Dynamic height growth model for Spanish and Tunisian cork oak (Quercus suber L.) forests

Abstract

Seven simple and advanced dynamic polymorphic functions were considered to develop a dominant height growth model for Spanish and Tunisian cork oak forests. Data from 115 stem analyses performed in two regions in each country were used to fit the equations. Parameter estimates were obtained using the Dummy variable method. Both numerical, graphical and biological consistency were used to compare alternative models. The dynamic equation finally selected was derived from the Hossfeld model by considering the shape parameter to be related to site productivity. An analysis of the dominant height growth patterns between the two countries indicated that the same dominant height growth model was valid for both countries. This dominant height growth model allows estimation of dominant height with a level of reliability of at least 83% from an age of 15 years for a prediction interval of less than 40 years.

Agents Affecting the Plant Functional Traits in National Soil and Water Conservation Demonstration Park (China)

“…The results showed that the effects of topographic conditions on plant diversity and functional traits were mainly reflected by the differences in altitude (ALT), slope aspect (ASP) and slope position (SP), as well as the different water, heat, and light conditions, thus, leading to the formation of life strategies with different combinations of functional traits in plants. Some scholars also confirmed the same problem, the study showed that topographic factors strongly influenced the pattern of community, ecosystem, and landscape, thus, affecting plant diversity (some studies pointed out that topographic factors were the main factor, limiting the change of vegetation distribution in the loess hilly region) [ 44 ].…”

Étude de l’influence des facteurs du milieu sur la croissance et la production du liège de la suberaie du Parc national de Theniet El Had (Tissemsilt, Algérie)

“…Elle correspond à la moyenne des profondeurs classées en trois catégories (Richard, 1988) : superficiel, assez profond et profond. Les peuplements ont été décrits selon trois critères : circonférence et hauteur dominante, variables liées à la fertilité stationnelle (Sánchez-González et al, 2010), la hauteur dominante correspondant à la moyenne arithmétique des 100 plus gros bois à l'hectare (Rondeux, 1999) ; la densité (Nt) qui reflète le degré de compétition entre les arbres (Rondeux, 1999) ; l'état de santé des peuplements. Ce dernier a été évalué par l'indice de santé (IS), calculé à partir du déficit foliaire (Df) estimé directement sur les arbres inventoriés.…”

Dynamic Top Height Growth Models for Eight Native Tree Species in a Cool-Temperate Region in Northeast China

“…Toward the end of the 19th century, it was realized that the stand mean height at a given reference age is a practical indicator of site productivity and a classification based on species and typical site-specific height development patterns were introduced [7][8][9][10]. Because of its importance in forestry, several height growth functions for estimation of site productivity have been developed over time [2,3,11]. Polymorphic growth models are able to express different curve shapes at different top height levels and having curve shape parameters be dependent on site-specific factors [10,12].…”

“…Today, top height growth models are mostly developed using base-age invariant approaches [11][12][13], making use of time series independent of the choice of reference age and site curves may be estimated from data without any inter-or extrapolations. In recent years, dynamic modelling approaches, such as the algebraic difference approach (ADA) [14] or the generalized algebraic difference approach (GADA) [15] have been applied together with the mixed-effects modelling to develop top height growth models or site index models with high accuracy, as these approaches are suited to both short-time series data with no common base age of the age-height series (NFI, PSP data) [16,17] and long-time series data with common base-age series (SA data) [18,19].…”