Fine-grain beta diversity in Palaearctic open vegetation: variability within and between biomes and vegetation types

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: Mucina et al. (2016) for classes occurring in Europe; Ermakov (2012) for classes restricted to Asia. Abbreviations: ANOVA = analysis of variance; EDGG = Eurasian Dry Grassland Group; SAR = species-area relationship.

Fine-grain beta diversity in Palaearctic open vegetation: variability within and between biomes and vegetation types

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: Mucina et al. (2016) for classes occurring in Europe; Ermakov (2012) for classes restricted to Asia. Abbreviations: ANOVA = analysis of variance; EDGG = Eurasian Dry Grassland Group; SAR = species-area relationship.

The Ecological Interpretation of Unbiased Estimator for the Taxonomic Ratio: Different Approaches for Local and Regional Flora

Taxonomic ratio in an ecological context is considered as an indicator of the level of competitive exclusion. In spite of more than a century of discussions on taxonomic ratio, the problem of finding an unbiased estimator for flora characterisation remains unsolved. The traditional form of taxonomic ratio (species/genus or species/families ratio) is biased, which depends on the area of territory for which the floral composition was established. This circumstance makes the taxonomic ratio an inadequate characteristic of the flora. To solve the problem of finding an unbiased estimator for the taxonomic ratio, we have combined two fundamental ecological generalisations. The first is that species that belong to the same genus usually live in similar habitats and have similar morphological features. The struggle for life between species from the same genus is, therefore, more intense than between species from different genera. The second is species–area relationship. We have considered the problem of finding an unbiased taxonomic relationship using the Arrhenius curves to fit species–area relationships. This combination allowed us to find a form of unbiased taxonomic relationship. The example of Cyanophyceae flora shows that this indicator is closely related to a wide range of ecological and biogeographical characteristics of vegetation. The residual of the linear equation of dependence of the logarithm of the number of species on the logarithm of the number of genera is an unbiased indicator of the taxonomic relation, which is independent of the number of genera (or number of families) and the sampling size (or area). An unbiased taxonomic relationship is a characteristic of regional flora, which depends on a wide range of its ecological and biogeographical features.

Island biogeography: an avenue for research in bryology

In the present review, we provide an updated account on the level of knowledge in island bryophyte biogeography. In the framework of the 50 most fundamental questions for present and future island biology research highlighted by Patiño et al. (2017), we summarize current knowledge in bryophyte island biogeography and outline main research avenues for the future in the field. We found that only about 50% of the key current questions in island biogeography have been addressed to some extent, at least once, in bryophytes. Even fundamental questions that have caught the attention of ecologists since more than one century, such as the species-area relationship, have only rarely been dealt with in bryophytes. The application of the Island Biogeography Theory therefore opens an avenue for research in bryology, and we discuss the most salient features, including species and community phylogenetics, biotic interactions, and invasion biology.

From Island Biogeography to Conservation: A Multi-Taxon and Multi-Taxonomic Rank Approach in the Tuscan Archipelago

Investigating the drivers that support species richness (S) in insular contexts can give insights for the conservation of insular biodiversity. Our aim was to decouple the effect of drivers (island area, distance from mainland and habitat diversity) accounted in three hypotheses or a combination of them in explaining S in seven islands of the Tuscan Archipelago: Area (species–area relationship, SAR), area and distance from mainland (equilibrium hypothesis, EQH) and habitat (habitat diversity hypothesis, HDH). We used published and original datasets to assess S (except aliens) for 42 taxa (14 animal and 28 plant taxa) in each island, and we used S as the dependent variable and the drivers as covariates in regression models. In 31 taxa, the data supported one of the tested hypotheses or a combination of them, and the most commonly supported hypotheses were SAR (12 taxa) and EQH (10 taxa). The effect of the area was also evident in SAR + HDH (five taxa) and EQH + HDH (one taxon), making it the prevailing driver in explaining S. Since distances are relatively short, and three out of four islands are land-bridge islands, the effect of distance was significant for 12 taxa. The effects of habitat diversity were evident for just nine taxa. The multi-taxon approach allowed us to understand the differential effect of drivers among taxa in influencing S in a single archipelago. Moreover, the multi-taxonomic rank approach highlighted how the information contained within higher taxonomic ranks (e.g., Division) can be substantially different from that derived from lower ranks (e.g., Family). These insights are of particular importance from a conservation perspective of the archipelago’s biodiversity, and this approach can be transferred to mainland fragmented systems.