Assessing the invasion threat of non-native plant species in protected areas using Herbarium specimen and ecological survey data. A case study in two rangeland bioregions in Queensland
Bibliographic record
Abstract
Naturalised non-native plants that become invasive pose a significant threat to the conservation of biodiversity in protected areas (areas dedicated and managed for long-term conservation of nature), economic productivity of agricultural businesses, and societal impacts including community, culture infrastructure and health. Quantifying the spread, potential dominance and invasion threat of these species is fundamental to effective eradication and development of threat mitigation policy. But this is often hampered by the lack of comprehensive data. This study used existing ecological survey data from 2548 sites and 64 758 Herbarium specimen records to document the status and abundance of non-native plants in two case study bioregions, Cape York Peninsula (CYP) and the Desert Uplands (DEU) in Queensland covering a total area of 186 697 km2. There were 406 non-native species in the CYP, 186 (45.6%) of which are known environmental weeds and 159 non-natives in DEU, of which 69 (43.5%) are environmental weeds. Inside the protected areas, there were 98 species of environmental weeds in CYP, 27 of which are listed as weeds of State significance (Weeds of National Significance (WONS), Queensland declared and non-declared pest plants categories). In DEU, there were 18 environmental weeds inside protected areas and none of them was listed as a weed of State significance. Non-native species that recorded foliage cover dominance in the ecological site data are generally recognised as environmental weeds in Queensland. The threat of weeds from outside of protected areas was serious, with 41 weeds of State significance found in CYP, five of which are WONS, and 25 weeds of State significance found in DEU, 10 of which are WONS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".