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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Afgan E, Baker D, Batut B, Van Den Beek M, Bouvier D, Ech M, Chilton J, Clements D, Coraor N, Grüning BA, Guerler A, Hillman-Jackson J, Hiltemann S, Jalili V, Rasche H, Soranzo N, Goecks J, Taylor J, Nekrutenko A, Blankenberg D (2018) The galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res 46:W537–W544. https://doi.org/10.1093/nar/gky379
Ahmed W, Stewart J, Gardner T, Powell D (2008) A real-time polymerase chain reaction assay for quantitative detection of the human-specific enterococci surface protein marker in sewage and environmental waters. Environ Microbiol 10:3255–3264. https://doi.org/10.1111/j.1462-2920.2008.01715.x
Ancion PY, Lear G, Lewis GD (2010) Three common metal contaminants of urban runoff (Zn, Cu & Pb) accumulate in freshwater biofilm and modify embedded bacterial communities. Environ Pollut 158:2738–2745. https://doi.org/10.1016/j.envpol.2010.04.013
Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26:32–46
Anderson MJ, Willis TJ (2003) Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology 84:511–525. https://doi.org/10.1890/0012-9658(2003)084[0511:CAOPCA]2.0.CO;2
Apté C, Weiss S (1997) Data mining with decision trees and decision rules. Futur Gener Comput Syst 13:197–210. https://doi.org/10.1016/S0167-739X(97)00021-6
Aranda CP, Valenzuela C, Matamala Y, Godoy FA, Aranda N (2015) Sulphur-cycling bacteria and ciliated protozoans in a Beggiatoaceae mat covering organically enriched sediments beneath a salmon farm in a southern Chilean fjord. Mar Pollut Bull 100:270–278. https://doi.org/10.1016/j.marpolbul.2015.08.040
Arsène-Ploetze F, Koechler S, Marchal M, Coppée JY, Chandler M, Bonnefoy V, Brochier-Armanet C, Barakat M, Barbe V, Battaglia-Brunet F, Bruneel O, Bryan CG, Cleiss-Arnold J, Cruveiller S, Erhardt M, Heinrich-Salmeron A, Hommais F, Joulian C, Krin E, Lieutaud A, Lièvremont D, Michel C, Muller D, Ortet P, Proux C, Siguier P, Roche D, Rouy Z, Salvignol G, Slyemi D, Talla E, Weiss S, Weissenbach J, Médigue C, Bertin PN (2010) Structure, function, and evolution of the Thiomonas spp. genome. PLoS Genet 6:e1000859. https://doi.org/10.1371/journal.pgen.1000859
Atashgahi S, Aydin R, Dimitrov MR, Sipkema D, Hamonts K, Lahti L, Maphosa F, Kruse T, Saccenti E, Springael D, Dejonghe W, Smidt H (2015) Impact of a wastewater treatment plant on microbial community composition and function in a hyporheic zone of a eutrophic river. Sci Rep 5:17284. https://doi.org/10.1038/srep17284
Auld RR, Myre M, Mykytczuk NCS, Leduc LG, Merritt TJS (2013) Characterization of the microbial acid mine drainage microbial community using culturing and direct sequencing techniques. J Microbiol Methods 93:108–115. https://doi.org/10.1016/j.mimet.2013.01.023
Aylagas E, Borja Á, Tangherlini M, Dell’Anno A, Corinaldesi C, Michell CT, Irigoien X, Danovaro R, Rodríguez-Ezpeleta N (2017) A bacterial community-based index to assess the ecological status of estuarine and coastal environments. Mar Pollut Bull 114:679–688. https://doi.org/10.1016/j.marpolbul.2016.10.050
Baker ME, King RS (2010) A new method for detecting and interpreting biodiversity and ecological community thresholds. Methods Ecol Evol 1:25–37. https://doi.org/10.1111/j.2041-210x.2009.00007.x
Ballaud F, Dufresne A, Francez AJ, Colombet J, Sime-Ngando T, Quaiser A (2016) Dynamics of viral abundance and diversity in a sphagnum-dominated peatland: temporal fluctuations prevail over habitat. Front Microbiol 6:1494. https://doi.org/10.3389/fmicb.2015.01494
Barkay T, Miller SM, Summers AO (2003) Bacterial mercury resistance from atoms to ecosystems. FEMS Microbiol Rev 27:355–384. https://doi.org/10.1016/S0168-6445(03)00046-9
Beck D, Foster JA (2014) Machine learning techniques accurately classify microbial communities by bacterial vaginosis characteristics. PLoS One 9:e87830. https://doi.org/10.1371/journal.pone.0087830
Belanche-Muñoz L, Blanch AR (2008) Machine learning methods for microbial source tracking. Environ Model Softw 23:741–750. https://doi.org/10.1016/j.envsoft.2007.09.013
Bergelson J, Mittelstrass J, Horton MW (2019) Characterizing both bacteria and fungi improves understanding of the Arabidopsis root microbiome. Sci Rep 9:24. https://doi.org/10.1038/s41598-018-37208-z
Boehm AB, Sassoubre LM (2014) Enterococci as indicators of environmental fecal contamination. In: Gilmore M, Clewell D, Ike Y, Shankar N (eds) Enterococci: from commensals to leading causes of drug resistant infection. Massachusetts Eye and Ear Infirmary, Boston, pp 1–21
Borcard D, Gillet F, Legendre P (2011) Numerical ecology with R. Springer-Verlag New York, New York
Borja A (2018) Testing the efficiency of a bacterial community-based index (microgAMBI) to assess distinct impact sources in six locations around the world. Ecol Indic 85:594–602. https://doi.org/10.1016/j.ecolind.2017.11.018
Borja A, Franco J, Perez V (2000) A marine biotic index to establish the ecological quality of soft-bottom benthos within European estuarine and coastal environments. Mar Pollut Bull 40:1100–1114
Bray JR, Curtis JT (1957) An ordination of the upland forest community of southern Wisconsin. Ecol Monogr 27:325–349
Buss DF, Carlisle DM, Chon TS, Culp J, Harding JS, Keizer-Vlek HE, Robinson WA, Strachan S, Thirion C, Hughes RM (2015) Stream biomonitoring using macroinvertebrates around the globe: a comparison of large-scale programs. Environ Monit Assess 187:4132. https://doi.org/10.1007/s10661-014-4132-8
Cabral L, Pereira de Sousa ST, Júnior GVL, Hawley E, Andreote FD, Hess M, de Oliveira VM (2018) Microbial functional responses to long-term anthropogenic impact in mangrove soils. Ecotoxicol Environ Saf 160:231–239. https://doi.org/10.1016/j.ecoenv.2018.04.050
Carignan V, Villard M-A (2002) Selecting indicator species to monitor ecological integrity: a review. Environ Monit Assess 78:45–61. https://doi.org/10.1023/A:1016136723584
Caruso G, Denaro R, Genovese M, Giuliano L, Mancuso M, Yakimov M (2004) New methodological strategies for detecting bacterial indicators. Chem Ecol 20:167–181. https://doi.org/10.1080/02757540410001690333
Casanovas-Massana A, Gómez-Doñate M, Sánchez D, Belanche-Muñoz LA, Muniesa M, Blanch AR (2015) Predicting fecal sources in waters with diverse pollution loads using general and molecular host-specific indicators and applying machine learning methods. J Environ Manag 151:317–325. https://doi.org/10.1016/j.jenvman.2015.01.002
Chen LX, Hu M, Huang LN, Hua ZS, Kuang JL, Li SJ, Shu WS (2015) Comparative metagenomic and metatranscriptomic analyses of microbial communities in acid mine drainage. ISME J 9:1579–1592. https://doi.org/10.1038/ismej.2014.245
Clark P, Niblett T (1989) The CN2 induction algorithm. Mach Learn 3:261–283. https://doi.org/10.1023/A:1022641700528
Clarke KR (1993) Non-parametric multivariate analyses of changes in community structure. Austral Ecology 18 (1):117-143. https://doi.org/10.1111/j.1442-9993.1993.tb00438.x
Clarke KR, Gorley RN (2006) PRIMER v6: user manual/tutorial
Collier K, Clapcott J, Neale M (2014) A macroinvertebrate attribute to assess ecosystem health of New Zealand waterways for the national objectives framework - issues and options. Environ Res Inst Rep 188:63–85. https://doi.org/10.1016/S1387-3806(98)14282-3
Colwell RR (1997) Microbial diversity: the importance of exploration and conservation. J Ind Microbiol Biotechnol 18:302–307. https://doi.org/10.1038/sj.jim.2900390
Compant S, Duffe B, Nowak J, Clement C, Barka EA (2005) Use of plant growth-promoting bacteria for biocontrol of plant diseases: principles, mechanisms of action, and future prospects. Appl Environ Microbiol 71:4951–4959. https://doi.org/10.1128/AEM.71.9.4951
Dasarathy BV (1991) Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos.
Davidson NC (2014) How much wetland has the world lost? Long-term and recent trends in global wetland area. Mar Freshw Res 65:934–941. https://doi.org/10.1071/MF14173
De Cáceres M, Legendre P (2009) Associations between species and groups of sites: indices and statistical inference. Ecology 90 (12):3566-3574. https://doi.org/10.1890/08-1823.1
De Cáceres M, Legendre P, Moretti M (2010) Improving indicator species analysis by combining groups of sites. Oikos 119:1674–1684
De Victorica J, Galván M (2001) Pseudomonas aeruginosa as an indicator of health risk in water for human consumption. Water Sci Technol 43:49–52
Dequiedt S, Saby NPA, Lelievre M, Jolivet C, Thioulouse J, Toutain B, Arrouays D, Bispo A, Lemanceau P, Ranjard L (2011) Biogeographical patterns of soil molecular microbial biomass as influenced by soil characteristics and management. Glob Ecol Biogeogr 20:641–652. https://doi.org/10.1111/j.1466-8238.2010.00628.x
Dillon R, Dillon V (2004) The gut bacteria of insects: nonpathogenic interactions. Annu Rev Entomol 49:71–92. https://doi.org/10.1146/annurev.ento.49.061802.123416
Dreiseitl S, Ohno-Machado L (2002) Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 35:352–359. https://doi.org/10.1016/S1532-0464(03)00034-0
Dubinsky EA, Butkus SR, Andersen GL (2016) Microbial source tracking in impaired watersheds using PhyloChip and machine-learning classification. Water Res 105:56–64. https://doi.org/10.1016/j.watres.2016.08.035
Falkowski PG, Fenchel T, Delong EF (2008) The microbial engine that drives the Earth’s biogeochemical cycles. Science 320:1034–1039
Feld CK, de BF, Dolédec S (2014) Biodiversity of traits and species both show weak responses to hydromorphological alteration in lowland river macroinvertebrates. Freshw Biol 59:233–248. https://doi.org/10.1111/fwb.12260
Fierer N, Jackson RB (2006) The diversity and biogeography of soil bacterial communities. PNAS 103:626–631. https://doi.org/10.1073/pnas.95.12.6578
Fortunato CS, Eiler A, Herfort L, Needoba JA, Peterson TD, Crump BC (2013) Determining indicator taxa across spatial and seasonal gradients in the Columbia River coastal margin. ISME J 7:1899–1911. https://doi.org/10.1038/ismej.2013.79
Frank E, Hall M, Pfahringer B (2003) Locally weighted naive Bayes. In: Proceedings of the nineteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., San Francisco, pp 249–256
Frontalini F, Coccioni R (2011) Benthic foraminifera as bioindicators of pollution: a review of Italian research over the last three decades. Rev Micropaleontol 54:115–127. https://doi.org/10.1016/j.revmic.2011.03.001
Fründ H, Graefe U, Tischer S (2011) Earthworms as bioindicators of soil quality. In: Karaka A (ed) Biology of earthworms. Soil Biology. Springer-Verlag, Berlin, pp 261–278
Glasl B, Webster NS, Bourne DG (2017) Microbial indicators as a diagnostic tool for assessing water quality and climate stress in coral reef ecosystems. Mar Biol 164:91. https://doi.org/10.1007/s00227-017-3097-x
Hallock P, Lidz BH, Cockey-Burkhard EM, Donnelly KB (2003) Foraminifera as bioindicators in coral reef assessment and monitoring: the foram index. Environ Monit Assess 81:221–238
Hamady M, Knight R (2009) Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res 19:1141–1152. https://doi.org/10.1101/gr.085464.108
Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013) High-resolution global maps of 21st-century forest cover change. Science 342:850–853. https://doi.org/10.1126/science.1244693
Harwood VJ, Staley C, Badgley BD, Borges K, Korajkic A (2014) Microbial source tracking markers for detection of fecal contamination in environmental waters: relationships between pathogens and human health outcomes. FEMS Microbiol Rev 38:1–40. https://doi.org/10.1111/1574-6976.12031
Head IM, Jones DM, Röling WFM (2006) Marine microorganisms make a meal of oil. Nat Rev Microbiol 4:173–182. https://doi.org/10.1038/nrmicro1348
Hermans SM, Buckley HL, Case BS, Curran-Cournane F, Taylor M, Lear G (2017) Bacteria as emerging indicators of soil condition. Appl Environ Microbiol 83:e02826–e02816
Ho TK (1995) Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition (1):278–282. https://doi.org/10.1109/ICDAR.1995.598994
Holt EA, Miller SW (2010) Bioindicators: using organisms to measure environmental impacts. Nat Educ Knowl 3:1–8
Hubbard CJ, Brock MT, Van Diepen LT, Maignien L, Ewers BE, Weinig C (2018) The plant circadian clock influences rhizosphere community structure and function. ISME J 12:400–410. https://doi.org/10.1038/ismej.2017.172
Izmalkova TY, Gafarov AB, Sazonova OI, Sokolov SL, Kosheleva IA, Boronin AM (2018) Diversity of oil-degrading microorganisms in the Gulf of Finland (Baltic Sea) in spring and in summer. Microbiology 87:261–271. https://doi.org/10.1134/S0026261718020054
Johnson HR, Trinidad DD, Guzman S, Khan Z, Parziale JV, DeBruyn JM, Lents NH (2016) A machine learning approach for using the postmortem skin microbiome to estimate the postmortem interval. PLoS One 11:e0167370. https://doi.org/10.1371/journal.pone.0167370
Karr JR, Dudley DR (1981) Ecological perspective on water quality goals. Environmental Management 5 (1):55-68. https://doi.org/10.1007/BF01866609
Kaufmann F, Lovley DR (2001) Isolation and characterization of a soluble NADPH-dependent Fe (III) reductase from Geobacter sulfurreducens. J Bacteriol 183:4468–4476. https://doi.org/10.1128/JB.183.15.4468-4476.2001
Kim H, Kaown D, Mayer B, Lee JY, Hyun Y, Lee KK (2015) Identifying the sources of nitrate contamination of groundwater in an agricultural area (Haean basin, Korea) using isotope and microbial community analyses. Sci Total Environ 533:566–575. https://doi.org/10.1016/j.scitotenv.2015.06.080
Knights D, Costello EK, Knight R (2011) Supervised classification of human microbiota. FEMS Microbiol Rev 35:343–359. https://doi.org/10.1111/j.1574-6976.2010.00251.x
Kostka JE, Prakash O, Overholt WA, Green SJ, Freyer G, Canion A, Delgardio J, Norton N, Hazen TC, Huettel M (2011) Hydrocarbon-degrading bacteria and the bacterial community response in Gulf of Mexico beach sands impacted by the Deepwater Horizon oil spill. Appl Environ Microbiol 77:7962–7974. https://doi.org/10.1128/AEM.05402-11
Laroche O, Wood SA, Tremblay LA, Ellis JI, Lear G, Pochon X (2018) A cross-taxa study using environmental DNA/RNA metabarcoding to measure biological impacts of offshore oil and gas drilling and production operations. Mar Pollut Bull 127:97–107. https://doi.org/10.1016/j.marpolbul.2017.11.042
Lau KEM, Washington VJ, Fan V, Neale MW, Lear G, Curran J, Lewis GD (2015) A novel bacterial community index to assess stream ecological health. Freshw Biol 60:1988–2002. https://doi.org/10.1111/fwb.12625
Lear G, Song B, Gault AG, Polya DA, Lloyd JR (2007) Molecular analysis of arsenate-reducing bacteria within Cambodian sediments following amendment with acetate. Appl Environ Microbiol 73:1041–1048. https://doi.org/10.1128/AEM.01654-06
Lear G, Niyogi D, Harding J, Dong Y, Lewis G (2009) Biofilm bacterial community structure in streams affected by acid mine drainage. Applied and Environmental Microbiology 75 (11):3455-3460. https://doi.org/10.1128/AEM.00274-09
Ley RE, Lozupone CA, Hamady M, Knight R, Gordon JI (2008) Worlds within worlds: evolution of the vertebrate gut microbiota. Nat Rev Microbiol 6:776–788. https://doi.org/10.1038/nrmicro1978
Li X, Wang H, Hu X, Hu C, Liao L (2016) Characteristics of corrosion sales and biofilm in aged pipe distribution systems with switching water source. Eng Fail Anal 60:166–175. https://doi.org/10.1016/j.engfailanal.2015.11.048
Liao H, Zheng C, Li J, Long J (2018) Dynamics of soil microbial recovery from cropland to orchard along a 20-year chronosequence in a degraded karst ecosystem. Science of The Total Environment 639:1051-1059. https://doi.org/10.1016/j.scitotenv.2018.05.246
Liu M, Fan L, Zhong L, Kjelleberg S, Thomas T (2012) Metaproteogenomic analysis of a community of sponge symbionts. ISME J 6:1515–1525
Liu Y, Delgado-baquerizo M, Bi L, Zhu J, He, J (2018) Consistent responses of soil microbial taxonomic and functional attributes to mercury pollution across China. Microbiome 6: 183.https://doi.org/10.1186/s40168-018-0572-7
Logares R, Lindström ES, Langenheder S, Logue JB, Paterson H, Laybourn-Parry J, Rengefors K, Tranvik L, Bertilsson S (2013) Biogeography of bacterial communities exposed to progressive long-term environmental change. ISME J 7:937–948. https://doi.org/10.1038/ismej.2012.168
Longford SR, Campbell AH, Nielsen S, Case RJ, Kjelleberg S, Steinberg PD (2019) Interactions within the microbiome alter microbial interactions with host chemical defences and affect disease in a marine holobiont. Sci Rep 9:1363. https://doi.org/10.1038/s41598-018-37062-z
Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 71:8228–8235. https://doi.org/10.1128/AEM.71.12.8228
Martiny JBH, Eisen JA, Penn K, Allison SD, Horner-Devine MC (2011) Drivers of bacterial β-diversity depend on spatial scale. PNAS 108:7850–7854. https://doi.org/10.1073/pnas.1016308108
McGeoch MA, Chown SL (1998) Scaling up the bioindicators. Trends Ecol Evol 13:46–47
McQuaig SM, Scott TM, Harwood VJ, Farrah SR, Lukasik JO (2006) Detection of human-derived fecal pollution in environmental waters by use of a PCR-based human polyomavirus assay. Appl Environ Microbiol 72:7567–7574. https://doi.org/10.1128/AEM.01317-06
Mills HJ, Hunter E, Humphrys M, Kerkhof L, McGuinness L, Huettel M, Kostka JE (2008) Characterization of nitrifying, denitrifying, and overall bacterial communities in permeable marine sediments of the northeastern Gulf of Mexico. Appl Environ Microbiol 74:4440–4453. https://doi.org/10.1128/AEM.02692-07
Moitinho-Silva L, Steinert G, Nielsen S, Hardoim CCP, Wu Y-C, McCormack GP, López-Legentil S, Marchant R, Webster N, Thomas T, Hentschel U (2017) Predicting the HMA-LMA status in marine sponges by machine learning. Front Microbiol 8:752. https://doi.org/10.3389/fmicb.2017.00752
Møller AK, Barkay T, Hansen MA, Norman A, Hansen LH, Sørensen SJ, Boyd ES, Kroer N (2014) Mercuric reductase genes (merA) and mercury resistance plasmids in high Arctic snow, freshwater and sea-ice brine. FEMS Microbiol Ecol 87:52–63. https://doi.org/10.1111/1574-6941.12189
Moran MA (2009) Metatranscriptomics: eavesdropping on complex microbial communities. Microbe 4:329–335. https://doi.org/10.1016/j.jbiotec.2009.03.022
Morrow KM, Fiore CL, Lesser MP (2016) Environmental drivers of microbial community shifts in the giant barrel sponge, Xestospongia muta, over a shallow to mesophotic depth gradient. Environ Microbiol 18:2025–2038. https://doi.org/10.1111/1462-2920.13226
Nath S, Deb B, Sharma I (2018) Isolation of toxic metal-tolerant bacteria from soil and examination of their bioaugmentation potentiality by pot studies in cadmium- and lead-contaminated soil. Int Microbiol 21:35–45. https://doi.org/10.1007/s10123-018-0003-4
Newton RJ, Huse SM, Morrison HG, Peake CS, Sogin ML, McLellan SL (2013) Shifts in the microbial community composition of gulf coast beaches following beach oiling. PLoS One 8:e74265. https://doi.org/10.1371/journal.pone.0074265
Ní Chadhain SM, Miller JL, Dustin JP, Trethewey JP, Jones SH, Launen LA (2018) An assessment of the microbial community in an urban fringing tidal marsh with an emphasis on petroleum hydrocarbon degradative genes. Mar Pollut Bull 136:351–364. https://doi.org/10.1016/j.marpolbul.2018.09.002
Nie Y, Chi CQ, Fang H, Liang JL, Lu SL, Lai GL, Tang YQ, Wu XL (2014) Diverse alkane hydroxylase genes in microorganisms and environments. Sci Rep 4:4968. https://doi.org/10.1038/srep04968
Nielsen MN, Winding A (2002) Microorganisms as indicators of soil health. Neri Tech. Rep. 388:1–85
Nir Y, Aviv R (2011) Impulse control: temporal dynamics in gene transcription. Cell 144:886–896. https://doi.org/10.1016/j.cell.2011.02.015 Impulse
Nunes I, Jacquiod S, Brejnrod A, Holm PE, Johansen A, Brandt KK, Priemé A, Sørensen SJ (2016) Coping with copper: legacy effect of copper on potential activity of soil bacteria following a century of exposure. FEMS Microbiol Ecol 92:fiw175. https://doi.org/10.1093/femsec/fiw17
Nyholm SV, McFall-Ngai MJ (2004) The winnowing: establishing the squid-Vibrio symbiosis. Nat Rev Microbiol 2:632–642. https://doi.org/10.1038/nrmicro957
Odonkor ST, Ampofo JK (2013) Escherichia coli as an indicator of bacteriological quality of water: an overview. Microbiol Res 4(e2):5–11. https://doi.org/10.4081/mr.2013.e2
Orgiazzi A, Ballabio C, Panagos P, Jones A, Fernández-Ugalde O (2018) LUCAS soil, the largest expandable soil dataset for Europe: a review. Eur J Soil Sci 69:140–153. https://doi.org/10.1111/ejss.12499
Panke-Buisse K, Poole AC, Goodrich JK, Ley RE, Kao-Kniffin J (2015) Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J 9:980–989. https://doi.org/10.1038/ismej.2014.196
Payne RJ, Dise NB, Stevens CJ, Gowing DJ, Partners B (2013) Impact of nitrogen deposition at the species level. PNAS 110:984–987. https://doi.org/10.1073/pnas.1214299109
Piotrowska-Seget Z, Cycoń M, Kozdrój J (2005) Metal-tolerant bacteria occurring in heavily polluted soil and mine spoil. Appl Soil Ecol 28:237–246. https://doi.org/10.1016/j.apsoil.2004.08.001
Qian X, Chen L, Guo X, He D, Shi M, Zhang D (2018) Shifts in community composition and co-occurrence patterns of phyllosphere fungi inhabiting Mussaenda shikokiana along an elevation gradient. PeerJ 6:e5767. https://doi.org/10.7717/peerj.5767
Quinn GP, Keough MJ (2002) Experimental design and data analysis for biologist. Cambridge University Press, Cambridge
R Core Team (2014). R: A language and environment for statistical computing [Internet], 2014. Available from https://doi.org/10.1007/978-3-540-74686-7
Raina JB, Tapiolas D, Willis BL, Bourne DG (2009) Coral-associated bacteria and their role in the biogeochemical cycling of sulfur. Appl Environ Microbiol 75:3492–3501. https://doi.org/10.1128/AEM.02567-08
Ranjard L, Dequiedt S, Jolivet C, Saby NPA, Thioulouse J, Harmand J, Loisel P, Rapaport A, Fall S, Simonet P, Joffre R, Bouré NC-P, Maron P-A, Mougel C, Martin MP, Toutain B, Arrouays D, Lemanceau P (2010) Biogeography of soil microbial communities: a review and a description of the ongoing French national initiative. Agron Sustain Dev 30:359–365. https://doi.org/10.1051/agro/2009033
Reis MP, Dias MF, Costa PS, Ávila MP, Leite LR, de Araújo FMG, Salim ACM, Bucciarelli-Rodriguez M, Oliveira G, Chartone-Souza E, Nascimento AMA (2016) Metagenomic signatures of a tropical mining-impacted stream reveal complex microbial and metabolic networks. Chemosphere 161:266–273. https://doi.org/10.1016/j.chemosphere.2016.06.097
Ross DE, Ruebush SS, Brantley SL, Hartshorne RS, Clarke TA, Richardson DJ, Tien M (2007) Characterization of protein-protein interactions involved in iron reduction by Shewanella oneidensis MR-1. Appl Environ Microbiol 73:5797–5808. https://doi.org/10.1128/aem.00146-07
Saulnier DM, Riehle K, Mistretta T, Diaz M, Mandal D, Raza S, Weidler EM, Qin X, Coarfa C, Milosavljevic A, Petrosino JF, Highlander S, Gibbs R, Lynch SV, Shulman RJ, Versalovic J (2011) Gastrointestinal microbiome signatures of pediatric patients with irritable bowel syndrome. Gastroenterology 141:1782–1791. https://doi.org/10.1053/j.gastro.2011.06.072
Schröder I, Johnson E, De Vries S (2003) Microbial ferric iron reductases. FEMS Microbiol Rev 27:427–447. https://doi.org/10.1016/S0168-6445(03)00043-3
Sedláček V, van Spanning RJM, Kučera I (2009) Ferric reductase A is essential for effective iron acquisition in Paracoccus denitrificans. Microbiology 155:1294–1301. https://doi.org/10.1099/mic.0.022715-0
Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C (2011) Metagenomic biomarker discovery and explanation. Genome Biol 12:R60. https://doi.org/10.1186/gb-2011-12-6-r60
Shanks OC, Kelty CA, Sivaganesan M, Varma M, Haugland RA (2009) Quantitative PCR for genetic markers of human fecal pollution. Appl Environ Microbiol 75:5507–5513. https://doi.org/10.1128/AEM.00305-09
Shosuke Y, Kasumi H, Toshihiko T, Ikuo T, Hironao Y, Yasuhito M, Kiyotsuna T, Kenji M, YoshiharuK K, Kohei O (2016) A bacterium that degrades and assimilates poly(ethylene terephthalate). Science 351:1196–1199. https://doi.org/10.1126/science.aad6359
Smith MB, Rocha AM, Smillie CS, Olesen SW, Paradis C, Wu L, Campbell JH, Fortney JL, Mehlhorn TL, Lowe KA, Earles JE, Phillips J, Techtmann SM, Joyner DC, Elias DA, Bailey KL, Hurt RA, Preheim SP, Sanders MC, Yang J, Mueller MA, Brooks S, Watson DB, Zhang P, He Z, Dubinsky EA, Adams PD, Arkin AP, Fields MW, Zhou J, Alm EJ, Hazen TC, Lindow SE (2015) Natural Bacterial Communities Serve as Quantitative Geochemical Biosensors. mBio 6: e00326-15. https://doi.org/10.1128/mBio.00326-15
Smith MJ, Kay WR, Edward DHD, Papas PJ, Richardson KSJ, Simpson JC, Pinder AM, Cale DJ, Horwitz PHJ, Davis JA, Yung FH, Norris RH, Halse SA (1999) AusRivAS: using macroinvertebrates to assess ecological condition of rivers in Western Australia. Freshw Biol 41:269–282. https://doi.org/10.1046/j.1365-2427.1999.00430.x
Šrut M, Menke S, Höckner M, Sommer S (2019) Earthworms and cadmium – heavy metal resistant gut bacteria as indicators for heavy metal pollution in soils? Ecotoxicol Environ Saf 171:843–853. https://doi.org/10.1016/j.ecoenv.2018.12.102
Stork NE, Samways MJ, Eeley HAC (1996) Inventorying and monitoring biodiversity. Trends Ecol Evol 11:39–40. https://doi.org/10.1016/0169-5347(96)81070-6
Sun MY, Dafforn KA, Johnston EL, Brown MV (2013) Core sediment bacteria drive community response to anthropogenic contamination over multiple environmental gradients. Environ Microbiol 15:2517–2531. https://doi.org/10.1111/1462-2920.12133
Teitzel GM, Parsek MR (2003) Heavy metal resistance of biofilm and planktonic Pseudomonas aeruginosa. Appl Environ Microbiol 69:2313–2320. https://doi.org/10.1128/AEM.69.4.2313
Terrat S, Horrigue W, Dequietd S, Saby NPA, Lelièvre M, Nowak V, Tripied J, Régnier T, Jolivet C, Arrouays D, Wincker P, Cruaud C, Karimi B, Bispo A, Maron PA, Prévost-Bouré NC, Ranjard L (2017) Mapping and predictive variations of soil bacterial richness across France. PLoS One 12:e0186766. https://doi.org/10.1371/journal.pone.0186766
Thomas T, Gilbert J, Meyer F (2012) Metagenomics - a guide from sampling to data analysis. Microb Inform Exp 2:3. https://doi.org/10.1186/2042-5783-2-3
Thomas T, Moitinho-Silva L, Lurgi M, Björk JR, Easson C, Astudillo-García C, Olson JB, Erwin PM, López-Legentil S, Luter H, Chaves-Fonnegra A, Costa R, Schupp PJ, Steindler L, Erpenbeck D, Gilbert J, Knight R, Ackermann G, Victor Lopez J, Taylor MW, Thacker RW, Montoya JM, Hentschel U, Webster NS (2016) Diversity, structure and convergent evolution of the global sponge microbiome. Nat Commun 7:11870. https://doi.org/10.1038/ncomms11870
Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, Prill RJ, Tripathi A, Gibbons SM, Ackermann G, Navas-Molina JA, Janssen S, Kopylova E, Vázquez-Baeza Y, González A, Morton JT, Mirarab S, Xu ZZ, Jiang L, Haroon MF, Kanbar J, Zhu Q, Song SJ, Kosciolek T, Bokulich NA, Lefler J, Brislawn CJ, Humphrey G, Owens SM, Hampton-Marcell J, Berg-Lyons D, McKenzie V, Fierer N, Fuhrman JA, Clauset A, Stevens RL, Shade A, Pollard KS, Goodwin KD, Jansson JK, Gilbert JA, Knight R, Agosto Rivera JL, Al-Moosawi L, Alverdy J, Amato KR, Andras J, Angenent LT, Antonopoulos DA, Apprill A, Armitage D, Ballantine K, Bárta J, Baum JK, Berry A, Bhatnagar A, Bhatnagar M, Biddle JF, Bittner L, Boldgiv B, Bottos E, Boyer DM, Braun J, Brazelton W, Brearley FQ, Campbell AH, Caporaso JG, Cardona C, Carroll JL, Cary SC, Casper BB, Charles TC, Chu H, Claar DC, Clark RG, Clayton JB, Clemente JC, Cochran A, Coleman ML, Collins G, Colwell RR, Contreras M, Crary BB, Creer S, Cristol DA, Crump BC, Cui D, Daly SE, Davalos L, Dawson RD, Defazio J, Delsuc F, Dionisi HM, Dominguez-Bello MG, Dowell R, Dubinsky EA, Dunn PO, Ercolini D, Espinoza RE, Ezenwa V, Fenner N, Findlay HS, Fleming ID, Fogliano V, Forsman A, Freeman C, Friedman ES, Galindo G, Garcia L, Garcia-Amado MA, Garshelis D, Gasser RB, Gerdts G, Gibson MK, Gifford I, Gill RT, Giray T, Gittel A, Golyshin P, Gong D, Grossart HP, Guyton K, Haig SJ, Hale V, Hall RS, Hallam SJ, Handley KM, Hasan NA, Haydon SR, Hickman JE, Hidalgo G, Hofmockel KS, Hooker J, Hulth S, Hultman J, Hyde E, Ibáñez-Álamo JD, Jastrow JD, Jex AR, Johnson LS, Johnston ER, Joseph S, Jurburg SD, Jurelevicius D, Karlsson A, Karlsson R, Kauppinen S, Kellogg CTE, Kennedy SJ, Kerkhof LJ, King GM, Kling GW, Koehler AV, Krezalek M, Kueneman J, Lamendella R, Landon EM, Lanede Graaf K, LaRoche J, Larsen P, Laverock B, Lax S, Lentino M, Levin II, Liancourt P, Liang W, Linz AM, Lipson DA, Liu Y, Lladser ME, Lozada M, Spirito CM, MacCormack WP, MacRae-Crerar A, Magris M, Martín-Platero AM, Martín-Vivaldi M, Martínez LM, Martínez-Bueno M, Marzinelli EM, Mason OU, Mayer GD, McDevitt-Irwin JM, McDonald JE, McGuire KL, McMahon KD, McMinds R, Medina M, Mendelson JR, Metcalf JL, Meyer F, Michelangeli F, Miller K, Mills DA, Minich J, Mocali S, Moitinho-Silva L, Moore A, Morgan-Kiss RM, Munroe P, Myrold D, Neufeld JD, Ni Y, Nicol GW, Nielsen S, Nissimov JI, Niu K, Nolan MJ, Noyce K, O’Brien SL, Okamoto N, Orlando L, Castellano YO, Osuolale O, Oswald W, Parnell J, Peralta-Sánchez JM, Petraitis P, Pfister C, Pilon-Smits E, Piombino P, Pointing SB, Pollock FJ, Potter C, Prithiviraj B, Quince C, Rani A, Ranjan R, Rao S, Rees AP, Richardson M, Riebesell U, Robinson C, Rockne KJ, Rodriguezl SM, Rohwer F, Roundstone W, Safran RJ, Sangwan N, Sanz V, Schrenk M, Schrenzel MD, Scott NM, Seger RL, Seguinorlando A, Seldin L, Seyler LM, Shakhsheer B, Sheets GM, Shen C, Shi Y, Shin H, Shogan BD, Shutler D, Siegel J, Simmons S, Sjöling S, Smith DP, Soler JJ, Sperling M, Steinberg PD, Stephens B, Stevens MA, Taghavi S, Tai V, Tait K, Tan CL, Taş N, Taylor DL, Thomas T, Timling I, Turner BL, Urich T, Ursell LK, Van Der Lelie D, Van Treuren W, Van Zwieten L, Vargas-Robles D, Thurber RV, Vitaglione P, Walker DA, Walters WA, Wang S, Wang T, Weaver T, Webster NS, Wehrle B, Weisenhorn P, Weiss S, Werner JJ, West K, Whitehead A, Whitehead SR, Whittingham LA, Willerslev E, Williams AE, Wood SA, Woodhams DC, Yang Y, Zaneveld J, Zarraonaindia I, Zhang Q, Zhao H (2017) A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551:457–463. https://doi.org/10.1038/nature24621
Tiquia SM (2008) Diversity of sulfate-reducing genes (dsrAB) in sediments from Puget Sound. Environ Technol 29:1095–1108. https://doi.org/10.1080/09593330802190608
Torsvik V, Torsvik V, Øvreås L, Thingstad TF (2002) Prokaryotic diversity — magnitude, dynamics, and controlling factors. Science 296:1064–1067. https://doi.org/10.1126/science.1071698
Tourna M, Maclean P, Condron L, O’Callaghan M, Wakelin SA (2014) Links between sulphur oxidation and sulphur-oxidising bacteria abundance and diversity in soil microcosms based on soxB functional gene analysis. FEMS Microbiol Ecol 88:538–549. https://doi.org/10.1111/1574-6941.12323
Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, Wu D, Paulsen I, Nelson KE, Nelson W, Fouts DE, Levy S, Knap AH, Lomas MW, Nealson K, White O, Peterson J, Hoffman J, Parsons R, Baden-Tillson H, Pfannkoch C, Rogers Y-H, Smith HO (2004) Environmental genome shotgun sequencing of the Sargasso Sea. Science 304:66–74
Wagner M, Loy A, Klein M, Lee N, Ramsing NB, Stahl DA, Friedrich MW (2005) Functional marker genes for identification of sulfate-reducing prokaryotes. Methods Enzymol 397:469–489. https://doi.org/10.1016/S0076-6879(05)97029-8
Wang J, Muyzer G, Bodelier PLE, Laanbroek HJ (2009) Diversity of iron oxidizers in wetland soils revealed by novel 16S rRNA primers targeting Gallionella-related bacteria. ISME J 3:715–725. https://doi.org/10.1038/ismej.2009.7
Wang Z, Zhang XX, Lu X, Liu B, Li Y, Long C, Li A (2014) Abundance and diversity of bacterial nitrifiers and denitrifiers and their functional genes in tannery wastewater treatment plants revealed by high-throughput sequencing. PLoS One 9:e113603. https://doi.org/10.1371/journal.pone.0113603
Weyens N, van der Lelie D, Taghavi S, Vangronsveld J (2009) Phytoremediation: plant-endophyte partnerships take the challenge. Curr Opin Biotechnol 20:248–254. https://doi.org/10.1016/j.copbio.2009.02.012
Wright JF (1995) Development and use of a system for predicting the macroinvertebrate fauna in flowing waters. Aust J Ecol 20:181–197. https://doi.org/10.1111/j.1442-9993.1995.tb00531.x
Wu S, Li Y, Wang P, Zhong L, Qiu L, Chen J (2016) Shifts of microbial community structure in soils of a photovoltaic plant observed using tag-encoded pyrosequencing of 16S rRNA. Appl Microbiol Biotechnol 100:3735–3745. https://doi.org/10.1007/s00253-015-7219-4
Wu H, Li Y, Zhang J, Niu L, Zhang W, Cai W, Zhu X (2017) Sediment bacterial communities in a eutrophic lake influenced by multiple inflow-rivers. Environ Sci Pollut Res 24:19795–19806. https://doi.org/10.1007/s11356-017-9602-4
WWF (2018) Living planet report - 2018: aiming higher. WWF, Gland, Switzerland
Yakimov MM, Timmis KN, Golyshin PN (2007) Obligate oil-degrading marine bacteria. Curr Opin Biotechnol 18:257–266. https://doi.org/10.1016/j.copbio.2007.04.006
Zhang H, Wan Z, Ding M, Wang P, Xu X, Jiang Y (2018) Inherent bacterial community response to multiple heavy metals in sediment from river-lake systems in the Poyang Lake, China. Ecotoxicology and Environmental Safety 165:314-324. https://doi.org/10.1016/j.ecoenv.2018.09.010
Zhang X, Tang S, Wang M, Sun W, Xie Y, Peng H, Zhong A, Liu H, Zhang X, Yu H (2019) Acid mine drainage affects the diversity and metal resistance gene profile of sediment bacterial community along a river. Chemosphere 217:790–799. https://doi.org/10.1016/j.chemosphere.2018.10.210
Zou H, Hastie T (2017) Regularization and variable selection via the elastic net. J R Stat Soc B 67(Part 2):301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x@10.1111/(ISSN)1467-9868.TOP_SERIES_B_RESEARCH
Funding
Funding for this review was provided by a University of Auckland FRDF grant (to GL) and the MBIE Endeavour programme C09X1613 (to BS).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical statement
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
ESM 1
(PDF 875 kb)
Rights and permissions
About this article
Cite this article
Astudillo-García, C., Hermans, S.M., Stevenson, B. et al. Microbial assemblages and bioindicators as proxies for ecosystem health status: potential and limitations. Appl Microbiol Biotechnol 103, 6407–6421 (2019). https://doi.org/10.1007/s00253-019-09963-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00253-019-09963-0