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.

Combining Laser-Induced Breakdown Spectroscopy (LIBS) and Visible Near-Infrared Spectroscopy (Vis-NIRS) for Soil Phosphorus Determination.

Conventional wet chemical methods for the determination of soil phosphorus (P) pools, relevant for environmental and agronomic purposes, are labor-intensive. Therefore, alternative techniques are needed, and a combination of the spectroscopic techniques-in this case, laser-induced breakdown spectroscopy (LIBS)-and visible near-infrared spectroscopy (vis-NIRS) could be relevant. We aimed at exploring LIBS, vis-NIRS and their combination for soil P estimation. We analyzed 147 Danish agricultural soils with LIBS and vis-NIRS. As reference measurements, we analyzed water-extractable P (Pwater), Olsen P (Polsen), oxalate-extractable P (Pox) and total P (TP) by conventional wet chemical protocols, as proxies for respectively leachable, plant-available, adsorbed inorganic P, and TP in soil. Partial least squares regression (PLSR) models combined with interval partial least squares (iPLS) and competitive adaptive reweighted sampling (CARS) variable selection methods were tested, and the relevant wavelengths for soil P determination were identified. LIBS exhibited better results compared to vis-NIRS for all P models, except for Pwater, for which results were comparable. Model performance for both the LIBS and vis-NIRS techniques as well as the combined LIBS-vis-NIR approach was significantly improved when variable selection was applied. CARS performed better than iPLS in almost all cases. Combined LIBS and vis-NIRS models with variable selection showed the best results for all four P pools, except for Pox where the results were comparable to using the LIBS model with CARS. Merging LIBS and vis-NIRS with variable selection showed potential for improving soil P determinations, but larger and independent validation datasets should be tested in future studies.