2.3 Statistical analyses
All statistics and graphics were carried out using R, version 3.2.5
(http://r-project.org).
(i) The effect of L. intestinalis infection on host fecundity was
tested using a generalized linear mixed-effects model (glmmPQL) fitted
with fecundity as a response variable (assuming Quasi-Poisson
distribution), and maturity stage and infection status as predictor
variables. Because the data were collected over a 10-year period, year
of sampling was included as a random effect factor in the model.
(ii) To test whether reproductive investment at maturity has increased
over time, we used a generalized linear model (glm) fitted with a
binomial distribution. The binomial response variable combined gonadal
weight of uninfected E. sardella and somatic weight. We chose to
use relative gonad weight at stage IV because this is the stage whereE. sardella reach reproductive maturity. Year was included as a
numerical predictor variable.
(iii) To test whether size of E. sardella at maturity has
decreased over time, we first fitted for each year a logistic regression
model with maturity status as a binomial response variable (0: immature;
1: mature), and body length as a continuous predictor variable
(Supplementary Figure S2). From the parameters of these logistic
regression equations, and following Diaz Pauli and Heino (2013), we
estimated for each year the length at which the probability of maturing
is 50% (i.e., LM50):
LM50\(=\frac{\text{Log}e(\frac{p}{1-p})-(a)}{b}\)
Where, p is the probability of maturity (0.5), a is the
intercept and b is the slope.
To test whether LM50 decreased over time, we fitted a
linear model (lm) with LM50 as a response variable and
year as a numerical predictor (linear and quadratic terms).