Evaluating the relative strengths of biotic versus abiotic controls on ecosystem processes

20 Pages·2013·0.88 MB·English

CHAPTER 5
Evaluating the relative strengths of
biotic versus abiotic controls on
ecosystem processes
M. A. Huston and A. C. McBride
5.1 Introduction increases with increasing species diversity (Naeem
et al. 1994a,b, 1995; Tilman 1996; Tilman et al. 1996,
There is no doubt that the rates of ecosystem pro
2001; Hector et al. 1999; see however, Hooper and
cesses and the resulting properties and structure of
Vitousek 1997; Wardle et al. 1997b; Hooper 1998;
ecosystems are influenced both by biotic factors,
Schwartz et al. 2000; Fridley in press) conflicts with
including species properties and interactions among
the fact that most of the high productivity eco
multiple species, and by abiotic factors, such as
systems found around the world have strikingly
climate, geology, and soils. The critical issue (and the
low plant diversity. This phenomenon was well
focus of the debate about diversity and ecosystem
known to early ecologists (Lawes et al. 1882) and
function) is the relative contribution of each of these
was succinctly summarized by Rosenzweig as the
general factors to any particular ecosystem pro
'paradox of enrichment' (1971), referring to the
cess, under any particular conditions, and at any
diversity-reducing effects of added nutrients (i.e.
particular scale.
eutrophication).
While much remains to be learnt from future
In many regions, plant diversity decreases with
experiments, we can nonetheless evaluate the rela
increasing productivity over most of the range of
tive effects of biotic and abiotic factors in the results
productivity (Huston 1980, 1994). Naturally pro
of current experiments, where they presumably
ductiVe ecosystems with low species diversity
result from processes operating in much the same
include phytoplankton and algal blooms, salt
way they operate under natural conditions. The
marshes, freshwater marshes, riparian forests in the
detection of both types of factors in diversity experi
tropics and temperate zones (e.g. Populus deltoides,
ments provides some insights into why these
Prioria copaifera), bamboo forests, redwood forests,
experiments have been so controversial. We will
Douglas fir forests, eucalypt forests etc. High
first review the patterns in natural systems that seem
diversity plant communities are generally found on
to contradict many of the experimental results, and
relatively unproductive sites, such as chalk grass
then examine some of the experiments in more
lands, Mediterranean shrublands, and rainforests
detail, in order to distinguish between biotic and
on oxisols and ultisols (Dawkins 1959, 1964; Grime
abiotic effects.
1979, 2001; Mahdi et al. 1989; Berendse 1994a;
Huston 1979, 1980, 1993, 1994). If productivity in
random-selection diversity experiments increases
5.2 Natural patterns of diversity
as a consequence of increasing diversity in experi
and productivity
ments, then either (1) the processes involved in the
The conclusion of virtually all of the 'diversity experiments differ from the processes involved in
productivity' experiments that productivity natural patterns of diversity; or (2) the experiments
47
48 BI 0 D I V ER S IT Y AND EC OS YS TEM FUN CT ION I N G
address a different portion of the complete range of a physical condition, such as temperature or pH)
productivity, where patterns may differ from those for other species, which then grow faster or larger
in other portions of the range. than they would in the absence of the facilitator
Notwithstanding the inconsistency of the (Vandermeer 1989; Hooper 1998). The most wide
experimental results with high productivity-low spread and important example of facilitation is
diversity and low productivity-high diversity nitrogen fixation by legumes, which has the effect
natural ecosystems, the experimental results are of increasing nitrogen availability for other plant
compatible with the observation that diversity species that may eventually overgrow and elimin
increases with increasing productivity under low ate the legumes (DeWit et al. 1966).
productivity conditions (Grime 1973a,b, 1979; In summary, variation in species richness can
Huston 1979, 1980, 1994; Huston and DeAngelis play only a minor role in natural patterns of pro
1994). When put together, the naturally occurring ductivity, which are regulated primarily by environ
increase in diversity with increasing productivity mental conditions such as climate and soils. Only
at low levels of productivity and the naturally when the dominant effects of environmental con
occurring decrease in diversity with increasing ditions on productivity are controlled experiment
productivity at high levels of productivity produce ally can the subtle effects of species composition
the unimodal 'hump-backed' diversity curve ori on productivity be detected. Consequently, the
ginally described by Grime (1973a,b, 1979; AI-Mufti failure to adequately control environmental condi
et al. 1977). tions is potentially a problem with any experiment
However, mere consistency of the experimental that attempts to evaluate the effect of species
pattern and the natural pattern under low pro richness, or any type of plant interactions, on
ductivity conditions does not mean that they have productivity.
the same cause. The natural pattern of increasing
plant diversity as productivity increases from very
low levels can be understood most simply as
5.3 The influence of soil heterogeneity
resource levels rising above the minimum required
on diversity-productivity experiments
for survival of various species with different min
imum resource requirements (Grime 1973a,b, 1979; Small differences in soil conditions can have a large
Huston 1979; Huston and DeAngelis 1994). This is effect on plant growth and species diversity
a phenomenon observed along gradients of increas (Fig. 5.1). Natural variation in soil nutrients and
ing resource supply in low resource environments water is associated with subtle differences in topo
(e.g. water in deserts), or potentially in experiments graphy, with even slight depressions generally
that use variation in resource availability as a having higher nutrient and water availability than
treatment (e.g. Lawes et al. 1882; Huston 1979, 1980, the surrounding area. Dealing with this natural
1994; Tilman 1987, 1988; Fridley 2001). variability is a major issue in the design of agri
The increase in average productivity with cultural and ecological field experiments (Trenbath
increasing species richness that has been observed 1974; Vandermeer 1989, see Chapter 19), and has led
in many of the diversity-productivity experiments to such designs as the 'Latin Square', replication
occurs under presumably· constant and uniform by blocks for ANOVA etc.
resource conditions (i.e. soil nutrients and water), Separation of 'block effects', some of which could
except to the degree that they are altered by the be due to differences in soil conditions, from the
plants themselves. Thus, any increase in product main treatment effects requires replication of the
ivity with increasing diversity is expected to result full experiment over a set of blocks that mayor may
from either (1) complementary resource use among not have significant differences in soil conditions.
multiple species (with no change in total resources, Such replication adds cost and complexity to any
just a more complete use of them) or (2) facilitative experimental design, and may be infeasible for
interactions among species, in which one or more experiments with many different treatment levels.
species improve conditions (resource availability or In random-selection diversity experiments, the
EVALUATING THE RELATIVE STRENGTHS OF BIOTIC VERSUS ABIOTIC CONTROLS 49
5
.--------------------------------------.600
u
400 ';;'
Figure 5.1 Natural variation in plant
~ 200 .~ species richness across the repeating
'g 12 J....===",..--=====~,,:::::=====::::=::-::=f-o g; topographic pattern of 'ridge and furrow'
g.. ~'" 10 ~ grassland in Great Britain. Note that
~~ .c 8 species richness is highest on the ridges,
P..~6 and lowest in the moister, more nutrient
o ,... 4 rich furrows, where two clonal species,
5) ~ 2 Agrostis stolonifera and Ranunculus
"8
0 repens, tend to exclude all the other
~ 10 20 30 species. (From unpublished data of G. R.
Position along transect (m) Sagar in Harper 1977).
relatively large number of plots within any par Sheffield was one of three sites that used an
ticular diversity level has generally precluded any experimental design suitable for distinguishing the
replication by spatial blocks. Consequently, the per effects of single species from multiple species using
formance of any treatment plot could potentially be analysis of 'over-yielding' (the other two being
influenced by three factors: (1) the number of species; Portugal and Sweden). However, Sheffield was the
(2) the particular species that are present; and/ or only site in the entire experiment in which the soil
(3) the soil conditions at the location of that plot. in the experimental field was artificially constructed
Separating the effects of these three factors is the by bringing in truckloads of soil, and mixing and
primary challenge in the analysis of the experimental spreading it uniformly over an existing field
results, as well as the primary cause for disagree (A. Hector, personal communication). The excep
ment about the interpretation of published results. tional homogeneity of the artificially constructed
Unfortunately, this is not a simple task, nor are soil at Sheffield seems to be the explanation for the
standard statistical approaches well suited for the extremely low variability among the plots at each
task (cf. Schmid et a/., Chapter 6). diversity level, particularly at the highest level
The effect of soil heterogeneity on experimental where all the plots have identical species composi
results can be seen in the results of the BIODEPTH tion (Huston et a/. 2000). This low variance within
experiments (Hector et a/. 1999, see Chapter 4), which each treatment level leads to the highest proportion
were conducted at eight sites in seven European of variance explained by the experimental treat
countries. The locations spanned a latitudinal gra ment at any of the eight sites (r2 = 0.49).
dient from Sweden to Greece, and included a range The extremely low variance among plots at the
of soil conditions and climates. In addition to the same level of species richness on the homogeneous
variation among the sites at which the experiments constructed soils of the Sheffield site suggests that
were conducted (not replicated, since all sites did the high variance observed at the other sites is at
not use the same design), there was inevitably soil least partially due to variance in soil conditions
heterogeneity within the field in which each across the experimental site. At Sheffield and the
experiment was conducted. other two sites designed for over-yielding analysis,
A striking property of the BIODEPTH results, the variability at the highest level of species richness
as well as the results of other experiments of this can be attributed primarily to soil variability because
type, is the high variability in the responses at all of the replicates are identical in species composi
any given level of species richness (Fig. 5.2). How tion. However, the other five sites include species
ever, this variability is not uniform across all sites, in the high diversity treatments that were not planted
which provides a critical insight into the influence of in the monocultures. Consequently, some of the vari
soil factors on the results of 'pure diversity' treat ance at these sites may be due to differences in
ments. A scattering of very high values, above the species composition even at the high species rich
level at which most plots are clustered, can be seen ness levels, as well as to the inevitable heterogeneity
at all of the sites but one, the site at Sheffield in UK. in soil conditions across the sites, and differences in
50 BI 0 DIVE RS ITY AN D ECOS YSTE M FU N CTION I N G
Germany Portugal Swetzerland 1500 Greece
1500 r2~0.15 1500 r2~ 0.00 1500 r'~0.26 r2~0.30
r2~0.1•5 r2~0.18 r2~0,02 r2~0.00
1000 I • 1000 1000 -- ~ . 1000
-- .:: ,.- .
500 500 ~. .--:--.- - 500 • ~ I .....! • 500
. . I :
. . !
""''
§oj O+-~~~-r~-T--~ O~-n~rr.-r-r--,
:.s 16 10 16 10 16 10 16 10
] Ireland Sweden UK (Sheffield) UK (Silwood)
~5r 1500 r2~0.20 1500 r2~0.01 1500 r2~0.11 1500
""
~ r2~0.00 r2~0.21 r2~0.49
1000 *--* 1000 1000 1000 -
iI _i ....i.. - -;i ~-i----;-_: .....
I r
500 500 ~--:--:500 ~ ·500
. I
: ~ • I r2~O.l3
o I O+--,~~~-r-'---, o r2~0.00
16 10 16 10 16 10 16 10
Species richness (log2 scale)
Figure 5.2 Results of the BIODEPTH experiment at eight sites across Europe. Only treatments with species richness less than or equal to
the number of species used in monoculture treatment are shown. Solid line is log-linear regression through all plots with two or more
species. Dotted line is the log-linear regression through plots expected to contain the species that was most productive in monoculture, for
evaluation of the effect of species richness on overyielding. Upper? value refers to the dotted line; lower value refers to solid line (from
Huston et al. 2000). Note that Sheffield, with artificially constructed soils, has the lowest within-treatment variance at h·lgh treatment levels.
species composition at the lower levels of species responses, rather than the hidden treatment factor
richness. that actually produced the response.
In summary, for diversity experiments in which
soil conditions are not controlled by site preparation,
5.4 Quasi-replication, representation,
variability in soil properties across an experimental
similarity, and inhomogeneity of
site can have a strong effect on the magnitude and
variance: a suite of hidden treatments
variability of the measured responses, such as
in random-selection experiments
above-ground biomass production. This introduces
the possibility that soil heterogeneity can act as a Given that there is inevitably some degree of soil
'hidden treatment' in these experiments, because heterogeneity in field experiments, the critical ques
soil variation can produce the same response, an tion becomes how inherent soil effects can be dis
increase in productivity, that is predicted to occur tinguished from the effects of the experimental
as a result of the diversity treatments. treatment, specifically from the effects of species
A hidden treatment is any factor that is correlated number or species composition. Obviously, experi
with the treatments in an experiment, but not ments can be established using designs capable of
expliCitly controlled, manipulated, measured, or distinguishing field effects from treatment effects.
considered in the experimental design and analysis Alternatively, the actual soil properties in each plot
of the experimental results (Huston 1997). If such can be measured and used as covariates in the
factors are not recognized or are ignored in the analysis. However, neither of these options have
analysiS of data, they can lead to misinterpretation been typically used in diverSity-ecosystem func
of causal relationships because the experimental tion experiments, primarily because the logistics
treatment is assumed (in the context of regression and cost of replicating species richness treatments
analyses or ANOVA) to cause the observed is already quite daunting.
EVALUATING THE RELATIVE STRENGTHS OF BIOTIC VERSUS ABIOTIC CONTROLS 51
The issue of replication in random-selection x species drawn from a total species pool of N is
diversity experiments is more complex than in calculated as N!/(x! (N - x)!). Thus, for an experi
standard experiments. Replication (or sample size) ment with a total species pool of 18, there are
is a central issue in the design and analysis of all 153 different two-species combinations, and also
experiments. In general, higher replication increases 153 different 16-species combinations, but only one
the accuracy of estimates of mean response, and 18-species combination (Fig. 5.3).
also increases the probability that any experimental True replication refers to the number of identical
response that is detected is statistically significant plots, i.e. plots with exactly the same set of planted
because it allows the response distribution to be species. For example, in an experiment with a total
determined more accurately. The pattern of variance species pool of 18 that includes a monoculture of
among responses at different treatment levels is a each species, representation at the one-species level
critical issue in statistical analysis because 'homo would be 100%. If there were two monoculture
geneity of variance' is a basic precondition for
the validity of many statistical tests, including
ANOVA. The critical assumption is that the vari
ance among replicates in any sample (or treatment Number of unique species combinations
level) is the same as the variance in any other in sets of N species drawn from 18 species
sample, i.e. variance is equal (or homogeneous) 18 153 3060 43,758 153 1
across all samples that are being compared (Sokal ill 1 ....
~ 0.9 If ...... _.
and Rohlf 1981). ~cH 0.8 '. ~ ~ .......0 1 ~
OH
Several inherent features in the design of random ~W w ~
selection diversity experiments cause the response ] 0.6 0.01 .S
_M ~
variance to differ between treatment levels, com CfJ 0.4 0"
promising the validity of most statistical tests that '~Ea 0.3 ~ 0.001 'ut
0.2 ~ ~
thhaevrem boeree,n tuhsee dp atott earnna loyfs eu ntheeqsuea el xvpaerriiamnecne tsis. Fcuorr J{ 0.1o -o f -----r"=2- --}-4- --+-8 -,---16-1,8- -+ 0.0001·£0P~ -<
related with treatment levels because the cause of
Number of species per set
variance changes predictably with an increasing
number of species. The assumption that variance is Figure 5.3 Statistical properties related to within-treatment
variance that co-vary with treatment level (number of species per set)
equal among treatments is invalid if the causes of
in random-selection diversity experiments. Jaccard's Similarity Index
variance differ among treatments. This correlation of (left-hand scale and thick line with bars for ± 1 SD), illustrates the
treatment level with the causes of within-treatment increasing similarity in species composition among randomly
variance increases the probability that the experi assembled sets of species (quasi-rep[icates) as the number of species
mental results will be misinterpreted. in the set approaches the total number of species, caused by the
variance reduction effect (Huston 1997). The proportion of variance
Understanding the patterns and causes of within
(• ••••) at a specific treatment level due to differences in species
treatment variance in random-selection diversity
composition is the inverse of the similarity among the quasi-replicates
experiments requires a careful evaluation of what at that [evel. The proportion of combinations represented (right-hand
replication means in these experiments. What is scale and --0--) is the number of randomly drawn quasi-replicates
treated as replication in these experiments actually at each treatment level (39, 35, 29, 30, 35 for the five treatment
levels) divided by the total number of unique combinations of species
has two very different components, 'representation'
at that treatment level (determined by combinatorial probabilities and
and 'true replication', that change in their relative
listed at the top of the figure). The consequences of these co-varying
proportions along the treatment gradient from low properties are that (1) treatment responses are underestimated at [ow
species richness to high species richness. treatment levels because of under-sampling of the high variability in
Representation is the proportion of the total num species composition (and therefore performance) among quasi
replicates, and (2) treatment effects are overestimated at high
ber of possible species compositions at a specific
treatment levels because the [ow variability in species composition
level of species richness (calculated using combin
at high treatment levels allows experimental error (such as
atorial probabilities) that is actually included in the uncontrolled variability in soil properties) to become the primary
experiment. The number of unique combinations of source of variability.
52 BI 0 DIVERS ITY AN D ECOS YSTE M FU N CTION I N G
plots of each species, the level of true replication dramatically from one experiment to another, and
would be 2, for a total of 36 plots. would be unlikely to capture any valid statistical
In a 'well-replicated' diversity-productivity properties of the actual distribution of performance.
experiment there may be 30 or more plots at each Interpretation of this symmetrical distribution of
level of species richness (e.g. 1,2,4,8,16). However, representation across the treatment levels in a
these plots are not true replicates but rather 'quasi random-selection diversity experiment is further
replicates' whose statistical properties are correl complicated by the 'variance reduction effect'
ated (and thus confounded) with the experimental (Huston 1997), which is another consequence of the
diversity treatments. Note that 'quasi-replicates' same probabilities for randomly selected sets that
represent a different phenomenon than 'pseudo produce the 'selection probability' or 'sampling'
replicates', sensu Hurlbert (1984). The quasi effect (Aarssen 1997; Huston 1997; Tilman et al.
replicates at each treatment level are composed of a 1997b). Just as the probability of drawing (without
variable proportion of the total possible number of replacement) any particular species out of a fixed
combinations, and, with a very low probability, some set of species increases as the number of species
true replicates of a few of the combinations. For an drawn increases, the probability of the same com
experiment with 35 plots of two species drawn bination of species appearing in different draws
from a pool of 18 species, a maximum of 22.8% increases as the number of species drawn increases.
(35/153) of all possible combinations could be The probability of all draws having the same spe
represented, and the probability that even a single cies composition reaches 100% when the number of
combination is drawn twice (i.e. replicated) is quite species drawn (without replacement) equals the
low. Even with a number of quasi-replicates as total number of species in the pool from which the
large as 35, over 75% of the possible species com draws are made.
binations will not be represented, so there is a high The variance reduction effect complicates the
probability that any specific pair of species (such as interpretation of representation and replication in
the two largest species, a particular legume and these experiments, because both of these properties
grass, or the most productive combination) is not reach a maximum in treatments with high species
even present in the experiment. richness. Consider the cases of two-species and
If all combinations are not evaluated (i.e. planted 16-species treatments in a random-selection experi
at least once in an experiment), then it is impossible ment with a total species pool of 18 (Fig. 5.3). Both
to conclude that the observed maximum production have the same number of unique species composi
is the actual maximum production, especially at low tions, 153, so if both treatment levels have the same
levels of species richness, because there is a high number of randomly assembled plots, both will
probability that the most productive combination have the same proportional representation of all
of species was not even planted. Assuming that possible combinations. However, as a consequence
there is a similar number of plots at each level of of the variance reduction effect, the 35 plots with
species richness, the proportional representation of 16 species are much more similar to one another
all possible combinations changes dramatically and than the 35 plots with two species. This occurs
non-linearly with increasing species richness in this because the each of the 16-species plots contains
type of experiment (Fig. 5.3). This pattern is sym most of the 18 possible species. Each plot must
metrical, with high representation at low levels of share a minimum of 12 species with each other
species richness, and also at high levels (near the plot, and any individual species is expected to occur
size of the total pool of species), but with extremely in 31 out of the 35 replicates. In contrast, any par
low representation at intermediate levels of rich ticular two-species plot does not necessarily share
ness. For example, at a treatment level of eight any species with another plot, and each individual
species drawn out of a pool of 18, 35 replicates species is expect to appear in only four out of the
represents 0.08% (35/43,758) of the total number 35 plots.
of combinations. Obviously, the results produced This pattern of increasing similarity (i.e. reduc
by such a small random sample could change tion in variance of species composition and related
EVALUAT[NG THE RELAT[VE STRENGTHS OF B[OT[C VERSUS AB[OT[C CONTROLS 53
properties) with increasing levels of species rich biomass. So, a normally distributed error should
ness can be simply illustrated using Jaccard's Index have little effect on the overall distribution, since
of similarity, a standard method for evaluating the most values that are displaced far from their true
similarity of species composition of pairs of eco values are still likely to be within the wide
logical communities (i.e. species lists). Jaccard's range of values produced by the different species
index is equal to an/(an + alO + aOl)' where an is the combinations.
number of species shared between list 1 and list 2, In contrast, because the I6-species plots are very
alO is the number of species that occur only in list 1, similar in species composition (i.e. nearly true rep
and aOI is the number that occurs only in list 2. The licates), they would be expected to have similar
index varies between 0 and 1.0. true values of the observed response. Application
Figure 5.3 shows the monotonic increase in of a normally distributed experimental error to
pairwise similarity among randomly drawn sets of these plots is very likely to produce values outside
species as the sets include larger numbers of species. the relatively narrow range of the true values. For
Similarity is 0 among unique single-species sets, and example, with a normally distributed error with a
1.0 among all I8-species sets. The critical distinction mean of 1.0, one might have 10% of values in each
is between the two-species and I6-species sets. tail of the distribution, e.g. below 0.7 or above 1.3.
Mean pairwise similarity among two-species plots If the error is applied as a multiplier of the true
is 7.6%, while mean similarity among 16-species value, as might be the case for error associated
plots is 80%. Although there is the same number of with variations in soil properties across an experi
compositionally distinct combinations (153) at both mental field, this would result in an average of 3.5
treatment levels, all 16-species plots are very similar high outliers and 3.5 low outliers out of 35 plots.
to one another, while all two-species plots are very This produces what is perhaps the most insidi
different from one another. ous hidden treatment in diversity/productivity
Consequently, the I6-species plots are almost research: the increasing probability of interpreting
complete replicates of the same species composi experimental error as a treatment effect at higher
tion, and should have many of the same properties levels of species richness. The probability of high
as true replicates in plant growth experiments, pos (or low) outliers that are outside the range of true
sibly including a normal distribution of values, values is much higher for high richness treatments
depending on the factors determining the experi than for low richness treatments. The 'experimental
mental error (i.e. unexplained variance in a regres error' that produces these outliers may come from
sion or within-subgroup mean squares in ANOVA). some undetermined and apparently random source
In contrast, all of the two-species plots are quite of variability, or from an easily understood source,
distinct from one another, and are unlikely to show such as slight variation in soil properties over the
the same response to error factors that would be experimental field. Combined with the higher mean
observed in the I6-species plots. value caused by the sampling effect at high species
Each unique species composition should produce richness, this variability is more likely to produce
a characteristic (or 'true') response under constant very high outliers at high levels of species richness.
or homogeneous conditions. This true response This may lead to the incorrect conclusion that the
could be estimated as the mean of replicate res treatment has produced an increase in response (e.g.
ponses under heterogeneous conditions. Assuming biomass or productivity) at high levels of diversity.
that there is some normally distributed error applied In reality, the presumed treatment effect is an error
to all plots in the experiment, this error should shift effect.
the value of each plot away from its true value, but This interaction of 'variance reduction' with
only a small percentage of the plots should be 'representation' and 'replication' produces reversed
shifted far away from their true value. Because two monotonic gradients in the causes of variation
species plots are so different from one another, they within treatment levels (i.e. experimental error)
would be expected to have true values that cover across the treatment gradient. This reversal in the
a broad range, e.g. from high biomass to low causes of within-treatment variance confounds the
54 BI 0 DI V ER S I TY AND EC OS YS TEM FUN CT ION I N G
interpretation and statistical analysis of random In summary, not only are the high diversity treat
selection diversity experiments. At low treatment ments most suitable for distinguishing the effects of
levels of species richness, most of the variance in soil heterogeneity (or other spatial factors) from the
response is the consequence of differences in species effects of species composition, they are also most
composition among the quasi-replicates, and a susceptible to the confounding of error effects with
relatively small proportion of the variance is due to treatment effects. The laws of probability produce a
other sources of variability, such as soil hetero continuous gradient of: (1) increasing similarity
geneity. In contrast, at high levels of the diversity among the quasi-replicate sets of species (e.g. dif
treatment, very little of the variance in response is ferent combinations of N species from a pool of
due to differences in species composition, because 18 species) drawn at any given diversity level, and
the quasi-replicates have converged with true (2) increasing number of replicates of each par
replicates because of their high similarity in species ticular subset of species (i.e. how many times
composition. Thus, the primary source of response exactly the same set of two or three species are
variance at high levels of species richness is not drawn from 18) at any given diversity level, as the
species composition, but rather whatever sources treatment level approaches the total number of
of error are present in the experiment, such as soil species. The fact that high diversity plots are more
heterogeneity. similar to one another than are low diversity plots
This phenomenon is clearly illustrated by the (the variance reduction effect) means that there are
BIODEPTH experiments, where all plots of the effectively more replicates of highly productive
highest diversity treatment were identical in species species combinations, and a greater chance of some
composition at the three sites designed to test for of the replicates falling on portions of the field with
over-yielding (Portugal, Sheffield, and Sweden). At anomalous soils. Therefore, there is a higher prob
these three sites, each of the high diversity plots ability of misinterpreting the effect of soil proper
contained all of the species in the total species pool ties, or other sources of experimental error, as
(i.e. all of the species that were grown as mono treatment effects at high levels of species richness.
cultures). Consequently, the variability in biomass
production among the plots of the high diversity
treatment could not have been due to differences
5.5 Simulating hidden treatment effects
in species composition, since all were identical
in a diversity-productivity experiment
in composition. This variability must have been
caused by some other factor, of which underlying One of the most contentious issues in the diversity
soil heterogeneity is the most likely candidate. The productivity controversy is whether the widely
role of soil heterogeneity as the primary cause of observed increase in mean productivity (or biomass
variability in biomass production is strongly sup etc.) with an increasing number of randomly selected
ported by the dramatically lower variability at species is (1) the consequence of interactions
Sheffield, the only site at which the soil was among many species, i.e. a true diversity effect
trucked in and homogenized (compare Portugal, caused by processes such as facilitation or comple
Sheffield, and Sweden in Fig. 5.2). Note that the mentary interactions among many species; or
variability of biomass at the lower levels of species (2) the result of the chance presence of one or a
richness (1,2,4) is relatively similar at the three few species, i.e. a statistical artefact of random
sites. This is expected because the primary source sampling, called the selection probability (Huston
of variability in randomly selected small numbers 1997) or sampling (Tilman et al. 1997b) effect.
of species is random differences in species com The sampling effect is the 'the increasing prob
position among the plots. It is primarily at the ability of selecting species with a specific property
highest level of species richness in these experi (e.g. large maximum height, stress tolerance,
ments, where all plots are identical, that the outliers nitrogen-fixation ability, high seed germination
from the error caused by soil heterogeneity across rate) in samples of increasing number that are ran
the field can be detected. domly selected from any group of species' (Huston
EVALUAT[NG THE RELAT[VE STRENGTHS OF B[OT[C VERSUS AB[OT[C CONTROLS 55
1997). This purely statistical phenomenon results remains unchanged while the mean response
from the random selection of anyone, two, or more increases with the number of randomly selected
objects (e.g. species) from a group of any number of species.
objects, according to the laws of probability. It can The most conservative criterion for distinguish
produce a pattern ofincreasing average productivity ing between these alternative hypotheses is a
of mixtures with an increasing number of species as metric from agricultural research known as 'trans
the result of (1) the increased probability of the gressive over-yielding'. Transgressive over-yielding
inclusion of a single large or fast-growing species occurs when the productivity of some mixture of
(Aarssen 1997; Huston 1997; Tilman et al. 1997b); or species exceeds the maximum productivity of the
(2) the inclusion of specific groups of two or more monocultures of all of the component species or of
species that interact positively either through facil mixtures with fewer species (Trenbath 1974; Mead
itation (e.g. the fertilization effect of a nitrogen and Riley 1981; Vandermeer 1989; Garnier et al. 1997;
fixing legume on a grass, Huston 1997; Hooper and Fridley 2001). For example, transgressive over
Vitousek 1997; Grime 1997; Hodgson et al. 1998; yielding above the two-species level occurs when
Wardle 1999; Huston et al. 2000; Loreau 2000a) or some plots with more than two species exceed the
through complementarity among groups of two or maximum production of all plots with two species.
more species (i.e. niche effects) to produce more In the case of multi-species niche complement
biomass than a smaller number of species could arity, there is over-yielding above each sequential
produce (Huston 1997; Tilman et al. 1997b; Hooper level of species richness, beginning with mono
1998; Loreau 2000a; Loreau et al. 2001; Fridley 2001; cultures, and continuing to the maximum number
Dukes 2001b). of species at which niche complementarity has a
The two primary alternative hypotheses to significant effect on production. For few-species
explain the observed increase in mean performance sampling effects, there is no over-yielding above
with increasing species richness in random-selection the number of species for which the sampling effect
diversity experiments are (1) the sampling effect for occurs. If the increase in productivity is determined
a small number of species (e.g. a highly productive by a sampling effect for one or more pairs of species
individual species, or a highly productive species (e.g. one or more specific legume-grass combina
pair, such as a legume and a grass); and (2) multi tions), no plots with more than two species will
species complementarity (niche effects) among a exceed the highest production level achieved by
large number of species. The definitive pattern for two-species combinations. The most productive
distinguishing between these two alternatives is not two-species combination(s) is assumed to be pre
the increase in mean production, which is expected sent in the most productive plots at all higher levels
to be the same both for interactions among many of species diversity.
species (niche effects) and for interactions among In most of the published experiments of this type
only a few species (sampling effects), but rather (Naeem et al. 1995; Tilman et al. 1996; Hector et al.
the shape of the upper boundary of the response 1999), as well as the first several years of the long
distribution. term experiment reported by Tilman et al. (2001),
'With niche complementarity, the upper bound none of the multi-species treatments exceeded the
increases with diversity because no monoculture is maximum productivity of monocultures, which
as productive as some combinations of two species, demonstrated that there was no over-yielding above
and no combination of N species is as productive as the single-species level. This result was quite sur
some combinations of N + 1 species' (Tilman et al. 2001, prising, since several of these experiments included
italics added). This definition requires a continuous nitrogen-fixing legumes, which are known to
increase in the maximum value, as well as of the increase the productivity of plants grown with
mean of the measured property (e.g. biomass pro them as a result of the nitrogen fertilization effect.
duction). In contrast, the 'signature' of the sampling There were undoubtedly positive interactions
effect is a constant upper bound for maximum between legumes and other species, but none that
biomass (or any other measured property) that resulted in transgressive over-yielding.
56 BI 0 DI V ER S IT Y AND EC OS YS TEM FUN CT ION I N G
However, in the last few years of the longest combination of a legume and a grass, a common
running diversity-productivity experiment, there and well-understood response, rather than the
was clear transgressive over-yielding above the niche effects of complementary interactions among
maximum production of monocultures (Tilman many (e.g. 9 to 13) species, as concluded by Tilman
et al. 2001), and published analyses demonstrate the et al. (2001).
expected strong effect of legumes (particularly There is no doubt that the most recent experi
Lupinus perennis) growing with C grasses. The mental results show several plots with above
4
critical question for interpreting these results is ground and total biomass that exceed the maximum
whether or not there was over-yielding above the amount at the two-species treatment level (Fig. 5.4).
maximum productivity at the two-species level. However, there are only a few plots (~1O% of the
If there was no over-yielding above the level 94 plots with four or more species) that exceed the
of two species, the observed increase in mean two-species overyielding level. Does this represent
productivity with increasing species richness is the increasing upper bound expected for multi
most likely the result of the sampling effect for the species niche effects, or just scattered outliers above
(a) 0.7 (b) 0.7
(1999) (2000)
'I'
Ei 0.6 0.6 X
co X
~ 0.5 ~ 0.5
''cE""oi 0.4 - - - -*- - -X - * 0.4 . .. .. .. ..
0 ~ ~~. ·~·-1 ~ ~ ~. ~ ~.t. ~ ~
;B ... *. ...... ~
'".C;::J:l 0.3 -------- -------- .. 0.3 X •
0 0.2 0.2
hc o ~
d; ~
0~ 0.1 ~ 0.1 X X
~ X
0 0
2 4 8 16 2 4 8 16
(e) 2.5 (d) 2.5
(1999) (2000) X
\ 2 X * 2 X
X
!! 1.5 - - - - 1- - XX -~ 1.5 - - - -X- - i - -
''cE""oi X -~- ---X-- -------~ -------X ------- ---- ----
0 ------------ ........~ ........ ------ ----
;B
~ ~
.l9
E0-< 0.5 ~ ~~ 0.5 I
X X
0 0
2 4 8 16 2 4 8 16
Number of species
Figure 5.4 Biomass production of all replkates at five levels of species rkhness in a random·selection diversity experiment (Tilman et at.
200 1). Data are shown for two years, and two measures of biomass: (a,b) above·ground biomass (approximating annual biomass
production); (c,d) total biomass, including both above· and below·ground (the accumulation of roots which had grown over the 6-7 years
of the experiment). Solid line indicates log·linear increase in average biomass with increasing number of species. Dotted line indicates the minimum
biomass value for transgressive over·yielding above the level of one species (set at the mean of the two highest biomass values, because
there are two replicates of each monoculture, and the mean is assumed to be the best estimate of the response of the most productive species).
Upper dashed lines indkate the m'mimum biomass value for transgressive over·yielding above the level of two species (set at the level of the
highest biomass value, because each of the two·species plots represents a unique species combination). The high biomass values in the
16-species treatments are most likely the result of experimental error caused by spatial variation in soil moisture or fertility across the
experimental field (see text).