Melaleuca (Myrtaceae): Biogeography of an important genus of trees and shrubs in a changing world
Bibliographic record
Abstract
The number of naturalised and invasive woody plant species has increased rapidly in recent decades. Despite the increasing interest in tree and shrub invasions, little is known about the invasion ecology of most species. This paper explores the global movement of species in the genus Melaleuca (Myrtaceae; here including the genus Callistemon). We assess the global introduction history, distribution and biogeographic status of the genus. Various global species occurrence databases, citizen science (iNaturalist), and the literature were used. Seventy-two species [out of 386 Melaleuca species; 19%] have been introduced to at least 125 regions outside their native range. The main regions of global Melaleuca introductions are Southeast Asia, the southern parts of North America, south-eastern South America, southern Africa and Europe. The earliest record of a Melaleuca species outside of the native range of the genus is 1789. First records of Melaleuca species outside their native range were most commonly recorded in the 1960s, with records from all over the world. The main reasons for Melaleuca introductions were for use in the tea tree (pharmaceutical value) and ornamental horticulture industries. Melaleuca introductions, naturalizations and invasions are recent compared to many other woody plant taxa. Experiences in Florida and South Africa highlight the potential of Melaleuca species to spread rapidly and have significant ecological impacts. It is likely that the accumulating invasion debt will result in further naturalization and invasion of Melaleuca species in the future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".