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Abstract: Regionalized variables with discrete distributions are commonly associated with counts of individuals (precious stones in ore deposits, wild animals in ecosystems, trees in forests, etc.), that can be represented by a spatial point process. In this paper, we propose to model the point distribution by a Cox process, i.e., a Poisson point process with a random regionalized intensity. The model is parsimonious and versatile, as it allows fitting the histogram of the count variable, its variogram and madogram. Simulation conditional to data is performed by recourse to iterative algorithms based on the Gibbs sampler. Computer programs are provided for parameter inference and for simulation, and an application to a forestry dataset is presented. [Copyright &y& Elsevier]

Abstract: This paper will discuss TRIPLEX-Flux, a process-based model that estimates net ecosystem production (NEP) as well as analyzing the level of sensitivity of the model''s response by simulating CO2 flux in an existing old black spruce BOREAS site in central Canada. The research objectives were: (1) to test the TRIPLEX-Flux model simulations against flux tower measurements; and (2) to examine parameter and input variable effects on model response via sensitivity analysis. Validation of NEP data at 30min intervals derived from tower and chamber measurements showed that the NEP data from the model corresponded well with the measured NEP from the BOREAS site (R 2 >0.65). Sensitivity analysis demonstrated different levels of sensitivity between morning and noon periods and from the current to doubled atmospheric CO2 concentrations. Additionally, the comparison of different algorithms to calculate stomatal conductance showed that the NEP predicted by the model using the iteration algorithm was consistent with the results using a constant C i/C a of 0.74 for current and 0.81 for doubled CO2 concentrations. Varying parameter and input variable values by ±10% resulted in a similar model response between morning and noon periods (less than or equal to 27.6% and 27.4%, respectively). Most parameters were more sensitive at noon than they were in the morning except for those that were correlated with air temperature, suggesting that air temperature has considerable influence over model sensitivity for these parameters/variables. Air temperature effect was greater under doubled than it was under current atmospheric CO2 concentrations. In contrast, model sensitivity to CO2 decreased under doubled CO2 concentrations. [Copyright &y& Elsevier]

A common approach for incorporating opening constraints into harvest scheduling is through the area-restricted model. This model is used to select which stands to include in each opening while simultaneously determining an optimal harvest schedule over multiple time periods. In this paper we use optimal benchmarks from a range of harvest scheduling problem instances to test a metaheuristic algorithm, simulated annealing, that is commonly used to solve these problems. Performance of the simulated annealing algorithm was assessed over a range of problem attributes such as the number of forest polygons, age-class distribution, and opening size. In total, 29 problem instances were used, ranging in size from 1269 to 36 270 binary decision variables. Overall, the mean objective function values found with simulated annealing ranged from approximately 87% to 99% of the optima after 30 min of computing time, and a moderate downward trend of the relationship between problem size and solution quality was observed. [ABSTRACT FROM AUTHOR]