Consequences for ectomycorrhizal fungi of the selective loss or gain of pine across landscapes
Bibliographic record
Abstract
Altered disturbance regimes and novel introductions are causing rapid shifts in the distribution of pines (Pinus L.). The functionally obligate symbiosis between pines and ectomycorrhizal (EM) fungi dictates that shifts in the distribution of one partner will affect the distribution of the other. In this review, we examine evidence for three hypotheses. (1) The loss of photosynthates through stress or tree mortality decreases the abundance of EM fungi and selects for less carbon-demanding species. (2) Pine introductions initiate establishment of novel EM fungal communities. (3) The extent of shifts in EM fungal abundance and community composition is mediated by surrounding vegetation. We find support for these hypotheses though changes in EM fungal abundance are variable and context-dependent. We posit that the consequences of shifts in EM fungal abundance and community composition extend beyond the individual tree to the landscape; these changes may affect population dynamics of both symbionts, ecosystem processes, and the conservation and evolution of fungi. In addition to conducting baseline surveys to assess the distribution of EM fungi, increasing our understanding of their function, morphology, propagation, and controls on host-specificity, and shifts would also assist in predicting the trajectory of ecosystems following the loss or gain of pine.
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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 | 0.000 | 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 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".