Beta‐diversity gradients of butterflies along productivity axes
Bibliographic record
Abstract
ABSTRACT Aim Several lines of evidence suggest that beta diversity, or dissimilarity in species composition, should increase with productivity: (1) the latitudinal species richness gradient is most closely related to productivity and associated latitudinal beta‐diversity relationships have been described, and (2) the scale dependence of the productivity–diversity relationship implies that there should be a positive productivity–beta‐diversity relationship. However, such a pattern has not yet been demonstrated at broad scales. We test if there is a gradient of increasing beta diversity with productivity. Location Canada. Methods Canada was clustered into regions of similar productivity regimes along three remotely sensed productivity axes (minimum and integrated annual productivity, seasonality of productivity) and elevation. The overall (βj), turnover (βsim) and nestedness (βnes) components of beta diversity within each productivity regime were estimated with pairwise dissimilarity metrics and related to cluster productivity with partial linear regression and with spatial autoregression. Tests were performed for all species, productivity breadth‐based subsets (e.g. species occurring in many and a moderate number of productivity regimes), and pre‐ and post‐1970 butterfly records. Beta diversity between adjacent clusters along the productivity gradients was also evaluated. Results Within‐cluster βj and βsim increased with productivity and decreased with seasonality. The converse was true for βnes. All species subsets responded similarly; however, productivity–beta‐diversity relationships were weaker for the post‐1970 temporal subset and strongest for species of moderate breadth. Between‐cluster beta diversity (βj) and nestedness (βnes) declined with productivity. Main conclusions As predicted, beta diversity of communities within productivity regimes was observed to increase with productivity. This pattern was driven largely by a gradient of species turnover. Therefore, beta diversity may make an important contribution to the broad‐scale gradient of species richness with productivity. However, this species richness gradient dominates regional beta diversity between productivity regimes, resulting in decreasing between‐productivity dissimilarity with productivity driven by a concurrent decline in nestedness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".