Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-10T08:33:47.037Z Has data issue: false hasContentIssue false

ANALYSIS OF RADIOCARBON DISTRIBUTION IN THE EUTROPHIC LAKE FISH ASSEMBLAGE USING STABLE C, N, S ISOTOPES

Published online by Cambridge University Press:  10 November 2022

Rūta Barisevičiūtė*
Affiliation:
State Research Institute Center for Physical Sciences and Technology, Savanorių ave. 231, LT-02300 Vilnius, Lithuania
Vytautas Rakauskas
Affiliation:
Laboratory of Fish Ecology, State Research Institute Nature Research Centre, Akademijos 2, Vilnius, LT- 08412, Lithuania
Tomas Virbickas
Affiliation:
Laboratory of Fish Ecology, State Research Institute Nature Research Centre, Akademijos 2, Vilnius, LT- 08412, Lithuania
Žilvinas Ežerinskis
Affiliation:
State Research Institute Center for Physical Sciences and Technology, Savanorių ave. 231, LT-02300 Vilnius, Lithuania
Justina Šapolaitė
Affiliation:
State Research Institute Center for Physical Sciences and Technology, Savanorių ave. 231, LT-02300 Vilnius, Lithuania
Vidmantas Remeikis
Affiliation:
State Research Institute Center for Physical Sciences and Technology, Savanorių ave. 231, LT-02300 Vilnius, Lithuania
*
*Corresponding author. Email: ruta.bariseviciute@ftmc.lt
Rights & Permissions [Opens in a new window]

Abstract

The carbon isotope distribution and its relationship with stable N and S isotope ratio values were investigated within a fish assemblage from the shallow lake Tapeliai, which is constantly affected by inflows of 14C depleted water from the surrounding watershed mires. The “conventional” radiocarbon age within the fish from this lake varied from 119 to 693 yr. The 14C/12C and δ13C values correlated significantly (r=0.85 p<0.001), which is not typical in lakes of the temperate zone. There were no observed statistical differences (Kruskal–Wallis ANOVA tests) in the 14C/12C values among different fish species. The radiocarbon dating values and 15N/14N measurements did not correlate. The radiocarbon measurement values also did not correlate with δ34S, however, the distribution of these isotopes in carp (119 yr and 1.3‰, respectively) and roach (344 yr and 4.5‰, respectively) indicated that fish may include allochthonous food sources in their diet.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press for the Arizona Board of Regents on behalf of the University of Arizona

INTRODUCTION

During the last decade, there has been an increase in the number of studies aimed at determining the reservoir effect on radiocarbon dating of remains from ancient settlements near water bodies. In addition, stable isotopes are used, since they could be useful in determining the contribution of proteins from this water ecosystem to the diet of these individuals (Sayle et al. Reference Sayle, Cook, Ascough, Gestsdóttir, Hamilton and McGovern2014, Reference Sayle, Hamilton, Gestsdóttir and Cook2016; Schulting et al. Reference Schulting, Ramsey, Bazaliiskii, Goriunova and Weber2014). It has been shown that the freshwater reservoir effect in the same water ecosystem varies between and even within different fish species, although they possess similar diets (Keaveney et al. Reference Keaveney and Reimer2012; Philippsen Reference Philippsen2013). This is not surprising, as the carbon isotope composition in DIC (dissolved inorganic carbon), as well as aquatic primary producers, varies within the lake (Li et al. Reference Li, Qiang, Jin, Liu, Zhou and Zhang2018). DIC concentration and its carbon isotope composition is determined by the air-water CO2 exchange rate, which depends on the organic matter production and decomposition rates, organic and inorganic matter import/export, and water residence time in that particular place (Hou et al. Reference Hou, D’Andrea and Liu2012; Mischke et al. Reference Mischke, Weynell, Zhang and Wiechert2013; Wang et al. Reference Wang, Chen, Kang, Hu and Wang2019). These processes also affect the content and composition of organic matter in the water column and sediments, thereby impacting on the diet of benthic suspension and primary pelagic feeders (Keaveney et al. Reference Keaveney, Reimer and Foy2015).

Stable isotope analysis is widely used to reconstruct diet and characterize trophic relationships in food webs and can be helpful in interpreting radiocarbon dating results. Nitrogen isotope ratios have been the main tool for determining trophic levels of consumers (Vander et al. Reference Vander and Rasmussen2001; Post Reference Post2002). The stable carbon isotope method allows separating littoral and pelagic habitat feeders/consumers, as littoral macrophytes have higher 13C concentrations (France Reference France1995). However, the δ13C signal in DIC, which is the main carbon source for phytoplankton and submerged water plants, depends on CO2 concentration in the water column, since its deficiency causes changes in fractionation during carbon fixation. During the bloom period, the carbon isotope composition in phytoplankton can vary by several ‰ (Savoye et al. Reference Savoye, Aminot, Tréguer, Fontugne, Naulet and Kérouel2003). In areas where C3 plants predominate, it is difficult to distinguish between autochthonous and allochthonous origin organic matter in freshwater ecosystems based on stable carbon isotope measurements alone, since the 13C/12C values overlap (Maksymowska et al. Reference Maksymowska, Richard, Piekarek-Jankowska and Riera2000). Meanwhile, 34S/32S ratio measurements can be used to separate terrestrial and freshwater origin food sources in diet (Richards et al. Reference Richards, Fuller, Sponheimer, Robinson and Ayliffe2003; Bocherens et al. Reference Bocherens, Drucker and Taubald2011), as well as in separation of benthic and pelagic habitat feeders (Fry Reference Fry1986a). Phytoplankton utilizes S from the water column and reflects its sulfate (with fractionation of 0–2‰ (Kaplan et al. Reference Kaplan and Rittenberg1964)) isotope composition. In anoxic sediments, sulfate-reducing bacteria discriminate against the heavier sulfur isotope resulting its depletion (Donahue et al. Reference Donahue, Werne, Meile and Lyons2008), which in marine environments reaches up to 50–60‰ (Thode Reference Thode1991). However, in lakes, especially those with low sulfate concentration in the water column, fractionation due to sulfate reduction is significantly lower (6–8‰) (Croisetière et al. Reference Croisetière, Hare, Tessier and Cabana2009; Karube et al. Reference Karube, Okada and Tayasu2012; Proulx et al. Reference Proulx and Hare2014). Sulfur isotope composition in sediments is more related with the origin of sediment forming fractions.

The present study focuses on the 14C/12C ratio variation within the fish assemblage of the shallow lake Tapeliai. During spring flood periods or after heavy rains, this lake is fed by an inflow of colored water from the surrounding watershed mires (Moisejenkova et al. Reference Moisejenkova, Tarasiuk, Koviazina, Maceika and Girgždys2012). Repetitive inflows of such water which is highly enriched in organic compounds not only affect photosynthetic activity, but also cause short-term changes in the content and composition of organic and inorganic matter that may impact radiocarbon distribution in the lake ecosystem.

The aim of this study was to investigate the radiocarbon distribution within fish species of different diet and trophic levels, and the possibility to relate radiocarbon distribution in fishes of this constantly changing ecosystem with their stable C, N, and especially S isotope ratio values.

MATERIALS AND METHODS

Sampling Area

Lake Tapeliai (54º46'28"N, 25º26'45"E) is located 17 km northeast of the city of Vilnius, Lithuania. The areal of the lake lies in the deepened up to 15–20 m tunnel valley with the glaciofluvial deposits (gravel with sand and other deposits containing up to 40 % of carbonates) of the Late Weichselian glaciation, which is a part of the plain of the Neris River (Bitinas et al. Reference Bitinas, Karmaziene and Jusiene2004; Bitinas Reference Bitinas2012). This lake belongs to a chain of small hydrologically connected lakes. In the north, Lake Tapeliai is connected by a brook with Lakes Juodis (54°46'49"N, 25°26'29"E) and was connected by an artificial ditch with the small humic Lake Lydekinis in the south. The ditch was opened between 1879 and 1923 causing an increase in sediment reservoir age of about 1175 ± 111 yr and closed in the 1960s (Tarasiuk et al. Reference Tarasiuk, Moisejenkova, Koviazina, Karpicz and Astrauskiene2009; Barisevičiute et al. Reference Barisevičiute, Maceika, Ežerinskis, Mažeika, Butkus, Šapolaite, Garbaras, Paškauskas, Jefanova and Karosiene2019). The surface area of the lake is ∼0.126 km2, the drainage basin area exceeds 0.7 km2 (Tarasiuk et al. Reference Tarasiuk, Moisejenkova, Koviazina, Karpicz and Astrauskiene2009; Moisejenkova et al. Reference Moisejenkova, Tarasiuk, Koviazina, Maceika and Girgždys2012). The lake is surrounded by peat bogs. Thus, during spring flood periods or after long-term rains, the lake is additionally fed by colored water (Moisejenkova et al. Reference Moisejenkova, Tarasiuk, Koviazina, Maceika and Girgždys2012).

According to the maximal and average depth measurements the lake is identified as a terminally shallow (polimictic) lake. The fish community is primarily composed of lentic water fish such as Esox lucius (Linnaeus 1758), Perca fluviatilis (Linnaeus 1758), Rutilus rutilus (Linnaeus 1758) and Abramis brama (Linnaeus 1758). In addition to these dominant species, the lake is inhabited by introduced Cyprinus carpio (Linnaeus 1758) which is not native and cannot breed under local thermal conditions (Virbickas Reference Virbickas2018). It is worth noting that the lake is under very intensive recreational use (city beaches, water sports, and angling) as it is close to the city of Vilnius.

Field Sampling

All samples were collected in September 2018. The fish were trapped using multi-mesh benthic gillnets, each of which was 40 m long and 3 m high. The mesh size (bar length) varied every 5 m and was 14, 18, 22, 25, 30, 40, 50, and 60 mm. Four such benthic gillnets, clustered into 2 sets for stability and convenience, were used. The nets were positioned randomly to cover different parts (western, eastern) and depths (2–7 m) of the lake, and left for 12 hr overnight, including sunset and sunrise. All trapped fish were identified to species level, measured to the nearest 1 mm (total body length—TL), weighed to the nearest 0.1 g, and their age was determined from scales (Thoresson Reference Thoresson1993). The standard catch per unit of effort (CPUE) was estimated as fish biomass trapped per 40-m-long multi-mesh benthic gillnet per sampling occasion. The fish taxonomy used in the present paper follows the taxonomy provided in FishBase (http://www.fishbase.org, accessed 2022.01.06).

Littoral snail was collected for distinguishing the second trophic level in the lake. Due to the constantly changing environmental conditions in the ecosystem, it was not appropriate to make measurements in DIC, water column sulfates, dissolved and particulate organic matter. The aquatic moss Fontinalis antipyretica Hedv. was collected instead. The absence of roots and other similar systems in this plant excludes the substrate influence on the uptake dynamics (Maberly Reference Maberly1985). The isotope composition in the tissues of this plant should reflect the average isotopic values in DIC and sulfates during its growth season.

Stable Isotope Analysis (SIA)

Fish for SIA were obtained from the standardized catches of autumn 2018. A sample of white dorsal muscle was cut out from each fish subjected to SIA. Up to three individuals of the same species and size were used for SIA. Perch were divided into two length groups (see Table 1) as it is known that this fish species undergoes ontogenetic niche shifts (Froese and Pauly Reference Froese and Pauly2022). The description of fish specimens’ size, age, and number of replicates are presented in Table 1. Additionally, gastropods Bithynia tentaculata were sampled as an integrator of the SIA signature of the benthic or littoral primary producers (Post Reference Post2002). The sampled mollusks were separated from shells, and only their soft tissues (mantle muscle, if possible) were used. Nine individuals of B. tentaculata were used to form one replicate for SIA. All samples were then oven dried to constant weight for 48 hr at 60ºC, ground to fine powder in an agate mortar, and placed into foil cups.

Table 1 Summary of SIA of different consumer species from Lake Tapeliai in 2018. Values are mean ± standard deviation, total length of fish (TL), fish age determined from scales, number of replicates (N), stable isotope values (δ13C, δ15N, δ34S, and 14C ‰), the radiocarbon age determined from 14C values. Perch size categories: small (S), large (L).

Carbon, nitrogen and sulfur stable isotope ratio measurements were performed using a Thermo Flash EA 1112 elemental analyser interfaced to a Thermo Scientific Delta V Advantage isotope ratio mass spectrometer (IRMS). All the results of the stable isotope ratio measurements were expressed relative to a standard using delta notation in units of permil (‰):

(1) $$\delta {\rm{X}} = \left[ {\left( {{{\rm{R}}_{{\rm{sample}}}}/{{\rm{R}}_{{\rm{standard}}}}} \right) - 1} \right)] \times 1000,$$

where X are 13C, 15N or 34S, respectively; R=13C/12C, 15N/14N or 34S/32S in the sample or in the standard. The stable carbon, nitrogen and sulfur isotope ratios were expressed relative to the Vienna Pee Dee Belamnite (V-PDB), atmospheric nitrogen and Vienna Canyon Diablo troilite (VCDT), respectively. The laboratory standards were calibrated using NIST Standard Reference Material IAEA600, IAES-N-2. IAEA-S-1, and IAEA-S-2. The long-term reference material measurements were performed with a precision of <0.15‰ for δ13C, <0.2‰ for δ15N and <0.6‰ for δ34S.

As lipids are depleted in 13C (DeNiro et al. Reference DeNiro and Epstein1977), any variation in lipid concentrations between consumer species could influence comparisons of δ13C. However, lipid removal in the consumer samples was not performed in order to keep the δ15N values unaffected by treatment (Post et al. Reference Post, Layman, Arrington, Takimoto, Quattrochi and Montaña2007). The C:N ratios in some of the samples were higher than the recommended limit for aquatic organisms (C:N>3.5), at which a lipid correction should be performed (Table 1). Therefore, consumer δ13C data were arithmetically lipid-normalised according to Post et al. (Reference Post, Layman, Arrington, Takimoto, Quattrochi and Montaña2007): δ13C = δ13Cuntreated –3.32 + 0.99 × C:N. We used lipid-normalised consumer δ13C data for all statistical analyses due to significant variation in the C:N ratios between different consumer species (see Table 1).

Radiocarbon Dating

Before radiocarbon dating, all samples were graphitized using Automated Graphitization Equipment AGE-3 (IonPlus AG). The measurements were performed with a 250 kV single stage accelerator mass spectrometer (SSAMS, NEC, USA) at the Center for Physical Sciences and Technology in Vilnius, Lithuania. Typical SSAMS system parameters can be found in the paper by (Ežerinskis et al. Reference Ežerinskis, Šapolaite, Pabedinskas, Juodis, Garbaras, Maceika, Druteikiene, Lukauskas and Remeikis2018). The background of the measurements was estimated to be 2.45 × 10−3 $${{\rm{F}}^{14}}{{\rm{C}}_{\rm{m}}}$$ (fraction of modern carbon) using phthalic anhydride (Alfa Aesar). The IAEA-C3 standard was used as a reference material (the percent of a modern carbon (pMC) value of 129.41). The 14C/12C ratio was measured with an accuracy better than 0.3%. For the 14C isotopic fractionation correction, the 13C/12C ratio measured with SSAMS was used.

The “conventional” radiocarbon age (RA) was calculated (Eriksson Stenström et al. Reference Eriksson Stenström, Skog, Geogiadou, Genberg and Johansson2011):

(2) $${{\rm{T}}_{{}^{14}{{\rm{C}}_{{\rm{yr}}}}}} = - 8033 \times {\rm{ln}}\;({{\rm{F}}^{14}}{{\rm{C}}_{\rm{m}}});$$

where $${{\rm{F}}^{14}}{{\rm{C}}_{\rm{m}}}$$ is fraction of modern carbon in aquatic samples.

Calculations and Data Analysis

Kruskal–Wallis ANOVA tests were applied to test for species effects on the stable δ13C, δ15N and δ34S isotope values of different sampled consumers. Spearman rank correlations were used to show connections between stable C and N isotopes. Non-parametric tests were used, as the data did not meet the normality assumption of parametric methods (Shapiro–Wilk’s W tests, P < 0.05). The analyses were performed using STATISTICA 12.0 software. The significance level of P< 0.05 was specified for all statistical analyses.

RESULTS

Fish Assemblage

Overall, seven species were encountered during this study, five species were considered as benthivorous, and one—piscivorous. Functional classification in Froese and Pauly (Reference Froese and Pauly2022) was followed. However, European perch is known to undergo a substantial ontogenetic diet shift from benthivory to piscivory (Froese and Pauly Reference Froese and Pauly2022); therefore, small specimens of this species were considered benthivorous, while large specimens were regarded as piscivores (Table 1). The species threshold for the diet switch was defined as the TL at which the percentage of fish-prey exceeds 50% of the total gut content biomass in most individuals. Thus, the threshold for P. fluviatilis was TL 20 cm (Hjelm et al. Reference Hjelm, Persson and Christensen2000). An average CPUE (the catch per unit effort) contained 3.1 kg of fish per 40-m-long multi-mesh benthic gillnet. Standard catches showed roach to be the dominant fish species by the proportion of number (78.2%), while carp and bream dominated the fish assemblage by their biomass (43.2 and 30.4%, respectively). The main top predators in the fish assemblage are represented by large-sized predatory fish species such as pike and perch. All together, they constituted 13.6% of the biomass in the fish catch.

Stable Isotopes (δ13C, δ15N, δ34S)

In total, 18 replicates of seven consumer species were analysed for stable δ13C, δ15N and δ34S isotope values in Lake Tapeliai (Table 1). δ13C values ranged between –35.1‰ and –28.7‰ (rangeδ13C = 6.4‰), and δ15N values between –2.0 and 10.2‰ (rangeδ15N = 8.2 ‰). δ34S range was also low, from 1.3‰ to 7.7‰ (rangeδ34S = 6.4 ‰). However, this follows the pattern typical of temperate freshwater lakes ranging from top predators (Esox lucius, large P. fluviatilis) to primary consumer (B. tentaculata) on the δ15N axis and from profundal dwelling fish (G. cernua) to littoral fitophagous fish (S. erythropthalmus) on the δ13C axis (Figure 1, Table 1).

Figure 1 Isotopic bi-plot showing the mean (±SD) δ13C and δ15N values in different fish species of Lake Tapeliai in 2018. The radiocarbon ages are shown adjacent to fish names.

The mean stable isotope values differed among consumer species (Figure 1). Although comparisons were limited due to differences in sample sizes, there was clear statistical evidence of differences in the mean δ13C among different consumers (Kruskal–Wallis ANOVA test: H8,18 = 15.84, P = 0.04). In the sampled consumers as a whole, the mean δ13C values varied between –35.1 ± 0.5‰ (G. cernua) and –28.7‰ (S. erythrophthalmus), with a difference of 6.4‰. Of all the consumers studied, G. cernua and S. erythrophthalmus were most isotopically distinct (Figure 1). G. cernua was more 13C-depleted suggesting their foraging for benthic-profundal food sources; while S. erythrophthalmus was 13C-enriched, indicating their reliance on vegetated-littoral food sources.

The mean δ15N values ranged between 2.0‰ (B. tentaculata) and 10.2‰ (large P. fluviatilis), and also varied significantly among species (Kruskal–Wallis ANOVA test: H8,18 = 15.25, P = 0.04). To illustrate isotopic variation in size, P. fluviatilis specimens were subsequently classified into small (S) and large (L) size groups (Figure 1 and Table 1). As expected, the most enriched in 15N in comparison with other fishes was the large P. fluviatilis and E. lucius indicating these fish species as top predators in the food chain of Lake Tapeliai. It is worth noting that C. carpio has also 15N-enriched δ15N value (Figure 1). Littoral primary consumer, snail B. tentaculata possess the most depleted δ15N values. There was no significant correlation between δ13C and δ15N values in sampled consumer specimens (Spearman rank order correlation: ρ = 0.14, p = 0.59).

The mean δ34S values ranged between 1.3‰ (C. carpio) and 7.7 ‰ (S. erythrophthalmus). Of all the consumers studied, C. carpio was the most isotopically distinct (Figure 2). There was no clear statistical evidence of differences in the mean δ34S among different consumers (Kruskal–Wallis ANOVA test: H8,17 = 12.74, P = 0.08). However, a significant correlation between δ34S and δ15N values in sampled fish specimens was observed (Spearman rank order correlation: r < 0.65, P = 0.004; and even more significant if carp was excluded: r < 0.83, P < 0.001). δ34S value the aquatic moss Fontinalis antipyretica Hedv. was 6.5‰ (Table 1).

Figure 2 Isotopic bi-plot showing the mean (±SD) δ34S and δ13C values in different fish species of Lake Tapeliai in 2018. The radiocarbon ages are shown adjacent to fish names.

Radiocarbon Dating

The results of radiocarbon dating and stable isotope analysis for Lake Tapeliai are shown in Table 1 and Figures 14. The $${{\rm{T}}_{{}_{}^{14}{{\rm{C}}_{{\rm{yr}}}}}}$$ value in the aquatic moss Fontinalis antipyretica Hedv. was 849 yr. The studied fish species present a wide range in radiocarbon ages, from 119 years for C. carpio (true age nine years) up to 693 years for G. cernua (true age of six years). Furthermore, there was also a wide range in the 14C ages within the same fish species. The results showed a difference of more than 300 years in $${{\rm{T}}_{{}_{}^{14}{{\rm{C}}_{{\rm{yr}}}}}}$$ for the same age (true age of five years) of the roach specimens. However, there was no statistical evidence of differences in the mean $${{\rm{T}}_{{}_{}^{14}{{\rm{C}}_{{\rm{yr}}}}}}$$ among different fish species (Kruskal–Wallis ANOVA test: H8,18 = 13.80, P = 0.09). Thus, 14C/12C values did not correlate with δ34S and δ15N. Only δ13C values were associated significantly with radiocarbon measurements (Table 2).

Figure 3 Isotopic bi-plot showing the mean (±SD) δ34S and δ15N values in different fish species of Lake Tapeliai in 2018. The radiocarbon ages are shown adjacent to fish names.

Figure 4 Isotopic bi-plot showing the mean (±SD) δ13C and 14C specific activity values in different fish species of Lake Tapeliai in 2018. The 14C ages are shown adjacent to fish names.

Table 2 Partial correlations for all explanatory variables for the RA value variation in fish muscle.

DISCUSSION

The lake fish assemblage is formed from a very low number of fish species. Only four native fish species represent the benthivorous fish guild in the lake. Additionally, introduced non-native carp enlarge the number of benthivorous fish species within the lake. Two species represent top predator’s guild, and one species—phytophagous. Overall, the observed fish assemblage is typical for such small, eutrophic lakes, where benthivorous species form most of the fish biomass, while piscivorous constitute 1/10 part of the overall fish assemblage.

The trophic chain of Lake Tapeliai appears to be rather short. For instance, the total difference in the mean δ15N of consumers ranging from the primary littoral consumer mollusk (B. tentaculate) to the top predators of the lake food chain such as large perch or pike is 8‰. If we use the typically quoted fractionation factor of 3.4‰ per trophic level (Vander et al. Reference Vander and Rasmussen2001; Post Reference Post2002), it turns out that the large perch or pike has the mean trophic level of 4.4, with only 2.4 trophic levels separating these top predators from the primary littoral consumers (Figure 1).

The main goal of this work was to determine the distribution of radiocarbon in the fish of a constantly changing lake ecosystem, and to investigate the possibility of linking the distribution of radiocarbon in those fish to their stable isotope values. A strong correlation (r=0.85 p<0.001, Table 2) between 14C/12C and δ13C values in fish tissues is not typical for the water ecosystems of the temperate zone, where C3 plants predominate. Carbon stable isotope values of modern (fresh plants), “old” (terrestrial soil, peat) terrestrial organic matter, as well as plants of the fresh water ecosystem overlap (Maksymowska et al. Reference Maksymowska, Richard, Piekarek-Jankowska and Riera2000; Xie et al. Reference Xie, Nott, Avsejs, Maddy, Chambers and Evershed2004; Esmeijer-Liu et al. Reference Esmeijer-Liu, Kürschner, Lotter, Verhoeven and Goslar2012). Thus, stable carbon isotope ratio values and radiocarbon dating values revealing the modern/terrestrial carbon fraction usually do not correlate in organic and inorganic matter of aquatic ecosystems.

As a result of constantly changing environmental conditions, it was not appropriate to perform sulfate and DIC (or phytoplankton/zooplankton) measurements in the water column. δ34S and radiocarbon measurements were performed in the aquatic moss Fontinalis antipyretica Hedv. instead, as the absence of roots excludes the nutrient uptake from the sediments (Maberly Reference Maberly1985). Thus, 14C/12C an δ34S values in the moss should reflect the averaged stable carbon and sulfur isotope distribution in DIC and water column sulfates, respectively during its growth season. All the investigated fishes were significantly younger than the moss (849 yr), even rudd (196 yr) known from its feeding preferences for littoral macrophytes (Table 1) which theoretically should correspond averaged over several months (time period tissues undergo biochemical change) radiocarbon distribution in DIC. The high 14C/12C value in rudd could be explained that shallow helophytes fixing atmospheric CO2 (not in water dissolved inorganic carbon) were the important food source in their diet.

The top predator large perch had the same highest $${{\rm{T}}_{{}_{}^{14}{{\rm{C}}_{{\rm{yr}}}}}}$$ value (686 ± 54 yr) as a bottom dwelling fish ruffe mostly relying on profundal organic source such as chironomids, ostracods, and mollusks. Their isotope ratio value differences Δδ13C = 1.1 ± 0.9‰, Δδ15N = 2.8 ± 0.4‰, and Δδ34S = 1.7±1.2 ‰ (typical for high-protein diet: 2.0 ± 0.65‰, McCutchan et al. Reference McCutchan, Lewis, Kendall and McGrath2003) showed that ruff was the main/important food source for large perch. The second most 14C depleted values (of 586 ± 88 yr) had bream and small perch (Table 1). Both species feed on benthic invertebrates, small perch also may rely on zooplankton. High δ34S value of 7.1 ± 0.4‰ in small perch (S) indicated that its diet was probably based on zooplankton feeding that reflects phytoplankton production rather than bottom invertebrate fauna affected by sulfate reduction. Meanwhile, lower δ34S value in bream revealed it feeding more on bottom invertebrate fauna.

A low δ34S value of 1.3‰ (Figures 2 and 3, Table 1) in carp could indicate its preferences on benthic production influenced by sulfur reduction in anoxic sediments. Carp can tolerate anoxic conditions and is extremely well adapted for feeding on sediment dwelling chironomids—they have specialized feeding behavior that allow them to penetrate deep (up to one meter) into sediments as well as mouthparts, which enable them to sort and retain food from sediments, and it appears they constantly sucking sediments in order to locate aggregations of chironomids (Lammens et al. Reference Lammens, Hoogenboezem, Winfield and Nelson1991). Strong discrimination of sulfate-reducing bacteria against 34S isotope leads to lower δ34S values in anoxic sediments comparing to sulfates in water column. This fractionation in some lakes was reported to vary from –3.7‰ to –14‰ (Fry Reference Fry1986b; Donahue et al. Reference Donahue, Werne, Meile and Lyons2008; Croisetière et al. Reference Croisetière, Hare, Tessier and Cabana2009; Karube et al. Reference Karube, Okada and Tayasu2012; Proulx et al. Reference Proulx and Hare2014) depending on sulfate concentration in water column. The difference between δ34S values in aquatic moss (6.5‰) and carp just falls within the range of values for fractionation due to sulfate reduction in lakes’ anoxic sediments. Another two fish species, ruffe and bream, are also benthivorous profundal dwelling fishes (Froese and Pauly Reference Froese and Pauly2022), thus they also may compete with carp for chironomid prey in such turbid waters as lake Tapeliai. δ34S values for these species were 5.7‰ and 5.6‰, respectively, and were between the most depleted value for carp and the value of the moss (Table 1). These fish may feed chironomids from the upper oxic sediment layers having more positive δ34S values than those from lower anoxic layers. Martin et al. Reference Martin, Proulx and Hare2008 showed that larvae of a Chironomus species feeding on oxic sediments had higher δ34S values than those that fed on anoxic sediments. Carp also had very low $${{\rm{T}}_{{}_{}^{14}{{\rm{C}}_{{\rm{yr}}}}}}$$ value (119 yr) comparing to the other bottom dwelling fishes bream and ruffe (586 ±88 yr and 693 ±56 yr, respectively, Table 1). However, this difference in $${{\rm{T}}_{{}_{}^{14}{{\rm{C}}_{{\rm{yr}}}}}}$$ of several hundred years cannot be explained by the adaptation of the carp to feeding in deeper sediment layers that could be affected by radiocarbon due to the “bomb peak.” Our previous study (Barisevičiute et al. Reference Barisevičiute, Maceika, Ežerinskis, Mažeika, Butkus, Šapolaite, Garbaras, Paškauskas, Jefanova and Karosiene2019) revealed that Tapeliai received 14C depleted organic (and possible inorganic) carbon due to the opening of the ditch with Lake Lydekinis at the beginning of the last century. Sediments at 25–30 cm (according 210Pb dating this layer corresponds the period of 1960–1942) are ∼1100 yr “older” than surface sediments. Thus, carp receiving a significant amount of their diet from chironomids from deeper layers would appear to be “older” than other fish species. Thus, only an additional allochthonous food source could be the reason for the low δ34S, unusually high δ15N and 14C/12C values in carp tissues. To our knowledge (V. Rakauskas personal observation), carp were rather intensively fed by anglers in Lake Tapeliai, that could explain the trace of additional allochthonous food sources in their tissues. Low δ34S values in general feeder roach tissues (δ34S value of 4.5 ± 0.5‰ was lower than in other bottom dwelling fishes such as ruff and bream) also likely indicate that their diet, like that of carp, contained additional allochthonous food sources. On the other hand, low δ15N values (7.4 ± 0.5‰) in roach tissues indicates that the fish could also include terrestrial plants in their diet (Table 1, Figure 3).

CONCLUSIONS

The 14C/12C ratio distribution and its relationship with stable S, C, and N isotopes among the fish of Lake Tapeliai was studied. The radiocarbon age of the fish in this lake, which was constantly affected by the inflows of the 14C depleted carbon sources from the surrounding watershed mires, ranged from 119 to 693 yr. No relationship of the carbon isotope ratio values with sulfur or nitrogen isotope ratios was observed in the studied ecosystem. However, 14C/12C measurements correlated significantly with δ13C values in fish tissues.

14C analysis is essential to trace the pathways of modern terrestrial carbon through the food web in freshwater lakes. However, in reconstructing the diet of fish such as carp, adapted to penetrate deep (up to one meter) into sediments, it is necessary to examine the sediments themselves and how they have been affected by 14C from the bomb peak. It was our previous sediment studies that showed that the deeper sediment layers were not enriched in 14C due to the bomb peak, but depleted in 14C, and this helped determine that carp was being fed by allochthonous food sources provided by anglers.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/RDC.2022.82

References

REFERENCES

Barisevičiute, R, Maceika, E, Ežerinskis, Ž, Mažeika, J, Butkus, L, Šapolaite, J, Garbaras, A, Paškauskas, R, Jefanova, O, Karosiene, J, et al. 2019. Tracing carbon isotope variations in lake sediments caused by environmental factors during the past century: a case study of Lake Tapeliai, Lithuania. Radiocarbon 61(4):885903.CrossRefGoogle Scholar
Bitinas, A. 2012. New insights into the last deglaciation of the south-eastern flank of the Scandinavian Ice Sheet. Quaternary Science Reviews 44:6980.CrossRefGoogle Scholar
Bitinas, A, Karmaziene, D, Jusiene, A. 2004. Glaciolacustrine Kame Terraces as an indicator of conditions of deglaciation in Lithuania during the Last Glaciation. Sedimentary Geology 165(3–4):285294.Google Scholar
Bocherens, H, Drucker, DG, Taubald, H. 2011. Preservation of bone collagen sulphur isotopic compositions in an Early Holocene river-bank archaeological site. Palaeogeography, Palaeoclimatology, Palaeoecology 310(1–2):3238.CrossRefGoogle Scholar
Croisetière, L, Hare, L, Tessier, A, Cabana, G. 2009. Sulphur stable isotopes can distinguish trophic dependence on sediments and plankton in Boreal lakes. Freshwater Biology 54(5):10061015.Google Scholar
DeNiro, MJ, Epstein, S. 1977. Mechanism of carbon isotope fractionation associated with lipid synthesis. Science 197(4300):261263.Google ScholarPubMed
Donahue, MA, Werne, JP, Meile, C, Lyons, TW. 2008. modeling sulfur isotope fractionation and differential diffusion during sulfate reduction in sediments of the Cariaco Basin. Geochimica et Cosmochimica Acta 72(9):22872297.Google Scholar
Eriksson Stenström, K, Skog, G, Geogiadou, E, Genberg, J, Johansson, A. 2011. A guide to radiocarbon units and calculations. LUNDFD6/(NFFR-3111). Lund University. 17 p.Google Scholar
Esmeijer-Liu, AJ, Kürschner, WM, Lotter, AF, Verhoeven, JTA, Goslar, T. 2012. Stable carbon and nitrogen isotopes in a peat profile are influenced by early stage diagenesis and changes in atmospheric CO2 and N deposition. Water, Air, and Soil Pollution 223(5):20072022.Google Scholar
Ežerinskis, Ž, Šapolaite, J, Pabedinskas, A, Juodis, L, Garbaras, A, Maceika, E, Druteikiene, R, Lukauskas, D, Remeikis, V. 2018. Annual variations of 14C concentration in the tree rings in the vicinity of Ignalina Nuclear Power Plant. Radiocarbon 60(4):12271236.CrossRefGoogle Scholar
France, RL. 1995. Differentiation between littoral and pelagic food webs in lakes using stable carbon isotopes. Limnology and Oceanography 40(7):13101313.Google Scholar
Froese, R, Pauly, D, editors. 2022. FishBase. World Wide Web electronic publication. www.fishbase.org, version (01/2022).Google Scholar
Fry, B. 1986a. Sources of carbon and sulfur nutrition for consumers in three meromictic lakes of New York State. Limnology and Oceanography 31(1):7988.Google ScholarPubMed
Fry, B. 1986b. Stable sulfur isotopic distributions and sulfate reduction in lake sediments of the Adirondack Mountains, New York. Biogeochemistry 2(4):329343.CrossRefGoogle Scholar
Hjelm, J, Persson, L, Christensen, B. 2000. Growth, morphological variation and ontogenetic niche shifts in perch (Perca fluviatilis) in relation to resource availability. Oecologia 122(2):190199.CrossRefGoogle ScholarPubMed
Hou, J, D’Andrea, WJ, Liu, Z. 2012. The influence of 14C reservoir age on interpretation of paleolimnological records from the Tibetan Plateau. Quaternary Science Reviews 48:6779.CrossRefGoogle Scholar
Kaplan, IR, Rittenberg, SC. 1964. Microbiological fractionation of sulphur isotopes. Journal of General Microbiology 34:195212.CrossRefGoogle ScholarPubMed
Karube, Z, Okada, N, Tayasu, I. 2012. Sulfur stable isotope signature identifies the source of reduced sulfur in benthic communities in macrophyte zones of Lake Biwa, Japan. Limnology 13(3): 269280.Google Scholar
Keaveney, EM, Reimer, PJ. 2012. Understanding the variability in freshwater radiocarbon reservoir offsets: a cautionary tale. Journal of Archaeological Science 39(5):13061316.CrossRefGoogle Scholar
Keaveney, EM, Reimer, PJ, Foy, RH. 2015. Young, old, and weathered carbon—Part 2: using radiocarbon and stable isotopes to identify terrestrial carbon support of the food web in an alkaline, humic lake. Radiocarbon 57(3):425438.CrossRefGoogle Scholar
Lammens, EHRR, Hoogenboezem, W. 1991. Diets and feeding behaviour. In: Winfield, IJ, Nelson, JS. editors. Cyprinid fishes: systematics, biology and explotation (Fish & Fisheries Series, 3), 1st ed. London: Chapman and Hall. 668 p.Google Scholar
Li, Y, Qiang, M, Jin, Y, Liu, L, Zhou, A, Zhang, J. 2018. Influence of aquatic plant photosynthesis on the reservoir effect of Genggahai Lake, northeastern Qinghai-Tibetan Plateau. Radiocarbon 60(2):561569.CrossRefGoogle Scholar
Maberly, SC. 1985. Photosynthesis by Fontinalis antipyretica: I. Interaction between photon irradiance, concentration of carbon dioxyde and temperature. New Phytologist 100(2):127140.CrossRefGoogle Scholar
Maksymowska, D, Richard, P, Piekarek-Jankowska, H, Riera, P. 2000. Chemical and isotopic composition of the organic matter sources in the Gulf of Gdansk (southern Baltic Sea). Estuarine, Coastal and Shelf Science 51(5):585598.CrossRefGoogle Scholar
Martin, S, Proulx, I, Hare, L. 2008. Explaining metal concentrations in sympatric chironomus species. Limnology and Oceanography 53(2):411419.CrossRefGoogle Scholar
McCutchan, JH, Lewis, WM, Kendall, C, McGrath, CC. 2003. Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102(2):378390.Google Scholar
Mischke, S, Weynell, M, Zhang, C, Wiechert, U. 2013. Spatial variability of 14C reservoir effects in Tibetan plateau lakes. Quaternary International 313–314:147155.CrossRefGoogle Scholar
Moisejenkova, A, Tarasiuk, N, Koviazina, E, Maceika, E, Girgždys, A. 2012. 137Cs in Lake Tapeliai, Lithuania. Lithuanian Journal of Physics 52(3):238252.CrossRefGoogle Scholar
Philippsen, B. 2013. The freshwater reservoir effect in radiocarbon dating. Heritage Science 1(1): 124.Google Scholar
Post, DM. 2002. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83:703718.CrossRefGoogle Scholar
Post, DM, Layman, CA, Arrington, DA, Takimoto, G, Quattrochi, J, Montaña, CG. 2007. Getting to the fat of the matter: models, methods and assumptions for dealing with lipids in stable isotope analyses. Oecologia 152(1):179189.CrossRefGoogle Scholar
Proulx, I, Hare, L. 2014. Differences in feeding behaviour among chironomus species revealed by measurements of sulphur stable isotopes and cadmium in larvae. Freshwater Biology 59(1):7386.Google Scholar
Richards, MP, Fuller, BT, Sponheimer, M, Robinson, T, Ayliffe, L. 2003. Sulphur isotopes in palaeodietary studies: a review and results from a controlled feeding experiment. International Journal of Osteoarchaeology 13:3745.CrossRefGoogle Scholar
Savoye, N, Aminot, A, Tréguer, P, Fontugne, M, Naulet, N, Kérouel, R. 2003. Dynamics of particulate organic matter δ15N and δ13C during spring phytoplankton blooms in a macrotidal ecosystem (Bay of Seine, France). Marine Ecology Progress Series 255:2741.CrossRefGoogle Scholar
Sayle, KL, Cook, GT, Ascough, PL, Gestsdóttir, H, Hamilton, WD, McGovern, TH. 2014. Utilization of δ13C, δ15N, and δ 34S analyses to understand 14C dating anomalies within a Late Viking Age community in northeast Iceland. Radiocarbon 56(2):811821.CrossRefGoogle Scholar
Sayle, KL, Hamilton, WD, Gestsdóttir, H, Cook, GT. 2016. Modelling Lake Mývatn’s freshwater reservoir effect: utilisation of the statistical program FRUITS to assist in the re-interpretation of radiocarbon dates from a cemetery at Hofstaðir, North-East Iceland. Quaternary Geochronology 36:111.Google Scholar
Schulting, RJ, Ramsey, CB, Bazaliiskii, VI, Goriunova, OI, Weber, A. 2014. Freshwater reservoir offsets investigated through paired human-faunal 14C dating and stable carbon and nitrogen isotope analysis at Lake Baikal, Siberia. Radiocarbon 56(3):9911008.CrossRefGoogle Scholar
Tarasiuk, N, Moisejenkova, A, Koviazina, E, Karpicz, R, Astrauskiene, N. 2009. On the radiocesium behavior in a small humic lake (Lithuania). Nukleonika 54(3):211220.Google Scholar
Thode, HG. 1991. Sulphur isotopes in nature and the environment: an overview. In: Krouse HR, Grinenko VA, editors. Stable isotopes: natural and anthropogenic sulphur in the environment. Scope 43:1–26.Google Scholar
Thoresson, G. 1993. Guidelines for coastal monitoring: fishery biology. Kustrapport 1. National Board of Fisheries, Institute of Coastal Research, Öregrund, Sweden.Google Scholar
Vander, MJ, Rasmussen, JB. 2001. Variation in studies δ15N andδ13C trophic fractionation: implications for aquatic food web. Limnology and Oceanography 46:20612066.Google Scholar
Virbickas, T. 2018. Fish assemblage structure, state of fish resources and fisheries recommendations of Lake Tapeliai. Internal report. Environmental Protection Agency. 7 p. In Lithuanian.Google Scholar
Wang, J, Chen, G, Kang, W, Hu, K, Wang, L. 2019. Impoundment intensity determines temporal patterns of hydrological fluctuation, carbon cycling and algal succession in a dammed lake of southwest China. Water Research 148:162175.CrossRefGoogle Scholar
Xie, S, Nott, CJ, Avsejs, LA, Maddy, D, Chambers, FM, Evershed, RP. 2004. Molecular and isotopic stratigraphy in an ombrotrophic mire for paleoclimate reconstruction. Geochimica et Cosmochimica Acta 68(13):28492862.Google Scholar
Figure 0

Table 1 Summary of SIA of different consumer species from Lake Tapeliai in 2018. Values are mean ± standard deviation, total length of fish (TL), fish age determined from scales, number of replicates (N), stable isotope values (δ13C, δ15N, δ34S, and 14C ‰), the radiocarbon age determined from 14C values. Perch size categories: small (S), large (L).

Figure 1

Figure 1 Isotopic bi-plot showing the mean (±SD) δ13C and δ15N values in different fish species of Lake Tapeliai in 2018. The radiocarbon ages are shown adjacent to fish names.

Figure 2

Figure 2 Isotopic bi-plot showing the mean (±SD) δ34S and δ13C values in different fish species of Lake Tapeliai in 2018. The radiocarbon ages are shown adjacent to fish names.

Figure 3

Figure 3 Isotopic bi-plot showing the mean (±SD) δ34S and δ15N values in different fish species of Lake Tapeliai in 2018. The radiocarbon ages are shown adjacent to fish names.

Figure 4

Figure 4 Isotopic bi-plot showing the mean (±SD) δ13C and 14C specific activity values in different fish species of Lake Tapeliai in 2018. The 14C ages are shown adjacent to fish names.

Figure 5

Table 2 Partial correlations for all explanatory variables for the RA value variation in fish muscle.

Supplementary material: Image

Barisevičiūtė et al. supplementary material

Barisevičiūtė et al. supplementary material 1

Download Barisevičiūtė et al. supplementary material(Image)
Image 156.4 KB
Supplementary material: Image

Barisevičiūtė et al. supplementary material

Barisevičiūtė et al. supplementary material 2

Download Barisevičiūtė et al. supplementary material(Image)
Image 99.8 KB