journal article Open Access Jan 01, 2026

Location‐scale models and cross validation to advance quantitative evidence synthesis

Ecology Vol. 107 No. 1 · Wiley
View at Publisher Save 10.1002/ecy.70303
Abstract
Abstract
Quantitative evidence synthesis is a prominent path towards generality in ecology. Generality is typically discussed in terms of central tendencies, such as an average effect across a compilation of studies, and the role of heterogeneity for assessing generality is less well developed. Heterogeneity examines the transferability of ecological effects across contexts, though between‐study variance is typically assumed as constant (i.e., homoscedastic). Here, I use two case studies to show how location‐scale models that relax the assumption of homoscedasticity and cross validation can combine to further the goals of evidence syntheses. First, I examine scale‐dependent heterogeneity for a meta‐analysis of plant native‐exotic species richness relationships, quantifying the relationships among unexplained effect size variation, spatial grain and extent. Second, I examine relationships among habitat fragment size, study‐level covariates and unexplained variation in patch‐scale species richness using a database of fragmentation studies. Heteroscedastic models quantify where effects can be transferred with more or less certainty and provide new descriptions of transferability for both case studies. Cross validation can be applied to a single or multiple models, adapted to either the goal of assessing intervention efficacy or generalization and, for the case studies examined here, showed that assuming homoscedasticity limits transferability.
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References
45
[2]
Betancourt M.2016.“Diagnosing Suboptimal Cotangent Disintegrations in Hamiltonian Monte Carlo.”ArXiv.
[6]
brms: An R Package for Bayesian Multilevel Models Using Stan

Paul-Christian Bürkner

Journal of Statistical Software 10.18637/jss.v080.i01
[7]
Stan: A Probabilistic Programming Language

Bob Carpenter, Andrew Gelman, Matthew D. Hoffman et al.

Journal of Statistical Software 10.18637/jss.v076.i01
[15]
Gelman A. A.Vehtari D.Simpson C. C.Margossian B.Carpenter Y.Yao L.Kennedy J.Gabry P. C.Bürkner andM.Modrák.2020.“Bayesian Workflow.”ArXiv.
[16]
Meta-analysis and the science of research synthesis

Jessica Gurevitch, Julia Koricheva, Shinichi Nakagawa et al.

Nature 10.1038/nature25753
[17]
The Elements of Statistical Learning

Trevor Hastie, Robert Tibshirani, Jerome Friedman

Springer Series in Statistics 10.1007/978-0-387-84858-7
[25]
Modrák M. "Simulation‐Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity" Bayesian Analysis (2023)
[29]
Peng S. N. L.Kinlock J.Gurevitch andS.Peng.2019a.“Data from: Correlation of Native and Exotic Species Richness: A Global Meta‐Analysis Finds No Invasion Paradox Across Scales [Dataset].”Dryad.https://doi.org/10.5061/dryad.59kv753 10.1002/ecy.2552
[32]
Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure

David R. Roberts, Volker Bahn, Simone Ciuti et al.

Ecography 10.1111/ecog.02881
[34]
Säilynoja T. M.Schmitt P.‐C.Bürkner andA.Vehtari.2025.“Posterior SBC: Simulation‐Based Calibration Checking Conditional on Data.”arXiv. 10.1007/s11222-026-10825-9
[35]
Heterogeneity in ecological and evolutionary meta‐analyses: its magnitude and implications

Alistair M. Senior, Catherine E. Grueber, Tsukushi Kamiya et al.

Ecology 10.1002/ecy.1591
[36]
Community ecology theory as a framework for biological invasions

K Shea

Trends in Ecology & Evolution 10.1016/s0169-5347(02)02495-3
[38]
Talts S. M.Betancourt D.Simpson A.Vehtari andA.Gelman.2020.“Validating Bayesian Inference Algorithms with Simulation‐Based Calibration.”arXiv.
[39]
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

Aki Vehtari, Andrew Gelman, Jonah Gabry

Statistics and Computing 10.1007/s11222-016-9696-4
[40]
Conducting Meta-Analyses inRwith themetaforPackage

Wolfgang Viechtbauer

Journal of Statistical Software 10.18637/jss.v036.i03
[42]
Williams D. R. J. E.Rodriguez andP. C.Bürkner.2021.“Putting Variation Into Variance: Modeling between‐Study Heterogeneity in Meta‐Analysis.”PsyArXiv. 10.31234/osf.io/9vkqy
[44]
Parsimonious model selection using information theory: a modified selection rule

Luke A. Yates, Shane A. Richards, Barry W. Brook

Ecology 10.1002/ecy.3475
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Published
Jan 01, 2026
Vol/Issue
107(1)
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Funding
Deutsche Forschungsgemeinschaft Award: FZT 118
European Commission
Cite This Article
Shane A. Blowes (2026). Location‐scale models and cross validation to advance quantitative evidence synthesis. Ecology, 107(1). https://doi.org/10.1002/ecy.70303
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