The American Lobster Settlement Index: History, lessons, and future of a long-term, transboundary monitoring collaborative
Bibliographic record
Abstract
Understanding the processes that influence recruitment variability in marine populations has been a long-standing challenge for resource management. Quantifying abundance at early life stages for marine fish and invertebrates with complex life histories can be difficult and require unconventional sampling approaches. However, the benefit of developing appropriate tools to sample early life stages is that, together with associated demographic and environmental information, the data can provide insights into the causes and consequences of recruitment variability, allowing prediction of older life stage abundance. Before the 1980s, the earliest benthic life stages of the American lobster ( Homarus americanus ) eluded quantitative field surveys. With the development of diver-based and vessel-deployed sampling methods over the past three decades, the American Lobster Settlement Index (ALSI) program has expanded into a regional, transboundary commitment to better understand lobster settlement processes and forecast future fishery trends for what has become the most valuable single-species fishery in North America. In this context, “settlement” is a shorthand for the annual recruitment of young-of-year lobster to coastal nurseries, as postlarvae settle to the seabed at the end of larval development. Here, we review the development and products of the ALSI program, first outlining the goals, methods, and data products of the program. We then highlight how the program has advanced the scientific knowledge on pre- and post-settlement processes that influence the fate of a cohort from egg hatch to harvest, which provides insight into the spawner-recruit relationship. Lastly, we provide guidance for future research recommendations building on the ALSI science to-date, some major elements of the program that have allowed for its success, and considerations for maintaining the ALSI program. By highlighting the uniqueness and contributions of the ALSI program, we hope it serves as a model for other scientists, managers, and industry collaborators aiming to understand recruitment processes for species over a broad geographic area.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".