Integrating complementary methods to improve diet analysis in fishery‐targeted species

Abstract

Developing efficient, reliable, cost‐effective ways to identify diet is required to understand trophic ecology in complex ecosystems and improve food web models. A combination of techniques, each varying in their ability to provide robust, spatially and temporally explicit information can be applied to clarify diet data for ecological research. This study applied an integrative analysis of a fishery‐targeted species group—Plectropomus spp. in the central Great Barrier Reef, Australia, by comparing three diet‐identification approaches. Visual stomach content analysis provided poor identification with ~14% of stomachs sampled resulting in identification to family or lower. A molecular approach was successful with prey from ~80% of stomachs identified to genus or species, often with several unique prey in a stomach. Stable isotope mixing models utilizing experimentally derived assimilation data, identified similar prey as the molecular technique but at broader temporal scales, particularly when prior diet information was incorporated. Overall, Caesionidae and Pomacentridae were the most abundant prey families (>50% prey contribution) for all Plectropomus spp., highlighting the importance of planktivorous prey. Less abundant prey categories differed among species/color phases indicating possible niche segregation. This study is one of the first to demonstrate the extent of taxonomic resolution provided by molecular techniques, and, like other studies, illustrates that temporal investigations of dietary patterns are more accessible in combination with stable isotopes. The consumption of mainly planktivorous prey within this species group has important implications within coral reef food webs and provides cautionary information regarding the effects that changing resources could have in reef ecosystems.

The importance of oceanic atoll lagoons for coral reef predators

“…UVC predator biomass data were calculated using length-weight relationships available on FishBase (https ://fishb ase.org) with the exception of Aethaloperca rogaa where length-weight relationships were taken from Mapleston et al (2009). Spatial differences in UVC predator biomass were investigated using a generalized linear model (GLM) with transect level biomass as the response variable and site nested within area as the predictor variable.…”

“…Consequently, reef predators may increase in density and diversity from shallow, lagoonal habitats to outer reef slopes (Friedlander et al 2010;Dale et al 2011). Outer reef habitats may provide a greater availability of resources, for example they host aggregations of planktivorous fishes (Hamner et al 1988(Hamner et al , 2007) that take advantage of increased plankton prey abundance (Wyatt et al 2013) and sustain reef predators (Frisch et al 2014;Matley et al 2018).…”

Reconstructing hyperdiverse food webs: Gut content metabarcoding as a tool to disentangle trophic interactions on coral reefs

“…Recent advances in molecular techniques have made DNA metabarcoding, the mass amplification of DNA barcodes from samples containing a multitude of prokaryotes and eukaryotes, an accessible and invaluable tool to assess marine biodiversity (Leray & Knowlton, , ). Over the past decade or so, DNA metabarcoding has been employed to analyse fish diet composition in both freshwater and marine environments (Corse et al, ; Leray, Meyer, & Mills, ; Leray, Yang, et al, ; Matley et al, ). However, this has only been conducted on a small scale with a limited scope, typically across a few species in the same family or for fishes at the same approximate trophic level (Leray et al, , ).…”

Refining trophic dynamics through multi‐factor Bayesian mixing models: A case study of subterranean beetles

“…Horizontal continuous lines indicate the estimated mean, and dashed lines refer to the median. Refer to Table S2 for the specific contribution mean values "who eats whom") (Ishikawa, 2018), the combination of these data within the framework of BMM has been proved to allow crucial refining of diet analyses in a vast number of studies (e.g., Franco-Trecu et al, 2013;Galvan, Sweeting, & Polunin, 2012;Matley et al, 2018;Traugott, Kamenova, Ruess, Seeber, & Plantegenest, 2013). Dietary contributions estimated via multi-factor BMM were compared with a classic δ 13 C with δ 15 N SIA BMM, conventional SIA coupled with genetic prior information (SIA + DNA BMM) and the "triple proxy approach" alone (multi-proxy BMM).…”