Trees do not always act their age: size-deterministic tree ring standardization for long-term trend estimation in shade-tolerant trees
Bibliographic record
Abstract
With increasing awareness of the consequences of climate change for global ecosystems, the focus and application of tree ring research have shifted to reconstruction of long-term climate-related trends in tree growth. Contemporary methods for estimating and removing biological growth trends from tree ring series (standardization) are ill-adapted to shade-tolerant species, leading to biases in the resultant chronologies. Further, many methods, including regional curve standardization (RCS), encounter significant limitations for species in which accurate age estimation is difficult. In this study we present and test two tree ring standardization models that integrate tree size in the year of ring formation into the estimation of the biological growth trend. The first method, dubbed size-deterministic standardization (SDS), uses tree diameter as the sole predictor of the growth trend. The second method includes the combined (COMB) effects of age and diameter. We show that both the SDS and COMB methods reproduce long-term trends in simulated tree ring data better than conventional methods; this result is consistent across multiple species. Further, when applied to real tree ring data, the SDS and COMB models reproduce long-term, time-related trends as reliably as traditional RCS and more reliably than other common standardization methods (i.e. C-method, basal area increments, conservative detrending). We recommend the inclusion of tree size in the year of ring formation in future tree ring standardization models, particularly when dealing with shade-tolerant species, as it does not compromise model accuracy and allows for the inclusion of unaged trees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".