Download Detritivore stoichiometric diversity alters litter processing efficiency in

Oikos 125: 1162–1172, 2016
doi: 10.1111/oik.02788
© 2015 The Authors. Oikos © 2015 Nordic Society Oikos
Subject Editor: Shawn Wilder. Editor-in-Chief: Dries Bonte. Accepted 24 October 2015
Detritivore stoichiometric diversity alters litter processing efficiency
in a freshwater ecosystem
Tamihisa Ohta, Sou Matsunaga, Shigeru Niwa, Kimitaka Kawamura and Tsutom Hiura­
T. Ohta ([email protected]), S. Matsunaga and T. Hiura, Tomakomai Research Station, Field Science Center for Northern Biosphere,
Hokkaido University, Takaoka, Tomakomai, JP-053-0035 Hokkaido, Japan. – TO also at: Graduate School of Environmental Science,
Hokkaido University, JP-060-0810 Sapporo, Japan. – S. Niwa, Network Center of Forest and Grassland Survey in Monitoring Sites 1000
Project, Japan Wildlife Research Center, Takaoka, Tomakomai, JP-053-0035 Hokkaido, Japan. – K. Kawamura, Inst. of Low Temperature
Science, Hokkaido University, JP-060-0819 Sapporo, Japan.­
Many studies have estimated relationships between biodiversity and ecosystem functioning, and observed generally positive effects. Because detritus is a major food resource in stream ecosystems, decomposition of leaf litter is an important
ecosystem process and many studies report the full range of positive, negative and no effects of diversity on decomposition.
However, the mechanisms underlying decomposition processes in fresh water remain poorly understood. Organism body
stoichiometry relates to consumption rates and tendencies, and decomposition processes of litter may therefore be affected
by diversity in detritivore body stoichiometry. We predicted that the stoichiometric diversity of detritivores (differences in
C: nutrient ratios among species) would increase the litter processing efficiency (litter mass loss per total capita metabolic
capacity) in fresh water through complementation regarding different nutrient requirements. To test this prediction, we
conducted a microcosm experiment wherein we manipulated the stoichiometric diversity of detritivores and quantified
mass loss of leaf litter mixtures. We compared litter processing efficiency among litter species in each microcosm with single
species detritivores, and observed detritivores with nutrient-rich bodies tended to prefer litter with lower C: nutrient ratios
over litter with higher C: nutrient ratios. Furthermore, litter processing efficiencies were significantly higher in the microcosms containing species of detritivores with both nutrient-rich and -poor bodies than microcosms containing species of
detritivores including only nutrient-rich or -poor bodies. This might mean a higher stoichiometric diversity of detritivores
increased litter processing efficiency. Our results suggest that ecological stoichiometry may improve understanding of links
between biodiversity and ecosystem function in freshwater ecosystems.
Rapid losses of biodiversity are occurring on a global scale
due to human impacts on ecosystems (Cardinale et al. 2012,
McGill 2015), and understanding the consequences of
biodiversity loss for ecosystem functioning is an urgent concern. Many studies in the last two decades have revealed relationships between biodiversity and ecosystem functioning
(B-EF) (Hooper et al. 2012). The litter processing rate, an
important ecosystem function, can increase with the diversity of the detritivore assemblage in fresh water (Jonsson and
Malmqvist 2000, McKie et al. 2008). However, neutral outcomes and negative effects have also been observed (McKie
et al. 2008, 2009), and the conditions and/or the mechanism that leads to the contrast results were not clear. Because
the mechanisms underlying these observed biodiversity
effects on decomposition processes are not well understood
(Gessner et al. 2010), the reasons underlying these differing results are also unclear. Many B-EF studies have focused
on linking empirical observations with concepts such as
complementarity or facilitation effects (Reiss et al. 2009).
Complementarity effects on litter processing rates are driven
by functional dissimilarity in traits such as body size, feeding
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efficiency and dietary flexibility among detritivorous species
(Reiss et al. 2009, Gessner et al. 2010). Facilitation effects
occur when some species of a community affect other species
in ways that enhance the contribution of ecosystem processes. Facilitation effects on litter processing rates are driven
by some factors such as fungal colonization and existence
of detritivore species that produce fine particulate organic
matter (FPOM) (Gessner et al. 2010).
Basal resources in food webs vary widely in their elemental composition and resource quality (Gulis et al. 2006),
whereas consumers often operate within more tightly constrained limits (Sterner and Elser 2002). In consequence,
consumers in streams face nutritional imbalances, with associated consequences for growth and reproduction (Sterner
and Elser 2002, Ohta et al. 2011, Fuller et al. 2015). Frost
et al. (2006) showed that animals have a threshold elemental
ratio (TER) at which growth limitation switches from one
element to another, and many researchers consider that TER
is a useful for prediction of organisms responces to alteration
of resource stoichiometry (but see Halvorson et al. 2015b).
Because of their higher body C:P (phosphorus) ratio and lower
growth rate, detritivore TERs for carbon and phosphorus
are significantly higher than those for grazers and predators.
However, recent studies have revealed that the stoichiometry of detritivores, such as the C (carbon): N (nitrogen): P
ratio, varies widely among species in stream (Evans-White
et al. 2005) and terrestrial ecosystems (Gonzalez et al. 2011).
Consequently, the strength of limitation effects, and thus
feeding behaviors, might depend on the C:N:P ratio in the
bodies of detritivores. In particular, nutrient-rich detritivores must ingest greater quantities of nutrients because of
their higher nutrient demands (Frost et al. 2006). Thus, we
expected that nutrient-rich detritivores would tend to prefer nutrient-rich litter more than nutrient-poor detritivores,
when resources of varying quality are available (Frost et al.
2006, Fuller et al. 2015). Moreover, the whole-body stoichiometric divergence among detritivores can be a measure
of functional dissimilarity, because differences in food selectivity yield complementary resource exploitation (Branquart
and Hemptinne 2000). We expected greater stoichiometric
divergence among detritivores may thus increase litter processing rates.
The C: nutrient ratios of leaf litter vary widely among
species, and these stoichiometric differences might affect
litter processing by detritivores (Ott et al. 2012, Halvorson
et al. 2015a). Zimmer et al. (2005) suggested that complementarity effects on decomposition mediated by detritivores
vary with resource nutrient quality. Leaf litter varies greatly
in its chemical composition (Cornwell et al. 2008). For
instance, the litter of Alnus may have high N content because
Alnus can be symbiotic with a nitrogen-fixing bacterium,
while the litters of other species, such as Pterostyrax hispida,
contain relatively high amounts of P (Osono and Takeda
2004). In aquatic ecosystems, differences in C:N:P ratios
affect fungal biomass on leaf litter (Jabiol and Chauvet 2012).
Aquatic hyphomycetes enhance litter quality (e.g. reducing
litter toughness and increasing its nutrient concentration)
to macroinvertebrate shredders, thereby indirectly facilitating decomposition (Jabiol and Chauvet 2012). Therefore,
because of its original C:N:P ratio and fungal colonization,
the quality of leaf litter deposited on a streambed may differ
widely among litter species. These species-based differences in
litter nutrient quality might affect litter mass loss (LML) by
detritivores. Previous studies on litter processing manipulated
consumer diversity only on single litter species (Jonsson and
Malmqvist 2000, McKie et al. 2008, 2009, Jabiol and
Chauvet 2012), although natural litter mixtures usually contain several litter species. To verify the assumption of feeding
differences between nutrient-poor and nutrient-rich detritivores and any consequences for the relationship between
detritivore diversity and litter processing, it is necessary to
conduct an experiment with a litter assemblage containing
multiple litter species (Jabiol and Chauvet 2012).
We conducted microcosm experiments to examine the
effects of stoichiometric differences among stream detritivores on litter processing. We predicted that 1) detritivores
with nutrient-rich bodies would prefer litter with low C:
nutrient ratios, while detritivores with nutrient-poor bodies
would tend to consume litters uniformly; and 2) litter processing efficiencies in microcosms containing both nutrientrich and nutrient-poor detritivores would be higher than
microcosms containing only nutrient-rich or nutrient-poor
detritivores. Further, we compared the relative importance
of body stoichiometric variation in detritivores, and other
aspects of their functional diversity (species richness, variations in body size and feeding type), in determining litter
processing efficiencies.
Material and methods
Our experiment manipulated the stoichiometric diversity
of stream detritivores by placing them into microcosms
with four litter bags each containing different litter species
(Supplementary material Appendix 1). Forty days after the
experiment began, we measured remaining litter weight in
each of the bags and calculated the total litter processing efficiency per microcosm and compared litter processing efficiency among treatments.
Focal invertebrates and field sampling
The focal detritivores [Amphipoda: Jesogammarus yesoensis
(Anisogammaridae) and Sternomoera yezoensis (Eusiridae),
Trichoptera: Goerodes satoi (Lepidostomatidae), Plecoptera: Nemoura sp. and Amphinemura sp. (Nemouridae) and
Ephemeroptera: Cincticostella nigra (Ephemerellidae)] were
collected from the upper and middle reaches of Horonai
stream, which runs through the Tomakomai Experimental
Forest (TOEF) of Hokkaido University, southwestern Hokkaido, Japan (42°43′N, 141°36′E). This cool-temperate forest comprises deciduous broad-leaved trees, and Horonai
stream is supplied with subsidiary litter from the riparian forest every autumn. The stream originates from a spring, and
its bed is underlain by pumice with an 8-mm mean particle
size; it is low in nutrient concentrations (Ohta et al. 2011).
Immediately prior to the experiment, we measured the C, N
and P contents in the bodies of 12 randomly selected individuals of each species, as described below. We then grouped
these six detritivore species into two stoichiometric groups
based on pre-experimental analysis: J. jesoensis, S. yezoensis, G.
satoi as species with nutrient-rich bodies (RB), and Nemoura
sp., Amphinemura sp. and C. nigra as species with nutrientpoor bodies (PB) (Table 1). For functional feeding groups
(FFGs), J. jesoensis, S. yezoensis, G. satoi, Nemoura sp. and
Amphinemura sp. were classified as shredders that chew leaf
litter, and C. nigra was classified as a collector–gatherer that
feeds on fine detritus (Merritt et al. 2008) (Table 1). These
six species dominate the upper reaches of the stream, and
feed on litter or detritus deposited on the streambed (Kawai
and Tanida 2005). For each species, we used detritivores of
the same life stages in the chemical analysis described below
and, as much as possible, in the experiment as a whole.
Leaf litters from Quercus crispula, Carpinus cordata, Alnus
japonica and Styrax obassia were collected from the TOEF
using litter-fall traps made of large nylon nets just before
the experiment began in late October 2012. These species
dominate both slopes and riparian areas, with the exception
of S. obassia. The four species have markedly different leaflitter nutrient qualities (Table 2). The collected leaf-litters
were sorted and dried at 60°C for 72 h. One gram of dried
leaf-litter was placed in a litter bag (ca 5  10 cm, 5-mm
mesh size). These leaf-litter portions were chopped into
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Table 1. C, N and P contents, C:N and C:P molar ratio and body mass (mean  1 SE) of the body tissues of each species of detritivore.
Functional feeding group
RB (detritivores with nutrient-rich bodies)
Jesogammarus jesoensis
shredder
Goerodes satoi
shredder
Sternomoera yezoensis
shredder
PB (detritivores with nutrient-poor bodies )
Nemoura sp.
shredder
Cincticostella nigra
collector-gatherer
Amphinemura sp.
shredder
%C
%N
%P
C:N
C:P
Bodymass
(mg)
49.59 (1.12)
53.12 (0.87)
59.81 (1.89)
12.81 (1.08)
11.42 (0.71)
12.46 (1.22)
1.04 (0.19)
1.11 (0.20)
1.13 (0.18)
5.47 (0.77)
5.00 (0.29)
4.85 (0.52)
45.17 (5.15)
46.19 (6.21)
53.17 (9.50)
1.89 (0.19)
1.07 (0.11)
0.79 (0.09)
56.11 (2.32)
51.80 (3.48)
57.48 (1.86)
6.96 (0.89)
9.14 (1.05)
7.02 (0.99)
0.50 (19.08)
0.43 (0.05)
0.48 (0.10)
7.92 (0.60)
5.60 (0.34)
8.24 (0.35)
108.04 (19.66)
121.85 (7.82)
123.90 (6.89)
0.68 (0.05)
0.63 (0.20)
0.71 (0.01)
small pieces (about 1  1 cm) to minimize the influence
of differences in thickness and toughness between samples
on feeding by detritivores. We constructed 760 litter bags
containing 1 g dried litter for each litter species, and 3040
in total.
Experimental system
The experiment was conducted from 27 October to 5 December
2012, coinciding with the beginning of the litter-fall season in the TOEF (40 days). We prepared 760 microcosms
(open-topped cylindrical polyethylene cups with a diameter
of 8 cm and height of 24 cm), into which we poured water
from Horonai stream. Into each of these 760 microcosms,
we placed four litter bags containing different litter species
(Supplementary material Appendix 1). One week after the
addition of the litter bags, 12 detritivores were introduced
into each microcosm (12 individuals of a single species, six
individuals each of two species, three individuals each of four
species, or two individuals each of six species; Supplementary material Appendix 2). Each of the detritivore species
treatments was replicated 20 times. This resulted in a total
of 120 microcosms for the single-species detritivore treatment (six species  20), 300 microcosms for the two-species
detritivore treatment (15 possible pairwise species combinations  20), 300 microcosms for the four-species detritivore
treatment (15 possible pairwise species combinations  20),
and 20 microcosms for the six-species detritivore treatment
(hereafter, ‘detritivore-present’ microcosms). We performed
four and six-species treatments, containing both RB and PB,
to determine the effects of functional dissimilarities other
than stoichiometric differences among detritivores. If the
litter processing efficiency were increased by factors other
than differences in the stoichiometry of the detritivores, the
litter processing efficiency might increase with the number
of species even if the microcosms contained RB and PB species. The remaining 20 microcosms contained no detritivores (hereafter, ‘detritivore-absent’ microcosms) to estimate
microbial-mediated decomposition rates. The body lengths
or head capsule widths of all detritivores placed into microcosms were measured from digital photographs using ImageJ
(ver. 1.41). We calculated biomass from the body-length measurements using length–mass regression equations (Johnston
and Cunjak 1999). The total biomass of detritivores ranged
from 23.61 to 7.52 mg in each microcosm. In this experiment, the growth rates of detritivores were not measured.
The 760 microcosms were randomly deployed in five
experimental channels (2.5  0.7  0.3 m). To maintain the
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water temperature in each microcosm equal to field conditions, water was supplied at a constant rate to the channels
from the nearby Horonai stream (4.9–8.3°C). The water
depth in each channel was about three-fourths the height of
the microcosms. Because the water supplied for cooling did
not mix with the water in the microcosms, we conducted
a complete water exchange tri-weekly using water pumped
from Horonai stream via a suction pump, plus a one-minute
aeration every day to avoid oxygen deficiencies. The water in
Horonai stream contains very low nutrient levels throughout
the year (inorganic nitrogen: about 20 mg l1, total phosphorus: about 1.0 mg l1) (Ohta et al. 2011). We checked
all microcosms every three days, and if the detritivores had
died, replaced them immediately with alternative individuals
of the same body length. The death rates of all species were
 8% over the experimental period.
We maintained the experimental system until the water
pipes froze (5 December). The litter bags in detritivore-present
microcosms were collected on the final day of the experiment,
the remaining leaf-litter in each bag dried, and its mass measured. We assumed the rate of decrease in litter mass in the
bags to be the litter processing rate, and compared litter processing rates among litter species. To approximate LML due to
detritivore activity, we subtracted the mean LML in detritivoreabsent microcosms (microbial decomposition) from the LML
in detritivore-present microcosms for each litter species.
(LML due to detritivore activity)  (TL) – (ML)
where TL indicates the combined LML of the four litter
species, ML indicates the combined mean LML of the four
litter species in detritivore-absent microcosms. The LML
due to detritivore activity in each microcosm were calculated
as grams of litter dry mass per metabolic capacity, as shown
in McKie et al. (2008). The metabolic capacity of detritivores correlates allometrically with body mass, as described
by Kleiber’s relationship (Kleiber 1932), which we used to
calculate the per capita metabolic capacity of each species in
each microcosm:
per capita metabolic capacity  [per capita mass (mg)]0.75
The exponent of 0.75 describes a general relationship
between metabolism and body size across all organisms,
and is a useful compromise when species-specific relationships are unknown, and the metabolic demand covers rates
of feeding or assimilation without measuring explicit single
rates (Brown et al. 2004). We quantified the total detritivore
metabolic capacity for each microcosm by summing the per
capita metabolic capacities across all individuals and species.
0.0058 (0.00027)a
0.0058 (0.00018)a
0.0078 (0.00038)b
0.0077 (0.00026)b
14.17 (1.84)a
16.70 (2.41)a
37.23 (4.20)b
35.93 (4.09)b
222.82 (3.85)ab
260.06 (9.67)bc
295.93 (19.79)e
291.05 (9.93)e
1692.60 (138.30)a
738.58 (63.98)b
542.81 (53.73)d
386.49 (6.27)b
0.029 (0.002)c
0.072 (0.006)ad
0.106 (0.009)e
0.124 (0.003)e
0.85 (0.056)a
2.11 (0.051)c
3.02 (0.080)e
1.40 (0.020)d
47.68 (0.60)b
51.99 (0.94)ab
55.34 (1.21)a
47.96 (1.50)bc
57.38 (4.10)a
24.73 (0.80)bc
18.34 (0.51)c
34.20 (1.11)b
185.27 (5.23)a
230.48 (3.82)ac
224.20 (2.95)bc
206.50 (6.97)ab
1622.98 (128.76)a
1164.27 (91.51)b
644.71 (68.49)c
265.93 (30.39)d
57.57 (1.53)a
27.80 (0.65)bc
22.80 (0.70)bc
32.37 (6.11)b
0.034 (0.002)a
0.055 (0.002)a
0.078 (0.009)acd
0.190 (0.026)b
0.96 (0.036)a
1.76 (0.080)b
2.12 (0.092)b
1.67 (0.30)bcd
54.19 (1.19)a
48.65 (1.75)b
48.06 (0.58)b
47.09 (1.60)bc
Before the experiment
Quercus crispula
Carpinus cordata
Alnus japonica
Styrax obassia
After the experiment
Quercus crispula
Carpinus cordata
Alnus japonica
Styrax obassia
%P
%N
%C
C:N ratio
C:P ratio
Lignin (mg g1)
Microbial
decomposition
rate (g d1)
Fungal
biomass
(mg g1)
Table 2. Chemical and microbial properties (mean  1 SE) of leaf litter before and after the experiment. Elemental ration of initial litter were calculated from 12 leafs collected from litter fall traps.
Chemical properties of leaf litter at before and after experiment were analysed separately. Different letters denote significant difference among litter species for a particular property (Tukey-HSD,
p  0.001).
After this amount of LML due to detritivore activity per
total capita metabolic capacity is defined ‘litter processing
efficiency’ in this paper.
The litter bags in detritivore-absent microcosms were also
collected on the final day of the experiment, freeze-dried
(48 h), and the remaining mass of leaf-litter in each bag
measured. The freeze-dried leaf litters were ground and the
following chemical analysis was conducted to estimate the
quantity of fungal biomass and the quality of leaf-litters for
detritivores (i.e. nutrient contents) during the experiment.
Treatment of samples
We collected and weighed three freeze-dried leaf discs (about
1  1 cm) per litter species from all detritivore-absent microcosms and used them for ergosterol determination, as a
proxy for fungal biomass (Gessner and Chauvet 1993). The
ergosterol in the leaf discs was extracted with 5 ml of hexane
mixed with approximately 50 ml of dichloromethane by
ultra-sonification. Next, 0.3 ml of KOH methanol solution
(8 g l1) was added to the extract. The extract was hydrolyzed
for 120 min at 120°C under reflux. After removing excess
KOH and hydrolyzed lipids with purified water, the organic
solvent phase (hexane) was concentrated using rotary evaporators. The extract was further concentrated with a gentle
argon flow to several tens of microliters; then, 1 ml of the
extract was injected into a gas chromatograph connected to
a mass spectrometer. Ergosterol in the samples was quantified by comparing the MS response with that of an internal
standard (cholesterol–2, 3, 4–13C), which was added into
the litter sample before the extraction. A conversion factor
of 5.5-mg ergosterol per gram of fungal dry mass (Gessner
and Chauvet 1993) was used to calculate fungal biomass per
gram of leaf-litter dry mass.
We measured the elemental composition of detritivores
and the pre- and post-experiment leaf litters in detritivoreabsent microcosms. To measure carbon and nitrogen contents each litter and detritivore species, we weighed 20 mg of
the ground litter sample and 5 mg of the ground detritivores.
Carbon and nitrogen contents of the leaf litters and detritivores were determined using a C/N analyzer. To measure
the content of phosphorus, samples of ground leaf-litters (20
mg) and ground detritivores (5 mg) were ashed at 490°C for
2 h, weighed and extracted with 15 ml of 1 M HCl at 80°C
for 1 h. The content of phosphorus in the extraction liquid
was determined using an inductively coupled plasma (ICP)
atomic emission spectrometer. The concentration of lignin
in leaf litter was estimated by gravimetry according to a standardized method using hot sulfuric acid digestion (King and
Heath 1967).
Statistical analysis
To verify prediction 1, firstly, we had to analyse differences
in chemical property among litter species. To test for differences in the fungal biomass, microbial decomposition
rate, C, N and P content and lignin concentration, C:N
and C:P ratios among litter species at the start and/or end
of the experiment (i.e. before and/or post-experiment leaf
litters), we used separate one-way ANOVAs with litter
species as the independent variable, followed by post hoc
1165
comparisons using Tukey’s HSD tests. Based on Bartlett’s
tests, the assumption of homogeneity of variances was
always met (p  0.05).
To verify prediction 1, we estimated whether there were
differences in feeding tendencies among detritivore species
(Fig. 1A). We compared species-specific litter processing
efficiencies (LML per unit of detritivore metabolic capacity)
among litter species using data from microcosms into which
a single species of a detritivore had been placed. At first,
litter species-specific processing efficiencies in each microcosm given a single detritivore species were analyzed using
two-way ANOVA with litter species and detritivore species
as independent variables to estimate interaction between litter and detritivore species, followed by post hoc comparisons
using Tukey’s HSD tests. And then, litter species-specific
processing efficiencies in each microcosm given a single
detritivore species were analyzed using a linear model (LM)
with the seven explanatory variables indicating litter quality
(fungal biomass, C, N and P content and lignin concentration, C:N ratio and C:P ratio in leaf litters) to test the effects
of litter traits on the feeding tendency of each detritivore
species. We selected best-fit models in a stepwise fashion
(R-package ‘MASS’) based on Akaike’s information criterion (AIC) to examine the contribution of each significant
explanatory variable to litter processing efficiency among leaf
litters (Venables and Ripley 2010). We used likelihood-ratio
tests to determine whether the data supported the selected
models over a null model.
To estimate the effects of the taxonomic and stoichiometric diversities of detritivores on litter processing efficiency,
the microcosms were classified into nine groups based on the
species number of RB and PB detritivores [1 species(sp) 
RB, 1sp  PB, 2sp  RB, 2sp  PB, 1sp  RB  1sp 
PB, 1sp  RB  3sp  RB, 2sp  RB  2sp  PB, 3sp 
RB  1sp  PB or 3sp  RB  3sp  PB], and the differences in litter processing efficiency per detritivore metabolic
capacity among these groups were analyzed using a one-way
ANOVA followed by Tukey’s HSD post hoc comparisons.
The homogeneity of variance across groups was confirmed
with a Bartlett’s test (p  0.05).
(A)
Jy
Sy
Gs
Jy
Sy
Gs
Ns
As
Cn
Ns
As
Cn
Feeding preference
4 litter species
detritivores combination
litter processing efficiency
4 litter species
Figure 1. The conceptual diagram of our analysis (LM). At first,
feeding tendencies among detritivore species were analysed (A), and
then effects of combinations of detritivore species on litter processing efficiency were analysed (B). Jy, Jesogammarus yesoensis; Sy,
Sternomoera yezoensis; Gs, Goerodes satoi; Ns, Nemoura sp.; As,
Amphinemura sp.; Cn, Cincticostella nigra.
1166
 ∆i 
Wi  exp  
 2
 ∆r 
∑ rR1 exp  
 2
where Δi indicates the difference in AIC values between
model i and the best model with the lowest AIC, and
R
 ∆i 
 ∆r 
∑ r1 exp  2  indicates the sum of the exp  2  of
all models (R: the number of the model). The relative importance of each explanatory variable was defined as the sum of
Wi of all models including the explanatory variable.
All statistical analyses were performed using R ver. 3.0.1
( www.r-project.org ).
Data deposition
Data available from the Dryad Digital Repository:
 http://dx.doi.org/10.5061/dryad.f5124  (Ohta et al.
2015).
(B)
detritivores
To test prediction 2, we analyzed the combined litter species processing efficiencies of detritivores using LMs with
four explanatory variables indicating different aspects of
detritivore taxonomic and functional diversity in the microcosms (Fig. 1B): number of stoichiometric groups [discrete
variable: 1 (PB or RB) or 2 (PB  RB)], standard deviation
of body mass (continuous variable), number of detritivore
species (discrete variable: 1, 2, 4 and 6) and number of FFGs
(Merritt et al. 2008) [discrete variable: 1 (shredder or collector–gatherer) or 2 (shredder  collector–gatherer)]. Variation in body size and litter processing efficiency were both
calculated by body size of detritivores. However, we used the
body mass variation as an explanatory variable in our model
because the litter processing efficiency is based on the total
of detritivore body mass and not on its variation which could
affect the litter mass loss through complementary consumption of litter by detritivores of variable body size.
First, we conducted a single regression analysis with each
of these four explanatory variables. Then, we calculated an
AIC value for a single or multiple regression model with
every combination of these four explanatory variables. Based
on these AIC values, we estimated the relative importance of
each explanatory variable using Akaike weights (Burnham
and Anderson 2002). Akaike weights (Wi), which are defined
by the following equation, can be used to evaluate the relative contributions of different variables in the models:
Results
Leaf litter and detritivores traits
Detritivore species displayed divergent body N and P contents and stoichiometry (Table 1). Body N and P content
of RB species (Jesogammarus yesoensis, Sternomoera yezoensis
and Goerodes satoi) were similar and greather than PB species (Nemoura sp., Amphinemura sp. and Cincticostella nigra).
These trends also contributed to differences in body C:N and
C:P ratios (Table 1). Mean body size of the detritivores were
similar to each other except for J. yesoensis.
Single elements C, N and P content, the lignin concentration, and the carbon-to-nutrient ratios C:N and C:P differed
significantly among all four litter species (Table 2; statistical
values are listed on Supplementary material Appendix 3).
Fungal biomass at the end of the experiment differed significantly among litter species (statistical values are listed on
Supplementary material Appendix 3), and was significantly
higher in the leaf litters of Alnus japonica and Styrax obassia
(Table 2; Tukey’s HSD, p  0.001), with the microbial
decomposition rates yielding similar results (Table 2, Supplementary material Appendix 3, Tukey’s HSD, p  0.001).
RB detritivores were significantly higher in leaf liter of A.
japonica and/or S. obassia (Fig. 2, Tukey-HSD, p  0.05),
and were predicted significantly with some traits, such as
fungal biomass, P content (%), and C:P ratio (Table 3).
Fungal biomass and P content (%) were significantly higher,
and C:P ratio was significant lower for litter of A. japonica
and S. obassia (Table 2). However, litter species-specific processing efficiency in microcosms with single species of PB
detritivores did not differ among litter species (Fig. 2, TukeyHSD, p  0.05). Across models predicting litter processing
efficiencies, N and C:N were not selected as significant
explanatory variables (Table 3). The amount of LML from
each combination of detritivores is shown in Supplementary
material Appendix 5.
We found significant effects of the stoichiometric combination on combined litter species processing efficiencies
(LML of all litter species) (one-way ANOVA, F8,2027  90.12,
Litter processing
Litter species-specific processing efficiency in microcosms
with single species detritivores differed significantly among
litter species, detritivore species and the interaction between
litter and detritivore species (statistical values are listed on
Supplementary material Appendix 4). Litter species-specific
processing efficiencies in microcosms with single species of
RB detritivores
PB detritivores
(d) Nemoura sp.
(a) Jesogammarus yesoensis
0.05
Litter species-specific processing efficiency(LML detritivoremetabolic capacity–1)
0.04
0.03
A
A
AB
B
A
A
A
A
0.02
0.01
0
(e) Amphinemura sp.
(b) Sternomoera yezoensis
0.05
B
0.04
0.03
A
B
A
A
A
A
A
A
A
Cc
Aj
So
A
0.02
0.01
0
0.05
(c) Goerodes satoi
0.04
0.03
(f) Cincticostella nigra
AB
A
B
A
A
0.02
0.01
0
Qc
Cc
Aj
Litter species
So
Qc
Litter species
Figure 2. Litter species-specific processing efficiency of detritivores (LML detritivore metabolic capacity1) in microcosms with a single
detritivore species. Mean and standard errors ( 1 SE) are shown. Significant differences between species of litters placed each single species
of detritivore are indicated by (post hoc Tukey-HSD tests, p  0.05).
1167
Table 3. The most parsimonious models for explaining the variation in litter processing efficiency among litter species in microcosms with a
single detritivore species. Explanatory variables (litter characteristics) of the best-fit models are showed in the second row. The modeling was
conducted using a linear model with stepwise selection based on AIC.
Species name
Explanatory variable
RB (detritivores with nutrient-rich bodies )
Jesogammarus yesoensis
intercept
%P
Sternomoera yezoensis
intercept
fungal biomass (mg g1)
C:P ratio
%P
Goerodes satoi
intercept
fungal biomass (mg g1)
%P
PB (detritivores with nutrient-poor bodies )
Nemoura sp.
intercept
fungal biomass (mg g1)
Amphinemura sp.
intercept
fungal biomass (mg g1)
Cincticostella nigra
intercept
C:P ratio
p  0.001, Fig. 3). In particular, there were significant differences in litter processing efficiencies among stoichiometric
combinations in the microcosms containing two detritivore
species (Fig. 3). Combined litter species processing efficiencies in the microcosms containing two species of detritivores
with both RB and PB were significantly higher than those
in microcosms containing two species of detritivores including only RB (Tukey-HSD, p  0.001) and microcosms
containing two species of detritivores including only PB
(Tukey-HSD, p  0.001; Fig. 3).
We found significant positive effects of the number of
stoichiometric groups, variation in body size, number of
detritivore species and number of FFGs on combined
litter species processing efficiencies (Table 4, Fig. 4). Furthermore, the greatest variation in decomposition efficiency was
explained by detritivore stoichiometric diversity (Table 4).
Discussion
We found that, due to differences in feeding tendency, stoichiometric differences among detritivores (i.e. RB and PB)
played an important role relating diversity to ecosystem
functioning. Species of RB tended to consume litter with
low C:P ratios or a high P content, and species of PB tended
to consume litters uniformly, supporting prediction 1. Moreover, combined litter species processing efficiencies in microcosms containing two stoichiometric groups were higher
than those in microcosms containing one stoichiometric
group, supporting prediction 2. This result might be caused
by complementarity resource exploitation of RB and PB species. Furthermore, the relative importance of the number of
stoichiometric groups to combined litter species processing
efficiency was the highest among explanatory variables (as
assessed by Akaike weight). Previous studies suggested that
functional diversity rather than species richness is relevant
for ecosystem functioning (Cardinale et al. 2012). For example, body size is an important functional trait that facilitates
differential modes of resource use (Bardgett and Wardle
1168
Coefficient (estimate  SE)
p-value
AIC
0.0251  0.001
0.0051  0.0088
0.0218  0.0180
0.0012  0.0003
0.00002  0.000008
0.0106  0.0043
0.0190  0.0027
0.0002  0.0001
0.0040  0.0019
 0.001
565.88
 0.001
607.40
 0.001
544.74
0.148
557.55
0.679
565.61
0.297
610.17
0.0220  0.0025
0.0001  0.0001
0.0271  0.0024
0.00004  0.000009
0.0257  0.0012
0.000001  0.000001
2010). Our results indicate stoichiometry of detritivores is
also a very important functional dissimilarity that should be
used more often to explain biodiversity–ecosystem function
relationships. Stoichiometry is a common index of organisms and can be easily measured to some extent. Therefore,
this concept that stoichiometry of organism is an important
functional trait for litter processing can be applicable to
other taxa.
Diversity of stream invertebrate depends on many
variable factors that can be changed by human activity, such
as water temperature, primary production and frequency of
disturbance (Jacobsen et al. 1997, Death and Zimmerman
2005). Therefore, reduction of diversity of stream invertebrate might lead to reduction of stoichiometric diversity
of detritivores. Furthermore, body C:P ratio of detritivores
can be reduced by increase in P concentration in stream and
stoichiometric diversity of detritivores also might be declined
in the stream (Evans-White et al. 2009). Therefore, stoichiometric variation of detritivores might be reduced by the
human activity such as nutrient enrichment.
Litter processing was affected not only by detritivore
species richness, variation in body size and number of FFGs,
but also by the number of stoichiometric groups (Table 4,
Fig. 4). Body size is an important functional trait that facilitates differential modes of resource use (Reiss et al. 2009,
Bardgett and Wardle 2010), and greater variation in body
size might correlate to greater variation in body stoichiometry. Although Liess and Hillebrand (2005) showed body C:P
and N: P ratios of stream invertebrate were positively related
to the body size, variation of body size among species is 10
times larger than our experiment. Therefore, body C:P and
N: P ratios of detritivores in our experiment might not greatly
depend on the body size and variation in body size might not
correlate to variation in body stoichiometry. In, addition,
because of low coefficient of determination (R2  0.111) and
our large sample size, the significance of variation in body
size may have to be evaluated carefully. Therefore, our data
confirmed the importance of factors identified in previous
studies (i.e. body size and feeding type) (Reiss et al. 2009),
Combined litter species processing efficiencies
(LML detritivore metabolic capacity–1)
while providing a new perspective (i.e. stoichiometric differences) on the study of relationships between detritivore
diversity and litter decomposition.
We found that feeding activity might depend on detritivore stoichiometry (Fig. 2). In particular, the consumption
of litter by RB species was significantly affected by litter
nutritional properties, especially P-content, while species
classified as PB were not affected (Table 3). As detritivores in
fresh water ecosystems maintain low N: P ratios in their bodies relative to detritus (Evans-White et al. 2005), they might
respond to P availability rather than N availability. Furthermore, fungal biomass on leaf litter alters feeding activity of
shredders through effects on nutrient composition of litter
(Cornut et al. 2015). RB species in our experiment also ate
more Alnus japonica and Styrax obassia with high microbial
biomass (Table 2, 3, Fig. 2). These differences in feeding
tendency might increase litter processing efficiency. In fact,
combined litter species processing efficiencies in microcosms
containing two species of detritivores differed significantly
between microcosms containing both RB and PB and those
containing either RB or PB (Fig. 3). Furthermore, there were
not significant difference of litter processing efficiencies in
microcosms that placed four and six species and contained
both RB and PB detritivores (Fig. 3). This might mean there
was not functional dissimilarity that yields significant effects.
Although many studies have tested whether litter processing rates were affected when species were lost from systems,
the patterns of species loss were equivocal (Jonsson and
Malmqvist 2000, McKie et al. 2008, 2009). Previous studies
proposed that the discrepancies among these results might
be caused by antagonistic species interactions produced by
density (McKie et al. 2008, 2009). Although our experimental system cannot rule out antagonistic species interactions,
our results indicate that these differences may be partially
explained by the stoichiometries of the detritivores used in
experiments (Ott et al. 2012).
Many studies have found consumption rate of animals
scale with body size with an exponent close to unity across
diverse taxa (Brown et al. 2004). In order to correct the litter
processing efficiencies of detritivores that have different body
size, we used a scaling patrameter (0.75). However, the scaling parameter for comsumption rates of organism is not still
fixed. For example, Maino and Kearney (2015) proposed a
scaling parameter of 0.89 for comsumption rates of insects.
We also calculated litter processing efficiency using this scaling parameter (0.89) and analysed our data. As a result, the
relative importance of the number of stoichiometric groups
to litter processing efficiency also was the highest among
explanatory variables (Supplementary material Appendix 6).
In our system, a collector species (C. nigra) contributed
to litter processing efficiency even in monoculture, although
collector species usually don’t contribute leaf litter processing (Merritt et al. 2008). Because water in the microcosms
was stagnant, FPOM could not easily slip through the litter bags. Thus, Cincticostella nigra may have fed on FPOM
made by shredders or microbes in the litter bags, decreasing
the weight of litter in the bags. Detritivores might mineralize
nutrients in litter through egestion of FPOM, which could
0.25
D
D
0.20
0.15
D
D
C
B
AB
A
AB
0.10
1sp � RB 1sp � PB 2sp � RB 2sp � PB 1sp � RB 3sp � RB
+
+
1sp � PB 1sp � PB
1 species
2 species
1 stoichiometric groups
2sp � RB 1sp � RB 3sp � RB
+
+
+
2sp � PB 3sp � PB 3sp � PB
4 species
6 species
2 stoichiometric groups
Figure 3. Combined litter species processing efficiency of detritivores (LML detritivore metabolic capacity1) in microcosms with different
species numbers and stoichiometric combination of detritivores. In this figure, the LMLs of four litter species in each microcosm were
combined and litter processing efficiencies were calculated. Significant differences between groups are denoted by different letters (post hoc
Tukey-HSD, p  0.05). RB refers to detritivores with nutrient-rich bodies, PB refers to detritivores with nutrient-poor bodies; -sp refers to
the number of RB- or PB-species in the microcosms.
1169
Table 4. Relationships between litter processing efficiency of detritivores in microcosms and taxonomic and functional diversities of
detritivore communities. Results of single regression analyses and relative importance (sum of Akaike weights) of the explanatory variables
are shown.
Single regression
Explanatory variable
Estimate  SE
t-value
p-value
The number of stoichiometric groups
The number of species
Variation of body size
The number of FFG
0.065  0.003
0.101  0.002
0.130  0.002
0.098  0.007
19.49
44.25
68.7
32.21
 0.001
 0.001
 0.001
 0.001
Relative importance of variable
(sum of Akaike weight)
1.000
0.991
0.989
0.269
*­ Estimate  SE is slope in the regression model
indirectly stimulate microbial breakdown (Halvorson et al.
2015a). Therefore, collector species might facilitate microbial breakdown of FPOM through production of egestion,
and indirectly contribute leaf litter processing.
When examining the effects of detritivore diversity on litter processing, it is important to consider that natural litter
mixtures usually contain several species. The quality of leaf
litter varies widely in these litter mixtures (e.g. C, N and
P contents and ratios and lignin concentration), and may
Combined litter species processing efficiencies (LML detritivore metabolic capacity–1)
(A)
affect the colonization of detritivores and the litter-processing rate (Ferreira et al. 2012). Additionally, differences in
litter quality affect microbial colonization, and change the
palatability to detritivores (Jabiol and Chauvet 2012). We
manipulated detritivore diversity in litter mixtures containing four litter species, and found that the feeding tendencies of some detritivores among litter species were derived
by stoichiometric differences of detritivores, affecting litter
processing efficiencies. However, because nutrient contents
(B)
R2 = 0.368
p < 0.001
0.25
B
0.20
A
0.15
0.10
1
2
Number of stoichiometric groups
1
2
(D)
(C)
0.25
A
4
Number of species
6
R2 = 0.111
p < 0.001
B
0.20
0.15
0.10
1
Number of FFGs
2
0
0.2
0.4
0.6
0.8
1.0
SDs of the body masses of the detritivores
placed in each microcosm
Figure 4. Combined litter species processing efficiency of detritivores (LML detritivore metabolic capacity1) among (A) number of
stoichiometric group, (B) number of species, (C) number of functional feeding group (FFG), (D) standard deviation (SD) of body mass of
the detritivores placed in each microcosm. They were analysed using a liner model. Significant difference is denoted by different letters
(likelihood ratio test, p  0.001).
1170
in leaf litter can change dramatically with incubation time in
the stream riffle zone through nutrient leaching (Gulis et al.
2006), stoichiometric differences among litter species may
vary temporally.
Water in our experimental system was stagnant, and the
leaf litter nutrient contents did not decrease greatly during
the experiment. Therefore, our results may not completely
reflect the decomposition process in a field environment; to
this end, there are some avenues of potential improvement,
particularly with respect to current and water exchange rate.
If our system was not stagnated, nitrogen and phosphorus in
the nutrient rich litter might be leached early in the experiment and stoichiometric differences among litter species
might be decreased. This means the difference in feeding
tendencies among litter species might be weakened, and the
effect of stoichiometric difference among detritivore species
might be decreased for litter processing efficiency in the last
half of our experiment. However, because turnover time of
water and nutrient retention in side pool of stream is greatly
lower than riffle zone (Hall et al. 2002, Ensign and Doyle
2005), our experimental system might reflect the field environment to some degree.
Given a month-long period of growth, body stoichiometry may have diverged from initial conditions in our
experiment. A resent study showed that the stoichiometry
of detritivores varies with life stage (Halvorson et al. 2015b).
However, Back and King (2013) showed the P-content of
each stream invertebrate declined exponentially with body
mass and the decline rates were reduced to almost zero in
later larval stage. Because we used almost last instar larva
of Plecoptera and Trichoptera or mature Amphipoda (Kawai
and Tanida 2005), there were not great change in body stoichiometry of detritivores.
Our study highlights the potential for ecological stoichiometry to indicate functional dissimilarity among organisms and explain patterns of biodiversity and ecosystem
functioning in a diversity of ecosystems. Stoichiometric
theory predicted low C: nutrients organism to produce
low excretion rate of nutrients (Elser and Urabe 1999).
This means variation of stoichiometry of organisms can be
potentially applied to another B-EF studies, such as consumer nutrient recycling (Halvorson et al. 2015a). Litter
decomposition is a key process not only in stream ecosystems but also in terrestrial ecosystems. Many studies have
verified the effects of diversity on decomposition (Gessner
et al. 2010). Some studies have shown that body size is
an important functional trait that facilitates differential
modes of resource use (Bardgett and Wardle 2010). However, no previous studies have focused on stoichiometric
differences in detritivores. In fact, Gonzalez et al. (2011)
showed that the P contents of terrestrial arthropods vary
widely among species. Therefore, our findings might also
be applicable to terrestrial communities, highlighting the
role of the stoichiometric diversity of detritivores as a
driver of ecosystem functioning.­­­­­­
Acknowledgements – We thank M. Yoshida, Y. Chitose, T. Tanaka
and Y. Kanazawa for their support during the study. We also thank
M. Ishihara, M. Nakaoka, T. Nakaji, I. Saeki and J. Urabe for
discussion and comments. This study was partly supported by a
Grant-in-Aid from JSPS (12J07244 to TO and 2566011103 to
TH) and from the Ministry of Environment (S-9-3 to TH).
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