Spatio‐temporal structure of multi‐storied, multi‐aged interior Douglas fir (<i>Pseudotsuga menziesii</i>var.<i>glauca</i>) stands
Bibliographic record
Abstract
Summary 1. We used replicated, repeated‐measures data to examine the spatio‐temporal structure of multi‐storied, multi‐aged interior Douglas fir ( Pseudotsuga menziesii var glauca (Mirb.) Franco) stands growing on dry sites under more than 50 years of fire protection. Along with the univariate and bivariate Ripley’s K and related functions, we used a random coefficients mixed model to investigate the variation in these functions over replicates and time. 2. The spatio‐temporal analyses revealed that trees greater than 1.3 m in height were clustered over space, and clustering was more evident for small trees (i.e. diameter at breast height (d.b.h.) ≤ 7.0 cm). 3. Bivariate functions indicated that small trees were spatially aggregated with large trees, indicating higher germination success and early survival near large trees. For these dry sites, moisture is more limiting than light and large trees provide moister microsites. 4. Dead trees were clustered, more commonly smaller in size and aggregated near large trees, indicating competition for moisture. 5. For the 16‐year period of the study, there was very little evidence that the spatio‐temporal patterns changed from a clustered to a more regular arrangement over time, unlike evidence from studies in other forest types. 6. Using the random coefficients mixed model approach, the majority of spatio‐temporal variation was due to differences among replicates, with little variation over time. 7. Under fire protection, interior Douglas fir stands on dry sites might be expected to have lower mortality but any increase in density will be limited by moisture availability. Since interior Douglas fir is moderately shade‐tolerant, this may result in an approach to a steady state of regeneration and death over the long term. 8. Synthesis . Spatio‐temporal analysis using replicated, repeated‐measures data, including a random coeffcieints mixed model approach, gave useful insights into mortality and recruitment in multi‐storied and multi‐aged stands. Similar patterns might be expected in other naturally occurring multi‐storied and multi‐aged stands. The use of replicated, repeated‐measures data rather than chronosequence data allows for the examination of true changes in spatial patterns over time.
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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 | 0.000 | 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) | 0.003 | 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".