Urbanization contributes to the loss of the world's biodiversity and the homogenization of its biota. However, comparative studies of urban biodiversity leading to robust generalities of the status and drivers of biodiversity in cities at the global scale are lacking. Here, we compiled the largest global dataset to date of two diverse taxa in cities: birds (54 cities) and plants (110 cities). We found that the majority of urban bird and plant species are native in the world's cities. Few plants and birds are cosmopolitan, the most common being Columba livia and Poa annua. The density of bird and plant species (the number of species per km 2 ) has declined substantially:& 2014 The Author(s) Published by the Royal Society. All rights reserved.on May 10, 2018 http://rspb.royalsocietypublishing.org/ Downloaded from only 8% of native bird and 25% of native plant species are currently present compared with estimates of non-urban density of species. The current density of species in cities and the loss in density of species was best explained by anthropogenic features (landcover, city age) rather than by non-anthropogenic factors (geography, climate, topography). As urbanization continues to expand, efforts directed towards the conservation of intact vegetation within urban landscapes could support higher concentrations of both bird and plant species. Despite declines in the density of species, cities still retain endemic native species, thus providing opportunities for regional and global biodiversity conservation, restoration and education.

Summary 1.Interpreting the functional diversity of vegetation is important in unravelling the relationship between environmental change, community composition and ecosystem processes. Functional diversity is the range and distribution of functional trait values in a community. It can be described, among other indicators, by community-level weighted means of trait values (CWM) and functional divergence. Standard methods exist for trait measurements but not for assessments of CWM and functional divergence in the field. No research has addressed the effects of different methods of estimating relative abundances, nor the need to estimate traits at individual, population or species level, or whether methods could be used that bypass taxonomy all together. 2. This study reviews and evaluates plot-level assessment methods of functional diversity in herbaceous vegetation. We asked: (i) Should the objective of the study influence the method for estimating relative abundance? (ii) What are the strengths and limitations of intensive vs. 'rapid' approaches, and when should either be applied? (iii) Are taxon-free methods robust in comparison to taxon-explicit methods of trait measurement? Under what circumstances might they be applied? 3. Our review of published studies that have measured functional diversity in the field showed that the choice of metric has not generally taken into account the link between the metric and the functions of interest, and that vegetation cover has been most widely used, regardless of study purpose. 4. We compared quantitatively in subalpine grasslands three methods for quantification of species abundances plus one taxon-free method. We found that: (i) data base trait values were robust across years for a diverse set of dominant species; (ii) CWM have little sensitivity to method for estimating relative abundances; this sensitivity also depends on traits, for example, seed mass results were less stable than leaf traits and heights; (iii) robust estimates of CWM were obtained from visual estimates of species ranks and biomass using a dry-weight ranking method (BOTANAL), whereas functional divergence was more sensitive to method; and (iv) the taxon-free method should be treated with more caution and performed particularly poorly for estimates of functional divergence. 5. We conclude that methodology can affect estimates of functional diversity. Although care should be taken in the choice of method and interpretation of results, rapid methods often offer promising avenues for sampling larger areas and/or repeated measures.

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