Phenotypic evolution is more constrained in simpler food webs
Bibliographic record
Abstract
Abstract Global change is simplifying the structure of ecological networks; however, we are currently in a poor position to predict how these simplified communities will affect the evolutionary potential of remaining populations. Theory on adaptive landscapes provides a framework for predicting how selection constrains phenotypic evolution, but often treats the community context of evolving populations as a “black box”. Here, we integrate ecological networks and adaptive landscapes to examine how changes in food-web complexity shape evolutionary constraints. We conducted a field experiment that manipulated the diversity of insect parasitoids (food-web complexity) that were able to impose selection on an insect herbivore. We then measured herbivore survival as a function of three key phenotypic traits. We found that more traits were under selection in simpler vs. more complex food webs. The adaptive landscape was more neutral in complex food webs because different parasitoid species impose different selection pressures, minimizing relative fitness differences among phenotypes. Our results suggest that phenotypic evolution becomes more constrained in simplified food webs. This indicates that the simplification of ecological communities may constrain the adaptive potential of remaining populations to future environmental change. “What escapes the eye, however, is a much more insidious kind of extinction: the extinction of ecological interactions.” Janzen (1974)
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".