Using soil bacterial communities to predict physico-chemical variables and soil quality
Abstract Background Soil ecosystems consist of complex interactions between biological communities and physico-chemical variables, all of which contribute to the overall quality of soils. Despite this, changes in bacterial communities are ignored by most soil monitoring programs, which are crucial t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-06-01
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| Series: | Microbiome |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40168-020-00858-1 |
| _version_ | 1818020212868382720 |
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| author | Syrie M. Hermans Hannah L. Buckley Bradley S. Case Fiona Curran-Cournane Matthew Taylor Gavin Lear |
| author_facet | Syrie M. Hermans Hannah L. Buckley Bradley S. Case Fiona Curran-Cournane Matthew Taylor Gavin Lear |
| author_sort | Syrie M. Hermans |
| collection | DOAJ |
| description | Abstract Background Soil ecosystems consist of complex interactions between biological communities and physico-chemical variables, all of which contribute to the overall quality of soils. Despite this, changes in bacterial communities are ignored by most soil monitoring programs, which are crucial to ensure the sustainability of land management practices. We applied 16S rRNA gene sequencing to determine the bacterial community composition of over 3000 soil samples from 606 sites in New Zealand. Sites were classified as indigenous forests, exotic forest plantations, horticulture, or pastoral grasslands; soil physico-chemical variables related to soil quality were also collected. The composition of soil bacterial communities was then used to predict the land use and soil physico-chemical variables of each site. Results Soil bacterial community composition was strongly linked to land use, to the extent where it could correctly determine the type of land use with 85% accuracy. Despite the inherent variation introduced by sampling across ~ 1300 km distance gradient, the bacterial communities could also be used to differentiate sites grouped by key physico-chemical properties with up to 83% accuracy. Further, individual soil variables such as soil pH, nutrient concentrations and bulk density could be predicted; the correlations between predicted and true values ranged from weak (R 2 value = 0.35) to strong (R 2 value = 0.79). These predictions were accurate enough to allow bacterial communities to assign the correct soil quality scores with 50–95% accuracy. Conclusions The inclusion of biological information when monitoring soil quality is crucial if we wish to gain a better, more accurate understanding of how land management impacts the soil ecosystem. We have shown that soil bacterial communities can provide biologically relevant insights on the impacts of land use on soil ecosystems. Furthermore, their ability to indicate changes in individual soil parameters shows that analysing bacterial DNA data can be used to screen soil quality. Video Abstract |
| first_indexed | 2024-04-14T08:01:58Z |
| format | Article |
| id | doaj.art-257b5cb91b3549b1bb13e4ba60ac4d80 |
| institution | Directory Open Access Journal |
| issn | 2049-2618 |
| language | English |
| last_indexed | 2024-04-14T08:01:58Z |
| publishDate | 2020-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Microbiome |
| spelling | doaj.art-257b5cb91b3549b1bb13e4ba60ac4d802022-12-22T02:04:52ZengBMCMicrobiome2049-26182020-06-018111310.1186/s40168-020-00858-1Using soil bacterial communities to predict physico-chemical variables and soil qualitySyrie M. Hermans0Hannah L. Buckley1Bradley S. Case2Fiona Curran-Cournane3Matthew Taylor4Gavin Lear5School of Biological Sciences, University of AucklandSchool of Science, Auckland University of TechnologySchool of Science, Auckland University of TechnologyMinistry for the Environment – Manatū Mō Te TaiaoWaikato Regional CouncilSchool of Biological Sciences, University of AucklandAbstract Background Soil ecosystems consist of complex interactions between biological communities and physico-chemical variables, all of which contribute to the overall quality of soils. Despite this, changes in bacterial communities are ignored by most soil monitoring programs, which are crucial to ensure the sustainability of land management practices. We applied 16S rRNA gene sequencing to determine the bacterial community composition of over 3000 soil samples from 606 sites in New Zealand. Sites were classified as indigenous forests, exotic forest plantations, horticulture, or pastoral grasslands; soil physico-chemical variables related to soil quality were also collected. The composition of soil bacterial communities was then used to predict the land use and soil physico-chemical variables of each site. Results Soil bacterial community composition was strongly linked to land use, to the extent where it could correctly determine the type of land use with 85% accuracy. Despite the inherent variation introduced by sampling across ~ 1300 km distance gradient, the bacterial communities could also be used to differentiate sites grouped by key physico-chemical properties with up to 83% accuracy. Further, individual soil variables such as soil pH, nutrient concentrations and bulk density could be predicted; the correlations between predicted and true values ranged from weak (R 2 value = 0.35) to strong (R 2 value = 0.79). These predictions were accurate enough to allow bacterial communities to assign the correct soil quality scores with 50–95% accuracy. Conclusions The inclusion of biological information when monitoring soil quality is crucial if we wish to gain a better, more accurate understanding of how land management impacts the soil ecosystem. We have shown that soil bacterial communities can provide biologically relevant insights on the impacts of land use on soil ecosystems. Furthermore, their ability to indicate changes in individual soil parameters shows that analysing bacterial DNA data can be used to screen soil quality. Video Abstracthttp://link.springer.com/article/10.1186/s40168-020-00858-1Bacterial communitiesBacterial indicatorsBiomonitoringEnvironmental monitoringRandom forest analysisSoil health |
| spellingShingle | Syrie M. Hermans Hannah L. Buckley Bradley S. Case Fiona Curran-Cournane Matthew Taylor Gavin Lear Using soil bacterial communities to predict physico-chemical variables and soil quality Microbiome Bacterial communities Bacterial indicators Biomonitoring Environmental monitoring Random forest analysis Soil health |
| title | Using soil bacterial communities to predict physico-chemical variables and soil quality |
| title_full | Using soil bacterial communities to predict physico-chemical variables and soil quality |
| title_fullStr | Using soil bacterial communities to predict physico-chemical variables and soil quality |
| title_full_unstemmed | Using soil bacterial communities to predict physico-chemical variables and soil quality |
| title_short | Using soil bacterial communities to predict physico-chemical variables and soil quality |
| title_sort | using soil bacterial communities to predict physico chemical variables and soil quality |
| topic | Bacterial communities Bacterial indicators Biomonitoring Environmental monitoring Random forest analysis Soil health |
| url | http://link.springer.com/article/10.1186/s40168-020-00858-1 |
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