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The most appropriate variance-covariate structure for each model was determined using a combination of AIC scores and the examination of plots of fitted values versus residuals based on a full model specification using restricted maximum likelihood (REML) [ 52].
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Goodness of fit for the PLS regression model was determined using the validation set.
Likelihood scores for each model were estimated in PAUP*4.0b10 [92] and the best model was determined using the Akaike Information Criterion AICC) [93].
For ML analyses, the best-fit likelihood model was determined using ProtTest [43].
† Models were determined using backward conditional multiple regression, with P≤.05 required for variable retention.
Optimal models are determined using the Bayesian Information Criterion.
The best-fit model of substitution for each partition was determined using the Bayesian information criterion.
The model of choice for each partition was determined using the program Modeltest [ 29].
The most appropriate model of nucleotide substitution for each dataset was determined using jModelTest v0.1.1 [78].
The final logistic regression model for each independent variable was determined using backward selection.
The best nucleotide substitution model for the each locus was determined using jMODELTEST [48].
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