Evaluating Coastal Douglas Fir Growth Responses to Nitrogen Application Using Tree Ring Chronologies
Bibliographic record
Abstract
Dendrochronology is a technique that can be applied as a retrospective monitoring (RM) approach to evaluate the performance of nutrient application in forest ecosystems. Applying the RM approach across operations lacks experimental controls, which may adversely affect accuracy and precision of estimates due to greater mismatches in stand conditions between treated and untreated plots. To test the rigor of the RM approach, we collected increment cores of coastal Douglas fir (Pseudotsuga menziesii var. menziesii) at eight sites of an experiment where stands were fertilized in 1971. First, we tested the approach under ideal conditions by sampling from treated and untreated plots of the experiment. Second, we tested the approach using newly established surrogate control (SC) plots, which differed in ecological site classification from those of the treated plots to understand how robust the approach was to mismatches in conditions between treated and untreated samples. We hypothesized that detrending ring width would mitigate error in responses to nutrient application resulting from mismatches in site classification. Within the experiment, the approach indicated an average increase of 15% (5% to 26%, p < 0.05) growth response to operational doses of urea. Different responses were found when the analysis relied on SC plots. Detrending low-frequency variation in ring widths eliminated differences in results arising, at least in part, to mismatches in site class. However, it also reduced the growth response using the experimental control plots to 10%. Dendrochronology with detrending shows promise in the ability to mitigate variation introduced by mismatches in ecological site classification that may occur in operational monitoring. Based on these results, we see potential to implement RM with operations to evaluate and optimize stand selection criteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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".