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In this paper, we present an approach employing multiobjective optimization to support decision making in forest management planning under risk. The primary objectives are biodiversity and timber cash flow, evaluated from two perspectives: the expected value and the value-at-risk (VaR). In addition, the risk level for both the timber cash flow and biodiversity values are included as objectives. With our approach, we highlight the trade-off between the expected value and the VaR, as well as between the VaRs of the two objectives of interest. We employ an interactive method in which a decision maker iteratively provides preference information to find the most preferred management plan and learns about the interdependencies of the objectives at the same time. The method is illustrated with a case study in which biodiversity is assessed through an index calculated from the characteristics of the forest. Uncertainty is included both through modifying the input data according to the accuracy of current inventory methods and through growth model errors. This uncertainty is described using a set of 25 scenarios. Involving multiple components of risk is a highly relevant approach in multiobjective forestry; however, estimation of the uncertainty of biodiversity needs further attention. [ABSTRACT FROM AUTHOR]

Mexican temperate forests, at the southernmost end of the distribution range of this ecosystem, are the world's centre of diversity of pine and oak, with 55 and 161 species, respectively. Such forests are threatened by land-use change, unsustainable forest management practices, and climate change; these threats reduce their diversity, alter the distribution ranges of species, modify disturbance regimes, and reduce ecosystem adaptability. This paper briefly reviews ( i) the ecology of the Mexican temperate forests, ( ii) the ecological basis for the unique diversity of pine and oak species, ( iii) the main disturbances as well as the main drivers of global changes affecting these forests, in particular climate change, and ( iv) the social, economic, and cultural factors to be considered in proposing a new forest management approach. It proposes a new conceptual framework to manage Mexican temperate forests that are in line with ( i) their natural dynamics, ( ii) the rapidly changing and uncertain global environmental, social, and economic conditions, and ( iii) the complex adaptive system approach. This new forest management combines functional zoning, multispecies plantations, and sylvicultural interventions to increase the adaptive capacity of forests as a way to balance the increasing need for timber products with the need for other ecosystem services facing rapidly changing and uncertain future environmental, social, and economic conditions. [ABSTRACT FROM AUTHOR]

The uniform angle index (UAI) is used to characterize the spatial distribution of a forest community or of individual tree species within that community. This study presents an empirical assessment of the performance of a new statistical test used in conjunction with the UAI. In this study, the UAI is applied to observed and simulated communities. The effect of plot size and density are examined and the results are compared with the criteria of the aggregation index R of Clark and Evan (CE-R) and Ripley's L function (RL). The results suggest that the UAI performs well in determining a particular class of spatial pattern and that, in contrast to the CE-R index, it is unaffected by population density because the standard deviation can be estimated when the density is known using a new empirical relationship presented in this paper. The results of this study represent an improvement in the development of the UAI, verifying its performance and confirming its overall utility. Our comparative analysis positions the UAI among its historical competitors, the CE-R and RL criteria. The UAI index, which does not require expensive mapping of tree positions, can produce results that are equivalent to those obtained by RL and more robust and reliable than those obtained by CE-R. [ABSTRACT FROM AUTHOR]