Phylogenetic structure and ecological and evolutionary determinants of species richness for angiosperm trees in forest communities in China
Bibliographic record
Abstract
Abstract Aim Site‐level species richness is thought to result from both local conditions and species’ evolutionary history, but the nature of the evolutionary effect, and how much it underlies the correlation with current environment, are debated. Although tropical conservatism is a widely used explanatory framework along temperature gradients, it is unclear whether cold tolerance is primarily a threshold effect (e.g. freezing tolerance) or represents a more continuous constraint. Nor is it clear whether cold tolerance is the only major axis of conservatism or whether others, such as water‐stress tolerance, are additionally important or trade‐off against cold tolerance. We address these questions by testing associated predictions for forest plots distributed across 35° latitude. Location China. Methods We recorded all trees within 57 0.1‐ha plots, generated a phylogeny for the 462 angiosperm species found, and calculated phylogenetic diversity (standardized PD ), net relatedness index ( NRI ) and phylogenetic species variability ( PSV ) for each plot. We tested the predictions using regression, variance partitioning and structural equation modelling to disentangle potential influences of key climate variables on NRI and PSV , and of all variables on species richness. Results Species richness correlated very strongly with minimum temperature, nonlinearly overall but linearly where freezing is absent. The phylogenetic variables also correlated strongly with minimum temperature. While NRI and PSV explained little additional variance in species richness, they accounted for part of the species richness–current climate correlation. Water stress added minimal explanatory power. All these variables showed strong latitudinal gradients. Main conclusions Minimum temperature appeared to primarily control tree species richness, via both a threshold‐like freezing effect and a linear relationship in climates without freezing. We found no clear signal of water‐stress effects. The modelled contribution of evolutionary history is consistent with cold‐tolerance conservatism, but could not account for all the species richness–climate relationship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".