Modeling the covariance structure between observations of the same individual in tree growth curve models of mesquite (Prosopis laevigata Humb & Bonpl. Ex Willd.) In the rural community of Chinobampo, El Fuerte, Sinaloa
DOI:
https://doi.org/10.35197/rx.11.01.e3.2015.13.erKeywords:
growth curves, mixed models, random coefficientsAbstract
Tree growth curves are modeled from a biological (non-linear functions) or empirical (polynomial functions) point of view. Because growth is assessed by repeated measurements over time on the same tree, the underlying correlation structure must be considered. Covariance models for estimating individual and population tree growth curves are described and compared.
In this work, series from slow-growing tree species from native mesquite forests in the rural community of Chinobampo, El Fuerte, Sinaloa are modeled. Mesquite, ten trees per species and a series of growth ring width per tree were used. The series were smoothed by moving averages to maximize the trend due to biological growth.
The covariance structure between observations of the same individual was modeled using the compound symmetry and autoregressive models for the covariance submatrix between the corresponding error terms and also by incorporating random effects in the model. The adjustments were made within the framework of the mixed linear models and compared from the corresponding likelihood functions. The model with random effects associated to each parameter of a second-order polynomial was the most efficient in estimating growth in both species. The incorporation of random effects allows to take into account the high variability of growth commonly observed between individuals of a species in natural forests.
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