Abstract
Long-term ecological research (LTER) is performed in many countries across the globe by scientists that gather and analyse multidecadal ecological observations and data, to support understanding and management of the environment. LTER data are used to describe the state and dynamics of an ecosystem. This valuable research is in charge of experts in ecology, and it is performed in the so-called LTER networks (for Europe, see http://www.lter-europe.net), usually organized at a national level; they consist of sites covering different ecosystem typologies, each one with research and monitoring facilities. In summer 2015, researchers of the Italian LTER network (LTER-Italy) have created a set of naturalistic trails, connecting sites of LTER-Italy (http://www.lteritalia.it/cammini), aiming to transfer and share the research results with citizens, through public events and informal science communication. Along one of these trails, performed on the Alps, the organizers proposed the use of two VGI apps to collect either biological or abiotic observations (http://www.lteritalia.it/it/content/citizenscience). The apps were chosen according to crucial characteristics, i.e. offline usage, operative system independence, strong development and support community, customization of user interfaces. This paper reports on this first test, discussing lessons learned and, in particular, the impact on the practice of research communities of participative, innovative tools, often neglected in the analysis of mobile application effects.
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Acknowledgements
This work has been partially funded by the Italian Flagship Project RITMARE, by LifeWatch-Italy (the Italian component of LifeWatch ERIC) and by the LTER-Italy network.
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Criscuolo, L., Carrara, P., Oggioni, A., Pugnetti, A., Antoninetti, M. (2018). Can VGI and Mobile Apps Support Long-Term Ecological Research? A Test in Remote Areas of the Alps. In: Bordogna, G., Carrara, P. (eds) Mobile Information Systems Leveraging Volunteered Geographic Information for Earth Observation. Earth Systems Data and Models, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-70878-2_3
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DOI: https://doi.org/10.1007/978-3-319-70878-2_3
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