TY  - GEN
AB  - 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.
AD  - University of Lausanne, Lausanne, Switzerland
AD  - University of Lausanne, Lausanne, Switzerland
AD  - University of Lausanne, Lausanne, Switzerland
AD  - University of Lausanne, Lausanne, Switzerland
AD  - University of Lausanne, Lausanne, Switzerland
AD  - Real Jardín Botánico-CSIC, Madrid, Spain
AD  - University of Helsinki, Helsinki, Finland
AD  - School of Viticulture and Enology, HES-SO University of Applied Sciences and Arts Western Switzerland
AD  - University of Lausanne, Lausanne, Switzerland
AD  - University of Lausanne, Lausanne, Switzerland
AD  - University of Lausanne, Lausanne, Switzerland
AU  - Verdon, Valentin
AU  - Malard, Lucie
AU  - Collart, Flavien
AU  - Adde, Antoine
AU  - Yashiro, Erika
AU  - Pandi, Enrique Lara
AU  - Mod, Heidi
AU  - Singer, David
AU  - Niculita-Hirzel, Hélène
AU  - Guex, Nicolas
AU  - Guisan, Antoine
CY  - Lund, Sweden
DA  - 2024-05
DO  - 10.1111/ecog.07086
DO  - DOI
EP  - To be published
ID  - 14957
JF  - Ecography
KW  - amplicon sequencing
KW  - archaea
KW  - bacteria
KW  - cross-validation
KW  - eDNA
KW  - fungi
KW  - protist
KW  - species distribution model
KW  - topsoil
L1  - https://arodes.hes-so.ch/record/14957/files/Verdon_2024_Can_we_accurately_predict_the_distribution_of_soil_microorganism_presence_and_relative_abundance.pdf
L2  - https://arodes.hes-so.ch/record/14957/files/Verdon_2024_Can_we_accurately_predict_the_distribution_of_soil_microorganism_presence_and_relative_abundance.pdf
L4  - https://arodes.hes-so.ch/record/14957/files/Verdon_2024_Can_we_accurately_predict_the_distribution_of_soil_microorganism_presence_and_relative_abundance.pdf
LA  - eng
LK  - https://arodes.hes-so.ch/record/14957/files/Verdon_2024_Can_we_accurately_predict_the_distribution_of_soil_microorganism_presence_and_relative_abundance.pdf
N2  - 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.
PB  - Nordic Society Oikos (NSO)
PP  - Lund, Sweden
PY  - 2024-05
SN  - 0906-7590
SP  - To be published
T1  - Can we accurately predict the distribution of soil microorganism presence and relative abundance ?
TI  - Can we accurately predict the distribution of soil microorganism presence and relative abundance ?
UR  - https://arodes.hes-so.ch/record/14957/files/Verdon_2024_Can_we_accurately_predict_the_distribution_of_soil_microorganism_presence_and_relative_abundance.pdf
Y1  - 2024-05
ER  -