Cascading impacts of overstory structure in managed forests on understory structure, microclimate conditions, and <i>Ixodes scapularis</i> (Acari: Ixodidae) densities
Bibliographic record
Abstract
Forest management practices designed to meet varied landowner objectives affect wildlife habitat and may interrupt the life-cycle stages of disease vectors, including the black-legged tick, Ixodes scapularis Say (Acari: Ixodidae). Ixodes scapularis transmits multiple pathogens including Borrelia burgdorferi, the causative agent of Lyme disease, which is the most common tick-borne disease in the United States. There is evidence that a range of active forest management practices (e.g., invasive plant removal, prescribed burning) can alter tick densities and pathogen transmission. However, few studies have investigated relationships between forest stand structural variables commonly manipulated by timber harvesting and tick ecology. Foresters may harvest timber to create certain forest structural conditions like the mean number of trees, or basal area, per hectare. This study used a spatially replicated experiment in a blocked design to compare forest stands with a range of overstory structures and document variations in the midstory, understory, and forest floor, as well as microclimate conditions within tick off-host habitat. Greater numbers of trees or basal area per hectare correlated with greater canopy closure but less understory cover, stabilized microclimate temperature, higher microclimate humidity, and greater I. scapularis nymph densities. A random forest model identified understory forest structure as the strongest predictor of nymph densities. There was no relationship between the number of trees or basal area per hectare and daily deer (Odocoileus virginianus Zimmermann) activity or nymphal infection prevalence. These findings provide a deeper understanding of tick-habitat associations within a forest stand and have the potential to inform forest management decisions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".