Global Biogeographic Patterns and Genetic Connectivity of Oyster Populations
Bibliographic record
Abstract
Oysters are widely distributed along coastal regions worldwide and serve as key ecological engineering species with significant value in maintaining coastal ecosystem functions and supporting fisheries. However, the biogeographic distribution patterns and genetic connectivity of oyster populations vary considerably across regions, influenced by a combination of paleoclimatic and geological history, oceanographic gradients, and human activities. This study provides a comprehensive overview of the systematics and distribution of major oyster groups globally, elucidates the biogeographic divisions and historical-ecological factors driving oyster population differentiation, and reviews recent advances in the application of molecular markers and population genomics in studying oyster genetic connectivity. Through case studies from representative regions—including Pacific oysters in the Northwest Pacific, eastern oysters in the North Atlantic of North America, and rock oysters in Europe and the Southern Hemisphere—we analyze the genetic structure and connectivity patterns of regional populations. The results reveal variations in genetic diversity and gene flow levels among oyster populations across different marine regions. Understanding genetic connectivity is crucial for biodiversity conservation and sustainable resource management, enabling the delineation of management units and guiding breeding, stock enhancement, and habitat restoration efforts. Under global climate change, oyster population distributions and connectivity patterns may undergo profound shifts, necessitating enhanced research on oysters’ genetic responses to environmental changes. This study advocates for incorporating genetic connectivity into habitat conservation and aquaculture management decisions to enhance oyster populations' adaptability to environmental changes and ensure the long-term maintenance of their ecological functions and economic value.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".