The terroir of the finch: How spatial and temporal variation shapes phenotypic traits in Darwin's finches
Bibliographic record
Abstract
The term terroir is used in viticulture to emphasize how the biotic and abiotic characteristics of a local site influence grape physiology and thus the properties of wine. In ecology and evolution, such terroir (i.e., the effect of space or "site") is expected to play an important role in shaping phenotypic traits. Just how important is the pure spatial effect of terroir (e.g., differences between sites that persist across years) in comparison to temporal variation (e.g., differences between years that persist across sites), and the interaction between space and time (e.g., differences between sites change across years)? We answer this question by analyzing beak and body traits of 4388 medium ground finches (Geospiza fortis) collected across 10 years at three locations in Galápagos. Analyses of variance indicated that phenotypic variation was mostly explained by site for beak size (η2 = 0.42) and body size (η2 = 0.43), with a smaller contribution for beak shape (η2 = 0.05) and body shape (η2 = 0.12), but still higher compared to year and site-by-year effects. As such, the effect of terroir seems to be very strong in Darwin's finches, notwithstanding the oft-emphasized interannual variation. However, these results changed dramatically when we excluded data from Daphne Major, indicating that the strong effect of terroir was mostly driven by that particular population. These phenotypic results were largely paralleled in analyses of environmental variables (rainfall and vegetation indices) expected to shape terroir in this system. These findings affirm the evolutionary importance of terroir, while also revealing its dependence on other factors, such as geographical isolation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".