Evaluating the suitability of reference sites to gauge the success of ecological restoration on arbuscular mycorrhizal fungal abundance and community composition
Bibliographic record
Abstract
Most ecological restorations aim to return degraded ecosystems to pre‐disturbance conditions, but community composition in restored ecosystems often differs from sites representative of a historical state and can more closely resemble semi‐natural or moderately disturbed ecosystems. We examined the effect of a prairie restoration on the community composition of arbuscular mycorrhizal (AM) fungi, an ecologically important group of soil microbes that form nutrient‐exchange mutualisms with most plant species. We determined AM fungal community composition using DNA amplicon sequencing of the 18S rRNA gene in recently restored prairies (8–10 years) on retired agricultural lands and compared these communities with those in three types of reference sites: remnant prairies that represent a historic state, managed prairies with a history of agricultural land use but lack nutrient pollution from fertilizers, and farm‐side meadows at the margins of cultivated fields. In soil samples, AM fungal species richness and phylogenetic diversity were 1.6× higher in restored prairies compared to remnant prairies and dissimilar in community composition because remnant prairies were dominated by one AM fungal family, Glomeraceae. AM fungal species richness was 1.3× higher in farm‐side meadows than in restored prairies and differed in the abundance of multiple families. By contrast, AM fungal species richness and community composition did not differ between restored and managed prairies. These results suggest that recent prairie restoration of retired agricultural fields can cause AM fungal composition to resemble older prairie restorations with histories of agricultural land use, rather than remnant prairies and farm‐side meadows.
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 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 | 0.001 | 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 | 0.001 | 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".