Body size is a better predictor of intra-than interspecific variation of animal stoichiometry across realms
Bibliographic record
Abstract
Animal stoichiometry influences critical processes from organismal physiology to biogeochemical cycles. However, it remains uncertain whether animal stoichiometry follows predictable scaling relationships with body mass and whether adaptation to terrestrial or aquatic environments constrains elemental allocation. We tested both interspecific and intraspecific body-mass scaling relationships for nitrogen (N), phosphorus (P), and N:P content using a subset of the StoichLife database, which includes 9,933 individual animals across 1,543 species spanning 10 orders of magnitude in body mass from terrestrial, freshwater, and marine realms. Our results show that body mass predicts intraspecific stoichiometric variation, accounting for 42-45% of the variation in 27% of vertebrate and 35% of invertebrate species. However, body mass was less effective at explaining interspecific variation, with taxonomic identity emerging as a more significant factor. Differences between aquatic and terrestrial organisms were observed only in invertebrate interspecific %N, suggesting that realm has a relatively minor influence on elemental allocation. Our study, based on the most comprehensive animal stoichiometry database to date, revealed that while body mass is a good predictor of intraspecific elemental content, it is less effective for interspecific patterns. This highlights the importance of evolutionary history and taxonomic identity over general scaling laws in explaining stoichiometric variation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".