Twin commercial fishing vessels are reflected in still water at a harbor in Haines, Alaska.
©TAGanner/iStock/Getty Images Plus
+Expand Caption

Understanding Linkages across Fisheries Can Inform More Effective Policy Design

Powerful new analytical tools can help improve understanding of how management decisions in one fishery can have cross-cutting effects for other fisheries in a region.

Managing fisheries is complex business. Across the country, US Regional Fishery Management Councils track fish stock abundance, economic outcomes such as revenue, and other factors to monitor the health of fisheries and inform decisionmaking. Policies that govern catch limits, fishing seasons, and how quota are allocated to fishers are typically implemented on a single-fishery basis. But fishery participants in a region may take part in numerous fisheries, targeting multiple species, in multiple areas, and using a variety of nets, pots, and other gear to harvest their catch. This complexity is not often reflected in the design and evaluation of management policies, making it challenging to create effective approaches, even with detailed data. Addressing this challenge is critical for economically vital fisheries—like those in Alaska, where the seafood industry generates over $5.4 billion in direct annual economic output.

The management councils that oversee the fisheries off the coast of Alaska issue permits on a fishery-by-fishery basis: a unique permit is required for each combination of species, location, and gear. If fishers own multiple permits, a policy change in one fishery may have “spillover” impacts that affect other fisheries, as participants adjust their efforts across the region. In other words, in response to a change in management strategy, a fisher might choose to shift fishing effort to another region, target a different species of fish, or use different gear.

Network analysis can be a useful tool for policy design by revealing “clusters” of closely linked fisheries and predicting the potential for spillover effects.

Using publicly available permitting data from Alaska, we illustrate cross-fishery permitting networks and show that preexisting network statistics can be useful for identifying the potential scope of policy-induced spillover impacts. We also identify clusters of similar fisheries (defined by their primary species, geography, and gear) that share a significant number of permit holders. The high degree of connectedness and clustering we see across Alaska fisheries indicates that the regional fishing sector is vulnerable to cross-fishery spillovers from network shocks, such as policy changes (e.g., implementation of catch shares) or changes in fish stock abundance.

We delved into this issue with collaborators Ethan T. Addicott and Justine Huetteman in a recent paper in the Canadian Journal of Fisheries and Aquatic Sciences. (Note: subscription required; read the related RFF working paper.) Our work shows that in Alaska’s commercial fisheries, cross-fishery permitting is extensive. With a large and diverse number of permits and fisheries regionally, managers face an exceedingly difficult task in trying to anticipate the potential for cross-fishery substitution—how a policy change in one fishery could have unintended consequences for another. We demonstrate how network analysis can be a useful tool for policy design by revealing “clusters” of closely linked fisheries and predicting the potential for spillover effects.

Map of Alaskan Fisheries and Their ConnectionsA map of Alaskan fisheries with each fishery placed as a node and lines showing the connections among themC-6-AC-6-AC-6-CC-6-CD-9-AD-9-AG-2-ZG-2-ZG-34-AG-34-AH-1-AH-1-AK-19-AK-19-AK-29-AK-29-AK-39-AK-39-AK-49-AK-49-AK-59-AK-59-AK-69-AK-69-AL-21-AL-21-AL-21-CL-21-CP-17-AP-17-AP-9-AP-9-AP-9-DP-9-DS-1-AS-1-AS-3-AS-3-AS-3-HS-3-HS-4-DS-4-DT-10-AT-10-AT-19-AT-19-AY-26-AY-26-AY-5-AY-5-AY-6-AY-6-AD-9-JD-9-JD-9-MD-9-MG-1-KG-1-KG-1-MG-1-MG-1-TG-1-TG-34-KG-34-KH-1-MH-1-MK-9-OK-9-OK-9-OAK-9-OAK-9-QK-9-QK-9-TK-9-TK-9-TAK-9-TAP-9-JP-9-JS-1-KS-1-KS-1-LS-1-LS-1-MS-1-MS-2-KS-2-KS-3-MS-3-MS-4-KS-4-KS-4-MS-4-MT-9-LT-9-LT-9-MT-9-MT-9-OT-9-OT-9-QT-9-QT-9-QAT-9-QATB-9-AKTB-9-AKC-5-EC-5-ED-9-HD-9-HG-1-AG-1-AG-1-EG-1-EG-1-HG-1-HG-34-EG-34-EG-34-HG-34-HL-21-EL-21-EP-9-EP-9-ES-1-ES-1-ES-1-HS-1-HS-3-ES-3-ES-4-ES-4-ES-4-HS-4-HG-34-NG-34-NG-34-SG-34-SG-34-TG-34-TG-34-UG-34-UG-34-VG-34-VG-34-WG-34-WG-34-YG-34-YG-34-ZG-34-ZK-9-ZK-9-ZK-9-ZEK-9-ZEL-12-TL-12-TS-3-TS-3-TS-4-PS-4-PS-4-TS-4-TS-4-WS-4-WS-4-XS-4-XS-4-YS-4-YS-4-ZS-4-ZS-8-PS-8-PJ-11-AJ-11-AQ-11-AQ-11-AQ-11-KQ-11-KU-11-AU-11-ACluster 1Cluster 2Cluster 3Cluster 4Cluster 5

= 4; = 2,261

Size of the circles indicates number of permits in the fishery.

Lines indicate shared permit holders. Note: for clarity, not all connections across clusters are shown.

Using publicly available permitting data from Alaska, we illustrate cross-fishery permitting networks and show that preexisting network statistics can be useful for identifying the potential scope of policy-induced spillover impacts. We also identify clusters of similar fisheries (defined by their primary species, geography, and gear) that share a significant number of permit holders. The high degree of connectedness and clustering we see across Alaska fisheries indicates that the regional fishing sector is vulnerable to cross-fishery spillovers from network shocks, such as policy changes (e.g., implementation of catch shares) or changes in fish stock abundance.

We find that fisheries with similar geographic proximity are more likely to be part of a highly connected cluster. In the interactive map above, users can see a visual demonstration of five closely connected clusters across fisheries in Alaska. For instance, the Aleutian Islands fisheries (Cluster 2) show strong connectivity through overlapping permit holders—even when fishers are targeting very different species, such as Dungeness crab and herring.

Seward, AK—Fishers clean their daily catch on a pier.
Seward, AK—Fishers clean their daily catch on a pier. © choja/iStock Unreleased/Getty Images

Although the data in our analysis represent Alaska fisheries, similar methods can be applied to other regions. This research demonstrates that network analysis can improve our understanding of the potential for impacts on one fishery from policy changes in another. Fishery management councils can use cross-permitting network analysis to enhance their knowledge of the likelihood of policy-induced spillovers and make better-informed policy recommendations. And innovative data visualization tools, such as the interactive map featured here, make economic linkages visible and comprehensible for all stakeholders—including fishery management council staff, fishers and seafood industry personnel, ocean conservation advocates, and others. When fisheries are managed with a more complete understanding of the linkages among users, managers can more effectively design policies to support healthy and sustainable fisheries and fishing communities.

About the Author

image of Kailin Kroetz

Kailin Kroetz

Kailin Kroetz is a fellow at RFF.

Author Profile
image of James N. Sanchirico

James N. Sanchirico

James N. Sanchirico is a university fellow at RFF and a professor and associate director of the Coastal and Marine Science Institute at the University of California, Davis.

Author Profile
image of Matthew Reimer

Matthew Reimer

Matthew Reimer is an assistant professor of economics at University of Alaska Anchorage’s Institute of Social and Economic Research.

Author Profile
image of Daniel K. Lew

Daniel K. Lew

Daniel K. Lew is an economist at NOAA’s Alaska Fisheries Science Center and a visiting scholar at the University of California, Davis.

Author Profile