to theInstitute for Tropical EcosystemsStudies, Uni-
versity of Puerto Rico, and theInternational Institute of Tropical Forestry as part of
the Long-Term Ecological Research Program in the Luquillo Experimental Forest.
Thefollowing participants in theworkshopcontributedto the discussion leading
to the manuscript: Linda Blum, Scott Collins, Stephen Cox, Katherine Gross, Jeff
Herrick, Michael Kaspari, Clarence Lehman, John Moore, Glenn Motzkin, Craig
Osenberg, Michael Rosenzweig, Samuel Scheiner, Lee Turner, Maria Vernet, and
BruceWallace.The manuscript was substantially improvedasaresultof comments
byStevenChown,Deborah Clark, Stephen Cox, James Grace, andMosheShackak.
Louise Williams prepared the tables, and Leida Rohena and Saioa de Urquiza
helped with the bibliography.
Visit the Annual Reviews home page at http://www.AnnualReviews.org
LITERATURE CITED
1. Aarssen LW. 1997. High productivity in
grassland ecosystems: effected by species
diversity or productive species? Oikos 80:
183–84
2. Abrams PA. 1988. Resource productivity-
consumer species diversity: simple models
of competition in spatially heterogeneous
environments. Ecology 69:1418–33
3. Abrams PA. 1995. Monotonic and unimodal
diversity-productivity gradients: What does
competition theory predict? Ecology 76:
2019–27
4. Adams DA. 1963. Factors influencing vas-
cular plant zonation in North Carolina salt
marshes. Ecology 44:445–56
5. Agard JBR, Griffith JK, Hubbard RH. 1996.
The relation between productivity, distur-
banceand thebiodiversity ofCaribbean phy-
toplankton: applicability of Huston’s dy-
namic equilibrium model. J. Exp. Mar. Biol.
Ecol. 202:1–17
6. Al-Mufti MM, Sydes CL, Furness SB,
Grime JP, Band SR. 1977. A quantitative
analysis of shoot phenology and dominance
in herbaceous vegetation. J. Ecol. 65:759–
91
7. ArrheniusO. 1921.Species andarea. J.Ecol.
9:95–99
8. Auclair AN, Goff FG. 1971. Diversity
relations of upland forests in the western
Great Lakes area. Am. Nat. 105:499–528
9. Bakker JP. 1985. The impact of grazing on
plant communities, plant populations and
soil conditions on salt marshes. Vegetatio
62:391–98
10. Bamberg SA, Vollmer AT, Klienkopf GE,
Ackerman TL. 1976. A comparison of sea-
sonal primary production of Mojave Desert
shrubs during wet and dry years. Am. Midl.
Natur. 95:398–405
11. Barbour CD, Brown JH. 1974. Fish species
diversity in lakes. Am. Nat. 108:473–89
12. BatzliGO, Collier BD, MacLean SF,Pitelka
FA, White RG. 1980. The herbivore-based
trophic system. In An Arctic Ecosystem:
The Coastal Tundra at Barrow, Alaska, ed. J
Brown, FL Bunnell, PC Miller, LL Tieszen,
pp. 335–410. Stroudsberg, PA: Dowden,
Hutchinson & Ross
13. Batzli GO, Henttonen H. 1990. Demogra-
phy and resource use by microtine rodents
near Toolik Lake, Alaska. Arct. Alp. Res.
22:51–64
14. Bazely DR, Jefferies RL. 1986. Changes in
the composition and standing crop of salt-
marsh communities in response to the re-
moval of a grazer. J. Ecol. 74:693–706
15. Beatley JC. 1969. Biomass of desert win-
ter annual populations in southern Nevada.
Oikos 20:261–273

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 291
16. Begon M, Harper JL, Townsend CR. 1990.
Ecology: Individuals, Populations and
Communities. Boston: Blackwell Sci. 2nd
ed.
17. Bertness MD. 1985. Fiddler crab regu-
lation of Spartina alterniflora production
on a New England salt marsh. Ecology
66:1042–55
18. Bertness MD. 1991. Interspecific interac-
tions among high marsh perennials in a
New England salt marsh. Ecology 72:125–
37
19. Bertness MD. 1991. Zonation of Spartina
patens and Spartina alterniflora inaNew
England salt marsh. Ecology 72:138–48
20. Bertness MD, Ellison AM. 1987. Deter-
minants of pattern in a New England salt
marsh community. Ecol. Monogr. 57:129–
47
21. Billings WD. 1992. Phytogeographic and
evolutionary potential of the arctic flora
and vegetation in a changing climate. In
Arctic Ecosystems in a Changing Cli-
mate, ed. FS Chapin III, RL Jefferies, JF
Reynolds, GR Shaver, J Svoboda, pp. 91–
109. New York: Academic
22. BillingsWD, Mooney HA. 1968.Theecol-
ogy of arctic and alpine plants. Biol. Rev.
43:481–529
23. Bliss LC. 1962. Adaptations of arctic and
alpine plants to environmental conditions.
Arctic 15:117–44
24. Bliss LC. 1977. General summary, Tru-
elove Lowland ecosystem. In Truelove
Lowland, Devon Island, Canada: A High
Arctic Ecosystem, ed. LC Bliss, pp. 657–
76. Edmonton: Univ. Alberta Press
25. Bliss LC. 1988. Arctic tundra and polar
desert biome. In North American Terres-
trial Vegetation, ed. MG Barbour, WD
Billings, pp. 1–32. New York: Cambridge
Univ. Press
26. BlissLC, Bliss DI,Henry GHR, SvobodaJ.
1994. Patterns of plant distribution within
two polar desert landscapes. Arct. Alp. Res.
26:46–55
27. Bliss LC, Matveyeva NV. 1992. Circum-
polar arctic vegetation. In Arctic Ecosys-
temsin a Changing Climate, ed. FS Chapin
III,RL Jefferies,JF Reynolds,GR Shaver,J
Svoboda, pp. 59–90. NewYork: Academic
28. Bliss LC, Svoboda J. 1984. Plant commu-
nities and plant production in the western
Queen Elizabeth Islands. Holarctic Ecol-
ogy 7:325–44
29. Bliss, LC, Svoboda J, Bliss DI. 1984. Polar
deserts, their plant cover and plant produc-
tionin the Canadian High Arctic. Holarctic
Ecology 7:305–24
30. Brenner FJ. 1993. Seasonal changes in
plankton communities in three surface
lakes in western Pennsylvania. J. Penn.
Acad. Sci. 67:59–64
31. Brewer JS, Grace JB. 1990. Plant commu-
nity structure in an oligohaline tidal marsh.
Vegetatio 90:93–107
32. Brown JH. 1973. Species diversity of seed-
eating desert rodents in sand dune habitats.
Ecology 54:775–87
33. Brown JH, Kodric-Brown A. 1977.
Turnoverratesin insular biogeography: ef-
fect of immigration on extinction. Ecology
58:445–49
34. Brown S, Lugo AE. 1982. The storage and
production of organic matter in tropical
forests and their role in the global carbon
cycle. Biotropica 14:161–87
35. Browne RA. 1981. Lakes as islands: bio-
geographicdistribution, turnoverrates, and
species composition in the lakes of central
New York. J. Biogeog. 8:75–83
36. Carleton TJ, Maycock PF. 1980. Vegeta-
tion of the boreal forests south of James
Bay: non-centered component analysis of
the vascular flora. Ecology 61:1199–212
37. Chabreck RH. 1972. Vegetation, water
and soil characteristics of the Louisiana
coastal region.Bull. No. 664, La. State
Univ. Agric. Exp. Stn. 75 pp.
38. Chapin FS III. 1974. Morphological and
physiological mechanisms of tempera-
ture compensation in phosphate absorp-
tion along a latitudinal gradient. Ecology
55:1180–98

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
292 WAIDE ET AL
39. ChapinFS III, EverettKR, Fetcher N, Kiel-
landK, Linkins AE.1988.Productivity and
nutrient cycling of Alaskan tundra: en-
hancement by flowing soil water. Ecology
69:693–702
40. Chapin FS III, Giblin AE, Laundre JA,
Nadelhoffer KJ, Shaver GR. 1995. Respo-
nses of arctic tundra to experimental and
observed changes in climate. Ecology 76:
694–711
41. Chapin FS III, Sala OE, Burke IC, Grime
JP, Hooper DU et al. 1998. Ecosystem con-
sequences of changing biodiversity. Bio-
Science 48:45–52
42. Chapin FS III, Shaver GR. 1985. Arctic.
In Physiological Ecology of North Amer-
ican Plant Communities, ed. BF Chabot,
HA Mooney, pp. 16–40. London: Chap-
man & Hall
43. Chew RM, Chew AE. 1965. The pri-
mary productivity of a desert shrub (Lar-
rea tridentata) community. Ecol. Monogr.
35:355–75
44. Coleman BD, Mares MA, Willig MR,
Hsieh Y. 1982. Randomness, area and
species richness. Ecology 63:1121–33
45. Connell JH, Orias E. 1964. The ecologi-
calregulation of species diversity.Am. Nat.
98:399–414
46. Crosswhite FS, Crosswhite CD. 1982. The
Sonoran Desert. In Reference Handbook of
Deserts of the World, ed. GL Bender, pp.
163–319. Westwood, CT: Greenwood
47. Day RT, Carleton T, Keddy PA, McNeill J.
1988. Fertility and disturbance gradients:
a summary model for riverine marsh vege-
tation. Ecology 69:1044–54
48. DiTommaso A, Aarssen LW. 1989. Re-
sourcemanipulations in natural vegetation:
a review. Vegetatio 84:9–29
49. Doak DF, Bigger D, Harding EK, Marvier
MA,O’MalleyME,Thomson D.1998. The
statisticalinevitability ofstability-diversity
relationships in community ecology. Am.
Nat. 151:264–76
50. Dodson SI. 1987. Animal assemblages in
temporarydesertrockpools: aspectsofthe
ecology of Dasyhelea sublettei (Diptera:
Ceratopogonidae). J. North Am. Benthol.
Soc. 6:65–71
51. Dodson SI. 1992. Predicting crustacean
zooplankton species richness. Limnol.
Oceanogr. 37:848–56
51a. Dodson SI, Arnott SE, Cottingham KL.
The relationship in lake communities be-
tweenprimary production andspecies rich-
ness. Ecology (in press).
52. Ehrlich PR, Ehrlich AH. 1981. Extinction:
The Causes and Consequences of the Dis-
appearance of Species. New York: Ran-
dom House
53. Elseth GD, Baumgardner KD. 1981. Pop-
ulation Biology. New York: Van Nostrand
54. EltonC. 1958. The Ecology of Invasionsby
Animals and Plants. London: Methuen
55. Ettershank G, Bryant M, Ettershank J,
Whitford WG. 1978. Effects of nitrogen
fertilization on primary production in a
Chihuahuan Desert ecosystem. J. Arid En-
viron. 1:135–39
56. Floret C, Pontanier R, Rambal S. 1982.
Measurement and modeling of primary
production and water use in a south
Tunisian steppe. J. Arid Environ. 5:77–90
57. Flynn KM, McKee KL, Mendelsshohn IA.
1995. Recovery of freshwater marsh vege-
tation after a saltwater intrusion event. Oe-
cologia 103:63–72
58. Ford MF. 1996. Impacts of fire and verte-
brate herbivores on plant community char-
acteristics and soil processes in a coastal
marshof easternLouisiana, USA. PhDdis-
sertation, La. State Univ., Baton Rouge.
119 pp.
59. Ford MF, Grace JB. 1998. Effects of her-
bivores on vertical soil accretion, shallow
subsidence, and soil elevation changes in
coastal Louisiana. J. Ecol. 86:974–82
60. Fox JF. 1985. Plant diversity in relation to
plant production and disturbance by voles
in Alaskan tundra communities. Arct. Alp.
Res. 17:199–204
61. Ganapati SV. 1940. The ecology of a tem-
ple tank containing a permanent bloom of

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 293
Microcystis aeruginosa (Kutz) Henfr. J.
Bombay Nat. Hist. Soc. 17:65–77
62. Garcia LV, Clemente L, Maranon T,
Moreno A. 1993. Above-ground biomass
and species richness in a Mediterranean
salt marsh. J. Veg. Sci. 4:417–24
63. Gentry AH. 1988. Changes in plant com-
munitydiversity andfloristic composition
onenvironmental andgeographicalgradi-
ents. Ann. Mo. Bot. Gard. 75:1–34
64. Gold WG, Bliss LC. 1995. Water limita-
tions and plant community development
in a polar desert. Ecology 76:1558–68
65. Goldberg DE, Miller TE. 1990. Effects
of different resource additions on species
diversity in an annual plant community.
Ecology 71:213–25
66. Gough L. 1996. Plant species diversity
and community structure in a Louisiana
coastalmarsh. PhD dissertation, La.State
Univ., Baton Rouge. 192 pp.
67. Gough L, Grace JB. 1998. Herbivore ef-
fects on plant species richness at varying
productivity levels. Ecology 79:1586–94
68. GoughL, Grace JB, TaylorKL.1994. The
relationshipbetweenspecies richness and
community biomass: the importance of
environmental variables. Oikos 70:271–
79
68a. Grace JB. 1999. The factors controlling
species density in herbaceous plant com-
munities: an assessment. Perspectives in
Plant Ecology, Evolutionand Systematics
2:1–28
69. GraceJB, Pugesek BH. 1997. Astructural
equation model of plant species richness
and its application to a coastal wetland.
Am. Nat. 149:436–60
70. Grillas T, Bonis A, van Wijck C. 1993.
The effect of salinity on the dominance-
diversityrelations of experimentalcoastal
macrophyte communities. J. Veg. Sci. 4:
453–60
71. Grime JP. 1973. Competitive exclusion in
herbaceous vegetation. Nature 242:344–
47
72. Grime JP. 1973. Control of species
density on herbaceous vegetation. J. En-
viron. Manage. 1:151–67
73. GuoQ, Berry WL. 1998. Species richness
and biomass: dissection of the hump-
shaped relationships. Ecology 79:2555–
59
74. Gutierrez JR, Da Silva OA, Pagani MI,
WeemsD,Whitford WG. 1988. Effects of
different patterns of supplemental water
and nitrogen fertilization on productivity
and composition of Chihuahuan Desert
annual plants. Am. Midl. Nat. 119:36–43
75. Hebert PDN, Hann BJ. 1986. Patterns in
the composition of arctic tundra pool mi-
crocrustacean communities. Can. J. Fish.
Aquatic Sci. 43:1416–25
76. Hector A. 1998. The effect of diversity on
productivity-detecting the role of species
complementarity. Oikos 82:597–99
77. Henry GHR, Freedman B, Svoboda J.
1986. Effects of fertilization on three tun-
dra plant communities of a polar desert
oasis. Can. J. Bot. 64:2502–7
78. Hik DS, Jefferies RL. 1990. Increases in
the net above-ground primary production
of a salt-marsh forage grass: a test of the
predictions of the herbivore-optimization
model. J. Ecol. 78:180–95
79. Hooper DU. 1998. The role of comple-
mentarity and competition in ecosystem
responses to variation in plant diversity.
Ecology 79:704–19
80. Hooper DU, Vitousek PM. 1997. The
effects of plant composition and diver-
sityon ecosystem processes. Science 277:
1302–5
81. Hugueny B. 1989. West African rivers as
biogeographic islands: species richness
of fish communities. Oecologia 79:236–
43
82. Huston MA. 1979. A general hypothesis
of species diversity. Am. Nat. 113:81–101
83. Huston MA. 1980. Soil nutrients and tree
species richness in Costa Rican forests. J.
Biogeog. 7:147–57
84. Huston MA. 1994. Biological Diversity:
The Coexistence of Species in Changing

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
294 WAIDE ET AL
Landscapes. Cambridge, UK: Cambridge
Univ. Press
85. Huston MA. 1997. Hidden treatments in
ecological experiments: re-evaluating the
ecosystem function of biodiversity. Oe-
cologia 110:449–60
86. Huston MA. 1998. Reconciling scales and
processes: the key to a general theory
of species diversity. Bull. Ecol. Soc. Am.
(Supp.) 79
87. Huston MA, DeAngelis DL. 1994. Com-
petition and coexistence-the effects of re-
source transport and supply. Am. Nat.
144:954–77
88. Inouye RS, Huntly NJ, Stilwell M, Tester
JR,TilmanD, et al. 1987. Old-field succes-
sion on a Minnesota sand plain. Ecology
68:12–26
89. Jacobson HA, Jacobson GL Jr. 1989.
Variability of vegetation in tidal marshes
of Maine, U.S.A. Can. J. Bot. 67:230–
38
90. Jefferies RL, Klein DR, Shaver GR. 1994.
Vertebrate herbivores and northern plant
communities: reciprocalinfluences andre-
sponses. Oikos 71:193–206
91. Johnson KH, Vogt KA, Clark HJ, Schmitz
OJ, Vogt DJ. 1996. Biodiversity and the
productivity and stability of ecosystems.
Trends Evol. Ecol. 11:372–77
92. Jonasson S. 1981. Plant communities and
species distributions of low alpine Betula
nana heaths in northernmost Sweden. Veg-
etatio 44:51–64
93. Jonasson,S. 1982. Organicmatter and phy-
tomasson three north Swedish tundra sites,
andsomeconnectionswith adjacent tundra
areas. Holarctic Ecology 5:367–75
94. Jonasson S. 1992. Plant responses to fer-
tilization and species removal in tundra re-
lated to community structure and clonality.
Oikos 63:420–29
95. Kajimoto T, Matsuura Y, Sofronov MA,
Volokitina AV, Mori S, et al. 1997. Above-
andbelow-ground biomass andannual pro-
duction rates of a Larix gmelinii stand near
tundrain central Siberia. In Proc. Symp. Jt.
Sib. Permafrost Stud. Between Jpn. Russia
1996,5
th, Tsukuba, Ibaraki, pp. 119–128.
Japan: Natl. Inst. Environ. Stud.
96. Kazantseva TI. 1980. Productivity and dy-
namics of above-ground biomass of desert
plants. In Problemy Osvoeiya Pustyn, No.
2,pp. 76–84. KomarovBot. Inst.Acad. Sci.
USSR
97. Keddy PA. 1990. Competitive hierarchies
and centrifugal organization in plant com-
munities. In Perspectives on Plant Compe-
tition, ed. JB Grace, D Tilman, pp.266–90.
New York: Academic
98. KolasaJ, Pickett STA(eds.). 1989.Ecolog-
ical Heterogeneity. New York: Springer-
Verlag
99. Komarkova V, Webber PJ. 1980. Two
low arctic vegetation maps near Atkasook,
Alaska. Arct. Alp. Res. 12:447–72
100. La Roi GH, Stringer MHL. 1976. Ecologi-
calstudies in the borealspruce-fir forests of
the North American taiga, II. Analysis of
thebryophyte flora. Can. J. Bot. 54:619–43
101. Latham PJ, Kitchens WM, Pearlstine LG.
1994. Species associations changes across
a gradient of freshwater, oligohaline, and
mesohaline tidal marshes along the Savan-
nah River. Wetlands 14:174–83
102. Lawton JH. 1994. What do species do in
ecosystems? Oikos 71:367–74
103. Lawton JH, Naeem S, Thompson LJ, Hec-
tor A, Crawley MJ. 1998. Biodiversity and
ecosystem function: getting the Ecotron
experiment in its correct context. Funct.
Ecol. 12:848–52
104. LeHouerou HN. 1984. Rain useefficiency:
a unifying concept in arid-land ecology. J.
Arid Environ. 7:213–47
105. Leibold MA. 1996. A graphical model of
keystone predators in food webs: trophic
regulation of abundance, incidence, and di-
versity patterns in communities. Am. Nat.
147:784–812
106. Leigh EG Jr. 1965. On the relationship be-
tween productivity, biomass, diversity and
stability of a community. Proc. Natl. Acad.
Sci. USA 53:777–83

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 295
107. LoreauM. 1998. Separatingsampling and
other effects in biodiversity experiments.
Oikos 82:600–2
108. Ludwig, JA. 1986. Primary production
variability in desert ecosystems. In Pat-
tern and Process in Desert Ecosys-
tems, ed. WG Whitford, pp. 5–17. Albu-
querque: Univ. New Mex. Press
109. Ludwig JA, Whitford WG, Cornelius JM.
1989. Effects of water, nitrogen and sul-
furamendments oncover,density andsize
ofChihuahuan Desert ephemerals. J. Arid
Environ. 16:35–42
110. Lutz HL. 1953. The effects of forest
fires on the vegetation of interior Alaska.
USDA For. Serv., Alaska For. Res. Cent.
Stn. Pap. No. 1. Juneau, Alaska
111. Lyons SK, Willig MR. 1999. A hemi-
spheric assessment of scale-dependence
in latitudinal gradients of species rich-
ness. Ecology. In press
112. MacArthur RH. 1955. Fluctuations of an-
imal populations and a measure of com-
munity stability. Ecology 36:533–36
113. MacArthur RH, Pianka ER. 1966. On the
optimal use of a patchy environment. Am.
Nat. 100:603–9
114. Marrs RH, Gough L, Grace JB. 1996. On
the relationship between plant species di-
versity and biomass: a comment on a
paper by Gough, Grace & Taylor. Oikos
75:323–26
115. Matveyeva NV, Parinkina OM, Cher-
nov YI. 1975. Maria Pronchitsheva Bay,
USSR. In Structure and Function of Tun-
dra Ecosystems, Ecol. Bull. 20, ed. T
Rosswall, OW Heal, pp. 61–72. Stock-
holm
116. McGrady-Steed J, Harris PM, Morin PJ.
1997. Biodiversity regulates ecosystem
predictability. Nature 390:162–65
117. McKee KL, Mendelssohn IA. 1989. Re-
sponse of a freshwater marsh plant com-
munityto increased salinity and increased
water level. Aq. Bot. 34:301–16
118. McKendrick JD, Batzli GO, Everett KR,
Swanson FC. 1980. Some effects of
mammalian herbivores and fertilization
on tundra soils and vegetation. Arct. Alp.
Res. 12:565–78
119. McKenzie D. 1982. The northern Great
Basin region. In Reference Handbook
on the Deserts of North America, ed.
GL Bender, pp. 67–102. Westport, CT:
Greenwood
120. Medellin-Leal D. 1982. The Chihuahuan
Desert. In Reference Handbook on the
Desertsof North America, ed.GL Bender,
pp. 321–381. Westport, CT: Greenwood
121. Medina E, Klinge H. 1983. Productiv-
ity of tropical forests and tropical wood-
lands. In Physiological Plant Ecology IV,
Ecosystem Processes: Mineral Cycling,
Productivity and Man’s Influence, ed. OL
Lange, PS Nobel, CB Osmond, H Ziegler,
9:281–303. New York: Springer-Verlag
122. Miller PC. 1982. Experimental and vege-
tational variation across a snow accumu-
lation area in montane tundra in central
Alaska. Holarctic Ecol. 5:85–98
123. Mitsch WJ, Gosselink JG. 1986. Wet-
lands.New York: VanNostrand Reinhold
124. Moore DRJ, Keddy PA. 1989. The re-
lationship between species richness and
standingcrop inwetlands: the importance
of scale. Vegetatio 79:99–106
125. Moore JC, deRuiter PC, Hunt HW. 1993.
Influence of productivity on the stability
of real and model ecosystems. Science
261:906–8
126. Morris JT. 1991. Effects of nitrogen load-
ing on wetland ecosystems with particu-
lar reference to atmospheric deposition.
Annu. Rev. Ecol. Syst. 22:257–79
127. Muc M, Freedman B, Svoboda J. 1994.
Aboveground standing crop in plant com-
munities of a polar desert oasis, Alexan-
dra Fiord, Ellesmere Island. In Ecol-
ogy of a Polar Oasis: Alexandra Fiord,
Ellesmere Island, Canada, ed. J Svoboda,
B Freedman, pp. 65–74. Toronto: Captus
Univ. Publ.
128. Muc M, Freedman B, Svoboda J. 1994.
Vascular plant communities of a polar

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
296 WAIDE ET AL
desert oasis, Alexandra Fiord, Ellesmere
Island. In Ecology of a Polar Oasis:
Alexandra Fiord, Ellesmere Island, Ca-
nada,ed.J Svoboda, BFreedman,pp. 53–
63. Toronto: Captus Univ. Publ.
129. Mun HT, Whitford WG. 1989. Effects
of nitrogen amendment on annual plants
in the Chihuahuan Desert. Plant Soil
120:225–31
130. Murphy PG. 1977. Rates of primary pro-
ductivity in tropical grassland, savanna
and forest. Geo-Eco-Trop 1:95–102
131. Murphy PG, Lugo AE. 1986. Ecology of
tropical dry forest. Annu. Rev. Ecol. Syst.
17:67–88
132. Naeem S, H˚akansson K, Lawton JH,
Crawley MJ, Thompson LJ. 1996. Bio-
diversity and plant productivity in a
model assemblage of plant species. Oikos
76:259–64
133. Naeem S, Lawlor SP, Thompson LJ,
Woodfin RM. 1994. Declining biodiver-
sity can alter the performance of ecosys-
tems. Nature 368:734–36
134. NaeemS,Thompson LJ, Lawler SP,Law-
ton JH, Woodfin RM. 1995. Empirical
evidence that declining biodiversity may
alter the performance of terrestrial eco-
systems. Philos. Trans. R. Soc. London B
347:249–62
135. Neill C. 1990. Effects of nutrients and
water levels on emergent macrophyte
biomass in a prairie marsh. Can. J. Bot.
68:1007–14
136. Neill C. 1990. Effects of nutrients and
water levels on species composition in
prairie whitetop (Scolochloa festucacea)
marshes. Can. J. Bot. 68:1015–20
137. Nilsson SG, Ericson L. 1992. Conserva-
tion of plant and animal populations in
theory and practice. In Ecological Princi-
ples of Nature Conservation, ed. L Hans-
son, pp. 71–112. London: Elsevier Appl.
Sci.
138. Nyman JA, Chabreck RH, Kinler NW.
1993. Some effects of herbivory and 30
years of weir management on emergent
vegetation in brackish marsh. Wetlands
13:165–75
139. OdumWE. 1988.Comparativeecology of
tidal freshwater and salt marshes. Annu.
Rev. Ecol. Syst. 19:147–76
140. Oechel WC, Van Cleve K. 1986. The role
of bryophytes in nutrient cycling in the
taiga.InForestEcosystems in the Alaskan
Taiga: A Synthesis of Structureand Func-
tion, ed. K Van Cleve, FS Chapin, PW
Flanigan, LA Viereck, CT Dyrness, pp.
121–137. New York: Springer-Verlag
141. Oksanen L, Arruda J, Fretwell SD,
Niemela P. 1981. Exploitation ecosys-
tems in gradients of primary productivity.
Am. Nat. 131:424–44
142. Oksanen T, Oksanen L, Gyllenberg M.
1992. Exploitation ecosystems in het-
erogeneous habitat complexes II: impact
of small scale heterogeneity on predator
prey dynamics. Evol. Ecol. 6:383–98
143. Olson JS. 1975. Productivity of for-
est ecosystems. Proc. Symp. Productivity
World Ecosystems, pp. 33–43. Washing-
ton, DC: Natl. Acad. Sci.
144. Orshan G, Diskin S. 1968. Seasonal
changes in productivity under desert con-
ditions. In Functioning of Terrestrial
Ecosystems at the Primary Production
Level. Proc. Copenhagen Symp., ed. FE
Echardt, pp. 191–201. Paris: UNESCO
145. Parmenter RR, Brantley SL, Brown JH,
Crawford CS, Lightfoot DC, Yates TL.
1995. Diversity of animal communities
on southwestern rangelands: species pat-
terns, habitat relationships, and land man-
agement. In Biodiversity on Rangelands.
Natural Resources and Environmental Is-
sues. Vol. IV, ed. NE West, pp. 50–71.
Logan: Utah State Univ. Press
146. Parmenter RR, Van Devender T. 1995.
The diversity, spatial variability and func-
tional roles of vertebrates in the desert
grassland. In The Desert Grassland, ed.
M McClaran T Van Devender, pp. 196–
229. Tucson: Univ. Ariz. Press
147. Pastor J, Downing A, Erickson HE.

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 297
1996. Species-area curves and diversity-
productivity relationships in beaver
meadows of Voyageurs National Park,
Minnesota, USA. Oikos 77:399–406
148. PatalasK. 1990. Diversity of zooplankton
communities in Canadian lakes as a func-
tion of climate. Verh. Int. Verein. Limnol.
24:360–68
149. Patten DT. 1978. Productivity and pro-
duction efficiency of an upper Sonoran
Desert ephemeral community. Am. J. Bot.
65:891–95
150. Pearson LC. 1965. Primary productivity
in grazed and ungrazed desert communi-
ties of eastern Idaho. Ecology 46:278–85
151. Pennings SC, Callaway RM. 1992. Salt
marsh plant zonation: the relative impor-
tance of competition and physical factors.
Ecology 73:681–90
152. Phillips OL, Gentry AH, Hall P, Sawyer
SA, Vasquez R. 1994. Dynamics and
species richness of tropical rain forests.
Proc. Natl. Acad. Sci. USA 91:2805–9
153. Pianka ER. 1966. Latitudinal gradients in
species diversity: a review of concepts.
Am. Nat. 100:33–46
154. Pickett STA, Kolasa J, Jones CG. 1994.
Ecological Understanding: The Nature
of Theory and the Theory of Nature. San
Diego, CA: Academic
155. Preston FW. 1962. The canonical distri-
bution of commonness and rarity: part I.
Ecology 43:185–215
156. Preston FW. 1962. The canonical distri-
bution of commonness and rarity: part II.
Ecology 43:410–32
157. Proctor J. 1984. Tropical forest litterfall
II: the data set. In Tropical Rain-Forest,
ed.AC Chadwick, SLSutton,pp.83–113.
Leeds: Leeds Philos. Lit. Soc.
158. RuessRW,HendrickRL, Bryant JP.1998.
Regulationof fineroot dynamics bymam-
malian browsers in early successional
taiga forests of interior Alaska. Ecology
79:2706–20
159. Ruess RW, Van Cleve K, Yarie J, Viereck
LA. 1996. Comparative estimates of fine
root production in successional taiga
forests on the Alaskan interior. Can. J.
For. Res. 26:1326–36
160. RiebesellJF.1974.Paradox ofenrichment
in competitive systems. Ecology 55:183–
87
161. Robinson CH, Wookey PA, Lee JA,
Callaghan TV, Press MC. 1998. Plant
community responses to simulated envi-
ronmental change at a high arctic polar
semi-desert. Ecology 79:856–66
162. Rosenzweig ML. 1971. Paradox of en-
richment: destabilization of exploitation
ecosystems in ecological time. Science
171:385–87
163. Rosenzweig ML. 1972. Stability of
enriched aquatic ecosystems. Science
171:564–65
164. Rosenzweig ML. 1977. Aspects of eco-
logicalexploitation. Q. Rev.Biol.52:371–
80
165. Rosenzweig ML. 1977. Coexistence and
diversity in heteromyid rodents. In Evo-
lutionary Ecology, ed. B Stonehouse, C
Perrins, pp. 89–99. London: Macmillan
166. Rosenzweig ML. 1992. Species diversity
gradients: We know more or less than we
thought. J. Mammal. 73:715–30
167. Rosenzweig ML. 1995. Species Diversity
inSpace andTime.Cambridge, UK: Cam-
bridge Univ. Press
168. Rosenzweig ML, Abramsky Z. 1993.
How are diversity and productivity re-
lated? In Species Diversity in Ecolog-
ical Communities: Historical and Geo-
graphical Perspectives, ed. RE Ricklef,
D Schluter, pp. 52–65. Chicago: Univ.
Chicago Press
169. Rowlands P, Johnson H, Ritter E, and
EndoA. 1982. The MojaveDesert. In Ref-
erence Handbook on the Deserts of North
America, ed. GL Bender, pp. 103–162.
Westport, CT: Greenwood
170. RundelPW,Gibson AC.1996. Ecological
Communities and Processes in a Mojave
Desert Ecosystem, Rock Valley, Nevada.
New York: Cambridge Univ. Press

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
298 WAIDE ET AL
171. SchulzeE-D, Mooney HA. 1994. Ecosys-
tem function of biodiversity: a summary.
In Biodiversity and Ecosystem Function,
ed. E-D Schulze, HA Mooney, pp. 497–
510. Berlin: Springer-Verlag
172. Shafi MI, Yarranton GA. 1973. Diversity,
floristic richness, and species evenness
during a secondary (post-fire) succession.
Ecology 54:897–902
173. Sharma BM. 1982. Plant biomass in the
semi-arid zone of India. J. Arid Environ.
5:29–33
174. Shaver GR, Cades DH, Giblin AE, John-
son LC, Laundre JA, et al. 1998. Biomass
accumulation and CO2flux in three
Alaskan wet sedge tundras: responses
to nutrients, temperature, and light. Ecol.
Monogr. 68:75–97
175. Shaver GR, Chapin FS III. 1991. Produc-
tion: biomass relationships and element
cycling in contrasting arctic vegetation
types. Ecol. Monogr. 61:1–31
176. Shaver GR, Giblin AE, Laundre JA,
Nadelhoffer KJ. 1996. Changes in live
plant biomass, primary production, and
species composition along a riverside
toposequence in arctic Alaska, U.S.A.
Arct. Alp. Res. 28:361–77
177. Shaver GR, Jonassen S. 1999. Produc-
tivity of arctic ecosystems. In Terrestrial
Global Productivity, ed. H Mooney, J
Roy, B Saugier, pp. 000–00. New York:
Academic. In press
178. Shipley B, Gaudet C, Keddy PA, Moore
DRJ. 1991. A model of species density in
shoreline vegetation. Ecology 72:1658–
67
179. Shmida A, Ellner SP. 1984. Coexistence
of plant species with similar niches. Veg-
etatio 58:29–55
180. Shmida A, Wilson MV. 1985. Biological
determinants of species diversity. J. Bio-
geog. 12:1–20
181. Silander JA, Antonovics J. 1982. Anal-
ysis of interspecific interactions in a
coastal plant community—a perturbation
approach. Nature 298:557–60
182. Smith SD, Monson RK, Anderson JE.
1997. Physiological Ecology of North
American Desert Plants. New York:
Springer-Verlag
183. SokalRR, Rohlf FJ. 1995. Biometry.New
York: Freeman. 3rd ed.
184. Swift MJ, Anderson JM. 1993. Biodi-
versity and ecosystem function in agri-
cultural systems. In Biodiversity and
EcosystemFunction, ed. ED Schulze, HA
Mooney, pp. 15–41, Berlin: Springer-
Verlag
185. Symstad A, Tilman D, Wilson J, Knops
JMH. 1998. Species loss and ecosys-
tem functioning: effects of species iden-
tity and community composition. Oikos
84:389–87
186. Tilman D. 1982. Resource competition
and community structure. Princeton, NJ:
Princeton Univ. Press
187. Tilman D. 1987. Secondary succession
and the pattern of plant dominance along
experimental nitrogen gradients. Ecol.
Monogr. 57:189–214
188. Tilman D, Downing JA. 1994. Biodiver-
sity and stability on grasslands. Nature
367:363–65
189. Tilman D, Lehman CL, Bristow CE.
1998. Diversity-stability relationships:
statistical inevitability or ecological con-
sequence? Am. Nat. 151:277–82
190. Tilman D, Lehman CL, Thomson KT.
1997. Plant diversity and ecosystem
productivity: theoretical considerations.
Proc. Natl. Acad. Sci. USA 94:1857–61
191. Tilman D, Pacala S. 1993. The mainte-
nance of species richness in plant com-
munities. In Species Diversity in Ecolog-
ical Communities: Historical and Geo-
graphical Perspectives, eds. RE Ricklefs,
D Schluter, pp. 13–25. Chicago: Univ.
Chicago Press
192. Tilman D, Wedin D, Knops J. 1996. Pro-
ductivity and sustainability influenced by
biodiversity in grassland ecosystems. Na-
ture 379:718–20
193. Valentine JW. 1976. Genetic strategies of

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
PRODUCTIVITY AND SPECIES RICHNESS 299
adaptation. In Molecular Evolution, ed.
FJ Ayala, pp. 78–94. Sunderland, MA:
Sinauer
194. Van Cleve K, Barney R, Viereck LA.
1981. Evidence of temperature control of
production and nutrient cycling in two
interior Alaska black spruce ecosystems.
Can. J. For. Res. 11:258–73
195. Van Cleve K, Chapin FS III, Dyrness CT,
Viereck LA. 1991. Element cycling in
taigaforests: state factorcontrol–a frame-
work for experimental studies of ecosys-
tem processes. Bioscience 41:78–88
196. Van Cleve K, Dyrness CT, Viereck LA,
Fox J, Chapin FS III, et al. 1983. Taiga
ecosystems in interior Alaska. Bioscience
33:39–44
197. Van Cleve K, Oliver LK, Schlentner R,
Viereck LA, Dyrness CT. 1983. Produc-
tivity and nutrient cycling in taiga forest
ecosystems. Can. J. For. Res. 13:747–66
198. Van Cleve K, Viereck LA. 1983. A com-
parison of successional sequences fol-
lowing fire on permafrost-dominated and
permafrost-free sites in interior Alaska.
Permafrost: 4th Int. Conf. Proc.:1286–90
199. Van Cleve K, Viereck LA, Schlentner
R. 1971. Accumulation of nitrogen in
alder (Alnus) ecosystems near Fairbanks,
Alaska. Arct. Alp. Res. 3:101–114
200. Van Cleve K, Yarie J. 1986. Interaction of
temperature, moisture, and soil chemistry
incontrolling nutrientcycling andecosys-
temdevelopment inthe taiga of Alaska. In
Forest Ecosystems in the Alaskan Taiga:
a Synthesis of Structure and Function, ed.
K Van Cleve, FS Chapin, PW Flanigan,
LA Viereck, CT Dyrness, pp. 160–189.
New York: Springer-Verlag
201. Van Cleve K, Yarie J, Adams PC, Erick-
sonR, DyrnessCT.1993. Nitrogen miner-
alization and nitrification in successional
ecosystems on the Tanana River flood-
plain, interior Alaska. Can. J. For. Res.
23:970–78
202. van de Koppel J, Huisman J, van der Wal
R, Olff H. 1996. Patterns of herbivory
along a productivity gradient: an empiri-
cal and theoretical investigation. Ecology
77:736–45
203. Vandermeer JH. 1988. The Ecology of In-
tercropping.New York: CambridgeUniv.
Press
204. Vermeer JG, Berendse F. 1983. The re-
lationship between nutrient availability,
shoot biomass and species richness in
grassland and wetland communities. Veg-
etatio 53:121–26
205. Viereck LA, Dyrness CT, Van Cleve K,
Foote B. 1983. Vegetation, soils, and for-
est productivity in selected forest types in
interior Alaska. Can. J. For. Res. 13:703–
20
206. Viereck LA, Van Cleve K, Adams PC,
Schlentner RE. 1993. Climate of the
Tanana River floodplain near Fairbanks,
Alaska. Can. J. For. Res. 23:899–913
207. Viereck LA, Van Cleve K, Dyrness CT.
1986.Forestecosystem distribution in the
taiga environment. In Forest Ecosystems
inthe AlaskanTaiga: aSynthesisof Struc-
ture and Function, ed. K Van Cleve, FS
Chapin, PW Flanigan, LA Viereck, CT
Dyrness, pp. 22–43. New York: Springer-
Verlag
208. Vollenweider RA. 1974. A Manual on
Methods for Measuring Primary Produc-
tion in Aquatic Environments.IBP Hand-
bookNo. 12. Oxford, UK: Blackwell. 2nd
ed.
209. Walker BH. 1992. Biodiversity and eco-
logical redundancy. Conserv. Biol. 6:18–
23
210. Walker MD. 1995. Patterns and causes
of arctic plant community diversity. In
Arctic and Alpine Biodiversity, ed. FS
Chapin III, C Korner, pp. 1–20. New
York: Springer-Verlag
211. Wardle DA, Bonner KI, Nicholson KS.
1997. Biodiversity and plant litter: ex-
perimental evidence which does not sup-
port the view that enhanced species rich-
ness improves ecosystem function. Oikos
79:247–58

P1: FNE/FGO P2: FLI/FDR
September 15, 1999 17:9 Annual Reviews AR093-10
?
300 WAIDE ET AL
212. Webb WL, Lauenroth WK, Szarek SR,
Kinerson RS. 1983. Primary production
and abiotic controls in forests, grass-
lands,and desert ecosystemsin the United
States. Ecology 64:134–151
213. Webber PJ. 1978. Spatial and temporal
variation of the vegetation and its produc-
tion, Barrow, Alaska. In Vegetation and
Production Ecology of an Alaskan Arctic
Tundra, ed. LL Tieszen, pp. 37–112. New
York: Springer-Verlag
214. Wheeler BD, Giller KE. 1982. Species
richness of herbaceous fen vegetation in
Broadland, Norfolk in relation to the
quantityof abovegroundmaterial.J. Ecol.
70:179–200
215. Wheeler BD, Shaw SC. 1991. Above-
ground crop mass and species richness of
the principal types of herbaceous rich fen
vegetationof lowlandEngland andWales.
J. Ecol. 79:285–301
216. Whitford WG. 1986. Pattern and Pro-
cess in Desert Ecosystems. Albuquerque:
Univ. New Mex. Press
217. Whittaker RH, Niering WA. 1975. Veg-
etation of the Santa Catalina Mountains,
Arizona. V. Biomass, production and di-
versity along the elevational gradient.
Ecology 56:771–90
218. Wiegert RG, Chalmers AG, Randerson
PF. 1983. Productivity gradients in salt
marshes: theresponse of Spartina alterni-
flora to experimentally manipulated soil
water movement. Oikos 41:1–6
219. Willig MR, Walker LR. 1999. Distur-
bance in terrestrial ecosystems: salient
themes, synthesis, and future directions.
In Ecology of Disturbed Ground, ed. LR
Walker, Amsterdam: Elsevier. In press
220. Wilson SD, Keddy PA. 1988. Species
richness, survivorship, and biomass ac-
cumulation along an environmental gra-
dient. Oikos 53:375–80
221. Wisheu IC, Keddy PA. 1989. Species
richness-standing crop relationships
along four lakeshore gradients: con-
straints on the general model. Can. J.
Bot. 67:1609–17
222. Wollkind DJ. 1976. Exploitation in three
trophic levels: an extension allowing in-
traspecies carnivore interaction. Am. Nat.
110:431–47
223. Wright DH, Currie DJ, Maurer BA. 1993.
Energy supply and patterns of species
richness on local and regional scales.
In Species Diversity in Ecological Com-
munities: Historical and Geographical
Perspectives, ed. R Ricklefs, D Schluter,
pp. 66–74. Chicago: Univ. Chicago
Press
224. Yarie J, Van Cleve K, Schlentner R. 1990.
Interaction between moisture, nutrients
and growth of white spruce in interior
Alaska. For. Ecol. Man. 30:73–89
225. Zedler JB, Williams P, Winfield T. 1980.
Salt marsh productivity with natural
and altered tidal circulation. Oecologia
44:236–40