Shinichi Nakagawa

A flexible modelling framework for estimating thermal tolerance and sensitivity

This paper introduces a flexible modelling framework using a four-parameter log-logistic model to estimate thermal tolerance and sensitivity, improving reliability over conventional two-stage analyses.

Music exposure reduces anxiety- and depression-like behavior in rodents: a systematic review and multilevel meta-analysis

This study reviews and analyzes preclinical rodent studies on music exposure's effects on anxiety and depression-like behaviors, finding a significant reduction in mean behaviors but no overall change in variability.

Fast phylogenetic generalised linear mixed-effects modelling using the glmmTMB R package

The paper compares R packages for phylogenetic generalised linear mixed models and shows glmmTMB is faster and more flexible for fitting PGLMMs with likelihood-based models.

Evidence that genome editing is preferable to transgenesis forenhancing animal traits

This study compares transgenesis and genome editing across 72 species and 55 genes to assess their effectiveness in enhancing animal production traits, finding gene editing more impactful overall.

Underappreciated role of environmental enrichment in alleviating depression and anxiety: Quantitative evidence synthesis of rodent models

This study quantitatively synthesizes rodent model data to show environmental enrichment reduces depressive and anxiety-like behaviors by 16% and decreases inter-individual variability by 12%.

Sperm storage reduces sperm and embryo quality in animals

This study investigates how in-vivo sperm storage affects sperm quality, fertilization success, and offspring quality across humans and non-human animals, finding that storage duration and other factors influence these outcomes.

YOLO-Behaviour: A simple, flexible framework to automatically quantify animal behaviours from videos

YOLOv8 detection with SORT event grouping and grid-search tuning quantifies visually distinct behaviours from video across 5 case studies, reaching f1-scores of 0.62 to 0.94.

Interpreting prediction intervals and distributions for decoding biological generality in meta-analyses

The paper evaluates generality in ecological and evolutionary meta-analyses, finding that 20% of meta-analyses show consistent effects across contexts.

Bivariate multilevel meta-analysis of log response ratio and standardized mean difference for robust and reproducible environmental and biological sciences

This paper introduces a bivariate multilevel meta-analysis method for log response ratio and standardized mean difference to improve reproducibility and reduce false positives in environmental and biological meta-analyses.

partR2: Partitioning R2 in generalized linear mixed models

partR2 quantifies part R2 and structure coefficients for fixed effects in generalized linear mixed models, providing variance decomposition and confidence intervals.

Embrace heterogeneity to improve reproducibility: A perspective from meta-analysis of variation in preclinical research

This paper argues for embracing heterogeneity in preclinical research to improve reproducibility and generalizability through meta-analysis of phenotypic variability in ischaemic stroke models.

Revisiting and expanding the meta-analysis of variation: The log coefficient of variation ratio, lnCVR

This paper reviews and proposes new estimators for effect size statistics lnVR and lnCVR to compare variability between two groups in meta-analyses, emphasizing the importance of accounting for group dependence.

Facultative adjustment of paternal care in the face of female infidelity in dunnocks

The paper tests the certainty of paternity hypothesis in dunnocks, finding within-male adjustment in paternal care when paternity is lost, but no strong between-male effect.

General methods for evolutionary quantitative genetic inference from generalised mixed models.

This paper provides methods to infer evolutionary quantitative genetic parameters from generalized linear mixed models, enabling prediction of response to selection for non-normal traits.