Towards the sustainable use of Europe’s forests: a centre for European forest science! – Some missing catalytic elements?

The Symposium, “Towards the sustainable use of Europe’s forests”, with sub-title “Forest ecosystem and landscape research: scientific challenges and opportunities” lists three fundamental substantive areas of research that are involved: Forest management and practices, Ecosystem processes and functional ecology, and Environmental economics and sociology. This paper argues that there are essential catalytic elements missing! Without these elements there is great danger that the aimed-for world leadership in the forest sciences will not materialize. What are the missing elements? All the sciences, and in particular biology, environmental sciences, sociology, economics, and forestry have evolved so that they include good scientific methodology. Good methodology is imperative in both the design and analysis of research studies, the management of research data, and in the interpretation of research finding. The methodological disciplines of Statistics, Modelling and Informatics (“SMI”) are crucial elements in a proposed Centre of European Forest Science, and the full involvement of professionals in these methodological disciplines is needed if the research of the Centre is to be world-class. Distributed Virtual Institute (DVI) for Statistics, Modelling and Informatics in Forestry and the Environment (SMIFE) is a consortium with the aim of providing world-class methodological support and collaboration to European research in the areas of Forestry and the Environment. It is suggested that DVI: SMIFE should be a formal partner in the proposed Centre for European Forest Science.

Digital Forestry: A white paper

Digital Forestry has been proposed as “the science, technology, and art of systematically acquiring, integrating, analyzing, and applying digital information to support sustainable forests.” Although rooted in traditional forestry disciplines, Digital Forestry draws from a host of other fields that, in the past few decades, have become important for implementing the concept of forest ecosystem management and the principle of sustainable forestry. Digital Forestry is a framework that links all facets of forestry information at local, national, and global levels through an organized digital network. It is anticipated that a new set of principles will be established when practicing Digital Forestry concept for the evolution of forestry education, research, and practices as the 21st century unfolds.

The Richit-Richards family of distributions and its use in forestry

Johnson's SB and the logit-logistic are four-parameter distribution models that may be obtained from the standard normal and logistic distributions by a four-parameter transformation. For relatively small data sets, such as diameter at breast height measurements obtained from typical sample plots, distribution models with four or less parameters have been found to be empirically adequate. However, in situations in which the distributions are complex, for example in mixed stands or when the stand has been thinned or when working with aggregated data, then distribution models with more shape parameters may prove to be necessary. By replacing the symmetric standard logistic distribution of the logit-logistic with a one-parameter “standard Richards” distribution and transforming by a five-parameter Richards function, we obtain a new six-parameter distribution model, the “Richit-Richards”. The Richit-Richards includes the “logit-Richards”, the “Richit-logistic”, and the logit-logistic as submodels. Maximum likelihood estimation is used to fit the model, and some problems in the maximum likelihood estimation of bounding parameters are discussed. An empirical case study of the Richit-Richards and its submodels is conducted on pooled diameter at breast height data from 107 sample plots of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.). It is found that the new models provide significantly better fits than the four-parameter logit-logistic for large data sets.