Original Research ARTICLE
Network-based biomonitoring: exploring freshwater food webs with stable isotope analysis and DNA metabarcoding
- 1Environment and Climate Change Canada, University of New Brunswick Fredericton, Canada
- 2Centre for Environmental Genomics Applications (CEGA), Canada
- 3Department of Biology, Canadian Rivers Institute, University of New Brunswick, Canada
- 4Environment and Climate Change Canada and Faculty of Forestry and Environmental Management, University of New Brunswick Fredericton, Canada
- 5Lancaster Environment Centre, Lancaster University, United Kingdom
- 6Department of Integrative Biology, Centre for Biodiversity Genomics, Biodiversity Institute of Ontario, University of Guelph, Canada
- 7Great Lakes Forestry Centre, Natural Resources Canada, Canada
- 8Department of Computer Science, University of New Brunswick Saint John, Canada
Threatened freshwater ecosystems urgently require improved tools for effective management. Food web analysis is currently under-utilised, yet can be used to generate metrics to support biomonitoring assessments by measuring the stability and robustness of ecosystems. Using a previously developed analysis pipeline, we combined taxonomic outputs from DNA metabarcoding with a text-mining routine to extract trait information directly from the literature. This pipeline allowed us to generate heuristic food webs for sites within the lower Saint John / Wolastoq River and the Grand Lake Meadows (GLM), Atlantic Canada’s largest freshwater wetland. While these food webs are derived from empirical traits and their structure has been shown to discriminate sites both spatially and temporally, the accuracy of their properties have not been assessed against other methods of trophic analysis. We explored two approaches to validate the utility of heuristic food webs. First, we qualitatively compared how well trophic position derived from heuristic food webs recovered spatial and temporal differences across the GLM complex in comparison to traditional stable isotope approaches. Second, we explored how the trophic position of invertebrates, derived from heuristic food webs, predicted trophic position measured from δ15N values. In general, both heuristic food webs and stable isotopes were able to detect seasonal changes in maximum trophic position in the GLM complex. Samples from the entire GLM complex demonstrated that prey-averaged trophic position measured from heuristic food webs strongly predicted trophic position inferred from stable isotopes (R2 = 0.60), and even stronger relationships were possible for some individual models (R2 = 0.78 for best model). Beyond their areas of congruence, heuristic food web and stable isotope analysis also appear to complement one another, suggesting a surprising degree of independence between community trophic niche width (assessed from stable isotopes) and food web size and complexity (assessed from heuristic food webs). Collectively, these analyses indicate that trait-based networks do generate properties that correspond to those of actual food webs, and support the routine adoption of food web metrics within ecosystem biomonitoring.
Keywords: food web, Traits, Ecological network, Stable istopes, Trophic position, DNA metabarcoding, Bayesian mixing models
Received: 22 Jun 2019;
Accepted: 04 Oct 2019.
Copyright: © 2019 Compson, Monk, Hayden, Bush, O'Malley, Hajibabaei, Porter, Wright, Baker, Al Manir, Curry and Baird. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mx. Zacchaeus G. Compson, University of New Brunswick Fredericton, Environment and Climate Change Canada, Fredericton, E3B 5A3, New Brunswick, Canada, email@example.com