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.