dataesr/scientific-paragraphs-categorization huggingface dataset:
Recent research has highlighted strong correlations between soil edaphic parameters and bacterial biodiversity. Here we seek to explore these relationships across the European Union member states with respect to mapping bacterial biodiversity at the continental scale. As part of the EU FP7 EcoFINDERs project, bacterial communities from 76 soil samples taken across Europe were assessed from eleven countries encompassing Arctic to Southern Mediterranean climes, representing a diverse range of soil types and land uses (grassland, forest and arable land). We found predictable relationships between community biodiversity (ordination site scores) and land use factors as well as soil properties such as pH. Based on the modelled relationship between soil pH and bacterial biodiversity found for the surveyed soils, we were able to predict biodiversity in ~1000 soils for which soil pH data had been collected as part of national scale monitoring.
We then performed interpolative mapping utilising existing EU wide soil pH data to present the first map of bacterial biodiversity across the EU member states. The predictive accuracy of the map was assessed again using the national scale data, but this time contrasting the EU wide spatial predictions with point data on bacterial communities. Generally the maps were useful at predicting broad extremes of biodiversity reflective of low or high pH soils, though predictive accuracy was limited for Britain particularly for organic/acidic soil communities. Spatial accuracy could however be increased by utilising published maps of soil pH calculated using geostatistical approaches at both global and national scales. These findings will contribute to wider efforts to predict and understand the spatial distribution of soil biodiversity at global scales.
This paper aside there are few studies which have examined in detail the predictive performance of such maps compared to simple interpolation. More widely, large scale spatial predictions of soil parameters are increasingly being disseminated through downloadable map resources (e. g soilgrids. org, ukso. org), and there is now a need to identify specific predictive limitations in order to further improve accuracy  (Hengl et al, 2014). Here as part of this special issue reporting results from the EU FP7 EcoFINDERs project coordinated soil sampling campaign, we seek to assess the bacterial communities in 76 soils sampled across Europe in order to produce a soil bacterial map at the European scale, which can be validated against national scale datasets. We predict that soil pH will be the strongest correlate with measures of community biodiversity, which will then allow us to predict and spatially interpolate communities based on publicly available European scale point data on soil pH (from the LUCAS survey: Tóth et al, 2013).
Sampling Bacterial communities were examined in soils sampled across the EU member states as part of the EcoFINDERs project "transect" sampling campaign, full details of which are prodived elsewhere in this issue  (Stone et al, 2015). Briefly, a range of sites spanning a gradient of soil properties (principally pH, organic matter and texture), climatic zones, and land uses (grassland, arable, forest) were targeted for sampling following examination of EU wide datasets (see supplementary material for site locations, S1). Samples were collected at the end of summer 2012 according to standardised protocols to 5cm depth, and sent to a central processing lab for homogenisation and distributing to various partner labs for further analyses. In total eighty-two soils from 11 countries encompassing Arctic to Southern Mediterranean climes of which 76 are assessed in this study.
Amplifications were performed in 50 µl reactions under the following conditions: 94⁰C for 90 s, followed by 35 cycles of 94⁰C for 45 s, 55⁰C for 1 min and 72⁰C for 3 min, followed by a final extension of 72⁰C for 10 min. Amplicons were then purified using the ZR-96 DNA clean-up kit (Zymo research, Freiburg, Germany), prior to enzymatic digestion. Purified bacterial DNA was digested with MspI restriction enzyme (New England Biolabs Inc. , Ipswich, MA, USA) at 37⁰C for 3 h. Fragment analysis was performed with a Beckman Coulter CEQ 2000XL capillary sequencer (Beckman Coulter Corporation, California, USA). Peak height data were analysed using GeneMarker software (Softgenetics, LLC, PA, USA). Relative abundances were calculated as the ratio between the fluorescence of each terminal restriction fragment (T-RF) and the total integrated fluorescence of all T-RFs.
Statistical Analyses A site by taxon (TRF) relative abundance table derived from the TRFLP analyses was used to explore community relationships with environmental variables, and calculate community scores (ordination site scores and diversity estimates) using standard routines in the vegan library within the R package (  R Core Development Team, 2005). Geostatistical calculations, manipulations and plots were also performed within R using the maptools, gstat, raster, and RColorBrewer libraries. Specifically, to produce the bacterial map we used the inverse distance distance weighted (IDW) interpolation method, on account of it's simplicity and widespread application  (Lam, 1983). The IDW method predicts a value at an unsampled location based on the weighted average of values at sampled point locations, with weights decreasing linearly with distance from that location.
We used the idw function of the R library gstat to perform the interpolation, using leave one out cross validation to establish the optimum power parameter value (determining how much the weightings decrease with distance) and evaluate the overall performance of the interpolation with respect to predictive power. For both the IDW interpolative mapping, and the prediction contrasts with observed data from a national scale dataset, predictive power was evaluated by assessing the coefficient of determination (R2) and root mean square error (RMSE) between observed and predicted values.
Predictability and mapping of soil bacterial communities The site scores for bacterial communities were clearly strongly aligned along the first axis of the NMDS ordination which corresponds with a gradient of soil pH. This afforded the opportunity to extrapolate and predict communities over larger spatial scales using wider soil pH datasets. Such datasets are available across 23 EU member states from the LUCAS topsoil survey  (Toth et al, 2013) , which provides data on the percentage of coarse fragments, particle size distribution, pH, organic carbon, carbonate content, phosphorous content, total nitrogen content, extractable potassium content, cation exchange capacity and multispectral properties from approximately 20000 soils. We therefore sought to model the relationships between soil pH and bacterial communities from the present survey, and then predict community NMDS scores for the 20000 data points across the EU of soil pH to enable the production of EU wide maps of predicted soil biodiversity using simple interpolative approaches.
The first step was to reliably model relationships between soil pH and the bacterial NMDS scores. Figure  2  shows the relationship observed between soil pH and the first axis bacterial community NMDS scores for the 76 soils assessed in this study. The relationship was visually assessed to be curvilinear and could be modelled with a simple second-degree polynomial (R 2 = 0. 93) of the equation: bacterial NMDS axis1 = -3. 748 + 0. 9188pH -0. 04954pH 2 To test the predictive power of the regression equation, we predicted the bacterial community NMDS axis 1 scores from a nationwide survey of over 1000 point measurements conducted across Great Britain using only the measured pH values as predictors (countrysidesurvey. org. uk/). Uniquely, this dataset also comprises bacterial TRFLP profiles  (Griffiths et al 2011)  therefore allowing us to independently test the predictive power of the regression equation on a different dataset.
Despite several differences in methodologies (soil sampling depth, DNA extraction, taxonomic binning) the modelled relationships between soil pH and bacterial community ordination scores from less than 100 soils across Europe provided a reasonable prediction of the bacterial scores in over 1000 soils across Britain (Figure  3 ). It is noteworthy that the ordination axis scores are themselves arbitrary, and only denote the (dis)similarity between samples analysed at any one time. This fact makes the strong correlations between the EU wide and national scale datasets all the remarkable, and is perhaps testament to the strength and global ubiquity of the relationships between soil pH and bacterial communities, provided a sufficient range of soils are sampled. To map bacterial communities across the EU member states we then predicted the NMDS axis 1 ordination scores for the ~20000 soils sampled in the LUCAS survey based on pH measurements and the equation outlined above.
The LUCAS datasets were downloaded subject to agreements from the JRC European Soil Portal (http://eusoils. jrc. ec. europa. eu/) and a map showing the sampled locations is provided in the supplementary material (S2). Predicted community scores were then mapped using inverse distance weighting interpolation. We firstly compared interpolative performance using different integer powers parameters (1-5), and the accuracy of predictions assessed by comparing the deviation from the measured data using a leave one out cross-validation procedure. The best performing interpolated map is shown in Figure  4. This was made using an IDW power parameter of 2 which yielded the lowest root mean square error (RMSE) for predicting bacterial NMDS1 scores (0. 28), together with the highest precision with respect to the relationship between observed and predicted values (R 2 =0.
Map validation and contrasts with other mapping approaches In order to assess the accuracy of the EU bacterial map wide we contrasted the spatial predictions with observed national scale data from the British Survey. The interpolated map was firstly converted to a raster, and then the predicted NMDS1 scores extracted using the sample locations of the British dataset, prior to correlation with the observed scores (figure  6a ). Despite a lack of a good linear fit and a tendency for the spatial predictions to cluster near the overall mean, the interpolation performed reasonably well at predicting the NMDS community scores across Britain (RMSE=0. 41, R 2 = 0. 29). Despite a lack of strong correlative relationships for lower pH communities, there was evidence that higher scoring (higher pH) community scores could be predicted to some extent.
This map of bacterial biodiversity therefore gives a very broad overview of the extreme types of communities likely to be found in different geographic locations across Europe, but for Britain it is of limited use in spatially predicting more subtle differences in communities. Its predictive power is limited by its reliance on the locations of the sampled LUCAS topsoil data points, the design of which has an inherent bias towards agricultural lands (Toth et al 2013). Few samples were taken from large areas of Scottish uplands in the LUCAS survey which may be explain the poor relationships between predicted and observed community scores across Britain. The lack of comparable national scale "test" datasets comprising bacterial data, means we are unable to assess the predictive accuracy of the map for other countries.
To assess whether the predictive accuracy could be enhanced by drawing on more advanced geostatistical predictions of soil pH, we next applied the pH-biodiversity transfer function to two existing soil pH maps: a recently published predictive map at the global scale (SoilGrids: soilgrids. org, Hengl et al 2014) and freely available maps of soil pH at the national scale from Britain (Countryside Survey data from ukso. org). Both these maps were constructed using geostatistical models applied to surveyed pH data to predict unknown values using wider landscape level datasets, using information such as land cover, parent material, climate etc. Maps were downloaded and rasterized where necessary, prior to extracting of pH values based on the GB survey coordinates. Using the detailed 1km resolution SoilGrids soil pH map offered some small improvements in predicting the national scale bacterial data (RMSE=0.
39, R 2 = 0. 36) particularly with respect to the acidic habitat scores (Figure  6b ). However the predictions again were focused around the mean, and extreme scores were not particularly well estimated. It was notable that in inspecting the range of soil pH values predicted across the UK and comparing with known UK level data that the extreme values were particularly underestimated in the SoilGrids predictions (e. g predictions of pH 4 or pH 8 soils were over or underestimated respectively). Possible explanations could be related to i) difference in pH determination between the Countryside survey and EU wide LUCAS datasets; ii) undersampling of certain habitats at the EU scale; and iii) geo-statistical artefacts. It is impossible to entirely discount (i) as comparable samples are not available from both surveys, but a cursory inspection of the range of pH values for both datasets indicated there were no systemic differences in the range of pH measurements.
With respect to ii) as already discussed, part of the reason for the poor fit on the negative side of the interpolated map is the lack of upland areas sampled in the Lucas survey -meaning the predicted pH for under sampled upland areas such as in Scotland would be higher than in reality. Whilst the SoilGrids predictions utilise global soil pH data in the model, the LUCAS dataset is also a large contributory dataset which could have a significant influence on the predictions. Finally with respect to geostatistical artefacts (iii), the predictive maps of pH provide a mean prediction based on a global model, which will always under or overestimate the higher or lower extremities of the pH range respectively. This could explain why the full range of NMDS1 scores was not adequately reflected in the model predictions.
Conclusions This study characterised bacterial biodiversity and explored environmental correlates in a range of soils sampled across continental Europe. In agreement with previous global studies land use, climate and soil abiotic properties were strongly associated with changes in bacterial communities, with soil pH being the best single correlate. Ultimately these findings point to the general conclusion that broad characteristics of soil bacterial communities can be considered as a dependent soil state variable related to other soil properties (and to some extent human land use); which are ultimately controlled by the independent soil forming factors of climate, relief, parent material, and time  (Jenny, 1941). These relationships therefore allow the global prediction of soil bacterial community features over large scales, and we present the first attempt to map bacterial communities across Europe along with a detailed evaluation of the predictions against observed data from national scale surveys of one of the EU member states.
The map performed adequately in predicting community characteristics (ordination axis scores) albeit for opposing ends of the soil biotic/abiotic gradient, and we further demonstrate how the map can be improved by making use of available predictive maps of soil pH, previously calculated using correlations with georeferenced data on wider soil forming factors. In doing so we highlight the current limitations in soil property maps at different geographic scales, with national scale predictions outperforming global scale maps. To avoid misinterpretation there are some notable caveats which we must stress with respect to the findings of this study and other large surveys of bacterial taxa utilising 16S rRNA amplicon approaches. Firstly when conducting such large scale studies, one necessarily focusses on broad patterns and, particularly in this study using a community profiling technique, broad taxonomic resolution.
Whilst field-scale resolution was not the purpose of this mapping exercise, we feel this should be a future ambition of global efforts to characterise soil biodiversity. Our study highlights the benefits of using advances geostatistical approaches for soil biodiversity mapping using high resolution remote sensed data. It is possible that similar approaches can be applied to other elements of soil biodiversity, including eukaryotes, given enhanced understanding of controlling environmental parameters. Such knowledge can be gained both by efforts to harmonise existing soil biodiversity datasets, but also by increased eukaryotic sampling in national surveys -a realistic possibility now with the availability of rapid molecular tools for eukaryotes (e. g  Ramirez et al, 2014). Finally to conclude, our map identifies that predictions are only as good as the surveyed "real" data used to build models.
Predictive accuracy will vary depending on the scale of the surveyed data (model inputs) and the spatial extent of the area we seek to predict. Global predictions at high spatial accuracy should therefore be the ultimate goal, which requires increased efforts to standardise and conduct soil biotic and abiotic surveillance at global scales. These advances will be facilitated by better spatial integration of distributed datasets (e. g. global harmonisation of localised climate, geological, remote sensed land cover, and soil datasets) and continued development and validation of mapping predictions against local surveyed data. 0. 8 q q q q q q q q q q q q q q q q Arable Forest Grass q q q q q pH 4 5 6 7 8.
Samples were submitted to an acidic SDS-PAGE, and phosphorylation was detected using a phosphorimaging device. Phosphorylation intensities were quantified using the Optiquant Software. 100% represents the phosphorylation level of HMA6 or HMA6 mutants in the absence of added metal (Ø). Fittings were performed using a Hill equation. The values correspond to the means of five independent experiments. B, membrane preparations (50 g) containing HMA6 or HMA6 mutants (C161A and H165A) were phosphorylated from ATP under reducing conditions (500 M Na 2 SO 3 ) with various concentration of CuSO 4. Samples were treated as described above. 100% corresponds to the maximum phosphorylation level of HMA6 or HMA6 mutants. The values correspond to the means of four independent experiments. The error bars indicate standard deviation.
The experiment then proceeded as for HMA6. Quantifications were made using the Optiquant software. 100% corresponds to the maximum phosphorylation level of the different proteins in the presence of copper before the addition of chelators. Values correspond to means of at least three independent experiments (three for HMA6, C161A, and H165A and four for HMA8 and H193A). The error bars indicate the standard deviation. Fittings were made using the SigmaPlot software and were used to determine the different dephosphorylation rates listed in B. E, temperature-dependent dephosphorylation after phosphorylation from P i. Membrane fractions (50 g) containing HMA6 or the HMA6 mutants (C161A and H165A) were phosphorylated in the same conditions as described in Fig. 8A  in the presence of BCA/BCS (100 M/10 M).
After 10 min, samples were put on ice, and the reaction was stopped at different times (between 20 and 280 s) by addition of 1 mM KH 2 PO 4 , 7% (v/v) TCA. Samples were submitted to an acidic SDS-PAGE, and phosphorylation was detected using a phosphorimaging device. Phosphorylation intensities were quantified using the Optiquant Software. In parallel, a time-dependent temperature decrease was measured in the same experimental conditions (without radioactive P i ). The values correspond to the mean of four independent experiments. 100% corresponds to the phosphorylation level after 20 s on ice and correspond to a temperature of 23 °C. The error bars indicate the standard deviation. Fittings were made using the SigmaPlot software with slopes of Ϫ1. 8 Ϯ 0. 12, Ϫ2. 1 Ϯ 0. 2, and Ϫ1. 6 Ϯ 0. 1 for HMA6, C161A, and H165A, respectively.
lactis membrane preparations, 20 mM HEPES (pH 6. 0), 10 mM MgCl 2 , and 20% (v/v) Me 2 SO (dimethyl sulfoxide), in the presence of various concentrations of copper. After a 5-min incubation at 30 °C, the reaction was started by the addition of 100 M 32 P i (10 -100 Ci nmol Ϫ 1 ) and stopped, 10 min later, by the addition of 1 ml of ice-cold 1 mM KH 2 PO 4 in 7% (v/v) trichloroacetic acid. After a 30-min incubation on ice, samples were treated as described in Ref. 8. In the dephosphorylation kinetics from E2P, samples were first phosphorylated from P i as described above during 10 min at 30 °C. They were then put on ice and stopped at different times by addition of 1 ml of ice-cold 1 mM KH 2 PO 4 in 7% (w/v) trichloroacetic acid. The phosphorylation signal was revealed using a phosphorimaging device (Cyclone, PerkinElmer Life Science) and analyzed using the Optiquant Software (Perkin-Elmer Life Sciences).
After 4 min, an aliquot was taken and acid-quenched. The experiment then proceeded as for HMA6. Quantifications were made using the Optiquant software. 100% corresponds to the maximum phosphorylation level of the different proteins in the presence of copper before the addition of chelators. Values correspond to means of at least three independent experiments (three for HMA6, C161A, and H165A and four for HMA8 and H193A). The error bars indicate the standard deviation. Fittings were made using the SigmaPlot software and were used to determine the different dephosphorylation rates listed in B. E, temperature-dependent dephosphorylation after phosphorylation from P i. Membrane fractions (50 g) containing HMA6 or the HMA6 mutants (C161A and H165A) were phosphorylated in the same conditions as described in Fig.
8Ain the presence of BCA/BCS (100 M/10 M). After 10 min, samples were put on ice, and the reaction was stopped at different times (between 20 and 280 s) by addition of 1 mM KH 2 PO 4 , 7% (v/v) TCA. Samples were submitted to an acidic SDS-PAGE, and phosphorylation was detected using a phosphorimaging device. Phosphorylation intensities were quantified using the Optiquant Software. In parallel, a time-dependent temperature decrease was measured in the same experimental conditions (without radioactive P i ). The values correspond to the mean of four independent experiments. 100% corresponds to the phosphorylation level after 20 s on ice and correspond to a temperature of 23 °C. The error bars indicate the standard deviation. Fittings were made using the SigmaPlot software with slopes of Ϫ1. 8 Ϯ 0.
* This work was supported by the  Commissariat à l'Energie Atomique et aux Energies Alternatives , the  Centre National de la Recherche Scientifique , the  French National Institute for Agricultural Research , and the  University of Grenoble Alpes. The authors declare that they have no conflicts of interest with the contents of this article. 1Supported by a joint grant from the  GRAL Labex  ( ANR-10-LABX-49-01 ) and the  CEA.
Climate and soil data processing / aggregation methods For climate data, time series of minimum, mean and maximum air temperature (2 m above ground), precipitation, global radiation, wind speed and relative humidity were used. For this purpose, daily records from 280 weather stations and an interpolated grid of 1 km resolution of monthly records were obtained from the German Meteorological Service for the period 1982 to 2011. The station records of daily temporal resolution and the monthly grids of 1 km spatial resolution were combined to daily time series of 1 km spatial resolution following  [28]. The climate properties of the region for the different spatial resolutions were given by  [11]. In addition, Fig  2  shows the inter-annual variability of the climatic water balance (cwb) calculated as precipitation (P) minus reference evapotranspiration (ET): cwb = P-ET.