Spatial connectivity and marine disease dispersal: missing links in aquaculture carrying capacity debates
Bibliographic record
Abstract
Abstract One major societal challenge is meeting the constantly increasing demand for (sea)food in a sustainable way. With marine aquaculture on the rise, it is crucial to define limits to aquaculture growth in order to ensure ocean health. Along these lines, the concept of aquaculture carrying capacity (CC) is increasingly intersected with the principles of the ecosystem approach to aquaculture. Its primary aims are to estimate sustainable production potential and limits of locally defined regions. However, the ocean is a fluid environment, subject to large- and small-scale dynamics, including ocean currents, tidal fluctuations, and human action. These dynamics introduce spatial connectivity between aquaculture sites and more distant ecosystems than considered in current CC estimates. We argue that far-reaching effects of aquaculture on the ocean, such as introduction and spread of invasive species and marine diseases, are thus underestimated when providing recommendations. Marine diseases can impact biodiversity, society, and overall ocean health and it is imperative to guide aquaculture development to reduce the risk of marine disease dispersal. We, therefore, suggest to embrace spatial ocean connectivity into the CC concept by using hydrodynamic modelling and dispersal simulations as high-throughput methods to estimate potential impact areas and provide risk assessments. In this work, we focus on the example of dispersing infectious diseases in bivalve farming and discuss ecological as well as social consequences of spatial connectivity. Both are applicable to a wide range of organisms and marine aquaculture systems internationally. Summary The concept of aquaculture carrying capacity (CC) aims at defining sustainable limits to aquaculture growth in order to ensure ocean health. Usually, estimations are based on locally defined regions and on the farm-scale. However, interactions of aquaculture with the ocean can have far-reaching effects, such as introduction and spread of invasive species and marine diseases. The ocean is a fluid environment, subject to large- and small-scale dynamics that introduce spatial connectivity between aquaculture sites and more distant ecosystems than considered in current CC estimates. We, therefore, suggest to embrace spatial ocean connectivity into the CC concept by using hydrodynamic modelling and dispersal simulations as high-throughput methods to estimate potential impact areas and provide risk assessments. Here, we focus on the example of dispersing infectious diseases in bivalve farming and discuss ecological as well as social consequences of spatial connectivity. Both are applicable to a wide range of organisms and marine aquaculture systems internationally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".