Demography and Dispersal Ability of a Threatened Saproxylic Beetle: A Mark-Recapture Study of the Rosalia Longicorn (Rosalia alpina)

The Rosalia longicorn or Alpine longhorn (Coleoptera: Cerambycidae) is an endangered and strictly protected icon of European saproxylic biodiversity. Despite its popularity, lack of information on its demography and mobility may compromise adoption of suitable conservation strategies. The beetle experienced marked retreat from NW part of its range; its single population survives N of the Alps and W of the Carpathians. The population inhabits several small patches of old beech forest on hill-tops of the Ralska Upland, Czech Republic. We performed mark-recapture study of the population and assessed its distribution pattern. Our results demonstrate the high mobility of the beetle, including dispersal between hills (up to 1.6 km). The system is thus interconnected; it contained ∼2000 adult beetles in 2008. Estimated population densities were high, ranging between 42 and 84 adult beetles/hectare a year. The population survives at a former military-training ground despite long-term isolation and low cover of mature beech forest (∼1%). Its survival could be attributed to lack of forestry activities between the 1950s and 1990s, slow succession preventing canopy closure and undergrowth expansion, and probably also to the distribution of habitat patches on conspicuous hill-tops. In order to increase chances of the population for long term survival, we propose to stop clear-cuts of old beech forests, increase semi-open beech woodlands in areas currently covered by conifer plantations and active habitat management at inhabited sites and their wider environs

Computergestuetztes Verfahren zur Beurteilung der Auswirkungen von Strassenplanungen auf die landschaftsoekologischen Potentiale. T. 1 Text

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A planetary health blind spot: the untapped potential of women to safeguard nature and human resilience in LMICs

Against the backdrop of civic engagement, such as the Fridays For Future movement, expectations were high for 2020 to be a year of accelerating commitment to climate, biodiversity, and women’s rights; marked by plans for the UN Climate Summit, the Convention on Biological Diversity Summit, and the 25th anniversary of the Beijing Declaration on women’s rights. Instead, the political agenda was defined by COVID-19, which has served as an unwelcome, although timely, reminder of the fundamental interconnectedness of environmental, social, and economic systems. True recovery and a transi tion to a more resilient global society can be achieved by adopting an approach based on planetary health with a core focus on reducing social inequalities. This focus is necessary because the most vulnerable people globally are also the least represented in powerful positions in government and industry

A nonparametric data mining approach for risk prediction in car insurance: a case study from the Montenegrin market

For prediction of risk in car insurance we used the nonparametric data mining techniques such as clustering, support vector regression (SVR) and kernel logistic regression (KLR). The goal of these techniques is to classify risk and predict claim size based on data, thus helping the insurer to assess the risk and calculate actual premiums. We proved that used data mining techniques can predict claim sizes and their occurrence, based on the case study data, with better accuracy than the standard methods. This represents the basis for calculation of net risk premium. Also, the article discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such as Montenegrin