Do Eucalypt Species Display Similar Potential Niche Patterns to North American Trees?
Bibliographic record
Abstract
A recent paper in Science has described the realized and potential thermal niches of 188 North American tree species in terms of mean annual temperature (MAT). Using PlantSearch data from outside species native distributions it found that species potential niches displayed a “centrifugal organization” of thermal niches. As a result “potential niches of cold-adapted species extend to warmer temperatures whereas potential niches of warm-adapted species extend to cooler temperatures”. These patterns could have important implications for tree species management under climate change, and the study described here aimed to determine if similar patterns could be found with the MAT niches of eucalypt species. The realized niches of 48 eucalypt species and subspecies were assessed in terms of MAT range using the Atlas of Living Australia (ALA). Potential niches for 42 species and subspecies were examined using ex situ data from the PlantSearch database of Botanic Gardens Conservation International (BGCI). Potential niches of five selected species were also examined using data from the ALA and/or the Global Biodiversity Information Facility (GBIF). Results from the whole study described here were less clear-cut that the North American study but some similarities were found. For example, the warmest-adapted species had little or no extensions into any warmer environments. Histograms used here for three species highlighted MAT tolerances supported by numerous observations but also indicated outliers that may include some errors. In summary, there was some limited support for the conclusions of the North American study. However, data from arboreta and botanic gardens should be used with care or levels of species climatic tolerance may be exaggerated. The collation of data from commercial trials, which would be more representative of broadscale areas and could also include provenance as well as species information, is recommended.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".