WetVegEurope: a database of aquatic and wetland vegetation of Europe

Landucci, Flavia; Řezníčková, Marcela; Šumberová, Kateřina; Chytrý, Milan; Auniņa, Liene; Biță‐Nicolae, Claudia; Bobrov, Alexander A.; Borsukevych, L. M.; Brisse, Henry; Čarni, Andraž; Csiky, János; Cvijanović, Dušanka; Bie, Els De; Ruffray, Patrice de; Dubyna, D. V.; Dimopoulos, Panayotis; Dziuba, Tetiana; Fitzpatrick, Úna; Font, Xavier; Gigante, Daniela; Голуб, В. Б.; Hennekens, S.M.; Hrivnák, Richard; Iemelianova, S.М.; Jandt, Ute; Jenačković, Dragana D.; Jansen, Florian; Kącki, Zygmunt; Lájer, Konrád; Matulevičiūtė, Dalytė; Mesterházy, Attila; Michalcová, Dana; Paal, Jaanus; Papastergiadou, Eva; Properzi, Alessandro; Radulović, Snežana; Rodwell, J. S.; Schaminée, J.H.J.; Šilc, Urban; Sinkevičienė, Zofija; Stančić, Zvjezdana; Stepanovich, Jazep; Teteryuk, Boris Yu.; Tzonev, Rossen; Venanzoni, Roberto; Weekes, Lynda; Willner, Wolfgang

doi:10.1127/phyto/2015/0050

Cited by 16 publications

(12 citation statements)

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“…Analogously, wetland vegetation has long been used as a key metric for determining the legal status of U.S. wetlands (Cowardin et al 1979), which is based on hydrology, vegetation, and soils, and for fens particularly the presence of 40 cm of peat (a characteristic dismissed by Wheeler and Proctor 2003). Wetland status also has been described for many individual plant species, plant associations, and faunal species in other developed countries and continents (e.g., Ellery 2004, Landucci et al 2015, Australian Plant Society 2018).…”

Section: Springs Ecosystem Classification and Approachesmentioning

confidence: 99%

Springs ecosystem classification

Stevens

Schenk

Springer

2020

Ecological Applications

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Springs ecosystems are globally abundant, geomorphologically diverse, and bioculturally productive, but are highly imperiled by anthropogenic activities. More than a century of scientific discussion about the wide array of ecohydrological factors influencing springs has been informative, but has yielded little agreement on their classification. This lack of agreement has contributed to the global neglect and degradation of springs ecosystems by the public, scientific, and management communities. Here we review the historical literature on springs classification variables, concluding that site-specific source geomorphology remains the most diagnostic approach. We present a conceptual springs ecosystem model that clarifies the central role of geomorphology in springs ecosystem development, function, and typology. We present an illustrated dichotomous key to terrestrial (non-marine) springs ecosystem types and subtypes, and describe those types. We identify representative reference sites, although data limitations presently preclude selection of continentally or globally representative reference springs of each type. We tested the classification key using data from 244 randomly selected springs of 13 types that were inventoried in western North America. The dichotomous key correctly identified springs type in 87.5% of the cases, with discrepancies primarily due to differentiation of primary vs. secondary typology, and insufficient inventory team training. Using that information, we identified sources of confusion and clarified the key. Among the types that required more detailed explanation were hypocrenes, springs in which groundwater is expressed through phreatophytic vegetation. Overall, springs biodiversity and ecosystem complexity are due, in part, to the co-occurrence of multiple intra-springs microhabitats. We describe microhabitats that are commonly associated with different springs types, reporting at least 13 microhabitats, each of which can support discrete biotic assemblages. Interdisciplinary agreement on basic classification is needed to enhance scientific understanding and stewardship of springs ecosystems, the loss and degradation of which constitute a global conservation crisis.

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Section: Springs Ecosystem Classification and Approachesmentioning

confidence: 99%

Springs ecosystem classification

Stevens

Schenk

Springer

2020

Ecological Applications

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“…The Wetlands data set was extracted from the WetVegEurope database (Landucci et al., ). It contains plots from Austria, Czech Republic, Germany, Hungary, Poland, Slovakia and the Netherlands.…”

Section: Methodsmentioning

confidence: 99%

Joint optimization of cluster number and abundance transformation for obtaining effective vegetation classifications

Lengyel

Landucci

Mucina

et al. 2018

J Vegetation Science

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Question Is it possible to determine which combination of cluster number and taxon abundance transformation would produce the most effective classification of vegetation data? What is the effect of changing cluster number and taxon abundance weighting (applied simultaneously) on the stability and biological interpretation of vegetation classifications? Locality Europe, Western Australia, simulated data. Methods Real data sets representing Hungarian sub‐montane grasslands, European wetlands, and Western Australian kwongan vegetation, as well as simulated data sets were used. The data sets were classified using the partitioning around medoids method. We generated classification solutions by gradually changing the transformation exponent applied to the species projected covers and the number of clusters. The effectiveness of each classification was assessed with a stability index. This index is based on bootstrap resampling of the original data set with subsequent elimination of duplicates. The vegetation types delimited by the most stable classification were compared with other classifications obtained at local maxima of the stability values. The effect of changing the transformation power exponent on the number of clusters, indexed according to their stability, was evaluated. Results The optimal number of clusters varied with the power exponent in all cases, both with real and simulated data sets. With the real data sets, optimal cluster numbers obtained with different data transformations recovered interpretable biological patterns. Using the simulated data, the optima of stability values identified the simulated number of clusters correctly in most cases. Conclusions With changing the settings of data transformation and the number of clusters, classifications of different stability can be produced. Highly stable classifications can be obtained from different settings for cluster number and data transformation. Despite similarly high stability, such classifications may reveal contrasting biological patterns, thus suggesting different interpretations. We suggest testing a wide range of available combinations to find the parameters resulting in the most effective classifications.

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“…In all issues of 2015 there have been articles within one of our two permanent sections -"Ecoinformatics" (Section Editors: Jürgen Dengler & Florian Jansen) and "Phytosociological Nomenclature" (Section Editors: Erwin Bergmeier & Wolfgang Willner). Together with this issue, Phytocoenologia has published five Long Database Reports (Landucci et al 2015;Peterka et al 2015;Peyre et al 2015;Korzeniak 2016;Vassilev et al 2016) and three Short Database Reports (Thiele et al 2015;Revermann et al 2016;Vanselow 2016) about vegetation databases registered in the Global Index of Vegetation Plot Databases (GIVD; Dengler et al 2011). We believe that these data papers will stimulate the cooperation between database owners to share data for approaching broader-scale scientific questions and at the same time provide a means to give credit to the data originators through citing these publications whenever using data from the respective databases.…”

Section: Permanent Sections and Special Issuesmentioning

confidence: 98%