HYPSOMETRIC MODELING USING COVARIATES IN A SEMIDECIDUOUS SEASONAL FOREST
DOI:
https://doi.org/10.31413/nat.v14i3.21169Keywords:
Traditional modeling, Mixed modeling, Native forestAbstract
The objective of this study was to evaluate the performance of traditional and mixed models in fitting the hypsometric relationship in a fragment of Semideciduous Seasonal Forest, using data from 19 sample plots located in Nova Baden State Park, in Lambari, Minas Gerais, Brazil. Two scenarios were developed: one based on the original database and another after removing outliers, for fitting hypsometric relationship models. The models were evaluated using the adjusted coefficient of determination, standard error of the estimate, Akaike Information Criterion, Bayesian Information Criterion, graphical residual analysis, F-test, and coefficient significance. The best equation was then subjected to the inclusion of covariates such as species, botanical family, and diameter class. The Trorey linear model was considered the most accurate in both scenarios. The removal of outliers resulted in more concise and less biased estimates. The inclusion of covariates in the models improved the explanation of height variation in the forest fragment, especially when the variability of the random effect of species was incorporated into the three model coefficients.
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