Simulating the Effects of Nutrient Loading Rates and Hypoxia on Bay Anchovy in Chesapeake Bay Using Coupled Hydrodynamic, Water Quality, and Individual-Based Fish Models

  • Aaron T. Adamack
  • Kenneth A. Rose
  • Carl F. Cerco


Water quality in the Chesapeake Bay has decreased since the 1950s due to an increase in nutrient loadings that have increased the extent and duration of hypoxic conditions. Restoration via large-scale reductions in nutrient loadings is now underway. How reducing nutrient loadings will affect water quality is well predicted; however, the effects of reduced nutrients (reduced food availability) and associated reduced hypoxia on fish are generally unknown as most water quality models do not include trophic levels higher than zooplankton. We dynamically coupled a spatially explicit, individual-based population dynamics model of juvenile and adult anchovy to the three-dimensional Chesapeake Bay eutrophication model. Growth rates of individual anchovy were calculated using a bioenergetics equation. Anchovy consumption rates were forced by zooplankton densities from the water quality model, and anchovy consumption of zooplankton was added as an additional mortality term on zooplankton in the eutrophication model. Anchovy mortality was size dependent and their movement depended on water temperature, dissolved oxygen, and zooplankton concentrations. Multi-year simulations with fixed annual recruitment were performed under decreased, baseline, and increased nutrient loadings scenarios. The results of our analyses show that anchovy responses to changed nutrient loadings are dominated by changes in productivity, including simultaneous changes in growth and mortality rates, and spatial distribution, and depend on life stage. As such, we recommend using full life cycle, spatially explicit population models that are dynamically coupled to water quality models as a tool for predicting the effects of changes in nutrient loadings on fish population dynamics.


Nutrient loading Hypoxia Bay anchovy Numerical modeling Population dynamics Individual-based model Chesapeake Bay 



The authors would like to thank T.J. Miller for providing data on the spatial distribution of bay anchovy in Chesapeake Bay and M. Noel for providing post-processing scripts for the analysis of CE-QUAL-ICM model output files. Additionally, we would like to thank D. Justic, J.H. Cowan Jr., J. Geaghan, and R. Malone for comments on an earlier version of this manuscript. The Chesapeake Bay eutrophication model was developed with the support of the US Army Engineer District Baltimore and the US Environmental Protection Agency Chesapeake Bay Program. ATA was supported by a graduate assistantship from the Department of Oceanography and Coastal Sciences at Louisiana State University and was partially supported by the Cooperative Institute for Limnology and Ecosystems Research at the University of Michigan and by the University of Canberra Postdoctoral Fellowship Scheme while completing this manuscript. This research was partially supported (KAR) by the National Oceanographic and Atmospheric Administration, Center for Sponsored Coastal Ocean Research (CSCOR) NGOMEX09 Grant number NA09NOS4780179 awarded to the University of Texas, and CHRP Grant number NA10NOS4780157 awarded to Louisiana State University. This is publication number 219 of the NOAA’s CSCOR NGOMEX and CHRP programs.


  1. Adamack AT (2007) Predicting water quality effects on bay anchovy (Anchoa mitchilli) growth and production in Chesapeake Bay: linking water quality and individual-based fish models. Dissertation, Louisiana State UniversityGoogle Scholar
  2. Adamack AT, Rose KA, Breitburg DL et al (2012) Simulating the effect of hypoxia on bay anchovy egg and larval mortality using coupled watershed, water quality, and individual-based predation models. Mar Ecol Prog Ser 445:141–160CrossRefGoogle Scholar
  3. Baird D, Ulanowicz RE (1989) The seasonal dynamics of the Chesapeake Bay ecosystem. Ecol Monogr 59:329–364CrossRefGoogle Scholar
  4. Batiuk R, Linker L, Cerco C (2013) Featured collection introduction: Chesapeake Bay total maximum daily load development and application. J Am Water Resour Assoc 49:981–985Google Scholar
  5. Boynton WR, Garber JH, Summers R, Kemp WM (1995) Inputs, transformations, and transport of nitrogen and phosphorus in Chesapeake Bay and selected tributaries. Estuaries Coasts 18:285–314Google Scholar
  6. Brandt SB, Mason DM (2003) Effect of nutrient loading on Atlantic menhaden (Brevoortia tyrannus) growth rate potential in the Patuxent River. Estuaries 26:298–309Google Scholar
  7. Breitburg DL, Adamack A, Rose KA et al (2003) The pattern and influence of low dissolved oxygen in the Patuxent River, a seasonally hypoxic estuary. Estuaries 26:280–297CrossRefGoogle Scholar
  8. Breitburg DL, Craig JK, Fulford RS et al (2009a) Nutrient enrichment and fisheries exploitation: interactive effects on estuarine living resources and their management. Hydrobiologia 629:31–47Google Scholar
  9. Breitburg DL, Hondorp DW, Davias LA et al (2009b) Hypoxia, nitrogen and fisheries: integrating effects across local and global landscapes. Annu Rev Mar Sci 1:329–349Google Scholar
  10. Burkadt J (2014) Geometry—geometric calculations. Accessed 8 Aug 2014
  11. Burton DT, Richardson LB, Moore CJ (1980) Effect of oxygen reduction rate and constant low dissolved oxygen concentrations on two estuarine fish. T Am Fish Soc 109:552–557CrossRefGoogle Scholar
  12. Caddy JF (1993) Toward a comparative evaluation of human impacts on fishery ecosystems of enclosed and semi-enclosed seas. Rev Fish Sci 1:57–95CrossRefGoogle Scholar
  13. Cerco C, Cole T (1993) Three-dimensional eutrophication model of Chesapeake Bay. J Environ Eng-ASCE 119:1006–1025CrossRefGoogle Scholar
  14. Cerco C, Kim S-C, Noel M (2010) The 2010 Chesapeake Bay eutrophication model. A report to the US Environmental Protection Agency Chesapeake Bay Program and to the US Army Engineer Baltimore District. Accessed 23 July 2014
  15. Cerco C, Noel M (2007) Can oyster restoration reverse cultural eutrophication in Chesapeake Bay? Estuar Coast 30:331–343CrossRefGoogle Scholar
  16. Cerco C, Noel M (2010) Monitoring, modeling, and management impacts of bivalve filter feeders in the oligohaline and tidal fresh regions of the Chesapeake Bay system. Ecol Model 221:1054–1064CrossRefGoogle Scholar
  17. Cerco CF, Noel MR (2013) Twenty-one-year simulation of Chesapeake Bay water quality using the CE-QUAL-ICM eutrophication model. J Am Water Resour Assoc 49:1119–1133Google Scholar
  18. Chesapeake Bay Program (2013) Chesapeake Bay Program Accessed 15 Dec 2013
  19. Chesney EJ, Houde ED (1989) Laboratory studies on the effect of hypoxic waters on the survival of eggs and yolk-sac larvae of the bay anchovy, Anchoa mitchilli. In: Houde ED, Chesney EJ, Newberger TA et al (eds) Population biology of bay anchovy in mid-Chesapeake Bay. Solomons, Maryland, pp 98–107Google Scholar
  20. Christensen V, Walters CJ (2004) Ecopath with Ecosim: methods, capabilities and limitations. Ecol Model 172:109–139CrossRefGoogle Scholar
  21. Cloern JE (2001) Our evolving conceptual model for the coastal eutrophication problem. Mar Ecol Prog Ser 210:223–253CrossRefGoogle Scholar
  22. Conley DJ, Paerl NW, Howarth RW et al (2009) Controlling eutrophication: nitrogen and phosphorus. Science 323:1014–1015CrossRefPubMedGoogle Scholar
  23. Cooper SR, Brush GS (1991) Long-term history of Chesapeake Bay anoxia. Science 254:992–996CrossRefPubMedGoogle Scholar
  24. Costantini M, Ludsin SA, Mason DM et al (2008) Effect of hypoxia on habitat quality of striped bass (Morone saxatilis) in Chesapeake Bay. Can J Fish Aquat Sci 65:989–1002CrossRefGoogle Scholar
  25. Cowan JH Jr, Rose KA, Houde ED et al (1999) Modeling effects of increased larval mortality on bay anchovy population dynamics in the mesohaline Chesapeake Bay: evidence for compensatory reserve. Mar Ecol Prog Ser 185:133–146CrossRefGoogle Scholar
  26. Cronin TM, Vann CD (2003) The sedimentary record of climatic and anthropogenic influence on the Patuxent estuary and Chesapeake Bay ecosystems. Estuaries 26:169–209CrossRefGoogle Scholar
  27. Dalyander P, Cerco C (2010) Integration of a fish bioenergetics model into a spatially explicit water quality model: application to menhaden in Chesapeake Bay. Ecol Model 221:1922–1933CrossRefGoogle Scholar
  28. Diaz RJ, Rosenberg R (2008) Spreading dead zones and consequences for marine ecosystems. Science 321:926–929CrossRefPubMedGoogle Scholar
  29. Di Toro D (2001) Sediment flux modeling. Wiley, New YorkGoogle Scholar
  30. Dortch MS, Chapman RS, Abt SR (1992) Application of three dimensional Lagrangian residual transport. J Hydraul Eng-ASCE 118:831–848CrossRefGoogle Scholar
  31. Elliott DT, Pierson JJ, Roman MR (2013) Copepods and hypoxia in Chesapeake Bay: abundance, vertical position and non-predatory mortality. J Plankton Res 35:1027–1034CrossRefGoogle Scholar
  32. Fisher TR, Hagy JD III, Boynton WR et al (2006) Cultural eutrophication in the Choptank and Patuxent estuaries of Chesapeake Bay. Limnol Oceanogr 51:435–447CrossRefGoogle Scholar
  33. Gray A (2013) Chesapeake Bay Fiscal 2014 budget overview. Department of Legislative Services, Office of Policy Analysis, Annapolis MDGoogle Scholar
  34. Hagy JD, Boynton WR, Keefe CW et al (2004) Hypoxia in Chesapeake Bay, 1950-2001: long-term change in relation to nutrient loading and river flow. Estuaries 27:634–658CrossRefGoogle Scholar
  35. Hartman KJ, Brandt SB (1995) Trophic resource partitioning, diets and growth of sympatric estuarine predators. T Am Fish Soc 124:520–537CrossRefGoogle Scholar
  36. Houde ED, Chesney EJ, Newberger TA et al (1989) Population biology of bay anchovy in mid-Chesapeake Bay. Solomons, MarylandGoogle Scholar
  37. Houde ED, Zastrow CE (1991) Bay anchovy. In: Funderburk SL, Mihursky JA, Jordan SJ et al (eds) Habitat requirements for Chesapeake Bay living resources, 2nd edn. Living Resources Subcommittee, Chesapeake Bay Program. Annapolis, Maryland, pp 8-1–8-14Google Scholar
  38. Humston R, Ault JS, Lutcavage M et al (2000) Schooling and migration of large pelagic fishes relative to environmental cues. Fish Oceanogr 9:136–146CrossRefGoogle Scholar
  39. Humston R (2001) Development of movement models to assess the spatial dynamics of marine fish populations. Dissertation, University of MiamiGoogle Scholar
  40. Johnson BH, Kim KW, Heath RE et al (1993) Validation of three-dimensional hydrodynamic model of Chesapeake Bay. J Hydraul Eng-ASCE 119:2–20CrossRefGoogle Scholar
  41. Jung S, Houde ED (2003) Spatial and temporal variabilities of pelagic fish community structure and distribution in Chesapeake Bay, USA. Estuar Coast Shelf S 58:335–351CrossRefGoogle Scholar
  42. Jung S, Houde ED (2004a) Production of bay anchovy Anchoa mitchilli in Chesapeake Bay: application of size-based theory. Mar Ecol Prog Ser 281:217–232Google Scholar
  43. Jung S, Houde ED (2004b) Recruitment and spawning-stock biomass distribution of bay anchovy (Anchoa mitchilli) in Chesapeake Bay. Fish B NOAA 102:63–77Google Scholar
  44. Kemp WM, Boynton WR, Adolf JE et al (2005) Eutrophication of Chesapeake Bay: Historical trends and ecological interactions. Mar Ecol Prog Ser 303:1–29CrossRefGoogle Scholar
  45. Kim S-C (2013) Evaluation of a three-dimensional hydrodynamic model applied to Chesapeake Bay through long-term simulation of transport processes. J Am Water Resour Assoc 49:1078–1090CrossRefGoogle Scholar
  46. Kolesar SE, Breitburg DL, Purcell JE et al (2010) Effects of hypoxia on Mnemiopsis leidyi, ichthyoplankton and copepods: clearance rates and vertical habitat overlap. Mar Ecol Prog Ser 411:173–188CrossRefGoogle Scholar
  47. Kolesar SE, Rose KA, Breitburg DL (2017) Hypoxia effects within an intraguild predation food web of Mnemiopsis leidyi ctenophores, larval fish, and copepods. In: Justice D (ed) Modeling coastal hypoxia. Springer, ChamGoogle Scholar
  48. Lett C, Rose KA, Megrey BA (2009) Biophysical models fish. In: Checkley D, Alheit J, Oozeki Y, Roy C (eds) Climate change and small pelagic fish. Cambridge University Press p 88–111Google Scholar
  49. Ludsin SA, Zhang X, Brandt SB et al (2009) Hypoxia-avoidance by planktivorous fish in Chesapeake Bay: implications for food web interactions and fish recruitment. J Exp Mar Biol Ecol 381:S121–S131CrossRefGoogle Scholar
  50. Luo J, Brandt SB (1993) Bay anchovy Anchoa mitchilli production and consumption in mid-Chesapeake Bay based on a bioenergetics model and acoustic measures of fish abundance. Mar Ecol-Prog Ser 98:223–236CrossRefGoogle Scholar
  51. Luo J, Hartman KJ, Brandt SB et al (2001) A spatially-explicit approach for estimating carrying capacity: an application for the Atlantic menhaden (Brevoortia tyrannus) in Chesapeake Bay. Estuaries 24:545–556CrossRefGoogle Scholar
  52. Luo J, Musick JA (1991) Reproductive biology of the bay anchovy in Chesapeake Bay. T Am Fish Soc 120:701–710CrossRefGoogle Scholar
  53. Magnien RE (1987) Monitoring for management actions: Maryland Office of Environmental Programs Chesapeake Bay Water Quality Monitoring Program, First biennial report. Maryland Office of Environmental Programs, 61 ppGoogle Scholar
  54. Maryland Department of Natural Resources, Annapolis (2017) Juvenile striped bass survey. Accessed 3 March 2017
  55. Mauchline J (1998) The biology of calanoid copepods. Adv Mar Biol 33Google Scholar
  56. Miller TJ, Nye JA, Loewensteiner DL (2008) Development and implementation of the Chesapeake Bay Fishery-Independent Multispecies Survey (CHESFIMS)—final report. Technical report series No TS-545-08-020 of the University of Maryland Center for Environmental Science, Solomons, MarylandGoogle Scholar
  57. Morin LG, Houde ED (1989) Hatch-date frequencies and young-of-the-year growth rates of bay anchovy in mid-Chesapeake Bay. In: Houde ED, Chesney EJ, Newberger TA et al (eds) Population biology of bay anchovy in mid-Chesapeake Bay, Solomons, Maryland, pp 98–107Google Scholar
  58. Newberger TA, Houde ED, Chesney EJ (1989) Relative abundance, age, growth and mortality of bay anchovy (Anchoa mitchilli) in the mid-Chesapeake Bay. In: Houde ED, Chesney EJ, Newberger TA et al (eds) Population biology of bay anchovy in mid-Chesapeake Bay. Solomons, Maryland, pp 17–77Google Scholar
  59. Nixon SE, Buckley BA (2002) “A strikingly rich zone”—nutrient enrichment and secondary production in coastal marine ecosystems. Estuaries 25:782–796CrossRefGoogle Scholar
  60. Pothoven SA, Vanderploeg HA, Höök TO et al (2012) Hypoxia modifies planktivore–zooplankton interactions in Lake Erie. Can J Fish Aquat Sci 69:2018–2028CrossRefGoogle Scholar
  61. Rabalais NN, Turner RE, Dortch Q et al (2002) Nutrient-enhanced productivity in the northern Gulf of Mexico: past, present and future. Hydrobiologia 475:39–63CrossRefGoogle Scholar
  62. Roman MR, Pierson JJ, Kimmel DG et al (2012) Impacts of hypoxia on zooplankton spatial distributions in the northern Gulf of Mexico. Estuar Coast 35:1261–1269CrossRefGoogle Scholar
  63. Rose KA, Cowan JH Jr, Clark ME et al (1999) An individual-based model of bay anchovy population dynamics in the mesohaline region of Chesapeake Bay. Mar Ecol Prog Ser 185:113–132CrossRefGoogle Scholar
  64. Rose KA, Cowan JH Jr, Winemiller KO et al (2001) Compensatory density dependence in fish populations: importance, controversy, understanding and prognosis. Fish Fish 2:293–327CrossRefGoogle Scholar
  65. Rose KA, Adamack AT, Murphy CA et al (2009) Does hypoxia have population-level effects on coastal fish? Musings from the virtual world. J Exp Mar Biol Ecol 381:S188–S203Google Scholar
  66. Rose KA, Justic D, Fennel K, Hetland R (2017) Numerical modeling of hypoxia and its effects: synthesis and going forward. In: Justice D (ed) Modeling coastal hypoxia. Springer, ChamGoogle Scholar
  67. Runge JA, Franks PJS, Gentleman WC et al (2004) Diagnosis and prediction of variability in secondary production and fish recruitment processes: developments in physical-biological modeling. In: Robinson AR, Brink KH (eds) The global coastal ocean: multi-scale interdisciplinary processes, vol 13. The Sea, p 413–473Google Scholar
  68. Scheffer M, Baveco JM, DeAngelis DL et al (1995) Super-individuals a simple solution for modeling large populations on an individual basis. Ecol Model 80:161–170CrossRefGoogle Scholar
  69. Snyder JP (1987) Map projections—a working manual. Geological Survey Professional Paper, 1395. U. S. Government Printing Office, Washington, D. CGoogle Scholar
  70. U.S. Environmental Protection Agency (2010) Chesapeake Bay total maximum daily load for nitrogen, phosphorus and sediment. U.S. Environmental Protection Agency Chesapeake Bay Program Office., Accessed 16 Dec 2013
  71. U.S Geological Survey (2014) Estimating stream flow entering Chesapeake Bay. Accessed 11 Aug 2014
  72. Vanderploeg HA, Ludsin SA, Ruberg SA et al (2009) Hypoxia affects spatial distributions and overlap of pelagic fish, zooplankton, and phytoplankton in Lake Erie. J Exp Mar Biol Ecol 381:S92–S107CrossRefGoogle Scholar
  73. Wang S-B, Cowan JH Jr, Rose KA et al (1997) Individual-based modeling of recruitment variability and biomass production of bay anchovy in mid-Chesapeake Bay. J Fish Biol 51(Suppl. A):101–120Google Scholar
  74. Watkins KS, Rose KA (2013) Evaluating the performance of individual-based animal movement models in novel environments. Ecol Model 250:214–234CrossRefGoogle Scholar
  75. Zastrow CE, Houde ED, Morin LG (1991) Spawning fecundity, hatch-date frequency and young-of-the-year growth of bay anchovy Anchoa mitchilli in mid-Chesapeake Bay. Mar Ecol-Prog Ser 73:161–171CrossRefGoogle Scholar
  76. Zhang H, Ludsin SA, Mason DM et al (2009) Hypoxia-driven changes in the behavior and spatial distribution of pelagic fish and mesozooplankton in the northern Gulf of Mexico. J Exp Mar Biol Ecol 381:S80–S91CrossRefGoogle Scholar
  77. Zhang H, Mason DM, Stow CA et al (2014) Effects of hypoxia on habitat quality of pelagic planktivorous fishes in the northern Gulf of Mexico. Mar Ecol Prog Ser 505:209–226CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aaron T. Adamack
    • 1
    • 3
  • Kenneth A. Rose
    • 1
  • Carl F. Cerco
    • 2
  1. 1.Department of Oceanography and Coastal SciencesLouisiana State University, Energy, Coast, and Environment BuildingLAUSA
  2. 2.U.S. Army Engineer Research and Development CenterVicksburgUSA
  3. 3.Institute for Applied Ecology, University of CanberraBruceAustralia

Personalised recommendations