Picking a way forward: valuing and managing traditional shellfish gathering for <i>Littorina littorea</i>
Bibliographic record
Abstract
Littorina littorea (periwinkles) have been harvested by hand picking from the shore since prehistoric times. Harvests are generally unregulated, catches are not linked to particular shores and fisheries statistics are considered to be unreliable. The absence of key data has made it difficult to develop harvesting recommendations. Surveys around Strangford Lough, Northern Ireland were used to investigate the size structure and relationships among densities in different size classes. Three size classes were identified in surveyed L. littorea , with mean shell lengths of 0.81, 1.56 and 2.48 cm. Assuming that the age classes represent year classes, data across different shores suggested that the ratio between densities in successive year classes was not constant. Proportionally fewer individuals were found in the larger, older, size class as the density of the smaller size class on a shore increased. This density-dependent relationship was modelled with a Ricker curve for the year 1 to year 2 and the year 2 to year 3 transitions. The predicted transition rates from Ricker curves were used in a size-structured model to describe L. littorea dynamics. An emergent property of the size-structured model is a decline in mean shell length with overall density of a population. This prediction was supported by the survey data from Strangford Lough and by an independent survey of Irish shores. The size-structured model predicts potential harvests of individuals above 2.06 cm as a function of recruitment rate. Maximum harvest was predicted for a density of 5 year 1 individuals m −2 , leading to 13.8 year 3 individuals m −2 or an estimated annual harvest weight of 67 g m −2 . Modelled estimates of production provide a means to value shores and develop harvest predictions for management purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".