Traits Explain Canopy Tree Occurrence Along Regional Environmental Gradients: A Subset Combine to Be Useful
Bibliographic record
Abstract
ABSTRACT Trait‐Species Distribution Models (trait‐SDM) help to understand the importance of plant strategies to niches, assess their generality across species and provide a path to predicting species distributions from a shortlist of traits. Yet published trait‐environment associations show considerable inconsistency. Region‐scale models may leverage more species, traits, trait ranges and climatic gradients, than at local scales, while retaining biogeographic coherence, which is lost in global compilations. Here we fit trait‐SDMs with six traits using multilevel models for over 90 eucalypt tree taxa. We model presence‐absence in 1 km 2 grid cells which contain multiple survey plots, arrayed along environmental gradients which span 120,000 km 2 , 8°C mean temperature and 900 mm annual precipitation. We found stem sapwood density, bark thickness, seed mass and maximum height were the most influential predictors in a multi‐trait model of environmental responses to temperature, water deficit, soil depth and pH. Combined, they explained 9%–19% of variance between species in environmental responses. We found less support for specific leaf area and leaf size. Species occurred unimodally along environmental gradients. Trait‐environment terms indicated species with dense stems were more likely in drier climates, thicker bark in warmer climates and that both thinner bark and larger seeds increased occurrence in shallow soils. Taller species were more common and more likely to occur towards sites that were warmer and wetter than average along the gradient. Our work has wider implications: trait‐SDMs help to test trait‐based theory about realised niches. Single trait models reflect the maximum potential explanation of niche differentiation by a trait, while multi‐trait models represent integrated phenotypes responding to multidimensional niches. For planning and management, such trait‐SDMs can provide useful predictions of where certain kinds of species occur or could be restored. But they will leave much uncertainty, especially for identifying which particular species occur where.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".