Biodiversity and Plant Productivity in a Model Assemblage of Plant ...

Biodiversity and Plant Productivity in a Model Assemblage of Plant Species
Shahid Naeem; Katarina Håkansson; John H. Lawton; M. J. Crawley; Lindsey J. Thompson
Oikos, Vol. 76, No. 2. (Jun., 1996), pp. 259-264.
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OIKOS 76: 259-264. Copenhagen 1996
Biodiversity and plant productivity in a model assemblage of
plant species
Shahid Naeem, Katarina Hlkansson, John H. Lawton, M. J. Crawley and Lindsey J. Thompson
Naeem, S., Hikansson, K., Lawton. J. H., Crawley, M. J. and Thompson, L. J. 1996.
Biodiversity and plant productivity in a model assemblage of plant species - Oikos
76: 259-264.
We examined productivity as a function of biotic diversity. We manipulated plant
species richness as an experimental factor to determine if productivity (net above
ground primary productivity or NPP) is affected by changes in plant diversity (species
richness). We constructed 164 assemblages that varied in species richness and
measured their biomass at the end of one growing season. The plants were drawn
from a pool of 16 species of self-pollinating annual herbs common to English weedy
fields. On average, species-poor assemblages were less productive. Results also
showed, however, that species-poor assemblages had wider ranges of possible productivities
than more diverse assemblages.
S. Naeem, K. Hikansson, J. H. Lawton and L. J. Thompson, Centre for Population
Biology, Imperial College at Silbvood Park, Ascot, Berks., UK SL5 7PY @resent
address of SN: Dept of Ecology, Evolution, and Behacior, Univ. of Minnesota, 100
Ecology Building, 1987 Upper Buford Circle, St. Paul, MN 55108, USA
(naeemOOl@maroon.tc.umn.edu)). - M. J. Crawley, Dept of Biolog., Imperial College
at Sihvood Park, Ascot, Berks.. UK SL5 7PY.
Productivity is a complex measure of the net effects of specles richness or community structure, have been
both abiotic and biotic processes within an ecosystem treated as dependent variables of such abiotic factors as
and serves as a convenient, single index for comparing energy, temperature, rainfall, and nutrient or water
the behaviors of different ecosystems. Intriguing pat- input, (e.g., Mellinger and McNaughton 1975. Whitterns
of association between productivity and biotic taker and Niering 1975, Kirchner 1977, Silvertown
and abiotic ecosystem characteristics have long been 1980, Tllman 1982, 1987, 1993, Goldberg and Miller
known (e.g., Whittaker and Likens 1971, Whittaker 1990, Rosenzweig and Abramsky 1994, Tilman and
and Niering 1975, Brown and Davidson 1977, Huston Downing 1994, Tilman and Pacala 1994, Wright et al.
1979, Tilman 1982, Brown and Gibson 1983, Boston et 1994). Though the limits of ecosystem productivity are
al. 1989, Owen 1990, Rosenzweig and Abramsky 1994, clearly determined by abiotic factors, such as climate
Cebrian and Duarte 1995), but the causes for these and geochemistry, the biotic characteristics of an
patterns remain unclear (reviewed in Rosenzweig and ecosystem can also strongly influence local productivity
Abramsky 1994 and Wright et al. 1994). (McNaughton 1993, Vitousek and Hooper 1993).
Although productivity is clearly a function of both Our study, in contrast to the majority of others,
abiotic and biotic processes (reviewed in Schlesinger explores how ecosystem productivity varies as a depen-
1991: 114-125), most studies have treated the biotic dent function of biotic diversity. We focus on net
characteristics of an ecosystem as dependent functions above-ground primary productivity (NPP) as a measure
of abiotic factors. That is, biotic characteristics such as of productivity and we focus on plant species richness
Accepted 21 December 1995
Copyright O OIKOS 1996
ISSN 0030-1299
Printed in Ireland - all rights reserved

as a measure of diversity. Other studies that have
examined the effects of changing plant diversity on
NPP differ from ours by having either indirectly manipulated
plant diversity or having manipulated only a few
( < 5) species. For example, Ewel et al. (1991), Tilman
and Downing (1994), and Naeem et al. (1994, 1995)
manipulated plant species richness, but Ewel et al. did
not report plant productivity, Tilman and Downing
manipulated plant diversity indirectly by fertilizer addition,
and Naeem et al. (1994, 1995) manipulated species
richness among several trophic levels, thereby confounding
the contributing factors to the change in plant
productivity they observed (thus, this paper is distinct
from Naeem et al. 1995). Intercropping studies have
directly manipulated plant (crop) diversity and measured
plant productivity (yield) as a response variable
(e.g. Vandermeer 1989, Swift and Anderson 1993), but
these studies are limited by the small number of species
(2 or 3) they manipulate (Swift and Anderson 1993).
Intercropping experiments are also designed with the
intent of uncovering means for achieving overyielding
in plant assemblages, usually made up of high-yielding,
domesticated plants that are either not found or do not
co-occur in nature. These studies therefore represent a
biased set of experiments and their general conclusion
that overyielding, on average. results from intercropping
may not apply broadly to non-agricultural plant
assemblages. Our study is, nevertheless, an extended
and more elaborate version of a traditional intercropping
experiment, but unlike these experiments, our
plants co-occur in nature, maximum diversity in our
experiment is substantially higher than most intercropping
studies. Although our specific intent was to explore
how changing plant biotic diversity affects
productivity under a given set of abiotic conditions, our
broader motivation was to examine the ecological consequences
of changing plant diversity on ecosystem
processes.
Theoretical considerations
Three different hypothetical relationships between NPP
and plant species diversity can be derived from current
theory. These hypothetical relationships provide estimates
for polyculture yield for a unit area over a unit
period of time, or NPP, based on monoculture yields of
the species found in the polyculture. They differ in the
extent to which they incorporate the effects of intraand
interspecific interactions in estimating yields.
The first, the 'proportional hypothesis', estimates the
expected productivity of an assemblage by assuming
that productivity is solely a function of the proportional
contributions of individuals based on their individual
performances when grown in monoculture. The
expected yield (El) can be calculated by the formula,
260
where s is the number of species in the assemblage, n is
the number of individual plants of the ith species, and
P, is the average biomass of an individual of the ith
species in monoculture, summed for all individuals in
the pot. NPP is therefore a function of intra-specific
population growth rates and not affected by interspecific
interactions.
A second expected yield (E,) is based on the law of
constant yield (discussed in Crawley 1986) that predicts
that a polyculture is always less productive than a
monoculture of its most productive species. Thus, E2 =
the monoculture productivity of the most productive
species in a polyculture. This hypothesis therefore assumes
that biomass is neither a function of intra- or
interspecific interactions; maximum net productivity is
determined by light, space, nutrient conditions, and the
species-specific growth rates under these conditions.
Polyculture yields higher than a monoculture of the
most productive species due to facilatory interactions,
for example, are considered unlikely.
A third expected yield (E,) is based on the assumption
that productivity is a function of species-specific
responses to their biotic environment. That is, the
species-specific responses to species-richness gradients.
For simplicity, we use a least square estimate for this
response. The expected yield based on species-specific
responses is formulated by.
8, = S,(ln S) + I,.
, = I
where S, and I, are the regression slope and intercept,
respectively, of the ith species' productivity response to
increasing species richness. Note that we chose a loglinear
relationship between species richness and NPP
because log-linear associations are typical of many biological
phenomena (reviewed in May 1975, Sugihara
1980), although the mechanistic bases for these relationships
are still debated (Naeem and Hawkins 1994).
A variety of other relationships (e.g., linear, log-log, or
polynomial) are possible and may yield better estimates
(D. Currie pers. comm.). For simplicity, however, we
explore only the more common log-linear relationship
in this study.
Materials and methods
For a small assemblage of species the number of different
combinations possible is formidable. If we manipulate
both the evenness (proportional representation of
species) and richness (number of species) of an assemblage,
the total number of possible combinations (C) is,
OIKOS 762 (1996)

C = n!i[r!(n- r)!],
where r = the number of species drawn at random from
a pool of n. Thus, for a 16-species assemblage, with
each assemblage limited to 16 individual plants, and
with numbers of species selected along a log, richness
gradient of 1, 2, 4, 8 and 16 species, an unreplicated full
experimental design would require producing 13 827
assemblages containing 221 232 plants.
Our resources did not permit an experiment of this
magnitude, so we simplified our experimental design.
We selected an assemblage of plant 16 species, all
self-pollinating herbs known to co-occur in British
weedy fields (Lawton et al. 1993). We constructed a
representative subset of the total combinations possible
from this species pool, using 164 combinations that
sampled across a log, scaled gradient of diversity from
monocultures to full richness polycultures. Our subsets
included one set which contained 4 replicates each of
the 16 possible monocultures; a second set which contained
20, 30, and 40 replicates each of intermediate
richness polycultures of 2, 4, and a third set which
contained 8 species. respectively; and 10 replicates of
full richness polycultures. Intermediate richness polycultures
were constructed by using a random number
generator to select species from the species pool, but
duplicate combinations were not used, thus all 90 polycultures
were unique. This procedure maximized our
coverage of the large number of combinations possible
per level of species richness. We further simplified the
design by keeping evenness the same in all polycultures.
That is, all species in polycultures had equal numbers
of individual plants with the total number of all individuals
held constant at 16 per pot. Note that this
design replicates only for species richness as an experimental
factor, but does not replicate for species composition.
except for monocultures and full species richness
assemblages. Thus, our inference is limited to statements
about species richness and its association with
NPP. A larger experimental design, in which combinations
of species are replicated, would be necessary to
expand our inference space to include species combination
as a factor associated with NPP.
Each of these assemblages was grown in a 7.5-1 pot
(20 cm diam.) containing a loamlsand mixture developed
for this plant system. Each individual of each
species was randomly assigned to a position in a 4 x 4
grid on the surface of the pot. This procedure ensured
that edge effects caused by the small size of our pots,
were distributed in an identical and unbiased fashion,
across all treatments, and experienced uniformly by all
species.
Seeds were planted in their assigned positions and
seedlings - were weeded to the final densities of one
individual per grid square or 16 individuals per pot,
Pots were randomly assigned positions within the
greenhouse. The experiment was conducted between 10
May 1993 and 16 July 1993 at which time all species
had flowered except Aphanes arvensis and Conyza
canadensis. Individual productivity was measured as the
above-ground dry weight of an individual plant in a pot
at the end of the experiment. Productivity of an assemblage
was measured as the sum of the individual productivities
of all plants in a pot. Note that our measures
of "productivity" are actually NPP, but the unit area
and unit time are the same for all pots so, for simplicity,
we do not include them in reported measures of
productivity.
Results
Results from the monocultures showed that the species
pool for our model assemblage uniformly covered a
wide range of species-specific productivities (Fig. 1).
Our most productive species (Sinapis arcensis) was over
25 times more productive than our least productive
species (Aphrznes uruensis). This distribution of speciesspecific
NPPs of monocultures, under the conditions of
our greenhouse, defines the limits (assuming no interspecific
facilitation among plant species) of the expected
total productivities of our polycultures.
Average polyculture NPP increased as plant diversity
increased (Fig. 2A). Comparing yields expected from
the three hypotheses discussed above. the results best fit
-.-
VA AA SO AT LP CC CH CBP W PA CA SPA TI SM SV SIA
species
Fig. 1. Species-specific plant productivities. Biomass = mean
dry weight of an individual plant in monoculture. Species
abbreviations are listed in Table 1. Each bar revresents the
mean of all plants from all 4 replicate monocultures (n = 64).
Error bars are one SE of 64 plants. (Note. this figure appears
in Naeem et al, 1995 as extrinsically determined calibration
data for Ecotron project.)

2C) which suggests that increasingly diverse polycultures
have increasingly lower productivities than can be
achieved by monocultures of their most productive
species. The respective positive and negative slopes for
comparisons based on E, and E2 suggest that the data
do not fit these hypotheses well, but the difference
between observed and E,, on average, remains constant
(Fig. 2D). Thus species-specific responses to diversity
gradients. estimated by a log-linear relationship, may,
of the three hypotheses examined, best explain our
observed average increase in NPP with increasing plant
species richness.
Slopes and intercepts used for E, estimates are shown
in Table 1. Fig. 3 shows four examples of the wide
variety of typical species-specific responses to speciesrichness
gradients we observed.
In summary, the overyielding, or greater average
yields of species-rich plant assemblages over average
yields of species poor assemblages, may be related to
the fact that the average species-specific productivity
response to increasing diversity was positive (x = 1.75,
see Table 1) in our plant assemblages. This implies that,
on average, intra-specific competition was stronger than
inter-specific competition in this assemblage. Thus, a
species-rich assemblage of similar density to a speciespoor
assemblage was more productive because it experienced
proportionally less intra-specific competition.
LOGJPLANT SPECIES)
Fig. 2. Mean, total observed and expected above-ground productivities
(plant biomass) for assemblages of different diversities.
A) Observed biomass for all assemblages. B) differences
between observed and expected biomass based on proportional
contributions to final productivity (E,). C) Differences between
observed and expected biomass based on constant yield
hypothesis (E,). D) Differences between observed and expected
biomass based on interspecific responses to diversity
gradients (E,). Diversity is plotted as the log, of number of
species (1, 2, 4, 8, and 16 species = 0, 1, 2, 3, 4 respectively).
The line represents the linear regression of mean biomass (dry
weight) on log, (number of plant species). Note that the points
are randomly scattered near the fixed diversity levels for the
purposes of clarity.
the third hypothesis, E, (Fig. 2B-D). The difference
between observed and E, increases positively with species
richness (Fig. 2B) which suggests that, on average,
increasingly diverse polycultures led to higher productivities
than would be expected based solely on intraspecific
responses. The difference between observed
and E, increases negatively with species richness (Fig.
Discussion
Plant diversity and plant productivity
Our general results suggest that whether a reduction (or
augmentation) in species will result in a net increase,
decrease, or no change in plant productivity (NPP) of
an ecosystem depends on three characteristics of the
system; (1) the distribution of species-specific productivities,
(2) the distribution of species-specific responses to
species-richness gradients, and (3) which species are
being lost (or added).
We apply several cautions to the interpretation of
our results. First, for an experiment such as this, replicates
of the highest species richness treatment are invariant
in composition and therefore have a lower
expected variance for NPP than those of other treatments.
Further, the highest range and variance in NPP
is expected for replicates of the monoculture treatment
since this treatment would always include the most
productive and the least productive species. Thus, variance
is likely to be heterogeneous among treatments
which makes parametric hypothesis testing inappropriate
for the overall relationship between NPP and species
richness. Second, we did not manipulate evenness
(see combinatorial considerations in Materials and
methods), and it is possible that changes solely in the
evenness of a plant assemblage could generate equally
OIKOS 76.2 (1996)

Table 1. Summary of plant species and their individual responses to changes in species richness in plant assemblages.
Abbr. = abbreviation used in Fig. 1. Family = family that contains species. NS = regression not significant. P = significance
probability, with critical value set at P < 0.05. SE = standard error of slope. Slope = slope of linear regression with dependent
variable set as dry weight of plant at harvest and independent variable set as log, of the number of species in assemblage.
Species
Aphanes arvensis
Arabidopsis thaliana
Capsella bursa-pastoris
Cardamine hirsuta
Chenopodium album
Conyza canadensis
Lamium purpureum
Poa annua
Senecio vulgaris
Sinapis avoensis
Sonchus oleraceus
Spergula arvensis
Stellaria media
Tripleurospermum inodorurn
Veronica arvensis
Veronica persica
Family
Rosaceae
Cruciferae
Cruciferae
Cruciferae
Chenopodiacae
Compositae
Labiatae
Graminae
Compositae
Cruciferae
Compositae
Caryophyllaceae
Caryophyllaceae
Compositae
Scrophulariaceae
Scrophulariaceae
Abbr. Slope SE P
A A -0.009 0.002

tion is currently the focus of much discussion concerning
the effects of declining biotic diversity on ecosystem
function (Ehrlich and Wilson 1991, Ehrlich and Ehrlich
1992, papers in Schulze and Mooney 1993). Our results
suggest that declining diversity within an ecosystem
may result in local changes in NPP and, by inference,
alterations of other ecosystem properties (rates of energy
and nutrient fluxes). Global productivity is therefore
likely to change as a function of the cumulative
changes in local productivity. Our study demonstrates,
however, that predicting the magnitude and direction of
this change requires considerable information on the
species-specific productivities and species-specific responses
to local biotic environments. The fate of global
productivity in the face of declining biotic diversity,
therefore, cannot be readily determined
Ackno~ljledgements- We thank G. Cooper, R. Jones, J. Radley,
G. Asefa, and S. Williamson for their assistance in all
aspects of the project. D. Tilman, S. Tjossem and P. Heads
critically read the manuscript. D. Currie provided invaluable
insights into the statistical and inferential limits of experimental
manipulations of biotic diversity.
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