VONBIT 2005 - Von Bertalanffy Iterative Approach
VONBIT stands for von Bertalanffy Iterative Approach and it is a computer software designed to run under Windows. The presented linear regression method for fitting the von Bertalanffy growth function to data on size at age is also directly applicable to tag and recapture data, thus offering a more integrated approach than other similar methods. Several comparison tests have revealed that the present approach shows lower error and bias and more flexibility of action than other commonly used models and systems.
The methodological notes included in the tutorial component of the system were taken from the original work that was published in 1989 by ICES with reference J. Cons. int. Explor. Mer. 45: 200-208.Methodology
In the method proposed here the driving parameter is k, trial values of which are used in simple linear regressions with age as the independent variable and observed size as the dependent variable. The advantage over general-purpose non-linear procedures is that the optimization process concentrates on values of k only and deals with stationary points on a curve, rather than on a surface. This makes it possible to select a single population value for k as a global optimum. The method involves very few computational steps, and although it remains an iterative approach (in that trial values of k are examined), the number of iteration steps as well as the accuracy of parameter estimation are under the control of the user.
Fitting data from tagging experiments
Extensive comparisons, some reported here, were made between the proposed method and other methods in current use. These comparisons reveal that the latter procedures may sometimes underestimate the true value of the overall population k. For instance, some methods make the basic assumption that all individuals have the same maximum asymptotic length and only their individual values of k are different. Others use the length at capture as independent variable, thus assuming that it is free of measurement errors. Such assumptions tend, in general, to underestimate k .
Operational guidelines and tutorial
Minimum system requirements
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