Trait-based approaches to global change ecology: from description to prediction
Bibliographic record
Abstract
As global change forces species’ ranges and abundances into novel configurations, traits-based approaches could allow predictions of community re-assembly. We present a quantitative review of traits-based research globally to (1) evaluate the extent to which this approach has been applied, and (2) evaluate moving from description and to prediction. We highlight the application of traits-based frameworks to describe ecological patterns; terrestrial plant morphology comprises >30% of the literature alone. But fewer than 3% of studies predict ecological effects of global change, mostly in the past five years. While organism size is the most common trait, we identified 2,430 other morphological, physiological, behavioural, and life history traits that mediate environmental filters of species’ ranges across ecosystems and taxonomy. Global change studies forecast range shifts from a few physiological or life history traits. Though uncommon, spatially-explicit models constructed from correlated multivariate trait assemblages (or ‘syndromes’) offer the best chance of predicting shifts under global change scenarios. Moving the field towards trait-based prediction requires (1) matching the scale of trait measurement to the ecological processes, (2) increasing the resolution of environmental gradients along which traits are measured, (3) moving from single to multivariate traits, and (4) accounting for intraspecific trait 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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".