Modeling unobserved variables in dendrochronological age structures improves inferences about population dynamics
Bibliographic record
Abstract
The loss of evidence poses a major challenge to historical ecology. For example, dendroecological studies aiming at relating tree establishment with past climate should consider the possibility that many plants might not survive to be recorded at the date of the study. A standard approach to deal with this data loss consists in fitting an exponential decay curve to the observed age structure and using the residuals of this fit as a proxy of tree establishment. Here, we show that hierarchical Bayesian analysis (HBA), where tree establishment is modeled as a latent variable, can outperform the standard approach. We illustrate the use of HBA with a simulation study in which the goal is to infer population dynamics from dendrochronological age structures. Both methods are also used to analyze empirical data from expanding Alnus acuminata Kunth forests in northwestern Argentina. The simulation study showed that the standard approach underestimated the association between rainfall and tree establishment. The HBA was unbiased and had narrower uncertainty around estimates. In the empirical study, the HBA detected effects of rainfall on tree establishment, which were deemed not significant by the standard approach. Besides these advantages, the flexibility of the HBA should allow for the analysis of more complex (and realistic) models.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".