Impact of plot size on tropical forest structure and diversity estimation / Impacto del tamaño de la parcela en la estructura y estimación de la diversidad de los bosques tropicales

Abstract Introduction: Inventories are essential for forest management, but, in the Amazon region, the absence of standardization produces information loss, low accuracy, and inconsistent measurements. This prevents valid comparisons and compromises the use of information in networks and software. Sampling unit size is of key importance in the inventory of native forests, particularly regarding accuracy and costs. Objective: To identify a plot size that provides adequate precision for dendrometric parameters in the Amazon. Methods: In Cotriguaçu, Mato Grosso, Brazil, we tested four plot sizes with six repetitions each: 2 500, 5 000, 7 500, and 10 000 m². We measured diameter at breast height, population density, basal area, and biomass. We applied Shannon and Jaccard indexes; Weibull 2P and Gamma functions to fit the diametric distribution; and the Akaike Information Criterion for the best model. Results: There was a directly proportional relationship between plot area and population similarity, but diversity did not indicate significant alterations. Plot size did not affect dendrometric attributes and diametric distribution. Larger plot areas led to lower coefficients of variation and smaller confidence intervals. The Gamma function was the best model to represent the distributions of different plot sizes. Conclusions: For similar forests, we recommend the 2 500 m² plot to evaluate diameter at breast height, population density, basal area, and biomass.

Resumen Introducción: Los inventarios son fundamentales para la gestión forestal, pero en la Amazonía la ausencia de estandarización produce pérdida de información, baja precisión y mediciones inconsistentes. Esto impide comparaciones válidas y compromete el uso de información en redes y programas. El tamaño de la unidad de muestreo es de importancia clave en el inventario de bosques nativos, particularmente en lo que respecta a la precisión y los costos. Objetivo: Identificar un tamaño de parcela que proporcione una precisión adecuada para los parámetros dendrométricos en la Amazonía. Métodos: En Cotriguaçu, Mato Grosso, Brasil, probamos cuatro tamaños de parcela con seis repeticiones cada una: 2 500, 5 000, 7 500 y 10 000 m². Medimos diámetro a la altura del pecho, densidad de población, área basal y biomasa. Se aplicaron los índices de Shannon y Jaccard; Funciones Weibull 2P y Gamma para adaptarse a la distribución diametral; y el Criterio de Información de Akaike para el mejor modelo. Resultados: Hubo una relación directamente proporcional entre el área de parcela y la similitud poblacional, pero la diversidad no indicó alteraciones significativas. El tamaño de la parcela no afectó los atributos dendrológicos y la distribución diametral. Las áreas de parcela más grandes dieron lugar a coeficientes de variación más bajos e intervalos de confianza más pequeños. La función Gamma fue el mejor modelo para representar las distribuciones de diferentes tamaños de parcela. Conclusiones: Para bosques similares, recomendamos la parcela de 2 500 m² para evaluar diámetro a la altura del pecho, densidad de población, área basal y biomasa.

Bivariate and generalized models for taper stem representation and assortments production of loblolly pine (Pinus taeda L.).

Modeling is an important statistical tool to Forest Science, especially to forest planning by predicting the forest's yield and assortments, for instance. This paper evaluated the accuracy of bivariate and generalized linear mixed modeling in the representation of the Pinus taeda L. trunk taper and the estimation of its assortments. To compose the fitting data, 558 trees from plantations located in the Southern region of Santa Catarina, Brazil, were scaled. Initially, the data's bivariate normality was evaluated, and the bivariate standard normal distribution was fitted. Six generalized linear mixed models were fitted for the bivariate representation of diameter and height in the trunk. Afterwards, some statistical indices were obtained to verify the quality of the fitted models and, in a complementary way, of the bivariate graphs of the residuals. Even with the application of Box-Cox transformation, the results indicate the non-normality of the variables, but the transformation contributed to improve the model fitting in 50%. The ordinal and exponential models obtained the best statistics for height representation, with the Akaike Information Criterion (AIC) value being reduced from 16,430.13 to 5,686.78 when considering normal distribution. When evaluating the assortments prediction, there were high discrepancies in the estimated values (246 logs for sawmill and 120 logs for veneers) versus the observed ones (881 logs for sawmill and 628 logs for veneers), which corresponds to a 75% underestimation of total logs per hectare. Thus, the generalized linear mixed modeling improved the trunk taper representation, and the bivariate modeling was not efficient to predict assortments production.