C (2009) Towards an integrated understanding of green space in the European built environment. Urban Forestry and Urban Greening, 8 (2). pp. 65-75.

No abstract

Recent studies of the global diversity of the lichenized fungal family Graphidaceae suggest that there are a large number of species remaining to be discovered. No less than 640 species have been described since 2002, including 175 new species introduced in a collaborative global effort in a single issue in this journal. These findings suggest that the largest family of tropical crustose lichens may have an even higher number of species than Parmeliaceae. To estimate whether the discovery of 175 new species is a significant step forward in cataloguing extant diversity in this family, we employed a parametric method to predict global species richness of Graphidaceae using a GIS-based grid map approach. The model employs linear regression between observed species richness and sample score and vegetation composition per grid to predict individual grid species richness, and interpolation of species grid distributions to predict global species richness. We also applied a non-parametric species-area curve approach and non-parametric species richness estimators (Chao, Jackknife, Bootstrap) to compare the results from the different methods. Our approach resulted in a prediction of 4,330 species of Graphidaceae, including approximately 3,500 (sub-)tropical species in the core subfamilies Fissurinoideae, Graphidoideae, Redonographoideae, plus 125 species restricted to extratropical regions (outside the zone between 30° northern and 30° southern latitude) and 700 species in subfamily Gomphilloideae. Currently, nearly 2,500 species are known in the family, including species not yet formally described. Thus, our model suggests that even after describing 175 species in this issue and with another approximately 140 awaiting publication, the number of species still to be discovered and described is more than 1,800, and much work remains to be done to close this substantial gap. Based on our approach, we predict that most of this undiscovered diversity is to be found in Mexico, the northern Andean region, the eastern Amazon and central and southern Brazil, tropical West Africa, continental Southeast Asia, Indonesia, and Papua New Guinea.

Studies were undertaken on the floristic composition and stand structure of four 1 hectare plots in the lowland forests of Kurupukari, Guyana. A total of 3897 trees, covering 153 species and 31 plant families were recorded at greater than 5 cm diameter at breast height (dbh). The number of species per hectare ranged from 61 to 84 (>5.0 cm dbh) and 50–71 (>10.0 cm dbh). The total number of trees per hectare varied two-fold between study plots, with 45–50% of the trees within the 5–10 cm size-class. Mean total basal area varied from 32.39–34.63 m2 per 100 m2. The four most dominant plant families represented 43.8% of the total number of trees, while representing only 11.2% of the species. No one plant family dominated in more than one of the four study plots, and all four plots held at least one plant family with more than 20% of the total number of trees. Although 14 tree species were common to all four plots, only 26%–35% of the species were represented by a single tree. Between three and seven species represented 50% of the trees within all size-classes, with species dominance occurring within the highest density plot.\ud \ud These tropical forest types of central Guyana may represent some of the lowest diversity forests in the neotropics, whereby the total number of tree species is relatively limited, typically with six dominant canopy species, but the relative abundance of these species is highly variable between the forest types. Mechanisms influencing the competitive interactions associated with species dominance are discussed in relation to the importance of mycorrhizae and the persistence of species dominance

Accuracy of taxonomic identifications is crucial to data quality in online repositories of species occurrence data, such as the Global Biodiversity Information Facility (GBIF), which have accumulated several hundred million records over the past 15 years. These data serve as basis for large scale analyses of macroecological and biogeographic patterns and to document environmental changes over time. However, taxonomic identifications are often unreliable, especially for non-vascular plants and fungi including lichens, which may lack critical revisions of voucher specimens. Due to the scale of the problem, restudy of millions of collections is unrealistic and other strategies are needed. Here we propose to use verified, georeferenced occurrence data of a given species to apply predictive niche modeling that can then be used to evaluate unverified occurrences of that species. Selecting the charismatic lichen fungus, Usnea longissima, as a case study, we used georeferenced occurrence records based on sequenced specimens to model its predicted niche. Our results suggest that the target species is largely restricted to a narrow range of boreal and temperate forest in the Northern Hemisphere and that occurrence records in GBIF from tropical regions and the Southern Hemisphere do not represent this taxon, a prediction tested by comparison with taxonomic revisions of Usnea for these regions. As a novel approach, we employed Principal Component Analysis on the environmental grid data used for predictive modeling to visualize potential ecogeographical barriers for the target species; we found that tropical regions conform a strong barrier, explaining why potential niches in the Southern Hemisphere were not colonized by Usnea longissima and instead by morphologically similar species. This approach is an example of how data from two of the most important biodiversity repositories, GenBank and GBIF, can be effectively combined to remotely address the problem of inaccuracy of taxonomic identifications in occurrence data repositories and to provide a filtering mechanism which can considerably reduce the number of voucher specimens that need critical revision, in this case from 4,672 to about 100.