Bridging Ecological Stoichiometry and Nutritional Geometry with homeostasis concepts and integrative models of organism nutrition
Bibliographic record
Abstract
Summary The role of nutrition in linking animals with their environment is increasingly seen as fundamental to explain ecological interactions. The two currently predominant frameworks for exploring questions in nutritional ecology – Nutritional Geometry (NG) and Ecological Stoichiometry (ES) – share common features, but also differ in their goals and origins. NG originates from behavioural ecology using terrestrial insects as model organisms in tightly controlled feeding experiments, while ES originates from biogeochemistry focusing on the transfer of key elements across trophic levels, mainly in aquatic environments. Here, we review the history of these two complementary frameworks, emphasizing the key concepts defining their respective aims, methodologies and focal taxa to answer questions at different ecological scales. We identify and explore homeostasis as a shared conceptual cornerstone of each framework that can be used to bridge knowledge gaps and for developing new hypotheses within nutritional ecology. Expanding on the concept of homeostasis, we introduce dynamic energy budget (DEB) models as a general way to address homeostatic regulation at its fundamental level. Specifically, we describe how a two‐reserve DEB model can be used to track metabolic pathways of nutrients as well as elements and suggest that multi‐reserve DEB models, when integrated and parameterized with NG and ES concepts, can form powerful components of agent‐based models to predict how animal nutrition influences individual and trophic interactions in food webs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".