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Using a qualitative model to explore the impacts of ecosystem and anthropogenic drivers upon declining marine survival in Pacific salmon

Published online by Cambridge University Press:  11 December 2017

KATHRYN L. SOBOCINSKI ,
CORREIGH M. GREENE  and
MICHAEL W. SCHMIDT
KATHRYN L. SOBOCINSKI*
Affiliation:
Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 2725 Montlake Blvd. E, Seattle, WA 98112, USA Long Live the Kings, 1326 5th Ave. #450, Seattle, WA 98101, USA
CORREIGH M. GREENE
Affiliation:
Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 2725 Montlake Blvd. E, Seattle, WA 98112, USA
MICHAEL W. SCHMIDT
Affiliation:
Long Live the Kings, 1326 5th Ave. #450, Seattle, WA 98101, USA
*
*Correspondence: Dr Kathryn L. Sobocinski email [email protected]
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Summary

Coho salmon (Oncorhynchus kisutch), Chinook salmon (Oncorhynchus tshawytscha) and steelhead (Oncorhynchus mykiss) in Puget Sound and the Strait of Georgia have exhibited declines in marine survival over the last 40 years. While the cause of these declines is unknown, multiple factors, acting cumulatively or synergistically, have likely contributed. To evaluate the potential contribution of a broad suite of drivers on salmon survival, we used qualitative network modelling (QNM). QNM is a conceptually based tool that uses networks with specified relationships between the variables. In a simulation framework, linkages are weighted and then the models are subjected to user-specified perturbations. Our network had 33 variables, including: environmental and oceanographic drivers (e.g., temperature and precipitation), primary production variables, food web components from zooplankton to predators and anthropogenic impacts (e.g., habitat loss and hatcheries). We included salmon traits (survival, abundance, residence time, fitness and size) as response variables. We invoked perturbations to each node and to suites of drivers and evaluated the responses of these variables. The model showed that anthropogenic impacts resulted in the strongest negative responses in salmon survival and abundance. Additionally, feedbacks through the food web were strong, beginning with primary production, suggesting that several food web variables may be important in mediating effects on salmon survival within the system. With this model, we were able to compare the relative influence of multiple drivers on salmon survival.

Keywords

network modelindicatorsecosystemsalmonqualitative
Type
Non-Thematic Papers
Information
Environmental Conservation , Volume 45 , Issue 3: Thematic section. Humans and Island Environments , September 2018 , pp. 278 - 290
Copyright
Copyright © Foundation for Environmental Conservation 2017 

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Footnotes

Supplementary material can be found online at https://doi.org/10.1017/S0376892917000509

References

REFERENCES

Beamish, R.J., Noakes, D.J., McFarlane, G.A., Pinnix, W., Sweeting, R. & King, J. (2000) Trends in coho marine survival in relation to the regime concept. Fisheries Oceanography 9: 114–119.Google Scholar
Beamish, R.J., Sweeting, R.M., Lange, K.L., Noakes, D.J., Preikshot, D. & Neville, C.M. (2010) Early marine survival of coho salmon in the Strait of Georgia declines to very low levels. Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science 2: 424–439.Google Scholar
Beckman, B.R. (2011) Perspectives on concordant and discordant relations between insulin-like growth factor 1 (IGF1) and growth in fishes. General and Comparative Endocrinology 170: 233–252.Google Scholar
Burke, B.J., Peterson, W.T., Beckman, B.R., Morgan, C., Daly, E.A. & Litz, M. (2013) Multivariate models of adult Pacific salmon returns. PLoS ONE 8: e54134.Google Scholar
Carey, M.P., Levin, P.S., Townsend, H., Minello, T.J., Sutton, G.R., Francis, T.B., Harvey, C.J., Toft, J.E., Arkema, K.K., Burke, J.L., Kim, C., Guerry, A.D., Plummer, M., Spiridonov, G. & Ruckelshaus, M. (2014) Characterizing coastal foodwebs with qualitative links to bridge the gap between the theory and the practice of ecosystem-based management. ICES Journal of Marine Science 71: 713–724.Google Scholar
Carpenter, S.R. & Brock, W.A. (2006) Rising variance: A leading indicator of ecological transition. Ecology Letters 9: 311–318.Google Scholar
Christensen, V. & Walters, C.J. (2004) Ecopath with ecosim: Methods, capabilities and limitations. Ecological Modelling 172: 109–139.Google Scholar
DeAngelis, D.L. & Waterhouse, J.C. (1987) Equilibrium and nonequilibrium concepts in ecological models. Ecological Monographs 57: 1–21.Google Scholar
Debertin, A.J., Irvine, J.R., Holt, C.A., Oka, G. & Trudel, M. (2017) Marine growth patterns of southern British Columbia chum salmon explained by interactions between density-dependent competition and changing climate. Canadian Journal of Fisheries and Aquatic Sciences 74: 1077–1087.Google Scholar
Dunne, J.A., Williams, R.J. & Martinez, N.D. (2002a) Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecology Letters 5: 558–567.Google Scholar
Dunne, J.A., Williams, R.J. & Martinez, N.D. (2002b). Food-web structure and network theory: The role of connectance and size. Proceedings of the National Academy of Sciences of the United States of America. 99: 12917–12922.Google Scholar
Greene, C.M., Kuehne, L., Rice, C.A., Fresh, K.L. & Penttila, D. (2015) Forty years of change in forage fish and jellyfish abundance across greater Puget Sound, Washington (USA): Anthropogenic and climate associations. Marine Ecology Progress Series 525: 153–170.Google Scholar
Harvey, C.J., Williams, G.D. & Levin, P.S. (2012) Food web structure and trophic control in central Puget Sound. Estuaries and Coasts 35: 821–838.Google Scholar
Harvey, C.J., Reum, J.C.P., Poe, M.R., Williams, G.D. & Kim, S.J. (2016) Using conceptual models and qualitative network models to advance integrative assessments of marine ecosystems. Coastal Management 44: 486–503.Google Scholar
Hoekstra, J.M., Bartz, K.K., Ruckelshaus, M.H., Moslemi, J.M. & Harms, T.K. (2007) Quantitative threat analysis for management of an imperiled species: Chinook salmon (Oncorhynchus tshawytscha). Ecological Applications 17: 2061–2073.Google Scholar
Hook, S., Gallagher, E. & Batley, G. (2014) The role of biomarkers in the assessment of aquatic ecosystem health. Integrated Environmental Assessment and Monitoring. 10: 327–341.Google Scholar
Holmes, E.E. (2001) Estimating risks in declining populations with poor data. Proceedings of the National Academy of Sciences of the United States of America 98: 5072–5077.Google Scholar
Irvine, J.R. & Ruggerone, G.T. (2016) Provisional estimates of numbers and biomass for natural-origin and hatchery-origin pink, chum, and sockeye salmon in the North Pacific, 1952–2015. NPAFC Doc. 1660. 45 pp. Fisheries and Oceans Canada, Pacific Biological Station and Natural Resources Consultants, Inc. [www document]. URL http://www.npafc.org/new/publications/Documents/PDF%202016/1660(Canada+USA).pdfGoogle Scholar
Ives, A.R. & Carpenter, S.R. (2007) Stability and diversity of ecosystems. Nature 317: 58–62.Google Scholar
Janssen, M.A., Bodin, Ö., Anderies, J.M., Elmqvist, T., Ernstson, H., McAllister, R.R.J., Olsson, P. & Ryan, P. (2006) A network perspective on the resilience of social–ecological systems. Ecology and Society 11: 15.Google Scholar
Johannessen, S.C. & McCarter, B. (2010) Ecosystem status and trends report for the Strait of Georgia ecozone. Fisheries and Oceans Canada, Science Advisory Secretariat, Research Document 2010/010. vi + 45 p. [www document]. URL http://waves-vagues.dfo-mpo.gc.ca/Library/341615.pdfGoogle Scholar
Kendall, N.W., Marston, G.W. & Klungle, M.M. (2017) Declining patterns of Pacific Northwest steelhead trout (Oncorhynchus mykiss) adult abundance and smolt survival in the ocean. Canadian Journal of Fisheries and Aquatic Sciences 74: 1275–1290.Google Scholar
Levins, R. (1974) The qualitative analysis of partially specified systems. Annals of the New York Academy of Sciences 231: 123–138.Google Scholar
Liu, J., Dietz, T., Carpenter, S.R., Folke, C., Alberti, M., Redman, C.L., Schneider, S.H., Ostrom, E., Pell, A.N., Lubchenco, J., Taylor, W.W., Ouyang, Z., Deadman, P., Kratz, T. & Provencher, W. (2007) Coupled human and natural systems. AMBIO 236: 639–649.Google Scholar
Mauger, G.S., Casola, J.H., Morgan, H.A., Strauch, R.L., Jones, B., Curry, B., Busch Isaksen, T.M., Whitely Binder, L., Krosby, M.B. & Snover, A.K. (2015) State of Knowledge: Climate Change in Puget Sound. Report Prepared for the Puget Sound Partnership and the National Oceanic and Atmospheric Administration. Seattle, WA, USA: Climate Impacts Group, University of Washington.Google Scholar
May, R.M. (1974) Stability and Complexity in Model Ecosystems. Princeton, NJ, USA: Princeton University Press.Google Scholar
Melbourne-Thomas, J., Wotherspoon, S., Raymond, B. & Constable, A. (2012) Comprehensive evaluation of model uncertainty in qualitative network analyses. Ecological Monographs 82: 505–519.Google Scholar
Ogden, A.D., Irvine, J.R., English, K.K., Grant, S., Hyatt, K.D., Godbout, L. & Holt, C.A. (2015) Productivity (recruits-per-spawner) data for sockeye, pink, and chum salmon from British Columbia. Canadian Technical Report of Fisheries and Aquatic Sciences 3130, vi + 57 p. [www document]. URL http://publications.gc.ca/collections/collection_2016/mpo-dfo/Fs97-6-3130-eng.pdfGoogle Scholar
O'Neill, S.M. & West, J.E. (2009) Marine distribution, life history traits, and the accumulation of polychlorinated biphenyls in Chinook salmon from Puget Sound, Washington. Transactions of the American Fisheries Society 138: 616–632.Google Scholar
Paine, R.T. (1966) Food web complexity and species diversity. American Naturalist 100: 65–76.Google Scholar
Pearcy, W.G. (1988) Factors affecting survival of coho salmon off Oregon and Washington. In: Salmon Production, Management, and Allocation, ed. McNeil, W.J., pp. 67–73. Corvallis, OR, USA: Oregon State University Press.Google Scholar
Pimm, S.L., Lawton, J.H. & Cohen, J.E. (1991) Food web patterns and their consequences. Nature 350, 669–674.Google Scholar
Preikshot, D.B. (2008) Public Summary – Computer Modelling of Marine Ecosystems: Applications to Pacific Salmon Management and Research. Vancouver, BC, Canada: Pacific Fisheries Resource Conservation Council.Google Scholar
PSEMP (2016) Puget Sound Marine Waters: 2015 Overview. Eds. Moore, S.K., Wold, R., Stark, K., Bos, J., Williams, P., Dzinbal, K., Krembs, C. and Newton, J. [www document]. URL www.psp.wa.gov/PSEMP/PSmarinewatersoverview.phpGoogle Scholar
Puccia, C.J. & Levins, R. (1985) Qualitative Modeling of Complex Systems: An Introduction to Loop Analysis and Time Averaging. Cambridge, MA, USA: Harvard University Press.Google Scholar
R Core Team (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria [www document]. URL www.r-project.orgGoogle Scholar
Raymond, B., McInnes, J., Dambacher, J.M., Way, S. & Bergstrom, D.M. (2011) Qualitative modelling of invasive species eradication on subantarctic Macquarie Island. Journal of Applied Ecology 48: 181–191.Google Scholar
Reum, J.C.P., Ferriss, B.E., McDonald, P.S., Farrell, D.M., Harvey, C.J., Klinger, T. & Levin, P.S. (2015) Evaluating community impacts of ocean acidification using qualitative network models. Marine Ecology Progress Series 536: 11–24.Google Scholar
Roberts, M., Mohamedali, T., Sackmann, B., Khangaonkar, T. & Long, W. (2014) Puget Sound and the Straits Dissolved Oxygen Assessment: Impacts of Current and Future Nitrogen Sources and Climate Change through 2070. Olympia, WA, USA: Washington Department of Ecology.Google Scholar
Ruff, C.P., Anderson, J.H., Kemp, I.M., Kendall, N.W., Mchugh, P. Velez-Espino, A., Greene, C.M., Trudel, M., Holt, C.A., Ryding, K.E. & Rawson, K. (2017) Salish Sea Chinook salmon exhibit weaker coherence in early marine survival trends than coastal populations. Fisheries Oceanography. Epub ahead of print. DOI: 10.1111/fog.12222.Google Scholar
Samhouri, J.F., Andrews, K.S., Fay, G., Harvey, C.J., Hazen, E.L., Hennessey, S., Holsman, K.K., Hunsicker, M.E., Large, S.I., Marshall, K., Stier, A.C., Tam, J. & Zador, S. (2017) Defining ecosystem thresholds for human activities and environmental pressures in the California Current. Ecosphere 8: e01860.Google Scholar
Scheffer, M., Carpenter, S., Foley, J.A., Folke, C. & Walker, B. (2001) Catastrophic shifts in ecosystems. Nature 413: 591–596.Google Scholar
Sih, A., Hanser, S.F. & McHugh, K.A. (2009) Social network theory: New insights and issues for behavioral ecologists. Behavioral Ecology and Sociobiology 63: 975.Google Scholar
Teo, S.L.H., Botsford, L.W. & Hastings, A. (2009) Spatio-temporal covariability in coho salmon (Oncorhynchus kisutch) survival, from California to southwest Alaska. Deep Sea Research Part II: Topical Studies in Oceanography 56: 2570–2578.Google Scholar
Zimmerman, M., Irvine, J.R., O'Neill, M., Anderson, J.H., Greene, C.M., Weinheimer, J., Trudel, M. & Rawson, K. (2015) Spatial and temporal patterns in smolt survival of wild and hatchery coho salmon in the Salish Sea. Marine and Coastal Fisheries 7: 116–134.Google Scholar
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