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

Abstract

Abstract Food web dynamics are vital in shaping the functional ecology of ecosystems. However, trophic ecology is still in its infancy in groundwater ecosystems due to the cryptic nature of these environments. To unravel trophic interactions between subterranean biota, we applied an interdisciplinary Bayesian mixing model design (multi‐factor BMM) based on the integration of faunal C and N bulk tissue stable isotope data (δ13C and δ15N) with radiocarbon data (Δ14C), and prior information from metagenomic analyses. We further compared outcomes from multi‐factor BMM with a conventional isotope double proxy mixing model (SIA BMM), triple proxy (δ13C, δ15N, and Δ14C, multi‐proxy BMM), and double proxy combined with DNA prior information (SIA + DNA BMM) designs. Three species of subterranean beetles (Paroster macrosturtensis, Paroster mesosturtensis, and Paroster microsturtensis) and their main prey items Chiltoniidae amphipods (AM1: Scutachiltonia axfordi and AM2: Yilgarniella sturtensis), cyclopoids and harpacticoids from a calcrete in Western Australia were targeted. Diet estimations from stable isotope only models (SIA BMM) indicated homogeneous patterns with modest preferences for amphipods as prey items. Multi‐proxy BMM suggested increased—and species‐specific—predatory pressures on amphipods coupled with high rates of scavenging/predation on sister species. SIA + DNA BMM showed marked preferences for amphipods AM1 and AM2, and reduced interspecific scavenging/predation on Paroster species. Multi‐factorial BMM revealed the most precise estimations (lower overall SD and very marginal beetles' interspecific interactions), indicating consistent preferences for amphipods AM1 in all the beetles' diets. Incorporation of genetic priors allowed crucial refining of the feeding preferences, while integration of more expensive radiocarbon data as a third proxy (when combined with genetic data) produced more precise outcomes but close dietary reconstruction to that from SIA + DNA BMM. Further multidisciplinary modeling from other groundwater environments will help elucidate the potential behind these designs and bring light to the feeding ecology of one the most vital ecosystems worldwide.

Keywords

• Bayesian mixing models
• food webs
• groundwater
• metagenomics
• radiocarbon
• stygofauna