Mycoheterotrophic plants as indicators of post-agricultural forest regeneration: abundance of <i>Hypopitys monotropa</i> and <i>Monotropa uniflora</i> in post-agricultural forests changes through time
Bibliographic record
Abstract
Herbaceous layers in second-growth forests are shaped by past land use. Disturbances such as agriculture may impact populations of mycoheterotrophs, non-photosynthetic mycorrhizal plants that obtain carbon from fungal networks by altering mycorrhizal communities or removing trees they derive carbon from. I tested the hypotheses that two mycoheterotrophic forest herbs increase in abundance during succession and become most common in older forests as plant communities reassemble through time. Distributions of Hypopitys monotropa and Monotropa uniflora were sampled in Athens County, Ohio, USA. I surveyed populations in a 40-site post-agricultural forest chronosequence with five upland and five valley sites in each of four age classes: 40–60, 61–80, 81–100, and >130 years since canopy closure. Aspect and elevation were measured to assess environmental influence. Both H. monotropa and M. uniflora were most common in older stands with EM tree-rich canopy composition and west- or south-facing aspects, indicating influence of historical, biotic, and edaphic factors. Hypopitys was exclusive to forests >80 years old, while M. uniflora was present in younger stands. Abundance of both species was also significantly predicted by Fagaceae basal area. Because EM trees were also most abundant in south- and west-facing uplands, environmental influence appears to be mediated through canopy composition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".