Ectomycorrhizal fungal diversity predicted to substantially decline due to climate changes in North American Pinaceae forests
Bibliographic record
Abstract
Abstract Aim Ectomycorrhizal fungi (ECMF) are partners in a globally distributed tree symbiosis implicated in most major ecosystem functions. However, resilience of ECMF to future climates is uncertain. We forecast these changes over the extent of North American Pinaceae forests. Location About 68 sites from North American Pinaceae forests ranging from Florida to Ontario in the east and southern California to Alaska in the west. Taxon Ectomycorrhizal fungi (Asco‐ and Basidiomycetes). Methods We characterized ECMF communities at each site using molecular methods and modelled climatic drivers of diversity and community composition with general additive, generalized dissimilarity models and Threshold Indicator Taxa ANalysis (TITAN). Next, we projected our models across the extent of North American Pinaceae forests and forecast ECMF responses to climate changes in these forests over the next 50 years. Results We predict median declines in ECMF species richness as high as 26% in Pinaceae forests throughout a climate zone comprising more than 3.5 million square kilometres of North America (an area twice that of Alaska state). Mitigation of greenhouse gas emissions can reduce these declines, but not prevent them. The existence of multiple diversity optima along climate gradients suggest regionally divergent trajectories for North American ECMF, which is corroborated by corresponding ECMF community thresholds identified in TITAN models. Warming of forests along the boreal–temperate ecotone results in projected ECMF species loss and declines in the relative abundance of long‐distance foraging ECMF species, whereas warming of eastern temperate forests has the opposite effect. Main Conclusions Our results reveal potentially unavoidable ECMF species‐losses over the next 50 years, which is likely to have profound (if yet unclear) effects on ECMF‐associated biogeochemical cycles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".