Bibliographic record
Abstract
Research Highlight: Brans, K. I., Tüzün, N., Sentis, A., De Meester, L., & Stoks, R. (2021). Cryptic eco-evolutionary feedback in the city: Urban evolution of prey dampens the effect of urban evolution of the predator. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.13601. Despite the strength and ubiquity of urban stressors on multiple taxa, there have been minimal attempts to determine the ecological consequences of urban evolution on multiple species. Brans & Tüzün et al. use a well-known predator prey system-damselfly nymphs Ischnura elegans and water fleas Daphnia magna-to test whether scenarios in which both species evolve in response to urbanization differ from scenarios in which only the predator or prey evolves. The authors show that urban damselflies showed higher encounter and predation rates when paired with rural prey, but that the advantages conferred by urbanization-driven adaptation disappeared when urban predators encountered urban prey. This represents a cryptic eco-evolutionary feedback, where evolution of both predator and prey concealed the effect of evolution in each partner individually. Results suggest that mismatches in the evolutionary responses of interacting species may have significant ecological consequences, and highlight the importance of a multi-species approach in eco-evolutionary dynamics research.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".