Geodiversity data for Europe

Abstract

Geodiversity is an essential part of nature's diversity. However, geodiversity is insufficiently understood in terms of its spatial distribution and its relationship to biodiversity over large spatial extents. Here, we present European geodiversity data at resolutions of 1 km and 10 km. We assess terrestrial geodiversity quantitatively as a richness variable (georichness) using a commonly employed grid-based approach. The data incorporate aspects of geological, pedological, geomorphological and hydrological diversity, which are also available as separate richness variables. To evaluate the data, we correlated European georichness with empirically tested national georichness data from Finland, revealing a positive correlation at both 1 km (r_p = 0.37, p < 0.001) and 10 km (r_p = 0.59, p < 0.001) resolutions. We also demonstrate potential uses of the European data by correlating georichness with vascular plant species richness in two contrasting example areas: Finland and Switzerland. The positive correlations between georichness and species richness in Finland (r_p = 0.34, p < 0.001) and Switzerland (r_p = 0.26, p < 0.001) further support the use of our data in geodiversity-biodiversity research. Moreover, there is great potential beyond geodiversity-biodiversity questions, as the data can be exploited across different regions, ecosystems and scales. These geodiversity data provide an insight on abiotic diversity in Europe and establish a quantitative large-scale geodiversity assessment method applicable worldwide. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Keywords: biodiversity; geological diversity; geomorphological diversity; georichness; hydrological diversity; pedological diversity.

Figure 1.

Total georichness at 1 km resolution (a), and at 10 km resolution (b). Panels (c–f) represent lithological richness, soil richness, terrain form richness and hydrological richness at 1 km resolution, respectively. For corresponding maps at 10 km resolution, see electronic supplementary material, appendix 3. The histograms represent the distribution of richness values (with mean values as $\overline{x}$). Topographical visualization on the background is a shaded relief image [60]. (Online version in colour.)

Figure 2.

Georichness maps of Europe at 1 km resolution classified based on equal intervals (a), and 20% quantiles (b), with distribution of values within each class (mean georichness, $\overline{x}=10$). Panel (a) is a true presentation of georichness value distribution, while panel (b) emphasizes their relative differences. Topographical visualization on the background is a shaded relief image [60]. (Online version in colour.)

Figure 3.

Total georichness and vascular plant species richness (in the grid cells that contain species richness data) in Finland (a–c). All richness values are mean values at 10 km resolution. Histograms represent the distribution of georichness values (with mean values as $\overline{x}:\hspace{0.17em}{\overline{x}}_A=11$, ${\overline{x}}_B=11$, ${\overline{x}}_C=44$). In the scatterplots (d–f), Pearson correlations (with p-values) and linear trendlines are included. Topographical visualization on the background is a hillshade image derived from a digital elevation model [61]. (Online version in colour.)

Figure 4.

Total georichness and vascular plant species richness in Switzerland at 10 km resolution (a,b). Histogram on top of the legend represents the distribution of georichness values (with mean values as $\overline{x}:\hspace{0.17em}{\overline{x}}_A=19$, ${\overline{x}}_B=819$). In the scatterplot (c), Pearson correlation coefficient (with p-value) and linear trendline is included. Topographical visualization on the background is a hillshade image derived from a digital elevation model [61]. (Online version in colour.)