000014957 001__ 14957
000014957 005__ 20250114134153.0
000014957 022__ $$a0906-7590
000014957 0247_ $$2DOI$$a10.1111/ecog.07086
000014957 037__ $$aARTICLE
000014957 039_9 $$a2025-01-14 13:41:53$$b0$$c2025-01-14 08:24:12$$d1000099$$c2024-11-05 08:43:23$$d0$$y2024-11-05 08:43:15$$z1000099
000014957 041__ $$aeng
000014957 245__ $$aCan we accurately predict the distribution of soil microorganism presence and relative abundance ?
000014957 260__ $$aLund, Sweden$$bNordic Society Oikos (NSO)
000014957 269__ $$a2024-05
000014957 300__ $$a15 p.
000014957 506__ $$avisible
000014957 520__ $$9eng$$aSoil microbes play a key role in shaping terrestrial ecosystems. It is therefore essential to understand what drives their distribution. While multivariate analyses have been used to characterise microbial communities and drivers of their spatial patterns, few studies have focused on predicting the distribution of amplicon sequence variants (ASVs). Here, we evaluate the potential of species distribution models (SDMs) to predict the presence–absence and relative abundance distribution of bacteria, archaea, fungi, and protist ASVs in the western Swiss Alps. Advanced automated selection of abiotic covariates was used to circumvent the lack of knowledge on the ecology of each ASV. Presence–absence SDMs could be fitted for most ASVs, yielding better predictions than null models. Relative abundance SDMs performed less well, with low fit and predictive power overall, but displayed a good capacity to differentiate between sites with high and low relative abundance of the modelled ASV. SDMs for bacteria and archaea displayed better predictive power than for fungi and protists, suggesting a closer link of the former with the abiotic covariates used. Microorganism distributions were mostly related to edaphic covariates. In particular, pH was the most selected covariate across models. The study shows the potential of using SDM frameworks to predict the distribution of ASVs obtained from topsoil DNA. It also highlights the need for further development of precise edaphic mapping and scenario modelling to enhances prediction of microorganism distributions in the future.
000014957 540__ $$acorrect
000014957 592__ $$aChangins
000014957 592__ $$bAucun institut
000014957 592__ $$cIngénierie et Architecture
000014957 6531_ $$9eng$$aamplicon sequencing
000014957 6531_ $$9eng$$aarchaea
000014957 6531_ $$9eng$$abacteria
000014957 6531_ $$9eng$$across-validation
000014957 6531_ $$9eng$$aeDNA
000014957 6531_ $$9eng$$afungi
000014957 6531_ $$9eng$$aprotist
000014957 6531_ $$9eng$$aspecies distribution model
000014957 6531_ $$9eng$$atopsoil
000014957 655__ $$ascientifique
000014957 700__ $$aVerdon, Valentin$$uUniversity of Lausanne, Lausanne, Switzerland
000014957 700__ $$aMalard, Lucie$$uUniversity of Lausanne, Lausanne, Switzerland
000014957 700__ $$aCollart, Flavien$$uUniversity of Lausanne, Lausanne, Switzerland
000014957 700__ $$aAdde, Antoine$$uUniversity of Lausanne, Lausanne, Switzerland
000014957 700__ $$aYashiro, Erika$$uUniversity of Lausanne, Lausanne, Switzerland
000014957 700__ $$aPandi, Enrique Lara$$uReal Jardín Botánico-CSIC, Madrid, Spain
000014957 700__ $$aMod, Heidi$$uUniversity of Helsinki, Helsinki, Finland
000014957 700__ $$aSinger, David$$uSchool of Viticulture and Enology, HES-SO University of Applied Sciences and Arts Western Switzerland
000014957 700__ $$aNiculita-Hirzel, Hélène$$uUniversity of Lausanne, Lausanne, Switzerland
000014957 700__ $$aGuex, Nicolas$$uUniversity of Lausanne, Lausanne, Switzerland
000014957 700__ $$aGuisan, Antoine$$uUniversity of Lausanne, Lausanne, Switzerland
000014957 773__ $$tEcography$$qTo be published
000014957 8564_ $$uhttps://arodes.hes-so.ch/record/14957/files/Verdon_2024_Can_we_accurately_predict_the_distribution_of_soil_microorganism_presence_and_relative_abundance.pdf$$yPublished version$$9b916e0f8-7098-42f3-a69c-89631ac77aed$$s2954277
000014957 906__ $$aGOLD
000014957 909CO $$ooai:hesso.tind.io:14957$$pGLOBAL_SET
000014957 950__ $$aaucun
000014957 980__ $$ascientifique
000014957 981__ $$ascientifique