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1. Halting biodiversity loss, a major environmental challenge, relies on the understanding of species richness patterns. The assessment of species richness is often hampered by limited taxonomic knowledge and the general dearth of trained systematists. Research has shown that we can predict the number of species in a community by the number of higher order taxonomic units present. Here, we test whether we need to know all the genera, families or orders in order to do so. Further, the number of common species in a region is a good predictor of total richness and we test if this predictability translates to using higher taxa. 2. We used data from 240 sites from the Natura 2000 network of protected areas in Greece, including 5148 plant species and subspecies, which are grouped in 1113 genera 174 families and 56 orders. We correlated species richness with the number of common genera, families or orders present. The analysis was repeated using the number of the most speciose higher orders instead of the most common. 3. We found that we do not need to know all higher order taxa present, in order to predict species richness. If we know how many out of the 30 most common orders are present, we can reliably predict the number of species. Similar results were obtained if we know how many of the 60 most common families or 200 most common genera are present. 4. Equally good results were obtained using the same numbers of the most speciose higher orders. 5. Synthesis and applications. Our analysis demonstrates that species richness can be predicted from the number of common or more speciose genera, families and orders present. These predictions hold without complete sampling of these higher taxa. The implication is that we need only limited systematic knowledge, resources and effort in order to predict species richness. Assuming these findings hold in other taxonomic groups and in other regions, we argue that the uncertainty introduced by limited knowledge of the systematics ... [ABSTRACT FROM AUTHOR]

Aim To examine the way in which ‘area’ and ‘habitat diversity’ interact in shaping species richness and to find a simple and valid way to express this interaction. Location The Natura 2000 network of terrestrial protected areas in Greece, covering approximately 16% of the national territory. Methods We used the Natura 2000 framework, which provides a classification scheme for natural habitat types, to quantify habitat heterogeneity. We analysed data for the plant species composition in 16,143 quadrats in which 5044 species and subspecies of higher plants were recorded. We built a simple mathematical model that incorporates the effect of habitat diversity on the species–area relationship (SAR). Results Our analysis showed that habitat diversity was correlated with area. However, keeping habitat diversity constant, species richness was related to area; while keeping area constant, species richness was related to habitat diversity. Comparing the SAR of the 237 sites we found that the slope of the species–area curve was related to habitat diversity. Main conclusions Discussion of the causes of the SAR has often focused on the primacy of area per se versus habitat heterogeneity, even though the two mechanisms are not mutually exclusive and should be considered jointly. We find that increasing habitat diversity affects the SAR in different ways, but the dominant effect is to increase the slope of the SAR. While a full model fit typically includes a variety of terms involving both area and habitat richness, we find that the effect of habitat diversity can be reduced to a linear perturbation of the slope of the species accumulation curve. [ABSTRACT FROM AUTHOR]

• Empirical test of functional differentiation of forests at a regional scale. • Functional identity but not diversity differentiates forest community types. • Decoupled functional diversity and identity patterns highlight their complementarity. We studied the differentiation among plant communities of deciduous broadleaved and mountain coniferous forests in terms of functional diversity and identity at a regional scale (northern and central Greece). We asked if patterns of functional differentiation among communities are consistent between the overstorey and understorey layers and if they can be influenced by deep past environmental conditions. Functional Richness (FRic) and Functional Dispersion (FDis), as well as their standardized effect sizes, were employed to assess the multivariate functional diversity of the community types. In contrast, single-trait Community Weighted Means (CWMs) were used as surrogates of functional identity. The aforementioned indices were calculated for three datasets, namely all the vascular plant taxa found in individual vegetation plots (total community), all phanerophyte (tree and shrub) taxa (overstorey) and all non-phanerophyte vascular plant taxa (understorey). We found that community types and especially four broad forest types (beech, ravine, pine and oak forests) are well differentiated in terms of functional composition (identity), as indicated by Non-Metric Multidimensional Scaling (NMDS). After conducting an NMDS for the three datasets, functional identity based on the total floristic composition was found to be the best discriminator of the studied communities. However, contrasting patterns were found for some specific traits or their categories between overstorey and understorey layers. The patterns of functional diversity of the community types (based on multivariate indices), revealed by calculating the standardized effect sizes of FRic and FDis based on the richness null model, did not differ substantially from random expectations for most of the studied community types when the dataset of all the vascular plant taxa was analyzed. However, the patterns revealed for the overstorey layer differed from those for the understorey layer. For the latter layer, the clustered structure was revealed in many community types based on the ses.FDis metric. Indications of deep past influence on the functional composition were found for certain community types (i.e. ravine forests) based on single-trait metrics, but no indication of such influence was found based on multivariate indices. Our findings highlight the complementarity and the additive explanatory value of the simultaneous use of single- and multi-trait approaches and their application to different layers in forests. [ABSTRACT FROM AUTHOR]

Modern water resource management requires biomonitoring of the structure and functioning of freshwater ecosystems, which may be better illuminated by functional trait distribution patterns and responses across human-induced pressure gradients. In this study, we applied the RLQ, the fourth-corner and their novel combination methods, in order to assess the relationship between the distribution of 30 aquatic macrophyte functional traits and 14 indicators of anthropogenic pressures across 16 freshwater lakes. Our findings showed that there is a statistically significant relationship between the distribution of specific functional traits and anthropogenic pressures. Eutrophication was the dominant pressure and the shift from a submerged-macrophyte dominated vegetation community to an emergent one was the most important functional response. Aerial reproduction and dispersal traits were found to replace water-related ones under higher nutrient concentrations. Trophic and light preferences of macrophytic species increased, while their leaf morphology was found to change from tubular/capillary leaf types with low leaf area values to entire leaf types with greater leaf area. These results provide hints on the changes in ecosystem functioning occurring as a response to human-induced drivers. Therefore, this assessment approach could provide important support to the tasks of biomonitoring, conservation and management planning in freshwater ecosystems. Image 1 • RLQ and fourth-corner combined were applied to assess traits-environment relations. • Strong correlation found between aquatic plant traits and anthropogenic pressures. • Emergent life forms replace submerged ones in increased eutrophication levels. • Reproduction, dispersal, trophic, light, and leaf traits respond to eutrophication. • Trait assessment approaches can support conservation and restoration management. [ABSTRACT FROM AUTHOR]