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All issues No 419 (2018) Knowl. Manag. Aquat. Ecosyst., 419 (2018) 49 Full HTML
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Knowl. Manag. Aquat. Ecosyst.
Number 419, 2018
Topical Issue on Fish Ecology
Article Number 49
Number of page(s) 13
DOI https://doi.org/10.1051/kmae/2018037
Published online 17 December 2018

© E.T. Nolan and J.R. Britton, Published by EDP Sciences 2018

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License CC-BY-ND (http://creativecommons.org/licenses/by-nd/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. If you remix, transform, or build upon the material, you may not distribute the modified material.

1 Introduction

Piscivorous fishes play an important role in regulating the structure of aquatic food-webs (Woodward and Hildrew, 2002). They can exert substantial top-down forces on prey communities, potentially initiating trophic cascades (Brett and Goldman, 1996; Pace et al., 1999; Drenner and Hambright, 2002). Alien piscivorous fishes that are introduced to enhance sport angling, such as largemouth bass Micropterus salmoides and peacock bass Cichla spp., also exert substantial top-down forces on prey fish communities, resulting in impacts including reduced prey abundances and decreased species diversity (Gratwicke and Marshall 2001; Pelicice and Agostinho, 2009). As the diets of piscivorous fishes tend to involve strong ontogenetic changes via increasing gape sizes (Zhao et al., 2014), the strength of trophic cascades can be strongly influenced by the resultant dietary shifts (Sato and Watanabe, 2014). Thus, an important step in the assessments of the ecological impacts of invasive piscivores is analyses of their diet composition, including assessing ontogenetic shifts in their prey selection.

Dietary assessments of piscivorous fishes are often reliant on stomach contents analysis (SCA) (Sandlund et al., 2016). Whilst providing information on diet composition of individual fish, the method usually utilises relatively large numbers of fish to maximise statistical power and to assist understandings of dietary patterns over time and space (Cortés, 1997). For piscivores such as Northern pike (Esox lucius) and pikeperch (S. lucioperca), an inherent issue in stomach contents analysis is that many of the fish often have empty stomachs, resulting in a paucity of dietary data from the sampled population. Piscivorous fishes in general and particularly those that consume prey whole have higher proportions of empty stomachs compared to lower trophic level fishes (Arrington et al., 2002), with feeding frequency thought to decrease through the consumption of energy-rich food items (Bowen et al., 1995). These methodological issues can potentially be overcome by using complementary dietary assessment methods, such as stable isotope analysis (SIA) (Cucherousset et al., 2012; Jensen et al., 2012). Indeed, in a study where an average of 36% of E. lucius had empty stomachs across 16 populations, stable isotope analysis showed no trophic position differences between fish with and without prey items in their stomachs, or between piscivorous and invertebrate feeders determined through stomach content analysis (Paradis et al., 2008), indicating opportunistic rather than specialist invertebrate feeding strategies. Therefore, integrative studies may often show that SCA and SIA provide contrasting dietary information due to, for example, differences in the temporal scales of the methods (i.e. short-term SCA versus long-term SIA diet assessments), (Locke et al., 2013; Busst and Britton, 2017), but these differences can provide insights where disintegrated studies cannot.

The issues of sacrificing relatively large numbers of piscivorous fish to satisfy the requirements needed for stomach contents analysis is also problematic when these fish have high values within sport angling. For example, catch and release angling (C&R) is increasingly applied to sport fishing for species such as M. salmoides, S. lucioperca and Cichla spp. Mortalities associated with C&R can be minimised via use of best practice angling techniques and fish handling codes (Siepker et al., 2007; Arlinghaus and Hallermann, 2007; Cook et al., 2015, Bower et al., 2016). Consequently, dietary assessments for piscivorous sport fishes based on destructive sampling are increasingly at odds with their fishery management and angling practises, even where the fishes are invasive (Hickley and Chare, 2004). Indeed, the fishery value of invasive fishes are increasingly recognised (Gozlan, 2008), especially when their populations are in large open systems in which their population management is inherently difficult (Britton et al., 2011; Britton and Orsi, 2012).

Pikeperch S. lucioperca is a large-bodied piscivorous freshwater fish whose native range in Europe extends from Germany in the West to Central Russia in the East (Maitland, 2004). The species has been introduced outside of this range, into countries such as France, Spain and Great Britain (Elvira and Almodóvar, 2001; Kopp et al., 2009), often with the primary purpose of increasing sport angling opportunities (Hickley and Chare, 2004). Following their initial introduction into Britain in 1878, there was a series of translocations of S. lucioperca into waters in Eastern England during the 1960s (Wheeler and Maitland, 1973). It was these releases that resulted in their invasion of river catchments across Eastern, Central and Southern England (Linfield and Rickards, 1979; Hickley, 1986; Copp et al., 2003). Whilst there were initial concerns on their impacts on prey populations, the species is now considered as an important angler target species in many fisheries (Hickley and Chare, 2004). Consequently, whilst studies on their diet previously utilised stomach contents analyses (e.g. Smith et al., 1997; Schulze et al., 2006), methods based on stable isotope analysis might now be more preferable (Kopp et al., 2009), especially where tissues can be utilised that can be collected non-lethally (Britton and Busst, 2018).

The diet of the S. lucioperca has been well studied both within their native and non-native ranges (e.g. Campbell 1992; Keskinen and Marjomäki 2004; Pérez-Bote and Roso 2012; Didenko and Gurbyk 2016). They are generally considered to be piscivorous within their first year of life (Mittelbach and Persson 1998), although this switch to piscivory can become delayed if individuals do not reach a size advantage over prey (Persson and Brönmark 2002) or if suitable prey fish are unavailable (Ginter et al., 2011). While S. lucioperca diet comprises of fish across a range of size classes, they can also be cannibalistic, with this acting as an important regulatory force (Mehner et al., 1996; Frankiewicz et al., 1999; Lappalainen et al., 2006). Individual S. lucioperca will also consume macro-invertebrates, with these prey items most frequently encountered in the diets of smaller individuals (Hansson et al., 1997; Argillier et al., 2012).

The application of SIA using multiple tissues in conjunction with SCA enables the dietary habits of the target population to be assessed across difference timescales. SCA provides ‘snapshot’ dietary information (Cortés, 1997). By contrast, SIA provides longer term dietary perspectives, with the timescale dependent on the analysed tissue (Fry, 2006; Newsome et al., 2007; Martínez del Rio et al., 2009). The aim of this study was thus to use S. lucioperca as a model fish exploited by C&R sport angling to assess how stable isotope analysis can be applied to assess their diet in relation to using tissues that are collected non-destructively. Objectives were to (1) quantify the relationships of the stable isotopes of δ13C and δ15N between dorsal muscle and three tissues that can be collected non-lethally; (2) utilise the stable isotope data to predict the diet composition of a S. lucioperca population using Bayesian mixing models (Stock et al., 2018); and (3) complete stomach contents analyses on the S. lucioperca population and assess the results in the context of the dietary predictions from the mixing models.

2 Materials and methods

2.1 Sample collection

The S. lucioperca population of the Grand Union Canal, Northamptonshire, in Central England was sampled by boat-mounted electric fishing (‘boom-boat’, power supplied by a 2 kVA generator) in April 2017. This canal is generally of 15 m maximum width and depth rarely exceeds 2 m. A series of locks overcome changes in the gradient of the surrounding land. Small-bodied cyprinid fishes are dominant in the fish community, especially roach Rutilus rutilus. Pikeperch have been present in the canal for at least 30 years (Hickley, 1986). A total of 180 individuals were captured by electric fishing that ranged in fork length (to nearest mm) between 169 and 551 mm (mean ± 95% CI; 355 ± 14 mm) and weight between 48 and 1924 g (mean ± 95% CI; 561 ± 71 g). Following their capture, the fish were euthanised and held on ice while being transferred to the laboratory where they were processed immediately.

2.2 Stable isotope analysis

Of the 180 sampled S. lucioperca, a sub-sample of 19 were processed for stable isotope analysis using fish from across the length range (mean ± 95% CI; 323 ± 54 mm). Following their measurement, the tissues that were sampled from each fish were dorsal muscle, pelvic fin tissue, scales and epidermal mucus. The epidermal mucus was collected by scraping the dorsal surface of each fish with a cover slip, with the sample then cleaned with forceps as per Maruyama et al. (2015) and transferred to a sample tube. This method was used in preference to the filtration method of Church et al. (2009), as it was demonstrated to result in reduced error (Maruyama et al., 2017). Scales were collected from the body area between the dorsal fin and the lateral line. Scale decalcification was not performed prior to isotopic analysis, since the removal of inorganic carbonates has been shown to have no significant effect on scale δ13C and δ15N values (Sinnatamby et al., 2007; Ventura and Jeppesen, 2010; Woodcock and Walther, 2014). Preparation thus focused on cleaning scales with distilled water prior to removing the outer portion of the scale for SIA, ensuring the tissue analysed was from recent growth only (∼1 year) (Hutchinson and Trueman, 2006; Bašić and Britton, 2015). A selection of all prey fish species (dorsal muscle only) and macroinvertebrates (cf. Stomach content analysis) were also prepared for stable isotope analysis recovered through dissection and removal of prey from the stomachs. These samples were based only on individual animals that were recovered in good condition, i.e. those very recently ingested, with negligible digestion and that were identifiable to species level. All samples were then dried at 60 °C for 48 h.

The samples were then analysed at the Cornell Isotope Laboratory, New York, USA, where they were ground to powder, weighed precisely to approximately 1000 μg and analysed on a Thermo Delta V isotope ratio mass spectrometer (Thermo Scientific, USA) interfaced to a NC2500 elemental analyser (CE Elantach Inc., USA). Verification of accuracy was against internationally known reference material and accuracy and precision of the sample runs was tested every 10 samples using a standard animal sample (mink). Delta (δ) isotope ratios were expressed as units per mil (‰). Analytical precision of the δ15N and δ13C sample runs was estimated at 0.42 and 0.15‰ respectively. Lipid correction was not necessary as C:N ratios indicated very low lipid content (Post et al., 2007).

2.3 Tissue comparisons

The significance of differences in the stable isotope ratios between the tissues were tested using pair-wise t-tests. Simple linear regression models tested the significance of the relationship between mucus and muscle, fin and muscle and scale and muscle for δ13C and δ15N isotope values. Models were run both with and without fish length. The best fitting model was chosen using regression statistics and the lowest value of Akaike's Information Criteria (AIC). Statistical analysis and graphical outputs were performed using R (R Core Team, 2018, version 3.4.3).

2.4 Bayesian mixing models

The stable isotope data were analysed to assess the effect of tissue type on fish diet predictions, including after conversion of the stable isotope data of the non-lethal tissues to dorsal muscle (as the standard tissue used in fish isotope studies). The primary tool for these analyses was the use of Bayesian mixing models (Phillips et al., 2014) allowing for predictions of the relative proportions of the putative prey resources that contributed to the diet of S. lucioperca for each tissue both before and after their conversion to dorsal muscle values. The models were run in the package ‘Mixing Models for Stable Isotope Analysis in R’ (MixSIAR; Parnell et al., 2013; Stock et al., 2018). All models were run using normal run length (chain length: 100,000 iterations with burn-in of 50,000, with posterior thinning (thin: 50) and 3 chains). Model diagnostics were based on Gelman-Rubin and Geweke, with sufficient convergence to accept the results (Stock and Semmens, 2016).

Five mixing models were run that covered the use of the S. lucioperca (as the consumer) stable isotope data from (1) dorsal muscle, (2) epidermal mucus, (3) scales, (4) epidermal mucus data converted to dorsal muscle values (using the linear models for δ13C and δ15N mucus to muscle), and (5) scale data converted to dorsal muscle values (using the linear models for δ13C and δ15N scale to muscle). The putative prey (source) data used within the mixing models was constant across all models, except for model (3) where fish muscle isotope data were converted to scale data based on conversion factors in Busst et al. (2015) to ensure consistency in predictions by accounting for differences in isotope values between the tissues of source and consumer. Dietary contributions were predicted by splitting the fish into two size classes, <350 and >350 mm, with distinctions made between the two groupings based on (1) the likelihood of sexual maturity at above approximately 350 mm (Lappalainen et al., 2003) and (2) differences in the contribution of prey items to the diet of individuals in each size class from stomach content analysis (cf. results).

In the mixing models, the isotopic fractionation values between the prey resources and S. lucioperca were varied according to the S. lucioperca tissue being used. For muscle and mucus, values were chosen based on standards proposed by Post (2002): δ15N 3.4 ± 0.5‰; δ13C 1 ± 0.5‰. For scales, the fractionation factors used were δ15N = 2.58 ± 1‰ and δ13C = 2.78 ± 1‰), based on the standards of Post (2002) but with correction for scales using the mean differences from three studies comparing fractionation between muscle and scale tissue (Δ15N −0.82‰, Δ13C 1.78‰) (Heady and Moore, 2013; Busst and Britton, 2015; Busst et al., 2015).

Reported outputs of the models were overall estimated posterior density contributions to diet given as summary statistics; mean, standard deviation and 95% confidence limits. Posterior density plots for each model are given in supple mentary material.

2.5 Stomach content analysis

S. lucioperca were measured (fork length, nearest mm) and weighed (nearest g), and then dissected and their stomach contents removed. Prey items from stomach contents were identified to their lowest possible taxonomic level, total stomach fullness (% in volume) was assessed, as was the contribution of each prey item to overall fullness. For subsequent analyses, stomach contents were categorised into three groupings consisting of (1) ‘Cyprinidae’ including roach Rutilis rutils, common bream Abramis brama and gudgeon Gobio gobio; (2) ‘Percidae’ including perch Perca fluviatilis and ruffe Gymnocephalus cernua; and (3) ‘Invertebrates’ where macro-invertebrates were identified to family level, and included Gammaridae, Chironomidae and Mysidae.

The contribution of each diet category was expressed as percentages in terms of frequency of occurrence and prey-specific abundance. Frequency of occurrence (% Fi ) of a given prey type was defined as the number of stomachs in which that prey occurred, expressed as a frequency of the total number of stomachs in which prey were present (Costello, 1990). For prey-specific abundance, prey-type contribution was first estimated in proportion to overall stomach fullness (in volume). The proportional fullness contribution of each diet category was then expressed as percentage prey-specific abundance (% Pi ):where Pi was the prey-specific abundance of prey i, Fi was the stomach content fullness for diet category i and Ft was the total stomach fullness in only those predators with prey i in their stomach (Amundsen et al., 1996). In addition, the fork length (mm) of each prey item was also taken to assess changes in prey use patterns with increasing body length of S. lucioperca using regression analysis (as prey: predator length ratios). Dietary contribution was predicted for size classes <350 and >350 mm as per Bayesian mixing models.

3 Results

3.1 Relationship of δ13C and δ15N values between S. lucioperca tissues

There was a significant difference in the δ13C values between scale and all other tissues (Tab. 1, Fig. 1), where scale was significantly enriched in δ13C relative to muscle (t-test, t = 12.6, P < 0.001), mucus (t-test, t = 12.4, P < 0.001) and fin (t-test, t = 8.1, P < 0.001). Although not significantly different, mucus was depleted in δ13C relative to muscle (−0.55‰; t-test, t = −1.8, P = 0.07), whilst fin was enriched in δ13C relative to muscle (+0.53‰; t-test, t = 1.6, P = 0.10). For δ15N, significant differences were also evident between scale and all other tissues (Tab. 1, Fig. 1), with scale depleted in δ15N relative to muscle (−1.25‰; t-test, t = −3.6, P < 0.001), mucus (t-test, t = −2.5, P < 0.001) and fin (t-test, t = −3.5, P < 0.001). There was no significant difference in δ15N between muscle and mucus (+0.38, t-test, t = 1.1, P = 0.27) or between muscle and fin (−0.01; t-test, t = −0.1, P = 0.97).

Significant relationships were found between S. lucioperca muscle isotope values (δ13C and δ15N) and all other tissue types ( Tab. 2, Fig. 2). Including length in the models improved their fit in all cases (according to AIC and regression statistics; Tab. 2). This is likely explained by the significant increase in δ13C with increasing fish length ( Fig. 3; muscle, R 2 = 0.68; F (1,17) = 39.2; P < 0.001; mucus, R 2 = 0.52; F (1,17) = 20.1; P < 0.001; fin, R 2 = 0.66; F (1,17) = 35.4; P < 0.001; scale, R 2 = 0.70; F (1,17) = 42.91; P < 0.001). Consequently, length was retained in the regression analyses across all tissue/isotope conversions for consistency. There was no relationship between δ15N and fish length (muscle, R 2 = 0.06; F (1,17) = 2.12; P =0.16; mucus, R 2 = 0.01; F (1,17) = 1.12; P =0.31; fin, R 2 = 0.02; F (1,17) = 1.32; P = 0.27; scale, R 2 = 0.01; F (1,17) = 0.25; P = 0.62).

Table 1

Number of individuals, tissue specific carbon (δ13C) and nitrogen (δ15N) stable-isotope ratios (Mean ± SD) indicating variation in isotope values between tissues.

thumbnail Fig. 1

Stable isotope bi-plot of δ13C versus δ15N showing individual (light grey) and mean (black) values for all tissue types (■ muscle; ▲mucus; ● fin; + scale), where error bars represent standard deviation.
Table 2

Linear regression statistics for the relationship between dorsal muscle stable isotope values (δ13C and δ15N) and those of epidermal mucus, fin and scales collected from S. lucioperca.

thumbnail Fig. 2

Linear relationships of (a) δ13C and (b) δ15N dorsal muscle versus epidermal mucus (‘mucus’), fin and scale, where the dotted line represents the relationship with length included in the model and the dashed line represents the relationship with length excluded.
thumbnail Fig. 3

Linear relationships of (a) δ13C and (b) δ15N for muscle (■), mucus (▲), fin (●) and scale (+). Significant relationships are fitted with 95% confidence intervals around the line for muscle (long dashed line, light grey), mucus (short dashed line), fin (solid line) and scale (long dashed line, dark grey).

3.2 Stable isotope mixing models

The mixing models predicted the diet category ‘invertebrates’ to be the most important item to the diet of S. lucioperca <350 mm, followed by Cyprinidae and then Percidae ( Tab. 3). This result was consistent across all models (Tab. 3, Fig. 4). For S. lucioperca >350 mm, Cyprinidae had the greatest predicted contribution to S. lucioperca diet, followed by invertebrates and then Percidae (Tab. 3, Fig. 4).

The difference in mean dietary contribution predictions across size classes between model 1 (muscle) and all other models was lowest for model 5 (scale data converted to dorsal muscle values) (Tab. 3, Fig. 4). Differences were greatest between model 1 (muscle) and model 2 (mucus) for mean dietary contribution predictions in size class <350 mm and for Percidae in size class >350 mm, whereas differences were greatest between model 1 (muscle) and model 4 (epidermal mucus data converted to dorsal muscle values) for Cyprinidae and Invertebrates in size class >350 mm (Tab. 3, Fig. 4).

Table 3

Mean predicted dietary contributions from Bayesian mixing models of ‘Cyprinidae’, ‘Invertebrates’ and ‘Percidae’ to the diet of S. lucioperca by size class (<350 mm and >350 mm), showing standard deviation and 95% confidence limits. Mixing models were (1) consumer as muscle values, (2) consumer as mucus values, (3) consumer as scale values, (4) consumer as muscle values based on conversion using the linear models for δ13C and δ15N mucus to muscle, and (5) consumer as muscle values based on conversion using the linear models for δ13C and δ15N scale to muscle.

thumbnail Fig. 4

Mean predicted dietary contributions (0–1) of ‘Cyprinidae’, ‘Invertebrates’ and ‘Percidae’ to the diet of S. lucioperca by size class (<350 and >350 mm) for each Bayesian mixing model. Models are represented by colour in sequence from light to dark, where model 1 is represented by light grey and model 5 by dark grey, and error bars represent the standard deviation. Mixing models were (1) consumer as muscle values, (2) consumer as mucus values, (3) consumer as scale values, (4) consumer as muscle values based on conversion using the linear models for δ13C and δ15N mucus to muscle, and (5) consumer as muscle values based on conversion using the linear models for δ13C and δ15N scale to muscle.

3.3 Stomach contents analysis

Of the 180 sampled S. lucioperca, 98 had empty stomachs (54%). Of the 82 fish with items in the stomach, analyses revealed that as S. lucioperca body size increased, the size of their prey significantly increased (Cyprinidae: R 2 = 0.41, F (1,65) = 46.48, P < 0.01; Percidae: R 2 = 0.43, F (1,6) = 6.28, P = 0.05) ( Fig. 5a). Between the two fish prey groups, there was no significant difference in their sizes (ANOVA F 1,72 = 0.35, P = 0.56). Regarding prey: predator length ratios, these ratios generally decreased as S. lucioperca body size increased, although the relationships were not significant (Cyprinidae: R 2 = 0.03, F (1,65) = 2.23, P = 0.14; Percidae: R 2 = 0.09, F (1,6) = 0.58, P = 0.47) (Fig. 5b). The maximum prey length to predator length ratio was 0.38, whilst the minimum was 0.08 (mean ± SD; 0.22 ± 0.06), with the majority of S. lucioperca consuming small prey sizes relative to their body size (85% of prey <0.3 prey length/predator length; Fig. 5c).

The prey-specific abundance (% Pi) was highest for Cyprinidae at 79.8%, followed by Percidae (13.1%) and then invertebrates (7.11%) (Tab. 4). Invertebrates were only represented in the diet of individuals up to 396 mm, whilst Cyprinidae were present in individuals from 204 to 532 mm and Percidae from 221 to 464 mm. Grouping S. lucioperca into the two size classes of <350 mm (194–340 mm, n = 41) and >351 mm (352–532 mm, n = 41) revealed the percentage prey abundance was higher for invertebrates in the smaller size category (<350 mm = 15.8%) than in the larger size class (>350 mm = 0.5%). For Percidae, the opposite pattern was evident, with higher % Pi for Percidae in the larger size class (>350 mm = 20.3%) than in the smaller size class (<350 mm = 3.7%). Percentage prey abundance remained similar for Cyprinidae in both size classes (<350 mm = 80.5%, >350 mm = 79.3%; Tab. 4).

thumbnail Fig. 5

S. lucioperca predator–prey relationships in the Grand Union Canal. (a) Predator size to prey size linear relationships (with 95% confidence intervals) for ‘Cyprinidae’ (● solid line) and ‘Percidae’ (▲ dashed line). (b) Prey: predator length ratios versus S. lucioperca body length, where lines represent relationships according to linear regression for ‘Cyprinidae’ (● solid line) and ‘Percidae’ (▲ dashed line). (c) Relative frequency distributions of prey: predator length ratios, where the mean prey size to predator size ratio is shown at 0.22.

4 Discussion

The predictable relationships between the SI data of dorsal muscle and from fins, scales and epidermal mucus revealed that the tissues that can be collected by non-destructive methods can be used reliably within trophic studies on S. lucioperca, negating the collection of dorsal muscle samples. Mucus and fin showed no significant differences in isotope values compared to muscle, while scale was significantly depleted in δ15N and enriched in δ13C. Moreover, the data provided here enables the application of the SI data from these tissues to Bayesian mixing models for predicting diet composition from putative prey SI data (Parnell et al., 2013; Phillips et al., 2014; Stock et al., 2018). In this study, the diet composition predictions from Bayesian mixing model results were broadly similar to those from stomach content analyses. The addition of stomach contents analysis, however, also provided data on the structured feeding relationships of these non-native piscivorous fish and their prey, revealing that these S. lucioperca were consuming small prey sizes relative to their body size. Finally, where diet assessments are being made in catch and release fisheries, the results suggest that tissue collection can successfully involve anglers, such as through scale collection (Kopp et al., 2009; Amat Trigo et al., 2017). In turn, this can help engage the public in research and build support for the conservation and management of aquatic resources (Cooke et al., 2013; Elmer et al., 2017; Arlinghaus et al., 2017).

The stomach contents analysis of this S. lucioperca population emphasised an inherent problem with the method; despite 180 fish being sampled, 98 had empty stomachs. Moreover, other studies that have utilised greater numbers of S. lucioperca have also reported this as an issue with, for example, over 20% of 376 sampled individuals having empty stomachs in a sample from an Iberian reservoir (Pérez-Bote and Roso, 2012), an average of 57.5% of S. lucioperca stomachs reported to be empty across seasons and years in a German lake (Schulze et al., 2012) and 42% of 591 sampled S. lucioperca from Lake Peipsi in Estonia with empty stomachs (Kangur and Kangur, 1998). Additionally, high proportions of empty stomachs could be due to the sampling period, as data were collected during the spawning period (Lappalainen et al., 2003), which is known to be associated with reduced feeding in other piscivorous fishes (Dörner et al., 2003). Where this type of sampling regime is considered problematic, such as where it removes large numbers of fish from fisheries, where S. lucioperca (or other piscivorous sport fishes) is an important target species for C&R (Hickley and Chare, 2004), stable isotope analysis clearly has high utility as a non-destructive dietary analysis tool.

Studies on the relationships of the SI values of fish dorsal muscle versus fin and scale tissues have shown that whilst differences in δ15N are usually minor and often non-significant, there tends to be predictable shifts in δ13C between the tissues (e.g. Pinnegar and Polunin, 1999; Tronquart et al., 2012; Vašek et al., 2017). For example, in cyprinid fishes such as chub Squalius cephalus, barbel Barbus barbus and goldfish Carassius auratus, there was a predictable pattern of significant δ13C enrichment from muscle to fin to scales (Busst et al., 2015; Busst and Britton, 2016). This pattern of δ13C enrichment between these tissues was also apparent here for S. lucioperca, although only significant from scales to muscle, mucus and fin. For epidermal mucus, however, studies have only recently started to determine how its SI values compare with other tissues, with limited differences in δ15N but with more variability in δ13C (e.g. Shigeta et al., 2017). Here, it was revealed that differences in δ13C between mucus and muscle were primarily in mucus being depleted, a contrast to fin and scales. In a study of catfish (Silurus asotus), there was also a general trend of depleted δ13C values of mucus relative to muscle (Maruyama et al., 2017), and depleted relative to both muscle and fin in three freshwater cyprinid species (Shigeta et al., 2017). The tissues used in this study are also known to have considerable differences in their stable isotope turnover rates, with mucus generally having shorter half-lives when compared with fin and scale tissues (Church et al., 2009; Maruyama et al., 2017; Shigeta et al., 2017). The complementary use of these tissues in SIA could therefore provide insights into diet over different timescales, although this was not able to be assessed here. The use of mucus in fish isotope studies is still relatively new compared to tissues such as muscle and fin (Church et al., 2009; Maruyama et al., 2015, 2017). As such, further development work is needed, both specifically for S. lucioperca and for fishes more generally, with increased focus required on the isotopic relationship of mucus with other tissues, their turnover rates and their fractionation factors with prey (Heady and Moore, 2013). This work should then enable the wider application of epidermal mucus to fish stable isotope studies, with this potentially highly advantageous due to its ability to be collected by non-invasive sampling techniques from live fish.

The results of both dietary assessment methods here revealed that this S. lucioperca population was functioning as an obligate piscivore, but only in its larger sizes. Some ontogenetic dietary shifts were evident, with smaller individuals having diets that included macroinvertebrates. Whilst S. lucioperca tend to switch to piscivory during the first year of life (Mittelbach and Persson, 1998), predictions from the Bayesian mixing models here suggested higher dietary contributions of invertebrates than fish for pikeperch <350 mm, where all fish were greater than 1 year old (E. Nolan, unpublished data). This pattern was also reflected in stomach content analyses. Obligate piscivory in S. lucioperca has been reported in a number of studies (e.g. Campbell, 1992; Kangur et al., 2007; Pérez-Bote and Roso, 2012), with the benefits of becoming piscivorous early in life being well documented (Mehner et al., 1996; van Densen et al., 1996; Mittelbach and Persson, 1998). However, in the absence of suitable-sized prey fish, S. lucioperca will continue to consume invertebrates species (Ginter et al., 2011), but are likely to grow slower than those that are completely piscivorous (Persson and Brönmark, 2008).

The stomach contents analysis of this S. lucioperca population also revealed that as S. lucioperca length increased, their prey fish size significantly increased, but that prey length to predator length ratios ranged between 0.08 and 0.38. These ratios were similar to those of Keskinen and Marjomäki (2004), who also revealed that while lengths of S. lucioperca and their prey were positively correlated, their prey:predator size ratio was negatively correlated. Most prey were thus relatively small to the size of the predator, an outcome that cannot be explained by gape size limitations alone (Dörner et al., 2007). There was also no significant relationship between S. lucioperca length and δ15N values, indicating that larger individuals were generally not feeding at higher trophic levels than smaller individuals (Post, 2002). Active prey choice is thought to be more important in explaining diet patterns in S. lucioperca than passive selection mechanisms (Turesson et al., 2002). This behavioural trait could explain the trends seen here, indicating that in the absence of suitable-sized fish prey, S. lucioperca will utilise the resources available (i.e. invertebrates), but when fish prey are available, prey sizes are chosen, which give the highest energy return per time spent foraging. These results on prey sizes highlight the value that SCA data can provide SIA studies, albeit with the caveat that its use is destructive or, if using non-lethal stomach evacuation techniques, is invasive to the individual fish.

Pikeperch also usually occupy higher trophic positions than other piscivorous fishes, with this apparent from across a range of habitat typologies (Campbell, 1992; Kangur and Kangur, 1998; Keskinen and Marjomäki, 2004). This has been attributable to their piscivory of omnivorous cyprinid fishes (Keskinen and Marjomäki, 2004) and, in larger S. lucioperca, on other piscivores such as perch P. fluviatilis (Kopp et al., 2009). Other studies have also highlighted that cannibalism can be feature of S. lucioperca diet that tends to increase in importance with lengths over 250 mm (Campbell, 1992; Didenko and Gurbyk, 2016; Hempel et al., 2016), and so can help explain the high trophic position of larger individuals versus other piscivores (Kopp et al., 2009). The results here are generally consistent with these findings, with both R. rutilus and P. fluviatilis being the principal prey items encountered in stomachs. However, there was minimal evidence suggesting that these S. lucioperca were cannibalistic. This might be explained by the time of sampling, as young-of-the-year (YOY) S. lucioperca would not have been present in the population due to timing of spawning (Lappalainen et al., 2003). Both inter- and intra-cohort cannibalism in pikeperch has been shown to correlate with the density of juveniles in a population (Frankiewicz et al., 1999; Lappalainen et al., 2006). Indeed, cannibalism in S. lucioperca is seen as a key regulatory force in some populations (Mehner et al., 1996; Frankiewicz et al., 1999; Lappalainen et al., 2006). This again points to the limitations of the stomach content analyses in providing accurate dietary assessments, as it was only completed at a single time of year.

In summary, this study has provided relationships on the stable isotope data of a range of tissues from S. lucioperca. The application of these data to Bayesian mixing models predicted strong ontogenetic dietary patterns, with shifts from macro-invertebrates/fish to fish only as S. lucioperca length increased. These ontogenetic patterns were similarly evident in SCA, but with these data also highlight that as S. lucioperca length increased, their prey size significantly increased, although prey items remained relatively small. In entirety, these results suggest that when non-destructive sampling is required for sport fishes such as S. lucioperca, SIA can be used to provide robust dietary assessments. However, if SCA can be completed, then it can provide dietary data that are complementary to SIA and so help provide greater insights into their piscivory and predation pressure on native prey fishes.

Acknowledgements

We thank John Ellis of the Canal and Rivers Trust for access to the fish.

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Cite this article as: Nolan ET, Britton JR. 2018. Diet of invasive pikeperch Sander lucioperca: developing non-destructive tissue sampling for stable isotope analysis with comparisons to stomach contents analysis. Knowl. Manag. Aquat. Ecosyst., 419, 49.

All Tables

Table 1

Number of individuals, tissue specific carbon (δ13C) and nitrogen (δ15N) stable-isotope ratios (Mean ± SD) indicating variation in isotope values between tissues.

In the text
Table 2

Linear regression statistics for the relationship between dorsal muscle stable isotope values (δ13C and δ15N) and those of epidermal mucus, fin and scales collected from S. lucioperca.

In the text
Table 3

Mean predicted dietary contributions from Bayesian mixing models of ‘Cyprinidae’, ‘Invertebrates’ and ‘Percidae’ to the diet of S. lucioperca by size class (<350 mm and >350 mm), showing standard deviation and 95% confidence limits. Mixing models were (1) consumer as muscle values, (2) consumer as mucus values, (3) consumer as scale values, (4) consumer as muscle values based on conversion using the linear models for δ13C and δ15N mucus to muscle, and (5) consumer as muscle values based on conversion using the linear models for δ13C and δ15N scale to muscle.

In the text

All Figures

thumbnail Fig. 1

Stable isotope bi-plot of δ13C versus δ15N showing individual (light grey) and mean (black) values for all tissue types (■ muscle; ▲mucus; ● fin; + scale), where error bars represent standard deviation.
In the text
thumbnail Fig. 2

Linear relationships of (a) δ13C and (b) δ15N dorsal muscle versus epidermal mucus (‘mucus’), fin and scale, where the dotted line represents the relationship with length included in the model and the dashed line represents the relationship with length excluded.
In the text
thumbnail Fig. 3

Linear relationships of (a) δ13C and (b) δ15N for muscle (■), mucus (▲), fin (●) and scale (+). Significant relationships are fitted with 95% confidence intervals around the line for muscle (long dashed line, light grey), mucus (short dashed line), fin (solid line) and scale (long dashed line, dark grey).
In the text
thumbnail Fig. 4

Mean predicted dietary contributions (0–1) of ‘Cyprinidae’, ‘Invertebrates’ and ‘Percidae’ to the diet of S. lucioperca by size class (<350 and >350 mm) for each Bayesian mixing model. Models are represented by colour in sequence from light to dark, where model 1 is represented by light grey and model 5 by dark grey, and error bars represent the standard deviation. Mixing models were (1) consumer as muscle values, (2) consumer as mucus values, (3) consumer as scale values, (4) consumer as muscle values based on conversion using the linear models for δ13C and δ15N mucus to muscle, and (5) consumer as muscle values based on conversion using the linear models for δ13C and δ15N scale to muscle.
In the text
thumbnail Fig. 5

S. lucioperca predator–prey relationships in the Grand Union Canal. (a) Predator size to prey size linear relationships (with 95% confidence intervals) for ‘Cyprinidae’ (● solid line) and ‘Percidae’ (▲ dashed line). (b) Prey: predator length ratios versus S. lucioperca body length, where lines represent relationships according to linear regression for ‘Cyprinidae’ (● solid line) and ‘Percidae’ (▲ dashed line). (c) Relative frequency distributions of prey: predator length ratios, where the mean prey size to predator size ratio is shown at 0.22.
In the text

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