Journal of Ecology 2012, 100, 742–749
doi: 10.1111/j.1365-2745.2011.01944.x
Forest productivity increases with evenness, species
richness and trait variation: a global meta-analysis
Yu Zhang1, Han Y. H. Chen1* and Peter B. Reich2,3
1
Faculty of Natural Resources Management, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1,
Canada; 2Department of Forest Resources, University of Minnesota, 115 Green Hall, 1530 Cleveland Ave. N., St.
Paul, MN 55108-6112, USA; and 3Hawkesbury Institute for the Environment, University of Western Sydney, Locked
Bag 1797, Penrith NSW 2751, Australia
Summary
1. Although there is ample support for positive species richness–productivity relationships in
planted grassland experiments, a recent 48-site study found no diversity–productivity relationship
(DPR) in herbaceous communities. Thus, debate persists about diversity effects in natural versus
planted systems. Additionally, current knowledge is weak regarding the influence of evenness on
the DPRs, how DPRs are affected by the variation in life-history traits among constituent species in
polycultures and how DPRs differ among biomes. The impacts of these factors on DPRs in forest
ecosystems are even more poorly understood.
2. We performed a meta-analysis of 54 studies to reconcile DPRs in forest ecosystems. We quantified the net diversity effect as log effect size [ln(ES)], the log ratio of the productivity in polycultures
to the average of those in monocultures within the same type of mixture, site condition and stand
age of each study. The first use of a boosted regression tree model in meta-analysis, a useful method
to partition the effects of multiple predictors rather than relying on vote-counting of individual
studies, unveiled the relative influences of individual predictors.
3. Global average ln(ES) was 0.2128, indicating 23.7% higher productivity in polycultures than
monocultures. The final model explained 21% of the variation in ln(ES). The predictors that substantially accounted for the explained variation included evenness (34%), heterogeneity of shade
tolerance (29%), richness (13%) and stand age (15%). In contrast, heterogeneity of nitrogen fixation and growth habits, biome and stand origin (naturally established versus planted) contributed
negligibly (each £ 4%). Log effect size strongly increased with evenness from 0.6 to 1 and with richness from 2 to 6. Furthermore, it was higher with heterogeneity of shade tolerance and generally
increased with stand age.
4. Synthesis. Our analysis is, to our knowledge, the first to demonstrate the critical role of species
evenness, richness and the importance of contrasting traits in defining net diversity effects in forest
polycultures. While testing the specific mechanisms is beyond the scope of our analysis, our results
should motivate future studies to link richness, evenness, contrasting traits and life-history stage to
the mechanisms that are expected to produce positive net biodiversity effects such as niche differentiation, facilitation and reduced Janzen–Connell effects.
Key-words: biomes, boosted regression trees, net diversity effect, plant development and
life-history traits, productivity, species evenness, stand origins
Introduction
The diversity–productivity relationship (DPR) has received
considerable attention during the past two decades, largely
because of the continuous loss of biodiversity (Hooper et al.
2005). Numerous empirical experiments, mostly in temperate
*Correspondence author. E-mail: [email protected]
grasslands, have demonstrated positive DPRs, also defined as
net biodiversity effect, i.e. polycultures have higher biomass
production than the average production of monocultures
(overyielding) (e.g. Tilman, Wedin & Knops 1996; Loreau &
Hector 2001; Cardinale et al. 2007; Isbell, Polley & Wilsey
2009). The niche complementarity hypothesis, i.e. positive
diversity effect is due to increased resource use and nutrient
retention via niche differentiation or partitioning and
2012 The Authors. Journal of Ecology 2012 British Ecological Society
Diversity and productivity relationships 743
interspecific facilitation (Tilman 1999; Loreau et al. 2001;
Hooper et al. 2005), has been the cornerstone of DPR studies.
However, it is rare for DPR studies to directly demonstrate the
link between the net biodiversity effect in polycultures and ecological mechanisms (Cardinale 2011). Additionally, there is a
lack of evidence for a positive DPR in naturally assembled herbaceous communities (Adler et al. 2011).
Research of DPRs is lagging behind in forest ecosystems
due to the longevity and size of trees and the complexity of forest ecosystems (Leuschner, Jungkunst & Fleck 2009). Previous
empirical DPR studies in forest ecosystems have reported positive (e.g. MacPherson, Lieffers & Blenis 2001; Garber &
Maguire 2004; Amoroso & Turnblom 2006; Pretzsch &
Schutze 2009; Brassard et al. 2011), insignificant or even
negative (e.g. Edgar & Burk 2001; Chen & Klinka 2003; Vila
et al. 2003; Cavard et al. 2010) effects of species diversity on
productivity. Possible causes of the observed contrasting
DPRs may include: choice of diversity indices, failing to
consider variation in life-history traits such as contrasting
shade tolerance, growth rate and nitrogen (N) fixation among
constituent species in polycultures, stand origins (experimental
plantations versus naturally established stands) and variation
in stand age. However, little research has been conducted to
determine the potential influences of these factors on DPRs.
The measure of species diversity in DPR studies is still
debated (Hillebrand & Matthiessen 2009; Hillebrand &
Cardinale 2010). Most DPR studies have chosen species richness as the measure of species diversity to define and interpret
DPRs. However, richness alone cannot fully represent species
diversity (Bock, Jones & Bock 2007) in relation to ecosystem
functioning because it ignores the influence of species evenness
(relative abundance) on interspecific interactions (Kirwan
et al. 2007; Hillebrand, Bennett & Cadotte 2008; Turnbull &
Hector 2010). The lack of understanding of species evenness in
DPRs is presumably limited by traditional experimental and
statistical methods (Isbell, Polley & Wilsey 2009). We hypothesize that both richness and evenness influence the diversity
benefits on productivity.
The trait-based approach has elicited much recent interest
for predicting changes in community composition and ecosystem functioning in response to the presence of competitors
along environmental niche axes (Hillebrand & Matthiessen
2009) because of the potential causal link between species traits
to niche occupancy and partitioning (Silvertown 2004). For
example, polycultures with different life-history traits among
constituent species can increase the spatial niche occupancy of
a site (Coomes et al. 2009; Brassard et al. 2011). Thus, we
hypothesize that variation in the DPRs (i.e. positive, neutral or
negative DPRs) in forest ecosystems is attributable in large
part to the presence or absence of life-history variation such as
contrasting shade tolerance, growth rate and nitrogen (N) fixation among constituent species in polycultures.
Stand origin may also be an important factor influencing
DPRs in forest ecosystems because experiments under controlled homogeneous environments, while allowing for a
mechanistic understanding of DPRs, may not reflect processes
in heterogeneous natural environments (e.g. Lepš 2004; Grace
et al. 2007). The discrepancy between the outcomes of DPR
studies conducted in experimental plantations versus naturally
established stands may be more pronounced in forest ecosystems because of the long-term dynamics associated with tree
establishment, competition and mortality (Wardle, Walker &
Bardgett 2004; Hart & Chen 2008). However, the opposite
view argues that the benefits of species diversity in natural environments have been underestimated (Duffy 2009) because
resource heterogeneity and the extended time frame associated
with observational studies arguably increase the realized effects
of niche complementarity (Stachowicz et al. 2008). To reconcile these divergent views, we hypothesize that natural stands
may exhibit greater diversity benefits on productivity than
experimental plantations. We also hypothesize that diversity
effects increase with stand age since species complementarity
tends to increase with time (Cardinale et al. 2007). Furthermore, we hypothesize that DPR patterns may differ among
biomes. For example, a recent analysis demonstrates that the
positive biodiversity effects are more apparent in the more
stressful environment of the boreal biome than in the temperate biome (Paquette & Messier 2011).
Here, we attempted to reconcile DPRs in forest ecosystems
at a global scale using a meta-analysis of 54 DPR studies. We
specifically examined how the effect size, a ratio of the aboveground productivity in polycultures to the average of those in
monocultures, responded to changes in species richness, evenness, the extent of life-history trait variation, stand origin,
stand age and biome.
Materials and methods
DATA COLLECTION
We conducted an extensive literature search for studies of DPRs in
forest ecosystems using the ISI Web of Science, Forest Science Database and Google Scholar. Different combinations of key words such
as basal area, volume, biomass, productivity, forest, tree, species richness, plantation, diversity, biodiversity, pure, mixed species, single
species, boreal, temperate and tropical were used for the search.
We included studies that met the following criteria: (i) studies
were published in reputable peer-reviewed journals; (ii) studies were
implemented purposely to isolate the effect of tree species diversity
from other factors such as soil conditions and topographic features
of sampling plots; resulting in the use of 54 studies (published
papers) in the meta-analysis (see Appendix S1 in Supporting Information). We extracted above-ground productivity measurements of
live trees, climate, geographical location, species diversity and lifehistory traits from the original papers. For studies with multiple
sampling dates, the latest data of productivity and species diversity
measurements were used in the analysis. When an original study
reported results graphically, we used SigmaScan Pro version 5
(Systat Software Inc., Point Richmond, CA, USA) to digitally
extract data from figures.
A meta-analysis weighing both the variances and replication sizes
from original studies is preferred. However, when the main goal of a
meta-analysis is to generalize the commonality and differences
between original studies, a drastic removal of studies would reduce
the power and generality of the meta-analysis (Hillebrand & Cardinale 2010). Since neither variance nor replication size were available
2012 The Authors. Journal of Ecology 2012 British Ecological Society, Journal of Ecology, 100, 742–749
744 Y. Zhang, H. Y. H. Chen & P. B. Reich
in 29 of the 54 studies, we used an un-weighted meta-analysis to avoid
a large loss of information.
We used both species richness and evenness as measures of diversity from the original studies (four of the 54 studies) or calculated
these metrics based on the species’ proportions by stand basal area
(41 studies). When basal area was not reported in the original study,
we used stem density (four studies) or crown cover (one study) to calculate richness and evenness. For the remaining four studies, evenness
could not be determined, and it was treated as a missing value in our
data set. Species evenness was estimated using J¢ index (Pielou 1969)
as the ratio of the observed Shannon’s diversity of a stand to its maximum value with the same number of species:
H0
J0 ¼
S
P
i¼1
¼
1
1
S lnðSÞ
H0
lnðSÞ
eqn 1
where H¢ is the observed Shannon’s index, and S is the species
richness.
We adopted a trait-based approach to the analysis (Hillebrand &
Matthiessen 2009) by using the extent of life-history variation,
selected a priori, as a measure of trait dissimilarity. Life-history variation in our study was qualified as discrete variables, because the traits
we were interested in were qualitatively classified in the literature.
Similar to our metric, life-history variation, qualified as non-objectively chosen a priori, has been found to be one of the best predictors
at explaining productivity in grassland experiments (Cadotte et al.
2009; Marquard et al. 2009). The classification of presence ⁄ absence
of life-history variation was based on the explicit description in the
original papers. When descriptions of interspecific differences in lifehistory traits were not presented in the original paper, we obtained
these life-history traits from the USDA plant data base (http://plants.
usda.gov/java/). This approach is practical because trait information
on individual species is usually available for plants (Statzner, Bonada
& Doledec 2007). The presence of life-history variation within a stand
was defined a priori as the presence or absence of the following three
contrasting traits among the dominant and co-dominant tree species
within a stand: shade tolerant versus shade intolerant, fast versus slow
growing and N fixation versus non-fixation. Stand origin and stand
age were determined based on the site description in the original studies. However, stand age was not available for four studies, and it was
treated as a missing value for these four studies. Biomes were identified as boreal, temperate or tropical.
DATA ANALYSIS
Effect size (ES) was calculated as a standardized measure of productivity across studies, using a response ratio:
ESij ¼
Pij
Mi
eqn 2
where ES (i = 1, 2,... 54; j = 1, 2,... n, n is the number of observations in each original study) is effect size of the jth observation
in the ith study, Pij is the observed productivity of the jth observation in the ith study and M is the mean productivity of all
monocultures within each study. When a study reported multiple
types of mixtures, site conditions or stand ages, the M was calculated separately for each mixture type, site condition and stand
age. Monocultures in most studies were pure single species
stands. In studies where pure single species stands were not available (nine of the 54 studies), consistent with most observational
studies (e.g. Brassard et al. 2011), monocultures were defined by
the single species comprising ‡80% of stand basal area. For stud-
ies that examined the DPRs across different stand ages and site
conditions, ES was calculated by using stands with the same age
and site condition. The productivity measures were chosen in
order of preference from biomass, volume and basal area from
original studies when multiple measurements were reported as
surrogates of above-ground productivity. As a recommended
practice in meta-analysis (Hedges, Gurevitch & Curtis 1999), controls, i.e. monocultures, were not included in analysis, resulting in
a total of n = 319 for analysis.
To partition independent influences of species richness, species
evenness and life-history traits, stand origin, stand age and biome on
ES, which was transformed by natural logarithm, we used boosted
regression trees (BRT). BRT is an advanced form of machine learning
method based on classification and regression trees and is ideal for
complex data with unidentified distributions (De’ath 2007). Furthermore, BRT can accommodate missing values in predictors (De’ath
2007; Elith, Leathwick & Hastie 2008). In BRT, multiple trees are fitted and combined in a forward stage-wise procedure to predict the
response of the dependent variable to multiple predictors (De’ath
2007).
There are four input settings for BRT models: loss function, learning rate, tree complexity, bagging fraction and folds of cross-validation (De’ath 2007). Gaussian error structure was chosen for the loss
function because of the nature of our response variable (Ridgeway
2010). The learning rate regulates the number of trees fitted. In general, a low learning rate (and a large number of trees) enables BRT to
generate highly complex response functions. A fast learning rate
requires fewer trees but is subject to more noise from the bagging and
a lack of smoothness in the response functions. The tree complexity,
the number of splits in each tree, indicates the level of interactions in
BRT, i.e. a value of three permits up to three-way interactions. The
bagging fraction introduces randomness into BRT to reduce overfitting by a random selection of a portion of the data for model training
and validation. The cross-validation specifies the number of times to
randomly divide the data for model fitting and validation. The monotonic constraint on continuous variables may further reduce overfitting and the filtering of data noise, resulting in a simpler model.
We fitted 36 BRT models with the combinations of the following
settings: learning rates of 0.05, 0.01, 0.005 and 0.001, bag fractions of
0.40, 0.50 and 0.60, 5-, 8- and 10-fold cross-validations and a tree
complexity of 4 to account for potential higher order interactions
(De’ath 2007). These values were chosen to find the optimal settings
based on the empirical rules recommended for ecological modelling
(De’ath 2007; Elith, Leathwick & Hastie 2008). Among the fitted
models, the best model had a cross-validation deviance, i.e. prediction
error (PE), of 0.101 [±one stand error = 0.014] from the learning rate
of 0.005, the bag fraction of 0.5 and 10-fold cross-validation. The consequent models were all fitted with these optimal settings.
Further BRTs were fitted as follows: (i) since N fixation, growth
habit and stand origin showed weak relative influences (<3%) in the
best model, the three predictors were dropped from the model, which
increased PE to by only 0.003 (to 0.104 ± 0.011), suggesting negligible
loss in the simplified model; (ii) fitting the BRT comprising main
effects without interactions increased the PE to 0.111 (±0.011), indicating that interactions were existent but negligible; (iii) applying
monotonic constraints to richness, evenness and stand age yielded a
PE of 0.113 (±0.012). Because the models with and without monotonic constraints show similar results (Figs 1 and 2), our interpretation focused on the model with monotonic constraints.
To interpret BRT results, we examined the relative influence of predictors by partitioning the total variation explained by each predictor
in percentage. In the boosted model, the relative influence was the
2012 The Authors. Journal of Ecology 2012 British Ecological Society, Journal of Ecology, 100, 742–749
Diversity and productivity relationships 745
40
average of all trees in a BRT model (Friedman & Meulman 2003).
We interpreted the response of ln(ES) to predictors from the partial
dependency plots, which illustrates the predicted effect of the individual predictor on the response variable while accounting for the average effects of other variables (De’ath 2007; Elith, Leathwick & Hastie
2008). All BRT analyses were carried out in R using the ‘gbm’ package (Ridgeway 2010) and supplemental functions (De’ath 2007; Elith,
Leathwick & Hastie 2008).
Relative influence (%)
Monotonic
Non-monotonic
30
20
Results
10
The global average of ln(ES) was 0.2128, i.e. ES = 1.237, indicating that polycultures on average had 23.7% higher productivity than monocultures. The BRT model explained 21% of
the variation in ln(ES). Species evenness and richness contributed 34% and 13% of the explained variation, respectively
(Fig. 1). ln(ES) increased with richness from 2 to 6, then
plateaued with richness ‡ 6 (Fig. 2a). When evenness was
<0.60, it had no effect on ln(ES). However, ln(ES) increased
markedly with evenness from 0.6 to 1 (Fig. 2b).
The extent of life-history variation had a total of 35% relative influence on ln(ES): 29% from shade tolerance, 4% from
N fixation and 2% from growth habit, respectively (Fig. 1).
The presence of contrasting shade tolerance in polycultures
or
ig
in
e
St
an
d
Bi
om
ha
G
ro
wt
h
Ag
e
bi
t
n
N
-fi
xa
tio
an
ce
to
le
r
en
n
Sh
ad
e
Ev
R
ic
hn
e
ss
es
s
0
Fig. 1. Results from boosted regression tree (BRT) analysis showing
the relative contributions of predictors in percentage on natural logtransformed effect size. Bars in black are values for the BRT model
with monotonic constraints on richness, evenness and stand age, and
those in grey for the model without the monotonic constraints.
n = 294 for evenness, 283 for stand age and 319 for remaining
predictors.
Predicted ln(ES)
(a)
(b)
0.3
0.3
0.2
0.2
0.2
0.1
0.1
0.1
0.0
0 2 4 6 8 10 12 14 16
0.0
0.2
0.4
Richness
Predicted ln(ES)
0.3
0.6
0.8
1.0
0.0
0.3
(d)
0.3
(e)
0
20 40 60 80 100 120
Stand age (years)
Evenness
0.2
0.2
0.2
0.1
0.1
(f)
0.1
0.0
–0.1
Absence
Presence
0.0
0.3
0.3
(g)
0.2
0.2
0.1
0.1
0.0
Bo
Te
Biome
Tr
Absence
Presence
Contrasting N-fixation
Contrasting shade tolerance
Predicted ln(ES)
(c)
0.3
0.0
0.0
Absence
Presence
Contrasting growth habit
(h)
N
P
Stand origin
Fig. 2. The predicted natural log-transformed effect size [ln(ES)] in relation to predictors. (a) species richness, (b) evenness, (c) stand age (years),
(d) presence or absence of contrasting shade tolerance, (e) presence or absence of contrasting nitrogen fixation, (f) presence or absence of contrasting growth habit, (g) biome (bo = boreal, te = temperate and tr = tropical) and (h) stand origin (N = natural stand, P = plantation).
n = 294 for evenness, 283 for stand age and 319 for remaining predictors. The responses of ln(ES) with monotonic constraints on richness, evenness and stand age are shown in black and without the constraints in grey.
2012 The Authors. Journal of Ecology 2012 British Ecological Society, Journal of Ecology, 100, 742–749
746 Y. Zhang, H. Y. H. Chen & P. B. Reich
had a predicted ln(ES) value of 0.16, while the absence of contrasting shade tolerance had a ln(ES) value of -0.03 (Fig. 2d).
The predicted ln(ES) was 0.132 and 0.103 with the presence
and absence of contrasting N fixation traits, and 0.119 and
0.10 with the presence and absence of contrasting growth habits, respectively (Fig. 2e,f).
Stand age had a 15% relative influence on ln(ES) (Fig. 1).
With increasing stand age, ln(ES) generally increased, showing
two steps of increase: a very modest one between 1.6 and 20
and a sharp, large one between 65 and 75 years of age
(Fig. 2c). Biome and stand origin had a 1.4% and 1.2% relative influence on ln(ES), respectively (Fig. 1), indicating that
neither biome nor stand origin is important for explaining the
variation in observed diversity effects. The predicted ln(ES) of
biomes was 0.11, 0.11 and 0.12 for boreal, temperate and tropical forest ecosystems, respectively (Fig. 2g). The predicted
ln(ES) of stand origin was 0.11 and 0.12 for natural stands and
plantations, respectively (Fig. 2h).
Discussion
This meta-analysis is, to our knowledge, the first to reveal
the distinct productivity responses to species richness, evenness, heterogeneity of life-history traits and stand age in forest ecosystems at the global scale. Our results demonstrate
that polycultures are generally more productive than monocultures, and our findings offer new insight to the ever-evolving debate surrounding DPR studies. First, the importance
of evenness as a central component of species diversity to
drive DPRs is supported, provoking further investigations to
unveil the underlying mechanisms. Second, our findings
highlight the important role of contrasting traits on productivity in forest communities, including natural ones, extending findings shown previously in heavily manipulated
grassland experiments (e.g. Cadotte et al. 2009). Third, our
results are, to some extent, consistent with the findings of a
previous meta-analysis, which showed net diversity effects on
productivity are generally positive and increase over time in
various types of plant communities (Cardinale et al. 2007).
Fourth, the expected greater diversity effect from the
enhanced expression of niche differentiation in observational
studies due to higher intrinsic resource and spatial heterogeneity was not found, suggesting that the potential bias in
diversity experiments in controlled environments may be
overestimated (Weis, Madrigal & Cardinale 2008). Lastly,
the limited influence on ln(ES) from biomes rejected the
hypothesis that DPR differs among biomes and supported
the hypothesis that the positive DPR is a global phenomenon in forest ecosystems.
As expected, ln(ES) increased with increasing species richness, but plateaued when richness ‡6. Consistent with the predictions of the niche complementarity hypothesis, the positive
relationship between productivity and richness observed in this
study can probably be attributed to improved resource partitioning and ⁄ or interspecific facilitation, especially as these
played out over the multiple years of the study (cf. Isbell et al.
2011). The plateau at the high range of species richness appears
to support the hypothesis that a ‘ceiling’ of productivity gain
may occur at a high level of species diversity due to functional
redundancy (Naeem et al. 2009); therefore, richness (alone) as
a reliable predictor of ecosystem functioning may be limited in
natural communities that usually have high richness values
(Baiser & Lockwood 2011).
Surprisingly, species evenness explained the greatest variation in ln(ES) among all predictors, showing that ln(ES)
increased with increasing evenness, although it had no effect in
low ranges. Our results concur with those of a large-scale grassland experiment, in which overyielding in polycultures was
mostly attributed to evenness (e.g. Kirwan et al. 2007). The
strong positive effects on DPRs from increased evenness provide strong empirical evidence to support the theoretical prediction (see Hillebrand, Bennett & Cadotte 2008) that evenness
affects the relative strength of interspecific and intraspecific
interactions within communities, therefore causing a shift of
DPR both in magnitudes and form. Greater evenness also
likely increases functional trait diversity, when calculated using
abundance-weighted values. The underappreciated role of
evenness in previous empirical studies can be attributed to the
limited levels of evenness (high and ‘realistically low’) in those
experiments (e.g. Polley, Wilsey & Derner 2003; Isbell, Polley
& Wilsey 2009). However, the underlying mechanism for the
evenness effect likely reflects both the extent of niche ⁄ resource
utilization and the heterogeneity of functional traits, but these
links have not been established, and we call for future experiments to incorporate a wide range of evenness treatments to
evaluate these potential links.
The presence of contrasting life-history traits contributed
considerably to the enhanced ln(ES) of polycultures, probably
because they are associated with important differences in plant
functional traits. This agrees with Darwin’s original idea
(1859) that the presence of a ‘divergence of character’, or variations in life-history traits among species, is essential for reduced
interspecific competition as a result of differentiated demands
for resources and, in turn, improves productivity (Hector &
Hooper 2002). Among all three traits, high shade tolerance
variation within a community is likely to be the most important for forest ecosystems. Heterogeneity in shade tolerance is
strongly associated with heterogeneity in important functional
traits (Reich et al. 2003) and likely leads to more efficient light
exploitation and utilization at the ecosystem level (Yachi &
Loreau 2007; Coomes et al. 2009). The weak effects of heterogeneity in N fixation on ln(ES) may reflect that N fixation from
trees only account for a part of the important ecological
process in forest ecosystems (Menge & Hedin 2009). The weak
effect from heterogeneity in growth rates, which were classified
based on growth performance in high-light environments, may
be attributable to the high dependence between tree growth
rate and shade tolerance (Pacala et al. 1994; Reich et al. 2003),
therefore, resulting in the negligible independent effect of the
heterogeneity in growth rates.
We found that ln(ES) generally increased with stand age,
and that the slopes of increasing age effects occurred during
two periods: very weakly from about 1 to 20 years and very
strongly from 65 to 75 years. The positive age effect is
2012 The Authors. Journal of Ecology 2012 British Ecological Society, Journal of Ecology, 100, 742–749
Diversity and productivity relationships 747
consistent with the findings of previous studies (Cardinale
et al. 2007; Fargione et al. 2007), in which net diversity effects
are found to be positive, and enhanced with life-history stages.
It is possible that the two ‘phase’ changes reflect two key transitional stages in forests (Chen & Popadiouk 2002; Franklin
et al. 2002) – first, from aggrading and open to increasingly
closed canopy (i.e. stand exclusion phase), and second, from
mature to ageing (passing out of stem exclusion phase as older
trees start to age and have dieback). Alternatively, however,
the stepwise pattern of age effects may, to a degree, reflects the
data availability. Particularly, the first rapid increase in age
effects may represent the response of effect size in tropical
plantations, while the second peak of age effects may represent
canopy transition in boreal and temperate forests (Taylor &
Chen 2011), in which the course of succession may impact the
DPRs.
Our results show that the stand origin had negligible effects
on DPRs. The similarity between natural and planted origins
in their effects on DPRs does not support the hypothesis that
natural stands may exhibit stronger niche complementarity.
Our result that natural stands showed biodiversity effects equal
to those observed in plantations may, to a degree, relieve the
doubt about the applicability of positive complementarity
effects found in controlled experiments to natural systems
(Lepš 2004). Thus, the positive biodiversity effects are likely to
be ubiquitous regardless of stand origin. Alternatively, the lack
of effects from stand origin may be partially a result of the fact
that plantations are field experiments at a fairly large scale and
are therefore subject to similar environmental variability as
observational studies in natural stands. Furthermore, the lack
of strong confounding variables that may hide diversity effects,
e.g. fertilization would lead to lower diversity but higher productivity in plantations, may also explain the similar responses
between natural stands and plantations.
In contrast to previous findings (Paquette & Messier 2011),
our results show that the complementarity effects are similar
across biomes. Paquette & Messier (2011) attributed the higher
diversity effects on productivity in boreal, compared with temperate, forests to a stronger beneficial species interaction in the
more environmentally stressed boreal climates. While this
attribution is consistent with the prediction of the stress-gradient hypothesis (Maestre et al. 2009), which is based on a gradient of local site conditions rather than regional climates, more
evidence would be required to generalize biome effects on the
interplay between competition and facilitation as an independent mechanism influencing the strength of species complementarity at the biome scale.
Testing specific mechanisms for the diversity benefits is
beyond the empirical scope of this study but merits conceptual consideration. Key mechanisms are likely to include
positive effects of niche differentiation and facilitation, supported by the importance of richness, evenness and trait
strategy (i.e. shade tolerance) diversity found in our metaanalysis. Additionally, negative soil–plant interactions via
host-specific pathogens and ⁄ or herbivores among conspecific
individuals predicted by the Janzen–Connell hypothesis
(Janzen 1970; Connell 1971) may also explain the higher
productivity in polycultures (Schnitzer et al. 2011). The
higher productivity with higher species evenness may be the
net outcome of both the reduced negative Janzen–Connell
effect and increased realization of niche differentiation and
facilitation. Furthermore, these mechanisms may change and
grow in number with stand development and site environments (Maestre et al. 2009; Cavard et al. 2011; Chen &
Taylor 2011), as comprehensively documented for herbaceous
communities (Isbell et al. 2011).
In conclusion, our results show an average of 23.7% higher
productivity in forest polycultures than monocultures. This
contrasts markedly with a recent paper showing no relationship between productivity and species richness in 48 herbaceous sites (Adler et al. 2011). Although, in part, this may
reflect differences in the gradients across stands of contrasting
diversity, it may also reflect undiscovered issues and processes
that differ in woody and herbaceous systems, and thus merits
further study. Additionally, our analysis highlights the critical
role of species evenness and the presence of contrasting life-history traits in defining DPRs in forest ecosystems and provides
a broad guide for forest management practices aimed at facilitating higher per-unit-area productivity by increasing evenness
and the extent of life-history traits among species in polycultures. The limitations of our analysis call for future studies to
reveal the mechanisms for the roles of evenness, richness and
the extent of life-history traits in defining DPRs.
Acknowledgements
We thank Ellen Macdonald, Phil Comeau and Yves Bergeron for their engaging discussion on the topic, Kevin Crowe, Dave Morris, Brian Brassard and
Margot Downey for their editorial comments and anonymous reviewers for
constructive comments to an earlier version of the manuscript. This study was
supported by the Natural Sciences and Engineering Research Council of Canada (DG 283336-09 and SPG 322297). P.B.R. was supported by the US
National Science Foundation LTER Program (DEB 0620652) and the Wilderness Research Foundation.
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Received 11 August 2011; accepted 25 November 2011
Handling Editor: David Coomes
Supporting Information
Additional Supporting Information may be found in the online
version of this article:
Appendix S1. Supplementary References: the list of references for the
54 published studies used in this meta-analysis.
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2012 The Authors. Journal of Ecology 2012 British Ecological Society, Journal of Ecology, 100, 742–749