Good day,
We are comparing size-at-age curves for polar bears in Southern Hudson Bay, Ontario, Canada.  We hypothesized that polar bears grew more slowly and possibly had smaller maximum sizes in recent years compared to previously.  Preliminary examination of the data supports this hypothesis, and furthermore indicates that the von Bertalanffy growth model fits polar bear size-at-age data quite well.  The data consist of size-at-age data for live-captured polar bears.  Many individuals were captured only once, but many were also captured 2 - 4 times over many years.

It would appear, therefore, that a non-linear mixed effects model, where at least two of the parameters of the von Bertalanffy growth function (growth rate, K, and maximum size Linf) could be modeled as functions of covariates, and bear.id could be included as a random effect, would provide a good framework for analysis and inference.

I have found a few articles presenting results from similar models.  For example:
Kimura (2008. Extending the von Bertalanffy growth model using explanatory variables. Can.J.Fish.Aquat Sci.) developed models that allowed for explanatory covariates, but not random effects, and with no implementation in R.

Baudron et al (2011. Implications of a warming North Sea for the growth of Haddock. Journal of Fish Biology) implemented a similar model in R.

I was hoping to find, but have not as yet, an R package or code that implements the von Bertalanffy growth model as a non-linear mixed effects model in R (e.g. relying on nlme or lme4).  I have also been working on coding one myself in nlme.

I haven't posted this question to r-sig-mixed-models.

Thanks in advance,
Eric


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