Woodstoich III: Integrating tools of nutritional geometry and ecological stoichiometry to advance nutrient budgeting and the prediction of consumer‐driven nutrient recycling
Bibliographic record
Abstract
Within the last two decades, ecological stoichiometry (ES) and nutritional geometry (NG, also known as geometric framework for nutrition) have delivered novel insights into core questions of nutritional ecology. These two nutritionally explicit frameworks differ in the ‘nutrient currency’ used and the focus of their past research; behavioural feeding strategies in NG, mainly investigating terrestrial organisms, and trophic ecology in ES, mainly in aquatic settings. However, both NG and ES have developed in explaining patterns across various scales of biological organization. Integrating specific tools of these frameworks could advance the field of nutritional ecology by unifying theoretical and empirical approaches from the organismal to ecosystem level processes. Toward this integration, we identified 1) nutrient/element budgets as a shared concept of both frameworks that encompass nutrient intake, retention, and release, 2) response surface plots of NG as powerful tools to illustrate processes at the organismal level and 3) the concept of consumer‐driven nutrient recycling (CNR) of ES as a useful tool bridging organism and ecosystem scales. We applied response surface plots to element budget data from an ES study to show how this approach can deliver new insights at the organismal level, e.g. by showing the interplay between egestion and excretion depending simultaneously on the consumed amount of carbon and phosphorus based on variation across individuals. By integrating concepts of ES and NG to model microbial uptake and mineralization of nitrogenous wastes reported in a NG study, we also demonstrate that considering biochemically explicit mineralization rates of organic wastes can improve predictions of CNR by reducing over‐ or underestimation of mineralization depending on the quality of the consumer's diet. Our presented tools and approaches can help to bridge the organismal and ecosystem level, advancing the predictive power of studies in nutritional ecology at multiple ecological scales.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".