Improved description of terrestrial habitat types by including microbial communities as indicators.

Soils host diverse communities of microorganisms essential for ecosystem functions and soil health. Despite their importance, microorganisms are not covered by legislation protecting biodiversity or habitats, such as the Habitats Directive. Advances in molecular methods have caused breakthroughs in microbial community analysis, and recent studies have shown that parts of the communities are habitat-specific. If distinct microbial communities are present in the habitat types defined in the Habitats Directive, the Directive may be improved by including these communities. Thus, monitoring and reporting of biodiversity and conservation status of habitat types could be based not only on plant communities but also on microbial communities. In the present study, bacterial and plant communities were examined in six habitat types defined in the Habitats Directive by conducting botanical surveys and collecting soil samples for amplicon sequencing across 19 sites in Denmark. Furthermore, selected physico-chemical properties expected to differ between habitat types and explain variations in community composition of bacteria and vegetation were analysed (pH, electrical conductivity (EC), soil texture, soil water repellency, soil organic carbon content (OC), inorganic nitrogen, and in-situ water content (SWC)). Despite some variations within the same habitat type and overlaps between habitat types, habitat-specific communities were observed for both bacterial and plant communities, but no correlation was observed between the alpha diversity of vegetation and bacteria. PERMANOVA analysis was used to evaluate the variables best able to explain variation in the community composition of vegetation and bacteria. Habitat type alone could explain 46% and 47% of the variation in bacterial and plant communities, respectively. Excluding habitat type as a variable, the best model (pH, SWC, OC, fine silt, and Shannon's diversity index for vegetation) could explain 37% of the variation for bacteria. For vegetation, the best model (pH, EC, ammonium content and Shannon's diversity index for bacteria) could explain 25% of the variation. Based on these results, bacterial communities could be included in the Habitats Directive to improve the monitoring, as microorganisms are more sensitive to changes in the environment compared to vegetation, which the current monitoring is based on.

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra.

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l'Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the 'upland model' was able to more accurately predict SOC compared with the 'upland & wetland model'. However, the separately calibrated 'upland and wetland model' did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).