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Rooney LM, Dupuy LX, Hoskisson PA, McConnell G. Construction and characterisation of a structured, tuneable, and transparent 3D culture platform for soil bacteria. MICROBIOLOGY (READING, ENGLAND) 2024; 170:001429. [PMID: 38289644 PMCID: PMC10866023 DOI: 10.1099/mic.0.001429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 01/19/2024] [Indexed: 02/01/2024]
Abstract
We have developed a tuneable workflow for the study of soil microbes in an imitative 3D soil environment that is compatible with routine and advanced optical imaging, is chemically customisable, and is reliably refractive index matched based on the carbon catabolism of the study organism. We demonstrate our transparent soil pipeline with two representative soil organisms, Bacillus subtilis and Streptomyces coelicolor, and visualise their colonisation behaviours using fluorescence microscopy and mesoscopy. This spatially structured, 3D approach to microbial culture has the potential to further study the behaviour of bacteria in conditions matching their native environment and could be expanded to study microbial interactions, such as competition and warfare.
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Affiliation(s)
- Liam M. Rooney
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Lionel X. Dupuy
- The James Hutton Institute, Invergowrie, Dundee, DD2 5DA, UK
- Present address: Department of Conservation of Natural Resources, Neiker, Basque Institute for Agricultural Research and Development, Derio, Spain
- Present address: Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Paul A. Hoskisson
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Gail McConnell
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
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Rusconi O, Broennimann O, Storrer Y, Le Bayon R, Guisan A, Rasmann S. Detecting preservation and reintroduction sites for endangered plant species using a two‐step modeling and field approach. CONSERVATION SCIENCE AND PRACTICE 2022. [DOI: 10.1111/csp2.12800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Affiliation(s)
- Olivia Rusconi
- Institute of Biology University of Neuchâtel Rue Emile‐Argand 11 Neuchâtel Switzerland
| | - Olivier Broennimann
- Department of Ecology and Evolution University of Lausanne Lausanne Switzerland
- Institute of Earth Surface Dynamics University of Lausanne Lausanne Switzerland
| | - Yannick Storrer
- Wildlife, Forest and Nature Cantonal Service (SFFN) Couvet Switzerland
| | - Renée‐Claire Le Bayon
- Institute of Biology University of Neuchâtel Rue Emile‐Argand 11 Neuchâtel Switzerland
| | - Antoine Guisan
- Department of Ecology and Evolution University of Lausanne Lausanne Switzerland
- Institute of Earth Surface Dynamics University of Lausanne Lausanne Switzerland
| | - Sergio Rasmann
- Institute of Biology University of Neuchâtel Rue Emile‐Argand 11 Neuchâtel Switzerland
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Mod HK, Buri A, Yashiro E, Guex N, Malard L, Pinto-Figueroa E, Pagni M, Niculita-Hirzel H, van der Meer JR, Guisan A. Predicting spatial patterns of soil bacteria under current and future environmental conditions. THE ISME JOURNAL 2021; 15:2547-2560. [PMID: 33712699 PMCID: PMC8397778 DOI: 10.1038/s41396-021-00947-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 02/16/2021] [Accepted: 02/19/2021] [Indexed: 02/01/2023]
Abstract
Soil bacteria are largely missing from future biodiversity assessments hindering comprehensive forecasts of ecosystem changes. Soil bacterial communities are expected to be more strongly driven by pH and less by other edaphic and climatic factors. Thus, alkalinisation or acidification along with climate change may influence soil bacteria, with subsequent influences for example on nutrient cycling and vegetation. Future forecasts of soil bacteria are therefore needed. We applied species distribution modelling (SDM) to quantify the roles of environmental factors in governing spatial abundance distribution of soil bacterial OTUs and to predict how future changes in these factors may change bacterial communities in a temperate mountain area. Models indicated that factors related to soil (especially pH), climate and/or topography explain and predict part of the abundance distribution of most OTUs. This supports the expectations that microorganisms have specific environmental requirements (i.e., niches/envelopes) and that they should accordingly respond to environmental changes. Our predictions indicate a stronger role of pH over other predictors (e.g. climate) in governing distributions of bacteria, yet the predicted future changes in bacteria communities are smaller than their current variation across space. The extent of bacterial community change predictions varies as a function of elevation, but in general, deviations from neutral soil pH are expected to decrease abundances and diversity of bacteria. Our findings highlight the need to account for edaphic changes, along with climate changes, in future forecasts of soil bacteria.
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Affiliation(s)
- Heidi K Mod
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.
- Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland.
| | - Aline Buri
- Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
| | - Erika Yashiro
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
- Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland
| | - Nicolas Guex
- Bioinformatics Competence Center, University of Lausanne, Lausanne, Switzerland
- Vital-IT, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Lucie Malard
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
| | | | - Marco Pagni
- Vital-IT, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Hélène Niculita-Hirzel
- Department of Occupational Health and Environment, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
| | | | - Antoine Guisan
- Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
- Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
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Shi T, Yang C, Liu H, Wu C, Wang Z, Li H, Zhang H, Guo L, Wu G, Su F. Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 272:116041. [PMID: 33272796 DOI: 10.1016/j.envpol.2020.116041] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/16/2020] [Accepted: 09/17/2020] [Indexed: 06/12/2023]
Abstract
Due to rapid urbanization in China, lead (Pb) continues to accumulate in urban topsoil, resulting in soil degradation and increased public exposure. Mapping Pb concentrations in urban topsoil is therefore vital for the evaluation and control of this exposure risk. This study developed spatial models to map Pb concentrations in urban topsoil using proximal and remote sensing data. Proximal sensing reflectance spectra (350-2500 nm) of soils were pre-processed and used to calculate the principal components as landscape factors to represent the soil properties. Other landscape factors, including vegetation and land-use factors, were extracted from time-sequential Landsat images. Two hybrid statistical approaches, regression kriging (RK) and geographically weighted regression (GWR), were adopted to establish prediction models using the landscape factors. The results indicated that the use of landscape factors derived from combined remote and proximal sensing data improved the prediction of Pb concentrations compared with useing these data individually. GWR obtained better results than RK for predicting soil Pb concentration. Thus, joint proximal and remote sensing provides timely, easily accessible, and suitable data for extracting landscape factors.
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Affiliation(s)
- Tiezhu Shi
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
| | - Chao Yang
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
| | - Huizeng Liu
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
| | - Chao Wu
- School of Geographic and Biologic Information & Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
| | - Zhihua Wang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - He Li
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Huifang Zhang
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Long Guo
- College of Resources and Environment, Huazhong Agricultural University, 430070, Wuhan, China
| | - Guofeng Wu
- MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China.
| | - Fenzhen Su
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
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Terrain Effects on the Spatial Variability of Soil Physical and Chemical Properties. SOIL SYSTEMS 2019. [DOI: 10.3390/soilsystems4010001] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Understanding topography effects on soil properties is vital to modelling landscape hydrology and establishing sustainable on-field management practices. This research focuses on an arable area (117 km2) in Southwestern Ethiopia where agricultural fields and bush cover are the dominant land uses. We postulate that adapting either of the soil data resources, coarse resolution FAO-UNESCO (Food and Agriculture Organization of the United Nations Educational, Scientific and Cultural Organization) soil data or pedo-transfer functions (PTFs) is not reliable to indicate future watershed management directions. The FAO-UNESCO data does not account for scale issues and assigns the same soil property at different landscape gradients. The PTFs, on the other hand, do not account for environmental effects and fail to provide all the required data. In this regard, mapping soil property spatial dynamics can help understand landscape physicochemical processes and corresponding land use changes. For this purpose, soil samples were collected across the watershed following a gridded sampling scheme. In areas with heterogeneous topography, soil is spatially variable as influenced by land use and slope. To understand the spatial variation, this research develops indicators, such as topographic index, soil topographic wetness index, elevation, aspect, and slope. Pearson correlation (r), among others, was used to investigate terrain effects on selected soil properties: organic matter (OM), available water content (AWC), sand content (%), clay content (%), silt content (%), electrical conductivity (EC), moist bulk density (MBD), and saturated hydraulic conductivity (Ksat). The results show that there were statistically significant correlations between elevation-based variables and soil physical properties. Among the variables considered, the ‘r’ value between topographic index and soil attributes (i.e., OM, EC, AWC, sand, clay, silt, and Ksat) were 0.66, 0.5, 0.7, 0.55, 0.62, 0.4, and 0.66, respectively. In conclusion, while understanding topography effects on soil properties is vital, implementing either FAO-UNESCO or PTFs soil data do not provide appropriate information pertaining to scale issues.
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