Climate response in dominant and suppressed spruce trees,<i>Picea abies</i>(L.) Karst., on a subalpine and lower montane site in Switzerland
Bibliographic record
Abstract
In most dendroclimatological reconstructions, cores are usually taken from the biggest and oldest trees in a stand. The objective of the present study was to investigate the possible influence of such a subjective sampling technique in climate reconstructions using tree ring data. In order to assess the implications of any influence, the study was carried out in two stands with different site elevations and different climate data was used for one of the sites. Twenty-seven spruce trees from an upper timberline site and 18 spruce trees from a lower montane site were investigated. On both sites, subsets of nine dominant and nine suppressed trees were established on the basis of social status and stem diameter. Four additional subsets were made up of trees from the timberline site. The subsets “winner”, “loser” and “indifferent” were based on cumulative basal increment growth, and the subset “damaged” only contained trees with strong stem wood injuries. The climate response of each subset was calculated using bootstrap response functions over the investigation period 1901-1995. Mean monthly temperature and monthly precipitation sums were used as independent variables. For the subalpine site, response function models with measured and modelled climate data were calculated. On the subalpine site, the results indicate significant positive correlation of tree-ring growth to monthly mean temperatures in June (<em>r</em> = 0.294) and July (<em>r</em> = 0.305), and on the lower montane site, significant negative correlation to June temperatures (<em>r</em> = -0.234). Shifts in the correlation values of single months between the subsets as well as between the models with measured and modelled climate data series were small. The largest difference in the tree-ring growth – climate relationship was found between the subalpine and lower montane sites. The results reveal that the common sampling strategy in dendroclimatology (oldest, largest and dominant trees) hardly affects the results in annual climate response. The results also confirm that ecological site conditions are the most determining factors in the growth models. The selection of meteorological stations can also affect the results but this is of secondary importance. The social status of the tree is of less importance for tree ring growth compared to site elevation and the weather stations used for the regression model. Thus, restricting sampling to the biggest and oldest trees does not seem to be a major problem for dendroclimatological reconstructions.
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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 | 0.000 | 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 | 0.000 |
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".