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.     

Resilience of the poorest: coping strategies and indigenous knowledge of living with the floods in Northern Namibia

In Northern Namibia, the climate is highly polarised between dry and wet seasons, and local communities have lived with these varying weather extremes for centuries. However, the recent changes in socio-environmental dynamics—associated with urbanisation, inappropriate spatial planning, and population growth—have disturbed the river system in the area. These changes, together with torrential seasonal rains, have aggravated the social impacts of the flood events. By using various qualitative and quantitative data sources, and comparative analyses between the flood dynamics in urban and rural environments, this research studies local residents’ coping strategies to endure the irregular flood events from the perspective of socio-ecological resilience. Particular interest is placed on the learning processes that enhance the residents’ capability to cope and the role of indigenous knowledge. Indigenous knowledge (IK) has been emphasised as a source of resilience in both theory and practice, as it is built upon learning from past experiences of natural hazards. The findings reveal that the floods are a result of complex and relational development without the necessary linear relationship between the causes and effects. The abrupt socio-ecological changes, together with the multiple stressors related to poverty, have made residents more vulnerable to the flood events and attenuated the communities’ coping strategies based on IK. Instead of focusing on the communities’ capacity to self-organise, the focus of resilience building needs to be directed to emphasising the broader socio-political processes, which are making the communities vulnerable in the first place.     

Comparative validation of amperometric and optical analyzers of dissolved oxygen: a case study

A comprehensive comparative validation for two different types of dissolved oxygen (DO) analyzers, amperometric and optical, is presented on two representative commercial DO analyzers. A number of performance characteristics were evaluated including drift, intermediate precision, accuracy of temperature compensation, accuracy of reading (under different measurement conditions), linearity, flow dependence of the reading, repeatability (reading stability), and matrix effects of dissolved salts. The matrix effects on readings in real samples were evaluated by analyzing the dependence of the reading on salt concentration (at saturation concentration of DO). The analyzers were also assessed in DO measurements of a number of natural waters. The uncertainty contributions of the main influencing parameters were estimated under different experimental conditions. It was found that the uncertainties of results for both analyzers are quite similar but the contributions of the uncertainty sources are different. Our results imply that the optical analyzer might not be as robust as is commonly assumed; however, it has better reading stability, lower stirring speed dependence, and typically requires less maintenance. On the other hand, the amperometric analyzer has a faster response and wider linear range. Both analyzers seem to have issues with the accuracy of temperature compensation. The approach described in this work will be useful to practitioners carrying out DO measurements for ensuring reliability of their measurements.