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The purpose of this paper is to give a general description and analysis of the key factors important for forest biodiversity, and to show the development of a strategy for choosing an indicator methodology for assessment of forest biodiversity. Different biodiversity indicator schemes (methodologies) have been developed recently and all should be capable of being adapted to the specific objectives of biodiversity assessment and to the forest types concerned.

A forest ecosystem comprises three main components: structure (e.g. physical characteristics); composition (e.g. number and population of species); and function (e.g. evapo‐transpiration) and it is upon these characteristics that forest biodiversity is based and determined. Forest ecosystems can therefore be characterised by key factors (parameters) which are important for the development of indicators and evaluation of biodiversity.

The key factors can be grouped or classified according to major ecosystem components and also according to spatial scale (national, regional, landscape and stand level). Selected indicators are the tools that can be used to assess the size/or level and quality of forest biodiversity.

Biodiversity key factors and indicators are crucial for studying and monitoring forest biodiversity.

This paper aims to present a general analysis and overview of forest biodiversity, emphasising the three main components (structure, composition, function) related to biodiversity. Following this theoretical consideration of forest biodiversity the current proposal of the European Environment Agency for biodiversity indicators is presented as a table with the application to forests identified and the direct/indirect nature of the indicator also indicated.

The biodiversity of forests is determined by a number of overall large‐scale factors that intimately affect the individual components of biodiversity. A more specific and direct measurement of forest biodiversity is presented with data assessed by the Fungib programme to provide a set of biodiversity indices that can be used to: create biodiversity baselines; compare sites; and follow biodiversity changes through time, all with the possibility of determining significance of change statistically. An example of data derived from a macrofungal survey is presented as an example of how this methodology can be adapted to many groups of organisms. Finally, a further example of using biodiversity quality data to understand changes in biodiversity is presented comparing butterfly biodiversity quality with nitrogen deposition.

This paper shows how an example of function (nitrogen deposition) has affected an element of composition (butterfly biodiversity). This has been made possible by the use of a rational sampling methodology (TRIM) used over a number of years. This metadata analysis of butterfly survey data shows clearly that changes in the butterfly biodiversity quality would have been missed if the normal approach of equating biodiversity with species richness had been adopted. In this example nitrogen‐sensitive species loss was compensated by nitrophilic species gain.

Such a methodology, therefore, has much to recommend it and it has been shown to be applicable to a wide range of organisms from bryophytes to beetles. For forest biodiversity monitoring the adoption of this kind of rationale will hopefully yield far greater information to the scientific community and policy makers for little extra effort.

The purpose of this paper is to show how corporate responsibility (CR) could be utilized to manage and develop individual, cultural, and biodiversity and turn them into business strategies.

Case studies of two multinational forest companies are used to demonstrate present and possible corporate values, words, and actions of diversity.

Forest companies tend to react on external pressure in diversity issues. They often take a confrontational or minimum legal compliance approach. Biodiversity, cultural diversity, and individual diversity form an intertwined hot topic all over the world.

For future prospects, forest companies should take initiative in diversity enhancement and become entrepreneurial, particularly as pulp and paper production is a declining industry and new business visions are needed. In cooperation with indigenous peoples, environmental organizations, governments, and other stakeholders forest companies could replace monocultural tree plantations with multicultural forest gardens, which provide many ecosystem services, natural products, and employment, and participate in large‐scale forest leasing for conservation. Multinational forest companies could also make better use of their diverse individual and cultural resources.

This is the first attempt to build a CR/diversity framework by studying different kinds of diversity issues through CR and by analyzing corporate values, words, and actions. Integrating diversity into CR helps companies to turn it into a business strategy.

This essay is a review of the recent literature on the methodology of economics, with a focus on three broad trends that have defined the core lines of research within the discipline during the last two decades. These trends are: (a) the philosophical analysis of economic modelling and economic explanation; (b) the epistemology of causal inference, evidence diversity and evidence-based policy and (c) the investigation of the methodological underpinnings and public policy implications of behavioural economics. The final output is inevitably not exhaustive, yet it aims at offering a fair taste of some of the most representative questions in the field on which many philosophers, methodologists and social scientists have recently been placing a great deal of intellectual effort. The topics and references compiled in this review should serve at least as safe introductions to some of the central research questions in the philosophy and methodology of economics.

To provide an explicit representation for wide‐sense stationary stochastic fields which can be used in stochastic finite element modelling to describe random material properties.

This method represents wide‐sense stationary stochastic fields in terms of multiple Fourier series and a vector of mutually uncorrelated random variables, which are obtained by minimizing the mean‐squared error of a characteristic equation and solving a standard algebraic eigenvalue problem. The result can be treated as a semi‐analytic solution of the Karhunen‐Loève expansion.

According to the Karhunen‐Loève theorem, a second‐order stochastic field can be decomposed into a random part and a deterministic part. Owing to the harmonic essence of wide‐sense stationary stochastic fields, the decomposition can be effectively obtained with the assistance of multiple Fourier series.

The proposed explicit representation of wide‐sense stationary stochastic fields is accurate, efficient and independent of the real shape of the random structure in consideration. Therefore, it can be readily applied in a variety of stochastic finite element formulations to describe random material properties.

This paper discloses the connection between the spectral representation theory of wide‐sense stationary stochastic fields and the Karhunen‐Loève theorem of general second‐order stochastic fields, and obtains a Fourier‐Karhunen‐Loève representation for the former stochastic fields.

This paper proposes the Student's t Dynamic Linear Regression (St-DLR) model as an alternative to the various extensions/modifications of the ARCH type volatility model. The St-DLR differs from the latter models of volatility because it can incorporate exogenous variables in the conditional variance in a natural way. Moreover, it also addresses the following issues: (i) apparent long memory of the conditional variance, (ii) distributional assumption of the error, (iii) existence of higher moments, and (iv) coefficient positivity restrictions. The model is illustrated using Dow Jones data and the three-month T-bill rate. The empirical results seem promising, as the contemporaneous variable appears to account for a large portion of the volatility.