A unified framework for diversity gradients: the adaptive trait continuum
Bibliographic record
Abstract
ABSTRACT Aim Adaptive trait continua are axes of covariation observed in multivariate trait data for a given taxonomic group. These continua quantify and summarize life‐history variation at the inter‐specific level in multi‐specific assemblages. Here we examine whether trait continua can provide a useful framework to link life‐history variation with demographic and evolutionary processes in species richness gradients. Taking an altitudinal species richness gradient for Mediterranean butterflies as a study case, we examined a suite of traits (larval diet breadth, adult phenology, dispersal capacity and wing length) and species‐specific habitat measures (temperature and aridity breadth). We tested whether traits and species‐specific habitat measures tend to co‐vary, whether they are phylogenetically conserved, and whether they are able to explain species distributions and spatial genetic variation in a large number of butterfly assemblages. Location Catalonia, Spain. Methods We formulated predictions associated with species richness gradients and adaptive trait continua. We applied principal components analyses (PCAs), structural equation modelling and phylogenetic generalized least squares models. Results We found that traits and species‐specific habitat measures covaried along a main PCA axis, ranging from multivoltine trophic generalists with high dispersal capacity to univoltine (i.e. one generation per year), trophic specialist species with low dispersal capacity. This trait continuum was closely associated with the observed distributions along the altitudinal gradient and predicted inter‐specific differences in patterns of spatial genetic variability (FSTand genetic distances), population responses to the impacts of global change and local turnover dynamics. Main conclusions The adaptive trait continuum of Mediterranean butterflies provides an integrative and mechanistic framework to: (1) analyse geographical gradients in species richness, (2) explain inter‐specific differences in population abundances, spatial distributions and demographic trends, (3) explain inter‐specific differences in patterns of genetic variation (FSTand genetic distances), and (4) study specialist–generalist life‐history transitions frequently involved in butterfly diversification processes.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".