Host–parasite distributions under changing climate:<i>Tsuga heterophylla</i>and<i>Arceuthobium tsugense</i>in Alaska
Bibliographic record
Abstract
Dwarf mistletoes ( Arceuthobium species) influence many processes within forested ecosystems, but few studies have examined their distribution in relation to climate. An analysis of 1549 forested plots within a 14.5 million ha region of southeast Alaska provided strong indications that climate currently limits hemlock dwarf mistletoe ( Arceuthobium tsugense (Rosendahl) G.N. Jones) to a subset of the range of its primary tree host, western hemlock ( Tsuga heterophylla (Raf.) Sarg.), with infection varying from a high of 20% of trees at sea level to only 5% by 200 m elevation. Three types of modeling approaches (logistic, most similar neighbors, and random forests) were tested for the ability to simultaneously predict abundance and distribution of host and pathogen as a function of climate variables. Current distribution was explained well by logistic models using growing degree-days, indirect and direct solar radiation, rainfall, snowfall, slope, and minimum temperatures, although accuracy for predicting A. tsugense presence at a particular location was only 38%. For future climate scenarios (A1B, A2, and B1), projected increases for A. tsugense habitat over a century ranged from a low of 374% to a high of 757%, with differences between modeling approaches contributing more to uncertainty than differences between climate scenarios.
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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 | 0.000 | 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 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".