The genetic dimension of pest pressure in the tropical rainforest
Bibliographic record
Abstract
Wet tropical forests are among the most diverse ecosystems on Earth and can host several hundreds of tree species per hectare. To maintain such diversity, the community must contain large numbers of relatively rare species rather than be dominated by a few very common trees, as is often the case in temperate forests. Explaining the mechanisms preventing dominance by common species has been a major task of tropical forest ecology. One of the most promising mechanisms is negative density dependence (NDD) of tree abundance driven by pests, including fungal diseases ('pest pressure'). NDD entails that the chance of survival of a sapling increases with the distance from a mature tree of the same species, thus preventing species from becoming locally dominant. Curiously, the strength of NDD is negatively correlated with abundance, meaning that tree species that are more common generally show weaker NDD (Comita et al. ). Interactions between plants and soil pathogens have been shown to play an important role in NDD (Klironomos ), and rare species are apparently more strongly affected (Mangan et al. ). However, the genetic mechanisms underlying this phenomenon have remained obscure. In this issue of Molecular Ecology, Marden et al. () suggest that reduced diversity of the genes involved in pathogen recognition (Resistance genes or R genes) could explain why NDD is stronger in locally rare species.
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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".