Bivalve aquaculture‐environment interactions in the context of climate change
Why this work is in the frame
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Bibliographic record
Abstract
Coastal embayments are at risk of impacts by climate change drivers such as ocean warming, sea level rise and alteration in precipitation regimes. The response of the ecosystem to these drivers is highly dependent on their magnitude of change, but also on physical characteristics such as bay morphology and river discharge, which play key roles in water residence time and hence estuarine functioning. These considerations are especially relevant for bivalve aquaculture sites, where the cultured biomass can alter ecosystem dynamics. The combination of climate change, physical and aquaculture drivers can result in synergistic/antagonistic and nonlinear processes. A spatially explicit model was constructed to explore effects of the physical environment (bay geomorphic type, freshwater inputs), climate change drivers (sea level, temperature, precipitation) and aquaculture (bivalve species, stock) on ecosystem functioning. A factorial design led to 336 scenarios (48 hydrodynamic × 7 management). Model outcomes suggest that the physical environment controls estuarine functioning given its influence on primary productivity (bottom-up control dominated by riverine nutrients) and horizontal advection with the open ocean (dominated by bay geomorphic type). The intensity of bivalve aquaculture ultimately determines the bivalve-phytoplankton trophic interaction, which can range from a bottom-up control triggered by ammonia excretion to a top-down control via feeding. Results also suggest that temperature is the strongest climate change driver due to its influence on the metabolism of poikilothermic organisms (e.g. zooplankton and bivalves), which ultimately causes a concomitant increase of top-down pressure on phytoplankton. Given the different thermal tolerance of cultured species, temperature is also critical to sort winners from losers, benefiting Crassostrea virginica over Mytilus edulis under the specific conditions tested in this numerical exercise. In general, it is predicted that bays with large rivers and high exchange with the open ocean will be more resilient under climate change when bivalve aquaculture is present.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it