Size-structured planktonic ecosystems: constraints, controls and assembly instructions
Bibliographic record
Abstract
Here we present a nutrient-phytoplankton-zooplankton (NPZ) model that has arbitrary size-resolution within the phytoplankton- and zooplankton-state variables. The model assumes allometric scaling of biological parameters. This particular version of the model (herbivorous zooplankton only) has analytical solutions that allow efficient exploration of the effects of allometric dependencies of various biological processes on the model's equilibrium solutions. The model shows that there are constraints on the possible combinations of allometric scalings of the biological rates that will allow ecosystems to be structured as we observe (larger organisms added as the total biomass increases). The diversity (number of size classes occupied) of the ecosystem is the result of simultaneous bottom-up and top-down control: resources determine which classes can exist; predation determines which classes do exist. Thus, the simultaneous actions of bottom-up and top-down controls are essential for maintaining and structuring planktonic ecosystems. One important conclusion from this model is that there are multiple, independent ways of obtaining any given biomass spectrum, and that the spectral slope is not, in and of itself, very informative concerning the underlying dynamics. There is a clear need for improved size-resolved field measurements of biological rates; these will both elucidate biological processes in the field, and allow strong testing of size-structured models of planktonic ecosystems.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.001 |
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".