Appendix A. A table of the 85 published studies of vascular plant species richness and soil pH.

A table of the 85 published studies of vascular plant species richness and soil pH

Scatterplots of beta-diversity indices against hypothetical ecological gradient for Scenario A.

<p>(a) β_Md (left axis) and β_Md-1 (right axis); linear regression trends, β_Md, for β_Md-1 ( for both). (b) β_Pt; linear regression trend, (). (c) β^*_Md, β^*_Md-1 (circles) and β^*_Pt (squares); linear regression trends, for β^*_Md β^*_Md-1, for β^*_Pt ( for all). Dashed trends in (a) and (b) depict linear trends of β^*_Md (and β^*_Md-1) and β^*_Pt retransformed to β_Md (and β_Md-1) and β_Pt, respectively. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110485#pone-0110485-t001" target="_blank">Table 1</a> for description of beta-diversity indices and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110485#pone-0110485-t002" target="_blank">Table 2</a> for data for Scenario A.</p

Values of γ- and α-diversity along a hypothetical ecological gradient for three scenarios.

<p>Number of local sites (<i>N</i>)  = 10.</p><p>Values of γ- and α-diversity along a hypothetical ecological gradient for three scenarios.</p

Mathematical definitions and expressions of beta-diversity indices.

<p>Low and High represent the lower and upper limits of beta-diversity indices as a function of the number of local sites (<i>N</i>).</p><p>Mathematical definitions and expressions of beta-diversity indices.</p

Appendix A. A table providing the 163 published studies of the plant productivity–diversity relationship (including latitude, longitude, mean annual temperature, annual precipitation, method of productivity measurement, productivity range, and reference) used in this study.

A table providing the 163 published studies of the plant productivity–diversity relationship (including latitude, longitude, mean annual temperature, annual precipitation, method of productivity measurement, productivity range, and reference) used in this study

Appendix B. Significant pairwise species associations and P values from spatially informed generalized estimation equations (GEEs) for above- and belowground data.

Significant pairwise species associations and P values from spatially informed generalized estimation equations (GEEs) for above- and belowground data

Appendix A. Molecular analysis.

Molecular analysis

Trait and biomass data from plants grown in pots by themselves, monoculture, and mixture

The data file contains data on shoot biomass, root biomass, average leaf area, average root diameter, specific leaf area, specific root length, and root to shoot ratios for 15 species. Traits were collected on plants grown alone, in monoculture, and in mixture

Spatially-Explicit Estimation of Geographical Representation in Large-Scale Species Distribution Datasets

<div><p>Much ecological research relies on existing multispecies distribution datasets. Such datasets, however, can vary considerably in quality, extent, resolution or taxonomic coverage. We provide a framework for a spatially-explicit evaluation of geographical representation within large-scale species distribution datasets, using the comparison of an occurrence atlas with a range atlas dataset as a working example. Specifically, we compared occurrence maps for 3773 taxa from the widely-used Atlas Florae Europaeae (AFE) with digitised range maps for 2049 taxa of the lesser-known Atlas of North European Vascular Plants. We calculated the level of agreement at a 50-km spatial resolution using average latitudinal and longitudinal species range, and area of occupancy. Agreement in species distribution was calculated and mapped using Jaccard similarity index and a reduced major axis (RMA) regression analysis of species richness between the entire atlases (5221 taxa in total) and between co-occurring species (601 taxa). We found no difference in distribution ranges or in the area of occupancy frequency distribution, indicating that atlases were sufficiently overlapping for a valid comparison. The similarity index map showed high levels of agreement for central, western, and northern Europe. The RMA regression confirmed that geographical representation of AFE was low in areas with a sparse data recording history (e.g., Russia, Belarus and the Ukraine). For co-occurring species in south-eastern Europe, however, the Atlas of North European Vascular Plants showed remarkably higher richness estimations. Geographical representation of atlas data can be much more heterogeneous than often assumed. Level of agreement between datasets can be used to evaluate geographical representation within datasets. Merging atlases into a single dataset is worthwhile in spite of methodological differences, and helps to fill gaps in our knowledge of species distribution ranges. Species distribution dataset mergers, such as the one exemplified here, can serve as a baseline towards comprehensive species distribution datasets.</p></div

Appendix A. Detailed reply to criticism from Robert J. Whittaker, provided in his Appendix A of "Meta-analyses and mega-mistakes: calling time on meta-analyses of the species richness–productivity relationship.

Detailed reply to criticism from Robert J. Whittaker, provided in his Appendix A of "Meta-analyses and mega-mistakes: calling time on meta-analyses of the species richness–productivity relationship