Genetic differentiation of populations of the threatened saproxylic beetle Rosalia longicorn, Rosalia alpine (Coleoptera: Cerambycidae) in Central and South-east Europe

Knowledge of patterns of genetic diversity in populations of threatened species is vital for their effective conservation. Rosalia longicorn (Rosalia alpina) is an endangered and strictly protected beetle. Despite a marked decline in part of its range, the beetle has recently expanded to the lowlands of Central Europe. To facilitate a better understanding of the species' biology, recent expansion and more effective conservation measures, we investigated patterns of genetic structure among 32 populations across Central and South-east Europe. Eight microsatellite loci and a partial mitochondrial gene (cytochrome c oxidase subunit I) were used as markers. Both markers showed a significant decline in genetic diversity with latitude, suggesting a glacial refugium in north-western Greece. The cluster analysis of the nuclear marker indicated the existence of two genetically distinct lineages meeting near the border between the Western and Eastern Carpathians. By contrast, one widespread mtDNA haplotype was dominant in most populations, leading to the assumption that a rapid expansion of a single lineage occurred across the study area. The genetic differentiation among populations from the north-western part of the study area was, however, surprisingly low. They lacked any substructure and isolation-by-distance on a scale of up to 600 km. This result suggests a strong dispersal capacity of the species, as well as a lack of migration barriers throughout the study area. That the lowland populations are closely related to those from the nearby mountains indicates repeated colonization of the lowlands. Our results further suggest that R. alpina mostly lives in large, open populations. Large-scale conservation measures need to be applied to allow for its continued existence

A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing

The first step to detect when a vineyard has any type of deficiency, pest or disease is to observe its stems, its grapes and/or its leaves. To place a sensor in each leaf of every vineyard is obviously not feasible in terms of cost and deployment. We should thus look for new methods to detect these symptoms precisely and economically. In this paper, we present a wireless sensor network where each sensor node takes images from the field and internally uses image processing techniques to detect any unusual status in the leaves. This symptom could be caused by a deficiency, pest, disease or other harmful agent. When it is detected, the sensor node sends a message to a sink node through the wireless sensor network in order to notify the problem to the farmer. The wireless sensor uses the IEEE 802.11 a/b/g/n standard, which allows connections from large distances in open air. This paper describes the wireless sensor network design, the wireless sensor deployment, how the node processes the images in order to monitor the vineyard, and the sensor network traffic obtained from a test bed performed in a flat vineyard in Spain. Although the system is not able to distinguish between deficiency, pest, disease or other harmful agents, a symptoms image database and a neuronal network could be added in order learn from the experience and provide an accurate problem diagnosis