Effects of artificial climate warming and competition on growth of Alliaria petiolata and Vincetoxicum rossicum
Bibliographic record
Abstract
Climate change and exotic invasive species are both major threats to global biodiversity. Experimental studies typically treat these as separate phenomena, but their impacts may be antagonistic. For example, studies investigating the ecology of invasive species usually focus on interactions with invasive species even though habitats often include many competing invaders. In this thesis, I study competition between two invasive species in southern Ontario, Vincetoxicum rossicum and Alliaria petiolata, where they threaten local biodiversity. First, I investigate the extent of co-existence between V. rossicum and A. petiolata at different spatial scales, using observational records and targeted field surveys. Second, I set up a field experiment conducted at the Queen’s University Biological Station (QUBS) using artificial warming chambers to test how future climate warming may affect future co-existence. Results from the field survey show that V. rossicum and A. petiolata frequently co-exist locally in southern Ontario, as close as 0.5 m2. In the QUBS experiment, A. petiolata was a stronger competitor than V. rossicum, having no significant difference in size between individuals growing in the two competition treatments. Conversely, V. rossicum was, on average, 20 % shorter when grown with A. petiolata than grown with other V. rossicum. Additionally, climate warming appears to benefit A. petiolata more than V. rossicum with A. petiolata height, number of leaves, and leaf area all becoming significantly larger under warming conditions. On the other hand, only V. rossicum height increased under warming conditions while leaf number and leaf area were unaffected. Based on these results, I predict that A. petiolata is more of a threat than V. rossicum and will become more so in the future, and therefore the former should be a higher priority for control when both species are present in an area.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".