Mapping the relative abundance of soil microbiome biodiversity from eDNA and remote sensing

Although an enormous number of plant and animal species have been directly observed and recorded in online databases, the spatial variation in the composition of the microbiome remains relatively largely unknown. In this study, for the first time, we demonstrate mapping of the relative abundance of...

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Published in:Science of Remote Sensing
Main Authors: Skidmore, Andrew K., Siegenthaler, Andjin, Wang, Tiejun, Darvishzadeh, Roshanak, Zhu, Xi, Chariton, Anthony, de Groot, G. Arjen
Format: Article in Journal/Newspaper
Language:English
Published: 2022
Subjects:
Online Access:https://researchers.mq.edu.au/en/publications/274db2be-9082-4b35-b857-a54e0ba82ee5
https://doi.org/10.1016/j.srs.2022.100065
https://research-management.mq.edu.au/ws/files/282288948/282263091.pdf
http://www.scopus.com/inward/record.url?scp=85166788503&partnerID=8YFLogxK
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author Skidmore, Andrew K.
Siegenthaler, Andjin
Wang, Tiejun
Darvishzadeh, Roshanak
Zhu, Xi
Chariton, Anthony
de Groot, G. Arjen
author_facet Skidmore, Andrew K.
Siegenthaler, Andjin
Wang, Tiejun
Darvishzadeh, Roshanak
Zhu, Xi
Chariton, Anthony
de Groot, G. Arjen
author_sort Skidmore, Andrew K.
collection Unknown
container_start_page 100065
container_title Science of Remote Sensing
container_volume 6
description Although an enormous number of plant and animal species have been directly observed and recorded in online databases, the spatial variation in the composition of the microbiome remains relatively largely unknown. In this study, for the first time, we demonstrate mapping of the relative abundance of the soil microbiome for three terrestrial ecosystems across North America (savanna, boreal and tundra) using airborne image spectroscopy and environmental DNA (eDNA) data. We identified field plots of publicly available eDNA data co-occurring with AVIRIS-NG hyperspectral imagery. An eDNA processing pipeline was developed to generate a consistent profile of the relative abundance for thousands of microbiome operational taxonomic units (OTU) and 225 microbiome families. Using Linear Discriminate Analysis (LDA) scores for the eDNA data, we identified 81 families with the greatest explanatory power based on the community composition between the three ecosystems. A case study example demonstrates our conceptual approach by selecting a dominant and functionally important bacterial family for each ecosystem, with each family representing a specific biomarker. A partial least squares regression (PLSR) was applied to spatially predict the relative abundance of each bacteria family (in the boreal, tundra, and savanna ecosystems) from the hyperspectral imagery. For the boreal, Pseudomonadaceae is shown to be a dominant family taxon, as it is a saprophytic family specialized in decomposing the dense organic matter of boreal forest soils. Members of an understudied family of Acidobacteria, so far indicated as AKIW659, are abundant in acidic Arctic soils and peat bogs. Finally, the Micromonosporaceae are dominant and functionally important in drier regions with grass-tree dominated woodlands, being a member of Actinobacteria with a high relative abundance in soils with high carbon content and nitrate leaching. We demonstrate for the first time how the spatial prediction of relative abundance of these bacteria taxa based on remote ...
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Tundra
geographic Arctic
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op_source Skidmore , A K , Siegenthaler , A , Wang , T , Darvishzadeh , R , Zhu , X , Chariton , A & de Groot , G A 2022 , ' Mapping the relative abundance of soil microbiome biodiversity from eDNA and remote sensing ' , Science of Remote Sensing , vol. 6 , 100065 , pp. 1-9 . https://doi.org/10.1016/j.srs.2022.100065
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spelling ftmacquarieunicr:oai:https://researchers.mq.edu.au:publications/274db2be-9082-4b35-b857-a54e0ba82ee5 2025-06-15T14:22:18+00:00 Mapping the relative abundance of soil microbiome biodiversity from eDNA and remote sensing Skidmore, Andrew K. Siegenthaler, Andjin Wang, Tiejun Darvishzadeh, Roshanak Zhu, Xi Chariton, Anthony de Groot, G. Arjen 2022-12 application/pdf https://researchers.mq.edu.au/en/publications/274db2be-9082-4b35-b857-a54e0ba82ee5 https://doi.org/10.1016/j.srs.2022.100065 https://research-management.mq.edu.au/ws/files/282288948/282263091.pdf http://www.scopus.com/inward/record.url?scp=85166788503&partnerID=8YFLogxK eng eng info:eu-repo/semantics/openAccess Skidmore , A K , Siegenthaler , A , Wang , T , Darvishzadeh , R , Zhu , X , Chariton , A & de Groot , G A 2022 , ' Mapping the relative abundance of soil microbiome biodiversity from eDNA and remote sensing ' , Science of Remote Sensing , vol. 6 , 100065 , pp. 1-9 . https://doi.org/10.1016/j.srs.2022.100065 Biodiversity Environmental DNA Image spectroscopy Species abundance article 2022 ftmacquarieunicr https://doi.org/10.1016/j.srs.2022.100065 2025-06-02T00:02:23Z Although an enormous number of plant and animal species have been directly observed and recorded in online databases, the spatial variation in the composition of the microbiome remains relatively largely unknown. In this study, for the first time, we demonstrate mapping of the relative abundance of the soil microbiome for three terrestrial ecosystems across North America (savanna, boreal and tundra) using airborne image spectroscopy and environmental DNA (eDNA) data. We identified field plots of publicly available eDNA data co-occurring with AVIRIS-NG hyperspectral imagery. An eDNA processing pipeline was developed to generate a consistent profile of the relative abundance for thousands of microbiome operational taxonomic units (OTU) and 225 microbiome families. Using Linear Discriminate Analysis (LDA) scores for the eDNA data, we identified 81 families with the greatest explanatory power based on the community composition between the three ecosystems. A case study example demonstrates our conceptual approach by selecting a dominant and functionally important bacterial family for each ecosystem, with each family representing a specific biomarker. A partial least squares regression (PLSR) was applied to spatially predict the relative abundance of each bacteria family (in the boreal, tundra, and savanna ecosystems) from the hyperspectral imagery. For the boreal, Pseudomonadaceae is shown to be a dominant family taxon, as it is a saprophytic family specialized in decomposing the dense organic matter of boreal forest soils. Members of an understudied family of Acidobacteria, so far indicated as AKIW659, are abundant in acidic Arctic soils and peat bogs. Finally, the Micromonosporaceae are dominant and functionally important in drier regions with grass-tree dominated woodlands, being a member of Actinobacteria with a high relative abundance in soils with high carbon content and nitrate leaching. We demonstrate for the first time how the spatial prediction of relative abundance of these bacteria taxa based on remote ... Article in Journal/Newspaper Arctic Tundra Unknown Arctic Science of Remote Sensing 6 100065
spellingShingle Biodiversity
Environmental DNA
Image spectroscopy
Species abundance
Skidmore, Andrew K.
Siegenthaler, Andjin
Wang, Tiejun
Darvishzadeh, Roshanak
Zhu, Xi
Chariton, Anthony
de Groot, G. Arjen
Mapping the relative abundance of soil microbiome biodiversity from eDNA and remote sensing
title Mapping the relative abundance of soil microbiome biodiversity from eDNA and remote sensing
title_full Mapping the relative abundance of soil microbiome biodiversity from eDNA and remote sensing
title_fullStr Mapping the relative abundance of soil microbiome biodiversity from eDNA and remote sensing
title_full_unstemmed Mapping the relative abundance of soil microbiome biodiversity from eDNA and remote sensing
title_short Mapping the relative abundance of soil microbiome biodiversity from eDNA and remote sensing
title_sort mapping the relative abundance of soil microbiome biodiversity from edna and remote sensing
topic Biodiversity
Environmental DNA
Image spectroscopy
Species abundance
topic_facet Biodiversity
Environmental DNA
Image spectroscopy
Species abundance
url https://researchers.mq.edu.au/en/publications/274db2be-9082-4b35-b857-a54e0ba82ee5
https://doi.org/10.1016/j.srs.2022.100065
https://research-management.mq.edu.au/ws/files/282288948/282263091.pdf
http://www.scopus.com/inward/record.url?scp=85166788503&partnerID=8YFLogxK