Organic substratesBasic magmatic and metamorphic rocks

LoessLimestone and other calc. rocks

Calcareous substrate

Acid magmatic and metamorphic rocksSand

Topsoil pH in water

Arable landIntensively cultivated land

Sedimentary rocks

Other non-consolidated sedimentsMean annual temperature

Altitude

Terrain ruggedness indexTemperature annual range

Mean annual AETMosaic land

GrasslandMean annual PET

Annual precipitation

Shrub landForests

Precipitation seasonality

Mean decrease in accuracy

0 20 40 60 80 120

Organic substratesBasic magmatic and metamorphic rocks

LoessSand

Other non-consolidated sediments

Sedimentary rocksAcid magmatic and metamorphic rocks

Arable land

Intensively cultivated landTopsoil pH in water

Mean annual temperature

Shrub landGrassland

Limestone and other calc. rocks

Mosaic landCalcareous substrate

ForestsPrecipitation seasonality

AltitudeTemperature annual range

Annual precipitation

Mean annual AETMean annual PET

Terrain ruggedness index

Mean decrease in node impurity (/1000)

0 200 400 600 800

Fine-resolution patterns of plant species richnessacross European forestsMartin Večeřa1, Milan Chytrý1, Jan Divíšek1,2 & data contributors*

1Department of Botany and Zoology, 2Department of Geography, Faculty of Science, Masaryk University, Brno, Czech Republic. Corresponding author: M. Večeřa (martinvec@seznam.cz)

Introduction

Up to now, most studies examining species richness of vascular plants have used coarse-resolution (i.e. spatially highly generalized) data based on atlases or inventories of large areas, which considerably limits ourunderstanding of species-richness patterns and underlying factors. In this study, we analysed vascular plant species richness in forest vegetation across major part of Europe using a large set of spatially referencedvegetation plots (phytosociological relevés).

Material

We used data on species richness from 27 national or regional vegetation-plot databases gathered inthe European Vegetation Archive. In total, we obtained 98 363 spatially referenced forest-vegetation plots withsampling sizes of 100–1000 m2. However, only 23–55% of these plots appeared to be suitable for analyses, dependingon the filtering criteria applied. We considered the following climatic, topographical, soil-geological and landscape-structure factors which might affect species richness at the continental scale:

Results

The random forest model explained 51.7% of variation in species richness. Residuals had almost normal distribution and they did not show any obvious pattern when weplotted them to the map. The relative importance of explanatory variables was expressed by two different measures called Mean decrease in accuracy and Mean decrease innode impurity. The higher the value of the first measure, the lower the chance that the effect of the selected variable on species richness is random. The second measureshows how good predictor of the species richness the selected variable is. Variables having high values of both measures can be considered as the most important predictors.In our case, these are both variables related to precipitation, area of forests in plots’ surroundings, mean potential and actual evapotranspiration. The most reliable (i.e. least random) predictor was precipitationseasonality. The best predictor according to the second measure was terrain ruggedness index.

The map suggests that the richest forests occur in mountainous regions with high proportion of calcareous bedrock and relatively high values of actual evapotranspiration. European forest diversity hotspots include the Limestone Alps, Western Alps and Jura Mountains, Southern and Northern Dinarides, and Western Carpathians in Slovakia. Other hotspots are located at the south-eastern foothills of the Romanian Carpathians, onthe Transylvanian Plateau, or in the north-eastern part of Poland. In contrast, species-poor forests predominate in north-western Europe, e.g. on the British Isles and in the Netherlands and adjacent lowland areas from France to north-western Poland.

These are just preliminary results which we are going to improve in the near future by adding more vegetation plots and by cross-validation testing.

Aims

Our aims were: (1) to map species richness of vascular plants in European forests; (2) to examine environmentalfactors determining the species-richness pattern; (3) to create a fine-resolution predictive grid map of vascular plantspecies richness in European forests

References: CEC, 2000. CORINE land cover technical guide – Addendum 2000. Copenhagen, European Environment Agency; CEC, 2004. The European Soil Database distribution version 2.0, European Commission and the European Soil Bureau Network (CD-ROM) EUR 19945 EN.; Hijmans R.J., Cameron S.E., Parra J.L., Jones P.G., Jarvis A., 2005. Very high resolution interpolated climate surfaces for global land

areas. International Journal of Climatology 25, 1965-1978; Jarvis A., Reuter H.I., Nelson A., Guevara E., 2008. Hole-filled SRTM for the globe Version 4. Available from the CGIAR-CSI SRTM 90m Database: http://srtm.csi.cgiar.org/; Panagos P., Van Liedekerke M., Jones A., Montanarella L., 2012. European Soil Data Centre: Response to European policy support and public data requirements. Land Use Policy, 29

(2), 329-338; Riley S.J., DeGloria S.D., Elliot R., 1999. A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain Journal of Sciences 5(1-4), 23-27; Trabucco A., Zomer R.J., 2010. Global Soil Water Balance Geospatial Database. CGIAR Consortium for Spatial Information; US Geological Survey (USGS), 1996. GTOPO30. Sioux Falls, SD: United States Geological Survey Center for Earth

Resources Observation and Science (EROS); Wieder W.R., Boehnert J., Bonan G.B., Langseth M., 2014. Regridded Harmonized World Soil Database v1.2. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA; Zomer R.J., Trabucco A., Bossio D.A., van Straaten O., Verchot L.V., 2008. Climate Change Mitigation: A

Spatial Analysis of Global Land Suitability for Clean Development Mechanism Afforestation and Reforestation. Agric. Ecosyst. Environm. 126, 67-80.

Methods

Data processing followed these steps:

(1) the numbers of species in each plot were counted; (2) plots older than 1970 were removed;

(3) plot sizes between 100 and 400 m2 were selected; (4) the number of species in each plot

was rescaled to 400 m2 according to species-area curve; (5) average number of species was

claculated in plots with identical geographical coordinates; (6) a circular buffer zone of an area

of approx. 25 km2 was created around each plot; (7) plots with location imprecision higher than

2821 m (the radius of the buffer zone) were removed; (8) buffer zones occupied by more than

50% of sea were removed; (9) average value for spatially continuous variables within each

buffer zone was claculated; (10) the area of occupancy for categorical spatially discontinuous

variables within each buffer zone was calculated.

Following this procedure we obtained the final dataset containing 49 433 records.Subsequently we calculated a predictive model using the Random Forest method. Results wereevaluated by visual inspection of the maps and histograms of residuals and by calculatingGlobal Moran’s I of residuals. Finally we created a predictive map of vascular plant species

richness of European forests with a 5 × 5 km resolution.

Variable Description, data source

Mean annual temperature Annual temperature rangeRaster data ~ 1 × 1 km, Hijmans et al. (2005)

Annual precipitation Precipitation seasonality

Mean annual potential evapotranspiration Raster data ~ 1 × 1 km, Zomer et al. (2008)

Mean annual actual evapotranspiration Raster data ~ 1 × 1 km, Trabucco et al. (2010)

Altitude Derived from digital elevation model ~ 90 × 90 m, Jarvis et al. (2008)

Terrain ruggedness index Based on USGS (1996), raster data ~ 1 × 1 km, Riley et al. (1999)

Topsoil pH measured in water Raster ~ 4.3 × 4.3 km, Wieder et al. (2014)

Main geological formations Calcareous substrateArea of occupancy within a buffer, vector data 1:1 000 000,

derived from CEC (2004), Panagos et al. (2012)

Acid magmatic and metamorphic rocks, Basic magmatic and metamorphic rocks, Limestone and other calcareous

rocks, Loess, Sand, Organic substrate, Sedimentary rocks, Other non-consolidated sediments

Land cover categories Area of occupancy within a buffer, vector data 1:100 000, CEC (2000)

Forests, Grassland, Arable land, Intensively cultivated land (i.e. arable land, rice fields, vineyards, fruit plantations, olive groves, annual crops associated with

permanent crops), Mosaic (semi-opened) land (i.e. pastures, natural grasslands, complex cultivation patterns, cultivated land with significant areas of natural vegetation,

agro-forestry areas), Shrub-land (i.e. moors and heathlands, sclerophyllous vegetation and transitional woodland-scrub)

Random Forest Result

No. of cases 49 433

No. of explanatory variables 24

No. of regression trees 500

No. of variables tried at each split 8

Variation explained (%) 51.7

Mean of squared residuals 102.9122

Moran’s I of residuals -0.04, p < 0.001

Emiliano Agrillo, Pierangela Angelini, Fabio Attorre, Christian Berg, Idoia Biurrun, Henry Brisse, Laura Casella, Panayotis Dimopoulos, Jörg Ewald, Úna FitzPatrick, Itziar García-Mijangos, Stephan Hennekens, Adrian Indreica, Ute Jandt, Florian Jansen, Zygmunt Kącki, Martin Kleikamp,

Vitaliy Kolomiychuk, Daniel Krstonošić, Flavia Landucci, Jonathan Lenoir, Vassiliy Martynenko, Dana Michalcová, Viktor Onyshchenko, Hristo Pedashenko, Valerijus Rašomavičius, John Rodwell, Patrice de Ruffray, Gunnar Seidler, Joop Schaminée, Jens-Christian Svenning, Grzegorz

Swacha, Jozef Šibík, Urban Šilc, Željko Škvorc, Ioannis Tsiripidis, Pavel Dan Turtureanu, Domas Uogintas, Milan Valachovič, Kiril Vassilev, Roberto Venanzoni, Lynda Weekes, Wolfgang Willner & Thomas Wohlgemuth

*

Predictive grid map of vascular plant species richness in European forests. The map is based on UTM grid cells, each of them spanning 5 × 5 km (species richness is predicted for an area of 400 m2).

Species richness of vascular plants in European forests in the final data set (49 433 vegetation plots)

Acknowledgements: We thank Ilona Knollová for her help with the data preparation, František Kuda and Petr Vybral for help with GIS and all the botanists whose vegetation plots we could use for analysing species richness. This study was supported by the Czech Science Foundation

(project no. 14-36079G, Centre of Excellence PLADIAS).