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Record W2886760664 · doi:10.1093/icb/icy110

A New Framework for Urban Ecology: An Integration of Proximate and Ultimate Responses to Anthropogenic Change

2018· article· en· W2886760664 on OpenAlexaff
Jenny Q. Ouyang, Caroline Isaksson, Chloé Schmidt, Pierce Hutton, Frances Bonier, Davide M. Dominoni

Bibliographic record

VenueIntegrative and Comparative Biology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsQueen's UniversityUniversity of Manitoba
FundersOffice of Integrative ActivitiesNational Institute of General Medical SciencesNational Science FoundationVetenskapsrådetNational Institutes of HealthSociety for Integrative and Comparative Biology (SICB)
KeywordsUrbanizationEcologyPsychological resilienceUrban ecologyWildlifeBiologyHabitatPopulationAdaptation (eye)Evolutionary ecologyFunctional ecologyGeographyEcosystemSociologyPsychology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.018
Scholarly communication0.0060.009
Open science0.0030.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.098
GPT teacher head0.373
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations74
Published2018
Admission routes1
Has abstractyes

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