Molecular analyses of carangid fish diets reveal inter‐predation, dietary overlap, and the importance of early life stages in trophic ecology

Abstract

Carangid fishes are commercially important in fisheries and aquaculture. They are distributed worldwide in both tropical and subtropical marine ecosystems. Their role in food webs is often unclear since their diet cannot be easily identified by traditional gut content analysis. They are suspected to prey on pelagic and benthic species, with clupeiform fishes being important dietary items for some species, though it is unknown whether carangids share food resources or show trophic segregation. Here, we used metabarcoding to overcome traditional challenges of taxonomic approaches to analyze the diet of seven carangid species caught as bycatch in the Brazilian southwest Atlantic sardine fishery. Stomach contents were processed from the following species: Caranx crysos, Caranx latus, Chloroscombrus chrysurus, Hemicaranx amblyrhynchus, Oligoplites saliens, Selene setapinnis, and Trachinotus carolinus. Identified diets were dominated by teleost fishes. The C. latus diet was the most distinct among the seven species, preferentially consuming Engraulis anchoita, but H. amblyrhynchus, O. saliens, and S. setapinnis also showed a trend of predominantly consuming small pelagic fishes. Finally, we found evidence of inter‐predation in carangids, especially strong between S. setapinnis and C. crysos, suggesting that consumption of early life stages may result in indirect competition through reduced recruitment in these fishes. These findings provide unprecedented insights into the biodiversity in marine ecosystems, especially the poorly known diet of carangid fishes.

Dietary metabarcoding of keystone sardine species reveals the importance of their ichthyoplankton prey in food webs of the Southern Brazilian Bight fisheries

“…Individual stomach contents were separated from preservative by three cycles of centrifugation, washed with ultrapure, and UV sterilized water. Four 650 μl subsample replicates (a-d) were prepared and stored in separate microcentrifuge tubes (Rosa et al, 2024). DNA from all replicates was extracted using the CTAB/phenol/chloroform protocol (Doyle & Doyle, 1987) in a decontaminated fume hood.…”

“…DNA from all replicates was extracted using the CTAB/phenol/chloroform protocol (Doyle & Doyle, 1987) in a decontaminated fume hood. Twohundred sixty-three subsamples representing all 87 samples across 31 species were successfully extracted and sequenced (successful amplification of replicates a-d is indicated in Table S1 as some replicates did not amplify, apparently because of PCR inhibition), as well as multiple negative controls generated at various steps, from sample processing (tubes opened and filled only with ethanol or water during sample preparation-one control between each homogenized sample), to DNA extraction (one negative control in each row of 12 tubes) and amplicon production (a minimum of two spatially separated negative controls on each PCR plate) steps (Rosa et al, 2024).…”

“…PCR amplifications of all subsamples and negative controls targeting the Cytochrome C oxidase subunit I marker (130 bp) were performed using the primers Minibar-Mod-F and Minibar-Mod-R (Berry et al, 2015), following index combinations as described in Fadrosh et al (2014). PCRs were carried out in 25 μl final volumes with final concentration of 1X Q5 High-Fidelity master mix (New England Biolabs), 2X Q5 enhancer (New England Biolabs), 100 nM of each primer, and 2-3 ng of template DNA (including unique combinations of dualindexing barcodes) (Ribas et al, 2021;Rosa et al, 2024). The PCR amplification protocol was as follows: initial denaturation at 98 C for 30 s; followed by 30 cycles of denaturation at 98 C for 10 s, annealing at 45 C for 15 s, and extension at 72 C for 15 s; and final extension of 2 min at 72 C. Amplicons were visualized on agarose gels and quantified using ImageLab Software v6.0.…”

“…To remove false positives and possible contaminants or sequencing errors, we applied the following rules: (i) the maximum number of reads detected in the controls was removed for each MOTU from all samples; (ii) MOTUs containing fewer than 10 reads were discarded; and (iii) obvious non-target species or MOTUs likely originating from carry-over contaminations were removed from the dataset (Ushio et al, 2018;Ribas et al, 2021;Rosa et al, 2024). The final assignments of dietary items were inferred based on similarity values and knowledge on geographic distributions in the literature accessed Species identity was considered confirmed for similarities above 97%, and the most probable geographically local representatives of the same genus or family identified when similarities were between 90% and 97% (Rosa et al, 2024). For example, a sample assigned with a similarity of 90-97% could be subsequently assigned at family level, genus level, or even a specific species in the genus known from the region if only one species is known for the region.…”

“…Detection of prey taxa was normalized, and secondary consumption of items was evaluated according to Rosa et al (2024), by using both thresholds of relative read abundance (RRA) within samples (<1% for exclusion and 1-5% as a flag to assess secondary consumption) and comparative data from the literature and from within the global metabarcoding dataset produced in this study. The number of reads was then averaged across subsamples (to avoid inflation of read abundance in samples with more subsamples, e.g., 4 vs. 3 vs. 2 subsamples) (Rosa et al, 2024). The dietary links of clupeiform fishes were visualized using Sankey diagrams.…”