Estimate length-at-age parameters and growth curves for Elasmobranchs

A multi-model growth estimation approach is applied to length-at-age data. Three models can be applied which include the von Bertalanffy (VB), logistic (Log) and Gompertz (Gom) models. AIC values and weights are calculated. The outputs will return a list of model parameter estimates and will either print a plot to the screen or output the length-at-age estimates as part of the list.Use of this function should cite Smart et al. (2016).

Estimate_Growth(
  data,
  models = c("VB", "Log", "Gom"),
  Birth.Len = NULL,
  correlation.matrix = FALSE,
  n.bootstraps = 1000,
  plots = T,
  Max.Age = NULL,
  plot.legend = T
)

Arguments

data: a data frame which includes 'Age' and 'Length - ideally with these names but the function will except some variation to these
models: a vector of models to be fitted. These can include" VB", "Log" and "Gom". A subset can also be used
Birth.Len: The length-at-birth to be used for two parameter models. If a value is provided, two parameter models are automatically run
correlation.matrix: Should the correlation matrix of parameters be returned? This is the only object returned if TRUE.
n.bootstraps: The number of bootstraps performed for model 95 confidence intervals
plots: Should plots be printed to the screen. If FALSE then the model estimates and CI's are returned as an additional output
Max.Age: Specify the max age for bootstrapped confidence intervals to be produced over. Default is the max age in the data.
plot.legend: Do you want a legend for the different models on the plot

Value

Returns a list of parameter estimates with errors and AIC results. If plots is TRUE then a plot is printed to the screen. If plots is FALSE then the length-at-age estimates are returned as a list element

References

Smart et al. (2016) Multi-model approaches in shark and ray growth studies: strengths, weaknesses and the future. Fish and Fisheries. 17: 955-971https://onlinelibrary.wiley.com/doi/abs/10.1111/faf.12154