Dr Jaret Bilewitch1, Amber Brooks1, Kristy Hogsden2, Lisa Smith1, Richard White2, Rick Stoffels2
1NIWA, Wellington, 2NIWA, Christchurch,
Biography:
Jaret Bilewitch is a molecular marine biologist with integrative experience in molecular systematics, population genetics, evolutionary biology, genomics and marine ecology. He specialises in developing molecular biological tools for application in environmental sciences, including conservation biology, biosecurity, biodiversity, oceanography, and fisheries sciences.
Natural and anthropogenic changes to flow rates of rivers are known to impact the physical characteristics of the riverbed, with associated changes in benthic invertebrate community composition. Predicting the extent and wider trophic impact of these changes is often difficult due to insufficient understanding of dietary preferences and plasticity in interactions with predatory fish. Dietary assessments of stomach contents obtained from small fish are hindered by their low biomass content and morphological degradation due to digestion, presenting an opportunity for alternative, genetic assessments of dietary diversity and relative abundance.
Abstract:
We assessed the invertebrate dietary composition of two common fish in the Selwyn River, New Zealand – the upland bully (Gobiomorphus breviceps) and the Canterbury galaxias (Galaxias vulgaris). Primary dietary constituents were identified over a three-year period using COI-metabarcoding of stomach contents. Observational data from field collections of macroinvertebrates and a curated reference database were both used to improve Amplicon Sequence Variant assignment of over 900 gut content samples. The resulting taxonomic information was incorporated into an ecosystem model forecasting how flow-induced changes to river hydraulics and sediment composition affect macroinvertebrate communities, and consequentially the trophic links between fishes and invertebrate prey. Work is currently underway to expand the study to other rivers, increasing the generality and scope of model predictions.