Exploring Predictions of Abundance from Body Mass Using Hierarchical Comparative Approaches
Bibliographic record
Abstract
Understanding and predicting how and why abundance varies is one of the central questions in ecology. One of the few consistent predictors of variation in abundance between species has been body mass, but the nature of this relationship has been contentious. Here I explore the relationship between body mass and abundance in birds of North America, using hierarchical partitioning of variance and regressions at taxonomic levels above the species. These analyses show that much variation in abundance is found across space, while a moderate amount of variation is found at the species/genus and also at the family/order level. However, body size and trophic level primarily vary at the family/order level, suggesting that mechanisms based on body size and energy should primarily explain only this moderate-sized, taxonomically conserved component of variation in abundance. Body size does explain more than 50% of the variation at this level (and almost 75% when trophic level is also included). This tighter relationship makes clear that energetic equivalence (slope = -3/4) sets an upper limit but does not describe the relationship between body mass and average abundance for birds of North America. Finally, I suggest that this hierarchical, multivariate approach should be used more often in macroecology.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".