Evidence map›Paper›PMID 40235724›Full record

ArticleEcology and evolution2025

Population Genetics Meets Ecology: A Guide to Individual-Based Simulations in Continuous Landscapes.

Elizabeth T Chevy, Jiseon Min, Victoria Caudill, Samuel E Champer, Benjamin C Haller, Clara T Rehmann, Chris C R Smith, Silas Tittes, Philipp W Messer, Andrew D Kern and 2 more

Erratum issuedAbstract read
In one paragraph

Article in Ecology and evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Resource-explicit interactions in spatial population models.Methods in ecology and evolution · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Elizabeth T ChevyCenter for Computational Molecular Biology Brown University Providence Rhode Island USA.ORCID https://orcid.org/0000-0003-2279-6129
Jiseon MinInstitute of Ecology and Evolution University of Oregon Eugene Oregon USA.ORCID https://orcid.org/0000-0002-1025-7761
Victoria CaudillInstitute of Ecology and Evolution University of Oregon Eugene Oregon USA.ORCID https://orcid.org/0000-0002-0577-5513
Samuel E ChamperDepartment of Computational Biology Cornell University Ithaca New York USA.ORCID https://orcid.org/0000-0002-4559-7627
Benjamin C HallerDepartment of Computational Biology Cornell University Ithaca New York USA.ORCID https://orcid.org/0000-0003-1874-8327
Clara T RehmannInstitute of Ecology and Evolution University of Oregon Eugene Oregon USA.ORCID https://orcid.org/0009-0006-3475-3053
Chris C R SmithInstitute of Ecology and Evolution University of Oregon Eugene Oregon USA.ORCID https://orcid.org/0000-0002-6470-3413
Silas TittesInstitute of Ecology and Evolution University of Oregon Eugene Oregon USA.ORCID https://orcid.org/0000-0003-4697-7434
Philipp W MesserDepartment of Computational Biology Cornell University Ithaca New York USA.ORCID https://orcid.org/0000-0001-8453-9377
Andrew D KernInstitute of Ecology and Evolution University of Oregon Eugene Oregon USA.ORCID https://orcid.org/0000-0003-4381-4680
Sohini RamachandranCenter for Computational Molecular Biology Brown University Providence Rhode Island USA.ORCID https://orcid.org/0000-0002-9588-7964
Peter L RalphInstitute of Ecology and Evolution University of Oregon Eugene Oregon USA.ORCID https://orcid.org/0000-0002-9459-6866

Funding

Deep learning for population geneticsR01HG010774 · NHGRI · UNIVERSITY OF OREGON · PI ANDREW D KERN · 2020 to 2026
$3.2M
Scaling up computational genomics with tree sequencesR01HG012473 · NHGRI · UNIVERSITY OF OREGON · PI PETER Lochhead RALPH · 2023 to 2026
$2.3M
Population-genetic methods for inferring recent histories and targets of selection in the biobank eraR35GM139628 · NIGMS · BROWN UNIVERSITY · PI Sohini Ramachandran · 2021 to 2026
$2.2M
Predoctoral Training Program in Biological Data Science at Brown UniversityT32GM128596 · NIGMS · BROWN UNIVERSITY · PI RAMACHANDRAN, SOHINI, SANDSTEDE, BJORN · 2018 to 2022
$1.5M
Computational Population GeneticsR35GM148253 · NIGMS · UNIVERSITY OF OREGON · PI ANDREW D KERN · 2023 to 2026
$1.5M
Population genetics of rapid evolutionary processesR35GM152242 · NIGMS · CORNELL UNIVERSITY · PI Philipp W Messer · 2024 to 2026
$1.2M
Deep learning for spatial population geneticsF32GM146484 · NIGMS · UNIVERSITY OF OREGON · PI SMITH, CHRIS C · 2022 to 2023
$122k
NHGRI NIH HHS R01 HG010774NHGRI NIH HHS R01 HG012473NIGMS NIH HHS F32 GM146484NIGMS NIH HHS R35 GM139628NIGMS NIH HHS R35 GM148253NIGMS NIH HHS R35 GM152242NIGMS NIH HHS T32 GM128596
6 · The paper itself

Abstract

Individual-based simulation has become an increasingly crucial tool for many fields of population biology. However, continuous geography is important to many applications, and implementing realistic and stable simulations in continuous space presents a variety of difficulties, from modeling choices to computational efficiency. This paper aims to be a practical guide to spatial simulation, helping researchers to implement individual-based simulations and avoid common pitfalls. To do this, we delve into mechanisms of mating, reproduction, density-dependent feedback, and dispersal, all of which may vary across the landscape, discuss how these affect population dynamics, and describe how to parameterize simulations in convenient ways (for instance, to achieve a desired population density). We also demonstrate how to implement these models using the current version of the individual-based simulator, SLiM. We additionally discuss natural selection-in particular, how genetic variation can affect demographic processes. Finally, we provide four short vignettes: simulations of pikas that shift their range up a mountain as temperatures rise; mosquitoes that live in rivers as juveniles and experience seasonally changing habitat; cane toads that expand across Australia, reaching 120 million individuals; and monarch butterflies whose populations are regulated by an explicitly modeled resource (milkweed).

Identifiers

PMID40235724
PMCPMC11997375

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.