Estuaries and Coasts

, Volume 37, Supplement 1, pp 31–45

Hydrologic Variability and Its Control of Phytoplankton Community Structure and Function in Two Shallow, Coastal, Lagoonal Ecosystems: The Neuse and New River Estuaries, North Carolina, USA

  • Hans W. Paerl
  • Nathan S. Hall
  • Benjamin L. Peierls
  • Karen L. Rossignol
  • Alan R. Joyner

DOI: 10.1007/s12237-013-9686-0

Cite this article as:
Paerl, H.W., Hall, N.S., Peierls, B.L. et al. Estuaries and Coasts (2014) 37(Suppl 1): 31. doi:10.1007/s12237-013-9686-0


Hydrologic conditions, especially changes in freshwater input, play an important, and at times dominant, role in determining the structure and function of phytoplankton communities and resultant water quality of estuaries. This is particularly true for microtidal, shallow water, lagoonal estuaries, where water flushing and residence times show large variations in response to changes in freshwater inputs. In coastal North Carolina, there has been an increase in frequency and intensity of extreme climatic (hydrologic) events over the past 15 years, including eight hurricanes, six tropical storms, and several record droughts; these events are forecast to continue in the foreseeable future. Each of the past storms exhibited unique hydrologic and nutrient loading scenarios for two representative and proximate coastal plain lagoonal estuaries, the Neuse and New River estuaries. In this synthesis, we used a 13-year (1998–2011) data set from the Neuse River Estuary, and more recent 4-year (2007–2011) data set from the nearby New River Estuary to examine the effects of these hydrologic events on phytoplankton community biomass and composition. We focused on the ability of specific taxonomic groups to optimize growth under hydrologically variable conditions, including seasonal wet/dry periods, episodic storms, and droughts. Changes in phytoplankton community composition and biomass were strongly modulated by the amounts, duration, and seasonality of freshwater discharge. In both estuaries, phytoplankton total and specific taxonomic group biomass exhibited a distinctive unimodal response to varying flushing rates resulting from both event-scale (i.e., major storms, hurricanes) and more chronic seasonal changes in freshwater input. However, unlike the net negative growth seen at long flushing times for nano-/microphytoplankton, the pigments specific to picophytoplankton (zeaxanthin) still showed positive net growth due to their competitive advantage under nutrient-limited conditions. Along with considerations of seasonality (temperature regimes), these relationships can be used to predict relative changes in phytoplankton community composition in response to hydrologic events and changes therein. Freshwater inputs and droughts, while not manageable in the short term, must be incorporated in water quality management strategies for these and other estuarine and coastal ecosystems faced with increasing frequencies and intensities of tropical cyclones, flooding, and droughts.


Phytoplankton Coastal ecosystem Neuse River Estuary New River Estuary Hydrology 


Estuarine and coastal watersheds support approximately three quarters of the world’s human population; both the percentage and total population density continue to increase (Vitousek et al. 1997; Vörösmarty et al. 2000). Expanding agricultural, urban, and industrial development resulting from population growth in these watersheds have led to rising nutrient, sediment, and other contaminant loads, putting increasing pressure on water and habitat quality, and the sustainability of receiving waters (NRC 2000; Boesch et al. 2001; Elmgren and Larsson 2001; Howarth 2008; Paerl and Piehler 2008). Superimposed on these stresses are climatic perturbations, which are increasing in frequency and intensity (IPCC 2007, 2012; Holland and Webster 2007; Allan and Soden 2008). In particular, tropical cyclone activity has risen in the Atlantic and Pacific Ocean Basins since the mid-1990s, with more severe storms (Category 2 and higher) projected to impact North America’s Atlantic and Gulf Coast coastal regions (Webster et al. 2005; Bender et al. 2010). These regions are also experiencing record droughts, leading to extreme hydrologic variability (Trenberth 2005). Finally, global warming has accelerated sea level rise, and a large proportion of low-lying coastal regions are now threatened with inundation (Kemp et al. 2011). The extremeness of hydrologic events has impacted the delivery, processing, and fate of nutrients, sediments and other pollutants by these resource-rich waters.

Hydrologic variability represents a challenge to nutrient management, including formulating nutrient input reductions and predicting their success in countering eutrophication and its detrimental effects (harmful algal blooms, hypoxia, fish kills, etc.). It is critically important to know how effective nutrient loading reduction strategies will be over the short and long term, and whether responses to these strategies may at times be modulated, masked, or overridden by changes in freshwater discharge and flushing rates. In estuaries, water exchange and replacement are controlled by tides and freshwater throughput, and there is a highly dynamic balance between net phytoplankton production (biomass) and hydrologic flushing, or biomass removal (Pinckney et al. 1999; Cloern 2001; Lucas et al. 2009). Furthermore, major estuarine phytoplankton taxonomic groups (chlorophytes, cryptophytes, cyanobacteria, diatoms, dinoflagellates) exhibit differential maximum intrinsic growth rates. Therefore, the net accumulation and composition of phytoplankton biomass may be controlled by these hydrologic forcings. This is especially significant in lagoonal systems where low flushing rates and long water residence times normally allow for maximum phytoplankton biomass accumulation of even the slowest-growing taxa. However, if this pattern is perturbed by changes in flushing rates, significant impacts on phytoplankton biomass and composition may occur (Pinckney et al. 1999; Adolf et al. 2006; Paerl et al. 2006a, b; Valdes-Weaver et al. 2006).

North Carolina’s coastline consists of a long chain of barrier islands punctuated by only a few shallow, narrow inlets. Inside the barrier islands lies a series of semi-lagoonal estuaries, including the largest lagoonal ecosystem in North America, the Albemarle–Pamlico Sound (APS). These estuaries are generally poorly flushed, with residence times ranging from weeks to months due to low regional tidal amplitudes and the limited oceanic exchange through the small inlets (Pietrafesa et al. 1996). High nutrient loads, long residence times, and their shallow nature (generally <5 m depth) make these systems highly sensitive to nutrient inputs and susceptible to the detrimental symptoms of nutrient-over-enrichment, including excessive algal production and harmful algal blooms, bottom water hypoxia, and fish kills (Copeland and Gray 1991; Paerl et al. 1995, 1998, 2006a, b, 2007; Burkholder et al. 2006).

In addition to pressures associated with eutrophication, the North Carolina coastal watersheds have experienced at least eight hurricanes, six tropical storms, major floods, a series of exceptionally strong nor'easters, and record droughts during the past 2 decades (1995–2011). This, combined with encroaching sea level rise has raised concerns about the long-term viability and sustainability of the North Carolina coastline (Band and Salveston 2009). A lot is at stake, yet we know little about how to assess the interactive effects of a rise in climatic (hydrologic) perturbations on phytoplankton communities, which in these systems form the base of major food webs and are key determinants of water and habitat quality.

Here, we evaluate a long-term environmental data set from one of the major tributaries (and its basin) of the APS, the Neuse River Estuary (NRE), as well as a shorter-term data set from the neighboring New River Estuary (NEW), in order to identify and quantify impacts of hydrologic forcing on phytoplankton biomass community structure. We include data from previous and ongoing studies to develop a synthesis of these impacts in the context of the need to develop management options aimed at minimizing the potentially deleterious effects of excessive nutrient loading in a time of extreme hydrologic variability.

These coastal plain, semi-lagoonal estuaries have agriculturally dominated watersheds and are located within 100 km of each other. They have also been impacted by identical storm systems and droughts. We identified impacts from these hydrologic stressors on phytoplankton community structure and function, especially their roles in determining water quality and trophic state. Major questions include the following: (1) To what extent do variable hydrologic conditions determine phytoplankton community structure and function in these systems? (2) Can we determine stressor–response relationships that will enable researchers and managers to establish quantitative, predictive relationships between the intensity of the stressor and taxonomic group responses within the phytoplankton community? (3) Based on the information obtained above, can we determine and recommend what is “manageable” and what is not? Hopefully, addressing these questions can aid in establishing achievable management steps that will most effectively protect these systems' resources and ensure their sustainability in a more hydrologically variable and extreme world.

Methods and Materials

Site Descriptions

Neuse River Estuary

The NRE is the largest sub-estuary of the APS (Fig. 1). The average depth is ~3.5 m (Table 1) and the astronomical tidal range is <0.1 m (Luettich et al. 2000). Flushing rates vary greatly, depending on riverine discharge, but generally range from 1 week to over 4 months (Luettich et al. 2000). Its watershed drains rapidly expanding agricultural (animal and row crop operations), urban (Raleigh–Durham Research Triangle) and industrial centers within the piedmont and coastal plain regions of NC. Primary production in the NRE is strongly controlled by nutrient, especially nitrogen (N), inputs (Rudek et al. 1991; Boyer et al. 1993; Paerl et al. 1995). Excessive nutrient loading (dominated by non-point sources, ~80 %) associated with these expanding human activities has promoted eutrophication, nuisance algal blooms, hypoxia, toxicity, and food web alterations (Copeland and Gray 1991; Mallin et al. 1993; Paerl et al. 1998, 2006a,b, 2007; Lenihan and Peterson 1998; Buzzelli et al. 2002; Burkholder et al. 2004).
Fig. 1

Maps of the a New River Estuary and b Neuse River Estuary, North Carolina, showing the locations of sampling stations, Autonomous Vertical Profilers and US Geological Service (USGS) river gauging stations. The USGS gauging station on the Neuse River is located at Ft. Barnwell approximately 26 km upstream from New Bern and out of the area covered by panel b

Table 1

General physical/hydrologic characteristics of the Neuse River Estuary and New River Estuary, North Carolina


Neuse River

New River

Watershed size (km2)



Surface area (km2)



Average depth (m)



Discharge (m3 s−1)



Freshwater flushing time (days)



The NRE benefits from extensive long-term complimentary observational programs, ModMon and FerryMon, designed to cover the NRE (Fig. 1). These programs are spatially and temporally intensive and they include an extensive array of measurements. ModMon is a collaborative university–State of North Carolina (NC Dept. of Environment and Natural Resources, Division of Water Quality [NC DENR-DWQ])–stakeholder (Lower Neuse Basin Association/Neuse River Compliance Association) program (, which has operated since 1994. It consists of biweekly visits to 11 mid-river stations along the estuarine portion of the NRE (Fig. 1) for vertical profiling and collection of near-surface and near-bottom water samples (Paerl et al. 1995, 2006b, 2007). NC-DENR’s DWQ also samples the NRE stations once a month for similar physical–chemical measurements and water samples, and these data are shared in a common long-term database maintained by University of North Carolina at Chapel Hill, Institute of Marine Sciences (UNC-CH IMS) and DENR-DWQ (

New River Estuary

The NEW is a relatively small (88 km2), shallow (~ 1.5 m mean depth) coastal plain estuary, located in Onslow County, southeastern North Carolina (Table 1, Fig. 1). Most of the estuary consists of three broad lagoons that reside within the US Marine Corps Base Camp Lejeune (MCBCL). Flushing time in the NEW also varies with riverine discharge ranging from 8 to 187 days, with an average of 70 days (Ensign et al. 2004). Similar to the NRE, its shallow nature and poor flushing makes the NEW highly sensitive to nutrient inputs and the NEW experiences periodic phytoplankton blooms, including harmful species (Tomas et al. 2007) and periods of seasonal bottom water hypoxia (Mallin et al. 2005; Hall et al. 2013).

The NEW watershed and its nutrient inputs are dominated by agricultural activities, including row crop and concentrated animal feeding operations (CAFOs) (Mallin et al. 2005). The other major source of nutrient input to the NEW is the city of Jacksonville (2009 population: 80,500) located near the head of the estuary, which has a history of nutrient inputs from its municipal wastewater treatment plant. During the 1970s–1990s, partially treated waste was discharged to the New River at Jacksonville, which promoted highly eutrophic conditions in the upper NEW (Mallin et al. 1997, 2005). Improved wastewater treatment, starting in the late 1990s greatly decreased the nutrient (both N and P) load at Jacksonville, which led to marked declines in chlorophyll a concentrations in the upper estuarine regions (NC DENR 1990; Mallin et al. 2005). However, nutrient inputs associated with burgeoning upstream CAFO and row crop operations have increased significantly over the past several decades, threatening to reverse improvements in water quality.

Field data utilized in this study were obtained from an eight-station transect and 2 Autonomous Vertical Profilers along the central axis of the NEW (Fig. 1). This transect was established as part of the Defense Coastal Estuarine Research Program ( aimed at evaluating the impacts of the New River watershed and MCBCL on water quality and habitat condition of the NEW.

Sample Collection

Monthly samples for measuring phytoplankton biomass and composition were collected along an eight-station downstream transect from October 2007 through 2011 in the NEW and biweekly along an 11 station transect from 1998 through 2011 in the NRE (Fig. 1). At each station, surface (0.2 m depth) and bottom (0.5 m above bottom) samples were collected using either a Van Dorn water sampler or more recently a non-destructive diaphragm pump and dispensed into 4-l polyethylene bottles. Subsamples from each 4-l bottle were transferred to 40-ml glass bottles and preserved with 1 % Lugol’s solution for microscopic phytoplankton identification and enumeration. Bottles were placed in coolers held in darkness at in situ temperatures and transported to the UNC-CH IMS for processing. Biweekly to monthly samples were also collected from surface waters with pre-cleaned polyethylene bottles at two United States Geologic Survey (USGS) gauging stations (starting in April 2008) near the head of the NEW (#0209303205 and #02093000) that continuously monitor water level, temperature, salinity, and flow velocity.

Photopigment Determinations

Chlorophyll a (Chl a) served as an indicator of total phytoplankton biomass (Paerl et al. 2010), while diagnostic chlorophyll and carotenoid photopigments were used as indicators of major phytoplankton taxonomic groups (chlorophytes, cryptophytes, cyanobacteria, diatoms, dinoflagellates; Pinckney et al. 2001; Paerl et al. 2003). For Chl a analysis, filters were extracted using a tissue grinder in 90 % acetone (EPA method 445.0; Arar et al. 1997). Concentration of the Chl a extracts was measured using the non-acidification method of Welschmeyer (1994) on a Turner Designs TD-700 fluorometer calibrated with purified liquid Chl a standards (Turner Designs, Sunnyvale, CA, USA). Accessory photopigments representing major phytoplankton divisions were measured using high-performance liquid chromatography (HPLC) from surface and bottom water samples. Approximately 400–500 ml of sample water was vacuum-filtered using 47 mm GF/F filters (nominal pore size 0.7 μm). Filters were frozen and subsequently extracted in 100 % acetone, sonicated, and stored at -20 °C for approximately 24 h. Extracts (200 μl) were then injected into the HPLC. The HPLC procedures are described by Van Heukelem et al. (1994) and Pinckney et al. (1996, 1998, 2001). Pigments were identified according to their absorption spectra, which were determined using commercially obtained pigment standards (DHI, Denmark). Dominant photopigments that were measured and algal classes represented by those pigments and common to both estuaries are shown in Table 2. Periodically (particularly during blooms), phytoplankton were microscopically identified and enumerated from preserved samples according to methods described by Hall et al. (2013). Volume-weighted mean pigment concentrations on each date for each entire estuary were calculated by summing the product of mean station concentrations and segment volumes (Peierls et al. 2012), and then dividing that sum by the total estuarine volume.
Table 2

The ten most abundant phytoplankton photopigments contained within classes microscopically identified in the Neuse and New River Estuaries


Classes represented by pigment

Chlorophyll a

All classes


Diatoms, raphidophytes, chrysophytes, dinoflagellatesa, haptophytes




Same as for fucoxanthin plus dinoflagellates


Cyanobacteria, chlorophytes, chrysophytes, raphidophytes, cryptophytes

Chlorophyll b


Chlorophyll c

Same as for fucoxanthin plus dinoflagellates and cryptophytes


Cryptophytes and ciliophora containing cryptophyte chloroplasts


Raphidophytes, chlorophytes, chrysophytes


Dinoflagellatesa, haptophytes

aThese dinoflagellates are a small group of HAB–forming genera that contain chloroplasts of haptophyte (e.g., Karlodinium veneficum) or diatom (e.g., Kryptoperidinium foliaceum) origin. Classes in bold type indicate the dominant contributors to the pigment in the Neuse and New River Estuaries. Phytoplankton class pigment compositions were from Hoek et al. (1997)

Size-Fractionated Photopigments

From May 2007 through 2011, size fractionated accessory photopigments were measured in the NRE from surface water collected at stations 70 and 180. Subsamples of NRE water were filtered onto 3-μm pore size, 47-mm Nuclepore polycarbonate membrane and Whatman GF/F filters. The picophytoplankton and combined nano-/microphytoplankton fraction were defined as the fraction that passed through the 3-μm filter divided by the total retained by the 0.7-μm filter (Picophytoplankton Fraction = 1 − Pigment3μm/Pigment0.7μm; Nano/Microphytoplankton Fraction = Pigment3μm/Pigment0.7μm). Some picophytoplankton may pass through GF/F filters, and therefore the picophytoplankton fraction may be underestimated (Gaulke et al. 2010). Results were used to evaluate the utility of the dominant accessory photopigments as indicators of these two size fractions within the long-term record (1998–2011) of pigment concentrations within the NRE.

Freshwater Discharge and Flushing Time

Freshwater discharge to the NRE and NEW were continuously quantified by the USGS gauging station on the Neuse River near Ft. Barnwell (USGS gage #0209265810) and the New River gauging station near Gum Branch (Fig. 1; USGS gage #02093000), respectively. New River flow measurements at Gum Branch commenced 1 September 1949 and Neuse River flow measurements near Ft. Barnwell commenced 1 October 1996. To extend the flow record to the beginning of the study period in 1994 and to calculate long-term 50 year means, flow at Ft. Barnwell (QFB) was back calculated using a polynomial regression of QFB on flow at the next gauging station upstream near Kinston, NC (QKin) for the period 1 October 1996 through 31 December 2011 [QFB = 4.15 + 1.30QKin + 0.0004QKin2, R2 = 0.95]. Flows at Ft. Barnwell and Gum Branch represent 69 and 22 % of the watersheds of the NRE and NEW, respectively (Peierls et al. 2012). Total freshwater inputs for use in flushing time calculation were estimated by multiplying the gauged freshwater discharge by the ratio of total to gauged watershed area, exclusive of the estuary water surface (Ensign et al. 2004).

Flushing time for each system was calculated using the date-specific freshwater replacement method (Alber and Sheldon 1999). This measure of transport time scale provides an estimate of the freshwater age (Sheldon and Alber 2006), and is used to provide a time course for examining the transformation of materials (nutrients, phytoplankton, etc.) as freshwater is advected and mixed downstream through the estuaries. Details of the calculation are provided by Peierls et al. (2012).

Prior to analyses, photopigments indicative of total phytoplankton biomass (Chl a) and of algal size classes (pico- and nano-/microplankton) were natural log transformed. In cases where pigment concentrations were undetectable, a minimal value (0.5 times the lowest measured concentration) was assigned. Relationships between flushing time and pigment concentrations were fitted using a segmented linear regression analysis, consisting of two segments. Continuity of the segments was assured by defining the start of the second modeled segment as the end of the first segment. The breakpoint at which the two model segments meet was determined by iteratively fitting the segmented model with each observed flushing time and retaining the model with the breakpoint that minimized the sum squared error of the residuals. Since the breakpoint location was allowed to vary across the full range of flushing times, a single-segment linear regression was possible. Model fitting was performed using the least squares curve fitting “lsqcurvefit” function in Matlab v.7.1 (The Mathworks, Natick, CT, USA).

The full segmented empirical model is defined by four parameters: (1) y-intercept of the first segment, (2) slope of the first segment, (3) breakpoint salinity, and (4) slope of the second segment. Confidence intervals for these parameters were estimated by standard bootstrapping (Hall et al. 2004). The binning and curve fitting procedures described above were repeated with 1000 resampled data sets each of the same size as the original. The upper and lower bounds for the 95 % confidence interval are then defined as the 26th and 975th rankings of each parameter estimate (Hall et al. 2004) and are used to infer differences in phytoplankton size–class relationships with flushing time.

Results and Discussion

Freshwater delivery to both estuaries varied by more than three orders of magnitude across seasonal, interannual, and event time scales (Figs. 2a and 3a). A weak seasonality is apparent with generally higher flows during the late winter/early spring period when evapotranspiration rates within the watersheds are lowest (Litaker et al. 1987). Impacts of interannual variability in precipitation on river flows are also clearly evident. In particular, the regional droughts of 2001–2002 (Fig. 2) and mid-2007 through late 2009 (NC DENR 2012) resulted in flows well below long-term seasonal means for both systems. The remaining variability in flow is primarily associated with large pulses of freshwater that resulted from intense precipitation events generated by storm systems such as tropical cyclones, nor'easters, and stalled fronts.
Fig. 2

Time series of Neuse River flow and contour plots of the spatio-temporal distribution of diagnostic photopigments. a Daily (solid line) and long-term, 50-year (dotted line) mean freshwater flow at Fort Barnwell, North Carolina. X-axis tick marks are the first day of each year. Off-scale flows are labeled beside the peak flow. b Contour plot of chlorophyll a versus time and distance downstream with overlays of the 10-day (solid black line) and 30-day (dotted black line) freshwater age contours. c Fucoxanthin, d Peridinin, e Zeaxanthin, f Chlorophyll b, g Alloxanthin

Fig. 3

Time series of Neuse River flow and contour plots of the spatio-temporal distribution of diagnostic photopigments with overlays of the 10-day and 30-day freshwater age contours. Figure configuration is identical to Fig. 2. Data from 6 km downstream represent the average of data from stations 6 and 8

Diagnostic Phytoplankton Group Photopigments

Contour plots show the distribution of averaged surface and bottom diagnostic pigment concentrations along the axis of the estuary from 1998 to 2011 for the NRE and from 2007 to 2011 for the NEW, as determined by HPLC pigment analysis (Figs. 2 and 3). These plots illustrate the variability in the taxonomic composition of the phytoplankton community over time and space. Variability in river flow had a strong influence on phytoplankton biomass and community composition.

For most pigments, the maximum concentrations occurred in the mesohaline region of the NRE (~20 to 30 km). This location coincides with where the estuary broadens and flushing slows down, and freshwater age reaches about 10 days (see overlay of 10- and 30-day contours of freshwater age in Fig. 2). This appears to be the amount of time required for maximum biomass development and depletion of riverine DIN loads (Peierls et al. 2012). The same general pattern is also true for the NEW. However, the region within the NEW where freshwater age reaches ~10 days occurs near the upstream boundary of the area sampled (0 to 2 km). On most sampling occasions, riverine nutrient loads were depleted upstream of the NEW sampling area as evidenced by a lack of detectable nitrate (Hall et al. 2013; Peierls et al. 2012), which is elevated in the river due to wastewater discharge, and intensive animal and row crop operations (Mallin et al. 2005).

Concentrations of fucoxanthin, which mainly represents diatoms, raphidophytes, and a few dinoflagellate taxa (Table 2), were variable during the study period. There were prolonged periods of elevated fucoxanthin in the NRE during 2002–2003 and 2011, with concentrations at times exceeding 13 μg l−1 (Fig. 2c). Microscopic analysis of several of these blooms indicated that fucoxanthin concentrations found during warmer seasons (summer–fall) belonged to raphidophytes, or fucoxanthin-containing dinoflagellates (e.g., Kryptoperidinium foliaceum and Karlodinium veneficum), while primarily representing diatoms during cooler seasons (winter–spring) (Hall, unpublished data). The NEW also contained elevated fucoxanthin (>3 and up to 19 μg l−1) during 2011 (Fig. 3c), which was similarly contributed by diatoms during cooler seasons and flagellates during the warmer season (Hall et al. 2013).

Peridinin, which represents dinoflagellates, was common in both systems with blooms often occurring in the winter–spring period (Figs. 2d and 3d). For example, the NRE had a very high and prolonged period bloom of the dinoflagellate Prorocentrum minimum during the 2006–2007 winter, with cell concentrations >50,000 cells ml−1 and peridinin as high as 90 μg l−1. The downstream maximum of this bloom, as well as the winter blooms of 2004, 2005, and 2010, all occurred at freshwater ages considerably older than 10 days but were generally bounded by the 30-day contour. Slow growth due to low winter temperatures, likely increases the time necessary for bloom development, leading to bloom distributions at longer freshwater ages and greater distances downstream. For the NEW, peridinin concentrations were elevated (>3 μg l−1) over much of the length of the estuary in the fall of 2011, with highest concentrations (>20 μg l−1) occurring in the middle of the estuary (Fig. 3d).

Seasonal temperature changes can also influence community composition. For example, zeaxanthin concentrations exhibited seasonal fluctuations for both the NRE and NEW, with low concentrations during colder months and high concentrations in warmer months (Figs. 2e and 3e). Zeaxanthin is primarily contained within picoplanktonic cyanobacteria (Gaulke et al. 2010), as was confirmed by a 5-year record of size-fractionated pigment data in the NRE. It also appears this group becomes a more substantial fraction of the phytoplankton community during summer months (Figs. 2e, 3e and 4a, b).
Fig. 4

Volume-weighted mean pigment concentration (colored areas) and freshwater discharge (solid line) versus time in both systems. Concentration of each pigment is added to the height of each preceding pigment. Peri peridinin, Chlb chlorophyll b, Zea zeaxanthin, Allo alloxanthin, Fuco fucoxanthin

Chlorophyll b (Chl b), specific to chlorophytes, was mostly present at low (<1 μg l − 1) concentrations for both estuaries, with occasional peaks >3 μg l−1; the highest Chl b concentration in the NEW (>22 μg l−1) occurred in the upper region of the estuary during the spring of 2009 (Fig. 3f). Because they are relatively fast growing, prefer high nutrient conditions and thrive under low salinity conditions, chlorophytes tend to respond positively to elevated freshwater input events. Examples include the time periods following the high rainfall-containing storm events, such as in the NRE during hurricanes Dennis and Floyd (fall, 1999), and the very wet summer of 2003 (Fig. 2f). Chlorophyte abundance was often higher than most other groups near the head of the estuary under low-flow conditions and the downstream location of maximum abundance was highly tied to changes in freshwater age associated with variability in flow. Together, this suggests that the chlorophytes within the estuary are most likely freshwater taxa that are capable of growth under highly flushed brackish conditions (Figs. 2f and 3f).

Cryptophyte biomass, represented by alloxanthin, rarely reached above 3 μg l−1 for either system and occurred sporadically throughout the year. In the NRE, cryptophyte biomass generally tracked seasonal and episodic hydrologic changes (Fig. 2g). However, cryptophytes seemed less tied to hydrology within the NEW but displayed higher abundances during the fall and winter (Fig. 3g).

The general pattern of hydrological control can also explain much of the variability in the total pigment inventories (i.e., volume weighted averages) of both estuarine systems. When flow is high, maximum biomass develops further downstream within each estuary (Figs. 2 and 3). Particularly within the NRE, the volumes of these deeper and broader regions constitute the majority of the estuarine volume. Thus, higher freshwater inputs lead to greater total inventories of algal biomass (Fig. 4). However, under exceptionally high runoff events, both estuaries showed substantial decreases in pigment concentrations (Fig. 4). For example, in October 2010, all accessory pigments (i.e., other than chlorophyll a) concentrations were less than 0.2 μg l−1 in both systems (Figs. 2 and 3) following a high runoff period that was associated with the remnants of Tropical Storm Nicole combined with a low pressure system, which impacted the region during this time.

Indicator pigments for picophytoplankton and nano-/microphytoplankton were assessed for the NRE based on their specificity toward a size fraction as well as their frequency of detection in the estuary. Similar analyses could not be performed for the NEW due to lack of size fractionated pigment data. Of the 16 accessory pigments examined (most of which are listed in Table 2), zeaxanthin was judged to be the best indicator of picophytoplankton biomass. On average, greater than 80 % of the zeaxanthin was found in the <3-μm size fraction and zeaxanthin was detectable in 95 % of samples collected (Fig. 5). Picocyanobacteria (cf. Synechococcus sp.) are the dominant source of zeaxanthin within the picophytoplankton community in the NRE (Gaulke et al. 2010). Fucoxanthin and peridinin were judged to be the best indicators of nano-/microphytoplankton biomass due to their low fractions (<25 % and <15 %, respectively) within the picophytoplankton size class (Fig. 5), comparatively low variability in size distribution, and nearly ubiquitous presence within the estuary (detection frequencies of 100 % and 89 %, respectively). Additionally, previous work in the NRE has shown that peridinin-containing dinoflagellates and fucoxanthin-containing diatoms, raphidophytes, chrysophytes, and dinoflagellates constitute the majority of nano-/microphytoplankton identified microscopically within the NRE (Mallin and Paerl 1992; Rothenberger et al. 2009; Hall and Paerl 2011). Despite its low abundance within the picophytoplankton and high frequency of occurrence, diadinoxanthin was judged an inappropriate indicator because it is a photoprotective pigment and its cellular concentration is known to fluctuate greatly with light exposure history (Brunet et al. 1993). Other pigments showed less specificity for a size class (e.g., alloxanthin and Chl b), highly variable specificity (e.g., β carotene and lutein), or low detection frequency within NRE samples (e.g., 19′-butanyloxyfucoxanthin and 19′-hexanyloxyfucoxanthin).
Fig. 5

Mean percent of phytoplankton photopigments within the picophytoplankton size fraction from stations 70 and 180 in the Neuse River Estuary from May 2007 to December 2011. Error bars represent the standard deviation of the mean (N = 201). Frequencies of samples with detectable concentrations of each pigment are written above each bar

Flushing Time Relationships

The relationships between flushing time and pigment indicators for total phytoplankton biomass (fluorometrically determined Chl a), nano-/microphytoplankton (peridinin + fucoxanthin) and picophytoplankton (zeaxanthin) were assessed for the period from 1998 through 2011 for the NRE. Data prior to 1998 were excluded from this analysis due to differences in Chl a measurement methods and the lack of data downstream of station 120. Results show a strong degree of control of phytoplankton biomass and size structure that is dependent on the age of freshwater within the system (Fig. 6). As nutrient-laden freshwater ages during transport down the estuary, phytoplankton biomass builds as the river-supplied nutrients are assimilated. Peak biomass occurs at the point of exhaustion of the riverine load (Peierls et al. 2012), which occurs at a freshwater age of about 1 week for the long-term NRE record. Similar analyses on a shorter 3-year record (2008–2010) from the NEW found peak biomass at a freshwater age of 10 days (Peierls et al. 2012). Peierls et al. (2012) used a continuous non-monotonic function (Shepherd function) to fit the data. Fitting the Shepherd curve to long-term NRE data set revealed a flushing time maximum of 15 days (not shown), which suggests that part of the difference between the long-term and shorter term results is due to the difference in which non-monotonic function (i.e., segmented linear regression or Shepherd function) is used to fit the data. With the large degree of scatter evident within the long-term data, it seems likely that differences in time to peak biomass may also be a reflection of stochastic variability.
Fig. 6

Relationships between pigment concentrations (μg l−1) indicative of total phytoplankton biomass, nano-/microphytoplankton, and picophytoplankton versus flushing time. Solid lines represent the best-fit segmented linear regression of natural log-transformed pigment against flushing time. Results were plotted on a natural log abscissa for enhanced visualization of the data at short flushing times

Once riverine nutrient loads have been depleted, total phytoplankton biomass declines slowly (note that Fig. 6 is log–log), indicating that loss processes (zooplankton and bivalve grazing, mortality due to viral infections and bacterial attack, sedimentation, dilution with low biomass ocean water, etc.) are greater than intrinsic growth, yielding negative net growth. Since riverine nutrients are assimilated prior to the decrease in phytoplankton biomass, the relatively slow Chl a decline indicates that regeneration and non-riverine nutrient inputs (sediments, atmospheric, ground-water) effectively maintain productivity and biomass at long flushing times. The importance of regeneration and cycling of ammonium for maintaining production in the NRE has been previously shown through network analysis ecosystem models (Christian and Thomas 2003) and through 15 N uptake studies (Boyer et al. 1994; Twomey et al. 2005; Hall and Paerl 2011). Turnover of the ammonium pool is rapid (minutes to hours) due to high phytoplankton standing stocks and low (often <1 μmol l−1) residual ammonium concentrations (Christian and Thomas 2003; Hall and Paerl 2011). Under these conditions of high phytoplankton biomass and low N availability, smaller phytoplankton with more efficient ammonium uptake would be predicted to become a more prominent component of the community (Sunda et al. 2006).

Pigment indicators for the nano-/microphytoplankton fraction show a decline at freshwater ages greater than about 1 week (Fig. 6). The slopes and breakpoints for this large fraction were not significantly different from total phytoplankton biomass (Chl a), a reflection of their general dominance within the phytoplankton community (Fig. 6, Table 3). Picophytoplankton also displayed a similar increase in biomass up to a freshwater age of about 1 week. However, in contrast to the nano-/microphytoplankton fraction, the breakpoint for picophytoplankton divided the data into an initial rapid rise at short flushing times and a segment at longer flushing times with a lower but still significantly positive slope (Table 3). At long flushing times (>1 week), the decrease in the larger size class and slow increase of the picophytoplankton results in a net increase of the picophytoplankton fraction. The data are consistent with the hypothesis that growth of the nano-/microphytoplankton fraction becomes increasingly N limited at long flushing times, which allows the picophytoplankton to become more prominent under these conditions of steady diffuse regenerated nutrient supplies. This trend is positively reinforced as the picophytoplankton fraction is capable of drawing residual ammonium concentrations down to very low levels, further limiting growth of the nano-/microphytoplankton fraction (Sunda et al. 2006). In fact, concentrations of ammonium as low as 22 nmol l−1 have been measured in the lower NRE during severely N-limited summer periods (Hall et al., unpublished data) using a high-sensitivity fluorometric method (Holmes et al. 1999).
Table 3

Coefficients for segmented linear regressions of natural log-transformed photopigment concentrations representative of phytoplankton size classes versus flushing time

Phytoplankton size class



Break point


(representative pigment)

(95 % CI)

(95 % CI)

(95 % CI)

(95 % CI)

Total phytoplankton





(chlorophyll a)

(0.49 to 0.70)

(0.32 to 0.44)

(5.62 to 7.32)

(−0.0076 to −0.0059)






(fucoxanthin + peridinin)

(−2.34 to −2.07)

(0.40 to 0.54)

(6.12 to 7.64)

(−0.0082 to −0.0057)







(−3.51 to −3.07)

(0.30 to 0.62)

(4.12 to 7.94)

(0.0017 to 0.0056)

Intercept, Slope 1, Break point, and Slope 2 represent the y-intercept of the first segment, slope of the first segment, salinity at the break point, and slope of the second segment, respectively. Values in bold indicate a significant difference between that size class and total phytoplankton biomass based on non-overlapping 95 % confidence intervals (CI)

Variability in river flow is a dominant driver of the dynamics of phytoplankton biomass in shallow, microtidal systems due to freshwater discharge simultaneously acting as a source of nutrients and as a control of phytoplankton transport time. While enhanced flow delivers biomass stimulating nutrients to the estuary, it also decreases the flushing time. Under moderate flow conditions, flushing times in the upper estuarine regions can become too short to allow significant biomass accumulation (Peierls et al. 2012; Hall et al. 2013). Even higher flows push the zone of maximum biomass accumulation further downstream. Under the most extreme flow conditions (i.e., after major flooding from tropical storms), freshwater spends so little time in the estuary that little biomass accumulation within the estuary is possible and bioavailable nutrients are exported directly from the estuary without assimilation by the phytoplankton community (Peierls et al. 2003; Tester et al. 2003; Paerl et al. 2006a, b). As long as river flows do not reach this extreme point, the riverine nutrient load will be completely assimilated. In these two systems, this occurs at a freshwater age of 1 to 2 weeks and is remarkably consistent between the two systems. At freshwater ages beyond this point, shifts in competitive dominance of the phytoplankton community from larger to picoplanktonic taxa are consistent with an increasing degree of nutrient limitation of phytoplankton growth rates (Sunda et al. 2006).

Ramifications of this shift in phytoplankton community structure for water quality, including harmful algal blooms and hypoxia, and secondary production are unclear but deserve further study. Due to their small size, picophytoplankton are unlikely to settle as individual cells or be grazed by fecal pellet producing mesozooplankton. Therefore, picophytoplankton may not be expected to contribute greatly to sediment carbon loading which fuels oxygen consumption in the bottom waters. However, mesozooplankton grazing on the abundant microzooplankton in these systems (Wetz et al. 2011) that are capable of efficiently grazing such small cells is likely an important mechanism for picophytoplankton carbon export to both the sediments and to higher trophic levels. The microphytoplankton fraction, while being of a size that is more readily grazed and settles faster, also contains most toxic and/or unpalatable species (Tomas et al. 2007; Hall et al. 2008).

Increases in chlorophyte and diatom biomass frequently coincided with periods of elevated river flow (Figs. 2 and 3). Cyanobacterial and dinoflagellate biomass were reduced during these extreme river discharge events, while cryptophyte biomass was more variable in response to hydrologic changes (Figs. 2 and 3). In general, diatoms and chlorophytes have rapid growth rates, relative to dinoflagellates and cyanobacteria (Elliot et al. 2005). This is particularly evident during cooler months, since chlorophyte and diatom growth rates tend to be optimal at relatively low temperatures (Elliot et al. 2005; De Senerpont Domis et al. 2007). It is therefore hypothesized that the efficient growth rates and enhanced nutrient uptake rates of chlorophytes and diatoms during late winter and springtime allow for the rapid utilization of the short-term supply of nutrients associated with high discharge and short flushing times at these times of the year. In certain years, late winter dinoflagellate blooms (e.g., Heterocapsa triquetra) were observed, and these tended to take place when elevated winter freshwater discharge was followed by periods of relatively low rainfall (discharge) and clear weather. In contrast, cyanobacteria demonstrated greater abundance when river discharge was minimal and water temperatures were relatively high (i.e., summer–fall). Their growth appears to be optimal during periods of long residence time and water column stratification, and when water temperatures are maximal, conditions that characterize summer months (Reynolds 2006; Paerl and Huisman 2008). This is also a period of extreme N limitation in the lower estuary, favoring the dominance by picoplanktonic cyanobacteria (Gaulke et al. 2010).

Hurricanes that cause high rainfall in the watershed have been shown to significantly impact the phytoplankton community in the NRE and the receiving Pamlico Sound (Paerl et al. 1998, 2001, 2006a, b; Peierls et al. 2003; Tester et al. 2003) by radically decreasing the flushing time. However, smaller storms and rainfall events can also have an impact on community structure and function. A recent example was the impact of Tropical Storm Ernesto, which struck the North Carolina coast during September, 2006. This storm dumped over 30 cm of rainfall in some parts of the Coastal Plain, including the NRE watershed. The freshet associated with Ernesto formed a freshwater lens that covered relatively high salinity water resulting from previously dry conditions that dominated during the summer of 2006. The freshet delivered a massive infusion of nutrients at a time when nutrient inputs are usually low. That, combined with the very strong vertical salinity stratification that resulted when the low density freshet spread out over much saltier, high-density estuarine salt water, allowed for an intense (200,000 cells ml−1; Chl a >250 μg l−1), localized bloom of the toxic dinoflagellate Karlodinium veneficum to form in a the mid-estuarine region where residence time rapidly increases (Hall et al. 2008). Flushing times during this period were between 15 and 24 days, which is similar to the estimate of flushing time with maximum biomass. This bloom preceded several fish kills in the same region (Hall et al. 2008). Another example of hydrological impacts on phytoplankton biomass, although not from a tropical cyclone, was the extended period of elevated discharge in winter 2007, which was followed by high Chl a concentrations in the spring when flushing times reached the 1- to 2-week range.

While the historical record is much shorter for the NEW, we were able to observe the estuarine response to several large storm events, including a period of high rainfall storms in early to mid-2010, the remnants of tropical storm Nicole (2010) and Hurricane Irene (2011). Furthermore, we were able to examine the effects of two protracted drought years, 2008 and 2009. It is evident that, like the NRE, primary production, phytoplankton community composition, overall trophic state and water quality in the NEW is strongly controlled by hydrologic forcing (Hall et al. 2013; Peierls et al. 2012).

Management Implications

It will be impractical, if not impossible, to contain and or impound massive freshwater discharges associated with tropical cyclones and other extreme rainfall events in order to manipulate flow for optimizing production and composition of algal assemblages deemed most desirable from food web and water quality perspectives. In part, this constraint exists because there is no significant upstream reservoir storage capability. Also, these systems are situated in the coastal plain, which is a very flat region, highly susceptible to inundation. Therefore, the most prudent and effective strategy is to minimize nutrient and sediment inputs associated with hydrologic pulsing, particularly since we have entered a period of increasing hydrologic variability, in particular higher frequencies and intensities of tropical cyclones (cf., Webster et al. 2005; Trenberth 2005; Holland and Webster 2007; Band and Salveston 2009; Bender et al. 2010). It will be important to control excessive N and P loading to either estuary, as they each exhibit sensitivity to both nutrients. P limitation and N and P co-limitation are common in the upstream freshwater riverine portions, while N limitation dominates the oligo- to euhaline estuarine segments (Rudek et al. 1991; Mallin et al. 1997, 2005; Paerl et al. 1995, 2005). Large storm and rainfall events account for highly significant shares of the total annual N and P load (Paerl et al. 1998, 2001, 2006b). Therefore, nutrient load reduction strategies should be focused on such events.

In the agriculture-dominated NRE and NEW watersheds, nutrient (both N and P) and sediment inputs are dominated by diffuse non-point sources (approximately 80 % for the NRE and 70 % for the NEW) (NC DENR 1990; 2001a, b; Mallin et al. 1997; Paerl et al. 1995, 2006b, 2010; Stow et al. 2001; Burkholder et al. 2006; Lebo et al. 2012). As such, their release, transport, and inputs to the respective estuaries are strongly controlled by rainfall and resultant freshwater discharge over a range of seasonal and episodic time scales. Therefore, these are the most obvious targets for reduction. A comprehensive nutrient reduction strategy should focus on: (1) reducing non-point sources of both N and P by applying chemical and organic (e.g., animal waste) fertilizers at seasonally adjusted rates and avoiding applications of animal waste during periods when croplands are dormant and when episodic rainfall events are most likely (summer–fall thunderstorm and tropical cyclone periods); (2) minimizing sediment losses, possibly by instituting a greater degree of no-till agriculture and maintaining natural vegetation and cover crops wherever possible; (3) minimizing impervious surfaces such as parking lots, paved lots, etc.; (4) reducing the use of chemical fertilizers in residential areas (lawns, gardens, golf courses) to minimize losses of such fertilizers to nearby waterways; (5) reducing and/or attenuating losses of water from the watershed to the estuary by impounding waters, minimizing ditching, and controlling stormwater runoff in urban and rural regions. In this regard, the development and reclamation of wetlands, retention ponds, and installation of flashboard risers on existing ditches should be encouraged.

This study has provided information on how phytoplankton community composition is affected by hydrologic variability and extremes in these shallow, lagoonal estuaries. This information, combined with existing knowledge of their sensitivity to nutrient enrichment (Rudek et al. 1991; Mallin et al. 1997, 2005; Paerl et al. 1995, 2005), will enhance our predictability of how the combined effects of anthropogenic nutrient inputs and various hydrologic conditions and regimes will affect and potentially alter the structure of phytoplankton communities mediating primary production, food web dynamics, and water quality in response to a range of climate change scenarios. This is particularly relevant for lagoonal systems like the NRE and NEW, in which nutrient and sediment inputs can be captured and recycled over relatively long water residence times (weeks to months), making these systems highly sensitive to eutrophication and associated water quality degradation (cf. Kennish and Paerl 2010). It is incumbent to develop appropriate and effective nutrient and sediment input control measures in order to ensure protection and sustainability of these valuable ecosystems in a hydrologically more extreme world.


We thank co-workers in the Paerl Laboratory who assisted with field and laboratory work, including J. Braddy, L. Kelly, M. Hoffman, B. Abare, and R. Sloup. This research was conducted under the Defense Coastal/Estuarine Research Program (DCERP), funded by the Strategic Environmental Research and Development Program (SERDP), Project SI-1413, the Lower Neuse Basin Association/Neuse River Compliance Association, the North Carolina Dept. of Environment and Natural Resources (ModMon Program), and National Science Foundation Projects DEB 1119704, OCE 0825466, OCE 0812913 and CBET 0932632.

Copyright information

© Coastal and Estuarine Research Federation 2013

Authors and Affiliations

  • Hans W. Paerl
    • 1
  • Nathan S. Hall
    • 1
  • Benjamin L. Peierls
    • 1
  • Karen L. Rossignol
    • 1
  • Alan R. Joyner
    • 1
  1. 1.Institute of Marine SciencesUniversity of North Carolina at Chapel HillChapel HillUSA

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