High co-occurrence of invasive wetland plants and species at risk in Canada’s biodiverse Carolinian
Bibliographic record
Abstract
Abstract Invasive species are a major driver of biodiversity loss, with invasive plants increasingly threatening wetland ecosystems. Southern Ontario’s Carolinian Zone, a biodiversity hotspot supporting 79% of Ontario’s non-fish Species at Risk (SAR), is especially vulnerable. Over half (54%) of these SAR rely on wetland or semiaquatic habitats, emphasizing the importance of wetland protection for their recovery. These habitats are fragmented and highly susceptible to plant invasions. To guide conservation, we conducted a spatial co-occurrence analysis of non-fish SAR dependent on wetlands and invasive wetland plant species. We identified 33 invasive species posing current (n = 26) or imminent (n = 7) threats in the Carolinian Zone. Overlap between SAR and invasive plants was greatest in Lake Erie’s coastal marshes and shallow waters, where invasions are well documented, and also in urban areas such as Toronto, Windsor, London, and Niagara, where SAR richness was unexpectedly high. Co-occurrence of SAR and invasive plants in these regions indicates that managing invasive plants in urban wetlands could directly support SAR recovery. Marsh-nesting birds, reptiles, and wetland plants were most exposed and vulnerable to habitat alteration and resource competition. Spatial analyses help pinpoint where invasive plants most threaten SAR, enabling targeted, effective management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".