Studied biology, with a focus on zoology and limnology, at the University of Innsbruck (Austria) and graduated in 2007. He obtained his doctoral degree (PhD) for his research on “Benthic Invertebrates and Reference Conditions in East European Running Waters: Case study in the headwaters of Volga River (Tver Region, Russia) and adjacent water bodies” in 2010. He was working on national and international scientific projects since 2003 and his research interests are concentrated in the following areas: monitoring, biodiversity (zoobenthos and other aquatic quality components), investigation of anthropogenic stressors at large spatial scales and in the context of environmental factors as well as river basin management. Since 2009 he is working at TIWAG – Tiroler Wasserkraft AG (Hydropower Planning Department) as head of the group “Ecology” and in this role he is responsible for planning and management of ecological aspects as well as R&D projects. Additionally, Dr. Schletterer has teaching assignments at the Institute for Modelling Hydraulic and Environmental Systems at University of Stuttgart (Germany), the University of Vienna (Austria) as well as at the Universidade de Lisboa (Portugal).
Associate Professor at the University Innsbruck. Holds a Master’s degree in Limnology and Taxonomy (Master thesis on «Vertical distribution of Zoobenthos of the hard stubstrate areas in Lake Piburg, Tirol, Austria». Supervision: Prof. Dr. Roland Pechlaner) and a Ph.D in Tropical Ecology and Stream Ecology (Doctoral thesis at the University of Innsbruck and Drexel University, Philadelphia, as a Fulbright scholar, on «Drift patterns in neotropical streams in Costa Rica». Supervisors: Prof. Dr. Eugen Rott, Innsbruck, and Prof. Dr. Susan Kilham, Philadelphia). Scientific interest on the structure and function of stream ecosystems with special attention to (high) alpine areas and the taxonomy of high alpine aquatic insects, involved in basic and applied research topics. Currently involved in teaching at the undergraduate and graduate level as well as supervision of Master and Ph.D students
Candidate of technical sciences, associate professor. In 1994 he graduated from Tver State Polytechnic Institute (Russia) in «Open pit mining» and in 2002 he defended his thesis on «Development of Methods for the Environmental Evaluation of the Wise Use of Peatlands». Since 2000 he is working at Tver State Technical University. Research interests: evaluation of the impact of natural landscapes on the hydrochemical regime of riverine systems. Since 2011 he is also the head of the Laboratory of Environmental Monitoring of the Tver Center for Hydrometeorology and Environmental Monitoring (ROSHYDROMET division in Tver region)
Graduated in 1973 from the Kalinin Polytechnic Institute (Russia), with the specialty «Technology and complex mechanization of peat deposits». In 2000 he defended his doctoral thesis «Geoecological basis for the rational use of peat bog resources of the Upper Volga Region». Since 1996 he works in international scientific projects. Research interests are focused in the following areas: the interaction of peat bogs and the environment, the composition and properties of the surface water as well as environmental impact assessment. Since 1973 he is working at Tver State Technical University, in 2004 he became head of the Chair of Nature Management and Ecology. He also supervises the department of Geology, exploration and technological design of the East European Institute of Peat Business
Junior researcher in the Centre for Biorobotics of the Tallinn University of Technology (Estonia). His research interests focus primarily on ecohydraulics, hydraulic modeling, fish passes and underwater robotics. Since 2009 he has collaborated in several research projects focused on design, simulation and evaluation of fishways (GEA-Ecohydraulics, University of Valladolid), and since 2014 he has been working with artificial lateral line technology and its applications for the evaluation of aquatic ecosystems as well as its use in underwater robotics
Received the B.Sc. degree in civil engineering from California Polytechnic State University, San Luis Obispo, USA, in 2004, and a M.Sc. degree in water resources engineering and management, in 2007, and his Dr.-Ing. degree in hydraulics from the University of Stuttgart, Germany, in 2011. At the Center for Biorobotics, Tallinn University of Technology, Estonia, he leads the Environmental Sensing and Intelligence Group. His research areas include environmental intelligence, bio-inspired fluid mechanics and remote sensing
Within the joint Russian-Austrian monitoring programme “REFCOND_VOLGA (2006 – 20XX)”, monitoring sites were established in the headwaters of the Volga (Tver Region). River Tudovka, a right tributary to the Volga River, was included within this monitoring programme as its catchment is partly protected and has only few anthropogenic activities. The monitoring activities include physico-chemical and hydraulic parameters as well as biota with a focus is on benthic organisms (diatoms and macrozoobenthos). In this work, the longitudinal patterns in community structure are classified in the lowland river Tudovka using a novel feature-based approach taken from signal processing theory. The method first clusters field sampling data into longitudinal classes (upper, middle, lower course). Community features based on the relative frequency of individual species occurring per class are then generated. We apply both generative and discriminative classification methods. The application of generative methods provides data models which predict the probability of a new sample to belong to an existing class. In contrast, discriminative approaches search for differences between classes and allocate new data accordingly. Leveraging both methods allows for the creation of stable classifications. On this basis we show how the community features can be used to predict the longitudinal class. The community features approach also allows for a robust cross-comparison of investigation reaches over time. In cases where suitable long-term data set are available, predictive models using this approach can also be developed.
The authors declare that there are no conflicts of interest present.