Metapopulation ecology in the sea: from Levins' model to marine ecology and fisheries science
Bibliographic record
Abstract
Abstract Marine and fisheries scientists are increasingly using metapopulation concepts to better understand and model their focal systems. Consequently, they are considering what defines a metapopulation. One perspective on this question emphasizes the importance of extinction probability in local populations. This view probably stems from the focus on extinction in Levins' original metapopulation model, but places unnecessary emphasis on extinction–recolonization dynamics. Metapopulation models with more complex structure than Levins' patch‐occupancy model and its variants allow a broader range of population phenomena to be examined, such as changes in population size, age structure and genetic structure. Analyses along these lines are critical in fisheries science, where presence–absence resolution is far too coarse to understand stock dynamics in a meaningful way. These more detailed investigations can, but need not, aim to assess extinction risk or deal with extinction‐prone local populations. Therefore, we emphasize the coupling of spatial scales as the defining feature of metapopulations. It is the degree of demographic connectivity that characterizes metapopulations, with the dynamics of local populations strongly dependent upon local demographic processes, but also influenced by a nontrivial element of external replenishment. Therefore, estimating rates of interpopulation exchange must be a research priority. We contrast metapopulations with other spatially structured populations that differ in the degree of local closure of their component populations. We conclude with consideration of the implications of metapopulation structure for spatially explicit management, particularly the design of marine protected area networks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".