Local adaptation to biotic interactions: a meta-analysis across latitudes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Adaptation to local conditions can increase species’ geographic distributions and rates of diversification, but which components of the environment commonly drive local adaptation—particularly the importance of biotic interactions—is unclear. Biotic interactions should drive local adaptation when they impose consistent divergent selection; if this is common we expect transplant experiments to detect more frequent and stronger local adaptation when biotic interactions are left intact. We tested this hypothesis using a meta-analysis of transplant experiments from >125 studies (mostly on plants). Overall, local adaptation was common and biotic interactions affected fitness. Nevertheless, local adaptation was neither more common nor stronger when biotic interactions were left intact, either between experimental treatments within studies (control vs. biotic interactions experimentally manipulated) or between studies that used natural vs. biotically-altered transplant environments. However, the effect of ameliorating negative interactions varied with latitude, suggesting that interactions may promote local adaptation more often in tropical vs. temperate ecosystems, though few tropical studies were available to test this. Our results suggest that biotic interactions often fail to drive local adaptation even though they strongly affect fitness, perhaps because temperate biotic environments are unpredictable at the spatiotemporal scales required for local adaptation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 0.011 |
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 it