A New Framework for Urban Ecology: An Integration of Proximate and Ultimate Responses to Anthropogenic Change
Bibliographic record
Abstract
As urban areas continue to grow, understanding how species respond and adapt to urban habitats is becoming increasingly important. Knowledge of the mechanisms behind observed phenotypic changes of urban-dwelling animals will enable us to better evaluate the impact of urbanization on current and future generations of wildlife and predict how animals respond to novel environments. Recently, urban ecology has emerged not only as a means of understanding organismal adaptation but also as a framework for exploring mechanisms mediating evolutionary phenomena. Here, we have identified four important research topics that will advance the field of urban ecology and shed light on the proximate and ultimate causes of the phenotypic differences commonly seen among species and populations that vary in their responses to urbanization. First, we address the ecological and socio-economic factors that characterize cities, how they might interact with each other, and how they affect urban species. Second, we ask which are the proximate mechanisms underlying the emergence over time of novel traits in urban organisms, focusing on developmental effects. Third, we emphasize the importance of understanding the ultimate causations that link phenotypic shifts to function. This question highlights the need to quantify the strength and direction of selection that urban individuals are exposed to, and whether the phenotypic shifts associated with life in the city are adaptive. Lastly, we stress the need to translate how individual-level responses scale up to population dynamics. Understanding the mechanistic underpinnings of variation among populations and species in their responses to urbanization will unravel species resilience to environmental perturbation, which will facilitate predictive models for sustainability and development of green cities that maintain or even increase urban biodiversity and wildlife health and wellbeing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".