The effects of seasonal processes on size spectrum dynamics
Bibliographic record
Abstract
The recent advent of dynamic size spectrum models has allowed the analysis of life processes in marine ecosystems to be carried out without the high complexity arising from interspecies interactions within dense food webs. In this paper, we use “mizer”, a size spectrum modelling framework, to investigate the consequences of including the seasonal processes of plankton blooms and batch spawning in the model dynamics. A multispecies size spectrum model is constructed using 12 common North Sea fish species, with growth, predation, and mortality explicitly modelled, before simulating both seasonal plankton blooms and batch spawning of fish (using empirical data on the spawning patterns of each species). The effect of seasonality on the community size spectrum is investigated; it is found that with seasonal processes included, the species spectra are more varied over time, while the aggregated community spectrum remains fairly similar. Growth of seasonally spawning mature individuals drops significantly during peak reproduction, although lifetime growth curves follow nonseasonal ones closely. On analysing properties of the community spectrum under different fishing scenarios, seasonality generally causes more varied spectrum slopes and lower yields. Under seasonal conditions, increasing fishing effort also results in greater temporal variability of fisheries yields due to truncation of the community spectrum towards smaller sizes. Further work is needed to evaluate robustness of management strategies in the context of a wider range of seasonal processes and behavioural strategies, as well as longer term environmental variability and change.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".