@article{WalzWegmannDechetal.2015,
  author    = {Walz, Yvonne and Wegmann, Martin and Dech, Stefan and Raso, Giovanna and Utzinger, J{\"u}rg},
  title     = {Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook},
  series = {Parasites \& Vectors},
  volume    = {8},
  journal   = {Parasites \& Vectors},
  number    = {163},
  doi       = {10.1186/s13071-015-0732-6},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148778},
  year      = {2015},
  abstract  = {Background: Schistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions. Methods: We reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised. Results: We found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is - in principle - far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from. Conclusions: Our findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited.},
  language  = {en}
}
@article{FlorenKruegerMuelleretal.2015,
  author    = {Floren, Andreas and Kr{\"u}ger, Dirk and M{\"u}ller, Tobias and Dittrich, Marcus and Rudloff, Renate and Hoppe, Bj{\"o}rn and Linsenmair, Karl Eduard},
  title     = {Diversity and interactions of wood-inhabiting fungi and beetles after deadwood enrichment},
  series = {PLoS ONE},
  volume    = {10},
  journal   = {PLoS ONE},
  number    = {11},
  doi       = {10.1371/journal.pone.0143566},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-145129},
  pages     = {e0143566},
  year      = {2015},
  abstract  = {Freshly cut beech deadwood was enriched in the canopy and on the ground in three cultural landscapes in Germany (Swabian Alb, Hainich-Dun, Schorfheide-Chorin) in order to analyse the diversity, distribution and interaction of wood-inhabiting fungi and beetles. After two years of wood decay 83 MOTUs (Molecular Operational Taxonomic Units) from 28 wood samples were identified. Flight Interception Traps (FITs) installed adjacent to the deadwood enrichments captured 29.465 beetles which were sorted to 566 species. Geographical 'region' was the main factor determining both beetle and fungal assemblages. The proportions of species occurring in all regions were low. Statistic models suggest that assemblages of both taxa differed between stratum and management praxis but their strength varied among regions. Fungal assemblages in Hainich-Dun, for which the data was most comprehensive, discriminated unmanaged from extensively managed and age-class forests (even-aged timber management) while canopy communities differed not from those near the ground. In contrast, the beetle assemblages at the same sites showed the opposite pattern. We pursued an approach in the search for fungus-beetle associations by computing cross correlations and visualize significant links in a network graph. These correlations can be used to formulate hypotheses on mutualistic relationships for example in respect to beetles acting as vectors of fungal spores.},
  language  = {en}
}