Darwin’s finches and climate change: insights from a resilient system
Bibliographic record
Abstract
Climate change is known to influence biodiversity worldwide, with changes in organismal traits observed in many populations and species. Such effects are not universal, however, with some traits showing remarkable stability through time. Time-series analyses that link environmental variables to trait values can generate useful insights into trait evolution and its ecological bases. We use 24 years of data for beak and body traits in two species of Darwin's finches in the Galápagos Islands, alongside data on temperature and precipitation, to answer three questions: (Q1) is climate change present in our study sites? (Q2) do time-series of beak and body traits show detectable trends that suggest climate change effects? and (Q3) to what extent does weather influence annual variation in beak and body traits? We found that temperature and precipitation have been increasing over the past two decades-although this trend is minor in comparison to year-to-year variation. We next found that time-series of beak and body traits showed no detectable signs of climate change impact, instead they behave either as random walks or stable trajectories. Finally, for both species, analyses for short-term responses show that precipitation has a lagged, negative correlation with beak and body traits (effect size: maximum -0.632, minimum -0.131). Increased precipitation followed smaller traits in subsequent years. Associations of finch traits with temperature were more variable. We discuss reasons why Darwin's finches react to short-term weather changes but not to long-term climatic trends, and how these results relate to other findings in other tropical systems.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".