Research Articles (Geography and Environmental Studies)

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    Phylogenetic structure of alien plant species pools from European donor habitats
    (John Wiley & Sons Ltd., 2021) Kalusova, Veronika; Cubino, Josep Padulles; Fristoe, Trevor S.; Chytry, Milan; Van Kleunen, Mark; Dawson, Wayne; Essl, Franz; Kreft, Holger; Mucina, Ladislav; Pergl, Jan; Pysek, Petr; Weigelt, Patrick; Winter, Marten; Lososova, Zdenka
    Aim: Many plant species native to Europe have naturalized worldwide. We tested whether the phylogenetic structure of the species pools of European habitats is related to the proportion of species from each habitat that has naturalized outside Europe (habitat’s donor role) and whether the donated species are more phylogenetically related to each other than expected by chance. Location: Europe (native range), the rest of the world (invaded range). Time period: Last c. 100 years. Major taxa studied: Angiospermae. Methods: We selected 33 habitats in Europe and analysed their species pools, including 9,636 plant species, of which 2,293 have naturalized outside Europe. We assessed the phylogenetic structure of each habitat as the difference between the observed and expected mean pairwise phylogenetic distance (MPD) for (a) the whole species pool and (b) subgroups of species that have naturalized outside Europe and those that have not. We used generalized linear models to test for the effects of the phylogenetic structure and the level of human influence on the habitat’s donor role.
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    Addressing the need for improved land cover map products for policy support
    (Elsevier, 2020-10) Szantoi, Zoltan; Geller, Gary N.; Tsendbazar, Nandin-Erdene; See, Linda; Griffiths, Patrick; Fritz, Steffen; Gong, Peng; Herold, Martin; Morah, Brice; Obregon, Andre
    The continued increase of anthropogenic pressure on the Earth’s ecosystems is degrading the natural environment and then decreasing the services it provides to humans. The type, quantity, and quality of many of those services are directly connected to land cover, yet competing demands for land continue to drive rapid land cover change, affecting ecosystem services. Accurate and updated land cover information is thus more important than ever, however, despite its importance, the needs of many users remain only partially attended. A key underlying reason for this is that user needs vary widely, since most current products – and there are many available – are produced for a specific type of end user, for example the climate modelling community. With this in mind we focus on the need for flexible, automated processing approaches that support on-demand, customized land cover products at various scales. Although land cover processing systems are gradually evolving in this direction there is much more to do and several important challenges must be addressed, including high quality reference data for training and validation and even better access to satellite data. Here, we 1) present a generic system architecture that we suggest land cover production systems evolve towards, 2) discuss the challenges involved, and 3) propose a step forward. Flexible systems that can generate on-demand products that match users’ specific needs would fundamentally change the relationship between users and land cover products – requiring more government support to make these systems a reality.
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    National coastal assessment & coastal climate change vulnerability assessment: Implications for the future
    (2019-09) Luck-Vogel, Melanie
    In this presentation the legacy of the coastal flood and erosion risk assessments conducted from the National Coastal Assessment project and the Coastal Climate Change Vulnerability Assessment is explained and the way forward is lined out.
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    Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR
    (Elsevier, 2016-05) Luck-Vogel, Melanie; Mbolambi, C.; Rautenbach, K.; Adams, J.; van Niekerk, L.
    This paper examines the value of very high-resolution multispectral satellite imagery and LiDAR-derived digital elevation information for classifying estuarine vegetation types. Satellite images used are fromtheWorldView-2, RapidEye, and SPOT-6 sensors in 2mand 5mresolution, respectively, acquired between 2010 and 2014. Ground truthing reference is a GIS-derived vegetation map based on field data from 2008. Supervised maximum likelihood classification produced satisfactory overall accuracies between 64.3% and 77.9% for the SPOT-6 and the WorldView-2 image, respectively,while the RapidEye-based classifications produced overall accuracies between 55.0% and 66.8%. The reasons for the misclassifications are mainly based on the highly dynamic environmental conditions causing discrepancies between the field data and satellite acquisition dates rather than technical issues. Dynamics in water levels and salinity caused rapid change in vegetation communities. Further, weather impacts such as floods and wind events caused water turbidity and led to bias in the reflective properties of the satellite images and thus misclassifications. These results show, however, that the spatial and spectral resolution of modern very high-resolution imagery is sufficient to satisfactory map estuarine vegetation and to monitor small-scale change. They emphasise, however, the importance of synchronisation of ground truthing data with actual image acquisition dates in these highly dynamic environments in order to achieve high classification accuracies. The results also highlight the importance of ancillary data for accurate interpretation of observed classification discrepancies and vegetation dynamics.
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    EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats
    (2020-07) Chytry, Milan; Tichy, Lubomir; Hennekens, Stephan M.; Knollova, Ilona; Janssen, John A. M.; Rodwell, John S.; Peterka, Tomas; Marceno, Corrado; Landucci, Flavia; Danihelka, Jiri; Hajek, Michal; Dengler, Jurgen; Novak, Pavel; Zukal, Dominik; Jimenez-Alfaro, Borja; Mucina, Ladislav; Abdulhak, Sylvain; Acic, Svetlana; Agrillo, Emiliano; Attorre, Fabio; Bergmeier, Erwin; Biurrun, Idoia; Boch, Steffen; Boloni, Janos; Bonari, Gianmaria; Braslavskaya, Tatiana; Bruelheide, Helge; Campos, Juan Antonio; Carni, Andraz; Casella, Laura; Cuk, Mirjana; Custerevska, Renata; De Bie, Els; Delbosc, Pauline; Demina, Olga; Didukh, Yakiv; Dite, Daniel; Dziuba, Tetiana; Ewald, Jorg; Gavilan, Rosario G.; Gegout, Jean-Claude; del Galdo, Gian Pietro Giusso; Golub, Valentin; Goncharova, Nadezhda; Goral, Friedemann; Graf, Ulrich; Indreica, Adrian; Isermann, Maike; Jandt, Ute; Jansen, Florian; Jansen, Jan; Jaskova, Anni; Jirousek, Martin; Kacki, Zygmunt; Kalnikova, Veronika; Kavgacı, Ali; Khanina, Larisa; Korolyuk, Andrey Yu.; Kozhevnikova, Mariya; Kuzemko, Anna; Kuzmic, Filip; Kuznetsov, Oleg L.; Laiviņs, Maris; Lavrinenko, Igor; Lavrinenko, Olga; Lebedeva, Maria; Lososova, Zdenka; Lysenko, Tatiana; Maciejewski, Lise; Mardari, Constantin; Marinsek, Aleksander; Napreenko, Maxim G.; Onyshchenko, Viktor; Perez-Haase, Aaron; Pielech, Remigiusz; Prokhorov, Vadim; Rasomavicius, Valerijus; Rojo, Maria Pilar Rodriguez; Rusina, Solvita; Schrautzer, Joachim; Sibik, Jozef; Silc, Urban; Skvorc, Zeljko; Smagin, Viktor A.; Stancic, Zvjezdana; Stanisci, Angela; Tikhonova, Elena; Tonteri, Tiina; Uogintas, Domas; Valachovic, Milan; Vassilev, Kiril; Vynokurov, Denys; Willner, Wolfgang; Yamalov, Sergey; Evans, Douglas; Lund, Mette Palitzsch; Spyropoulou, Rania; Tryfon, Eleni; Schaminee, Joop H. J.
    Abstract: Aim: The EUNIS Habitat Classification is a widely used reference framework for European habitat types (habitats), but it lacks formal definitions of individual habitats that would enable their unequivocal identification. Our goal was to develop a tool for assigning vegetation-plot records to the habitats of the EUNIS system, use it to classify a European vegetation-plot database, and compile statistically-derived characteristic species combinations and distribution maps for these habitats. Location: Europe. Methods: We developed the classification expert system EUNIS-ESy, which contains definitions of individual EUNIS habitats based on their species composition and geographic location. Each habitat was formally defined as a formula in a computer language combining algebraic and set-theoretic concepts with formal logical operators. We applied this expert system to classify 1,261,373 vegetation plots from the European Vegetation Archive (EVA) and other databases. Then we determined diagnostic, constant and dominant species for each habitat by calculating species-to-habitat fidelity and constancy (occurrence frequency) in the classified data set. Finally, we mapped the plot locations for each habitat. Results: Formal definitions were developed for 199 habitats at Level 3 of the EUNIS hierarchy, including 25 coastal, 18 wetland, 55 grassland, 43 shrubland, 46 forest and 12 man-made habitats. The expert system classified 1,125,121 vegetation plots to these habitat groups and 73,188 to other habitats, while 63,064 plots remained unclassified or were classified to more than one habitat. Data on each habitat were summarized in factsheets containing habitat description, distribution map, corresponding syntaxa and characteristic species combination. Conclusions: EUNIS habitats were characterized for the first time in terms of their species composition and distribution, based on a classification of a European database of vegetation plots using the newly developed electronic expert system EUNIS-ESy. The data provided and the expert system have considerable potential for future use in European nature conservation planning, monitoring and assessment.