Beyond blight: Phytophthora root rot under climate change limits populations of reintroduced American chestnut
Bibliographic record
Abstract
Abstract American chestnut ( Castanea dentata ) was functionally extirpated from eastern US forests by chestnut blight, caused by a fungus from Asia. As efforts to produce blight‐resistant American chestnut germplasm advance, approaches to reintroduce chestnut throughout its former range are being developed. However, chestnut is also quite susceptible to a root disease in the southern half of its former range, and the pathogen that causes the disease ( Phytophthora cinnamomi ) is expected to move northward as climate warms. Genetic resistance to root rot appears to vary among individual chestnut trees, and the prevalence of resistance is highly uncertain. Because restoration of a self‐sustaining chestnut population is ultimately a landscape‐scale problem, we used a process‐based forest landscape model (LANDIS‐II) to conduct experiments to quantify the effects of root rot on the effectiveness of chestnut population restoration efforts in the center of the former range of chestnut under various climate scenarios. We developed a new LANDIS‐II extension to simulate root rot‐induced tree mortality as a function of temperature and soil moisture. We conducted a factorial simulation experiment with climate and resistance to root rot as factors and found that root rot greatly reduced chestnut biomass on the landscape, even when resistance to root rot infection was at the highest levels currently observed in published studies. Warming climate enhanced the virulence of the pathogen and resulted in a greater reduction in chestnut biomass. Results indicate that root rot has the potential to seriously hamper chestnut restoration efforts if resistance of chestnut is not enhanced through breeding and biotechnology, suggesting restoration efforts will be more successful if targeted to latitudes, elevations, and site conditions where root rot is not expected to be present well into the future, including areas north of the historical chestnut range (Canada). These results demonstrate the vital importance of incorporating root rot resistance into the larger blight resistance breeding program.
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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 | 0.000 | 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".