Indirect effects drive evolutionary responses to global change
Bibliographic record
Abstract
Anthropogenic environmental changes pose significant threats to plant and animal populations. These changes also may affect the evolution of natural populations either directly or indirectly by altering the outcome of species interactions that are important drivers of evolution. This latter indirect pathway may be especially important for evolutionary responses to elevated atmospheric CO2 concentrations (eCO2), which appear to have minimal direct effects on plant evolution but have large effects on interspecific interactions, such as competition. We manipulated competitive and CO2 environments of experimental Arabidopsis thaliana populations to test whether eCO2 alters evolutionary trajectories indirectly by altering selection imposed by competitors. We found that interspecific competition increased selection on growth traits, reduced heritabilities, and altered genetic covariances between traits and that the magnitude of these effects depended upon the CO2 environment. Although eCO2 had minimal direct effects on evolutionary processes, eCO2 typically reduced the strength of selection imposed by competitors and, therefore, relaxed selection on plant traits when competitors were present. Our results indicate that global changes may affect plant evolution indirectly by altering competitive interactions and underscore the importance of conducting research in natural communities when attempting to predict population responses to global 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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".