where is body mass at age = 0, and is the growing season length (days/year). Using a W0 A D mass-length relationship = we can express Eq. (S2.2) as a function of length: W ΩLb, (S2.3) In order to simplify analysis, it is convenient and reasonable to assume the initial size as zero (L0 = 0), which allows taking a logarithmic transformation on both and resulting in: L A, (S2.4) which rearranged becomes: (S2.5) A linear regression of log(L) on log(A) allows estimating 1/[b(1- as the slope which can B, β*)] be rearranged to give = 1 1/(bB). β* – Using this method, we carried out linear regressions using length and age values from Lake Trout individual back-calculated data including only pre-maturation years (A = 1 to = A A Amat). histogram of estimates of for all individuals is presented in Figure S2.1. β* Figure S2.1. Frequency distribution of estimates of the production exponent from linear regressions on β* length-at-age data. Supplement S3. Model comparisons based on Akaike Information Criterion (AIC). S3.1. AIC values, (difference from the lowest AIC), and weights (w) from logistic regression of maturity ΔAIC at age (A), length (L). R = predator/prey mass ratio; S = sampling period. refers to predictors included in “Model” the regression. Best models are highlighted in bold. w(AIC) Predictor (X) Sex Model AIC ΔAIC
where W0 is body mass at age A = 0, and D is the growing season length (days/year). Using a mass-length relationship W = ΩLb, we can express Eq. (S2.2) as a function of length: (S2.3) In order to simplify analysis, it is convenient and reasonable to assume the initial size as zero (L0 = 0), which allows taking a logarithmic transformation on both L and A, resulting in: (S2.4) which rearranged becomes: (S2.5) A linear regression of log(L) on log(A) allows estimating 1/[b(1- β*)] as the slope B, which can be rearranged to give β* = 1 – 1/(bB). Using this method, we carried out linear regressions using length and age values from Lake Trout individual back-calculated data including only pre-maturation years (A = 1 to A = Amat). A histogram of estimates of β* for all individuals is presented in Figure S2.1. Figure S2.1. Frequency distribution of estimates of the production exponent β* from linear regressions on length-at-age data. Supplement S3. Model comparisons based on Akaike Information Criterion (AIC). S3.1. AIC values, ΔAIC (difference from the lowest AIC), and weights (w) from logistic regression of maturity at age (A), length (L). R = predator/prey mass ratio; S = sampling period. “Model” refers to predictors included in the regression. Best models are highlighted in bold. Predictor (X) Sex Model AIC ΔAIC w(AIC)
cjfas-2015-0190%20supplement%201.docx