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 (12C/13C, expressed as
δ13C) and nitrogen
(14N/15N, expressed as
δ15N) of stable isotopes have been widely used. While
δ13C can be used to track the origin of the carbon
source in organisms´ diet and the base of the food web,
δ15N is especially useful to determine the organisms´
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 δ13C and
δ15N, estimates of the trophic discrimination factor
(TDF, Δ13C and Δ15N) are
needed. This factor represents the difference in δ13C
(or δ15N) 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 (Martínez 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 O2 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 12C, 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 (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 Δ13C and
Δ15N 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.