Data from: Using a ‘time machine’ to test for local adaptation of aquatic microbes to temporal and spatial environmental variation
Bibliographic record
Abstract
Local adaptation occurs when different environments are dominated by different specialist genotypes, each of which is relatively fit in its local conditions and relatively unfit under other conditions. Analogously, ecological species sorting occurs when different environments are dominated by different competing species, each of which is relatively fit in its local conditions. The simplest theory predicts that spatial, but not temporal, environmental variation selects for local adaptation (or generates species sorting), but this prediction is difficult to test. Although organisms can be reciprocally transplanted among sites, doing so among times seems implausible. Here we describe a reciprocal transplant experiment testing for local adaptation or species sorting of lake bacteria in response to both temporal and spatial variation in water chemistry. The experiment used a −80 °C freezer as a ‘time machine’. Bacterial isolates and water samples were frozen for later use, allowing transplantation of older isolates ‘forward in time’ and newer isolates ‘backward in time’. Surprisingly, local maladaptation predominated over local adaptation in both space and time. Such local maladaptation may indicate that adaptation, or the analogous species sorting process, fails to keep pace with temporal fluctuations in water chemistry. This hypothesis could be tested with more finely-resolved temporal data.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.018 |
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".