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

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          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 (δ 13C and δ 15N) with radiocarbon data (Δ 14C), 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 (δ 13C, δ 15N, and Δ 14C, 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.

          Abstract

          Understanding of cryptic ecosystems is enabled by multi‐factor models and interdisciplinary research.

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          Most cited references49

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          The Trophic-Dynamic Aspect of Ecology

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            Food Webs: Linkage, Interaction Strength and Community Infrastructure

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              Uncertainty in source partitioning using stable isotopes.

              Stable isotope analyses are often used to quantify the contribution of multiple sources to a mixture, such as proportions of food sources in an animal's diet, or C3 and C4 plant inputs to soil organic carbon. Linear mixing models can be used to partition two sources with a single isotopic signature (e.g., δ(13)C) or three sources with a second isotopic signature (e.g., δ(15)N). Although variability of source and mixture signatures is often reported, confidence interval calculations for source proportions typically use only the mixture variability. We provide examples showing that omission of source variability can lead to underestimation of the variability of source proportion estimates. For both two- and three-source mixing models, we present formulas for calculating variances, standard errors (SE), and confidence intervals for source proportion estimates that account for the observed variability in the isotopic signatures for the sources as well as the mixture. We then performed sensitivity analyses to assess the relative importance of: (1) the isotopic signature difference between the sources, (2) isotopic signature standard deviations (SD) in the source and mixture populations, (3) sample size, (4) analytical SD, and (5) the evenness of the source proportions, for determining the variability (SE) of source proportion estimates. The proportion SEs varied inversely with the signature difference between sources, so doubling the source difference from 2‰ to 4‰ reduced the SEs by half. Source and mixture signature SDs had a substantial linear effect on source proportion SEs. However, the population variability of the sources and the mixture are fixed and the sampling error component can be changed only by increasing sample size. Source proportion SEs varied inversely with the square root of sample size, so an increase from 1 to 4 samples per population cut the SE in half. Analytical SD had little effect over the range examined since it was generally substantially smaller than the population SDs. Proportion SEs were minimized when sources were evenly divided, but increased only slightly as the proportions varied. The variance formulas provided will enable quantification of the precision of source proportion estimates. Graphs are provided to allow rapid assessment of possible combinations of source differences and source and mixture population SDs that will allow source proportion estimates with desired precision. In addition, an Excel spreadsheet to perform the calculations for the source proportions and their variances, SEs, and 95% confidence intervals for the two-source and three-source mixing models can be accessed at http://www.epa.gov/wed/pages/models.htm.
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                Author and article information

                Contributors
                mattia.sacco@postgrad.curtin.edu.au
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                20 July 2020
                August 2020
                : 10
                : 16 ( doiID: 10.1002/ece3.v10.16 )
                : 8815-8826
                Affiliations
                [ 1 ] WA‐Organic Isotope Geochemistry Centre The Institute for Geoscience Research School of Earth and Planetary Sciences Curtin University Perth WA Australia
                [ 2 ] Collections and Research Centre Western Australian Museum Welshpool WA Australia
                [ 3 ] School of Biological Sciences University of Western Australia Crawley WA Australia
                [ 4 ] Australian Centre for Evolutionary Biology and Biodiversity School of Biological Sciences University of Adelaide Adelaide SA Australia
                [ 5 ] Evolutionary Biology Unit South Australian Museum Adelaide SA Australia
                [ 6 ] Department of Environmental Science The Connecticut Agricultural Experiment Station New Haven CT USA
                [ 7 ] Australian Nuclear Science and Technology Organisation (ANSTO) Kirrawee DC NSW Australia
                [ 8 ] Trace and Environmental DNA Lab School of Molecular and Life Sciences Curtin University Perth WA Australia
                Author notes
                [*] [* ] Correspondence

                Mattia Saccò, WA‐Organic Isotope Geochemistry Centre, The Institute for Geoscience Research, School of Earth and Planetary Sciences, Curtin University, Perth 6102, WA, Australia.

                Email: mattia.sacco@ 123456postgrad.curtin.edu.au

                Author information
                https://orcid.org/0000-0001-6535-764X
                Article
                ECE36580
                10.1002/ece3.6580
                7452819
                83d64e50-7767-480c-8aac-fa3f04e83ace
                © 2020 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 November 2019
                : 21 June 2020
                : 24 June 2020
                Page count
                Figures: 5, Tables: 3, Pages: 12, Words: 7961
                Funding
                Funded by: AINSE Postgraduate research grant
                Award ID: AINSE PGRA Mattia Sacco
                Funded by: Australian Research Council (ARC) linkage grant
                Award ID: LP140100555
                Funded by: Curtin International Postgraduate Research Scholarship
                Award ID: CIPRS Mattia Sacco
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                August 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.8 mode:remove_FC converted:27.08.2020

                Evolutionary Biology
                bayesian mixing models,food webs,groundwater,metagenomics,radiocarbon,stygofauna
                Evolutionary Biology
                bayesian mixing models, food webs, groundwater, metagenomics, radiocarbon, stygofauna

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