Model correlation in stochastic forest simulators—A case of multilevel multivariate model for seedling establishment

Forest development can be predicted by the use of forest simulators based on various statistical models describing the forest and its dynamics. One potential approach to study the reliability of the simulators is to utilise Monte Carlo simulation techniques to generate a predictive distribution of a forest characteristic. One problem in examining the effect of model uncertainty in forestry decision making, however, is correlation between the models. If this is not taken into account, predictions of the model systems may become biased, and the effect of errors on decision making may be underestimated. In reality, the models often are interdependent, but the correlations usually are not known because the models have been estimated in separate studies. The aim of this paper is to study the impacts of between-model dependencies on the predictive distribution of forest characteristics by Monte Carlo simulation techniques. We utilise a case of predicting seedling establishment of planted Norway spruce (Picea abies (L.) Karst.) stands as an example with multivariate multilevel model structures. Regardless of low cross-correlations between the models, ignoring them led to significant underestimation of the amount of competing broadleaves to be removed in pre-commercial thinning. Therefore, we recommend that between-model dependencies are clarified and considered in stochastic simulations. In our case, between-model interdependencies can be reliably estimated with a limited dataset. In addition, estimating the models separately and using the model residuals to estimate interdependencies between models were also sufficient to take the between-model dependencies into account when producing stochastic predictions for silvicultural decision making.     

Influence of diesel concentration on the fate of phenanthrene in soil

The aim of this study was to investigate the influence of diesel on the loss and bioavailability of soil-associated [14C]phenanthrene with time. In addition, the temporal development of phenanthrene catabolic activity and the impact of co-contaminant mixtures on the soil microflora were also assessed. With respect to compound fate, the results suggested that competitive effects between dissimilar co-contaminants did influence [14C]phenanthrene loss. Where diesel was present at a concentration of 0, 20, 200 and 2000 mg kg(-1), increased phenanthrene loss was observed with increasing diesel concentrations. In the 20,000 mg kg(-1) diesel treatment, however, a significantly higher amount of the initial [14C]activity remained after 225 days. Furthermore, initial degradation of phenanthrene in this treatment was retarded as a result of repressed phenanthrene catabolic activity. These results were complemented by a 4-fold increase in total culturable bacterial cell numbers in the 20,000 mg kg(-1) treatment when compared with the 2000 mg kg(-1) after 225 days of incubation time.     

Exposure to insecticides in the use of cold fog generators in greenhouses

The exposure of greenhouse workers to deltamethrin and dichlorvos was studied when low‐volume misters (LVM) were used for the application of these insecticides. Exposure to deltamethrin was mainly by skin contamination and the level of exposure was low. Exposure to dichlorvos was clearly higher than to deltamethrin both through skin and inhalation. These two insecticides are different from chemical structure and character dichlorvos being a highly volatile chemical. The re‐entry period for deltamethrin was calculated to be about ten hours and for dichlorvos two or three days.     

Biological monitoring of wood dust exposure in nasal lavage by high-performance liquid chromatography

A high-performance liquid chromatography (HPLC) method for biomonitoring of occupational wood dust exposure based on nasal lavage as a biomonitoring matrix was developed. Gallic acid (GA) was chosen as the indicator compound for oak dust exposure. From the chromatographic profile of ash dust, four peaks were chosen as indicator compounds. Phenolic indicator compounds were analysed by HPLC. Personal dust samples and corresponding nasal lavage samples were collected from 16 workers exposed to oak dust and six to ash dust. The dust concentrations in the workers' breathing zone varied between 0.7 and 13.8 mg m(-3). The indicators revealed the nature of the wood dust inhaled. For the workers who did not use respirators, the correlation between the dust and corresponding indicator compound in their nasal lavage was significant; r2 = 0.59 (n = 12) for oak dust and r2 = 0.58 (n = 6) for ash dust, respectively. Further, the correlation for oak dust workers who used respirators was r = 0.67 (n = 4). Nasal lavage sampling and HPLC analysis of polyphenol indicator compounds are promising tools for measuring wood dust exposure. Although further validation is necessary, determination of the individual dose may prove invaluable in prospective epidemiological studies.     

Evaluating the management strategies of a forestland estate--the S-O-S approach

Connecting Multiple Criteria Decision Support (MCDS) methods with SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis yields analytical priorities for the factors included in SWOT analysis and makes them commensurable. In addition, decision alternatives can be evaluated with respect to each SWOT factor. In this way, SWOT analysis provides the basic frame within which to perform analyses of decision situations. MCDS methods, in turn, assist in carrying out SWOT more analytically and in elaborating the results of the analyses so that alternative strategic decisions can be prioritized also with respect to the entire SWOT. The A'WOT analysis is an example of such hybrid methods. It makes combined use of the Analytic Hierarchy Process (AHP) and SWOT. In this study, a hybrid method of the Stochastic Multicriteria Acceptability Analysis with Ordinal criteria (SMAA-O) and SWOT is developed as an elaboration of the basic ideas of A'WOT. The method is called S-O-S (SMAA-O in SWOT). SMAA-O enables the handling of ordinal preference information as well as mixed data consisting of both ordinal and cardinal information. Using SMAA-O is enough to just rank decision elements instead of giving them cardinal preference or priority ratios as required by the most commonly used MCDS methods. Using SMAA-O, in addition to analyzing what the recommended action is under certain priorities of the criteria, enables one to analyze what kind of preferences would support each action. The S-O-S approach is illustrated by a case study, where the shareholders of a forest holding owned by a private partnership prepared the SWOT analysis. Six alternative strategies for the management of their forest holding and of old cottage located on the holding were formed. After S-O-S analyses were carried out, one alternative was found to be the most recommendable. However, different importance orders of the SWOT groups would lead to different recommendations, since three of the six alternatives were efficient according to S-O-S analyses.