Ecology, 92(1), 2011, pp. 160169 2011 by the Ecological Society of America
Incubation time, functional litter diversity, and habitat characteristicspredict litter-mixing effects on decomposition
ANTOINE LECERF,1,5 GUILLAUME MARIE,1 JOHN S. KOMINOSKI,2,6 CARRI J. LEROY,3 CAROLINE BERNADET,1
AND CHRISTOPHER M. SWAN4
1Universite de Toulouse, UPS, INP, CNRS, EcoLab (Laboratoire dEcologie Fonctionnelle),29 Rue Jeanne Marvig, F-31055 Toulouse, France
2Department of Forest Sciences, University of British Columbia, Vancouver, British Columbia V6T1Z4 Canada3Environmental Studies Program, Evergreen State College, Olympia, Washington 98505 USA
4Department of Geography and Environmental Systems, University of Maryland, Baltimore County, Baltimore, Maryland 21250 USA
Abstract. Plant diversity inuences many fundamental ecosystem functions, includingcarbon and nutrient dynamics, during litter breakdown. Mixing different litter species causeslitter mixtures to lose mass at different rates than expected from component species incubatedin isolation. Such nonadditive litter-mixing effects on breakdown processes often occuridiosyncratically because their direction and magnitude change with incubation time, litterspecies composition, and ecosystem characteristics. Taking advantage of results from 18 littermixture experiments in streams, we examined whether the direction and magnitude ofnonadditive mixing effects are randomly determined. Across 171 tested litter mixtures and 510incubation time-by-mixture combinations, nonadditive effects on breakdown were commonand on average resulted in slightly faster decomposition than expected. In addition, we foundthat the magnitude of nonadditive effects and the relative balance of positive and negativeresponses in mixtures change predictably over time, and both were related to an index offunctional litter diversity and selected environmental characteristics. Based on these, it shouldbe expected that nonadditive effects are stronger for litter mixtures made of functionallydissimilar species especially in smaller streams. Our ndings demonstrate that effects of litterdiversity on plant mixture breakdown are more predictable than generally thought. We furtherargue that the consequences of current worldwide homogenization in the composition of planttraits on carbon and nutrient dynamics could be better inferred from long-durationexperiments that manipulate both functional litter diversity and ecosystem characteristics inhotspots of biodiversity effects, such as small streams.
Key words: biodiversity; diversity; ecosystem functioning; functional regularity; leaf litter; litterbreakdown; riparian forest.
INTRODUCTION
Increasing human-induced threats to biodiversity
have prompted extensive research linking biodiversity
to ecosystem functioning. There is now compelling
evidence of the reliance of several key ecosystem
functions on the diversity of plants, microorganisms,
and/or animals across ecosystem types (e.g., Heemsber-
gen et al. 2004, Swan and Palmer 2004, Lecerf et al.
2005, Balvanera et al. 2006, Cardinale et al. 2007, Meier
and Bowman 2008, Srivastava et al. 2009). Recent
syntheses of these data shed light on some general
patterns, as well as fundamental differences, in richness
function relationships across ecosystem functions, tro-
phic levels, and ecosystem types (Balvanera et al. 2006,
Cardinale et al. 2006, Schmid et al. 2009, Srivastava et
al. 2009). Cardinale et al. (2006) found that aquatic and
terrestrial ecosystems could be equally sensitive to loss
of species richness at a single trophic level. Moreover,
not all ecosystem properties are inuenced predictably
by species richness. Plant diversity has been shown to
consistently increase community-aggregated plant bio-
mass, often through niche complementarity effects
(Cardinale et al. 2007). By contrast, no such general
relationship exists between litter species richness and the
rate of plant-litter decomposition, as both positive and
negative effects result in an overall neutral trend
(Srivastava et al. 2009). The relative unpredictability of
litter diversity effects on breakdown of litter mixtures
may be due to differences in environmental conditions,
experimental design (e.g., inclusion of macro-consumers,
experimental duration), and choice of litter species
(Swan and Palmer 2004, Schadler and Brandl 2005,
LeRoy and Marks 2006, Lecerf et al. 2007, Madritch
and Cardinale 2007, Jonsson and Wardle 2008, Srivas-
tava et al. 2009, Rosemond et al. 2010). Furthermore,
there is no reason to expect a strong directional effect of
Manuscript received 12 February 2010; revised 8 June 2010;accepted 16 June 2010. Corresponding Editor: W. V. Sobczak.
5 E-mail: [email protected] Present address: Odum School of Ecology, University of
Georgia, Athens, Georgia 30602 USA.
160
litter species richness on litter mixture breakdown
because the number of litter species has been proven
to be much less important than taxonomic composition
of litter mixtures in controlling litter breakdown rates
(Wardle et al. 1997, Schadler and Brandl 2005, Lecerf et
al. 2007, Schindler and Gessner 2009, Swan et al. 2009).
The difculty in elucidating effects of plant assem-
blages on breakdown is attributed to litter mixing effects
resulting in accelerated or decelerated mass loss of litter
mixtures relative to expected rates based on component
species in isolation (Wardle et al. 1997, Gartner and
Cardon 2004, Hattenschwiler et al. 2005). A meta-
analysis of 23 terrestrial studies and 162 litter mixtures
showed that such nonadditive effects were common for
litter mixtures (approximately two-thirds of tested litter
mixtures), sometimes causing deviations from expected
mass loss as high as 65% (Gartner and Cardon 2004).Synergistic effects (acceleration) on loss rates were also
found to prevail over antagonistic (deceleration) effects
(47% vs. 19% of tested litter mixtures; Gartner andCardon 2004). Nonadditive mixing effects are likely a
consequence of complex interactions between litter
species mediated by abiotic factors and litter consumers.
There are three classes of nonexclusive mechanisms,
which can be disentangled by examining responses of
individual species within litter mixtures (Hattenschwiler
et al. 2005, Gessner et al. 2010). (1) Essential constitu-
ents (i.e., nutrients) or phenolic compounds released
from litter species rich in these elements can diffuse
within litter mixtures, resulting in synergistic or antag-
onistic effects on litter mixture mass loss, respectively
(Hattenschwiler et al. 2005). In addition, litter exploi-
tation by consumers and ultimately litter mass loss may
be faster for diverse mixtures of litters containing
variable relative abundances of key elements (e.g., C,
N, P) because such chemically diverse resources would
increase the opportunity of meeting consumer stoichio-
metric needs (Frost et al. 2005). (2) The physical
structure of litter patches can be altered by mixing
litters of contrasting toughness. This may promote
consumer-mediated decomposition as a result of in-
creased habitat complexity and stability (Hattenschwiler
et al. 2005, Kominoski et al. 2009, Sanpera Calbet et al.
2009) and may result in reduced abiotic breakdown of
the softest species in streams (Swan et al. 2008). (3)
Preferential feeding by invertebrate detritivores may
accelerate or decelerate the loss rate of high-quality
litters or low-quality litters, respectively (Swan and
Palmer 2006b). However, the mechanism of food
selection reported from microcosm experiments may be
modulated in natural systems by biotic interactions; for
instance, competition and predation might force detri-
tivores to feed on less-preferred litters (Bastian et al.
2008).
The functional trait approach represents a promising
framework to explain patterns of nonadditive litter-
mixing effects across species mixtures (Epps et al. 2007,
Meier and Bowman 2008). Following recent ndings on
effects of litter chemical diversity on soil carbon and
nitrogen dynamics (Meier and Bowman 2008) and
current mechanistic understanding of litter mixture
decomposition (Hattenschwiler et al. 2005), it is
expected that nonadditive litter-mixing effects will be
stronger in litter mixtures consisting of species with
dissimilar physical and chemical traits than in litter
mixtures made of functionally equivalent species
(Schindler and Gessner 2009). To date, this hypothesis
has received little support in few experimental studies
examining the inuence of mixture heterogeneity on
litter breakdown (Wardle et al. 1997, Hoorens et al.
2003, Smith and Bradford 2003, Schindler and Gessner
2009). Because these past studies used a single index of
functional diversity each time, their results should be
interpreted with caution in light of methodological
variation in functional diversity assessment (Petchey et
al. 2004, 2009, Mason et al. 2005, Villegier et al. 2008,
Poos et al. 2009). There is growing recognition that the
choice of functional diversity metric could be as critical
as the choice of species trait to detect functional litter
diversity effects (Petchey et al. 2004, Mason et al. 2005,
Villegier et al. 2008, Poos et al. 2009). Given the lack of
a consensus on how functional diversity should be
measured, the best guarantee against overlooking
ecologically relevant effects of functional diversity is to
incorporate several independent metrics (Mason et al.
2005, Villegier et al. 2008, Petchey et al. 2009).
Here we synthesize information available on litter
breakdown of assembled litter mixtures incubated in
streams (see Plate 1). Litter breakdown refers to litter
disappearance from mesh bags due to (catabolic)
decomposition processes and both biotic and abiotic
fragmentation (Gessner et al. 1999). Stream ecosystems
strongly rely on carbon and nutrients released by
decaying terrestrial plant litter (Gessner et al. 1999,
Wallace et al. 1999). Although litter disappears generally
faster in aquatic ecosystems, the underlying biotic
mechanisms in streams are not fundamentally different
from those operating in soils (Wagener et al. 1998,
Gessner et al. 2010). Contribution of stream studies to
research on litter diversitybreakdown relationships has
been substantial (e.g., Kominoski et al. 2007, Lecerf et
al. 2007, Schindler and Gessner 2009, Swan et al. 2009,
Rosemond et al. 2010). For this synthesis, we took
advantage of the opportunity to gather a wide array of
raw data, including time series. Unlike a recent meta-
analysis (Srivastava et al. 2009), which examined effects
of consumer and litter species richness on decomposi-
tion, our database includes individual lines of data for
each species assemblage, enabling us to calculate
functional litter diversity indices for each mixture and
independently assess effects of incubation time on
nonadditive effects.
We estimated the magnitude and direction of nonad-
ditive litter-mixing effects on breakdown and the sources
of variation both within and across experiments. We
specically tested the following hypotheses: (1) nonad-
January 2011 161CONTROLS ON LITTER-MIXING EFFECTS
ditive effects occur frequently in stream experiments; (2)
the direction (synergistic vs. antagonistic) and magni-tude of nonadditive effects change with incubation time
and functional litter diversity within experiments; and(3) differences between experiments are explained by
ecosystem characteristics.
METHODS
Data collection
We identied a total of 18 experiments reported in 11published or unpublished studies assessing the break-
down of 171 assembled mixtures of leaf litter enclosed inmesh bags and incubated in streams (Appendix A). All
but two experiments had mixtures of leaf species; the twoexceptions focused on litter genotypes, which we
included here under the umbrella term species forthe sake of consistency. An experiment consisted of a
trial including 1 to 30 mixtures assembled from a pool of3 to 20 litter species, enclosed in mesh bags, and
incubated in a single stream site during a speciedseason. Litter mixtures differed within experiments in
terms of composition and, sometimes, relative initialmass of the component species (evenness). Breakdown oflitter mixtures was measured from 18 sampling dates,
and varied by experiment. The data set included 510 linesof data corresponding to the number of incubation time-
by-mixture combinations (Appendix A).
Response variables
Observed (O) and expected (E) mass remaining of
litter mixtures was expressed as a fraction of initial littermass. Expected mass remaining was dened as the mean
mass remaining of the component litter species decom-posing in isolation weighted by their relative initial mass
in the mixture (Gartner and Cardon 2004, Lecerf et al.2007). The difference between O and E indicates
deviation from additivity. As in Wardle et al. (1997),we calculated a signed (O E) and an unsigneddeviation (jO Ej0.33 with the 0.33 exponent used toachieve normal distribution of data) to assess the
direction and magnitude of nonadditive mixing effectson breakdown, respectively. Negative deviations indi-cate synergistic responses (acceleration of litter break-
down), and positive deviations indicate antagonisticresponses (deceleration of litter breakdown) of litter
mixtures.
Functional litter diversity (FLD)
Following Mason et al. (2005) and Villegier et al.
(2008) we calculated three complementary functionaldiversity indices: functional richness, functional regular-
ity, and functional divergence of litter mixtures (Appen-dix B). Briey, functional richness is the standardized
range of trait values for species mixtures. It takes themaximal value of 1 for litter mixtures comprising the
species with the lowest and highest trait valuesregardless of the number of species. Functional regular-
ity is a measure of deviation from a null model of
uniform distribution of continuous traits in mixtures
with no less than three litter species (Mouillot et al.
2005). This index, which cannot be calculated for two-
species mixtures, has a maximal value of 1 when species
are equally spaced along the trait axis and have equal
initial mass. Lower values can be due to unequal
distances between species along the trait axis and/or
variable relative mass by species (Mouillot et al. 2005).
Functional divergence is calculated as the abundance-
weighted variance of trait values across the component
species followed by an arctangent transformation
constraining the variation with a 01 range (Mason et
al. 2005). High values are indicative of wide dispersion
of trait values around the mean.
The three functional diversity indices were calculated
from a single trait, specic litter degradability, which
was easily accessible from all the reported experiments.
Specic litter degradability combines important infor-
mation on litter persistence, toughness, and nutritional
value (Cadisch and Giller 1997, Cornwell et al. 2008).
Within-experiment specic litter degradability varied on
average over a 10.5-fold range. Because functional
richness, regularity, and divergence were not indepen-
dent of each other, we condensed information contained
in these variables into two composite, orthogonal
functional litter diversity indices (FLD1 and FLD2)
using principal component analysis (PCA). This analysis
was performed using a nonlinear iterative partial least
squares (NIPALS) algorithm, an interpolation process
which replaced nonexisting values of the functional
regularity index for two-species mixtures. PCA loadings
indicated that FLD1 increased with both functional
richness and divergence whereas FLD2 was independent
of these indices and increased with functional regularity
(Appendix B). Thus, the two synthetic indices were able
to discriminate between homogeneous (low FLD1 and
FLD2 values) and heterogeneous (high FLD1 and
FLD2 values) mixtures.
Ecosystem characteristics and experimental designs
We recorded abiotic ecosystem characteristics for
each experiment, including information on the geo-
graphic location (longitude, latitude, and elevation),
stream size (Strahler order), and mean water tempera-
ture. As our database did not cover full ranges of
latitude and longitude, we proceeded to run a compar-
ison between high (.208 N) and low (208 S208 N)latitudes, and between continents (Europe vs. [North South America]).
Data analysis
Mixed-effects linear models and model selection
procedures were used to assess the sources of variation
of signed and unsigned deviations from additivity
between and within experiments (Zuur et al. 2009).
Experiment was dened as a random term and
explanatory variables as xed terms. Quadratic terms
were used as necessary to model the U-shaped relation-
ANTOINE LECERF ET AL.162 Ecology, Vol. 92, No. 1
ship with time (Appendix C). A general form of the
model used was:
Yij l aij bi ai eij; ai ;N 0; r2a; eij ;N 0; r2i where Yij is the value of signed or unsigned deviation
from additivity of the observation j from the experiment
i, l is a xed intercept, aij is a matrix of within-experiment variables (FLD1 FLD2 incubationtime), bi is a matrix of between-experiment variables, aiis a random intercept for the experiment i, and eij is theresidual error. Here, we assumed that the error term has
different variances (r2i ) for each experiment (Pinherosand Bates 2000).
For each signed and unsigned deviation, an optimal
model was tted using a top-down approach (Zuur et al.
2009). We rst constructed beyond-optimal models,
using a restricted set of uncorrelated explanatory
variables (Appendix C). A second step involved
sequential backward deletion of nonsignicant and least
important xed effects according to F tests using type III
sum of squares. The signicance threshold was set at P0.05. Akaikes Information Criterion calculated with the
maximum likelihood method (ML-AIC) was recorded
at each step to check for goodness of t (Zuur et al.
2009). The optimal model was the model with the lowest
ML-AIC value, which included only signicant xed
effects. Mixed-effects models were performed in R with
the nlme library (Pinheros and Bates 2000). The
stepAIC procedure in the MASS package in R was
used for backward deletions (Venables and Ripley
2002).
RESULTS
General trends in nonadditive mixing effects
The bivariate relationship between observed and
expected litter mass remaining revealed frequent detect-
able deviations of individual litter mixtures from the 1:1
line, thus providing evidence of nonadditive breakdown
(Fig. 1A). Only 17.4% of litter mixtures decomposed
additively (i.e., observed and expected litter mass
remaining differed by ,0.01). The other litter mixturesdecomposed either faster (43.9%) than expected as a
result of synergistic responses (points below the 1:1 line
on Fig. 1A) or slower (38.7%) than expected as a result
of antagonistic responses (points above the 1:1 line on
Fig. 1A). The magnitude of the deviations from
additivity was as high as 0.48 for synergistic mixing
effects and 0.30 for antagonistic mixing effects (Fig. 1A).
The grand mean of the signed deviations from
additivity was signicantly lower than 0, indicating a
general trend of accelerated breakdown in litter mixtures
across the data set (Fig. 1B). Moreover, considerable
differences existed among experiments. Mean values of
the signed deviation for eight of 18 experiments were
signicantly different from 0 as a result of the prevalence
of antagonistic mixing effects in four experiments (O E
. 0) or synergistic mixing effects (O E , 0) in fourothers. No such directional nonadditive effects were
found in the other 10 experiments where the condence
intervals crossed 0 (Fig. 1B).
FIG. 1. (A) Observed vs. expected litter mass remaining ofthe 510 individual mixtures of litter species incubated instreams. The 1:1 axis (solid line) represents additivity. (B)Signed deviation (mean with 95% CI) from additivity (observedminus expected litter mass remaining) by experiment (graydots) and across all experiments (black dot). Negativedeviations from additivity indicate synergistic responses (accel-eration of litter breakdown), and positive deviations indicateantagonistic responses (deceleration of litter breakdown) oflitter mixtures. Experiment identication codes are displayedalong the vertical axis (see Appendix A).
January 2011 163CONTROLS ON LITTER-MIXING EFFECTS
Predicting nonadditive mixing effects
Within-experiment variation in nonadditive effects on
litter mixture breakdown was accounted for by incuba-
tion time and functional litter diversity. Optimal models
for signed and unsigned deviations from additivity
included both incubation time (1190 days; median: 35
days) and an index of functional litter diversity (FLD2)
as signicant predictors (Table 1). Incubation time was
negatively related to signed deviation and positively
related to unsigned deviation (Fig. 2A, B). This was due
to an increase in frequency of synergistic effects (45.8%of the mixtures below and 60.4% above the median valueof incubation time; Fig. 2A) and in the magnitude of
nonadditive mixing effects with incubation time (Fig.
2B). FLD2 was used as a surrogate of functional
regularity of litter species mixtures and was independent
of functional richness and divergence (FLD1) (Appen-
dix B). The positive relationship between FLD2 and
signed deviation was due to a shift in the balance of
synergistic and antagonistic mixing effects (Fig. 1C).
Synergistic effects were more frequent below the median
FLD2 value (56.2% of the mixtures), whereas there wasan almost exact balance of synergistic (50.9%) andantagonistic (49.1%) effects above the median FLD2value. Importantly, we found that, irrespective of their
direction, the magnitude of nonadditive mixing effects
increased linearly with FLD2 (Fig. 2D).
Optimal mixed models reveal that two environmental
factors were likely important in accounting for cross-
experiment variability of nonadditive mixing effects
(Table 1). A U-shaped relationship between signed
deviation and mean water temperature highlighted a
nontrivial effect of temperature across a wide gradient
(2228C: Fig. 2E). The lowest and highest temperatureswere associated with nonadditive antagonistic effects or
additive breakdown, whereas intermediate temperatures
(6108C) were most associated with synergisticresponses of litter mixtures (Fig. 2E). For instance, the
four experiments where synergistic mixing effects
prevailed were all conducted in streams with intermedi-
ate mean temperatures (7.88.98C) whereas the fourexperiments where antagonistic mixing effects prevailed
were conducted in colder (4.58C) or warmer (16.922.08C) streams (see also Fig. 1B). Additionally,unsigned deviation was negatively related to stream size
(Table 1), indicating that the largest effect of litter
mixing occurred in the smallest streams studied (Fig.
2F).
Robustness of results
The striking similarity of maximum likelihood (ML)
and restricted maximum likelihood (REML) estimates
(Table 1) suggested no computational issues for mixed-
effects models. One problem in interpretation of the
TABLE 1. Assessing the source of variation of signed and unsigned deviations by mixed-effects models and model selection.
Model description ML-AIC df F P
Estimates
ML REML
Signed deviation
Beyond-optimal model
Intercept incubation time FLD1 FLD2 temperature temperature2
1343.1
FLD1 1344.9 Temperature 1346.5
Optimal model
Intercept 1, 490 11.4 0.0008 0.0215 0.0217Incubation time 1, 490 14.6 0.0001 0.00025 0.00025FLD2 1, 490 8.0 0.0049 0.00564 0.00564Temperature2 1, 16 14.0 0.0018 0.00042 0.00043
Unsigned deviation
Beyond-optimal model
Intercept incubation time FLD1 FLD2 stream size
765.4
FLD1 766.4Optimal model
Intercept 1, 490 1266 ,0.0001 0.3359 0.3363Incubation time 1, 490 43.6 ,0.0001 0.00075 0.00076FLD2 1, 490 9.9 0.0017 0.01402 0.01351Stream size 1, 16 6.5 0.0224 0.02710 0.02760
Notes: Beginning with a beyond-optimal model presented in Table C2 (Appendix C), we removed the least important xedfactors sequentially ( FLD1, Temperature). We calculated the Akaike Information Criterion using maximum likelihoodmethods (ML-AIC) at each step. We stopped removing xed factors when the lowest ML-AIC was reached, corresponding tooptimal models. Signicance of xed factors in the optimal model was tested using an F test based on type III sum of squares. Afterrecording ML estimates, optimal models were retted using the restricted maximum likelihood (REML) approach for validation.Note that estimates are given for centered variables.
ANTOINE LECERF ET AL.164 Ecology, Vol. 92, No. 1
inuence of the FLD2 index on nonadditive effects
could have stemmed from interpolation of missing
functional regularity values for two species mixtures.
To assess if FLD2 estimates would change after the
removal of interpolated values, we tted new mixed
models on a subset of data without the two species
mixtures (n 343). Signed deviation from additivityremained positively related to FLD2 (estimate 0.0048) even though this effect was only marginallysignicant (F1, 325 2.9, P 0.0844). Unsigned deviationremained positively related to FLD2 (estimate 0.0132) and was still a signicant predictor (F1, 325 4.2, P 0.0413). These analyses conrm that FLD2 hadan inuence on the magnitude of nonadditive mixing
effects.
Last, we found support for our hypothesis that cross-
experiment variation of nonadditive effects was not
driven by experimental design. Correlation analyses did
not reveal signicant association between potentially
important design factors (mesh size of litter bags,
experimental duration, and maximal functional litter
diversity as determined by FLD1 and FLD2) and the
ecosystem characteristics included in the optimal model
of signed (water temperature) and unsigned (stream size)
deviations. For instance, no correlation was found
between mesh size of litter bags (ne [2 mm] vs. coarse[.5 mm]) and water temperature (r 0.02) or streamsize (r 0.09). The strongest correlation (r , 0.41),which was found between stream size and maximum
FLD2, remained nonsignicant (P 0.088).DISCUSSION
Our synthesis of 18 experiments conducted in
American (North and South) and European streams
examining the breakdown rate of 171 assembled
mixtures of leaf litter revealed that breakdown of litter
mixtures was often not equal to the weighted average of
mass loss values for the component species breaking
down in isolation. We also found that synergistic effects
were more frequent than antagonistic effects. The same
trends were reported from a previous meta-analysis of
terrestrial studies (Gartner and Cardon 2004), hinting at
a general pattern across ecosystem types (Cardinale et
al. 2006, Srivastava et al. 2009). The mechanisms
responsible for nonadditive litter-mixing effects on
breakdown may, however, differ between aquatic and
terrestrial ecosystems although this assumption is based
on indirect evidence rather than experimental support
FIG. 2. Signed and unsigned deviation between observed (O) and expected (E) litter mass remaining as a function ofexplanatory xed variables in optimal mixed-effects models (see Table 1). Variables include the number of days litter mixtures wereincubated in streams, an index of functional litter diversity (FLD2), stream temperature, and stream size. Stream size increases fromleft to right as Strahler order increases. Regression lines and curves are drawn from back-transformed (decentered) restrictedmaximum likelihood (REML) estimates.
January 2011 165CONTROLS ON LITTER-MIXING EFFECTS
(Gessner et al. 2010). It is worth noting that frequent
synergistic mixing effects on litter breakdown are
compatible with the lack of a general relationship
between litter species richness and decomposition
reported by Srivastava et al. (2009). Such a disconnect
between net and gross effects of litter diversity could be
driven by idiosyncratic variation in nonadditive mixing
effects over litter species richness and by an overriding
control of litter trait composition (additive effects) on
mixture mass loss rates (Wardle et al. 1997, Schadler
and Brandl 2005, Lecerf et al. 2007, Schindler and
Gessner 2009).
This study showed that incubation time is an
important design consideration if the goal is to capture
the full range of litter diversity effects on breakdown.
Srivastava et al. (2009) also reported a trend for larger
effects of litter species richness on decomposition in
long-duration experiments across aquatic and terrestrial
studies. These ndings are consistent with outcomes of a
recent synthesis of diversityproductivity experiments
showing that productivity increases with time due to
strengthened complementarity effects (Cardinale et al.
2007). The latter explanation may hold true for litter
processing, which involves a successional change in
dominant litter consumers (fungidetritivoresbacteria)
that work synergistically at breaking down leaf litter
(Gessner et al. 1999, Hattenschwiler et al. 2005, Lecerf et
al. 2005, Gessner et al. 2010). In addition, it is possible
that early dominant colonizers, such as fungi, do not
cause substantial deviation from additive breakdown
(Schadler and Brandl 2005, Swan and Palmer 2006a,
Sanpera Calbet et al. 2009, Schindler and Gessner 2009).
By contrast, large detritivores, which usually colonize
litter patches following microbes, are more likely to
generate powerful nonadditive mixing responses in litter
breakdown as reported from invertebrate addition
experiments (Hattenschwiler and Gasser 2005, Swan
and Palmer 2006a, b). Niche complementarity effects
and detritivore colonization may thus explain, in part,
why the magnitude of nonadditive mixing effects
increased with incubation time.
Our results conrm the notion that interspecic
functional dissimilarity could explain net (i.e., nonaddi-
tive) biodiversity effects on ecosystem functioning
(Heemsbergen et al. 2004, Meier and Bowman 2008).
Meier and Bowman (2008) found that after controlling
for litter chemical composition, soil respiration tended
to be higher when chemically distinct litter species were
assembled. Our results may corroborate this inuence of
litter chemical diversity on organic matter decomposi-
tion, assuming that specic litter degradability is a
correlate of the concentrations of key litter chemical
constituents (Cadisch and Giller 1997, Cornwell et al.
2008). Moreover, mixing litter species of contrasting
degradability may improve microhabitat structure and
persistence through time, which could in turn modify
consumerresource interactions in ways that cause
strong nonadditive mixing effects (Kominoski et al.
2009, Sanpera Calbet et al. 2009). These contrasting
hypotheses emphasize the need for an integrated
mechanistic understanding of how functional litter
diversity inuences decomposition and consumer diver-
sity, which requires disentangling the relative contribu-
tion of various structural and chemical traits of litter as
well as consumer traits to functional litter diversity
effects on decomposition.
Our study sheds light on a general relationship
between functional litter diversity and the magnitude
of nonadditive litter-mixing effects on breakdown. The
few studies that have specically assessed the inuence
of functional litter dissimilarity in streams and soils have
produced equivocal results (Wardle et al. 1997, Hoorens
et al. 2003, Smith and Bradford 2003, Schindler and
Gessner 2009). However, these studies did not use more
thorough indices to test functional litter diversity. This is
an important consideration, as study outcomes can be
greatly inuenced by the choice of functional diversity
metric (Petchey et al. 2004, Poos et al. 2009). Here, we
demonstrate that acknowledging the multifaceted nature
of functional diversity could protect against overlooking
their ecologically relevant effects (Mason et al. 2005,
Villegier et al. 2008). We found that only one (FLD2) of
the two tested independent indices detected functional
litter diversity effects. FLD2 was used as a surrogate of
functional regularity, an overlooked facet of functional
diversity (Mouillot et al. 2005). The functional regular-
ity index is by denition heavily penalized by species
trait overlaps (Mouillot et al. 2005), whereas the
complementary facets of functional diversity (functional
richness and divergence), which have been used to
measure functional litter diversity (Wardle et al. 1997,
Hoorens et al. 2003), are likely to overestimate true
functional diversity when functionally equivalent species
are present (Mason et al. 2005, Villegier et al. 2008).
Our synthesis reveals that, if nonadditive mixing
effects occurred in experiments frequently, predicting
their direction and magnitude must consider the
environmental context. Although several previous stud-
ies have reported considerable variability in nonadditive
mixing effects across experiments, trials, and/or sites,
regardless of ecosystem type, most of these studies fell
short in elucidating the underlying determinants (Swan
and Palmer 2004, LeRoy and Marks 2006, Lecerf et al.
2007, Madritch and Cardinale 2007). The relationship
between stream temperature and signed deviation
indicates seasonal shift of the direction and magnitude
of nonadditive breakdown in temperate streams, at
least. This was rst evidenced by Swan and Palmer
(2004) who found additive breakdown in autumn (mean
temperature ;48C) and antagonistic response of littermixture in summer (mean temperature ;228C).Nonadditivity appears to emerge as much stronger in
low-order reaches, and decreases downstream as stream
size increases. The importance of considering ecosystem
size on litter mixing has also been illustrated in
ANTOINE LECERF ET AL.166 Ecology, Vol. 92, No. 1
terrestrial ecosystems (Jonsson and Wardle 2008),
whereby island size inuenced the patterns of decom-
position for litter mixtures in soils. This previous
(Jonsson and Wardle 2008) and our present study may
provide indirect evidence of bottom-up regulation of
microbial decomposition in terrestrial and aquatic
ecosystems; soil fertility increased along the gradient of
island size (Jonsson and Wardle 2008), and solute
nutrients are expected to progressively increase along
the upstreamdownstream gradient. Alternatively, we
propose that decreased magnitude of nonadditive
mixing effects along the river continuum might be
explained, in part, by a shift from strong biotic
interactions typical of headwater streams (Power and
Dietrich 2002, Meyer et al. 2007), and weaker interac-
tions downstream owing to an increase in hydrological
unpredictability and frequent sediment disturbance
(Benda et al. 2004). In headwater streams, hydrological
disturbance, outside of drying, is generally weak, and
should contribute less to the physical abrasion of
particulate organic matter than in downstream loca-
tions. In addition, density of invertebrate detritivores
may be maximal in low-order stream reaches (Jonsson et
al. 2001). As nonadditive litter-mixing effects on
breakdown depend on feeding activity of invertebrates
and litter conditioning by microbial consumers (Hat-
tenschwiler and Gasser 2005, Schadler and Brandl 2005,
Swan and Palmer 2006a, b), locations where consumers
have the strongest effect on litter breakdown are likely
also to exhibit higher magnitudes of nonadditive effects.
In conclusion, this synthesis demonstrates that
nonadditive effects on litter mixture breakdown occur
frequently and are more predictable than generally
expected from individual studies. The striking inuences
of incubation time and functional litter diversity on the
magnitude of nonadditive litter-mixing effects suggest
that short-term experiments assessing effects of taxo-
nomic attributes of litter mixtures could underestimate
PLATE 1. Studies of leaf breakdown processes in forest streams, such as the one shown above, have provided important insightsinto the functional role of biodiversity in ecosystems. Photo credit: A. Lecerf.
January 2011 167CONTROLS ON LITTER-MIXING EFFECTS
the genuine importance of litter diversity for organic
matter decomposition. This nding should encourage
the design of long-duration experiments based on litter
mixtures spanning a broad gradient of chemical and
structural diversity. Although functional regularity
(FLD2) appears to be a good candidate metric to assess
functional litter diversity in this case, it may be too early
to recommend the use of single index rather than
multiple independent indices in further studies. The
spatial scale at which functional litter diversity is
manipulated should also be enlarged in order to
encompass ecological processes operating at a scale
larger than what occurs in litter bags. Finally, we
contend that a better grasp of the determinants of
nonadditive mixing effects could be gained by conduct-
ing experiments in hotspots of biodiversity effects,
such as small temperate streams, where ecosystem
characteristics could be manipulated concomitantly
(e.g., Rosemond et al. 2010). Current and future studies
will have important implications for predicting the
consequences of current worldwide homogenization of
plant trait composition on carbon and nutrient dynam-
ics through afterlife effects (Meier and Bowman 2008,
Laliberte et al. 2010).
ACKNOWLEDGMENTS
This work was supported by grants from the French Ofcefor Water and Aquatic Ecosystems (ONEMA) to A. Lecerf.Constructive comments by two reviewers helped improve theclarity of the paper.
LITERATURE CITED
Balvanera, P., A. B. Psterer, N. Buchmann, J. S. He, T.Nakashizuka, D. Raffaelli, and B. Schmid. 2006. Quantifyingthe evidence for biodiversity effects on ecosystem functioningand services. Ecology Letters 9:11461156.
Bastian, M., R. G. Pearson, and L. Boyero. 2008. Effects ofdiversity loss on ecosystem function across trophic levels andecosystems: a test in a detritus-based tropical food web.Austral Ecology 33:301306.
Benda, L. E., N. L. Poff, D. Miller, T. Dunne, G. H. Reeves, G.Pess, and M. Pollock. 2004. The network dynamicshypothesis: how channel networks structure riverine habitats.BioScience 54:413427.
Cadisch, G., and K. E. Giller. 1997. Driven by nature. Plantlitter quality and decomposition. CAB International, Wall-ingford, UK.
Cardinale, B. J., D. S. Srivastava, J. E. Duffy, J. P. Wright,A. L. Downing, M. Sankaran, and C. Jouseau. 2006. Effectsof biodiversity on the functioning of trophic groups andecosystems. Nature 443:989992.
Cardinale, B. J., J. P. Wright, M. W. Cadotte, I. T. Carroll, A.Hector, D. S. Srivastava, M. Loreau, and J. J. Weis. 2007.Impacts of plant diversity on biomass production increasethrough time due to species complementarity. Proceedings ofthe National Academy of Sciences USA 104:1812318128.
Cornwell, W. K., et al. 2008. Plant species traits are thepredominant control on litter decomposition rates withinbiomes worldwide. Ecology Letters 11:10651071.
Epps, K. Y., N. B. Comerford, J. B. Reeves III, W. P. Cropper,Jr., and Q. R. Araujo. 2007. Chemical diversityhighlightinga species richness and ecosystem function disconnect. Oikos116:18311840.
Frost, P. E., M. E. Evans-White, Z. V. Finkel, T. C. Jensen, andV. Matzek. 2005. Are you what you eat? Physiologicalconstraints on organismal stoichiometry in an elementallyimbalanced world. Oikos 109:1828.
Gartner, T. B., and Z. G. Cardon. 2004. Decompositiondynamics in mixed-species leaf litter. Oikos 104:230246.
Gessner, M. O., E. Chauvet, and M. Dobson. 1999. Aperspective on leaf litter breakdown in streams. Oikos 85:377384.
Gessner, M. O., C. M. Swan, C. K. Dang, B. G. McKie, R. D.Bardgett, D. H. Wall, and S. Hattenschwiler. 2010. Diversitymeets decomposition. Trends in Ecology and Evolution 25:372380.
Hattenschwiler, S., and P. Gasser. 2005. Soil animals alter plantlitter diversity effects on decomposition. Proceedings of theNational Academy of Sciences USA 102:15191524.
Hattenschwiler, S., A. V. Tiunov, and S. Scheu. 2005.Biodiversity and litter decomposition in terrestrial ecosys-tems. Annual Review of Ecology and Systematics 36:191218.
Heemsbergen, D. A., M. P. Berg, M. Loreau, J. R. van Hal,J. H. Faber, and H. A. Verhoef. 2004. Biodiversity effects onsoil processes explained by inter-specic functional dissimi-larity. Science 306:10191020.
Hoorens, B., R. Aerts, and M. Stroetenga. 2003. Does initiallitter chemistry explain litter mixture effects on decomposi-tion? Oecologia 442:578586.
Jonsson, M., B. Malmqvist, and P.-O. Hoffsten. 2001. Leaflitter breakdown rates in boreal streams: does shredderspecies richness matter? Freshwater Biology 46:161171.
Jonsson, M., and D. A. Wardle. 2008. Context dependency oflitter-mixing effects on decomposition and nutrient releaseacross a long-term chronosequence. Oikos 117:16741682.
Kominoski, J. S., T. J. Hoellein, J. J. Kelly, and C. M. Pringle.2009. Does mixing litter of different qualities alter streammicrobial diversity and functioning on individual litterspecies? Oikos 118:457463.
Kominoski, J. S., C. M. Pringle, B. A. Ball, M. A. Bradford,D. C. Coleman, D. B. Hall, and M. D. Hunter. 2007.Nonadditive effects of leaf-litter species diversity on break-down dynamics in a detritus-based stream. Ecology 88:11671176.
Laliberte, E., et al. 2010. Land use intensication reducesfunctional redundancy and response diversity in plantcommunities. Ecology Letters 13:7686.
Lecerf, A., M. Dobson, C. K. Dang, and E. Chauvet. 2005.Riparian plant species loss alters trophic dynamics indetritus-based stream ecosystems. Oecologia 146:432442.
Lecerf, A., G. Risnoveanu, C. Popescu, M. O. Gessner, and E.Chauvet. 2007. Decomposition of diverse litter mixtures instreams. Ecology 88:219227.
LeRoy, C. J., and J. C. Marks. 2006. Litter quality, streamcondition, and litter diversity inuence decomposition ratesand macroinvertebrate communities. Freshwater Biology 51:605617.
Madritch, M. D., and B. J. Cardinale. 2007. Impacts of treespecies diversity on litter decomposition in northern temper-ate forests of Wisconsin, USA: a multi-site experiment alonga latitudinal gradient. Plant Soil 292:147159.
Mason, N. W. H., D. Mouillot, W. G. Lee, and J. B. Wilson.2005. Functional richness, functional evenness and functionaldivergence: the primary components of functional diversity.Oikos 111:112118.
Meier, C. L., and W. D. Bowman. 2008. Links between plantlitter chemistry, species diversity, and below-ground ecosys-tem function. Proceedings of the National Academy ofSciences USA 105:1978019785.
Meyer, J. L., D. L. Strayer, J. B. Wallace, S. L. Eggert, G. S.Helfman, and N. E. Leonard. 2007. The contribution ofheadwater streams to biodiversity in river networks. Journalof the American Water Resources Association 43:86103.
ANTOINE LECERF ET AL.168 Ecology, Vol. 92, No. 1
Mouillot, D., N. W. H. Mason, O. Dumay, and J. B. Wilson.2005. Functional regularity: a neglected aspect of functionaldiversity. Oecologia 142:353359.
Petchey, O. L., A. Hector, and K. J. Gaston. 2004. How dodifferent measures of functional diversity perform? Ecology85:847857.
Petchey, O. L., E. OGorman, and D. F. B. Flynn. 2009. Afunctional guide to functional diversity measures. Pages 4959 in S. Naeem, D. E. Bunker, A. Hector, M. Loreau, and C.Perrings, editors. Biodiversity, ecosystem functioning, andhuman wellbeing: an ecological and economic perspective.Oxford University Press, Oxford, UK.
Pinheros, J. C., and D. M. Bates. 2000. . Mixed-effects modelsin S and S-PLUS. Springer-Verlag, New York, New York,USA.
Poos, M. S., S. C. Walker, and D. A. Jackson. 2009.Functional-diversity indices can be driven by methodologicalchoices and species richness. Ecology 90:341347.
Power, M. E., and W. E. Dietrich. 2002. Food webs in rivernetworks. Ecological Research 17:451471.
Rosemond, A. D., C. M. Swan, J. S. Kominoski, and S. E. Dye.2010. Non-additive effects of litter mixing are canceled in anutrient-enriched stream. Oikos 19:326336.
Sanpera Calbet, I., A. Lecerf, and E. Chauvet. 2009. Leafdiversity inuences in-stream litter decomposition througheffects on shredders. Freshwater Biology 54:16711682.
Schadler, M., and R. Brandl. 2005. Do invertebrate decom-posers affect the disappearance rate of litter mixtures? SoilBiology and Biochemistry 37:329337.
Schindler, M., and M. O. Gessner. 2009. Functional leaf traitsand biodiversity effects on litter decomposition in a stream.Ecology 90:16411649.
Schmid, B., P. Balvanera, B. J. Cardinale, J. Godbold, A. B.Psterer, D. Raffaelli, M. Solan, and D. S. Srivastava. 2009.Consequences of species loss for ecosystem functioning:meta-analyses of data from biodiversity experiments. Pages1429 in S. Naeem, D. E. Bunker, A. Hector, M. Loreau, andC. Perrings, editors. Biodiversity, ecosystem functioning, andhuman wellbeing: an ecological and economic perspective.Oxford University Press, Oxford, UK.
Smith, V. C., and M. A. Bradford. 2003. Do non-additiveeffects on decomposition rates in litter-mix experiments result
from differences in resource quality between litters? Oikos102:235243.
Srivastava, D. S., B. J. Cardinale, A. L. Downing, J. E. Duffy,C. Jouseau, M. Sankaran, and J. P. Wright. 2009. Diversityhas stronger top-down than bottom-up effects on decompo-sition. Ecology 90:10731083.
Swan, C. M., M. A. Gluth, and C. L. Horne. 2009. Leaf litterspecies evenness inuences nonadditive breakdown in aheadwater stream. Ecology 90:16501658.
Swan, C. M., B. Healey, and D. C. Richardson. 2008. The roleof native riparian tree species in decomposition of invasivetree of heaven (Ailanthus altissima) leaf litter in an urbanstream. Ecoscience 15:2735.
Swan, C. M., and M. A. Palmer. 2004. Leaf diversity alters litterbreakdown in a Piedmont stream. Journal of the NorthAmerican Benthological Society 23:1528.
Swan, C. M., and M. A. Palmer. 2006a. Composition ofspeciose leaf litter alters stream detritivore growth, feedingactivity and leaf breakdown. Oecologia 147:469478.
Swan, C. M., and M. A. Palmer. 2006b. Preferential feeding byan aquatic detritivore mediates non-additive decompositionof speciose leaf litter. Oecologia 149:107114.
Venables, W. N., and B. D. Ripley. 2002. Modern appliedstatistics with S. Fourth edition. Springer-Verlag, New York,New York, USA.
Villegier, S., N. W. H. Mason, and D. Mouillot. 2008. Newmultidimensional functional diversity indices for a multifac-eted framework in functional ecology. Ecology 89:22902301.
Wagener, S. M., M. W. Oswood, and J. P. Schimel. 1998.Rivers and soils: parallels in carbon and nutrient processing.BioScience 48:104108.
Wallace, J. B., S. L. Eggert, J. L. Meyer, and J. R. Webster.1999. Effects of resource limitation on a detrital-basedecosystem. Ecological Monographs 69:409442.
Wardle, D. A., K. I. Bonner, and K. S. Nicholson. 1997.Biodiversity and plant litter: experimental evidence whichdoes not support the view that enhanced species richnessimproves ecosystem function. Oikos 79:247258.
Zuur, A. F., E. N. Ieno, N. J. Walker, A. A. Saveliev, andG. M. Smith. 2009. Mixed effects models and extensions inecology with R. Statistics for Biology and Health. Springer-Verlag, New York, New York, USA.
APPENDIX A
A summary of studies and experiments selected for the synthesis (Ecological Archives E092-012-A1).
APPENDIX B
Quantication of functional litter diversity (Ecological Archives E092-012-A2).
APPENDIX C
Beyond optimal mixed-effects models for signed and unsigned deviations from additivity (Ecological Archives E092-012-A3).
SUPPLEMENT
Data set used in the synthesis (Ecological Archives E092-012-S1).
January 2011 169CONTROLS ON LITTER-MIXING EFFECTS
/ColorImageDict > /JPEG2000ColorACSImageDict > /JPEG2000ColorImageDict > /AntiAliasGrayImages false /CropGrayImages false /GrayImageMinResolution 150 /GrayImageMinResolutionPolicy /OK /DownsampleGrayImages false /GrayImageDownsampleType /Average /GrayImageResolution 300 /GrayImageDepth 8 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages true /GrayImageFilter /FlateEncode /AutoFilterGrayImages false /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict > /GrayImageDict > /JPEG2000GrayACSImageDict > /JPEG2000GrayImageDict > /AntiAliasMonoImages false /CropMonoImages false /MonoImageMinResolution 1200 /MonoImageMinResolutionPolicy /OK /DownsampleMonoImages false /MonoImageDownsampleType /Average /MonoImageResolution 1200 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict > /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (U.S. Web Coated \050SWOP\051 v2) /PDFXOutputConditionIdentifier (CGATS TR 001) /PDFXOutputCondition () /PDFXRegistryName (http://www.color.org) /PDFXTrapped /Unknown
/Description > /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [ > > /FormElements true /GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks false /IncludeInteractive false /IncludeLayers false /IncludeProfiles true /MarksOffset 6 /MarksWeight 0.250000 /MultimediaHandling /UseObjectSettings /Namespace [ (Adobe) (CreativeSuite) (2.0) ] /PDFXOutputIntentProfileSelector /UseName /PageMarksFile /RomanDefault /PreserveEditing true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling /LeaveUntagged /UseDocumentBleed false >> ] /SyntheticBoldness 1.000000>> setdistillerparams> setpagedevice
Top Related