Three decades post‐reforestation has not led to the reassembly of arbuscular mycorrhizal fungal communities associated with remnant primary forests
Bibliographic record
Abstract
The negative effects of deforestation can potentially be ameliorated through ecological restoration. However, reforestation alone may not reassemble the same ecological communities or functions as primary forests. In part, this failure may be owed to forest ecosystems inherently involving complex interactions among guilds of organisms. Plants, which structure forest food webs, rely on intimate associations with symbiotic microbes such as root-inhabiting mycorrhizal fungi. Here, we leverage a large-scale reforestation project on Hawai'i Island underway for over three decades to assess whether arbuscular mycorrhizal (AM) fungal communities have concurrently been restored. The reference ecosystem for this restoration project is a remnant montane native Hawaiian forest that provides critical habitat for endangered birds. We sampled soils from 12 plots within remnant and restored forest patches and characterized AM fungal communities using high-throughput amplicon sequencing. While some AM fungal community metrics were comparable between remnant and restored forest (e.g. species richness), other key characteristics were not. Specifically, community membership and the identity of AM fungal keystone species differed between the two habitat types, as well as the primary environmental factors influencing community composition. Remnant forest AM fungal communities were strongly associated with soil chemical properties, especially pH, while restored forest communities were influenced by the spatial proximity to remnant forests. We posit that combined, these differences in soil AM fungal communities could be negatively affecting the recruitment of native plant hosts and that future restoration efforts should consider plant-microbe interactions as an important facet of forest health.
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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 | 0.001 | 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) | 0.001 | 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".