Evidence map›Paper›PMID 42253486›Full record

ArticleEvolutionary applications2026

Predictions From Evolutionary Theory for Urban Environments.

Ailene MacPherson, Aude E Caizergues, Paul Savary, Kuangyi Xu, Philipp W Messer, Mia N Akbar, Marie-Josée Fortin, Robert D Holt, Amber Gigi Hoi, Nicole Mideo and 4 more

Abstract read
In one paragraph

Article in Evolutionary applications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Ailene MacPhersonDepartment of Mathematics Simon Fraser University Burnaby British Columbia Canada.ORCID https://orcid.org/0000-0003-1783-4127
Aude E CaizerguesDepartment of Biology University of Toronto Mississauga Mississauga Ontario Canada.ORCID https://orcid.org/0000-0003-4467-3912
Paul SavaryDepartment of Biology Concordia University Montreal Quebec Canada.
Kuangyi XuDepartment of Ecology and Evolutionary Biology University of Toronto Toronto Ontario Canada.
Philipp W MesserDepartment of Computational Biology Cornell University Ithaca New York USA.
Mia N AkbarDepartment of Ecology and Evolutionary Biology University of Toronto Toronto Ontario Canada.
Marie-Josée FortinDepartment of Ecology and Evolutionary Biology University of Toronto Toronto Ontario Canada.
Robert D HoltDepartment of Biology University of Florida Gainesville Florida USA.ORCID https://orcid.org/0000-0002-6685-547X
Amber Gigi HoiDepartment of Ecology and Evolutionary Biology University of Toronto Toronto Ontario Canada.
Nicole MideoDepartment of Ecology and Evolutionary Biology University of Toronto Toronto Ontario Canada.ORCID https://orcid.org/0000-0002-8719-3620
Rob NessDepartment of Biology University of Toronto Mississauga Mississauga Ontario Canada.
Pedro R Peres-NetoDepartment of Biology Concordia University Montreal Quebec Canada.
James S SantangeloDepartment of Biology University of Ottawa Ottawa Ontario Canada.
Marc T J JohnsonDepartment of Biology University of Toronto Mississauga Mississauga Ontario Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urbanization drives rapid and extreme environmental change, profoundly shaping the ecology and evolution of populations. In this Perspective, we call for the integration and development of evolutionary theory and empirical research through collaboration between theoretical and experimental biologists to provide new insights into urban evolutionary ecology. We argue that mathematical models derived from ecological and evolutionary theory can be tailored to provide a powerful framework for generating predictions that can guide empirical research in urban ecology and evolution. At the same time, empirical results can motivate and inform the development and analysis of new theoretical models specific to urban systems. We illustrate how existing evolutionary theory can be harnessed to generate specific predictions of how urbanization can influence evolution. These predictions span the range of urban impacts on all main evolutionary processes, including mutation, gene flow, genetic drift, non-random mating, and selection. We provide a summary of evidence supporting each prediction and outline empirical approaches available to test them. Importantly, these predictions require distinct modeling approaches that can be applied more broadly to better utilize theory for research on urban environments. To facilitate this, we provide an overview of these existing modeling approaches ranging from the application and syntheses of classic model results to the development of novel probabilistic predictions. We advocate for increased integration of theoretical and empirical research through the development of novel models using parameterization specific to urban systems, empirical tests grounded in theoretical models, model-based empirical tests, and model-based data analysis and inference to advance our understanding of evolution in urban environments.

Indexed as

anthropoceneanthropogenic disturbanceurban ecologyurban ecosystemurban evolution

Identifiers

PMID42253486
PMCPMC13239718

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.