Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

The mental health of expatriate and Kosovar Albanian humanitarian aid workers

The mental health consequences of exposure to traumatic events and the risk factors for psychological morbidity among expatriate and Kosovar Albanian humanitarian aid workers have not been well studied. In June 2000, we used standardised screening tools to survey 285 (69.5%) of 410 expatriate aid workers and 325 (75.8%) of 429 Kosovar Albanian aid workers from 22 humanitarian organizations that were implementing health programmes in Kosovo. The mean number of trauma events experienced by expatriates was 2.8 (standard deviation: 2.7) and by Kosovar staff 3.2 (standard deviation: 2.8). Although only 1.1% of expatriate and 6.2% of Kosovar aid workers reported symptoms consistent with the diagnosis for post-traumatic stress disorder, 17.2% and 16.9%, respectively, reported symptoms satisfying the definition of depression. Regression analysis demonstrated that the number of trauma events experienced was significantly associated with depression for the two sets of workers. Organisational support services may be an important mediating factor and should be targeted at both groups.     

Types of narrow-skulled vole (<Emphasis Type="Italic">Lasiopodomys gregalis</Emphasis>) colonies in Northern Kulunda and seasonal dynamics of their demographic composition

Demographic structure and its seasonal dynamics have been studied in colonies of narrow-skulled voles (Lasiopodomys gregalis Pall.) in Northern Kulunda. Three types of colonies have been distinguished: (I) true colonies (large, complex families), (II) simple families (nuclear or incomplete), and (III) all-male colonies. Changes in their sex, age, and functional composition (based on animal migration activity) during the breeding season have been analyzed.     

Analytic framework for assessing participation processes and outcomes of climate change adaptation tools

The assumptions and empirical evidence linking climate change adaptation to development cooperation have led to the emergence of a range of climate change adaptation tools aiming at improving the quality of development cooperation initiatives in times of global change. In order to assess the quality of these tools, we develop an analytic framework to critically assess the social learning and sustainable development outcomes of climate change adaptation tools. The analytic framework defines project objectives, participation typology, participation tools, participation stages, scenarios development, modelling exercises, stakeholder analysis and risk communication strategies that support quality participation outcomes.     

Bioanalytical screening methods for dioxins and dioxin-like compounds a review of bioassay/biomarker technology.

Determination of environmental pollutants utilizing biodetectors such as bioassays, biomarkers, enzyme immunoassays (EIAs), or other bioanalytical tools is a continuously growing area. The present literature review describes the principles and advantages/limitations of several bioanalytical detection methods (BDMs) for the screening and diagnosis of dioxin and dioxin-like compounds. This study characterizes briefly the family of dioxin and dioxin-like compounds, discusses potential Ah receptor (AhR) ligands and cytochrome P-450 (CYP) 1A1-enzyme-inducing compounds. 'Milestones' in the development of BDMs are summarized and explained in detail for a number of bioanalytical tools that can be used to detect these classes of dioxin-like persistent bioaccumulative toxicants (PBTs). The design of a screening profile with a battery of bioassays/biomarkers coupled with the chemical analysis is evaluated. The relative potencies (REPs) to 2,3,7,8-TCDD for dioxin-like compounds are reviewed for various BDMs and the differences are noted.