Fine‐grain beta diversity of Palaearctic grassland vegetation

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 R² = 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.

Scale dependence of species–area relationships is widespread but generally weak in Palaearctic grasslands

“…They were mostly determined in the field, but some additionally retrieved via the plot coordinates (Appendix S1). For simplicity and following a previous paper using the same dataset (Dembicz et al, 2021b), we group them into the following categories, acknowledging that some variables can relate to more than one category: (c) heterogeneity, and (4) vegetation typologies. In the following, we briefly introduce the variables of categories ( 2)-( 4), while details are provided in Appendix S1.…”

“…If there should be peaks of local z‐values at certain grain sizes this would indicate how spatial heterogeneity of plant communities is organized spatially. As for fine‐grain beta diversity in general (Drakare et al, 2006; Dembicz et al, 2021b) one should also expect peak location of local z ‐values to depend on taxonomic group and various environmental factors. Unfortunately, there are so far no comprehensive macroecological studies to examine the prevalence of peaks in local z ‐values and which drivers determine their position.…”

Benchmarking plant diversity of Palaearctic grasslands and other open habitats

“…Moreover, as the example in Figure5illustrates, the curves only rarely cross each other, meaning that vegetation types mainly differ in their c-values (corresponding to α-diversity), while there are few systematic differences concerning z-values (corresponding to β-diversity). As shown byDembicz et al (2021), z-values are much more affected by disturbance regimes and heterogeneity -which are largely independent of vegetation type.…”

Macroecology of vegetation — Lessons learnt from the Virtual Special Issue

“…Based on the previous finding that the power function is generally the best approximation of the species–area relationship (SAR) even at very fine grains (10 −4 –10 3 m 2 ; Dengler et al, 2020); two studies used the modelled exponent of the power function ( z ‐value) as a measure of multiplicative beta diversity within nested‐plot series (Dembicz et al, 2021; Zhang et al, 2021). Dembicz et al (2021) found consistent differences in z ‐values between three studied taxonomic groups in relation to elevation and to land‐use intensity and used their findings to propose a new conceptual model for separating causes of fine‐grain beta diversity. Zhang et al (2021) went a step further and asked whether the small deviations from the power law show any regularities.…”

“…A total of 15 contributions used fine‐grain plant community data to address macroecological questions at various extents: global (Kusumoto et al, 2021; Testolin et al, 2021), across the whole Palaearctic (Biurrun et al, 2021; Dembicz et al, 2021; Zhang et al, 2021), across Europe (Axmanová et al, 2021; Boonman et al, 2021; Padullés Cubino et al, 2021; Sporbert et al, 2021; Večera et al, 2021), larger parts of Europe (Cao Pinna et al, 2021; Wagner et al, 2021) or at state level (Bourgeois et al, 2021; Craven et al, 2021). Most of these studies rely on two large vegetation‐plot databases established and maintained by two working groups of the International Association for Vegetation Science (IAVS), the European Vegetation Archive (EVA; Chytrý et al, 2016) by the European Vegetation Survey (Axmanová et al, 2021; Boonman et al, 2021; Cao Pinna et al, 2021; Padullés Cubino et al, 2021; Sporbert et al, 2021; Večera et al, 2021; Wagner et al, 2021) and the GrassPlot database (Dengler et al, 2018) by the Eurasian Dry Grassland Group (Biurrun et al, 2021; Dembicz et al, 2021; Zhang et al, 2021). Testolin et al (2021) used data from the global vegetation‐plot database sPlot (Bruelheide et al, 2019), and four relied on regional data compilations (Bourgeois et al, 2021; Craven et al, 2021; Kusumoto et al, 2021; Tordoni et al, 2021).…”