"Models arise from questions about biology and the manner in which biological systems function." p. 16

"We recommend developing a set of candidate models prior to intensive data analysis, selecting one that is "best", and estimating the parameters of that model and their precision (using maximum likelihood or least square methods)." p. 22

"AIC is useful in selecting the best model in the set; however, if all the models are very poor, the AIC will still select the one estimated to be best, but even that relatively best model might be poor in an absolute sense." p. 62

"It is not the absolute size of the AIC value, it is the relative values over the set of models considered, and particularly the differences between AIC values, that are important." p.63

"AICc merely has an additional bias-correction term." "Unless the sample size is large with respect to the number of estimated parameters, use of AICc is recommended." p. 66

"change in AIC = AICi - AICmin"
"The model estimated to be best has change in AIC ~ 0" p. 71

"We can order teh change in AIC from smallest to largest, and the same ordering of the models indicates how good they are as an approximation to teh actual, expected K-L best model." p. 72

Burnham and Anderson, as below