Evidence map›Paper›PMID 31417626›Full record

ArticleEvolutionary applications2019

Local adaptation stops where ecological gradients steepen or are interrupted.

Jon R Bridle, Masakado Kawata, Roger K Butlin

Open access · goldAbstract read
In one paragraph

Article in Evolutionary applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
5.1field-weighted citation impact, top 5% of its field
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

17 citing papers in PubMed, 46 citations in OpenAlex.

  1. Evolution of species' range and niche in changing environments.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Impacts of limits to adaptation on population and community persistence in a changing environment.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Environmental variation and biotic interactions limit adaptation at ecological margins: lessons from rainforestPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Article
  9. Understanding the biology of species' ranges: when and how does evolution change the rules of ecological engagement?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Article
  10. Introduction to the theme issue 'Species' ranges in the face of changing environments'.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Article
  11. The role of phenotypic plasticity in the establishment of range margins.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Causes of maladaptation.Evolutionary applications · 2019
    Review
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

3 authors at 3 institutions in 3 countries.

Jon R BridleSchool of Biological Sciences University of Bristol Bristol UK.ORCID https://orcid.org/0000-0002-5999-0307
Masakado KawataGraduate School of Life Sciences Tohoku University Sendai Japan.
Roger K ButlinDepartment of Animal and Plant Sciences University of Sheffield Sheffield UK.
Tohoku University · JPUniversity of Bristol · GBUniversity of Gothenburg · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Population genetic models of evolution along linear environmental gradients cannot explain why adaptation stops at ecological margins. This is because, unless models impose reductions in carrying capacity at species' edges, the dominant effect of gene flow is to increase genetic variance and adaptive potential rather than swamping local adaptation. This allows the population to match even very steep changes in trait optima. We extend our previous simulations to explore two nonlinear models of ecological gradients: (a) a sigmoid (steepening) gradient and (b) a linear gradient with a flat centre of variable width. We compare the parameter conditions that allow local adaptation and range expansion from the centre, with those that permit the persistence of a perfectly adapted population distributed across the entire range. Along nonlinear gradients, colonization is easier, and extinction rarer, than along a linear gradient. This is because the shallow environmental gradient near the range centre does not cause gene flow to increase genetic variation, and so does not result in reduced population density. However, as gradient steepness increases, gene flow inflates genetic variance and reduces local population density sufficiently that genetic drift overcomes local selection, creating a finite range margin. When a flat centre is superimposed on a linear gradient, gene flow increases genetic variation dramatically at its edges, leading to an abrupt reduction in density that prevents niche expansion. Remarkably local interruptions in a linear ecological gradient (of a width much less than the mean dispersal distance) can prevent local adaptation beyond this flat centre. In contrast to other situations, this effect is stronger and more consistent where carrying capacity is high. Practically speaking, this means that habitat improvement at patch margins will make evolutionary rescue more likely. By contrast, even small improvements in habitat at patch centres may confine populations to limited areas of ecological space.

Indexed as

ecological marginslocal adaptationniche expansionpatchinesspopulation genetics

Identifiers

PMID31417626
PMCPMC6691213
OpenAlexW2921710116

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.