Incubation time, functional litter diversity, and habitat characteristics predict litter-mixing effects on decomposition

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).


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.


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.


(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


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.


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.

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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).


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Incubation time, functional litter diversity, and habitat characteristics predict litter-mixing effects on decomposition