Skip to main content
SpringerLink
Log in

Multi-Level Responses of Yellow Perch (Perca flavescens) to a Whole-Lake Nanosilver Addition Study

  • Published:
Archives of Environmental Contamination and Toxicology Aims and scope Submit manuscript

Abstract

Silver nanoparticles (AgNP) are widely used as antibacterial agents in both commercial products and for industrial applications. As such, AgNP has a high potential for release into freshwater environments. As part of a whole-lake ecosystem experiment to examine the impacts of AgNP exposure at low µg/L concentrations over multiple years, we evaluated biological responses in Yellow Perch (Perca flavescens) before, during, and after AgNP additions to a freshwater lake. Yellow Perch were monitored for responses to in situ AgNP additions at the cellular (suite of biomarkers), individual (growth, prey consumption, and metabolism), and population (abundance and gross prey consumption) scales. At the cellular level, several biomarkers of oxidative stress in liver tissues revealed down-regulation, including decreased mRNA levels of catalase and glutathione peroxidase in Yellow Perch collected during AgNP exposure, and elevated ratios of reduced to oxidized glutathione. At the individual level, Yellow Perch bioenergetic models revealed that prey consumption and total metabolism significantly declined during AgNP additions and remained depressed one year after AgNP addition. At the population level, Yellow Perch densities and gross prey consumption declined after AgNP was added to the lake. Together, these results reveal a holistic assessment of the negative impacts of chronic exposure to environmentally relevant AgNP concentrations (i.e., µg/L) on Yellow Perch at cellular, individual, and population levels.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Availabilityof Data and Material

All data are available from the corresponding author on request.

Code availability

Rcode available from the corresponding author on request.

References

  • Arnason AN, Schwarz CJ, Boyer G (1998) POPAN-5: A data maintenance and analysis system for mark-recapture data. Technical Report, University of Manitoba, Department of Computer Science

  • Asharani PV, Lian WY, Gong Z, Valiyaveettil S (2008) Toxicity of silver nanoparticles in zebrafish models. Nanotech 19:1–8

    Google Scholar 

  • Bacchetta C, Ale A, Simoniello MF, Gervasio S, Davico C, Rossi AS, Desimone MF, Poletta G, López G, Monserrat JM, Casenave J (2017) Genotoxicity and oxidative stress in fish after short-term exposure to silver nanoparticles. Ecol Indicat 76:230–239

    CAS  Google Scholar 

  • Beyers DW, Rice JA, Clements WH, Henry CJ (1999) Estimating physiological cost of chemical exposure: integrating energetics and stress to quantify toxic effects in fish. Can J Fish Aquat Sci 56:814–822

    CAS  Google Scholar 

  • Bilberg K, Malte H, Wang T, Baatrup E (2010) Silver nanoparticles and silver nitrate cause respiratory stress in Eurasian Yellow Perch (Perca fluviatilis). Aquat Toxicol 96:159–165

    CAS  Google Scholar 

  • Bustin SA, Benes VB, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nola T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT (2009) The MIQE guidelines: minimum information for the publication of quantitative real-time PCR experiments. Clin Chem 55:611–622

    CAS  Google Scholar 

  • Buzea C, Pacheco I, Robbie K (2007) Nanomaterials and nanoparticles: sources and toxicity. Biointerphases 2:17–71

    Google Scholar 

  • Cappello T, Brandão F, Guilherme S, Santos MA, Maisano M, Mauceri A, Canário J, Pacheco M, Pereira P (2016) Insights into the mechanisms underlying mercury-induced oxidative stress in gills of wild fish (Liza aurata) combining 1H NMR metabolomics and conventional biochemical assays. Sci Total Environ 548-549:13–24

    CAS  Google Scholar 

  • Carlson C, Schrand AM, Braydich-Stolle LK, Hess KL, Jones RL, Schlager JJ, Hussain SM (2008) Unique cellular interaction of silver nanoparticles: size-dependent generation of reactive oxygen species. J Phys Chem B 112:13608–13619

    CAS  Google Scholar 

  • CCME (2015) Canadian water quality guidelines for the protection of aquatic life: silver. In: Canadian environmental quality guidelines, Canadian Council of Ministers of the Environment, Winnipeg, MB, Canada, p 16

  • Chae YJ, Pham CH, Lee J, Bae E, Yi J, Gu MB (2009) Evaluation of the toxic impact of silver nanoparticles on Japanese medaka (Oryzias latipes). Aquat Toxicol 94:320–327

    CAS  Google Scholar 

  • Colman BP, Espinasse B, Richardson CJ, Matson CW, Lowry GV, Hunt DE, Wiesner MR, Bernhardt ES (2014) Emerging contaminant or an old toxin in disguise? Silver nanoparticle impacts on ecosystems. Environ Sci Technol 48:5229–5236

    CAS  Google Scholar 

  • Conine A (2017) The effects of particulate matter on the fate and toxicity of silver nanoparticles. Dissertation, Trent University

  • Conine AL, Rearick DC, Paterson MJ, Xenopoulos MA, Frost PC (2018) Addition of silver nanoparticles has no long-term effects on natural phytoplankton community dynamics in a boreal lake. Limnol Oceanogr Lett 3(4):311–319

    CAS  Google Scholar 

  • Eckmann R (2004) Overwinter changes in mass and lipid content of Perca fluviatilis and Gymnocephalus cernuus. J Fish Biol 65:1498–1511

    Google Scholar 

  • Farmen E, Mikkelsen HN, Evensen O, Einset J, Heier LS, Rosseland BO, Salbu B, Tollefsen KE, Oughton DH (2012) Acute and sub-lethal effects in juvenile Atlantic salmon exposed to low µg/L concentrations of Ag nanoparticles. Aquat Toxicol 108:78–84

    CAS  Google Scholar 

  • Ferriss BE, Essington TE (2014) Can fish consumption rate estimates be improved by linking bioenergetics and mercury mass balance models? Application to tunas. Ecol Model 272:232–241

    CAS  Google Scholar 

  • Furtado LM, Bundschuh M, Metcalfe CD (2016) Monitoring the fate and transformation of silver nanoparticles in natural waters. B Environ Contam Tox 97:449–455

    CAS  Google Scholar 

  • Garner KL, Suh S, Lenihan HS, Keller AA (2015) Species sensitivity distributions for engineered nanoparticles. Environ Sci Technol 49:5753–5759

    CAS  Google Scholar 

  • Griffitt RJ, Brown-Peterson NJ, Savin DA, Manning CS, Boube I, Ryan RA, Brouwer M (2012) Effects of chronic nanoparticulate silver exposure to adult and juvenile sheepshead minnows (Cyprinodon variegatus). Environ Toxicol Chem 31(1):160–167

    CAS  Google Scholar 

  • Gottschalk F, Sun T, Nowack R (2013) Environmental concentrations of engineered nanomaterials: Review of modeling and analytical studies. Environ Pollut 181:287–300

    CAS  Google Scholar 

  • Hanson PC, Johnson TB, Schindler DE, Kitchell JF (1997) Fish Bioenergetics 3.0. University of Wisconsin, Sea Grant Institute, Technical Report WISCU-T-97-001, Madison, WI, USA

  • Hayes JD, McLellan LI (1999) Glutathione and glutathione-dependent enzymes represent a co-ordinately regulated defence against oxidative stress. Free Radical Res 31:273–300

    CAS  Google Scholar 

  • Hayhurst LD (2018) Bioenergetic evaluation of a whole-lake nanosilver addition on Yellow Perch (Perca flavescens). Dissertation, Lakehead University

  • Isermann DA, Knight CT (2005) A computer program for age-length keys incorporating age assignment to individual fish. NA J Fish Manag 25:1153–1160

    Google Scholar 

  • Kennedy AJ, Hull MS, Bednar AJ, Goss JD, Gunter JC, Bouldin JL, Vikesland PJ, Steevens JA (2010) Fractionating nanosilver: importance for determining toxicity to aquatic test organisms. Environ Sci Technol 44:9571–9577

    CAS  Google Scholar 

  • Kimura DA (1977) Statistical assessment of the age-length key. J Fish Res Board Can 34:317–324

    Google Scholar 

  • Laban G, Nies LF, Turco RF, Bickham JW, Sepúlveda MS (2010) The effects of silver nanoparticles on fathead minnow (Pimephales promelas) embryos. Ecotoxicology 19:185–195

    CAS  Google Scholar 

  • Leadley TA, McLeod AM, Johnson TB, Heath D, Drouillard KG (2015) Uncovering adaptive versus acclimatized alterations in standard metabolic rate in brown bullhead (Ameiurus nebulosus). Can J Fish Aquat Sci 73:973–981

    Google Scholar 

  • Maes GE, Raeymaekers JAM, Hellemans B, Geeraerts C, Parmentier K, De Temmerman L, Volckaert FAM, Belpairte C (2013) Gene transcription reflects poor health status of resident European eel chronically exposed to environmental pollutants. Aquat Toxicol 126:242–255

    CAS  Google Scholar 

  • Maillard J-Y, Hartemann P (2013) Silver as an antimicrobial: facts and gaps in knowledge. Crit Rev Microbiol 39(4):373–383

    CAS  Google Scholar 

  • Martin JD, Colson T-L, Langlois VS, Metcalfe CD (2017a) Biomarkers of exposure to nanosilver and silver accumulation in yellow Perch (Perca flavescens). Environ Toxicol Chem 36:1211–1220

    CAS  Google Scholar 

  • Martin JD, Telgmann L, Metcalfe CD (2017b) A method for preparing silver nanoparticle suspensions in bulk for ecotoxicity testing and ecological risk assessment. Bull Environ Contam Toxicol 98:589–594

    CAS  Google Scholar 

  • Martin JD, Frost PC, Hintelmann H, Newman K, Paterson MJ, Hayhurst L, Rennie MD, Xenopoulos MA, Yargeau V, Metcalfe CD (2018) Accumulation of silver in Yellow Perch (Perca flavescens) and Northern Pike (Esox lucius) from a lake dosed with nanosilver. Environ Sci Technol 52:11114–11122

    CAS  Google Scholar 

  • Massarsky A, Trudeau VL, Moon TW (2014) Predicting the environmental impact of nanosilver. Environ Toxicol Pharmacol 38:861–873

    CAS  Google Scholar 

  • McShan D, Ray PC, Yu H (2014) Molecular toxicity mechanisms of nanosilver. J Food Drug Anal 22:116–127

    CAS  Google Scholar 

  • Murray L, Rennie MD, Enders E, Pleskach K, Martin J (2017a) Effect of nanosilver on cortisol release and morphometrics in rainbow trout (Oncorhynchus mykiss). Environ Toxicol Chem 36(6):1606–1613

    CAS  Google Scholar 

  • Murray L, Rennie MD, Svendsen JC, Enders EC (2017b) Effect of nanosilver on metabolism in rainbow trout (Oncorhynchus mykiss): an investigation using different respirometric approaches. Environ Toxicol Chem 36:2722–2729

    CAS  Google Scholar 

  • Notter DA, Mitrano DM, Nowack B (2014) Are nanosized or dissolved metals more toxic in the environment? A meta-analysis. Environ Toxicol Chem 33:2733–2739

    CAS  Google Scholar 

  • Nowack B, Ranville J, Diamond S, Gallego-Urrea J, Metcalfe C, Rose J, Horne A, Koelmans AA, Klaine SJ (2012) Potential scenarios for nanomaterial release and subsequent alteration in the environment. Environ Toxicol Chem 31:50–59

    CAS  Google Scholar 

  • Ogle DH (2016) A computer program for age-length keys incorporating age assignment to individual fish. NA J Fish Manag 25:1153–1160

    Google Scholar 

  • Pazzia I, Trudel M, Ridgway M, Rasmussen JB (2002) Influence of food web structure on the growth and bioenergetics of lake trout (Salvelinus namaycush). Can J Fish Aquat Sci 59:1593–1605

    Google Scholar 

  • Pham CH, Yi J, Gu MB (2012) Biomarker gene response in male medaka (Oryzias latipes) chronically exposed to silver nanoparticle. Ecotoxicol Environ Safe 78:239–245

    CAS  Google Scholar 

  • Pierron F, Bourret V, St-Cyr J, Campbell PGC, Bernatchez L, Couture P (2009) Transcriptional responses to environmental metal exposure in wild yellow Perch (Perca flavescens) collected in lakes with differing environmental metal concentrations (Cd, Cu, Ni). Ecotoxicology 18:620–631

    CAS  Google Scholar 

  • Pulit-Prociak J, Stokłosa K, Banach M (2014) Nanosilver products and toxicity. Environ Chem Lett 13:59–68

    Google Scholar 

  • Quinn GP, Keough MJ (2002) Experimental design and data analysis for biologists. Cambridge University Press, New York

    Google Scholar 

  • Rand PS, Stewart DJ (1998) Prey fish exploitation, salmonine production, and pelagic food web efficiency in Lake Ontario. Can J Fish Aquat Sci 55:318–327

    Google Scholar 

  • Rearick DC, Telgmann L, Hintelmann H, Frost PC, Xenopoulos MA (2018) Spatial and temporal trends in the fate of silver nanoparticles in a whole-lake addition study. PLoS ONE 13(8):e0201412

    Google Scholar 

  • Rennie MD, Venturelli PA (2015) The ecology of lifetime growth in percid fishes. In: Kestemont P, Dabrowski K, Summerfelt RC (eds) Biology and culture of percid fishes: principles and practices. Springer, Netherlands, pp 499–536

    Google Scholar 

  • Rennie MD, Verdon R (2008) Development and evaluation of condition indices for the Lake Whitefish. N Am J Fish Manag 28:1270–1293

    Google Scholar 

  • Rennie MD, Collins NC, Shuter BJ, Rajotte JW, Couture P (2005) A comparison of methods for estimating activity costs of wild fish populations: more active fish observed to grow slower. Can J Fish Aquat Sci 62(4):767–780

    Google Scholar 

  • Rennie MD, Purchase CF, Shuter BJ, Collins NC, Abrams PA, Morgan GE (2010) Prey life-history and bioenergetics response across a predation gradient. J Fish Biol 77:1230–1251

    CAS  Google Scholar 

  • Rennie MD, Johnson TB, Sprules WG (2012) Energy acquisition and allocation patterns of lake whitefish (Coregonus clupeaformis) are modified when dreissenids are present. Can J Fish Aquat Sci 69:41–59

    Google Scholar 

  • Rennie MD, Kennedy PJ, Mills KH, Rodgers CMC, Charles C, Hrenchuk LE, Chalanchuk S, Blanchfield PJ, Paterson MJ, Podemski CL (2019) Impacts of freshwater aquaculture on fish communities: a whole-ecosystem experimental approach. Freshw Biol 64:870–885

    Google Scholar 

  • Scown TM, Santos EM, Johnston BD, Gaiser B, Baalousha M, Mitov S, Lead JR, Stone V, Fernandes TF, Jepson M, Aerle RV, Tyler CR (2010) Effects of aqueous exposure to silver nanoparticles of different sizes in rainbow trout. Toxicol Sci 115(2):521–534

    CAS  Google Scholar 

  • Sherwood GD, Rasmussen JB, Rowan DJ, Brodeur J, Hontela A (2000) Bioenergetic costs of heavy metal exposure in yellow Perch (Perca flavescens): in situ estimates with a radiotracer (Cs-137) technique. Can J Fish Aquat Sci 57(2):441–450

    CAS  Google Scholar 

  • Sherwood GD, Kovecses J, Hontela A, Rasmussen JB (2002) Simplified food webs lead to energetic bottlenecks in polluted lakes. Can J Fish Aquat Sci 59:1–5

    Google Scholar 

  • Sun TY, Gottschalk F, Hungerbühler K, Nowack B (2014) Comprehensive probabilistic modelling of environmental emissions of engineered nanomaterials. Environ Poll 185:69–76

    CAS  Google Scholar 

  • R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://R-project.org

  • Trudel M, Tremblay A, Schetagne R, Rasmussen JB (2000) Estimating food consumption rates of fish using a mercury mass balance method. Can J Fish Aquat Sci 57:414–428

    Google Scholar 

  • Trudel M, Tremblay A, Schetagne R, Rasmussen JB (2001) Why are dwarf fish so small? An energetic analysis of polymorphism in lake whitefish (Coregonus clupeaformis). Can J Fish Aquat Sci 58:394–405

    Google Scholar 

  • Valerio-Garcia RC, Carbajal-Hernandez AL, Martinez-Ruiz EB, Jarquin-Diaz VH, Haro-Perez C, Martinez-Jeronimo F (2017) Exposure to silver nanoparticles produces oxidative stress and affects macromolecular and metabolic biomarkers in the godeid fish Chapalychthys pardalis. Sci Total Environ 583:308–318

    CAS  Google Scholar 

  • Wang Z, Chen J, Li X, Shao J, Peijenburg WJGM (2012) Aquatic toxicity of nanosilver colloids to different trophic organisms: contributions of particles and free silver ion. Environ Toxicol Chem 31(10):2408–2413

    CAS  Google Scholar 

  • White G, Burnham K (1999) Program MARK: Survival estimation from populations of marked animals. Bird Study 46(suppl.) S120–139

  • Willis DW, Guy CS, Murphy BR (1991) Development and evaluation of a standard weight (B) equation for yellow perch. N Am J Fish Manage 11:374–380

    Google Scholar 

Download references

Acknowledgments

Funding for the whole-lake addition project was provided by the Natural Sciences and Engineering Research Council (NSERC) of Canada through the Strategic Grants Program (No. 413230-2011) and matching grants from Environment and Climate Change Canada (ECCC) to CDM and MDR. The IISD-ELA provided in-kind support for the project. Biologists Lee Hrenchuk and Chandra Rodgers, Patrick Kennedy, Brandon Greenaway, and IISD-ELA students assisted with fish collection. Andrea Conine, Daniel Rearick, Joseph Tonin, and Daniel Gilchrist provided additional field assistance. Michael Paterson, Holger Hintelmann, and Paul Frost contributed to the design of the whole-lake experiment. Vince Palace and Paul Frost provided comments on earlier drafts of the manuscript. Support for biomarker analyses was provided by the National Contaminants Advisory Group of Fisheries and Oceans Canada to CDM and VSL. Mercury analyses, energy density analysis, and bioenergetics modeling were supported by infrastructure funded by the Canadian Foundation for Innovation and by NSERC Discovery, with additional support from IISD-ELA and the Mitacs Accelerate internship program. Emianka Sotiri analyzed 2012 samples for energy density and the Alberta Centre for Toxicology analyzed 2012 samples for mercury. Susan Mann (retired) and Hilary Pritchard from Northwest Biodiversity and Monitoring Ageing Laboratory with the OMNRF provided training and age validation. Johane Joncas from the Lakehead University Environmental Laboratory conducted methylmercury analyses, and Jane Kirk with Environment and Climate Change Canada assisted with validation of mercury results. VSL and MDR hold Canada Research Chairs and are grateful for their generous support.

Funding

Funding for the whole-lake addition project was provided by the Natural Sciences and Engineering Research Council (NSERC) of Canada through the Strategic Grants Program (No. 413230-2011) and matching grants from Environment and Climate Change Canada (ECCC). The IISD-ELA provided in-kind support for the project. Support for biomarker analyses was provided by the National Contaminants Advisory Group of Fisheries and Oceans Canada. Mercury analyses, energy density analysis, biomarker analyses, and bioenergetics modeling were supported by infrastructure funded by the Canadian Foundation for Innovation, NSERC Discovery, and the Canada Research Chairs program, with support from IISD-ELA and the Mitacs Accelerate internship program (Awards No. 1465260: Recovery of fish populations from environmental nanosilver release, August 2016).

Author information

Authors and Affiliations

  1. Department of Biology, Lakehead University, Thunder Bay, ON, Canada

    Lauren D. Hayhurst & Michael D. Rennie

  2. IISD Experimental Lakes Area Inc., Winnipeg, MB, Canada

    Lauren D. Hayhurst & Michael D. Rennie

  3. School of the Environment, Trent University, Peterborough, ON, Canada

    Jonathan D. Martin, Marguerite A. Xenopoulos & Chris D. Metcalfe

  4. Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec City, QC, Canada

    Sarah J. Wallace & Valérie S. Langlois

Authors
  1. Lauren D. Hayhurst
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonathan D. Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sarah J. Wallace
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Valérie S. Langlois
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Marguerite A. Xenopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chris D. Metcalfe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Michael D. Rennie
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CDM, MAX, and MDR were among the team of investigators that designed the whole-lake addition study. LDH, JDM, and MDR collected fish. JDM conducted analysis of glutathione and TBARS biomarkers, SJW, and VSL conducted biomarker analyses, and CDM compiled biomarker data. LDH modelled fish energetics and population estimates. LDH, MDR, and SJW conducted statistical analysis and prepared figures. LDH, MDR, SJW, VSL, and CDM wrote the manuscript. All authors contributed actively to the editing and final preparation of the manuscript.

Corresponding author

Correspondence to Lauren D. Hayhurst.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethics Approval

Fish for the study were collected and handled under approval from the Animal Care Committee at Fisheries and Oceans Canada (2012 to 2013); the University of Manitoba (2014, AUP Nos. F14-007 and F14-008), Trent University (2014 to 2016; AUP Nos. 23694 and 23287), and Lakehead University (2015 and 2016; AUP Nos. 1464693, 1464399, 1454655, and 1464656, and Biosafety Approval No. 1464768).

Consent to Participate

All authors consent to participate in the publication.

Consent for Publication

All authors provide consent for publication.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 305 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hayhurst, L.D., Martin, J.D., Wallace, S.J. et al. Multi-Level Responses of Yellow Perch (Perca flavescens) to a Whole-Lake Nanosilver Addition Study. Arch Environ Contam Toxicol 79, 283–297 (2020). https://doi.org/10.1007/s00244-020-00764-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00244-020-00764-5

Search

Navigation