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Background: Dissimilarity in community composition is one of the most fundamental and conspicuous features by which different forest ecosystems may be distinguished. Traditional estimates of community dissimilarity are based on differences in species incidence or abundance (e.g. the Jaccard, Sørensen, and Bray-Curtis dissimilarity indices). However, community dissimilarity is not only affected by differences in species incidence or abundance, but also by biological heterogeneities among species.Methods: The objective of this study is to present a new measure of dissimilarity involving the biological heterogeneity among species. The "discriminating Avalanche" introduced in this study, is based on the taxonomic dissimilarity between tree species. The application is demonstrated using observations from five stem-mapped forest plots in China and Mexico. We compared three traditional community dissimilarity indices (Jaccard, Sørensen, and Bray-Curtis) with the "discriminating Avalanche" index, which incorporates information, not only about species frequencies, but also about their taxonomic hierarchies.Results: Different patterns emerged for different measures of community dissimilarity. Compared with the traditional approaches, the discriminating Avalanche values showed a more realistic estimate of community dissimilarities, indicating a greater similarity among communities when species were closely related.Conclusions: Traditional approaches for assessing community dissimilarity disregard the taxonomic hierarchy. In the traditional analysis, the dissimilarity between Pinus cooperi and Pinus durangensis would be the same as the dissimilarity between P. cooperi and Arbutus arizonica. The dissimilarity Avalanche dissimilarity between P. cooperi and P. durangensis is considerably lower than the dissimilarity between P. cooperi and A. arizonica, because the taxonomic hierarchies are incorporated. Therefore, the discriminating Avalanche is a more realistic measure of community dissimilarity. This main result of our study may contribute to improved characterization of community dissimilarities.

This contribution complements Forest Ecosystems' Thematic Series on "Forest Observational Studies". We provide essential clarification regarding the definition and purpose of long-term field studies, review some of the extensive literature and discuss different approaches to collecting field data. We also describe two newly established forest observational networks that serve to illustrate the scope and diversity of forest field studies. The first is a large-scale network of forest observational studies in prominent natural forest ecosystems in China. The second example demonstrates observational studies in mixed and uneven-aged pine-oak forests which are selectively managed by local communities in Mexico. We summarize the potential for analysing and modeling forest ecosystems within interdisciplinary projects and provide argumentation in favour of long-term institutional commitment to maintaining forest observational field studies.

En este trabajo se comparan dos procedimientos de ajuste de modelos expresados en diferencias algebraicas generalizadas, para la construcción de curvas dinámicas de índice de sitio basadas en datos procedentes de análisis troncales de árboles dominantes de Pinus cooperi Blanco. La principal ventaja del método de diferencias algebraicas generalizado (GADA) es que la ecuación base puede ser expandida de acuerdo con diversas teorías sobre el crecimiento (p. ej., tasa de crecimiento y asíntota), lo que permite que más de un parámetro de cada modelo dependa de la calidad de estación, que las curvas obtenidas sean más flexibles, y así obtener curvas de índice de sitio que sean a la vez polimórficas y con múltiples asíntotas. El objetivo es obtener curvas que sean invariantes con respecto a la edad de referencia y que estimen directamente la altura dominante y el índice de sitio a cualquier altura y edad. Debido a la estructura longitudinal de los datos empleados, se corrige la dependencia de los errores al considerar la estructura del error como un proceso autorregresivo durante el proceso de ajuste. La ecuación aquí obtenida, derivada del modelo de Chapman-Richards, es muy flexible ya que se puede utilizar para cualquier edad de referencia, sin afectar las predicciones de la altura dominante o del índice de sitio.

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