Characterizing coastal foodwebs with qualitative links to bridge the gap between the theory and the practice of ecosystem-based management

• Published in: ICES journal of marine science Vol. 71; no. 3; pp. 713 - 724
• Main Authors: Carey, Michael P., Levin, Phillip S., Townsend, Howard, Minello, Thomas J., Sutton, Glen R., Francis, Tessa B., Harvey, Chris J., Toft, Jodie E., Arkema, Katie K., Burke, Jennifer L., Kim, Choong-Ki, Guerry, Anne D., Plummer, Mark, Spiridonov, Georgi, Ruckelshaus, Mary
• Format: Journal Article
• Language: English
• Published: Oxford University Press 01.04.2014
• Subjects: Decapoda; Marine; foodweb structure; Galveston Bay; Chesapeake Bay; demersal fisheries; Puget Sound; ecosystem-based management; loop analysis
• ISSN: 1054-3139; 1095-9289
• DOI: 10.1093/icesjms/fst012

Tools that integrate the complexity of natural systems are needed to facilitate ecosystem-based management (EBM). Loop analysis is an underutilized qualitative tool for EBM that uses information on foodweb links (e.g. predator–prey interactions) and the resulting pathways (cycles) through the foodweb to predict responses to press perturbations. We explore the utility of loop analysis related to management actions: increasing crab abundance and reducing eutrophication in coastal foodwebs. We found that crab-related management actions propagated through the foodweb, with positive and negative impacts. Several negatively impacted species support important fisheries; their declines illustrate the challenge of developing multispecies plans. In our analysis, the management actions decreasing eutrophication reduced most functional groups. However, these predictions were unreliable, suggesting indiscernible bottom-up effects in the foodwebs. Simultaneously managing for crab increases and reducing eutrophication created mostly decreasing abundances in other functional groups and reduced the predictability of the responses. The response to each management action suggests trade-offs between goals, and the qualitatively unreliable predictions could result from variation in the strength of species interactions or indicate knowledge gaps. EBM can benefit from both the explicit articulation of trade-offs and the identification of gaps in our understanding of these systems.