This interactive visualization maps the governance network for shellfish aquaculture in South Carolina. It is a component of an MPA/EVSS dual-degree thesis examining why South Carolina — a state with significant biophysical capacity for shellfish mariculture — has substantially less acreage in active production than its ecological potential would support.
The analysis applies two complementary theoretical frameworks. Node size reflects betweenness centrality — actors who sit between otherwise disconnected parts of the network and therefore hold disproportionate influence over information and resource flows. Border color reflects coalition membership. Red-orange edges indicate burden-generating regulatory relationships.
Identifies actor coalitions organized around shared policy beliefs about mariculture expansion. Coalitions: pro-expansion, regulatory-cautious, conservation, and neutral (science-extension brokers).
Identifies which governance relationships generate learning, compliance, and psychological costs for shellfish operators. 117 of 275 raw edges are burden-generating.
Classifies broker roles: Gatekeepers filter cross-group flows, Coordinators operate within their own cluster, Liaisons bridge distinct disconnected sectors.
All 104 nodes and 423 directed edges. Use the dropdown to filter by actor type, the search box to locate a specific node, or click any node to highlight its direct connections. Hover for full centrality details.
103 nodes · 417 edges
This subgraph isolates the 117 burden-generating edges across 39 nodes — relationships where regulatory authority, compliance requirements, or administrative processes impose learning, compliance, or psychological costs on shellfish operators (Herd & Moynihan, 2019).
40 nodes · 117 edges
Summary statistics for use in thesis methods and findings sections.
Betweenness centrality identifies actors whose position mediates flows between otherwise disconnected actors. Hub score (HITS algorithm) measures outbound structural influence — who pushes relationships to well-connected nodes. In a directed governance network, hub score captures regulatory power more precisely than eigenvector centrality.
| # | Actor | Type | Coalition | Betweenness | Degree | In | Out | Hub Score |
|---|---|---|---|---|---|---|---|---|
| 1 | SC Sea Grant Consortium (SCSGC) | science | neutral | 0.3673 | 62 | 31 | 31 | 0.1838 |
| 2 | South Carolina Shellfish Growers Association (SCSGA) | advocacy | pro-expansion | 0.3581 | 56 | 29 | 27 | 0.4642 |
| 3 | SCDNR Shellfish Management Section | regulatory | reg-cautious | 0.2805 | 67 | 19 | 48 | 1 |
| 4 | SCDNR MRRI / Shellfish Research | regulatory | reg-cautious | 0.0835 | 27 | 13 | 14 | 0.1148 |
| 5 | SCDES Shellfish Sanitation Section | regulatory | reg-cautious | 0.0747 | 43 | 6 | 37 | 0.8931 |
| 6 | Matt Gorstein | science | neutral | 0.0634 | 20 | 10 | 10 | 0.0787 |
| 7 | College of Charleston (institutional) | science | neutral | 0.0525 | 12 | 6 | 6 | 0.0569 |
| 8 | South Carolina Aquarium | advocacy | pro-expansion | 0.0377 | 8 | 4 | 4 | 0.0268 |
| 9 | Trey McMillan | industry | pro-expansion | 0.0376 | 9 | 8 | 1 | 0.0247 |
| 10 | Caitlyn Mayer | industry | pro-expansion | 0.0233 | 12 | 8 | 4 | 0.0581 |
| 11 | Susan Lovelace | science | neutral | 0.0214 | 16 | 8 | 8 | 0.1059 |
| 12 | Clemson University (institutional) | science | neutral | 0.0184 | 10 | 5 | 5 | 0.028 |
| 13 | SCDNR Marine Permitting Office (Indigenous Molluscan Importation) | regulatory | reg-cautious | 0.018 | 5 | 2 | 3 | 0.0589 |
| 14 | Andy Hollis | regulatory | reg-cautious | 0.0179 | 4 | 2 | 2 | 0.0304 |
| 15 | Bill Cox | industry | pro-expansion | 0.0168 | 4 | 2 | 2 | 0.0249 |
The Gould-Fernandez brokerage framework classifies how actors mediate flows between groups. Gatekeepers control what enters their group from outside. Coordinators mediate within their own group. Liaisons bridge two groups they don't belong to — the most fragile and analytically significant broker position for understanding governance bottlenecks.
Bridge two distinct groups they don't belong to. High liaison = fragile cross-sector bridge.
Filter or control flows entering their group from outside actors.
Mediate within their own cluster — internal cohesion nodes.
Coalition membership follows the Advocacy Coalition Framework (Sabatier & Jenkins-Smith, 1993). The regulatory-cautious coalition holds mean hub score 6× higher than the neutral science-extension cluster, despite similar betweenness. The largest coalition (pro-expansion) has the least structural power.
| Coalition | N | Mean Betweenness | Mean Degree | Mean Hub Score | Interpretation |
|---|---|---|---|---|---|
| regulatory-cautious | 26 | 0.0208 | 12.0 | 0.168 | Highest hub score — disproportionate outbound influence |
| neutral | 30 | 0.0206 | 7.6 | 0.028 | Science-extension brokers — internal coordination hubs |
| pro-expansion | 42 | 0.0130 | 6.7 | 0.038 | Largest coalition, lowest structural power |
| conservation | 5 | 0.0018 | 2.2 | 0.021 | Structurally peripheral — not a blocking actor |
This network was constructed using a hybrid observed-plus-survey methodology with a three-tier evidence hierarchy. All analysis conducted in R using igraph and visNetwork.
Tier 1: formal authority, statutory mandate, signed agreement. Tier 2: co-membership, documented joint project, survey self-report. Tier 3: documented co-attendance, role-overlap inference. Conflict edges require behavioral evidence from documentary record.
Follows ACF (Sabatier & Jenkins-Smith, 1993). Coded from role relationship and documented behavioral evidence, not self-report. Four categories: pro-expansion, regulatory-cautious, conservation, neutral.
Administrative burden assigned from role relationship per Herd & Moynihan (2019). Regulatory authority edges coded burden-generating = TRUE by default. Burden type (learning, compliance, psychological) inferred from relationship context.
Former actors retained per governance path-dependency argument — regulatory structures they shaped remain operative. Former nodes rendered with reduced opacity. Excluded from centrality calculations.
Gould-Fernandez brokerage roles computed via sna package (Butts, 2024). Five role types: Coordinator, Gatekeeper, Representative, Itinerant, Liaison. Group membership = actor_type.
Edge coverage reflects documented relationships as of May 2026. Absence of edge reflects absence of documentation, not confirmed absence of relationship (Laumann, Marsden & Prensky, 1983).
Key citations: Sabatier & Jenkins-Smith (1993); Herd & Moynihan (2019); Knoke & Yang (2008); Schneider et al. (2003); Henry (2011); Laumann et al. (1983); Cribbs et al. (2024); Von Nessen (2021); Gould & Fernandez (1989).