@article{Verdon:14957,
      recid = {14957},
      author = {Verdon, Valentin and Malard, Lucie and Collart, Flavien  and Adde, Antoine and Yashiro, Erika and Pandi, Enrique  Lara and Mod, Heidi and Singer, David and Niculita-Hirzel,  Hélène and Guex, Nicolas and Guisan, Antoine},
      title = {Can we accurately predict the distribution of soil  microorganism presence and relative abundance ?},
      publisher = {Nordic Society Oikos (NSO)},
      journal = {Ecography},
      address = {Lund, Sweden. 2024-05},
      number = {ARTICLE},
      pages = {15 p.},
      abstract = {Soil microbes play a key role in shaping terrestrial  ecosystems. It is therefore essential to understand what  drives their distribution. While multivariate analyses have  been used to characterise microbial communities and drivers  of their spatial patterns, few studies have focused on  predicting the distribution of amplicon sequence variants  (ASVs). Here, we evaluate the potential of species  distribution models (SDMs) to predict the presence–absence  and relative abundance distribution of bacteria, archaea,  fungi, and protist ASVs in the western Swiss Alps. Advanced  automated selection of abiotic covariates was used to  circumvent the lack of knowledge on the ecology of each  ASV. Presence–absence SDMs could be fitted for most ASVs,  yielding better predictions than null models. Relative  abundance SDMs performed less well, with low fit and  predictive power overall, but displayed a good capacity to  differentiate between sites with high and low relative  abundance of the modelled ASV. SDMs for bacteria and  archaea displayed better predictive power than for fungi  and protists, suggesting a closer link of the former with  the abiotic covariates used. Microorganism distributions  were mostly related to edaphic covariates. In particular,  pH was the most selected covariate across models. The study  shows the potential of using SDM frameworks to predict the  distribution of ASVs obtained from topsoil DNA. It also  highlights the need for further development of precise  edaphic mapping and scenario modelling to enhances  prediction of microorganism distributions in the future.},
      url = {http://arodes.hes-so.ch/record/14957},
      doi = {https://doi.org/10.1111/ecog.07086},
}