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In our study, we used a binomial error distribution, which best fit the nature of our data.
For prevalence, we used a binomial error structure, and for parasite intensity (log-transformed as the data were eggs per gram of faeces), we used a Gaussian error structure.
To test for effects on the binary responses of hatching probability and nestling paternity (within-pair vs. extra-pair), we used a binomial error distribution with a logit link function.
When the proportion of WPP was assigned as the dependent variable, we used a binomial error distribution with a logit link function with the number of social fledglings as the denominator.
We initially transformed the time that individuals spent in a tree into ordinal data, and used a Poisson error structure to model differences, but because the data were still overdispersed, we subsequently used a binomial error structure and logit link function to look at whether individuals stayed arboreal for longer than 200 s or not.
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Survival was modeled using a binomial error distribution.
We therefore analysed seedling germination patterns with a generalised linear mixed-effects model with penalised quasi-likelihood (hereafter GLMM) (glmmPQL function in R.2.4.1, using the MASS library [36]), with plateau, distance, propagule, cage and Time as fixed effects, maternal tree as a random effect, and using a binomial error structure.
Models 2 and 4 were fitted using a binomial error structure and logit link function.
Using a binomial error distribution requires binomial data, as for example "LLR male" and "non-LRR male".
It is recommended that you model this outcome with a generalized linear mixed model, using a binomial error distribution.
The spot check data for mating rate were modeled using a binomial error distribution as the number of mating opportunities (e.g., the number of females in each treatment × the number of spot checks) taken vs. not taken.
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