Corresponding author: Jürgen Dengler ( dr.juergen.dengler@gmail.com ) Academic editor: Arkadiusz Nowak
© 2021 Iwona Dembicz, Jürgen Dengler, François Gillet, Thomas J. Matthews, Manuel J. Steinbauer, Sándor Bartha, Juan Antonio Campos, Pieter De Frenne, Jiri Dolezal, Itziar García-Mijangos, Riccardo Guarino, Behlül Güler, Anna Kuzemko, Alireza Naqinezhad, Jalil Noroozi, Robert K Peet, Massimo Terzi, Idoia Biurrun.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Dembicz I, Dengler J, Gillet F, Matthews TJ, Steinbauer MJ, Bartha S, Campos JA, De Frenne P, Dolezal J, García-Mijangos I, Guarino R, Güler B, Kuzemko A, Naqinezhad A, Noroozi J, Peet RK, Terzi M, Biurrun I (2021) Fine-grain beta diversity in Palaearctic open vegetation: variability within and between biomes and vegetation types. Vegetation Classification and Survey 2: 293-304. https://doi.org/10.3897/VCS/2021/77193
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Aims: To quantify how fine-grain (within-plot) beta diversity differs among biomes and vegetation types. Study area: 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 spanning broad geographic and ecological gradients. Next, we calculated the slope parameter (z-value) of the power-law species–area relationship (SAR) to use as a measure of multiplicative beta diversity. We did this separately for vascular plants, bryophytes and lichens and for the three groups combined (complete vegetation). We then tested whether z-values differed between biomes, ecological-physiognomic vegetation types at coarse and fine levels and phytosociological classes. Results: We found that z-values varied significantly among biomes and vegetation types. The explanatory power of area for species richness was highest for vascular plants, followed by complete vegetation, bryophytes and lichens. Within each species group, the explained variance increased with typological resolution. In vascular plants, adjusted R2 was 0.14 for biomes, but reached 0.50 for phytosociological classes. Among the biomes, mean z-values were particularly high in the Subtropics with winter rain (Mediterranean biome) and the Dry tropics and subtropics. Natural grasslands had higher z-values than secondary grasslands. Alpine and Mediterranean vegetation types had particularly high z-values whereas managed grasslands with benign soil and climate conditions and saline communities were characterised by particularly low z-values. Conclusions: In this study relating fine-grain beta diversity to typological units, we found distinct patterns. As we explain in a conceptual figure, these can be related to ultimate drivers, such as productivity, stress and disturbance, which can influence z-values via multiple pathways. The provided means, medians and quantiles of z-values for a wide range of typological entities provide benchmarks for local to continental studies, while calling for additional data from under-represented units.
Syntaxonomic references:
Abbreviations: ANOVA = analysis of variance; EDGG = Eurasian Dry Grassland Group; SAR = species-area relationship.
biome, bryophyte, fine-grain beta diversity, GrassPlot, lichen, mean occupancy, Palaearctic grassland, phytosociological class, species–area relationship (SAR), vascular plant, vegetation type, z-value
The Palaearctic biogeographic realm (sensu
The GrassPlot database (
A recent study using GrassPlot (
According to
Conceptual figure summarizing our main hypotheses for how different drivers could influence fine-grain β-diversity via changing mean occupancy of species, based on the findings of
Beyond testing these expectations, our aim is to provide information on typical z-values of biomes and vegetation types. This could help to detect habitat-specific deviations, related for example to anthropogenic disturbances (see, e.g.,
The geographic scope of GrassPlot and of this study is the whole Palaearctic biogeographic realm. The nested-plot data used cover wide geographic gradients but have the highest density in Europe (Figure
We used plot data from the collaborative vegetation-plot database GrassPlot (
For those nested-plot series with more than one plot for a certain grain size, we averaged richness values per grain size. Thus, we obtained one single richness value for each grain size within each nested-plot series for each of the sampled species groups.
We fitted a power function to each dataset (i.e. a species group within a nested-plot series) using the non-transformed “S-space” (S = c Az) and the “logarithmic S-space” (log10 S = log10 c + z log10 A) with S = species richness, A = area in m², and c and z the fitted “intercept” and “slope” parameters, respectively. Both approaches are valid, have been widely used in the literature, and have different advantages and limitations (see
To fit the power model in log S-space, we used linear regression and the standard ‘lm’ function in R. The fitting in S-space followed the approach of
We excluded nested-plot series with zero reported species for the investigated species group as well as the very few nested-plot series where the model fitting did not converge or resulted in theoretically impossible values of z > 1 (
We tested how the modelled z-values depended on biome and vegetation types of three different typologies. First, as a biome typology, we used the ecozones of
As the visual inspection of the boxplots did not yield severe violations of the assumptions of linear models (see
For visualisation of the distribution of the observed values, we used violin plots, a method of plotting numeric data that is a hybrid of boxplots and kernel density plots, able to identify multimodality (R package ‘ggplot2’;
To avoid strong unequal sample sizes and heterogeneous variances among categories when comparing taxa, which could make F-tests unreliable, we restricted comparisons to biomes, vegetation types and phytosociological classes represented in complete vegetation by at least ten nested-plot series. All computations were performed with R 4.1.1 (
As the results were qualitatively similar for log S-space and S-space, and as we had slightly more replicates for log S-space, we present the results from the ANOVAs and violin plots only for log S-space in the main text. Descriptive statistics (number of replicates, means, medians, 10% and 90% quantiles) for both spaces are provided in Suppl. material
The mean and median z-values of most biomes and vegetation types were around 0.25, although the range was from 0.15 to 0.50 (Figures
Comparison of fine-grain z-values of vascular plants between the biomes included in GrassPlot with suitable data. The biomes are sorted in descending order of latitude and elevation. The circles represent the means, the horizontal lines the medians and the letters homogeneous groups according to Tukey’s HSD post hoc test following a significant ANOVA (in decreasing order). Numbers at the top of the violin plots indicate the number of nested-plot series in each biome.
Among the biomes, the Temperate midlatitudes had the lowest mean z-value, but were hardly different from Alpine, Boreal zone and Dry midlatitudes (Figure
Comparison of fine-grain z-values of vascular plants between the six coarse-level vegetation types distinguished in GrassPlot. The circles represent the means, the horizontal lines the medians and the letters homogeneous groups according to Tukey’s HSD post hoc test following a significant ANOVA (in decreasing order). Numbers at the top of the violin plots indicate the number of nested-plot series in each coarse-level vegetation type.
For all four vegetation typologies considered, the explained variance was highest for vascular plants, followed by complete vegetation, whereas it was relatively low in bryophytes and lowest in lichens (Suppl. material
Comparison of fine-grain z-values of vascular plants between those fine-level vegetation types distinguished in GrassPlot that were represented by at least 10 observations. A1 = alpine grasslands, A3 = xeric grasslands and steppes, A4 = rocky grasslands, B1 = sandy dry grasslands, B2 = meso-xeric grasslands, B3 = mesic grasslands, B4 = wet grasslands, B5 = Mediterranean grasslands, C1 = dunes, C2 = rocks and screes, C3 = saline communities, C4 = saline steppes and semi-deserts, C5 = wetlands, D1 = lowland heathlands, D2 = arctic-alpine heathlands, D3 = garrigues and thorn-cushion communities, E1 = tall forb communities, E2 = ruderal communities, F2 = cold deserts and semi-deserts. The circles represent the means, the horizontal lines the medians and the letters homogeneous groups according to Tukey’s HSD post hoc test following a significant ANOVA (in decreasing order). Numbers at the top of the violin plots indicate the number of nested-plot series in each fine-level vegetation type.
Comparison of fine-grain z-values of vascular plants between those phytosociological classes that were represented by at least 10 observations. The circles represent the means, the horizontal lines the medians and the letters homogeneous groups according to Tukey’s HSD post hoc test following a significant ANOVA (in decreasing order). Numbers at the top of the violin plots indicate the number of nested-plot series in each phytosociological class.
Similar to previous studies, we found large variation in z-values within most of the typological units considered (
Second, we found that the explained variance increased the finer resolved our typology was: while biomes explained only around 14% of the variance, phytosociological classes accounted for more than 50%. This finding is not surprising and mainly reflects that our typological units are meaningful entities that differ in their vegetation patterns as well as their average productivity, stress and disturbance (see
Third, we found a clear decrease in explained variance (or in other words, in distinctness of the patterns) from vascular plants via complete vegetation to bryophytes and lichens. This is consistent with findings of two previous GrassPlot studies that looked at other aspects of fine-grain z-values (
The two subtropical biomes (Subtropics with winter rain, Dry tropics and subtropics) had clearly higher z-values than the rest, which is consistent with the increase in z-values from 50 °N southward reported by
Second, natural grasslands had systematically higher z-values than secondary grasslands. This corroborates the previously reported clear negative effects of land use intensity on z-values (
Nested-plot z-values are mathematically closely linked to mean occupancy (
Our findings with regard to the different typological entities fit well into the hypothetical schema of Figure
This is the most comprehensive study to date that relates fine-grain β-diversity as measured using z-values to different vegetation typologies. We thus complement the recent study of
We found that despite important variation, there are clear differences in mean z-values among typological units. While there is not a single reason for low or high z-values, the values themselves can still be used as informative tools to assess the influence of certain drivers, particularly land-use intensity (see also
The vegetation-plot data used are stored in and available from the GrassPlot database (https://edgg.org/databases/GrassPlot;
J.D. initiated the data collection and, together with I.D. and S.B., conceived the idea for this paper. Most authors contributed data. J.D. served as custodian and I.B. as database manager of the GrassPlot database. F.G., M.J.S. and T.J.M. conducted the statistical analyses, while I.D. and J.D. led the writing. All authors checked, improved and approved the manuscript.
We thank all vegetation scientists who carefully collected the multi-scale plant diversity data from Palaearctic Grasslands available in GrassPlot. The Eurasian Dry Grassland Group (EDGG) and the International Association for Vegetation Science (IAVS) supported the EDGG Field Workshops that generated a core part of the GrassPlot data. The Bavarian Research Alliance (via the BayIntAn scheme) and the Bayreuth Center of Ecology and Environmental Research (BayCEER) funded the initial GrassPlot workshop during which the database was established and the current paper was initiated (grants to J.D.). I.B., J.A.C. and I.G.-M. were supported by the Basque Governement (IT936-16). Data of R.K.P. were collected with support of US NSF grant # BSR-8506098. A.N. was supported by the University of Mazandaran under a continuous “Master Research Plan”. We thank Arkadiusz Nowak and two anonymous reviewers for fast handling of and constructive recommendations on the manuscript of this paper.