Sea Grant 3rd Annual Science Symposium Lobsters as Model Organisms for Interfacing Behavior, Ecology, and Fisheries: Discussion Session Summary on Metapopulation Dynamics and Implications for Management
Bibliographic record
Abstract
Understanding patterns of connectivity among marine populations has emerged as a dominant research focus in the last decade as interest in metapopulation dynamics in the oceans has increased. For our purposes here we will adopt a broad definition of a metapopulation as a collection of spatially distributed populations linked through dispersal processes. One of Stan Cobb's earliest contributions to lobster behavior and population biology in fact concerned connections between offshore lobster groups and near shore populations (Rogers et al., 1968). Dispersal was inferred by carefully tracking larval stage distributions along an inshore-offshore transect and noting the predominance of early stage larvae offshore and settlement stage lobsters inshore in these synoptic surveys. Subsequent work by Stan and colleagues elucidated the behavioral mechanisms favoring directional larval transport (Cobb et al., 1989) and the linkage between hydrodynamics and dispersal potential (Katz et al., 1994). Building on these themes, Rom Lipcius of the College of William and Mary, led an insightful discussion of metapopulations and the links to fishery management, drawing examples from different crustacean taxa from tropical to temperate seas. Metapopulations can exhibit substantial resilience to external stressors (both natural and anthropogenic), if they exhibit some degree of asynchrony in their fluctuations. A population driven to low levels can be ‘rescued’ by a subsidy from a linked population and understanding the dispersal pathways is critical to understanding metapopulation dynamics.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.014 |
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".