Published February 2, 2022 | Version v1
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Submerged aquatic vegetation, water quality (pH, salinity, and turbidity) and waterfowl abundance data from 1991-2017 in Back Bay, Virginia

  • 1. Environmental Resources Management (United Kingdom)
  • 2. Naturalis Biodiversity Center
  • 3. United States Fish and Wildlife Service
  • 4. University of Oxford

Description

Back Bay, Virginia, has been documented as an important foraging area for waterfowl since at least the mid-1800s. Expansive submerged plant beds historically supported diverse assemblages of non-breeding waterfowl, however coastal development and other anthropogenic influences have since led to fluctuations in submerged aquatic vegetation (SAV) and an associated decline in waterfowl abundance in the bay. To gain insight into the effects of environmental drivers on waterfowl foraging guilds, our study explores the effects of SAV frequency and water quality on the abundance of dabbling ducks, diving ducks, and swans and geese in Back Bay. We use 8 years of SAV, water quality, and waterfowl monitoring data collected by state and federal agencies to model the effects of salinity, turbidity, pH, and percent frequency of SAV on the relative abundance of waterfowl by foraging guild in Back Bay. The appropriateness of the data and reasonability of the preliminary results were then evaluated through semi-structured interviews with 11 local informants representing state, federal, and non-governmental organizations. Quantitative results indicated that dabbling ducks are affected differently than other guilds by water quality and percent frequency of SAV. Thematic analysis of the interview data revealed a number of potential explanations for the model results, as well as highlighted areas of uncertainty in need of further research. In a test of face validity, participants demonstrated a significant degree of belief in turbidity, salinity, and SAV as drivers of waterfowl abundance, but were not convinced by the potential effects of pH as demonstrated by the model. This mixed methods study provides insights that could potentially influence the management and conservation of non-breeding waterfowl populations by challenging the assumption that particular environmental conditions serve all foraging groups equally.

Notes

REFERENCES

Aho, K., Derryberry, D. & Peterson, T. (2014). Model selection for ecologists: The worldviews of AIC and BIC. Ecology, 95(3), 631–636. https://doi.org/10.1890/13-1452.1

Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4', Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01

Bartoń, K. (2019). Package 'MuMIn', CRAN.

Eggeman, D.R., & Johnson, F.A. (1989). Variation in effort and methodology for the midwinter waterfowl inventory in the Atlantic Flyway. Wildl Soc Bul,l 17(3), 227-233. https://www.jstor.org/stable/3782374

Fredrickson, L.H., & Reid, F.A. (1988). Nutritional values of waterfowl foods. In Waterfowl Management Handbook. U.S. Fish and Wildlife Service.

Gelman, A. (2008). Scaling regression inputs by dividing by two standard deviations. Statistics in Medicine, 27, 2865–2873. https://doi.org/ 10.1002/sim.3107

Guillemain, M., Fritz, H., & Blais, S. (2000). Foraging methods can affect patch choice: an experimental study in Mallard (Anas platyrhynchos). Behav Processes, 50(2–3), 123–129. https://doi.org/10.1016/S0376-6357(00)00095-4

Gyimesi, A., de Vries, P.P., de Boer, T., & Nolet, B.A. (2011). Reduced tuber banks of fennel pondweed due to summer grazing by waterfowl. Aquat Bot, 94(1), 24-28. https://doi.org/10.1016/j.aquabot.2010.10.002

Hothorn, T., Bretz, F. and Westfall, P. (2008) Simultaneous Inference in General Parametric Models. Biometrical Journal, 50(3), 346–363. https://doi.org/ 10.1002/bimj.200810425

Huesmann, W. (1999). Let's get rid of the midwinter waterfowl inventory in the Atlantic Flyway. Wild Soc Bull, 27(3), 559-565. https://www.jstor.org/stable/3784074

Lenth, R. (2016). Least-Squares Means: The R Package lsmeans. Version 2916. Journal of Statistical Software, 61(1), 1–33. https://doi.org/10.18637/jss.v069.i01[NWQMC] National Water Quality Monitoring Council. (1995). Standard methods: 2130 B: turbidity by nephelometry. National Environmental Methods Index. https://www.nemi.gov/methods/method_summary/9645/

[NWQMC] National Water Quality Monitoring Council. (1995). Standard methods: 2130 B: turbidity by nephelometry. National Environmental Methods Index. https://www.nemi.gov/methods/method_summary/9645/

[NWQMC] National Water Quality Monitoring Council. (2020). Standard methods: 4500- H+B: pH in water by potentiometry. National Environmental Methods Index. https://www.nemi.gov/methods/method_summary/4707/

[NWQMC] National Water Quality Monitoring Council. (2021). Water Quality Data. https://www.waterqualitydata.us/portal/

Nelms, K.D., Ballinger, B., & Boyles, A. (2007). Wetland Management for Waterfowl Handbook. Mississippi River Trust, Natural Resources Conservation Service, United States Fish and Wildlife Service. https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_016986.pdf

Pöysä, H. (1983). Resource Utilization Pattern and Guild Structure in a Waterfowl Community. Oikos, 40(2), 295. https://doi.org/10.2307/3544594

Schwab, D., Settle, F.H., Halstead, O., & Ewell, R.L. (1991). Submerged aquatic vegetation trends of Back Bay, Virginia. In Proceedings of the Back Bay Ecological Symposium. Old Dominion University Digital Commons, 265–269. https://digitalcommons.odu.edu/backbay1990_flora/6

Sibley, D.A. (2000). The Sibley Guide to Birds. Alfred A. Knopf, Inc.

Sincock, J.L., Johnston, K.H., Coggin, J.L., Wollitz, R.E., Kerwin, J.A., Dickson, A.W., Crowell, T., Grandy III, J., Davis, J.R., & McCartney, R. (1965). Back Bay - Currituck Sound data report: Introduction and vegetation studies, Volume I. https://www.fws.gov/southeast/pdf/report/backbay-currituck-sound-vegatation.pdf

Tatu, K.S., Anderson, J.T., Hindman, L.J., & Seidel, G. (2007). Mute swans' impact on submerged aquatic vegetation in Chesapeake Bay. J Wildl Manage, 71, 1431–1439. https://doi.org/10.2193/2006-130

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