Literature DB >> 31243501

Microbial assemblages and bioindicators as proxies for ecosystem health status: potential and limitations.

Carmen Astudillo-García1, Syrie M Hermans2, Bryan Stevenson3, Hannah L Buckley4, Gavin Lear2.   

Abstract

Microorganisms play fundamental roles in the diversity and functional stability of environments, including nutrient and energy cycling. However, microbial biodiversity loss and change because of global climate and land use change remain poorly understood. Many microbial taxa exhibit fast growth rates and are highly sensitive to environmental change. This suggests they have potential to be efficient biological indicators to assess and monitor the state of the habitats within which they occur. Here, we describe and illustrate a range of univariate and multivariate statistical approaches that can be used to identify effective microbial indicators of environmental perturbations and quantify changes in microbial communities. We show that the integration of multiple approaches, such as linear discriminant analysis effect size and indicator value analysis, is optimal for the quantification of the effects of perturbation on microbial communities. We demonstrate the most prevalent techniques using microbial community data derived from soils under different land uses. We discuss the limitations to the development and use of microbial bioindicators and identify future research directions, such as the creation of reliable, standardised reference databases to provide baseline metrics that are indicative of healthy microbial communities. If reliable and globally-relevant microbial indicators of environmental health can be developed, there is enormous potential for their use, both as a standalone monitoring tool and via their integration with existing physical, chemical and biological measures of environmental health.

Keywords:  Ecosystem health; Microbial indicators; Molecular; Statistics

Year:  2019        PMID: 31243501     DOI: 10.1007/s00253-019-09963-0

Source DB:  PubMed          Journal:  Appl Microbiol Biotechnol        ISSN: 0175-7598            Impact factor:   4.813


  9 in total

1.  Metagenomics versus total RNA sequencing: most accurate data-processing tools, microbial identification accuracy and perspectives for ecological assessments.

Authors:  Christopher A Hempel; Natalie Wright; Julia Harvie; Jose S Hleap; Sarah J Adamowicz; Dirk Steinke
Journal:  Nucleic Acids Res       Date:  2022-08-18       Impact factor: 19.160

Review 2.  Potential of Meta-Omics to Provide Modern Microbial Indicators for Monitoring Soil Quality and Securing Food Production.

Authors:  Christophe Djemiel; Samuel Dequiedt; Battle Karimi; Aurélien Cottin; Walid Horrigue; Arthur Bailly; Ali Boutaleb; Sophie Sadet-Bourgeteau; Pierre-Alain Maron; Nicolas Chemidlin Prévost-Bouré; Lionel Ranjard; Sébastien Terrat
Journal:  Front Microbiol       Date:  2022-06-30       Impact factor: 6.064

3.  Quantifying stream periphyton assemblage responses to nutrient amendments with a molecular approach.

Authors:  James D Hagy Iii; Katelyn A Houghton; David L Beddick; Joseph B James; Stephanie D Friedman; Richard Devereux
Journal:  Freshw Sci       Date:  2020-05-05       Impact factor: 2.353

4.  Flooding and ecological restoration promote wetland microbial communities and soil functions on former cranberry farmland.

Authors:  Rachel L Rubin; Kate A Ballantine; Arden Hegberg; Jason P Andras
Journal:  PLoS One       Date:  2021-12-17       Impact factor: 3.240

Review 5.  Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges.

Authors:  James M W R McElhinney; Mary Krystelle Catacutan; Aurelie Mawart; Ayesha Hasan; Jorge Dias
Journal:  Front Microbiol       Date:  2022-04-25       Impact factor: 6.064

6.  Predictable Changes in Eelgrass Microbiomes with Increasing Wasting Disease Prevalence across 23° Latitude in the Northeastern Pacific.

Authors:  Deanna S Beatty; Lillian R Aoki; Brendan Rappazzo; Chelsea Bergman; Lia K Domke; J Emmett Duffy; Katie Dubois; Ginny L Eckert; Carla Gomes; Olivia J Graham; Leah Harper; C Drew Harvell; Timothy L Hawthorne; Margot Hessing-Lewis; Kevin Hovel; Zachary L Monteith; Ryan S Mueller; Angeleen M Olson; Carolyn Prentice; Fiona Tomas; Bo Yang; John J Stachowicz
Journal:  mSystems       Date:  2022-07-20       Impact factor: 7.324

7.  Evaluation of Three Prokaryote Primers for Identification of Prokaryote Community Structure and Their Abode Preference in Three Distinct Wetland Ecosystems.

Authors:  Kavita Kumari; Malay Naskar; Md Aftabuddin; Soma Das Sarkar; Bandana Das Ghosh; Uttam Kumar Sarkar; Subir Kumar Nag; Chayna Jana; Basanta Kumar Das
Journal:  Front Microbiol       Date:  2021-07-14       Impact factor: 5.640

8.  Using soil bacterial communities to predict physico-chemical variables and soil quality.

Authors:  Syrie M Hermans; Hannah L Buckley; Bradley S Case; Fiona Curran-Cournane; Matthew Taylor; Gavin Lear
Journal:  Microbiome       Date:  2020-06-02       Impact factor: 14.650

9.  Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture.

Authors:  Bin Liu; Heike Sträuber; João Saraiva; Hauke Harms; Sandra Godinho Silva; Jonas Coelho Kasmanas; Sabine Kleinsteuber; Ulisses Nunes da Rocha
Journal:  Microbiome       Date:  2022-03-25       Impact factor: 14.650

  9 in total

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