A large-scale assessment of lakes reveals a pervasive signal of land use on bacterial communities

Abstract

Lakes play a pivotal role in ecological and biogeochemical processes and have been described as “sentinels” of environmental change. Assessing “lake health” across large geographic scales is critical to predict the stability of their ecosystem services and their vulnerability to anthropogenic disturbances. The LakePulse research network is tasked with the assessment of lake health across gradients of land use on a continental scale. Bacterial communities are an integral and rapidly responding component of lake ecosystems, yet large-scale responses to anthropogenic activity remain elusive. Here, we assess the ecological impact of land use on bacterial communities from over 200 lakes covering more than 660,000 km2 across Eastern Canada. In addition to community variation between ecozones, land use across Eastern Canada also appeared to alter diversity, community composition, and network structure. Specifically, increasing anthropogenic impact within the watershed lowered diversity. Likewise, community composition was significantly correlated with agriculture and urban development within a watershed. Interaction networks showed decreasing complexity and fewer keystone taxa in impacted lakes. Moreover, we identified potential indicator taxa of high or low lake water quality. Together, these findings point to detectable bacterial community changes of largely unknown consequences induced by human activity within lake watersheds.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Bacterial community composition of four ecozones across Eastern Canada.
Fig. 2: Graphical representation of the structural equation model testing the impact of human impact index (HII) on environmental principal components (PCs) and in turn on Shannon–Weaver diversity.
Fig. 3: Plot of the distance-based redundancy analysis (db-RDA) coordinates of lake communities.
Fig. 4: Co-occurrence networks of high, moderate, and low-impact lake communities.

References

  1. 1.

    Adrian R, O’Reilly CM, Zagarese H, Baines SB, Hessen DO, Keller W, et al. Lakes as sentinels of climate change. Limnol Oceanogr. 2009;54:2283–97.

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Tranvik LJ, Downing JA, Cotner JB, Loiselle SA, Striegl RG, Ballatore TJ, et al. Lakes and reservoirs as regulators of carbon cycling and climate. Limnol Oceanogr. 2009;54:2298–314.

    CAS  Google Scholar 

  3. 3.

    Arbuckle KE, Downing JA. The influence of watershed land use on lake N: P in a predominantly agricultural landscape. Limnol Oceanogr. 2001;46:970–5.

    Google Scholar 

  4. 4.

    Taranu ZE, Gregory-Eaves I. Quantifying relationships among phosphorus, agriculture, and lake depth at an inter-regional scale. Ecosystems. 2008;11:715–25.

    CAS  Google Scholar 

  5. 5.

    Heisler J, Glibert PM, Burkholder JM, Anderson DM, Cochlan W, Dennison WC, et al. Eutrophication and harmful algal blooms: a scientific consensus. Harmful Algae. 2008;8:3–13.

    PubMed  PubMed Central  CAS  Google Scholar 

  6. 6.

    Scavia D, David Allan J, Arend KK, Bartell S, Beletsky D, Bosch NS, et al. Assessing and addressing the re-eutrophication of Lake Erie: Central basin hypoxia. J Gt Lakes Res. 2014;40:226–46.

    CAS  Google Scholar 

  7. 7.

    Bastviken D, Cole J, Pace M, Tranvik L. Methane emissions from lakes: dependence of lake characteristics, two regional assessments, and a global estimate. Glob Biogeochem Cycles. 2004;18:1–12.

    Google Scholar 

  8. 8.

    Novotny EV, Murphy D, Stefan HG. Increase of urban lake salinity by road deicing salt. Sci Total Environ. 2008;406:131–44.

    PubMed  CAS  Google Scholar 

  9. 9.

    Dugan HA, Bartlett SL, Burke SM, Doubek JP, Krivak-Tetley FE, Skaff NK, et al. Salting our freshwater lakes. Proc Natl Acad Sci USA. 2017;114:4453–8.

    PubMed  CAS  Google Scholar 

  10. 10.

    Hobbie SE, Finlay JC, Janke BD, Nidzgorski DA, Millet DB, Baker LA. Contrasting nitrogen and phosphorus budgets in urban watersheds and implications for managing urban water pollution. Proc Natl Acad Sci. 2017;114:4177–82.

    PubMed  CAS  Google Scholar 

  11. 11.

    Shade A, Kent AD, Jones SE, Newton RJ, Triplett EW, McMahon KD. Interannual dynamics and phenology of bacterial communities in a eutrophic lake. Limnol Oceanogr. 2007;52:487–94.

    CAS  Google Scholar 

  12. 12.

    Kara EL, Hanson PC, Hu YH, Winslow L, McMahon KD. A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic Lake Mendota, WI, USA. ISME J. 2013;7:680–4.

    PubMed  Google Scholar 

  13. 13.

    Marmen S, Blank L, Al-Ashhab A, Malik A, Ganzert L, Lalzar M, et al. The role of land use types and water chemical properties in structuring the microbiome of a connected lake system. Front Microbiol. 2020;11:1–16.

    Google Scholar 

  14. 14.

    Environment Canada Whole organism responses and intersex severity in rainbow darter (Etheostoma caeruleum) following exposures to municipal wastewater in the Grand River basin, ON, Canada. Part A, Municipal Water Use Rep. 2011;159:2011–301.

    Google Scholar 

  15. 15.

    Huot Y, Brown CA, Potvin G, Antoniades D, Baulch HM, Beisner BE, et al. The NSERC Canadian Lake Pulse Network: a national assessment of lake health providing science for water management in a changing climate. Sci Total Environ. 2019;695:133668.

    PubMed  CAS  Google Scholar 

  16. 16.

    Lu Y, Wang R, Zhang Y, Su H, Wang P, Jenkins A, et al. Ecosystem health towards sustainability. Ecosyst Heal Sustain. 2015;1:1–15.

    Google Scholar 

  17. 17.

    Hering D, Borja A, Carvalho L, Feld CK. Assessment and recovery of European water bodies: Key messages from the WISER project. Hydrobiologia 2013;704:1–9.

    Google Scholar 

  18. 18.

    U.S. Environmental Protection Agency. National Lake Assessment: a collaborative survey of the Nation’s Lakes. Washington, DC: EPA 841-R-09-001; 2009.

  19. 19.

    Ecological Stratification Working Group. A national ecological framework for Canada. Urbana-Champaign, Illinois: Ecological Stratification Working Group; 1996.

  20. 20.

    Glaz P, Gagné JP, Archambault P, Sirois P, Nozais C. Impact of forest harvesting on water quality and fluorescence characteristics of dissolved organic matter in eastern Canadian Boreal Shield lakes in summer. Biogeosciences. 2015;12:6999–7011.

    Google Scholar 

  21. 21.

    Patton C, Kryskalla J. Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory—evaluation of alakline digestion as an alternative to kjedahl digestion for determination of total and dissolved nitrogen and phosphorous. Denver, Colorado: Water-Resources Investigations Report 03; 2003.

  22. 22.

    U.S. Environmental Protection Agency. Method 200.7: determination of metals and trace elements in water and wastes by inductively coupled plasma-atomic emission spectrometry. Cincinatti, Ohio: U.S. Environmental Protection Agency; 1994.

  23. 23.

    U.S. Environmental Protection Agency. Method 300.1: determination of inorganic anions in drinking water by ion chromatography. Cincinatti, Ohio; 1997.

  24. 24.

    Wu Y. Barcode Demultiplex for Illumina I1, R1, R2 fastq.gz files. 2014.

  25. 25.

    Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10.

    Google Scholar 

  26. 26.

    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.

    PubMed  PubMed Central  CAS  Google Scholar 

  27. 27.

    Rohwer RR, Hamilton JJ, Newton RJ, McMahon KD. TaxAss: leveraging a custom freshwater database achieves fine-scale taxonomic resolution. mSphere. 2018;3:1–14.

    Google Scholar 

  28. 28.

    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.

    Google Scholar 

  29. 29.

    McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.

    PubMed  PubMed Central  CAS  Google Scholar 

  30. 30.

    Price MN, Dehal PS, Arkin AP. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Dray S, Dufour A-B. The ade4 Package: implementing the duality diagram for ecologists. J Stat Softw. 2007;22:1–20.

    Google Scholar 

  32. 32.

    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, Mcglinn D, et al. Vegan: community ecology package. 2016. https://cran.r-project.org; https://github.com/vegandevs/vegan.

  33. 33.

    Hair J, Tatham R, Anderson R, Black W. Multivariate data analysis. 5th ed. London: Prentice-Hall; 1998.

    Google Scholar 

  34. 34.

    R Development Core Team T. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2009.

    Google Scholar 

  35. 35.

    Pinheiro J, Bates D, DebRoy S, Sarkar D, R Development Core Team T. nlme: linear and nonlinear mixed effect models. R package version. 3.1-141; 2019.

  36. 36.

    Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–51.

    Google Scholar 

  37. 37.

    Rosseel Y. Lavaan: an R package for structural equation modeling. J Stat Softw. 2012;48:1–37.

    Google Scholar 

  38. 38.

    Albanese D, Filosi M, Visintainer R, Riccadonna S, Jurman G, Furlanello C. Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers. Bioinformatics. 2013;29:407–8.

    PubMed  CAS  Google Scholar 

  39. 39.

    Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015;11:e1004226.

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Banerjee S, Walder F, Büchi L, Meyer M, Held AY, Gattinger A, et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 2019;13:1722–36.

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6:343–51.

    PubMed  Google Scholar 

  42. 42.

    Stegen JC, Lin X, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 2013;7:2069–79.

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Benlloch S, López-López A, Casamayor EO, Øvreås L, Goddard V, Daae FL, et al. Prokaryotic genetic diversity throughout the salinity gradient of a coastal solar saltern. Environ Microbiol. 2002;4:349–60.

    PubMed  Google Scholar 

  44. 44.

    Abed RMM, Kohls K, De Beer D. Effect of salinity changes on the bacterial diversity, photosynthesis and oxygen consumption of cyanobacterial mats from an intertidal flat of the Arabian Gulf. Environ Microbiol. 2007;9:1384–92.

    PubMed  CAS  Google Scholar 

  45. 45.

    Lozupone CA, Knight R. Global patterns in bacterial diversity. Proc Natl Acad Sci USA. 2007;104:11436–40.

    PubMed  CAS  Google Scholar 

  46. 46.

    Wu QL, Zwart G, Schauer M, Kamst-Van Agterveld MP, Hahn MW. Bacterioplankton community composition along a salinity gradient of sixteen high-mountain lakes located on the Tibetan Plateau, China. Appl Environ Microbiol. 2006;72:5478–85.

    PubMed  PubMed Central  CAS  Google Scholar 

  47. 47.

    Wang J, Yang D, Zhang Y, Shen J, van der Gast C, Hahn MW, et al. Do patterns of bacterial diversity along salinity gradients differ from those observed for macroorganisms? PLoS ONE. 2011;6:e27597.

    PubMed  PubMed Central  CAS  Google Scholar 

  48. 48.

    Kelly VR, Lovett GM, Weathers KC, Findlay SEG, Strayer DL, Burns DJ, et al. Long-term sodium chloride retention in a rural watershed: legacy effects of road salt on streamwater concentration. Environ Sci Technol. 2008;42:410–5.

    PubMed  CAS  Google Scholar 

  49. 49.

    Corsi SR, Graczyk DJ, Geis SW, Booth NL, Richards KD. A fresh look at road salt: aquatic toxicity and water-quality impacts on local, regional, and national scales. Environ Sci Technol. 2010;44:7376–82.

    PubMed  PubMed Central  CAS  Google Scholar 

  50. 50.

    Levine SN, Schindler DW. Influence of nitrogen to phosphorus supply ratios and physicochemical conditions on cyanobacteria and phytoplankton species composition in the Experimental Lakes Area, Canada. Can J Fish Aquat Sci. 1999;56:451–66.

    Google Scholar 

  51. 51.

    Stockner JG, Shortreed KS. Response of Anabaena and Synechococcus to manipulation of nitrogen: phosphorus ratios in a lake fertilization experiment. Limnol Oceanogr. 1988;33:1348–61.

    CAS  Google Scholar 

  52. 52.

    Thad Scott J, McCarthys MJ. Nitrogen fixation may not balance the nitrogen pool in lakes over timescales relevant to eutrophication management. Limnol Oceanogr. 2010;55:1265–70.

    Google Scholar 

  53. 53.

    Håkanson L, Blenckner T, Bryhn AC, Hellström SS. The influence of calcium on the chlorophyll-phosphorus relationship and lake Secchi depths. Hydrobiologia. 2005;537:111–23.

    Google Scholar 

  54. 54.

    Eiler A, Heinrich F, Bertilsson S. Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J. 2012;6:330–42.

    PubMed  CAS  Google Scholar 

  55. 55.

    Peura S, Bertilsson S, Jones RI, Eiler A. Resistant microbial cooccurrence patterns inferred by network topology. Appl Environ Microbiol. 2015;81:2090–7.

    PubMed  PubMed Central  CAS  Google Scholar 

  56. 56.

    Logares R, Tesson SVM, Canbäck B, Pontarp M, Hedlund K, Rengefors K. Contrasting prevalence of selection and drift in the community structuring of bacteria and microbial eukaryotes. Environ Microbiol. 2018;20:2231–40.

    PubMed  Google Scholar 

  57. 57.

    Lindström ES, Kamst-Van Agterveld MP, Zwart G. Distribution of typical freshwater bacterial groups is associated with pH, temperature, and lake water retention time. Appl Environ Microbiol. 2005;71:8201–6.

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75:5111–20.

    PubMed  PubMed Central  CAS  Google Scholar 

  59. 59.

    Xiong J, Liu Y, Lin X, Zhang H, Zeng J, Hou J, et al. Geographic distance and pH drive bacterial distribution in alkaline lake sediments across Tibetan Plateau. Environ Microbiol. 2012;14:2457–66.

    PubMed  PubMed Central  CAS  Google Scholar 

  60. 60.

    Findlay DL, Kasian SEM. Phytoplankton community responses to acidification of lake 223, experimental lakes area, northwestern Ontario. Water Air Soil Pollut. 1986;30:719–26.

    CAS  Google Scholar 

  61. 61.

    Findlay DL, Kasian SEM. The effect of incremental pH recovery on the Lake 223 phytoplankton community. Can J Fish Aquat Sci. 1996;53:856–64.

    Google Scholar 

  62. 62.

    Maberly SC. Diel, episodic and seasonal changes in pH and concentrations of inorganic carbon in a productive lake. Freshw Biol. 2008;35:579–98.

    Google Scholar 

  63. 63.

    Tong Y, Lin G, Ke X, Liu F, Zhu G, Gao G, et al. Comparison of microbial community between two shallow freshwater lakes in middle Yangtze basin, East China. Chemosphere. 2005;60:85–92.

    PubMed  CAS  Google Scholar 

  64. 64.

    Romina Schiaffino M, Unrein F, Gasol JM, Massana R, Balagué V, Izaguirre I. Bacterial community structure in a latitudinal gradient of lakes: the roles of spatial versus environmental factors. Freshw Biol. 2011;56:1973–91.

    Google Scholar 

  65. 65.

    Zeng J, Yang L, Li J, Liang Y, Xiao L, Jiang L, et al. Vertical distribution of bacterial community structure in the sediments of two eutrophic lakes revealed by denaturing gradient gel electrophoresis (DGGE) and multivariate analysis techniques. World J Microbiol Biotechnol. 2009;25:225–33.

    CAS  Google Scholar 

  66. 66.

    Canfield DE, Bachmann RW. Prediction of total phosphorus concentrations, chlorophyll a, and Secchi depths in natural and artificial lakes. Can J Fish Aquat Sci. 1981;38:414–23.

    Google Scholar 

  67. 67.

    Meeuwig JJ, Peters RH. Circumventing phosphorus in lake management: a comparison of chlorophyll a predictions from land-use and phosphorus-loading models. Can J Fish Aquat Sci. 1996;53:1795–806.

    CAS  Google Scholar 

  68. 68.

    Yang L, Lei K, Meng W, Fu G, Yan W. Temporal and spatial changes in nutrients and chlorophyll-α in a shallow lake, Lake Chaohu, China: an 11-year investigation. J Environ Sci (China). 2013;25:1117–23.

    CAS  Google Scholar 

  69. 69.

    Kraemer SA, Soucy JPR, Kassen R. Antagonistic interactions of soil pseudomonads are structured in time. FEMS Microbiol Ecol. 2017;93:1–9.

    CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the genome Quebec and Genome Canada-funded ATRAPP Project (Algal blooms, Treatment, Risk Assessment, Prediction and Prevention) (awarded to BJS), by the NSERC Canadian LakePulse network (Strategic Partnership network NETG 479720-15), by NSERC Discovery Grant #6693-2016 and by the NSERC Canadian Research Chair #230456 (DW) and FQRNT and NSERC/CREATE-GRIL fellowships (NBDC). We thank the coordinators and field team members of the LakePulse 2017 sampling campaign for their efforts. We also would like to thank members of the network, and specifically B. Beisner and V. Fugere, for helpful discussions during the manuscript preparation.

Author information

Affiliations

Authors

Corresponding author

Correspondence to S. A. Kraemer.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kraemer, S.A., Barbosa da Costa, N., Shapiro, B.J. et al. A large-scale assessment of lakes reveals a pervasive signal of land use on bacterial communities. ISME J (2020). https://doi.org/10.1038/s41396-020-0733-0

Download citation

Search