Time of naturalization is key to explaining non-native plant invasions on islands
Bibliographic record
Abstract
AimRelatively long periods of time can elapse between the naturalization and spread of non-native plant species. However, time lags on islands are poorly understood, especially if integrating plant life histories. We asked whether (1) there is a time lag in the invasion process, (2) there were distinct periods of naturalization in the non-native plants that invaded islands and (3) non-native plants that naturalized more recently occur more frequently on islands that are large, less isolated and close to urban areas. Then, we contrasted trends across growth forms, dispersal modes and biogeographic origins.Location264 offshore islands in northern Aotearoa New ZealandTaxa Vascular plant speciesMethodsWe combined field surveys and published data for 848 non-native plant species. We categorized each species according to its growth form, dispersal mode and biogeographic origin and identified its year of naturalization in Aotearoa New Zealand. We contrasted period of naturalization, time lags and relationships with island area, isolation and distance from the nearest urban area by growth form, dispersal mode and biogeographic origin using ANCOVA and generalized linear models (GLMs).ResultsWe identified time lags, similar across all trait and biogeographic origin categories. Species with different trait and biogeographic origin categories first naturalized at different periods in time. Herbaceous species, those with unspecialized dispersal modes, and those originating from Eurasia and the Mediterranean basin were disproportionately introduced earlier than other categories. Non-native plants that naturalized more recently occur more frequently on large islands close to urban areas, but not less isolated ones. Relationships with island characteristics did not differ among trait and biogeographic origin categories.Main ConclusionsOverall, we found that the time of naturalization is more important than trait and biogeographic origin categories in explaining non-native plant invasion patterns on islands. Since similar time lags were identified for all categories, management bodies should focus on species of trait and biogeographic origin categories that naturalized more consistently in recent times (e.g. woody species from other regions within Oceania), and on large islands close to urban areas. Plant life histories do not always play a role in explaining plant distributions on islands.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".