56 research outputs found

    Appendix A. Occurrence of plant species in the seed bank and vegetation.

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    Occurrence of plant species in the seed bank and vegetation

    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.

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    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 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.

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    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

    Mycorrhizal trait data and niche characteristics for vascular plants in the Dutch flora

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    File includes updated mycorrhizal status, type and flexibility data with new additional references, and realized niche optima, widths and volumes along soil fertility, pH, moisture, salinity, light and temperature axes for the vascular plant species in the Dutch flora. Also, a legend is provided as a separate sheet in the file. More details can be found in the README file

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

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    <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

    Spatial autocorrelation, expressed as Moran’s <i>I</i>, with incrementing distance class for the full datasets of the Hultén & Fries atlas (dotted line) and AFE (solid line), and for the residuals of the reduced major axis model of the two (dashed line).

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    <p>Spatial autocorrelation, expressed as Moran’s <i>I</i>, with incrementing distance class for the full datasets of the Hultén & Fries atlas (dotted line) and AFE (solid line), and for the residuals of the reduced major axis model of the two (dashed line).</p

    Residual distribution of the reduced major axis (RMA) analysis between species richness of AFE and the Hultén & Fries atlas for (a) the complete atlases, (b) the intersection of species occurring in both atlases, (c) the independent data subset of species mapped in both atlases and (d) the dependent data subset of species.

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    <p>In each of the panels, the deviation from RMA predicted species richness was standardized using a seven-category scale legend, where a red tone intensity illustrated the degree to which the Hultén & Fries atlas richness estimation was higher than the RMA model prediction while the blue tone intensity illustrated level of deviation of the AFE richness estimation.</p

    Frequency distributions of the area of occupancy (number of grid cells occupied) and total species number (<i>n</i>) for (a) the complete AFE atlas (n = 3773), (b) the complete Hultén & Fries atlas (<i>n</i> = 2049), the intersection of species co-occurring in (c) AFE (<i>n</i> = 601) or (d) the Hultén & Fries atlas (<i>n</i> = 601), species exclusive to (e) AFE (<i>n</i> = 3172) or (f) the Hultén & Fries atlas (<i>n</i> = 1448).

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    <p>Frequency distributions of the area of occupancy (number of grid cells occupied) and total species number (<i>n</i>) for (a) the complete AFE atlas (n = 3773), (b) the complete Hultén & Fries atlas (<i>n</i> = 2049), the intersection of species co-occurring in (c) AFE (<i>n</i> = 601) or (d) the Hultén & Fries atlas (<i>n</i> = 601), species exclusive to (e) AFE (<i>n</i> = 3172) or (f) the Hultén & Fries atlas (<i>n</i> = 1448).</p
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