<b>Table 1.</b> Akaike weights for North Pacific bigeye tuna data.

a, Properly defined Akaike weights [37], calculated here from the raw data (all individuals pooled together) using the equations in Box 1 of [14]. Respective log-likelihoods are and , giving Akaike Information Criteria of 236,256 and 232,599. b, Data for each individual were binned using the log-binning with normalization (LBN, [13]) technique, and regression lines fitted to all the points plotted on one figure (see Supplementary Fig. 3 of [27]). c, LBN method for all individuals pooled together [27]. d, LBN method with generalised linear mixed-effect models, using individual as a random factor [27]. e, Bayesian (rather than Akaike) Information Criteria [37] weights based on fitting linear regressions to rank/frequency plots [27] for all individuals pooled together. f, Same method as e but calculated here for just two models (result can also be deduced from Supplementary Table 7 of [27]).