MODELING THE EFFECT OF ANTHROPOGENIC STRESSORS ON AQUATIC VEGETATION AND INTRICATE BIOGEOCHEMICAL INTERACTIONS IN A SHELLFISH-DOMINATED SHALLOW ESTUARY
Bibliographic record
Abstract
The increasing severity of estuarine nitrogen pollution worldwide, primarily from land-based activities, remains a significant contributor to eutrophication. The incidence of potential nutrient over-enrichment, attributed to these anthropogenic stressors, is becoming more prevalent in many estuaries in the southern Gulf of Saint Lawrence (sGSL). This phenomenon has far-reaching implications, as it not only disrupts the delicate timing and abundance of primary production but can also degrade water quality and fundamentally alter the dynamics of entire ecosystems, ultimately threatening the ecological balance. Moreover, determining and assessing the trophic condition of such estuaries is becoming more critical when 58% of this intricate system is occupied by natural populations of bivalve species and 9% is covered with submerged aquatic vegetation (SAV), such as eelgrass. To understand these intricate estuarine biogeochemical interactions, we developed for the first time a high-resolution 3-D coupled physical-biogeochemical model for the Bouctouche Estuary in Canada (one of the significant estuaries of sGSL), based on the Finite Volume Coastal Ocean Model and Integrated Compartment Model (FVCOM-ICM). Further, the water quality kinetics of this coupled model is integrated with a benthic filter-feeder Dynamic Energy Budget (DEB) ecophysiological submodule, and a SAV module. The aim of this study is to assess the present estuarine physical-biogeochemical conditions, predict pelagic-benthic activity responses to varying river nitrate loads, and promote phyto-based, bivalve-mediated bioremediation strategies. Our findings revealed that areas exposed to river nitrate loading are characterized by biogeochemically distinct waters exhibiting low dissolved oxygen levels. Moreover, estuarine biogeochemical processes are influenced by water renewal time. The role of SAV highlights further complex biogeochemical interactions while serving as a critical buffer against anthropogenic stressors in managing dissolved oxygen levels within these ecosystems. Besides, modeling scenarios indicated that nutrient competition between phytoplankton and SAV, along with filter-feeding activities of bivalves, can potentially mitigate the effects of estuarine eutrophication.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".