Nested vs non-nested model comparison (Myrtaceae)

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Method synopsis

Compares non-nested linear models using only species identity as predictor versus nested mixed models that account for genotype variation within species. Evaluates model performance using R², AIC, and significance testing.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure as described in the canonical source

Steps below were extracted from the paper that introduces this protocol — Accounting for the nested nature of genetic variation across levels of organization improves our understanding of biodiversity and community ecology (2016), Oikos. Implementations in other papers (listed below) may differ.

1. Fit statistical models
   Applied two statistical approaches: (1) non-nested linear models with trait as response variable and species as categorical fixed effect, and (2) nested mixed models with trait as response variable, species as categorical fixed effect, and genotype as categorical random effect nested within species
   Quantities: Models fit to all simulated datasetsDuration: Per dataset analysisConditions: Statistical analysis environment
   Equipment: R language, lme4 function in lmer package, restricted maximum likelihood method

Typical Equipment

• R language
• lme4 function in lmer package

Output Measurements

• R² values
• AIC scores
• p-values
• effect sizes

Papers Using This Protocol (1)

Species Studied With This Protocol

Places Where Used