Susceptibility of endangered <i>Cornus florida</i> (eastern flowering dogwood) to the introduced fungal pathogen <i>Discula destructiva</i> (dogwood anthracnose) in the Canadian Carolinian forest: insights from environmental, ecological, and population genetics assessments
Bibliographic record
Abstract
Forest fragmentation and introduced pathogens are negatively impacting trees and forests globally, including the Carolinian forest of southern Ontario, Canada. Multiple species at risk live in this threatened but biodiverse forest, including the endangered Cornus florida (eastern flowering dogwood), which is now limited to fragmented woodlots, and has been decimated by the introduced fungal pathogen Discula destructiva (dogwood anthracnose). Ongoing management of C. florida in Canada is challenged by multiple knowledge gaps, two of which we aimed to address in this study. We first evaluated the association between anthracnose disease prevalence and a suite of ecological and environmental variables across 21 sites. Across our sites, larger trees tended to have the highest disease incidence, and trees on shallow slopes had the most crown dieback. We then quantified genetic diversity and gene flow, and found that genetic structure has not been substantially impacted by habitat fragmentation, although dispersal typically covers short distances. However, genetic diversity is relatively low in smaller populations and in younger trees. Localized dispersal and eroding genetic diversity may limit future adaptation and hence exacerbate population declines. We recommend that managers prioritize plantings in small populations, avoid shallow slopes, and track younger trees to evaluate age-related mortality.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".