Evidence map›Paper›PMID 36479028›Full record

ArticleEcology and evolution2022

EcoEvoApps: Interactive apps for theoretical models in ecology and evolutionary biology.

Rosa M McGuire, Kenji T Hayashi, Xinyi Yan, Marcel Caritá Vaz, Damla Cinoğlu, Madeline C Cowen, Alejandra Martínez-Blancas, Lauren L Sullivan, Sheila Vazquez-Morales, Gaurav S Kandlikar

Open access · goldAbstract read
In one paragraph

Article in Ecology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.5field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Ecology and evolution · 2026
    Article
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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

10 authors at 5 institutions in 2 countries.

Rosa M McGuireDepartment of Ecology and Evolutionary Biology University of California Los Angeles California USA.ORCID https://orcid.org/0000-0002-9102-6892
Kenji T HayashiDepartment of Ecology and Evolutionary Biology University of California Los Angeles California USA.ORCID https://orcid.org/0000-0002-7598-1865
Xinyi YanDepartment of Integrative Biology University of Texas at Austin Austin Texas USA.ORCID https://orcid.org/0000-0003-0480-5044
Marcel Caritá VazInstitute for Environmental Science and Sustainability Wilkes University Wilkes-Barre Pennsylvania USA.ORCID https://orcid.org/0000-0002-6003-8583
Damla CinoğluDepartment of Integrative Biology University of Texas at Austin Austin Texas USA.
Madeline C CowenDepartment of Ecology and Evolutionary Biology University of California Los Angeles California USA.
Alejandra Martínez-BlancasDepartamento de Ecología y Recursos Naturales, Facultad de Ciencias Universidad Nacional Autónoma de Mexico Ciudad de México Mexico.
Lauren L SullivanDivision of Biological Sciences University of Missouri Columbia Missouri USA.ORCID https://orcid.org/0000-0002-4198-3483
Sheila Vazquez-MoralesDivision of Biological Sciences University of Missouri Columbia Missouri USA.
Gaurav S KandlikarDivision of Biological Sciences University of Missouri Columbia Missouri USA.ORCID https://orcid.org/0000-0003-3043-6780
University of California, Los Angeles · USUniversity of Missouri · USThe University of Texas at Austin · USUniversidad Nacional Autónoma de México · MXWilkes University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of theory and data drives progress in science, but a persistent barrier to such integration in ecology and evolutionary biology is that theory is often developed and expressed in the form of mathematical models that can feel daunting and inaccessible for students and empiricists with variable quantitative training and attitudes towards math. A promising way to make mathematical models more approachable is to embed them into interactive tools with which one can visually evaluate model structures and directly explore model outcomes through simulation. To promote such interactive learning of quantitative models, we developed EcoEvoApps, a collection of free, open-source, and multilingual R/Shiny apps that include model overviews, interactive model simulations, and code to implement these models directly in R. The package currently focuses on canonical models of population dynamics, species interactions, and landscape ecology. These apps help illustrate fundamental results from theoretical ecology and can serve as valuable teaching tools in classroom settings. We present data from student surveys which show that students rate these apps as useful learning tools, and that using interactive apps leads to substantial gains in students' interest and confidence in working with mathematical models. This points to the potential for interactive activities to make theoretical models more accessible to a wider audience, and thus facilitate the feedback between theory and data across ecology and evolutionary biology.

Indexed as

ecological theorymathematical modelingR packageshiny appsteaching

Identifiers

PMID36479028
PMCPMC9719042
OpenAlexW4311025598

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.