Life stage and taxonomy the most important factors determining vertebrate stoichiometry: A meta‐analysis
Bibliographic record
Abstract
Whole-body elemental composition is a key trait for determining how organisms influence their ecosystems. Using mass-balance, ecological stoichiometry predicts that animals with higher concentrations of element X will selectively retain more X and will recycle less X in their waste than animals with lower X concentrations. These animals will also store high quantities of X during their lives and after their deaths (prior to full decomposition). Vertebrates may uniquely impact nutrient cycling because they store high quantities of phosphorus (P) in their bones. However, vertebrates have diverse body forms and invest variably in bone. Current analyses of vertebrate elemental content predominately evaluate fishes, typically neglecting other vertebrates and leaving much of the diversity unexplored. We performed a systematic review and identified 179 measurements of whole-body percent phosphorus (%P), percent nitrogen (%N), and N to P ratio (N:P) from 129 unique species of non-fish vertebrates (amphibians: 39 species; reptiles: 19 species; birds: 27 species; mammals: 46 species). We found that %P (mean: 1.94%; SD [standard deviation] = 0.77) and N:P (mean: 12.52) varied with taxonomy and life stage, while %N (mean: 10.51%; SD = 3.25) varied primarily with taxonomy. Habitat, diet, and size had small and inconsistent effects in different groups. Our study highlights two research gaps. Life stage, which is frequently neglected in stoichiometric studies, is an important factor determining vertebrate %P. Furthermore, amphibians dominate our dataset, while other vertebrate taxa are poorly represented in the current literature. Further research into these neglected vertebrate taxa is essential.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.018 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".