Interactions between macrobiota (wild and aquacultured) and the physical-planktonic environment: insights from a new 3-D end-to-end modelling framework
Bibliographic record
Abstract
Marine ecosystem-based management requires end to end models, which are models capable of representing the entire ecosystem including physical, chemical and biological processes, anthropogenic activities, and multiple species with different sizes, life histories and from different trophic levels. To adequately represent ecosystem dynamics in shallow coastal regions, end-to-end models may need to include macrobiota species (wild and aquacultured) and may have to allow feedbacks (i.e. two-way coupling) between macrobiota and planktonic ecosystem dynamics. This is because the biomass of macrobiota can locally exceed the biomass of plankton, thus influencing the distribution of planktonic ecosystem tracers and altering the overall food web structure. Here, I describe a hybrid (Eulerian/Individual-Based) ecosystem framework, implemented in the Regional Ocean Modeling System (ROMS), a state-of-the-art 3-D ocean circulation model. The framework was applied to a model of a synthetic embayment containing seagrass, rockweed and kelp beds, a wild oyster reef, a mussel ranch and a fish farm. I found that two-way coupling is essential to reproduce expected spatial patterns of all variables and to conserve mass in the system. I also developed a shellfish ecophysiology model (SHELL E) and compared its results against water samples collected over 5 years in Ship Harbour, a fjord with mussel aquaculture in Nova Scotia, Eastern Canada. Also, from a high-resolution bio-optical survey of the fjord, I found that mussels decrease phytoplankton biomass inside the farm, but also cause a bloom of phytoplankton outside the farm. Using ROMS/SHELL-E, I determined that the increase of phytoplankton around the farm is caused by the waste products of the farmed bivalves, which have a fertilization effect, enhancing phytoplankton production outside the farm during nutrient-limited and light-replete conditions (i.e. late spring to late fall in Ship Harbour). The main conclusion of this thesis is that—in shallow coastal regions—ecosystem models must represent bilateral interactions between macrobiota and physical-planktonic dynamics, in a spatially-explicit setting, to adequately represent mass flows and ecosystem dynamics. The hybrid end-to-end modelling system provides a computationally efficient framework for describing these interactions and, through careful comparisons against observations, can be a powerful tool to test hypotheses and generate insights into coastal ecosystems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".