Hostname: page-component-8448b6f56d-qsmjn Total loading time: 0 Render date: 2024-04-24T21:00:27.782Z Has data issue: false hasContentIssue false

Determining diets for fishes (Actinopterygii) from a small interior British Columbia, Canada stream: a comparison of morphological and molecular approaches

Published online by Cambridge University Press:  19 May 2020

Adam D.C. O’Dell
Affiliation:
Fisheries and Oceans Canada, PO Box 1586, Iqaluit, Nunavut, X0A 0H0, Canada
Anne-Marie Flores
Affiliation:
Ecosystem Science and Management Programme, University of Northern British Columbia, 3333 University Way, Prince George, British Columbia, V2N 4Z9, Canada
Marla D. Schwarzfeld
Affiliation:
Canadian National Collection of Insects, Arachnids, and Nematodes, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, Ontario, K1A 0C6, Canada
Daniel J. Erasmus
Affiliation:
Chemistry Programme, University of Northern British Columbia, 3333 University Way, Prince George, British Columbia, V2N 4Z9, Canada
Daniel D. Heath
Affiliation:
Great Lakes Institute for Environmental Research and Department of Integrative Biology, University of Windsor, 401 Sunset Avenue, Windsor, Ontario, N9B 3P4, Canada
Dezene P.W. Huber
Affiliation:
Ecosystem Science and Management Programme, University of Northern British Columbia, 3333 University Way, Prince George, British Columbia, V2N 4Z9, Canada
J. Mark Shrimpton*
Affiliation:
Ecosystem Science and Management Programme, University of Northern British Columbia, 3333 University Way, Prince George, British Columbia, V2N 4Z9, Canada
*
*Corresponding author. Email: mark.shrimpton@unbc.ca

Abstract

Analysis of food webs is important for defining functional components of ecosystems, but dietary data are often difficult to obtain and coarsely characterised. We compared three methods of rainbow trout (Oncorhynchus mykiss (Walbaum); Salmoniformes: Salmonidae) and prickly sculpin (Cottus asper Richardson; Scorpaeniformes: Cottidae) gut content analysis: traditional morphological taxonomy of prey items, genetic sequencing of individual prey items, and next-generation sequencing of homogenised gut contents. Prey analysis of invertebrates by morphological identification allowed order-level classifications and produced ecologically important count and mass data. Sequencing individual specimens provided greater taxonomic resolution, while next-generation sequencing of stomach contents revealed more prey diversity in the diets of both fish species as it was possible to detect prey that were degraded beyond visual recognition. Both fish species exhibited generalist feeding characteristics; however, terrestrial Insecta were a large diet component for rainbow trout. This study demonstrates an efficient approach for prey analysis using molecular techniques that complement traditional taxonomy.

Type
Research Papers
Copyright
© The Author(s), 2020. Published by The Entomological Society of Canada and Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Subject editor: Cory Sheffield

References

Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research, 25: 33893402.CrossRefGoogle ScholarPubMed
Bardach, J.E. 1962. Experimental ecology of the feeding fishes. Copeia, 1962: 234236.CrossRefGoogle Scholar
Benson, D.A., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J., and Wheeler, D.L. 2007. GenBank. Nucleic Acids Research, 35: D21D25.CrossRefGoogle ScholarPubMed
Brandon-Mong, G.J., Gan, H.M., Sing, K.W., Lee, P.S., Lim, P.E., and Wilson, J.J. 2015. DNA metabarcoding of insects and allies: an evaluation of primers and pipelines. Bulletin of Entomological Research, 105: 717727.CrossRefGoogle ScholarPubMed
Carreon-Martinez, L. and Heath, D.D. 2010. Revolution in food web analysis and trophic ecology: diet analysis by DNA and stable isotope analysis. Molecular Ecology, 19: 2527.CrossRefGoogle ScholarPubMed
Carreon-Martinez, L., Johnson, T.B., Ludsin, S.A., and Heath, D.D. 2011. Utilization of stomach content DNA to determine diet diversity in piscivorous fishes. Journal of Fish Biology, 78: 11701182.CrossRefGoogle ScholarPubMed
Chen, J. 2012. GUniFrac: generalized UniFrac distances. R Package Version 1. R Foundation for Statistical Computing, Vienna, Austria. Available from https://cran.r-project.org/web/packages/GUniFrac/index.html [accessed 22 March 2020].Google Scholar
Clifford, H.F. 1991. Aquatic invertebrates of Alberta: an illustrated guide. University of Alberta, Edmonton, Alberta, Canada.Google Scholar
Cristescu, M.E. 2014. From barcoding single individuals to metabarcoding biological communities: towards an integrative approach to the study of global biodiversity. Trends in Ecology & Evolution, 29: 566571.CrossRefGoogle Scholar
Cummins, K.W. and Klug, M.J. 1979. Feeding ecology of stream invertebrates. Annual Review of Ecology and Systematics, 10: 147172.CrossRefGoogle Scholar
Cummins, K.W., Merritt, R.W., and Andrade, P.C. 2005. The use of invertebrate functional groups to characterize ecosystem attributes in selected streams and rivers in south Brazil. Studies on Neotropical Fauna and Environment, 40: 6989.CrossRefGoogle Scholar
Deagle, B.E., Jarman, S.N., Coissac, E., Pompanon, F., and Taberlet, P. 2014. DNA metabarcoding and the cytochrome c oxidase subunit I marker: not a perfect match. Biology Letters, 10: 2014056220140562.CrossRefGoogle ScholarPubMed
Deagle, B.E., Thomas, A.C., Shaffer, A.K., Trites, A.W., and Jarman, S.N. 2013. Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count? Molecular Ecology Resources, 13: 620633.CrossRefGoogle Scholar
De Barba, M., Miquel, C., Boyer, F., Mercier, C., Rioux, D., Coissac, E., and Taberlet, P. 2014. DNA metabarcoding multiplexing and validation of data accuracy for diet assessment: application to omnivorous diet. Molecular Ecology Resources, 14: 306323.CrossRefGoogle ScholarPubMed
Derocles, S.A.P., Bohan, D.A., Dumbrell, A.J., Kitson, J.J.N., Massol, F., Pauvert, C., et al. 2018. Biomonitoring for the 21st century: integrating next-generation sequencing into ecological network analysis. Advances in Ecological Research, 58: 162.CrossRefGoogle Scholar
Diaz, R.J., Solan, M., and Valente, R.M. 2004. A review of approaches for classifying benthic habitats and evaluating habitat quality. Journal of Environmental Management, 73: 165181.CrossRefGoogle ScholarPubMed
Edgar, R.C. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26: 24602461.CrossRefGoogle ScholarPubMed
Edgar, R.C. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods, 10: 996998.CrossRefGoogle ScholarPubMed
Elbrecht, V. and Leese, F. 2015. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass - sequence relationships with an innovative metabarcoding protocol. Public Library of Science One, 10: e0130324.Google ScholarPubMed
Folmer, O., Black, M., Hoeh, W., Lutz, R., and Vrijenhoek, R. 1994. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and Biotechnology, 3: 294299.Google ScholarPubMed
Hammer, Ø., Harper, D.A.T., and Ryan, P.D. 2001. PAST: paleontological statistics software package for education and data analysis. Palaeontologia Electronica, 4: 19.Google Scholar
Hebert, P.D.N., Cywinska, A., Ball, S.L., and deWaard, J.R. 2003. Biological identifications through DNA barcodes. Proceedings of the Royal Society B: Biological Sciences, 270: 313321.CrossRefGoogle ScholarPubMed
Hilsenhoff, W.L. 1982. Using a biotic index to evaluate water quality in streams. Technical Bulletin 132. Department of Natural Resources, Madison, Wisconsin, United States of America.Google Scholar
Huson, D.H., Beier, S., Flade, I., Górska, A., El-Hadidi, M., Mitra, S., et al. 2016. MEGAN community edition - interactive exploration and analysis of large-scale microbiome sequencing data. Public Library of Science Computational Biology, 12: e1004957. https://doi.org/10.1371/journal.pcbi.1004957.Google ScholarPubMed
Hyslop, E.J. 1980. Stomach contents analysis—a review of methods and their application. Journal of Fish Biology, 17: 411429.CrossRefGoogle Scholar
Jackson, J.K., Battle, J.M., White, B.P., Pilgrim, E.M., Stein, E.D., Miller, P.E., and Sweeney, B.W. 2014. Cryptic biodiversity in streams: a comparison of macroinvertebrate communities based on morphological and DNA barcode identifications. Freshwater Science, 33: 312324.CrossRefGoogle Scholar
Jacobus, L.M. 2019. Ephemeroptera of Canada. ZooKeys, 819: 211225.CrossRefGoogle Scholar
Kerans, B.L. and Karr, J.R. 1994. A benthic index of biotic integrity (B-IBI) for rivers of the Tennessee Valley. Ecological Applications, 4: 768785.CrossRefGoogle Scholar
Kislalioglu, M. and Gibson, R.N. 1976. Some factors governing prey selection by the 15-spined stickleback, Spinachia spinachia (L.). Journal of Experimental Marine Biology and Ecology, 25: 159169.CrossRefGoogle Scholar
Kondratieff, B.C., DeWalt, R.E., and Verdone, C.J. 2019. Plecoptera of Canada. ZooKeys, 819: 243254.CrossRefGoogle Scholar
Lenat, D.R. and Resh, V.H. 2001. Taxonomy and stream ecology—The benefits of genus- and species-level identifications. Journal of the North American Benthological Society, 20: 287298.CrossRefGoogle Scholar
Lévesque, L.M. and Dubé, M.G. 2007. Review of the effects of in-stream pipeline crossing construction on aquatic ecosystems and examination of Canadian methodologies for impact assessment. Environmental Monitoring and Assessment, 132: 395409.CrossRefGoogle ScholarPubMed
McCune, B. and Grace, J.B. 2002. Analysis of ecological communities. MjM Software, Gleneden Beach, Oregon, United States of America.Google Scholar
McCune, B. and Mefford, M.J. 2011. PC-ORD. Multivariate analysis of ecological data. MjM Software, Gleneden Beach, Oregon, United States of America.Google Scholar
McPhail, J.D. 2007. Freshwater fishes of British Columbia. University of Alberta Press, Edmonton, Alberta, Canada.Google Scholar
Merz, J.E. 2002. Comparison of diets of prickly sculpin and juvenile fall-run chinook salmon in the lower Mokelumne River, California. The Southwestern Naturalist, 47: 195204.CrossRefGoogle Scholar
Minchin, P.R. 1987. An evaluation of the relative robustness of techniques for ecological ordination. Vegetatio, 69: 89107.CrossRefGoogle Scholar
Pawluczyk, M., Weiss, J., Links, M.G., Egana Aranguren, M., Wilkinson, M.D., and Egea-Cortines, M. 2015. Quantitative evaluation of bias in PCR amplification and next-generation sequencing derived from metabarcoding samples. Analytical and Bioanalytical Chemistry, 407: 18411848.CrossRefGoogle ScholarPubMed
Pompanon, F., Deagle, B.E., Symondson, W.O.C., Brown, D.S., Jarman, S.N., and Taberlet, P. 2012. Who is eating what: diet assessment using next generation sequencing. Molecular Ecology, 21: 19311950.CrossRefGoogle ScholarPubMed
R Core Team. 2013. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Ratnasingham, S. and Hebert, P.D.N. 2007. BOLD: The barcode of life data system (http://www.barcodinglife.org). Molecular Ecology Notes, 7: 355364.CrossRefGoogle ScholarPubMed
Savage, J., Borkent, A., Brodo, F., Cumming, J.M., Curler, G., Currie, D.C., et al. 2019. Diptera of Canada. ZooKeys, 819: 397450.CrossRefGoogle Scholar
Sharma, P. and Kobayashi, T. 2014. Are “universal” DNA primers really universal? Journal of Applied Genetics, 55: 485496.CrossRefGoogle ScholarPubMed
Sheffield, C.S., deWaard, J.R., Morse, J.C., and Rasmussen, A.K. 2019. Trichoptera of Canada. ZooKeys, 819: 507520.CrossRefGoogle Scholar
Sheppard, S.K. and Harwood, J.D. 2005. Advances in molecular ecology: tracking trophic links through predator-prey food-webs. Functional Ecology, 19: 751762.CrossRefGoogle Scholar
Smock, L. 1980. Relationships between body size and biomass of aquatic insects. Freshwater Biology, 10: 375383.CrossRefGoogle Scholar
Symondson, W.O.C. 2002. Molecular identification of prey in predator diets. Molecular Ecology, 11: 627641.CrossRefGoogle ScholarPubMed
Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C., and Willerslev, E. 2012. Towards next-generation biodiversity assessment using DNA metabarcoding. Molecular Ecology, 21: 20452050.CrossRefGoogle ScholarPubMed
Thomas, A.C., Jarman, S.N., Haman, K.H., Trites, A.W., and Deagle, B.E. 2014. Improving accuracy of DNA diet estimates using food tissue control materials and an evaluation of proxies for digestion bias. Molecular Ecology, 23: 37063718.CrossRefGoogle Scholar
Tipu, H.N. and Shabbir, A. 2015. Evolution of DNA sequencing. Journal of the College of Physicians and Surgeons Pakistan, 25: 210215.Google ScholarPubMed
Townsend, C.R. and Hildrew, A.G. 1979. Resource partitioning by two freshwater invertebrate predators with contrasting foraging strategies. Journal of Animal Ecology, 48: 909920.CrossRefGoogle Scholar
Utne-Palm, A.C. 1999. The effect of prey mobility, prey contrast, turbidity and spectral composition on the reaction distance of Gobiusculus flavescens to its planktonic prey. Journal of Fish Biology, 54: 12441258.CrossRefGoogle Scholar
Vannote, R.L., Minshall, G.W., Cummins, K.W., Sedell, J.R., and Cushing, C.E. 1980. The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences, 37: 130137.CrossRefGoogle Scholar
Woodward, G., Perkins, D.M., and Brown, L.E. 2010. Climate change and freshwater ecosystems: impacts across multiple levels of organization. Philosophical Transactions of the Royal Society B-Biological Sciences, 365: 20932106.CrossRefGoogle ScholarPubMed
Zeale, M.R.K., Butlin, R.K., Barker, G.L.A., Lees, D.C., and Jones, G. 2011. Taxon-specific PCR for DNA barcoding arthropod prey in bat faeces. Molecular Ecology Resources 11: 236244.CrossRefGoogle ScholarPubMed
Zhou, X., Jacobus, L.M., DeWalt, R.E., Adamowicz, S.J., and Hebert, P.D.N. 2010. Ephemeroptera, Plecoptera, and Trichoptera fauna of Churchill (Manitoba, Canada): insights into biodiversity patterns from DNA barcoding. Journal of the North American Benthological Society, 29: 814837.CrossRefGoogle Scholar
Supplementary material: PDF

O’Dell et al. Supplementary Materials

O’Dell et al. Supplementary Materials

Download O’Dell et al. Supplementary Materials(PDF)
PDF 531 KB