Evidence map›Paper›PMID 42141815›Full record

ArticleThe Journal of animal ecology2026

A new approach to quantify isotopic (co)variation across levels of biological organization using double-hierarchical generalized linear models.

Federico Garrido-de León, Julien G A Martin, Raul Costa-Pereira, Valentina Franco-Trecu, Denis Réale

Abstract read
In one paragraph

Article in The Journal of animal ecology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Federico Garrido-de LeónDepartamento de Ecología y Evolución, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay.ORCID https://orcid.org/0000-0001-8922-9714
Julien G A MartinDepartment of Biological Sciences, University of Ottawa, Ottawa, Canada.ORCID https://orcid.org/0000-0001-7726-6809
Raul Costa-PereiraDepartamento de Biologia Animal, Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Campinas, São Paulo, Brazil.ORCID https://orcid.org/0000-0003-2370-5866
Valentina Franco-TrecuDepartamento de Ecología y Evolución, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay.ORCID https://orcid.org/0000-0003-1791-010X
Denis RéaleDépartement des Sciences Biologiques, Université du Québec à Montréal, Montréal, Québec, Canada.ORCID https://orcid.org/0000-0002-0419-7125

Funding

ANII (National Agency for Research and Innovation, Uruguay), Clemente Estable fund 2019 156387Fonds de Recherche Québec Nature et Technologie (FRQNT)Fundação de Amparo à Pesquisa do Estado de São Paulo 2020/11953-2Instituto Serrapilheira R-2011-37572Natural Science and Engineering Research Council of Canada, NSERC Discovery grantNorwegian University of Science and Technology (NTNU), SQuID INTPART grant
6 · The paper itself

Abstract

Double-hierarchical generalized linear models (DHGLMs) offer a powerful yet overlooked quantitative approach for analysing ecological data with hierarchical structures. DHGLMs account for unit-specific residual variances, allowing the estimation of variance components that expand our ability to quantify biodiversity patterns across levels of biological organization-from individuals to communities. A particularly promising application of these models lies in isotopic ecology, where stable isotopes of carbon (𝛿

Indexed as

Carbon IsotopesEcologyFood ChainNitrogen IsotopesAnimalsDietLinear ModelsModels, BiologicalCarbon IsotopesNitrogen Isotopesdispersion modelhierarchical dataisotopic ecologymixed modelsresidual variancetrophic niche variation

Identifiers

PMID42141815
PMCPMC13536250

What OpenQuestion holds

Textmetadata
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.