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\begin{document}

\title{Intra-specific differences in metabolic rates shape carbon stable
isotope trophic discrimination factors of muscle tissue in the common
teleost Eurasian perch (Perca fluviatilis)}



\author[1]{Kristin Scharnweber}%
\author[1]{Matilda Andersson}%
\author[1]{Fernando Chaguaceda}%
\author[2]{Peter Eklöv}%
\affil[1]{Uppsala Universitet}%
\affil[2]{Evolutionary Biology Centre}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
1. Stable isotopes represent a unique approach to provide insights into
the ecology of organisms. \selectlanguage{greek}δ\selectlanguage{english}13C and \selectlanguage{greek}δ\selectlanguage{english}15N have specifically be used to
obtain information on the trophic ecology and food web interactions. The
trophic discrimination factor (TDF, \selectlanguage{greek}Δ\selectlanguage{english}13C and \selectlanguage{greek}Δ\selectlanguage{english}15N) describes the
isotopic fractionation occurring from diet to consumer tissue and this
value is critical to obtain precise estimates within any application of
\selectlanguage{greek}δ\selectlanguage{english}13C and \selectlanguage{greek}δ\selectlanguage{english}15N. It is widely acknowledged that metabolism influences the
TDF, being responsible for different TDFs between tissues of variable
metabolic activity (e.g. liver vs. muscle tissue) or species body size
(small vs. large). However, the connection between the variation of
metabolism occurring within a single species during its ontogeny and
TDFs has rarely been considered. 2. Here, we conducted a 9-month feeding
experiment to report \selectlanguage{greek}Δ\selectlanguage{english}13C and \selectlanguage{greek}Δ\selectlanguage{english}15N of muscle and liver tissue for
several weight classes of Eurasian perch (Perca fluviatilis), a
widespread teleost often studied using stable isotopes, but without
established TDFs for feeding on a natural diet. In addition, we assessed
the relationship between the standard metabolic rate (SMR) and TDFs by
measuring their oxygen consumption of the individuals. 3. Our results
showed a significant negative relationship of SMR with \selectlanguage{greek}Δ\selectlanguage{english}13C, but not
with \selectlanguage{greek}Δ\selectlanguage{english}15N of muscle or TDFs of liver tissue. SMR was significantly
higher in perch juveniles, which translated to significantly lower \selectlanguage{greek}Δ\selectlanguage{english}13C
of muscle tissue. 4. In summary, our results emphasize the role of
metabolism in shaping specific TDFs (i.e. \selectlanguage{greek}Δ\selectlanguage{english}13C of muscle tissue), and
especially highlight the substantial differences between individuals of
different ontogenetic stages within a species. Our findings thus have
direct implications for the use of stable isotope data and the
applications of stable isotopes in food web studies.%
\end{abstract}%



\sloppy


\textbf{Introduction:}

Stable isotope analysis has become an established tool of ecologists for
numerous applications, including research on ecosystem functioning (e.g.
Mehner et al. 2016), animal migration (e.g. Hobson 1999),
ecophysiological processes (e.g. Gannes et al. 1998), and parasitism
(e.g. Lafferty et al. 2008). Furthermore, it provides a useful tool for
elucidating trophic interactions in food-web research (Post 2002,
Boecklen et al. 2011, Layman et al. 2012). For these purposes, the ratio
of carbon (\textsuperscript{12}C/\textsuperscript{13}C, expressed as
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C) and nitrogen
(\textsuperscript{14}N/\textsuperscript{15}N, expressed as
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N) of stable isotopes have been widely used. While
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C can be used to track the origin of the carbon
source in organisms\selectlanguage{ngerman}´ diet and the base of the food web,
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N is especially useful to determine the organisms\selectlanguage{ngerman}´
trophic level (DeNiro and Epstein 1978, 1981, Peterson and Fry 1987).
Combining these two approaches can provide information on resource and
habitat use, thus allowing inference of the ecological niche of
individuals, species or communities (Bearhop et al. 2004, Newsome et al.
2007, Martínez del Rio et al. 2009).

For most applications of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N, estimates of the trophic discrimination factor
(TDF, \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C and \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N) are needed.
This factor represents the difference in \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C (or
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N) between the consumer and its diet. Most studies
rely on average values for this parameter found in the literature, but
the use of inaccurate TDFs has been described as a major source of
uncertainties in the use of mixing models to calculate the contributions
of food items to the diet of a consumer (Phillips et al. 2014).
Therefore, to allow precise interpretations of isotope data, accurate
and appropriate TDF values obtained from relevant species-specific
trophic interactions are necessary (Mart\selectlanguage{ngerman}ínez del Rio et al. 2009, Wolf
et al. 2009).

TDFs may vary considerably within and between species (Post 2002),
influenced e.g. by diet quality (Gaye-Siessegger et al. 2003), feeding
rates (Barnes et al. 2007), and the metabolic processes which shape the
rate of diet incorporation (MacAvoy et al. 2005, MacAvoy et al. 2006,
Pecquerie et al. 2010). Metabolism describes the sum of all anabolic
(synthesizing) and catabolic (degrading) processes of living organisms.
Metabolic processes produce energy by consuming O\textsubscript{2} and
part of this energy is used during anabolic processes to produce
macromolecules (i.e. carbohydrates, proteins or lipids). This will lead
to an increase in tissue mass, resulting in growth, or to replacement of
tissue, which are both important underlying processes shaping TDFs.
Therefore, metabolic rate has strong implications for the rate at which
isotopes are incorporated (Carleton and Martínez del Rio 2010). It is
generally acknowledged, that more metabolically active tissues (e.g.
liver) have a faster turnover, resulting in lower TDFs due to the
preferential incorporation of \textsuperscript{12}C, compared to tissues
with slower turnover (e.g. muscle) (McIntyre and Flecker 2006, Xia et
al. 2013, Matley et al. 2016). However, this framework has rarely been
applied to the overall metabolism of an organism.

Fundamental differences in metabolic rates exist across the animal
kingdom with higher metabolic rates in smaller species compared to
larger ones (Kleiber 1947). In addition to phylogenic differences in
metabolism, metabolic rates also vary over ontogeny in individuals of
the same species (Wieser 1984, Chabot et al. 2016). To achieve high
growth rates in younger individuals, these ontogenetic live stages are
characterized by high metabolic rates (e.g. Hou et al. 2008, Yagi et al.
2010). In addition, a whole research field studies the consequences of
metabolic differences between individuals irrespective of ontogenetic
stages, and their influence on general behavior and performance
(Metcalfe et al. 1995, Careau et al. 2008, Biro and Stamps 2010),
including social dominance, aggressive behavior, and activity levels
(Røskaft et al. 1986, Reidy et al. 2000).

One aspect of an organism's metabolism is standard metabolic rate (SMR)
which is the minimum metabolic rate needed for subsistence (Hulbert and
Else 2004, Chabot et al. 2016). SMR is typically measured by the oxygen
consumption in a respirometer. This baseline is ecologically relevant in
how it translates to differences in ``maintenance costs'' and thereby
fitness, between conspecifics (Burton et al. 2011). Previous studies
have been able to correlate individual metabolic rates to the
differences of TDFs found between species, sexes and laboratory strains
of endothermic animals with a high metabolism, such as birds and rodents
(Ogden et al. 2004, MacAvoy et al. 2006, MacAvoy et al. 2012), but this
concept has not been broadened to ectothermic organisms with a slower
metabolism, such as fish.

In this study, we examined TDF for Eurasian perch (\emph{Perca
fluviatilis} ), which is a ubiquitous fish in Europe and Asia (Froese
and Pauly 2020). It is the dominant predatory species in many aquatic
habitats including freshwater (Mehner et al. 2007) and brackish systems
(Ådjers et al. 2006), playing a fundamental role in structuring food
webs (e.g. Svanbäck et al. 2015, Bartels et al. 2016, Marklund et al.
2019). Nonetheless, species-specific stable isotope TDFs for perch
feeding natural diets have not been established. As many vertebrate
predators, perch grow several orders of magnitude in body size during
the ontogeny (e.g. Hjelm et al. 2000), making this species an excellent
model for studying the relationship of metabolism and TDFs over
ontogeny.

The motivation for this study was two-fold. First, we wanted to
experimentally derive \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C and \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N
for different weight classes of Eurasian perch, to allow more accurate
estimates of trophic positions, ecological niches and other potential
food-web inferences for this common teleost. Second, we aimed at
identifying the role of metabolic rate on the TDFs. We hypothesized that
juvenile individuals have a significantly higher SMR that is translated
into different TDFs of muscle and liver tissue compared to the TDFs of
adult perch.

\textbf{Material and Methods:}

\emph{Fish collection, husbandry and experimental design}

Between August 21st and 28\textsuperscript{th} 2018, we sampled perch
from the lake Erken (59\selectlanguage{ngerman}°50'09.6''N, 18°37'52.3''E) in Central Sweden by
angling from littoral and pelagic habitats. We caught additional
young-of-the year juveniles by beach seining. Using cooled and aerated
boxes, we brought the fish to the aquarium facility at Uppsala
University.

Fish were anesthetized using 60 mg L\textsuperscript{-1} benzocaine and
weighted to the nearest 0.01g. We grouped the fish according to their
habitat of origin and the body weight at the beginning of the experiment
into weight classes: \textless{}20 g (juveniles of approximately 4 g),
20-30 g, 30-40 g, and 40-50 g. Perch of the weight class 20-30 g were
caught in both littoral and pelagic habitats. Due to potential
differences, we analyzed this weight class habitat-specifically. 3-9
perch individuals were placed together in 30l --aquaria (50 x 25 x 25cm)
with the bottom covered by a 3cm thick layer of sand. Room temperature
of the facility was set at 15 C°. Aquaria were equipped with a
flow-through system of fresh tap water. As the tap water became colder
during the winter months (November to March), we noticed a drop in the
tank water temperatures from 18 C° to 14 °C. We fed the fish daily with
commercially available Chironomids, with an amount corresponding to
approximately 15\% of the individual wet weight \textsuperscript{--day}.
Unfortunately, we noticed strong variation in isotopic signatures of
Chironomids and were forced to switch suppliers to obtain more stable
signatures in perch diet (final food: \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C: -30.6 %0 \selectlanguage{ngerman}±
0.5, \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N: 14.0 %0 \selectlanguage{ngerman}± 3.7; average ± SD, respectively).
Using the new food, we started the feeding experiment to estimate TDFs
in perch on October 10\textsuperscript{th} 2018 (day 0). We ended the
trials between July 12\textsuperscript{th} (day 275) and
25\textsuperscript{th} 2019 (day 288), i.e. the experiment ran slightly
longer than 9 months. At this time, perch had approximately doubled
their original body mass on average (132.7 \% weight gain ± 89.4, Table
1). This is an approximate value as we did not track the weight increase
individually, but set initial weights as the average values of the
respective weight class. We assumed perch to be in isotopic equilibrium
with the Chironomid diet based on the equations for isotopic half-life
of vertebrate muscle tissue and liver reported in Vander Zanden et al.
(2015) and the predictive turnover equations presented by Thomas and
Crowther (2015). For both predictions, we assumed isotopic equilibrium
in 4-5 times the half-live (Thomas and Crowther 2015). Before the fish
were sacrificed at the end of the experiment with an overdose of
benzocaine, we conducted metabolic trials to obtain individual SMR (see
section below). We weighed the sacrificed fish to the nearest 0.01g and
measured total length to the nearest mm. We dissected a sample of the
dorsal muscle tissue and the entire liver for stable isotope analyses
and dried the tissue in a drying oven at 60°C for 48 hours.

Over the course of the experiment, we sacrificed a subset of 5
individuals every 6-10 weeks to observe the development of the TEFs over
time. In total, we analyzed the \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C and
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N in muscle and liver for 48 and 47 individuals
respectively. In addition to the fish sacrificed over the course of the
experiment, 28 individuals were maintained for the entire 9-month period
and SMR measurements were taken for 26 of them.

The study was approved by the Uppsala Animal Ethic Committee with permit
number: C59/15.

\emph{Stable isotope analyses and calculation of trophic discrimination
factors}

We ground oven-dried tissues samples to a fine powder using a mortar and
pestle, and transferred approximately 1 mg of the powder to tin
capsules. Elemental and stable isotope analyses of carbon and nitrogen
were conducted at the University of California, Davis Stable Isotope
Facility, California, USA, using a PDZ Europa ANCA-GSL elemental
analyzer coupled to a PDZ Europa 20-20 isotope ratio mass spectrometer
(Sercon, Cheshire, UK). As in all samples, C/N was low (3.2 \selectlanguage{ngerman}± 0.01;
average ± SD), no lipid normalization was performed (Kiljunen et al.
2006). We express our results using the \selectlanguage{greek}δ \selectlanguage{english}notation, thus, referring the
ratios of samples to the international standards (Vienna Pee Dee
Belemnite and Air for carbon and nitrogen, respectively). Measurement
error was 0.2%0 for \textsuperscript{13}C and 0.3%0
for\textsuperscript{15}N.

We calculated the TDF (i.e. \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C and
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N) for perch as

\({X}_{\text{perch}}=\ \selectlanguage{greek}\text{δX}\selectlanguage{english}_{\text{perch}}-\ \selectlanguage{greek}\text{δX}\selectlanguage{english}_{\text{Chironomid}}\),

using average values for \(\selectlanguage{greek}\text{δX}\selectlanguage{english}_{\text{Chironomid}}\), where
\(X\)stands for the heavy isotope of C
(\textsuperscript{13}C) and N (\textsuperscript{15}N).

\emph{Measurements of standard metabolic rate (SMR)}

SMR was measured using intermittent flow respirometry system (Loligo
Systems, Viborg, Denmark).. A detailed description of the respirometry
set-up is described in Andersson et al. (early view). Briefly, our
set-up was comprised of four acrylic respirometry chambers, submerged in
two aquaria (two chambers per tank) which contained water maintained at
18.1 \selectlanguage{ngerman}± 0.07 °C (average ± SD) and air-stones to maintain oxygen at
air-saturation levels. Oxygen concentration in each chamber was measured
using fiber-optic optodes connected to flow-through oxygen cells in
conjunction with a Wiltrox4 oxygen meter (Loligo Systems). Chambers were
size matched to each fish. Measurement loops for the trials consisted of
a 180 sec flush phase, a 30 sec wait phase and a 210 sec measurement
phase. Measurement loops for calculating background oxygen consumption
rate (M\selectlanguage{english}O\textsubscript{2} (m gO\textsubscript{2} h\textsuperscript{-1}))
consisted of a 180 sec flush phase, a 30 sec wait phase and a 900 sec
measurement phase. The entire system was drained and flushed with bleach
between trials to prevent the build-up of microbes over the course of
the experiment. At the beginning of each trial, the system was refilled
with aerated tap water that had been maintained at 18 \selectlanguage{ngerman}± 0.5°C for a
minimum of 24h.

Fish were fasted for approximately 24 hours before the start of the
respirometry trial. Individuals were removed from their husbandry tank
using a mesh net and transferred to the respirometer in a 9-liter bucket
filled with aerated 18 ± 0.5°C tap water. Fish were placed in the
respirometer in the afternoon and remained in the respirometer for a
minimum of 19 hours and 43 minutes. Following the trial, fish were
killed with an overdose of benzocaine and frozen for stable isotope
analysis. Measurements for background respiration were taken directly
before and after each trial. AutoResp software (Loligo Systems, version
2.2.0), was used to calculate M\selectlanguage{english}O\textsubscript{2}
(mgO\textsubscript{2}h\textsuperscript{-1}) from the linear decrease in
dissolved oxygen over each measure phase. Chamber specific background
respiration was calculated by fitting a linear regression between all of
the starting and ending background values with R\textsuperscript{2}
\textgreater{} 0.1 grouped by chamber. The linear regressions between
all values, as opposed to just values specific to each trial, were used
so that measures with R\textsuperscript{2} \textless{} 0.1 could be
excluded, while still allowing for chamber specific background, which
accounts for slight differences in calibration between each chamber.
Each trial was adjusted for background respiration by subtracting fitted
values estimating background respiration from measures of
M\selectlanguage{english}O\textsubscript{2}for each fish at each timepoint. Background corrected
measures were divided by fish weight to calculate mass-specific
M\selectlanguage{english}O\textsubscript{2}(mgO\textsubscript{2} kg\textsuperscript{-1}
h\textsuperscript{-1}) and any estimate of M\selectlanguage{english}O\textsubscript{2} with an
R\textsuperscript{2}\textless{} 0.95 was removed prior to calculating
SMR. SMR was calculated as the mean of the lowest 10\% of
M\selectlanguage{english}O\textsubscript{2}measures (Baktoft et al. 2016, Andersson et al. early
view). R-code for calculations of all metabolic measures is available on
the data repository Zenodo (http://doi.org/10.5281/zenodo.4433723).

\emph{Statistical analyses}

We size-corrected TDFs of muscle and liver tissue with the attempt to
adjust for the variation explained solely by growth (Fry and Arnold
1982). Thus, for all analyses, we used the residuals of the significant
regression of TDFs and fish length. To analyze the variation in TDFs
between weight classes, we conducted four different ANOVAs with
subsequent Tukey\selectlanguage{ngerman}´s posthoc comparisons using size-corrected
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C and size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N, for
the two tissue types muscle and liver. Additionally, we conducted four
linear regressions with the size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C and
size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N, for muscle and liver tissue
respectively as dependent variables and SMR as the predictor in order to
analyze the relationship between TDFs and SMR. We compared SMR between
the different weight classes using an ANOVA with subsequent Tukey\selectlanguage{ngerman}´s
posthoc comparisons. The assumptions of normal distribution and
homogeneities of variances were met for all parametric analyses. We used
IBM SPSS (version 25) for statistical analyses.

\textbf{Results:}

Over the course of the experiment, \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C of muscle
tissue gradually decreased from an average of -25.9 %0 \selectlanguage{ngerman}± 0.3 (SD)
measured after 8 weeks of feeding on a Chironomid diet to -26.9 \selectlanguage{english}%0 +- 0.4
(Figure S1a), while the \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N of muscle tissue
gradually increased from 14.2 %0 \selectlanguage{ngerman}± 0.6 to 14.9 \selectlanguage{english}%0 +- 0.2 (Figure S1b)
during the same time interval. Compared to muscle tissue, stable isotope
values in liver tissue were depleted in \textsuperscript{13}C, leading
to more negative values of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C and variation at each
time point was high (Figure S1c). \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N of liver
decreased from 16.5 %0 \selectlanguage{ngerman}± 0.4 to 15.5 \selectlanguage{english}%0 +- 0.3 and liver tissue was
generally more enriched in \textsuperscript{15}N compared to muscle
tissue (Figure S1d).

\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C of perch muscle ranged from 2.6 %0 to 4.4 %0 and
for \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N from 0.5 to 3.0 %0 (Table 1, Figure S2a, b).
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C of perch liver tissue ranged from -0.1 %0 to 1.9
and for \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N from 0.2 to 2.0 %0 (Table 1, Figure S2c,
d). For muscle tissue, size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C differed
significantly between weight classes (ANOVA: F\textsubscript{4,28}:
6.289, P= 0.001), with significantly lower TDF in juvenile individuals
(i.e., weight class \textless{}20 g at the start of the experiment) and
20-30 g individuals caught in the pelagic zone that were significantly
different to the higher values found in individuals of 40-50 g. In
addition, size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C in juveniles differed
significantly from size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C in 20-30 g
individuals caught in the pelagic zone, as well as from 30-40 g
individuals. Size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N in muscle tissue
differed significantly between weight classes (ANOVA:
F\textsubscript{4,28}: 8.781, P\textless{} 0.001), with significant
higher \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N observed in \textless{}20 g individuals
compared to all other weight classes (Figure 1b). For liver tissue,
size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C differed significantly between
weight classes (ANOVA: F\textsubscript{4,27}: 3.028, P= 0.039), with
significantly lower TDF observed in juveniles compared to 20-30 g
individuals caught in the pelagic zone (Figure 1c). No significant
differences were found in the size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N of
liver between weight classes (Figure 1d).

SMR between the individuals varied substantially and ranged from 55.6 to
106.5 mg O\textsubscript{2} kg
\textsuperscript{-1}hr\textsuperscript{-1} (Table 1). SMR differed
significantly between perch of the different weight classes (ANOVA:
F\textsubscript{4,26}: 11.322, P\textless{} 0.001). SMRs were highest in
\textless{}20 g individuals and 20-30 g individuals caught in the
pelagic zone that were significantly different to the low values found
in individuals of 40-50 g (Figure 2). In addition, SMR in juveniles
differed significantly from SMR in 20-30 g individuals caught in the
pelagic zone, as well as from 30-40 g individuals (Figure 2).

For muscle tissue, linear regression showed that SMR had a significant
negative effect on the size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C (t=- 3.375,
P= 0.003, Figure 3a). For size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N in
muscle, a positive trend could be observed, but regressions were not
significant (Figure 3b). Furthermore, no significant relationship could
be identified between SMR and size corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C, or
size corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N in liver tissue (Figure 3 c, d).

\textbf{Discussion:}

Metabolic rate can be viewed as the most fundamental biological rate,
explaining the rate at which organisms take up, transform, and expend
energy (Brown et al. 2004). Thus, it must be assumed, that the
individual metabolic rate has profound implications for shaping the
TDFs. To the best of our knowledge, this framework has only been applied
to the study of TDFs in small mammals (i.e. mice and rats: MacAvoy et
al. 2006, MacAvoy et al. 2012), or birds (Ogden et al. 2004). However,
in ectothermic teleosts, where metabolism is comparably slower, this
process was assumed to be negligible. This view needs a revision, as our
results show a clear and gradual relationship between
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C and metabolic rate on the individual level for
muscle tissue. However, we could not detect any association between
metabolic rate and \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N in muscle tissues, nor to TDFs
in liver tissue. In accordance with Herzka (2005), our results highlight
the need for establishing different TDFs for specific ontogenetic stages
to allow more precise interpretation of isotopic data.

Trueman et al. (2005) reported differences in individual growth rates of
Atlantic salmon (\emph{Salmo salar} ) to be associated to variable TDFs.
The authors suggested that this pattern could be explained by
intraspecific differences in metabolism, but direct measurements of
metabolic rates were not included. In many studies of TDFs in ecothermic
species, isotopic change has been attributed to growth rather than
metabolism (Hesslein et al. 1993, Bosley et al. 2002), but see (Herzka
et al. 2001, Tarboush et al. 2006, Sun et al. 2012). Unfortunately, we
are not able to separate the isotopic change into contributions of
growth and metabolic rate \emph{sensu} Fry and Arnold (1982) as we did
not track the precise individual weight increase. .We approached this
issue by correcting our data to potential variation from growth by using
the residuals from the regression of TDFs on size (total length at the
end of the experiment). Here, the strongest differences in
size-corrected TDF could be detected comparing juvenile to adult perch.
Besides having the highest SMR, juveniles were also characterized by the
strongest approximate weight increase (Table 1). When measuring
individual metabolic rate immediately before assessing their TDFs we
found fine scale differences that correlated strongly with their
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C isotopes indicating that changes in metabolic
rates could translate into variations in TDFs (Kleiber 1932, 1947,
Boecklen et al. 2011).

Results of posthoc comparisons of SMR between perch with an initial
weight of 20-30 g caught in different habitats (pelagic and littoral)
were not significant, but when analyzed separately, a significant
difference appears with pelagic having a higher SMR (t-test:
t\textsubscript{7.633} = -2.406; P= 0.044), similar to previous results
(Andersson et al. unpublished data). Generally, it is known that such
intra-specific differences in metabolic rates within adult individuals
exist in many species, including fish (Biro and Stamps 2010). However,
less is known about differences between individuals living in different
habitats. In many Swedish lakes including Erken, which is the origin of
the perch used in this study, littoral and pelagic perch of the
intermediate class size differ in their individual specialization for
respective food items, which is even translated into adaptations of
their morphology. While pelagic perch predominately ingest pelagic
zooplankton and have a more streamlined body form, littoral perch
include benthic macroinvertebrates in their diet to a higher degree and
are characterized by a deeper body (Svanb\selectlanguage{ngerman}äck and Persson 2004, Marklund
et al. 2019). Potentially, habitat-specific differences in activity
levels could be related to the differences found in SMR (Kahilainen et
al. 2014). Pelagic perch need to be endurance swimmers in order to catch
the smaller fast-moving prey, while littoral perch forage on larger prey
items of lower mobility (Svanbäck and Eklöv 2004). Future research is
needed to resolve the underlying causes for the differences found in SMR
between littoral and pelagic perch. Interestingly, average
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C in muscle tissue of 20 -- 30 g pelagic perch was
lower compared to littoral perch of the same weight class (Table 1) but
this difference was not significant. Thus, our data reflect a trend that
the inverse relationship (i.e. elevated SMR leads to lower
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C), holds true not only between juveniles and
adults, but also between the habitat-specific individuals of the same
weight class.

While the effect of SMR was strong for \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C in muscle
tissue, and a trend (though not significant) was visible for the
association of SMR and \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N in muscle tissue, we did
not observe any relationship of SMR with the TDFs of liver tissue,
indicating that individual metabolic rate has a stronger effect of
tissue types of slower isotopic turnover. Generally, liver had lower
TDFs (\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C: 1.1 %0 \selectlanguage{ngerman}± 0.4; \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N: 1.1 %0
\selectlanguage{ngerman}± 0.5) compared to the TDFs of muscle tissues (\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C:
3.7 %0 \selectlanguage{ngerman}± 0.5; \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N: 1.3 %0 \selectlanguage{ngerman}± 0.6), showing relatively
little change in the isotope values between consumer and prey. Our
results are in line with the findings of other studies on
tissue-specific difference in TDFs in fish (Buchheister and Latour 2010,
Matley et al. 2016), and the observed pattern might be due to different
biochemical composition of the tissue types, e.g. the abundances of
specific amino acids (Pinnegar and Polunin 1999).

Our values of \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N in muscle tissue are relatively low
compared to the highly cited average value of 3.4 %0 (Post 2002).
However, consumers raised on a invertebrate diet typically show a lower
TDF compared to consumers raised on a high-protein diet (McCutchan et
al. 2003). Protein is the principal source of energy for perch, which
are ammoniotelic organisms, and thus characterized by a relatively high
N use efficiency that is linked to lower \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N (Trueman
et al. 2005).

In contrast, our derived values of \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C for muscle
tissue were rather high compared to the typically assumed value of 0.3 %0
(Post 2002) or 0.4 %0 (McCutchan et al. 2003). However, previous studies
have reported similarly high values (e.g.Pinnegar and Polunin 1999,
Barnes et al. 2007, Busst and Britton 2016). Vollaire et al. (2007)
reported \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C of 4.02 \selectlanguage{ngerman}± 0.13 \selectlanguage{english}%0 for juvenile perch
feeding on artificial diet. A potential reason for variation in
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C could be a process termed ``isotopic routing'',
where resource constituents, such as proteins, lipids and carbohydrates
are allocated to different tissue types (Schwarcz 1991). Furthermore,
the organisms lipid content may lead to substantial variation, as lipids
are generally\textsuperscript{13}C-depleted (DeNiro and Epstein 1978,
Focken and Becker 1998) However, as C/N in the perch used in this study
was generally low (3.2 \selectlanguage{ngerman}± 0.1, average ± SD), lipid correction is not
recommended (Kiljunen et al. 2006). Altogether, we thus agree with Wolf
et al. (2009) and acknowledge that further studies are urgently needed
to understand variation of \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C in fish.

Variation in \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N, specifically in liver tissue was
higher compared to the variation in \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C. We assume
that this variability can be attributed to the fact that the
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N of the diet was rather high (14.0 %0 \selectlanguage{ngerman}± 3.7).
Commercially raised Chironomids are maintained in big flumes and
cannibalism might occur which would result in higher
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N of individual organisms. Another aspect that
could potentially influence the variation in \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N, but
also in \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C is the individual food intake, which was
shown to influence TDFs (Bosley et al. 2002, Barnes et al. 2007). In our
study, perch shoals were fed at high feeding rates (approximately 15\%
of the individual wet weight\textsuperscript{--day}), and left-over food
was removed. However, this does not imply that all fish individuals fed
until satiation. Strong hierarchies exist in perch shoals (Magnhagen
2012) and were observed in some of our tanks. Dominance behavior of
single individuals might have prevented subordinates the access to food.
This artefact of the experimental design adds to the previously
mentioned confounding aspects and highlight again the complexity and
difficulty involved in experimentally accessing general and widely
applicable TDFs.

\textbf{Acknowledgements:}

We are grateful to Holger Villwock, Leonie Haferkemper and Robyn
\selectlanguage{ngerman}Övergaard for taking excellent care on the perch during the experiments.
In addition, Holger Villwock greatly helped during the sampling campaign
and Robyn Övergaard assisted during metabolic trials.

\textbf{Author Contributions:} KS did the conceptualization and wrote
the first draft. KS, MLA, FC, and PE collected data; KS and MLA analyzed
the data. All authors helped writing and gave final approval for
publication.

\textbf{Data availability} : Data and R scripts are available from the
Zenodo Digital Repository (http://doi.org/10.5281/zenodo.4433723).

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\textbf{Figures:}

\textbf{Figure 1:} Size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C and
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N of muscle and liver tissue (i.e. residuals of
regressions of TDFs on fish length) of different weight classes in
perch. Weight classes with the same letter are not significantly
different (Tukey\selectlanguage{ngerman}´s pairwise comparisons). Boxplots depict median, 25th
and 75th percentile, and whiskers extend to maximum and minimum values,
except for outliers (\textgreater{}1.5 times box height, represented by
dots).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2:} Standard metabolic rate (SMR) of different weight
classes in perch. Weight classes with the same letter are not
significantly different (Tukey\selectlanguage{ngerman}´s pairwise comparisons). Boxplots depict
median, 25th and 75th percentile, and whiskers extend to maximum and
minimum values.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 3} : Relationship between standard metabolic rate (SMR)
and size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C, and size-corrected
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N of muscle and liver tissue in perch individuals,
respectively. Colors depict different weight classes of perch at the
beginning of the experiment. Regression lines (including regression
coefficient R\textsuperscript{2}) in a) indicate significant linear
regressions between size-corrected \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C and SMR in
muscle tissue.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Table 1:} Overview of averages (\selectlanguage{ngerman}± standard deviations) of muscle
and liver tissue TDFs (\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C and
\selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N) for the different weight classes of perch, and
standard metabolic rate (SMR), including sample sizes, weight at end of
the experiment, and percent approximate change of weight (initial weight
was not measured, but it was assumed to be the average of the respective
weight classes).\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
\begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Weight class at start (g)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Sample size\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Habitat\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Muscle \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C (%0)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Muscle \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N (%0)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Liver \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{13}C (%0)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Liver \selectlanguage{greek}Δ\selectlanguage{english}\textsuperscript{15}N (%0)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
SMR (mg O\textsubscript{2} kg \textsuperscript{-1}
hr\textsuperscript{-1} )\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Weight at end (g)\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Percent approximate change of weight\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textless{}20 (juvenile)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
littoral\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.8 \selectlanguage{ngerman}± 0.2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.6 \selectlanguage{ngerman}± 0.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.4 \selectlanguage{ngerman}± 0.5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.2 \selectlanguage{ngerman}± 0.4\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
97.2 \selectlanguage{ngerman}± 8.9\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
16.6 \selectlanguage{ngerman}± 1.0\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
316.2 \selectlanguage{ngerman}± 24.2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
20-30\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
littoral\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.9 \selectlanguage{ngerman}± 0.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.1 \selectlanguage{ngerman}± 0.4\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.1 \selectlanguage{ngerman}± 0.2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.8 \selectlanguage{ngerman}± 0.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
73.8 \selectlanguage{ngerman}± 6.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
56.2 \selectlanguage{ngerman}± 7.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
124.7 \selectlanguage{ngerman}± 29.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
20-30\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
pelagic\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.6 \selectlanguage{ngerman}± 0.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.1 \selectlanguage{ngerman}± 0.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.3 \selectlanguage{ngerman}± 0.4\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.2 \selectlanguage{ngerman}± 0.5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
83.7 \selectlanguage{ngerman}± 8.8\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
57.7 \selectlanguage{ngerman}± 11.0\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
137.3 \selectlanguage{ngerman}± 58.8\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
30-40\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
pelagic\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.9 \selectlanguage{ngerman}± 0.4\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.1 \selectlanguage{ngerman}± 0.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.2 \selectlanguage{ngerman}± 0.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.1 \selectlanguage{ngerman}± 0.6\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
73. 8 \selectlanguage{ngerman}± 3.8\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
40.4 \selectlanguage{ngerman}± 23.1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
73.4 \selectlanguage{ngerman}± 45.3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
40-50\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
littoral\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4.0 \selectlanguage{ngerman}± 0.3\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.1 \selectlanguage{ngerman}±0.2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.3 \selectlanguage{ngerman}± 0.1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.8 \selectlanguage{ngerman}± 0.5\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
61.7 \selectlanguage{ngerman}± 8.3.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
54.4 \selectlanguage{ngerman}± 21.2\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
56.0 \selectlanguage{ngerman}± 35.1\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

\selectlanguage{english}
\FloatBarrier
\end{document}

