Geographic gradients in a functional trait: Drivers of body size and size diversity of ground invertebrate communities
Bibliographic record
Abstract
Abstract Body size is a key functional trait governing how an animal community transforms resources and conditions into performance, abundance, and fitness. Here we use the National Ecological Observatory Network of pitfall traps to explore how an ecosystem's plant productivity, temperature, and growing season length accounts for the range of body size across 99 ground invertebrate communities. The 19‐fold continental variation in mean body size failed to covary with latitude, while common ordinal subtaxa grew smaller (e.g., myriapods) to larger (e.g., acari) from Puerto Rico to Alaska. Communities with a larger mean size arose when winters were longer and gross primary productivity was high. The diversity of body sizes in a community (measured as the CV) varied ninefold and decreased with latitude (r2 = 0.24) consistently across common orders. Size‐diverse communities were less likely in ecosystems with long winters (suggesting constraints on the time to build, r2 = 0.34) and those with high invertebrate activity (and hence trap catch, r2 = 0.12). Body size distributions thus appeared to arise from conflicting combinations of constraint (i.e., the ability to build large bodies) and performance (utility of large size in surviving long winters). As warming promotes growing season length, populations of larger, rarer individuals may benefit.
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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 | 0.000 | 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 | 0.001 | 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".