Distribution changes of the toxic mushroom Amanita phalloides under climate change scenarios and its potential risk over indigenous communities in Mexico
Bibliographic record
Abstract
Amanita phalloides is a native European deathly ectomycorrhizal mushroom that was introduced to North America and has been expanding its distribution during the last decades. This species is morphologically similar to wild edible mushrooms and if its distribution expands to Mexico, it could represent a risk in terms of food security for local communities. The aim of this study was to evaluate the potential climatic suitability that exists for A. phalloides in North America and overlay it with the distribution of mycophilic communities in Mexico under a baseline climatic scenario and climate change scenarios. To find climatic suitability we modeled its potential distribution with the algorithm that had the best predictive power after pilot test (MaxEnt) using species presences and eight climatic variables chosen with biological and statistical criteria. We worked with CanESM5 because it is one of the best models to simulate climate in North America and SSP5-8.5 scenario in order to be consistent with the precautionary principle. Our results suggest that even when the species has not yet been registered in Mexico, when using European records to model, this country presents 33.61% of climatic suitability for this species under the baseline scenario, potentially affecting about 70% of indigenous communities which are the main consumers of edible mushrooms. Under climate change scenarios, an increase in climatic suitability is expected in Mexico, while decreases are expected in United States and Canada. When using North American records to model, almost no climatic suitability is found in Mexico; however, the implementation of warning campaigns in Mexico is still needed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".