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Submerged aquatic vegetation, water quality (pH, salinity, and turbidity) and waterfowl abundance data from 1991-2017 in Back Bay, Virginia

Citation

Sibilia, Carly; Aguirre-Gutiérrez, Jesús; Mowbray, Lauren; Malhi, Yadvinder (2022), Submerged aquatic vegetation, water quality (pH, salinity, and turbidity) and waterfowl abundance data from 1991-2017 in Back Bay, Virginia, Dryad, Dataset, https://doi.org/10.5061/dryad.jh9w0vtbd

Abstract

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.

Methods

STUDY AREA

Back Bay, Virginia, USA (36°36’49” N, 75°56’40” W) is narrowly separated from the Atlantic Ocean by sand dunes and marsh impoundments to the east, bordered by emergent wetlands and maritime forest to the north and west, and connected to Currituck Sound through Knotts Island Channel to the south (Morton and Kane, 1994; USFWS, 2010) (Fig. 1). As Back Bay is situated approximately 128 km north of the nearest marine inlet, Oregon Inlet, North Carolina, the waterbody experiences no lunar tidal action; instead, water levels are dictated by the prevailing wind direction and speed (Morton & Kane, 1994; USFWS, 2010). Wind tides coupled with precipitation and watershed inputs influence the salinity levels of the bay, which has been historically characterized as oligohaline, ranging 0-3 parts per thousands (ppt) (Norman, 1991; USFWS, 2010).

WATERFOWL AERIAL SURVEYS

Aerial surveys designed to estimate wintering waterfowl abundance and distribution were conducted by the VDWR, in January of each year between 1998 and 2017, and by the Back Bay National Wildlife Refuge biologist in 2011 (January, February, March, November, December), 2012 (January, February, March), and 2013 (November, December). In both the VDWR and USFWS aerial surveys, waterfowl numbers were recorded by species in generalized locations in and around Back Bay. Mid-winter inventory flight transects were designed to provide complete aerial coverage of the mid-winter inventory survey unit, however USFWS surveys placed a greater focus on refuge and state park property (see Settle & Schwab, 1991). Count data from the refuge and state park impoundments were excluded here to retain compatibility with the SAV and water quality datasets.

Waterfowl counts by species and location were spoken into a handheld recorder (Sony ICD-BX140 4GB Digital Voice Recorder, San Diego, USA, or similar), and later reproduced onto handwritten datasheets. Scanned copies of the original datasheets were provided by the respective agencies and electronically transcribed. While pilots varied between surveys, the VDWR and USFWS surveys were conducted by the same two biologists, one state and one federal, respectively, thus reducing the probability of observer bias (Pearse et al., 2008). While Huesmann (1999) and Eggeman and Johnson (1989) caution against using aerial mid-winter survey data, the latter identify Virginia as an Atlantic Flyway state where surveys have been conducted with consistency in methods, personnel, route coverage, and survey effort. Waterfowl were categorized into guilds (dabbling ducks, swans and geese, and diving ducks) based foraging behavior (Table S1). Dabbling ducks normally feed by dabbling their bills or tipping forward in water ranging in depth from 5 to 30 cm (Guillemain et al., 2000; Sibley, 2000; Nelms et al., 2007). Swans and geese feed similarly, by tipping up or grazing, foraging at water depths of 0 to 10 cm (Fredrickson & Reid, 1988; Sibley, 2000; Tatu et al., 2007; Gyimesi et al., 2011; Nelms et al., 2007). Diving ducks prefer water greater than 25 cm in depth, where they can dive for SAV as well as animal matter such as clams, fish, and various other invertebrates (Pöysä, 1983; Sibley, 2000; Nelms et al., 2007).

WATER QUALITY MONITORING STATIONS

Monthly water quality data for salinity, pH, and turbidity collected by the Virginia Department of Environmental Quality was downloaded from the National Water Quality Monitoring Council’s Water Quality Portal for nine stations in and around the perimeter of Back Bay (NWQMC, 2021; Fig. 1). Salinity was measured via the electrical conductivity method, and pH value in water was measured by potentiometry using a standard hydrogen electrode (NWQMC, 2020). Turbidity was measured in nephelometric turbidity units (NTU) by the nephelometric method (NWQMC, 1995). While water quality measurements were collected with consistent methodologies, variation in the time of day that the samples were taken, and the speed and direction of wind during sampling events may have introduced variability into otherwise standardized measurement procedures.

SUBMERGED AQUATIC VEGETATION TRANSECTS

Submerged aquatic vegetation surveys in Back Bay were established in 1958 (Sincock et al., 1965) and have been surveyed annually by the VDWR since 2009 following the methods of Schwab et al. (1991) (G.R. Costanzo, VDWR, unpublished data). During each survey event, vegetation samples were taken at 500 m intervals along eight transect lines traversing Back Bay (Fig. 1). At each 500 m interval, three two-square-foot bottom samples were taken using modified oyster tongs, and species of SAV was recorded along with a visual estimate of percent cover or density (trace, low, medium, and high) (Fig. S2). The one or two-day sampling events were conducted annually between September and November. Annual percent frequency of SAV in Back Bay was derived from dividing the number of samples with any SAV by the total number of samples taken each year.

STATISTICAL ANALYSIS

All quantitative analyses were conducted in the R statistical platform with the ‘lme4’, ‘MuMIn’, ‘lsmeans’ and ‘multcomp’ packages (Version 1.1.463, 2009-2018) (Hothorn et al., 2008; Bates et al., 2015; Lenth, 2016; Bartoń, 2019). As the data were not subject to overdispersion, we used a Poisson distribution model to investigate the impact of the selected environmental drivers on waterfowl abundance by foraging guild. The water quality and SAV variables were centered and standardized before analyses to allow for direct comparison of model coefficients between variables with different units (z-scores; Gelman, 2008). A Bayesian information criterion (BIC) comparison, which penalizes more complex models by excluding terms that explain only little variability in the data, was used to evaluate three models with three-way interactions between feeding guild, SAV percentage, and each water quality characteristic (pH, salinity, and turbidity) (Aho et al., 2014; Table 1). The first model comprises three, three-way interactions, including all additive terms and lower order interactions. The second model builds on the first by adding an offset for the number of waterfowl surveys per year. In addition to the added offset, the third model incorporates year as a random factor. The BIC comparison indicated that the first and second models were equally parsimonious. The second model, which includes three, three-way interactions between waterfowl guild, average water quality measurements, and annual vegetation frequencies from 2010 to 2017, as well as an offset for the number of waterfowl surveys per year, was chosen to evaluate the data (R2Adjusted = 0.46; Table S2). A Tukey multiple comparison of means analysis was subsequently run to assess differences between abundances across feeding guilds.

Usage 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