ECOSYSTEM ECOLOGY - ORIGINAL RESEARCH

Global meta-analysis for controlling factors on carbon stableisotope ratios of lotic periphyton

Naoto F. Ishikawa • Hideyuki Doi • Jacques C. Finlay

Received: 10 February 2011 / Accepted: 13 March 2012 / Published online: 31 March 2012

� Springer-Verlag 2012

Abstract Carbon stable isotope ratios (d13C) are widely

used to trace resource transfer pathways, yet d13C variation

in freshwater autotrophs is not yet fully understood. We

have analyzed data from 42 published studies, supple-

mented with some unpublished data, to show the deter-

minants of lotic periphyton d13C. At large spatial scales,

we observed broad differences in periphyton d13C among

biomes and consistent longitudinal variation related to

watershed area. Longitudinal increases in d13C indicate the

importance of in-stream processes on lotic carbon cycles

and autotroph d13C variation. At local spatial scales,

periphyton d13C was negatively related to canopy cover

and water current velocity and positively related to chlo-

rophyll a density. Autotroph d13C varied among taxonomic

groups. Cyanobacteria and red algae had significantly

higher and lower d13C than other taxa, respectively. A

hierarchical model across spatial scales showed that local

controls for periphyton d13C were nested by regional

controls, which suggested that productivity and CO2

availability determine d13C. Overall, our results reveal

general patterns of periphyton d13C and provide improved

information for study design and the use of d13C in isotopic

mixing models in lotic food web studies.

Keywords d13C variation � Regional control � Local

control � Structural equation modeling � Lotic ecosystem

Introduction

Most ecosystems receive resource subsidies from other

systems. Estimating the contributions of these spatial

subsidies to organisms provides information on food web

production and trophic structure by taking into account

resource movement across ecosystem boundaries (Polis

et al. 1997). Lotic food webs are a good example of sub-

sidized systems that are supported by two carbon sources:

autochthonous (i.e., periphytic algae, mosses, and other

aquatic plants attaching to a substrate) and allochthonous

(i.e., terrestrial litter and invertebrates; Nakano and

Murakami 2001) resources. Distinguishing the contribu-

tions of these two sources to lotic food webs is challenging

Communicated by Robert Hall.

N. F. Ishikawa and H. Doi contributed equally. N. F. Ishikawa and

H. Doi conceived and designed the study. J. C. Finlay provided

logistical support. N. F. Ishikawa, H. Doi and J. C. Finlay gathered

and analyzed the data. N. F. Ishikawa, H. Doi and J. C. Finlay wrote

the manuscript.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00442-012-2308-x) contains supplementarymaterial, which is available to authorized users.

N. F. Ishikawa (&)

Center for Ecological Research, Kyoto University,

2-509-3 Hirano, Otsu, Shiga 520 2113, Japan

e-mail: naoto-i@ecology.kyoto-u.ac.jp

H. Doi

Institute for Chemistry and Biology of the Marine Environment,

Carl-von-Ossietzky University Oldenburg, Schleusenstrasse 1,

26382 Wilhelmshaven, Germany

Present Address:H. Doi

Institute for Sustainable Sciences and Development,

Hiroshima University, 1-3-1 Kagamiyama,

Higashihiroshima, Hiroshima 739 8530, Japan

J. C. Finlay

Department of Ecology, Evolution and Behavior,

University of Minnesota, 1987 Upper Buford Circle,

St. Paul, MN 55108, USA

123

Oecologia (2012) 170:541–549

DOI 10.1007/s00442-012-2308-x

because it is difficult to directly observe food sources.

Carbon stable isotope ratios (d13C) are widely used to

discriminate between periphytic and terrestrial production

in lotic ecosystems (e.g., Fry 1991; Finlay 2001).

While the use of d13C has many advantages as a natural

tracer, growing awareness of the large spatial and temporal

variability in d13C values of lotic periphyton suggest an

intrinsic limitation to d13C applications under specific cir-

cumstances (e.g., Finlay et al. 1999; Zah et al. 2001). The

presence of a large variability in the d13C values of periph-

yton can introduce uncertainty in the determinations of the

isotopic baseline of food webs and consequently influence

the accuracy of food web analyses (McCutchan and Lewis

2001; Zah et al. 2001). Since two-source mixing models

require isotopic separation between potential sources, the

variability in the d13C values of periphyton can compromise

the precise estimation of the relative contribution of carbon

sources to food webs. For example, Finlay et al. (1999)

reported large variations in the d13C values of periphyton and

herbivores over small spatial scales (1–10 m) in a stream that

were related to in situ water current velocity. Such effects

greatly complicate the applications of mixing models.

Many researchers have pointed out the importance of

understanding the pattern of variability in d13C values of

lotic periphyton for the purpose of precise food web

analysis (MacLeod and Barton 1998; Finlay et al. 1999;

Trudeau and Rasmussen 2003; Singer et al. 2005; Doi et al.

2007; Hill et al. 2008). Although the importance of

understanding the variability in periphyton d13C is well

recognized, the factors that determine the d13C values of

periphyton are complex due to the influences of spatial

heterogeneity at multiple spatial scales, such as local

habitat, reach, and watershed (Doi et al. 2007). Conse-

quently, both regional controls (e.g., biome or river size)

and local controls (e.g., light/flow conditions, taxonomic

composition) must be considered if the factors controlling

periphyton d13C values are to be better understood. These

diverse controls over periphyton d13C have not yet been

adequately classified and brought into a hierarchically

structured and causally plausible framework.

In this study, we conduct a global meta-analysis to

identify the factors that determine the d13C value of lotic

periphyton. For this meta-analysis, we use a dataset that

contains a total of 765 records from 42 references and some

unpublished data. Using the dataset, we analyze regional

and local drivers that have been hypothesized to affect

periphyton d13C. In particular, we focus on biome, season,

and watershed area as regional controls because they

strongly affect periphytic production and dissolved inor-

ganic carbon (DIC) availability in lotic ecosystems. Can-

opy cover, water current velocity, chlorophyll a density,

and dominant taxa in periphyton are used as local controls.

Canopy cover regulates light intensity, chlorophyll a, and

dominant taxa in periphyton and in turn controls DIC

demand. Water current velocity regulates DIC availability

for periphyton. Finally, we build a hierarchical model and

synthesize controlling pathways of periphyton d13C.

Materials and methods

Data sources

Published sources were searched on 24 February 2009 for

data on carbon stable isotope values of lotic periphyton using

the ISI Web of Science (http://www.isiknowledge.com). The

search terms included ‘periphyt*’, ‘algae’, ‘isotope’, and

‘stream’. This search resulted in the identification of 192

studies. To these 192 studies, we added studies found in the

journals Canadian Journal of Aquatic and Fisheries Science,

Ecology, Freshwater Biology, Hydrobiologia, Journal of the

North American Benthological Society, Limnology and

Oceanography, and Marine and Freshwater Research that

had not been detected in our Web of Science search. We also

included unpublished data from our own studies. For the

purpose of this meta-analysis, we defined periphyton as

predominantly algal material attached to solid surfaces. In

this context, attached autotrophic algae described by authors

as benthic algae, epilithic algae, a lgal-dominated epilithon,

filamentous algae, littoral algae, micro algae, epiphyton,

periphytic biofilms, phototrophic biofilms, and phytomi-

crobenthos were regarded as periphyton. Data were used for

epilithon only if authors specified the dominance of epilithic

algae in the epilithic material. Neither periphyton nor epili-

thon are pure algae, and the terminology is not necessarily

consistent among researchers. Some studies have defined

epilithon as a mixed assemblage of algae, fungi, and bacteria

(e.g., England and Rosemond 2004), and others regard

epilithon as a surrogate of autochthonous materials (e.g.,

Watanabe et al. 2008) because they are highly influenced by

polysaccharides derived from microalgal production (Lock

1993). Data on periphyton were often a mix of microal-

gae,but information for specific taxonomic groups was

extracted where possible. Data on macrophytes and aquatic

bryophytes were also retrieved.

We accepted data that met following conditions:

1. The study was conducted in a field setting

2. The study provided periphyton d13C and environmen-

tal drivers

3. The study did not use 13C tracer additions

When the data were presented in figures, we extracted

data using graph digitizing software PlotDigitizer X ver.

2.0.1 (http://www.surf.nuqe.nagoya-u.ac.jp/*nakahara/Soft

ware/PlotDigitizerX/index-e.html). For the meta-analysis,

we ultimately selected 42 papers and used 765 data points.

542 Oecologia (2012) 170:541–549

123

Explanatory variables

Explanatory variables for periphyton d13C were extracted

from individual studies or, in a small number of cases, pro-

vided directly by authors. Biome (alpine; arctic; boreal;

temperate; tropical), sampling season, and watershed area

(km2; hereafter WA) of the study sites were used as ‘regional

controls’. We categorized biome of the study sites from the

location of the rivers studied. Sampling periods were cate-

gorized into groups approximating the four seasons, with the

periods March–May, June–August, September–November,

and December–February corresponding to spring, summer,

autumn, and winter for the Northern hemisphere and to

autumn, winter, spring, and summer for the Southern hemi-

sphere. The effects of seasonality on periphyton d13C were

categorized as a regional control because of the strong

relationship between season and productivity. Canopy cover

percentage (%; hereafter CC), water current velocity

(cm s-1; hereafter, WCV), chlorophyll a density (mg m-2;

hereafter, CHLA), and dominant taxa (bryophytes; cyano-

bacteria; diatoms; green algae; macrophytes; red algae;

multiple taxa mixtures) were used as ‘local controls’.

All numerical variables, excluding percentage or cate-

gorical variables, were analyzed after transformation by

log10 x for WA and CHLA and by log10 (x ?1) for WCV

because there were zero values in the WCV (Table 1),

except for missing values. Insufficient data for a statisti-

cally robust sample size for aqueous CO2 concentration and

d13C signatures were present in the meta-analysis dataset,

although these factors are important for determining the

d13C of periphyton (Finlay 2004).

Linear and non-linear modeling procedures

We tested the relationships between periphyton d13C and

the potential explanatory variables and analyzed the effects

of ‘regional’ and ‘local’ controls separately by building

two general linear models (GLMs) to test the relative

contributions of potential explanatory variables to periph-

yton d13C as follows:

Full model (regional controls):

Periphyton d13C ¼ biome + season + WA

Full model (local controls):

Periphyton d13C ¼ CC + WCV + CHLA + dominant taxa

We selected the best GLM by downward stepwise

selection according to the Akaike Information Criterion

(AIC; Akaike 1974) and also calculated AIC differences

(Di) and Akaike weight (xi), which is considered to be the

weight of evidence in favor of a candidate model being the

best model out of the set of models considered (Burnham

and Anderson 2002). For the individual relationship

between each explanatory variable and periphyton d13C,

we compared both regressions of single linear and non-

linear models with the generalized cross validation (GCV)

score: the smaller the GCV score, the higher the fitness to

the models (Wood 2008). Differences in periphyton d13C

among biomes, season, and dominant taxa were tested by

post hoc multiple comparison (Holm’s test). When velocity

data were available for rivers, the taxa-specific or river-

specific relationships between periphyton d13C and the

water current velocity were analyzed using analysis of

covariance (ANCOVA; dominant taxa and rivers as the

covariance of each ANCOVA model; n = 283).

Structural equation modeling procedure

Structural equation modeling (SEM) was used to consider

hierarchical orders among regional and local controls. The

dataset (n = 765) included four numerical variables (WA;

CC; WCV; CHLA) and periphyton d13C. The SEM pro-

cedure for ecological studies is described in detail else-

where (e.g., Shipley 2000; Grace 2006). Briefly, the

observed correlation matrix among the numerical variables

was set to calculate a path-coefficient between two

Table 1 Summary of variables with abbreviation, type of data, units, category, and statistical transformation for modeling

Control Variable Abbreviation Type of

data

Unit Category Statistical

transformation

Regional Biome Categorical Alpine; arctic; boreal; temperate; tropical

Season Categorical Spring; summer; autumn; winter

Watershed area WA Numerical km2 log10 x

Local Canopy cover CC Numerical % NA

Water current velocity WCV Numerical cm s-1 log10 (x ? 1)

Chlorophyll a density CHLA Numerical mg m-2 log10 x

Dominant taxa Categorical Bryophytes; cyanobacteria; diatoms;

green algae; macrophytes; red algae;

multiple taxa mixtures

Oecologia (2012) 170:541–549 543

123

variables [see Electronic Supplementary Material (ESM)].

Biome, season, and dominant taxa could not be included in

the SEM because they were the nominal variables and

therefore could not be used to set reasonable correlation

matrices.

To build a hierarchical model to explain periphyton

d13C, we used a three-step analysis procedure applying a

correlation matrix among causal and response variables.

First, non-hierarchical models that assumed all possible

correlations between variables were examined. Second, all

reasonable pathways among variables were included a pri-

ori in the model. Third, downward stepwise selection of the

model was performed based on whether a path-coefficient

was significant (p \ 0.05) or not (p [ 0.05), ultimately

arriving at the final model. The model fit to the data was

assessed with the v2 test (Grace 2006). The SEM approach

logically and statistically takes account of multiple co-

linearity among the numerical variables and thus allows a

more direct identification of causal associations.

The significance level of all statistical tests was assessed

at a = 0.05. We performed all statistical analyses and

graphics using R ver. 2.13.1 software (R Development

Core Team 2011) with DAAG (Maindonald and Braun

2011), MASS (Venables and Ripley 2002), mgcv (Wood

2011), and sem (Fox et al. 2010) packages in the library.

Results

Variation in d13C signatures of periphyton

Periphyton d13C ranged from -47.3 to -9.3 % (Fig. 1).

The mean periphyton d13C in the whole dataset was

-25.7 ± 6.8 % (mean ± 1 standard deviation, n = 765).

The most frequent values of periphyton d13C (-28 to

-26 %) overlapped with the typical values for terrestrial

C3 plants (Fig. 1); about 7 % of the data fell into this range.

Regional patterns and controls

The model that had the lowest AIC value for regional

controls included all explanatory variables (Table 2). We

adopted the non-linear model as the best-fit model for the

relationship between WA and periphyton d13C (Fig. 2a)

because it explained more variation than the linear model

(GCV 44.3 for non-linear model, 47.5 for linear model).

There was a significant relationship between periphyton

d13C and WA (df of smooth term 5.69, R2 = 0.18,

p \ 0.001, n = 269; Fig. 2a). Arctic and boreal rivers had

significantly lower periphyton d13C than other biomes

(Holm’s test p \ 0.05; Fig. 3a). Periphyton d13C was sig-

nificantly higher in winter than in the spring and summer

(Holm’s test p \ 0.001; Fig. 3b).

Local patterns and controls

The model that had the lowest AIC value for local controls

included canopy cover and dominant taxa, but the Di and xi

values of the model were similar to those of the full model

(Table 2). We again adopted the non-linear model as the

best-fit model for the relationship between CC and

periphyton d13C (Fig. 2b) because it explained more vari-

ation than the linear model (GCV: 28.8 for non-linear, 39.1

for linear). There was a significant relationship between

periphyton d13C and CC (df of smooth term 6.33,

R2 = 0.40, p \ 0.001, n = 244; Fig. 2b). Cyanobacteria

(e.g., Nostoc) had significantly higher d13C than the other

groups (Holm’s test p \ 0.001; Fig. 3c). Red algae (e.g.,

Batrachospermum and Lemanea) had significantly lower

d13C than the other taxa except for the bryophytes and

macrophytes (Holm’s test p \ 0.001; Fig. 3c).

The effect of WCV on periphyton d13C was significantly

different among dominant taxa and rivers (ANCOVA;

dominant taxa, p \ 0.001; rivers studied, p \ 0.001;

interaction WCV 9 dominant taxa, p = 0.59; interaction

WCV 9 rivers, p = 0.048). The d13C values of cyano-

bacteria and bulk periphyton (multiple taxa mixtures) had

significantly negative relationships with WCV (cyanobac-

teria: slope -4.2, R2 = 0.26, p = 0.01, n = 22; multiple

taxa mixtures: slope -1.8, R2 = 0.02, p = 0.04, n = 200),

while other taxa did not (diatoms: p = 0.92, n = 19; green

algae: p = 0.09, n = 42; bryophyte, macrophyte, red

algae: no data) (Fig. 2c). The relationship between CHLA

and periphyton d13C was significantly positive (slope 3.7,

R2 = 0.08, p \ 0.001, n = 204; Fig. 2d).

–40 –30 –20 –100

1

2

3

4

5

6

Periphyton δ 13C (‰)

Freq

uenc

y (%

)

Fig. 1 Frequency of carbon stable isotope ratios (d13C) of lotic

periphyton (%; mean -25.7 %, standard deviation 6.8; n = 765).

Black bar indicates overlap with typical values of terrestrial C3 plants

(-28 to -26 %)

544 Oecologia (2012) 170:541–549

123

Structural equation modeling

The SEM returned three models, although we present only

the final reduced model that contains only significant path-

coefficients. The non-hierarchical null model indicated that

three variables (CC, WCV, and CHLA) significantly

influenced periphyton d13C. The path-coefficient between

WA and periphyton d13C was not significantly different

from zero (p = 0.45). All explanatory variables were sig-

nificantly correlated with each other, indicating that they

were not completely independent. Since GLM results

showed that WA was a strong regional control, the null

model suggested that WA affected periphyton d13C by

regulating other local controls.

A hierarchical model that contained all reasonable

pathways was saturated (i.e., df 0). The model indicated

that WA occupied the top level of the hierarchy,

controlling all other variables, but a direct path from WA to

periphyton d13C was not significantly different from zero

(p = 0.45). Path-coefficients from WA to CC, WCV, and

CHLA were all significantly different from zero

(p \ 0.001). CC and WCV occupied the same layer, and

both significantly controlled CHLA. There was no corre-

lation between CC and WCV (p = 0.36). CHLA occupied

the lowest level of the hierarchy. CC, WCV, and CHLA

had significant paths to periphyton d13C (p \ 0.001).

Downward stepwise selection based on p values deleted

insignificant paths in the saturated model. Direct regression

from WA to periphyton d13C was not significant, but WA

strongly regulated local CC and CHLA, which controlled

periphyton d13C (Fig. 4). Correlation between CC and

WCV was also deleted from the model. Insignificant v2

(p [ 0.05) indicated that the model provided an acceptable

fit to the data (Grace 2006).

Table 2 Stepwise tests for regional and local controls to explain the periphyton carbon stable isotope ratio (d13C)

Variables df Sum of squares RSS AIC Di xi

Regional controls

Full model 8,997 964.2 0.999

? WA ? biome 4 897.2 9,894 981.8 17.6 \0.001

? Season ? biome 1 1,957.1 10,954 1,015.1 50.9 \0.001

? WA ? season 4 2,810 11,807 1,029.3 65.1 \0.001

Local controls

? Dominant taxa ? WCV ? CC 1 5.1 2,447 365.8 0.48

? Dominant taxa ? CHLA ? CC 1 21.6 2,463 366.6 0.8 0.32

Full model 2,441 367.5 1.7 0.20

? CC ? WCV ? CHLA 1 270.6 2,712 377.9 12.1 \0.001

? Dominant taxa ? WCV ? CHLA 1 666.2 3,108 394.0 28.2 \0.001

Di and xi, Akaike Information Criterion (AIC) differences and AIC weights, respectively

For abbreviations of variables listed in column 1, see Table 1

–40

–30

–20

–10

1 0 20 60 100 10 10240 80 1 10 102

Watershed area(WA: km2)

a

Per

iphy

ton

δ13C

(‰

)

Canopy cover(CC: %)

b

Water current velocity(WCV: cm s–1)

c

Chlorophyll a density(CHLA: mg m–2)

dBryophytes Cyanobacteria Diatoms Green algae Macrophytes Multiple taxa mixturesRed algae

10 102 103 104 1

Fig. 2 Relationships between periphyton d13C (%) and watershed

area (WA, km2; n = 269) (a), canopy cover (CC, %; n = 244) (b),

water current velocity (WCV, cm s-1; n = 283) (c), and chlorophyll

a density (CHLA, mg m-2; n = 204) (d). The lines of a and b are

given by smooth spline regression, purple dashed line of c is given by

linear regression for d13C of cyanobacteria, black lines of c and d are

given by linear regression for d13C of multiple taxa mixtures (color

version available online)

Oecologia (2012) 170:541–549 545

123

Discussion

The mean d13C value of periphyton in various lotic eco-

systems is close to or overlaps that of terrestrial litter for

C3 plants. However, large variations in periphyton d13C are

evident, such that periphyton d13C are distinct from

terrestrial sources in many streams and rivers. In the fol-

lowing sections, we integrate current understanding of

regional and local controls of periphyton d13C into a

hierarchical framework, which is useful in designing

studies with carbon stable isotopic techniques.

Regional patterns and controls

The significant d13C difference among biomes suggests

that variation in stream productivity controls, at least par-

tially, the fractionation of 13C in photosynthesis. Although

often unshaded by terrestrial vegetation, arctic and boreal

rivers have low water temperatures and are nutrient poor,

resulting in very low rates of primary productivity (Peter-

son et al. 1986). Low primary production promotes dis-

crimination against 13CO2 during photosynthesis (Finlay

2001) and, consequently, isotopic fractionation is likely to

be larger, resulting in lower periphyton d13C in arctic and

boreal rivers than in other biomes. Alpine rivers often have

little canopy shading and show higher d13C than rivers in

the arctic and boreal biomes, probably owing to higher

d13C of DIC and/or higher primary production (e.g.,

McCutchan and Lewis 2001; Zah et al. 2001).

An influence of primary production is also likely to

underlie the positive relationship between watershed area

and periphyton d13C. Gross primary production increases

with watershed area due to channel widening, nutrient

loading, and increased light availability (Battin et al. 2008;

Bernot et al. 2010; Finlay 2011). Thus, photosynthetic

activity generally increases with river size, while CO2

concentrations tend to decrease due to uptake and degas-

sing (Finlay 2003; Butman and Raymond 2011). These

Watershedarea

Canopy cover

Water currentvelocity

Chlorophyll adensity

Periphyton13C

–0.58

0.52

0.150.12

0.11

–0.39

–0.32

0.27

e

0.66

e

0.98

e

0.68

e

0.75

Fig. 4 Path-diagram containing only significant paths. For the non-

hierarchical null model (including the multiple linear regression

model) and for the full model with all reasonable combinations of

paths, see Electronic Supplementary Material. The numbers near each

arrow indicate the standardized path-coefficient. One-sided arrowsindicate causality. Solid, dashed arrows indicate positive and negative

relationships, respectively, arrow thickness is scaled to the path-

coefficient value. e Represents unexplained variance of dependent

variables. Insignificant v2 of 1.40 (df 2, n = 765, p = 0.50) indicates

that the model provides an acceptable fit to the data (Grace 2006)

Alpine Arctic Boreal Temperate Tropical

–40

–30

–20

–10

Spring Summer Autumn Winter

Bryop

hyte

s

Diatom

s

Mac

roph

ytes

Mult

iple

taxa

mixt

ures

ba c

Biome

Per

iphy

ton

δ13C

(‰

)

Season Dominant taxaCya

noba

cteria

Green

alga

e

Red a

lgae

Fig. 3 Box plot for periphyton d13C (%) and biome (alpine, n = 30;

arctic, n = 19, boreal, n = 104; temperate: n = 570; tropical,

n = 42) (a), season (spring, n = 87; summer, n = 329; autumn,

n = 95; winter, n = 114) (b), and dominant taxa (bryophytes,

n = 16; cyanobacteria, n = 46; diatoms, n = 52; green algae,

n = 94; macrophytes, n = 8; red algae, n = 22; multiple taxa

mixtures, n = 527) (c). The box and bar depict inter-quartile (Q1

and Q3) and median, respectively, whisker represents the most

extreme data point that is no more than 1.5-fold the inter-quartile

range. Outliers are shown where applicable

546 Oecologia (2012) 170:541–549

123

changes increase carbon limitation effects on isotopic

fractionation in productive rivers, leading to the pattern of

increasing periphyton d13C with increasing river size

(Finlay 2001).

Primary production increases in deciduous forest rivers

during the winter because seasonal defoliation reduces

canopy cover (Roberts et al. 2007). Canopy opening

increases primary production, resulting in smaller frac-

tionation and higher d13C. Seasonality in periphyton d13C

has been reported in studies conducted in alpine and tem-

perate rivers (e.g., McCutchan and Lewis 2001; Finlay

2004). Since our dataset was biased towards temperate

rivers, the seasonal influence we observed is most relevant

to temperate biomes. We did not have sufficient data to

address the mechanisms driving relationships with sea-

sonality, but the patterns shown here indicate that regional

influences on in-stream productivity and possibly water-

shed biogeochemistry are controlling periphyton d13C

variation.

Local patterns and controls

Periphyton d13C was lower under highly shaded, low-light

conditions and, in addition, a positive relationship was

observed between chlorophyll a density and periphyton

d13C. Both results support the hypothesis that algal pho-

tosynthetic activity determines isotopic fractionation

between DIC and algal cells. Photosynthetic activity

decreases with increased canopy cover because of low light

availability and colder temperature for primary producers,

leading to greater isotopic fractionation (Behmer and

Hawkins 1986; Lamberti and Steinman 1997; Finlay 2011).

Thus, decreasing periphyton d13C with increased canopy

cover indicates that there is considerable isotopic dis-

crimination against 13CO2 between aqueous CO2 and algae

during photosynthesis, or possibly less HCO3- use by

algae in shaded habitats (Doi et al. 2007; Finlay 2004; Hill

and Middleton 2006; Hill et al. 2008).

The negative relationship between water current veloc-

ity and periphyton d13C values suggests that the increased

CO2 supply to algal cells with high water current velocity

allows more selective 12CO2 uptake by algae, thereby

intensifying the isotopic fractionation between aqueous

CO2 and periphyton (MacLeod and Barton 1998; Finlay

et al. 1999; Trudeau and Rasmussen 2003; Singer et al.

2005). Although field and laboratory studies have often

revealed that faster current velocity strongly decreases

periphyton d13C, the general influence of water velocity on

periphyton d13C was weaker in the meta-analyses than in

individual studies. This difference may exist because the

flow history, rather than the instantaneous current velocity,

has a greater effect on d13C at sites with variable hydro-

graph conditions (Singer et al. 2005). Overall, water

current velocity effects on periphyton d13C values must be

considered with other environmental variables.

Our analyses show that variation in physiology among

taxa exerts a strong influence on 13C fractionation patterns.

Cyanobacteria typically concentrate inorganic carbon more

efficiently than any other group and are thus able to use

HCO3- under low water current velocity (Merz-Preiß and

Riding 1999; Badger 2003). The d13C value of HCO3- is

higher than that of aqueous CO2 (Mook et al. 1974),

thereby explaining the higher d13C of cyanobacteria com-

pared to all of the other taxa examined. In contrast, red

algae and bryophytes had lower d13C than other taxa. For

red algae, isotopic discrimination during inner-cell carbon

transportation is large, as most red algae cannot concen-

trate inorganic carbon within their cells (Raven et al. 2005).

Furthermore, most bryophytes cannot use HCO3- as a

photosynthetic substrate (Prins and Elzenga 1989). The

significant d13C difference among taxa suggests that the

researchers should pay attention to the community struc-

ture of freshwater autotrophs.

Relationships between regional and local controls

Our hierarchical model shows that multilevel controls

determine periphyton d13C. The effect of watershed area on

periphyton d13C was indirect in our model. Increasing

watershed area is associated with a higher discharge,

channel widening, and reduced canopy shading above the

water surface. Thus, watershed area generally controls

canopy cover and water current velocity, which in turn

locally control periphyton d13C. The significant direct

paths from the watershed area to chlorophyll a density

suggests that large rivers can support a higher biomass of

periphyton, probably due to the greater availability of light

and nutrients.

The model is closely related to the structure of geo-

morphology and productivity in lotic ecosystems (e.g.,

Vannote et al. 1980), indicating that periphyton d13C

changes along with river continuum through several dif-

ferent pathways. In a food web context, a recent study

showed that d13C gradients of macroinvertebrates from

upland to lowland rivers reflect longitudinal patterns of

their resource base (Kobayashi et al. 2011). Variability in

periphyton d13C across multiple spatial scales is clearly

transferred to the lotic food webs, showing the importance

of multiscale analyses for furthering our understanding

food web dynamics using d13C signatures in fluvial

networks.

In our dataset, however, the low R2 values of both non-

hierarchical and hierarchical models suggest that unex-

plained d13C variation in lotic periphyton remains. The

lack of data on dissolved CO2 concentrations and/or d13C

values of DIC in the study sites might limit our modeling

Oecologia (2012) 170:541–549 547

123

approach for the prediction of periphyton d13C. For

example, the positive relationship between watershed area

and periphyton d13C may reflect a lower CO2 concentration

in large rivers (Finlay 2003; Butman and Raymond 2011).

DIC d13C variation may also have been important since

values typically range between -13 and -9 % across most

rivers (Finlay 2003).

Since periphyton carbon has a longer turnover time than

aqueous CO2 and HCO3-, the d13C of periphyton integrates

short-term fluctuations in DIC d13C caused by variations in

discharge (Finlay 2003; Doctor et al. 2008). Such fluctua-

tions are about 2–4 % and therefore much smaller than the

observed variation in periphyton d13C (i.e., approx. 15 %)

at small scales. In addition to the effect of DIC, short-term

variations in water current velocity and primary production

may also be important for periphyton d13C (France and

Cattaneo 1998; Singer et al. 2005). Finer resolution studies,

rather than global meta-analysis, are thus required to

address temporal variation in periphyton d13C.

Perspectives for food web studies

While a global model to predict specific d13C values of

periphyton was beyond the scope of this study, our anal-

yses provide information on the likelihood of overlap or

isotopic separation of organic matter sources. In settings

where the d13C value of periphyton are most likely to

overlap with that of terrestrial litter, alternative methods

can be used to avoid the expense associated with extensive

sampling for d13C. We suggest the use of alternative iso-

topes, such as carbon-14 natural abundance (D14C) and

sulfur and hydrogen stable isotopes, to identify the food

web structure and energy sources (Doi et al. 2006; Finlay

et al. 2010; Ishikawa et al. 2010). For example, the D14C

value of periphyton is corrected by d13C and therefore is

not affected by isotopic fractionation (Ishikawa et al.

2012). These alternative isotope tracers may in some sit-

uations provide a better estimation of food web attributes

than d13C. The combined use of such isotopes together

with d13C analysis will be helpful for gaining a better

understanding of the contribution of the carbon source to

freshwater food webs.

Acknowledgments We thank C.M. Godwin and A.W. Walters for

providing their unpublished data for the meta-analysis. Comments

from R.O. Hall and an anonymous reviewer greatly improved the

early draft of this manuscript. H. Doi and N.F. Ishikawa were

financially supported by the Japan Society for the Promotion of

Science to conduct this study.

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