Sampling sufficiency in stomach content surveys of omnivorous fish species

ABSTRACT

Feeding research in fish has offered understanding into the ecology of individuals, populations, and ecosystems. In these studies, analyzing enough stomachs is crucial to determine a species’ diet. This research evaluates the number of stomachs needed to characterize the diet of omnivorous fish species by employing cumulative prey curves, non-parametric estimators, and rarefaction/extrapolation curves. The Feeding Index was calculated by utilizing a wide taxonomic classification of food items to determine the number of individuals needed to characterize trophic groups. The stomach contents of three species: Megaleporinus garmani (n = 174), Trachelyopterus galeatus (n = 99), and Wertheimeria maculata (n = 68) was analyzed. The cumulative prey curves fail to reach an asymptote for any species. In comparison to richness estimators, the observed diversity of prey varied between 73 and 91% across different species. The extrapolation curves approach an asymptote in the case of T. galeatus and W. maculata, reinforcing the declining counts of food item singletons and doubletons noted in their dietary accumulation curves. According to the Feeding Index, analyzing 30 to 60 stomachs is adequate to determine the primary dietary components. A reduced number of stomachs (25 to 35) is needed to identify feeding guilds. Due to the wider diets of omnivorous species, a larger sample is required to properly characterize their diet. Elements like research goals, required detail level, seasonal variations, and species-specific characteristics should be taken into account when determining the number of stomachs to analyze, to guarantee strong and dependable outcomes.

KEYWORDS:
Feeding Index; fish diet; food item diversity; richness estimators; sample size

INTRODUCTION

The analysis of gut contents provides valuable information on fish feeding patterns, interactions among species, predator-prey population dynamics, and energy intake and transfer (Manko 2016, Brodeur et al. 2017). Feeding studies contribute to understanding fish ecology on individual, population, and ecosystem levels. The broad applicability of such studies has led to the development of several approaches and techniques to analyze this type of data (Manko 2016).

A series of reviews focusing on methodology and statistics (Hyslop 1980, Ferry and Cailliet 1996, Cortés 1997, Hahn and Delariva 2003, Simenstad and Cailliet 2017, da Silveira et al. 2020) have compiled the various stomach content analysis methods, highlighting their strengths and weaknesses.

One type of approach to stomach content analysis is based on the use of compound indices. Compound indices encompass a combination of two or more variables, such as number, mass, volume, and occurrence of the items, to express more information (Mahesh et al. 2018). The Feeding Index (Kawakami and Vazzoler 1980) is based on the frequency of occurrence and the volume of food items. It returns the relative importance of each item in the diet. Besides analyzing the predominant items in the diet, the diet breadth can also be studied, assessing all prey items consumed by a species.

The food items can be assessed by estimating the richness of those items in the stomach of individuals of a species. Species richness may be defined as the number of species present in a particular community; this is one of the simplest measures of diversity used to characterize a community (Chao 2006). Despite the simplicity of the species richness metric, data sampling to measure this variable is usually difficult. For one, the richness observed in samples generally tend to underestimate the real number of species present in the community (Gotelli and Colwell 2011). Additionally, in practice, sampling all species present in a community is generally difficult to do (Chao and Chiu 2016). Despite that, it is possible to estimate species richness within a community, and to compare species richness among different communities. The non-asymptotic methods, rarefaction and extrapolation, allow for the comparison between samples of standardized sizes. In the asymptotic method, the value at which the curve of accumulation of a species reaches its asymptote is estimated, and this value is used to compare different communities (Chao and Chiu 2016).

Given the impacts of anthropogenic pressures on natural communities, species richness is often used in efforts to conserve biodiversity (Gotelli and Chao 2013). Despite the great importance and diverse applications of richness estimation, few studies in the literature have estimated richness in the diet of fish (Côté et al. 2013). In addition, after reviewing 200 papers about fish dietary habits, Ferry and Cailliet (1996) stated that none of the papers used a technique to determine if a sufficient sample size was used to allow diets to be compared. This aspect is particularly important for species with opportunistic feeding behavior, whose diet varies with environmental conditions (de Carvalho et al. 2019). This work aimed to use the omnivorous fish species Megaleporinus garmani (Borodin, 1929), Trachelyopterus galeatus (Linnaeus, 1766), and Wertheimeria maculata Steindachner, 1877 as models to (i) apply richness estimation methods for food items using species accumulation curves and rarefaction/extrapolation curves and (ii) estimate the necessary number of individuals to properly calculate the Feeding Index and define trophic groups.

MATERIAL AND METHODS

Study area

The Jequitinhonha River basin drains an area of ca. 70,315 km², taking place across the states of Minas Gerais and Bahia, Brazil (Fig. 1). The mean annual rainfall averages from 600 to 1,600 mm irregularly distributed throughout the year. Rainfall mostly occurs from October to March (IBGE 1997). The Jequitinhonha River rises into the Espinhaço mountain range in the municipality of Serro, Minas Gerais and runs 1,086 km northeast to the municipality of Belmonte, Bahia, where it flows into the Atlantic Ocean (Guerrero 2009). Two of the main tributaries on the left bank are the Itacambiruçu and Vacaria rivers (IBGE 1997). Three species that occur in the Jequitinhonha River basin were used in this study: Megaleporinus garmani, Trachelyopterus galeatus, and Wertheimeria maculata which have been classified, respectively, as obligate omnivorous digger (Azevedo et al. 2011), omnivorous with a tendency towards herbivory (Santin et al. 2015), and omnivorous with a tendency towards herbivory (Vono and Birindelli 2007).

Figure 1
Map of the Jequitinhonha River Basin indicating the nine sampling sites.

Data sampling

Fish were sampled in the Jequitinhonha, Itacambiruçu, and Vacaria rivers from 2011 to 2013, 2015, and 2017 to 2018 (Fig. 1). In total, sampling was performed 22 times: once in a month during these years, including both rainy and dry seasons.

To collect fish, we used gillnets with mesh sizes 2.4, 3, 4, 5, 6, 7, 8, 10, 12, 14 and 16 cm between opposite nodes. All net lengths were 30.0 x 1.5 m height, except for the 2.4 mesh net which was 20.0 x 1.5 m height. Nets were deployed at 6:00 PM and retrieved at 6:00 AM, resulting in a total exposure time of 12 hours. We fixed the individuals in formaldehyde and preserved them in alcohol in the laboratory. Specimens were measured (total and standard length), weighed, and then dissected to remove stomachs which were stored in 70° GL alcohol solution. Under a stereomicroscope, stomach contents were analyzed to the lowest possible taxonomic level. Identification was made with the aid of identification tools (Ward and Whipple 1918, Costa et al. 2006, Mugnai et al. 2010). The wet weight of the items was determined with a precision scale (Ohaus Adventurer, AR 2140, 0.0001 g). The sampling methods were approved by the Minas Gerais state authorities in accordance with Brazilian regulations, and comply with the guidelines of the Brazilian Council for Control of Animal Experimentation (CONCEA 2013).

Data analysis

Food item accumulation curves were constructed by cumulatively adding items found in the stomachs to verify the sample sufficiency for the three species. Because the order in which stomachs are added to the analysis can interfere with the shape of the curve (Colwell and Coddington 1994), sampling was randomized 100 times. We used the most detailed categories of food items to construct the accumulation curve. The variety and richness of food item estimators were calculated with Estimate S v9.1.0 software (Colwell 2013) and the graphics were made with R software (R Core Team 2024) with ggplot2 package (Wickham 2016). Three non-parametric estimators were used: Chao2, Jackknife 1, and bootstrap. These estimators focus on rare items in the sample to calculate the frequency of items that were not sampled (Gotelli and Chao 2013). We did not count the number of individuals ingested in each stomach, so the estimators were based on the incidence (presence/absence) of the different items in each stomach. The Chao2 estimator takes into account the items present in only one stomach (singletons) and the items present in two stomachs (doubletons); the estimator of Jackknife 1 considers only the presence of singletons. Bootstrap uses frequency data of all items to calculate richness. Such non-parametric estimators have presented robust results in the analyses, especially Jackknife and Chao (Walther and Moore 2005, Chao and Chiu 2014). A rarefaction/extrapolation curve using the incidence of food items was calculated for the three species. Samples were extrapolated doubling the reference sample size for each species to verify if the curves reach an asymptote. This analysis used the iNEXT package v3.0.1 on the R software v4.4.0 (Hsieh et al. 2016, R Core Team 2024).

To determine the relative importance of each food item, the Feeding Index (FIi) (Kawakami and Vazzoler 1980) was calculated, using the weight (Bennemann et al. 2006) and the frequency of occurrence (Hyslop 1980) of each food item: FO = number of stomachs with the item i / number of stomachs with food × 100 combined using the following equation:

$\mathrm{F}\mathrm{I}i=\frac{\mathrm{F}\mathrm{i}\mathrm{}\mathrm{x}\mathrm{}\mathrm{P}\mathrm{i}}{\sum _{\mathrm{i}=1}^{\mathrm{n}}\mathrm{}\left(\mathrm{F}\mathrm{i}\mathrm{}\mathrm{x}\mathrm{}\mathrm{P}\mathrm{i}\right)}\mathrm{}\mathrm{x}\mathrm{}100$

Where FIi = Feeding Index, Fi = Frequency of occurrence (%) of food item i and Pi = Weight (%) of food item i.

To create the graph of the feeding index (FIi), we calculated cumulatively the FIi for the stomachs analyzed in a random order. The items were grouped on the following categories: Algae, Cladocera (macrozooplankton), filamentous algae, fish remains, fish scales, Hymenoptera, insect remains, macrophytes, Odonata, plant remains, and sediment. In order to determine the most consumed items of each species, we considered the items that accounted for 90% of the feeding index at the end of this first analysis. Then, we created graphics cumulatively integrating the FIi into five feeding guilds: detritivore, invertivore, herbivore, piscivore, and zooplanktivore. The classification was made grouping the main consumed items in each feeding guild. Species that consume vegetal/animal items in similar proportions are classified as omnivores. The graphics were made with R software (R Core Team 2024) with the ggplot2 package (Wickham 2016).

RESULTS

We analyzed 192 stomachs of M. garmani, 157 of T. galeatus, and 86 of W. maculata. After the empty stomachs were excluded, a total of 174, 99, and 68, were included in the analysis, respectively. Megaleporinus garmani ingested 22 different food items, T. galeatus 19, and W. maculata 17 (Table 1).

Table 1
Feeding index (FIi) for the food items each species consumed. Items that comprise up to 90% of the diet of each species are highlighted in bold.

Food item	Feeding index (%)
	M. garmani	T. galeatus	W. maculata
Acari	<0.01	<0.01
Algae	77.64	0.05
Algae (Filamentous)			5.07
Amphipoda	<0.01
Bivalvia	<0.01
Cladocera	<0.01		4.09
Coleoptera	<0.01	3.77
Detritus	<0.01
Diptera	1.30	<0.01	1.53
Ephemeroptera	0.29	0.04	0.42
Fish remains	<0.01	21.35	23.00
Fish scale	<0.01	4.28	19.58
Hemiptera	<0.01	0.03
Hymenoptera	<0.01	42.31	2.35
Insect remains		23.19	19.48
Lepidoptera	<0.01
Macrophyte	8.65		1.16
Nematoda		0.03
Odonata	<0.01	0.66	6.76
Orthoptera		0.36
Ostracoda	<0.01		<0.01
Plant remains	0.22	3.83	12.85
Plecoptera		<0.01
Sediment	9.82	0.05	1.62
Trichoptera	0.04	0.03	<0.01
Unknown	1.98	0.02	2.05
Zooplankton	<0.01		0.03

None of the curves for the accumulation of food item diversity reached an asymptote (Fig. 2). For M. garmani, the count of singletons and doubletons stayed consistent at the largest sample size, whereas the curve of the asymptotic richness estimators kept rising. For T. galeatus and W. maculata, the count of singletons began to decline while the count of doubletons remained consistent. At the largest sample size, Chao 2 and Jackknife estimators continued to show greater values than the recorded food item diversity, with both estimators leveling off at around 60 stomachs sampled for T. galeatus and at 35 stomachs for W. maculata. The three asymptotic richness estimators indicated that the observed prey diversity was between 73-87% for M. garmani, 82-90% for T. galeatus, and 85-91% for W. maculata. Bootstrap was the sole richness estimator within the confidence interval for the three species analyzed. The extrapolation curves show that an asymptote was reached for T. galeatus and W. maculata. For M. garmani the curve did not stabilize, indicating that food item variety can still increase (Fig. 3).

Figure 2
Food items variety estimation for (A) Megaleporinus garmani, (B) Trachelyopterus galeatus, and (C) Wertheimeria maculata. CI - 95% Confidence interval.

Figure 3
Sampling curve for food item variety of the three analysed species. The dotted line represents the extrapolation for each species. Mgar: Megaleporinus garmani, Tgal: Trachelyopterus galeatus, Wmac: Wertheimeria maculata.

The feeding index graphics show the variation of items that were more important for each species. The feeding index stabilized after the analysis of 30 stomachs of M. garmani, 60 stomachs of T. galeatus, and 35 of W. maculata. The trophic guilds of the species stabilized after the analysis of 25 stomachs for M. garmani, 25 for T. galeatus, and 35 for W. maculata (Fig. 4).

Figure 4
Feeding Index for food items (left column) and for trophic guilds (right column) for Megaleporinus garmani (A, B), Trachelyopterus galeatus (C, D), and Wertheimeria maculata (E, F). The grey-dashed lines indicate the number of stomachs needed to stabilize the variations.

DISCUSSION

The accumulation curves for a range of food items did not attain an asymptote for any of the three species evaluated. The extrapolation curves for T. galeatus and W. maculata show an asymptote, and the opposite was observed for M. garmani. However, when a broader category is considered, anywhere from 30 to 60 stomachs would produce a satisfactory feeding index, and a lower number (25 to 35) would suffice to determine a trophic guild. These estimations were also species-dependent: fewer stomachs are necessary in the case of the more specialized species (M. garmani).

Several authors use cumulative prey curves to determine sample size sufficiency (Pielou 1966, Karpov and Cailliet 1979, Logan et al. 2013, Eddy et al. 2016). The asymptote of the prey curve would indicate the required number of stomachs to describe the diet, and a quantitative method to evaluate the slope of the curve was developed (Bizzarro et al. 2007). A thorough sampling of prey items is particularly beneficial when stomach contents can serve as an effective sampling method in benthic macroinvertebrate studies, especially in large, deep, swift rivers that are challenging to sample with conventional sediment collection equipment (Tupinambás et al. 2015). Complete sampling might also be more useful when stomach content analysis is used to assess the impact of invasive species on their prey in the new habitat (Côté et al. 2013). However, it is harder to reach sample sufficiency since fish feed on various food items. Chao et al. (2009) demonstrated trough simulations that the sampling effort needed to collect all the rare species in a community would be substantially large. An alternative to this issue can include aiming to achieve a lower percentage of food item variety, such as 95%, which would yield a more realistic sampling effort since a complete sampling is hardly achievable with real data samplings (Walther and Martin 2001, Chang and Colwell 2005). Based on the asymptotic richness estimators, the estimated food item variety was 12 to 26% higher than the variety observed for M. garmani, 9 to 17% higher for T. galeatus, and 8 to 14% higher to W. maculata. The species with observed food item variety values nearer to the estimated variety (M. garmani and W. maculata) also showed a declining number of singletons as the sampling size increased. The quantity of singletons declines as sampling increases, and once all singletons are gone, it can be inferred that complete food item diversity has been reached (Walther and Moore 2005). The asymptote for the extrapolation curve of these species also supports that the sampling was representative.

More specific taxonomic identifications influenced the number of stomachs needed to stabilize variations in the cumulative feeding index plots. Using detailed categories of prey items, as expected, required more stomachs than the trophic guild categories. Fewer samples are required to describe prey diversity than to describe prey variety (Dunn 2009). It is important to point out that since the broad classification of food items synthesize information, the number of stomachs required to stop variation can be small and may not represent the species’ true diet.

Seasonality is another important factor to consider. Seasonal variations in the hydrological cycle of freshwater systems influence habitat structure and the availability of food resources (Poff et al. 1997). As generalist feeders, omnivorous species can consume a wide range of food items (Gerking 1994). Their trophic plasticity allows them to adjust their diet according to the abundance and quality of resources in the environment (Abelha et al. 2001). Consequently, individuals of the same species sampled during wet and dry seasons may be assigned to different feeding guilds depending on seasonal conditions.

Our study specifically focused on omnivorous species, excluding other feeding guilds. Because these species consume a wider variety of dietary items, a larger number of stomachs is necessary to reach sampling sufficiency. For example, in M. garmani, a predominantly herbivorous species, the number of stomachs required to achieve sufficiency was lower. Therefore, our results provide a useful reference for feeding guilds with greater dietary specialization, which are expected to require fewer individuals to adequately characterize their diet.

ACKNOWLEDGMENTS

Thanks to Francisco Andrade for acquisition of funding and all the support during this study. Thanks to Carina Porto (Universidade Federal de Lavras) for the support on stomach content analysis and Jessica Schulte for the English revision of the manuscript.

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ADDITIONAL NOTES

• ZooBank register
  https://zoobank.org/6E6AAB3A-4966-48EB-BFD8-659A2E21EFF6
• Data Availability
  Datasets related to this article will be available upon request to the corresponding author.
• Funding
  Companhia Energética de Minas Gerais (4570015931), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (32004010017P3) This study was financed by Programa Peixe Vivo from Companhia Energética de Minas Gerais (Process: 4570015931) and by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance Code: 32004010017P3).
• How to cite this article
  Fráguas PS, Pompeu PS (2025) Sampling sufficiency in stomach content surveys of fish omnivorous species. Zoologia 42: e24063. https://doi.org/10.1590/S1984-4689.v42.e24063
• Published by
  Sociedade Brasileira de Zoologia at Scientific Electronic Library Online - https://www.scielo.br/zool

Edited by

• Editorial responsibility
  Ricardo Moratelli

Data availability

Datasets related to this article will be available upon request to the corresponding author.

Publication Dates

• Publication in this collection
  10 Oct 2025
• Date of issue
  2025

History

• Received
  30 Sept 2024
• Accepted
  23 Mar 2025