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Environmental Science and Pollution Research

, Volume 26, Issue 11, pp 11355–11370 | Cite as

Exploring inter-species sensitivity to a model hydrocarbon, 2-Methylnaphtalene, using a process-based model

  • Adriana E. SardiEmail author
  • Starrlight Augustine
  • Gro H. Olsen
  • Lionel Camus
Research Article
  • 55 Downloads

Abstract

We compared inter-species sensitivity to a model narcotic compound, 2-Methylnaphthalene, to test if taxonomical relatedness, feeding guilds, and trophic level govern species sensitivities on species distributed in different regions. We fitted a toxicokinetic-toxicodynamic model to survival patterns over time for 26 species using new and raw data from the literature. Species sensitivity distributions provided little insight into understanding patterns in inter-species sensitivity. The range of no-effect concentrations (NEC) obtained for 26 species showed little variation (mean 0.0081 mM; SD 0.009). Results suggest that the NEC alone does not explain the complexity of the species tolerances. The dominant rate constant and the derived time to observe an effect (t0), a function of concentration, might provide the means for depicting patterns in sensitivity and better ecotoxicological testing. When comparing the t0 functions, we observed that Arctic species have shorter time frames to start showing effects. Mollusks and second trophic level species took longer to build up a lethal body burden than the rest. Coupling our results with fate and transport models would allow forecasting narcotic compounds toxicity in time and thus improve risk assessment.

Keywords

No effect concentration Narcotics Survival SSD GUTS Toxicokinetics Toxicodynamics 

Notes

Acknowledgments

We wish to thank Gisele C. Morais for her valuable help during the sampling and conduction of the experiments of subtropical species. We also thank Thayanne Lima for helping during the sampling of subtropical species, and Carl Ballantine for providing raw data sets for estimating weights and lengths of Arctic species. Finally, we would like to thank the anonymous reviewers for their comments, which helped to improve the quality of our manuscript.

Funding information

The Norwegian Research Council funded this study through the “Latin Amerika” research program attributed to Akvaplan-niva AS under the Project number 227180/H30.

Supplementary material

11356_2019_4423_MOESM1_ESM.pdf (18.7 mb)
ESM 1 (PDF 19163 kb)

References

  1. Anderson MJ (2001) A new method for non parametric multivariate analysis of variance. Austral Ecol 26(2001):32–46Google Scholar
  2. Ashauer R, Boxall ABA, Brown CD (2006) New ecotoxicological model to simulate survival of aquatic invertebrates after exposure to fluctuating and sequential pulses of pesticides. Environ Sci Technol 41:1480–1486CrossRefGoogle Scholar
  3. Ashauer R, O’Connor I, Hintermeister A, Escher BI (2015) Death dilemma and organism recovery in ecotoxicology. Environ Sci Technol 49(16):10136–10146CrossRefGoogle Scholar
  4. Ashauer R, Albert C, Augustine S, Cedergreen N, Charles S, Ducrot V, Focks A, Gabsi F, Gergs A, Goussen B et al (2016) Modelling survival: exposure pattern, species sensitivity and uncertainty. Sci Rep 6(July):29178CrossRefGoogle Scholar
  5. Baas J, Jager T, Kooijman SALM (2009) A model to analyze effects of complex mixtures on survival. Ecotoxicol Environ Saf 72(3):669–676CrossRefGoogle Scholar
  6. Baas J, Jager T, Kooijman B (2010) Understanding toxicity as processes in time. Sci Total Environ 408(18):3735–3739CrossRefGoogle Scholar
  7. Baas J, Kooijman SALM (2015) Sensitivity of animals to chemical compounds links to metabolic rate. Ecotoxicology 24(3):657–663CrossRefGoogle Scholar
  8. Baas J, Spurgeon D, Broerse M (2015) A simple mechanistic model to interpret the effects of narcotics. SAR QSAR Environ Res 26(3):165–180CrossRefGoogle Scholar
  9. Bedaux JJM, Kooijman SALM (1994) Statistical analysis of bioassays, based on hazard modeling. Environ Ecol Stat 1:303–314CrossRefGoogle Scholar
  10. Cardoso FD, Dauner ALL, Martins CC (2016) A critical and comparative appraisal of polycyclic aromatic hydrocarbons in sediments and suspended particulate material from a large South American subtropical estuary. Environ Pollut 214:219–229CrossRefGoogle Scholar
  11. de Abreu-Mota MA, de Moura Barboza CA, Bícego MC, Martins CC (2014) Sedimentary biomarkers along a contamination gradient in a human-impacted sub-estuary in Southern Brazil: a multi-parameter approach based on spatial and seasonal variability. Chemosphere 103:156–163CrossRefGoogle Scholar
  12. de Hoop, L.; Schipper, A. M.; Huijbregts, M. A. J.; Olsen, G. H.; Smit, M. G. D.; Hendriks, A. J. Sensitivity of polar and temperate marine organisms to oil components. Environ Sci Technol 2011, 45:9017-9023. 1–28Google Scholar
  13. Delignette-Muller ML, Dutang C (2014) Fitdistrplus: an R package for fitting distributions. J Stat Softw 65(4):1–34Google Scholar
  14. Dutang C, Goulet V, Mathieu P (2008) Actuar: an R package for actuarial science. J Stat Softw 25(7):38Google Scholar
  15. EFSA (2013) Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters. EFSA J 11:1–268Google Scholar
  16. Falk-Petersen I-B, Saethre L, Lonning S (1982) Toxic effects of naphthalene and methylnaphthalenes on marine plankton organisms. SARSIA 67:171–178CrossRefGoogle Scholar
  17. Jager T (2014) Reconsidering sufficient and optimal test design in acute toxicity testing. Ecotoxicology 23(1):38–44CrossRefGoogle Scholar
  18. Jager T (2016) Predicting environmental risk: a road map for the future. J Toxicol Environ Heal Part A 79(13–15):572–584CrossRefGoogle Scholar
  19. Jager T. Making sense of chemical stress: applications of dynamic energy budget theory in ecotoxicology and stress ecology (2017). Version 1.3, Leanpub: https://leanpub.com/debtox_book
  20. Jager T, Kooijman SALM (2009) A biology-based approach for quantitative structure-activity relationships (QSARs) in ecotoxicity. Ecotoxicology 18(2):187–196CrossRefGoogle Scholar
  21. Jager T, Albert C, Preuss TG, Ashauer R (2011) General unified threshold model of survival - a toxicokinetic-toxicodynamic framework for ecotoxicology. Environ Sci Technol 45(7):2529–2540CrossRefGoogle Scholar
  22. Jager T, Øverjordet IB, Nepstad R, Hanse BH (2017) Dynamic links between lipid storage, toxicokinetics and mortality in a marine copepod exposed to dimethylnaphthalene. Environ Sci Technol 51(13):7707–7713CrossRefGoogle Scholar
  23. Jager T and Ashauer R (2018). Modelling survival under chemical stress. A comprehensive guide to the GUTS Framework Version 1.0, Leanpub: https://leanpub.com/ guts_book
  24. Kennedy CJ (1990) Toxicokinetic studies of chlorinated phenols and polycyclic aromatic hydrocarbons in rainbow trout (Oncorhynchus mykiss), Simon Frase UniversityGoogle Scholar
  25. Klok C, Nordtug T, Tamis JE (2014) Estimating the impact of petroleum substances on survival in early life stages of cod (Gadus morhua) using the Dynamic Energy Budget theory. Mar Environ Res 101(1):60–68CrossRefGoogle Scholar
  26. Kon Kam King G, Delignette-Muller ML, Kefford BJ, Piscart C, Charles S (2015) Constructing time-resolved species sensitivity distributions using a hierarchical toxico-dynamic model. Environ Sci Technol 49(20):12465–12473CrossRefGoogle Scholar
  27. Kooijman SALM, Bedaux JJM (1996) The analysis of aquatic toxicity data. VU University Press, Amsterdam, NetherlandsGoogle Scholar
  28. Kooijman SALM (1996) An alternative for NOEC exists, but the standard model has to be abandoned first. Oikos 75:310–316CrossRefGoogle Scholar
  29. Kooijman SALM (2010) Dynamic Energy Budget theory for metabolic organisation: summary of concepts of the third edition, WaterGoogle Scholar
  30. Kwok KWH, Leung KMY, Lui GSG, Chu SVKH, Lam PKS, Morritt D, Maltby L, Brock TCM, Van den Brink PJ, Warne MSJ et al (2007) Comparison of tropical and temperate freshwater animal species’ acute sensitivities to chemicals: implications for deriving safe extrapolation factors. Integr Environ Assess Manag 3(1):49–67CrossRefGoogle Scholar
  31. Leppanen M (1995) The role of feeding behaviour in bioaccumulation of organic chemicals in benthic organisms. Ann Zool Fenn 32(3):247–255Google Scholar
  32. Moen FE, Svensen E (2004) Marine fish and invertebrates of Northern Europe, First edit.Google Scholar
  33. Nahrgang J, Brooks SJ, Evenset A, Camus L, Jonsson M, Smith TJ, Lukina J, Frantzen M, Giarratano E, Renaud PE (2013) Seasonal variation in biomarkers in blue mussel (Mytilus edulis), Icelandic scallop (Chlamys islandica) and Atlantic cod (Gadus morhua): implications for environmental monitoring in the Barents Sea. Aquat Toxicol 127(9037):21–35CrossRefGoogle Scholar
  34. Neff JM. (2004) Bioaccumulation in marine organisms. Effect of contaminants from oil well produced water, Second edi.; ElsevierGoogle Scholar
  35. Nyman AM, Schirmer K, Ashauer R (2014) Importance of toxicokinetics for interspecies variation in sensitivity to chemicals. Environ Sci Technol 48(10):5946–5954CrossRefGoogle Scholar
  36. OECD. (2003) Draft Guidance Document for on the Statistical Analysis of Ecotoxicity Data Environment. ISO TC 147/SC 5 N 18, ISO/WD, . Publishing, Paris. http://www.oecd.org/chemicalsafety/testing/2956192.pdf
  37. OECD. (2014) Current Approaches in the Statistical Analysis of Ecotoxicity Data: A guidance to application (annexes to this publication exist as a separate document), OECD series on testing and assessment, no. 54, OECD Publishing, Paris,  https://doi.org/10.1787/9789264085275-en
  38. Olsen GH, Klok C, Hendriks AJ, Geraudie P, De Hoop L, De Laender F, Farmen E, Grøsvik BE, Hansen BH, Hjorth M et al (2013) Toxicity data for modeling impacts of oil components in an Arctic ecosystem. Mar Environ Res 90:9–17CrossRefGoogle Scholar
  39. Olsen GH, Smit MGD, Carroll J, Jæger I, Smith T, Camus L (2011) Arctic versus temperate comparison of risk assessment metrics for 2-methyl-naphthalene. Mar Environ Res 72(4):179–187CrossRefGoogle Scholar
  40. Posthuma L, Suter II GW, and Traas TP. (2010) Species sensitivity distributions in ecotoxicology. CRC pressGoogle Scholar
  41. R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  42. Renaud PE, Tessmann M, Evenset A, Christensen GN (2011) Benthic food-web structure of an Arctic fjord (Kongsfjorden, Svalbard). Mar Biol Res 7:13–26CrossRefGoogle Scholar
  43. Renaud PE, Løkken TS, Jørgensen LL, Berge J, Johnson BJ (2015) Macroalgal detritus and food-web subsidies along an Arctic fjord depth-gradient. Front Mar Sci 2:31CrossRefGoogle Scholar
  44. Saethre L, Falk-petersen I-B, Sydnes LK, Lønning S, Naley AM (1984) Toxicity and chemical reactivity of naphthalene and methylnaphthalenes. Aquat Toxicol 5:291–396CrossRefGoogle Scholar
  45. Sandrini-Neto L, Pereira L, Martins CC, Silva de Assis HC, Camus L, Lana PC (2016) Antioxidant responses in estuarine invertebrates exposed to repeated oil spills: effects of frequency and dosage in a field manipulative experiment. Aquat Toxicol 177:237–249CrossRefGoogle Scholar
  46. Sardi AE, Sandrini-Neto L, da S Pereira L, Silva de Assis H, Martins CC, Lana PC, Camus L (2016a) Oxidative stress in two tropical species after exposure to diesel oil. Environ Sci Pollut Res 23(20):20952–20962CrossRefGoogle Scholar
  47. Sardi AE, Renaud PE, da Cunha Lana P, Camus L (2016b) Baseline levels of oxidative stress biomarkers in species from a subtropical estuarine system (Paranaguá Bay, southern Brazil). Mar Pollut Bull 113(1–2):496–508CrossRefGoogle Scholar
  48. Tamelander T, Renaud PE, Hop H, Carroll ML, Ambrose WG, Hobson KA (2006) Trophic relationships and pelagic-benthic coupling during summer in the Barents Sea Marginal Ice Zone, revealed by stable carbon and nitrogen isotope measurements. Mar Ecol Prog Ser 310:33–46CrossRefGoogle Scholar
  49. US Department of Energy. International energy statistics: Total primary energy consumption. Data retrieved, January 2017 http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=44&pid=44&aid=2
  50. Wang Z, Kwok KWH, Lui GCS, Zhou GJ, Lee JS, Lam MHW, Leung KMY (2014) The difference between temperate and tropical saltwater species’ acute sensitivity to chemicals is relatively small. Chemosphere 105:31–43CrossRefGoogle Scholar
  51. Wheeler JR, Grist EPM, Leung KMY, Morritt D, Crane M (2002) Species sensitivity distributions: data and model choice. Mar Pollut Bull 45(1–12):192–202CrossRefGoogle Scholar
  52. Wickham H (2009) ggplot2: elegant graphics for data analysis; Springer, Ed.; Springer, New YorkGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Akvaplan-nivaHigh North Research Centre for Climate and the EnvironmentTromsøNorway
  2. 2.Faculty of Science, Faculty of Science and Technology, Department of Science & SafetyUniversity of TromsøTromsøNorway

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