2.3.4. Statistical analysis
The full models for ion leakage and compound content included
the predictors: temperature, day length, and cold acclimatisation
and their second- and third-order interactions. For statistical inferences,
we employed model selection using Akaike’s information
criterion corrected for small sample sizes, (AICc), a likelihoodbased
measure of model plausibility that penalises models with a
higher number of parameters. In cases with DAIC < 1, the model
with the fewer parameters was assumed better (Burnham &
Anderson, 2002). For freeze tolerance, the response variable ion
leakage is a proportion, hence a general linear model with normal
distribution and a logit link was used. For other responses, a general
linear model with normal distribution and identity link was
assumed. Particular variables were considered significant if their
95% confidence interval did not overlap zero. Differences among
levels of a variable were identified using the Tukey test at the
95% significance level. Data were log transformed when necessary
to ensure homogeneity of the variance. Data presented here are
back-transformed for clarity. The statistical package R (version
2.15.0, R Development Core Team 2010; R Foundation for StatisticalStatistical
Computing, Vienna, AT) was used for all statistical analyses
with the MuMIn package for model selection
2.3.4. Statistical analysisThe full models for ion leakage and compound content includedthe predictors: temperature, day length, and cold acclimatisationand their second- and third-order interactions. For statistical inferences,we employed model selection using Akaike’s informationcriterion corrected for small sample sizes, (AICc), a likelihoodbasedmeasure of model plausibility that penalises models with ahigher number of parameters. In cases with DAIC < 1, the modelwith the fewer parameters was assumed better (Burnham &Anderson, 2002). For freeze tolerance, the response variable ionleakage is a proportion, hence a general linear model with normaldistribution and a logit link was used. For other responses, a generallinear model with normal distribution and identity link wasassumed. Particular variables were considered significant if their95% confidence interval did not overlap zero. Differences amonglevels of a variable were identified using the Tukey test at the95% significance level. Data were log transformed when necessaryto ensure homogeneity of the variance. Data presented here areback-transformed for clarity. The statistical package R (version2.15.0, R Development Core Team 2010; R Foundation for StatisticalStatisticalComputing, Vienna, AT) was used for all statistical analyseswith the MuMIn package for model selection
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