Does functional redundancy stabilize fish communities?
Bibliographic record
Abstract
Abstract Rice, J., Daan, N., Gislason, H., and Pope, J. Does functional redundancy stabilize fish communities? – ICES Journal of Marine Science, 70: 734–742. Functional redundancy of species sharing a feeding strategy and/or maximum size has been hypothesized to contribute to increased resilience of marine fish communities (the “portfolio effect”). A consistent time-series of survey data of fish in the North Sea was used to examine if trophic functional groups or maximum length of species (Lmax) groups with larger numbers of species had lower coefficients of variation in abundance and biomass over time than did groupings with fewer species. Results supported this hypothesis. However, the stabilizing effect of numbers of species in a group on variation in abundance or biomass could be accounted for by the Law of Large Numbers, providing no evidence that specific ecological processes or co-adaptations are necessary to produce this effect. This implies that successful conservation policies to maintain the resilience of a marine fish community could be based on strategies to maintain the number of species in functional groups, without having to know the detailed ecological interactions between the species.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".