Influence of plant abundance on disease incidence in a Mexican tropical forest
Bibliographic record
Abstract
In this paper we investigate to what extent the occurrence of foliar diseases is affected by plant relative abundance in the understory community of the Los Tuxtlas tropical rain forest, how this changes with season of the year, and how plant species with different life histories present at the understory are affected by disease. To provide a context to these analyses we also include a general description of the floristic composition of the understory community. Using the eight most common species, we found that their relative abundance in each sampling location significantly explained the proportion of diseased plants. Accordingly, using the relative abundance of all plant species, we found that the probability of a host plant species being free of infection showed a significant decrement with abundance. At the plant level, we found that relative abundance had the greatest effect on the variation in leaf area/plant affected by pathogens, although the proportion of explained deviance was only 12%. Seasonality did not affect disease incidence and disease levels per plant. Throughout the year, plant relative abundance was very much lower in lianas, tree seedlings, and palms than in perennial herbs and ferns, and disease incidence was very much higher in the latter two, the most abundant life forms. These results collectively suggest that both intraspecific and interspecific variation in plant relative abundance explain variation in leaf damage by pathogenic fungi in tropical forest understories.
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 | 0.000 | 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 | 0.000 | 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".