The Complexity of Urban Eco-evolutionary Dynamics
Bibliographic record
Abstract
Abstract Urbanization is changing Earth's ecosystems by altering the interactions and feedbacks between the fundamental ecological and evolutionary processes that maintain life. Humans in cities alter the eco-evolutionary play by simultaneously changing both the actors and the stage on which the eco-evolutionary play takes place. Urbanization modifies land surfaces, microclimates, habitat connectivity, ecological networks, food webs, species diversity, and species composition. These environmental changes can lead to changes in phenotypic, genetic, and cultural makeup of wild populations that have important consequences for ecosystem function and the essential services that nature provides to human society, such as nutrient cycling, pollination, seed dispersal, food production, and water and air purification. Understanding and monitoring urbanization-induced evolutionary changes is important to inform strategies to achieve sustainability. In the present article, we propose that understanding these dynamics requires rigorous characterization of urbanizing regions as rapidly evolving, tightly coupled human–natural systems. We explore how the emergent properties of urbanization affect eco-evolutionary dynamics across space and time. We identify five key urban drivers of change—habitat modification, connectivity, heterogeneity, novel disturbances, and biotic interactions—and highlight the direct consequences of urbanization-driven eco-evolutionary change for nature's contributions to people. Then, we explore five emerging complexities—landscape complexity, urban discontinuities, socio-ecological heterogeneity, cross-scale interactions, legacies and time lags—that need to be tackled in future research. We propose that the evolving metacommunity concept provides a powerful framework to study urban eco-evolutionary dynamics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".