Subdividing the spectrum: quantifying host specialization in mistletoes
Bibliographic record
Abstract
Parasites necessarily depend on their hosts, but the number of host species used by a parasite varies from one to hundreds. Estimating host range and identifying the preferred host species that influence distributional boundaries and confer greater advantage to the parasite has proven elusive. As well as the confounding effects of sampling effort, characterising host specificity and preference has been hindered by considering host-use without accounting for availability. We selected three mistletoe species (Lysiana exocarpi, Amyema quandang, and Amyema lucasii) and sampled mistletoe–host interactions and host availability free from sampling bias. To quantify host specificity and identify preferred host species we applied specialist/generalist scores (G) and resource selection ratios (ω) respectively. Host specificity and preference were assessed at four scales. The generalist L. exocarpi was found to parasitise 31 plant species. Even at small scales, G values and host species turnover were high, with eight preferred hosts identified. Amyema quandang had a low G score with significant preference for half of its Acacia hosts. Amyema lucasii significantly preferred one host, consequently having low G values at all scales. By collecting potential host data and applying G scores and ω, the parasite host spectrum can be quantitatively estimated rather than qualitatively described.
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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) | 0.000 | 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".