Mooij et al. (1999) examined length-at-age data of European Perch (*Perca fluviatilis*) from Lake Tjeukemeer (The Netherlands) to identify possible sexual dimorphism in growth trajectories. Their data consisted of fork length (FL; cm), ages (yrs) from otoliths, and sex from 69 fish and are recorded in this CSV file (these data are also available in `EuroPerchTJ`

from `FSAdata`

).

Use these data, and results from this exercise, to answer the following questions.

- Plot FL versus age with different symbols for each sex.
- Do you foresee any model fitting problems with these data?
- Do you observe any possible differences in growth between the sexes?

- Mooij et al. (1999) actually fit an alternative paramaterization of the von Bertalanffy growth function (VBGF) to these data. That parameterization can be viewed with
`vbModels()`

in`FSA`

. Fit the additive errors version of this paramaterization in a model where all parameters differ by sex.- Assess the assumptions from this model fit.
- Compute point and bootstrapped 95% confidence interval estimates for each parameter in this model. Describe any problems that you encountered.

- Find the most parsimonius model that is a subset of the model fit above.
- Using the likelihood ratio test.
- Using the \(AICc\) criterion.
- Use the extra sums-of-squares test.
- Summarize (in words) the results of the most parsimonious model identified with the extra sums-of-squares test.

- Fit the this VBGF parameterizations seprately to both sexes.
- Compute point and bootstrapped 95% confidence interval estimates for each parameter in the separate models.
- Describe any problems that you encountered.
- How do the point estimates from these separate models compare to the point estimates from the most complex model in #2 above?
- Do you see any issues with the confidence intervals? If so, describe.

- Construct a summary graphic that shows the growth trajectories superimposed on the observed data for both sexes.
- Make the following comparisons between the results from fitting this parameterization to the results from fitting the typical parameterization in this exercise.
- Point estimates for \(L_\infty\).
- Summary graphic.
- Any issues with model convergence or interval estimates.

from Derek H. Ogle , created 08-Mar-19, updated 26-Dec-21, Comments/Suggestions.