Consequences of decreasing the number of cored trees per plot on chronology statistics and climate–growth relationships: a multispecies analysis in a temperate climate
Bibliographic record
Abstract
Existing literature investigates the effect of the number of cored trees per plot (N) on chronology statistics. The present study sought to highlight (i) the effect of N on the reliability of both chronology and climate–growth relationships and (ii) its variability across five European tree species with differentiated ecophysiological patterns. Fifty-eight pure, even-aged forests were sampled across France. For each plot, dendroecological investigations were carried out using chronologies built from 28 to three trees. Chronology reliability was studied using the mean intertree correlation (rbt) and the expressed population signal (EPS), whereas the climate–growth relationships were evaluated through the bootstrapped correlation coefficients (BCC). The accuracy of the dendroecological investigations decreased with decreasing N: EPS and BCC approached zero, implying that the signal common to all trees weakened. Thus, most of the significant correlations became nonsignificant when the sample size decreased from 28 to three trees per plot. Differences were found between species. For a given sample size, the shade-intolerant species Quercus petraea and Pinus sylvestris displayed lower intertree differences in growth-index series (higher rbt) and higher EPS than the shade-tolerant species Abies alba, Fagus sylvatica, and Picea abies. These latter species also displayed a greater sensitivity to sample size decrease, with a stronger BCC weakening and a higher proportion of changes in correlation significance. The EPS threshold of 0.85 was reached for around six to 10 trees for shade-intolerant species versus 20–30 for the shade-tolerant ones and generally corresponded to a mean correlation precision of around 0.06. We finally propose a general method to estimate this precision.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".