Using larval connectivity to inform conservation management of the endemic and threatened Atlantic mud-piddock (Barnea truncata) in the Minas Basin Canada
Bibliographic record
Abstract
Understanding metapopulation dynamics is critical for effective species conservation, but they are not always considered for marine species due to difficulties in assessing connectivity in marine environments. This is particularly true for species that are rare or threatened, as demographic and life history data are usually sparse. We employ Lagrangian Particle Tracking (LPT) to assess metapopulation dynamics and inform spatial management measures for the Atlantic Mud Piddock (AMP; Barnea truncata), a poorly studied and threatened marine bivalve mollusk in Canada, whose distribution in the country is limited to a single population in the Minas Basin, Nova Scotia. In a series of simulations designed to account for uncertainty in biological attributes of AMP, we identified that sub-populations along the southern coastline of the Minas Basin were the most strongly connected to other sub-populations by acting as the greatest sources and sinks of simulated larvae. Propagules released from the Minas Basin dispersed as far as the US coast of the Gulf of Maine, which harbors the closest known population of AMP outside of the Minas Basin. However, there was no exchange of larvae in the opposite direction, from the US population of AMP in the Gulf of Maine to the Minas Basin. These results suggest that sub-populations in the Minas Basin are self-sustaining (i.e., sub-populations that exchange larvae and ultimately act as a meta-population), supporting the need to protect critical source sites along the southern coastline for the regional persistence of this species. More generally, these results show how LPT outputs can be directly applied to conservation planning, and used to identify key knowledge gaps to address with future work to reduce uncertainty in model predictions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".