Using the resurrection approach to understand contemporary evolution in changing environments
Bibliographic record
Abstract
The resurrection approach of reviving ancestors from stored propagules and comparing them with descendants under common conditions has emerged as a powerful method of detecting and characterizing contemporary evolution. As climatic and other environmental conditions continue to change at a rapid pace, this approach is becoming particularly useful for predicting and monitoring evolutionary responses. We evaluate this approach, explain the advantages and limitations, suggest best practices for implementation, review studies in which this approach has been used, and explore how it can be incorporated into conservation and management efforts. We find that although the approach has thus far been used in a limited number of cases, these studies have provided strong evidence for rapid contemporary adaptive evolution in a variety of systems, particularly in response to anthropogenic environmental change, although it is far from clear that evolution will be able to rescue many populations from extinction given current rates of global changes. We also highlight one effort, known as Project Baseline, to create a collection of stored seeds that can take advantage of the resurrection approach to examine evolutionary responses to environmental change over the coming decades. We conclude that the resurrection approach is a useful tool that could be more widely employed to examine basic questions about evolution in natural populations and to assist in the conservation and management of these populations as they face continued environmental change.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".