Traits and tradeoffs among non‐native ectomycorrhizal fungal symbionts affect pine seedling establishment in a Hawaiian coinvasion landscape
Bibliographic record
Abstract
Pine invasions lead to losses of native biodiversity and ecosystem function, but pine invasion success is often linked to coinvading non-native ectomycorrhizal (EM) fungi. How the community composition, traits, and distributions of these fungi vary over the landscape and how this affects pine success is understudied. A greenhouse bioassay experiment was performed to test the effects of changes in EM fungal community structure from a pine plantation, to an invasion front to currently pine-free areas on percent root colonization and seedling biomass. Soils were also analysed by qPCR to determine changes in inoculum and spore density over distance for a common coinvading EM fungus, Suillus pungens. Percent colonization increased with distance from the plantation, which corresponded with an increase in seedling biomass and stark changes in EM fungal community membership where Suillus spp. dominated currently pine-free areas. However, there was a negative relationship between S. pungens inoculum potential versus root colonization over distance. We conclude that the success of pine invasions is facilitated by specific traits of Suillus spp., but that the success of Suillus is contingent on a lack of competition with other ectomycorrhizal fungi.
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 | 0.000 | 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) | 0.001 | 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".