Tree-ring analysis of the fungal disease Swiss needle cast in western Oregon coastal forests
Bibliographic record
Abstract
Swiss needle cast (SNC), an important fungal disease of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), has increased in severity throughout its natural and introduced range over the last half century. The role of climate change and forest management practices in the increase is unclear. We analyzed tree-ring chronologies from six late-successional Douglas-fir stands in the western Oregon Coast Range using time-series intervention analysis (TSIA) to address how climate relates to the impact of SNC on tree growth. Tree-ring chronologies of western hemlock (Tsuga heterophylla (Raf.) Sarg.), a species not susceptible to the fungus Phaeocryptopus gaeumannii (Rhode) Petrak, were used as a climate proxy in the TSIA. We found that growth reductions associated with SNC dated back to the 1590s, the earliest record in our dendritic data. Growth reductions were synchronous across the six sites, indicating that the disease severity was largely influenced by climatic conditions. SNC impact peaked in 1984–1986 at all six study sites, followed by unprecedented disease impacts of 100% in 1996 and 2004 at one site, while decreasing to previous levels at the other five sites. Our SNC index of impact significantly correlated with winter and summer temperatures and summer precipitation. Winter conditions were more strongly associated with disease impact at wetter, cooler sites, whereas summer conditions were more important at less humid, warmer sites. With climate change, SNC impacts are likely to increase in coastal areas where June–July precipitation is much higher than the P. gaeumannii-limiting threshold of ∼110 mm, and decrease where summer precipitation is at or below the threshold. Warmer winters will increase disease severity at higher elevation, north along the coast from northern Oregon to British Columbia, and at inland sites where current winter temperatures limit fungal growth.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".