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Fine‐grain beta diversity of Palaearctic grassland vegetation

Iwona Dembicz, Jürgen Dengler, Manuel J. Steinbauer, Thomas J. Matthews, Sándor Bartha, Sabina Burrascano, Alessandro Chiarucci, Goffredo Filibeck, François Gillet, Monika Janišová, Salza Palpurina, David Štorch, Werner Ulrich, Svetlana Aćić, Steffen Boch, Juan Antonio Campos, Laura Cancellieri, Marta Carboni, Giampiero Ciaschetti, Timo Conradi, Pieter De Frenne, Jiří Doležal, Christian Dolnik, Franz Essl, Edy Fantinato, Itziar García‐Mijangos, Gianpietro Giusso del Galdo, John‐Arvid Grytnes, Riccardo Guarino, Behlül Güler, Jutta Kapfer, Ewelina Klichowska, Łukasz Kozub, Анна Куземко, Swantje Löbel, Michael Manthey, Corrado Marcenò, Anne Mimet, Alireza Naqinezhad, Jalil Noroozi, Arkadiusz Nowak, Harald Pauli, Robert K. Peet, Vincent Pellissier, Remigiusz Pielech, Massimo Terzi, Emin Uğurlu, Orsolya Valkó, Iuliia Vasheniak, Kiril Vassilev, Denys Vynokurov, Hannah J. White, Wolfgang Willner, Manuela Winkler, Sebastian Wolfrum, Jinghui Zhang, Idoia Biurrun

2021Journal of Vegetation Science53 citationsDOIOpen Access PDF

Abstract

Abstract Questions Which environmental factors influence fine‐grain beta diversity of vegetation and do they vary among taxonomic groups? Location Palaearctic biogeographic realm. Methods We extracted 4,654 nested‐plot series with at least four different grain sizes between 0.0001 m² and 1,024 m² from the GrassPlot database, covering a wide range of different grassland and other open habitat types. We derived extensive environmental and structural information for these series. For each series and four taxonomic groups (vascular plants, bryophytes, lichens, all), we calculated the slope parameter ( z ‐value) of the power law species–area relationship (SAR), as a beta diversity measure. We tested whether z ‐values differed among taxonomic groups and with respect to biogeographic gradients (latitude, elevation, macroclimate), ecological (site) characteristics (several stress–productivity, disturbance and heterogeneity measures, including land use) and alpha diversity ( c ‐value of the power law SAR). Results Mean z ‐values were highest for lichens, intermediate for vascular plants and lowest for bryophytes. Bivariate regressions of z‐values against environmental variables had rather low predictive power (mean R ² = 0.07 for vascular plants, less for other taxa). For vascular plants, the strongest predictors of z ‐values were herb layer cover (negative), elevation (positive), rock and stone cover (positive) and the c ‐value (U‐shaped). All tested metrics related to land use (fertilization, livestock grazing, mowing, burning, decrease in naturalness) led to a decrease in z ‐values. Other predictors had little or no impact on z ‐values. The patterns for bryophytes, lichens and all taxa combined were similar but weaker than those for vascular plants. Conclusions We conclude that productivity has negative and heterogeneity positive effects on z ‐values, while the effect of disturbance varies depending on type and intensity. These patterns and the differences among taxonomic groups can be explained via the effects of these drivers on the mean occupancy of species, which is mathematically linked to beta diversity.

Topics & Concepts

LichenBeta diversityGrasslandEcologyVegetation (pathology)Vascular plantAlpha diversityRange (aeronautics)TaxonTaxonomic rankHabitatBiologyPhysical geographyGeographySpecies richnessMedicineComposite materialPathologyMaterials scienceEcology and Vegetation Dynamics StudiesLichen and fungal ecologyPlant and animal studies
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