Subtropical estuaries worldwide face increased pressure on their ecosystem health and services due to increasing human population growth and associated land use/land cover changes, expansion of ports, and climate change. We investigated freshwater inflows (river discharge) and the physico-chemical characteristics of Galveston Bay (Texas, USA) as mechanisms driving variability in phytoplankton biomass and community composition between February 2008 and December 2009. Results of multivariate analyses (hierarchical cluster analysis, PERMANOVA, Mantel test, and nMDS ordination coupled to environmental vector fitting) revealed that temporal and spatial differences in phytoplankton community structure correlate to differences in hydrographic and water quality parameters. Spatially, phytoplankton biomass and community composition responded to nutrient loading from the San Jacinto River in the northwest region of the bay (consistent with nutrient limitation) while hydraulic displacement (and perhaps other processes) resulted in overall lower biomass in the Trinity River delta (northeast region). The influence of inflows on phytoplankton diminished along a north to south gradient in the bay. Temporally, temperature and variables associated with freshwater inflow (discharge volume, salinity, inorganic nitrogen and phosphorus concentrations) were major influences on phytoplankton dynamics. Dissolved inorganic nitrogen: phosphorus (DIN:DIP) ratios suggest that phytoplankton communities will be predominately nitrogen limited. Diatoms dominated during periods of moderate to high freshwater inflows in winter/spring and were more abundant in the upper bay while cyanobacteria dominated during summer/fall when inflow was low. Given the differential influences of freshwater inflow on the phytoplankton communities of Galveston Bay, alterations upstream (magnitude, timing, frequency) will likely have a profound effect on downstream ecological processes and corresponding ecosystem services.
Citation: Dorado S, Booe T, Steichen J, McInnes AS, Windham R, Shepard A, et al. (2015) Towards an Understanding of the Interactions between Freshwater Inflows and Phytoplankton Communities in a Subtropical Estuary in the Gulf of Mexico. PLoS ONE 10(7): e0130931. https://doi.org/10.1371/journal.pone.0130931
Editor: Senjie Lin, University of Connecticut, UNITED STATES
Received: March 20, 2015; Accepted: May 27, 2015; Published: July 2, 2015
Copyright: © 2015 Dorado et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: All relevant data are within the paper.
Funding: This research was supported by grants from Texas Sea Grant (NA06OAR4170076), Texas Water Development Board (0904830894 and 1004831016), the Texas Commission for Environmental Quality (CE-00F208-01-0) and the Coastal Coordination Council under NOAA (NA08NOS4190458).
Competing interests: The authors have declared that no competing interests exist.
The health and welfare of aquatic and marine ecosystems has changed significantly over the last 100–200 years [1–6]. Substantial milestones in the principles and objectives governing freshwater management in the US include, but are not limited to, the establishment of the Army Corps of Engineers (in 1802; http://www.usace.army.mil/) and the Reclamation Service (in 1902 http://www.usbr.gov/) which have created water efficiencies for irrigation, navigation, and flood protection (e.g., the Hoover Dam). Important 20th century environmental management legislation included the National Environmental Policy Act (1969), the Clean Water Act (1972) and the Endangered Species Act (1973), which deal specifically with regulating impacts to US waterways (www.epa.gov). Similar governance and protection of freshwater resources has been applied worldwide (see United Nations Environmental Programme; www.unep.org/). In order to inform modern management strategies and regulation procedures scientists are challenged to define processes within target systems, specifically estuaries and coasts, where landscapes are shifting in response to human activities and climate change.
Changes in land use, riverine diversions and construction (e.g., levies, dams) have altered both quantities and ratios of nutrients and sediment loading into our rivers, estuaries and coasts. Significant hypoxia and algal (some harmful) blooms observed on the Louisiana continental shelf in the Gulf of Mexico are argued to be the result of nutrient enrichment in the Mississippi River, one of the world’s longest rivers and the largest drainage system in North America [7–9]. Similar observations have been made for other highly modified complexes including the Nile River in Africa , the Yangtze River in Asia , the Baltic Sea in Europe  and others [1,3–5,13].
Coastal development is occurring at an unprecedented pace such that concerns about the susceptibility and resilience of these ecotones need to be addressed. Desalination processes, for example, are required to produce freshwater for human consumption in many rapidly developing but arid countries. These processes however, result in the salinization of coastal waters, which has been linked to increased frequency of algal blooms [14–15]. When toxins are present, contaminated water supplies can no longer be used to support human requirements. Shellfish and finfish populations may be subjected to these toxins resulting in fish kill events  and in some cases overfishing is also occurring, making it difficult to separate one phenomenon from the other . Further, shipping traffic and the consequent discharge of ballast waters into receiving ports has led to an increase in propagule pressure and concurrently an increase in the appearance of invasive species in many coastal systems [18–19].
Identifying relationships between phytoplankton and freshwater inflows (river discharge into estuaries) is important for predicting ecological impacts resulting from urbanization and industrialization upstream as well as climate change and sea level rise associated with coastal processes downstream [20–21]. Phytoplankton populations are especially sensitive to changes in water chemistry and nutrient regimes [1,6,13,22–23]. Nutrient loading driven by inflow events temper primary production and alter phytoplankton community composition [1,2,4–6,22–24]. When supplied in the appropriate concentrations and ratios, nutrients contribute positively to estuarine water quality and resident primary producers [24–26]. While nitrogen is primarily considered, phosphate stimulation of phytoplankton is also observed [22,27–30]. Variability of phytoplankton responses (biomass, community composition, turnover rates, timing and magnitude of blooms) in turn influences higher trophic levels that depend on them for the assimilation of organic matter. While much of the work to date has been done in temperate ecosystems such as Chesapeake Bay [28,31] and San Francisco Bay , less is understood about interactions between freshwater inflows and phytoplankton dynamics in subtropical estuaries.
As the second largest estuary in the northern Gulf of Mexico (~1554 km2) and seventh largest in the United States, Galveston Bay, receives ~60% of the urban and industrial waste water (treated and untreated) of Texas . The Galveston Bay watershed, which includes the Dallas/Fort Worth metroplex and Houston, is home to nearly half the residents of Texas. The population within this watershed is expected to double by 2050 . Commercial and recreational fishing on Galveston Bay generates over one billion dollars annually . The areas around the bay are home to some of the nation’s largest petrochemical and industrial complexes; nearly half of all U.S. petrochemical production occurs in the greater Houston area. Further, it is home to three major Ports (Houston, Texas City, and Galveston) which handle upwards of 8000 ships annually (average from 2005–2010), discharging > 108 metric tons of ballast water directly into the bay [18–19]. The actual numbers and volume may be higher since this only reflects vessels reporting. Point (industrial, municipal) and non-point (agricultural, urban, suburban, and rural) discharges are major pollution sources [23,35]. These have been linked to fish kills over the decades, with Thronson and Quigg  identifying this bay as a hot spot in Texas. The Galveston Bay Estuary Program (one of 28 EPA National Estuary Programs in the US), identified beneficial freshwater inflows as a priority area in its comprehensive conservation management action plan, considering they ‘support the salinity, nutrient, and sediment loading regime needed to ensure the welfare of economically important and ecologically characteristic species in Galveston Bay .
The objective of this study was to develop an understanding of the downstream ecological influences of freshwater inflows on water quality and phytoplankton (biomass, community composition) spatio-temporal patterns. Building on the findings of Pinckney  and Roelke et al. , we used multivariate community analysis methods to assess the phytoplankton variability as correlated to environmental parameters. Given that freshwater inflows have differential influences on the phytoplankton communities of Galveston Bay, alterations upstream (magnitude, timing, frequency) will have a profound effect on downstream ecological processes.
Materials and Methods
No specific permissions were required for these locations/activities as we were collecting water samples on public waters. There was no animal research or other activities requiring any kinds of permits.
Galveston Bay, also referred to as the Trinity-San Jacinto Estuary (29.5°N, 94.8°W), is composed of four major sub-bays: San Jacinto Bay (Stn. 5), Trinity Bay (Stn. 6), East Bay (Stn. 3), and West Bay (Stn. 2) (Fig 1). It functions as the drainage basin for the largest watershed in Texas (85,470 km2). Guthrie et al.  updated surface inflows for the nine rivers, creeks, and bayous draining into Galveston Bay for 1977–2005. Based on the historical hydrologic record, the Trinity River (55%) is the largest provider, followed by the San Jacinto River (16%) and Buffalo Bayou (12%), with significantly smaller contributions from the remaining sub-watersheds (total– 17%). Indirect exchange with coastal water from the Gulf of Mexico occurs primarily through a narrow pass separating Galveston Island and Bolivar Peninsula (Station 1). The tidal regime is microtidal (0.15–0.5 m) such that wind forcing and freshwater inflows are important mixing mechanisms in this shallow bay (~2.1 m average). Daily river flow from the Trinity River was compiled from gauged data (US Geological Survey, Romayor Station #08066500) located ~48 km upriver and above the tidally influenced salt wedge. A monthly average (discharge) and the maximum value for each month (max discharge) were used for the statistical analyses. Rainfall data were obtained from Station Anahuac 6E, Wallisville, near the mouth of the Trinity River (weathersource.com). Fig 1 was prepared using the software ESRI ArcGIS Version 10.2.
Monthly sampling (February 2008 to December 2009) was performed from a small vessel (R/V Phyto I). Surface (0–0.5 m) samples collected in acid-cleaned dark polycarbonate bottles from six fixed stations (Fig 1) were stored in an insulated cooler for transportation to the lab (6–8 h; due to size of bay). Nutrient analyses were conducted on water samples filtered under low vacuum pressure (< 130 kPa) through pre-combusted Whatman GF/F filters and the filtrate frozen (-20°C) immediately in acid cleaned high density polyethylene bottles previously triple rinsed with extra filtrate. Nitrate (NO3-), nitrite (NO2-), ammonium (NH4+), and inorganic phosphorus (DIP) were analyzed according to procedures in . Dissolved inorganic nitrogen (DIN) concentrations were calculated by summing NO3-, NO2-, and NH4+. The detection limit for NH4+ was 0.070 μmol L-1; any values below detection were considered 0.069 μmol L-1 for the calculation of DIN. NOx is the sum of NO3- + NO2-; NOx was never below detection. The detection limit for DIP was 0.030 μmol L-1; any values below detection were considered 0.029 μmol L-1 for calculation of DIN:DIP ratios. Total nitrogen (TN) and total phosphorus (TP) analysis were performed on unfiltered samples according to . The filter from the nutrient analysis was dried at 60°C until a constant weight was reached to determine the total suspended solid (TSS) concentration.
A Fluorescence Induction and Relaxation (FIRe) system was used to calculate the quantum yield for photochemistry (Fv/Fm) in surface waters after performing a blank correction . The concentration of chlorophyll (chl) a and phytoplankton accessory pigments were measured using HPLC according to Pinckney et al.  on samples filtered and immediately frozen (-80°C). Pigment peaks were identified by comparing retention times and absorption spectra with pure standards (DHI, Denmark). The synthetic carotenoid β-apo-8’-carotenal was used as an internal standard. Accessory pigments provide reliable measures of the relative abundance of characteristic algal groups, including fucoxanthin for diatoms, alloxanthin for cryptophytes, peridinin for dinoflagellates, chl b for green algae, and zeaxanthin for cyanobacteria [37–39]. Traditional microscopic methods were used to verify that the dominant groups were indeed present as indicated by the HPLC analysis.
Averages are presented plus or minus standard deviation (±SD). Variation in the concentration and relative abundance of accessory pigments was assessed using multivariate community analysis methods using the R computing environment (Version 2.9.0)  and a suite of packages, mainly the “vegan” library (Version 1.15–3) . Environmental data were log(x+1) transformed to scale data before subjecting it to any analysis. To calculate the relative pigment abundance, a community data matrix was developed using Eqs 1 and 2 in which each accessory pigment concentration was divided by the respective chl a concentration (standardization to maximum) for that station: (1) and then divided by the sum of all the accessory pigments (standardize to total): (2) where xAP is the concentration of an accessory pigment (μg L-1) and xChl is the concentration of chl a (μg L-1) in Eqs 1 and 2 respectively. The result is equal to the fraction of each accessory pigment in relation to 1 chl a molecule (see also ) and is equivalent to Wisconsin double standardization commonly used in ecological community analysis methods . This pigment matrix was used to determine the relative importance of algal groups using environmental vector fitting.
Triangular similarity matrices of pigment concentration and relative abundance data were calculated using the Bray-Curtis index in order to characterize samples according to their similarity in pigment profiles. This pigment concentration matrix was then subjected to hierarchical cluster analysis using Ward’s minimum variance method  and a similarity profile test (SIMPROF) was performed using the “clustsig” package in R . SIMPROF allowed us to identify whether the clusters were significantly different from each other based on p < 0.05. Averages of the concentration and relative abundance of accessory pigments within each of the clusters was then calculated allowing us to classify groupings according to quantitative and qualitative differences in pigments. The frequency of each month and station between clusters was then assessed to summarize temporal and spatial trends within the sample groupings. Assessing the relative frequency of month and stations among clusters allowed us to assign samples to a season (Winter/Spring, Summer/Fall, and Transitional) and area (North Bay, Mid Bay, East Bay and South Bay).
We then used permutational multivariate analysis of variance (PERMANOVA)  to identify whether the overall pigment concentration and relative abundance data were significantly different between designated areas and seasons. We separated the phytoplankton data and ran PERMANOVA for each individual pigment which served as a univariate counterpart to this test. We repeated this to pinpoint the specific pigments whose concentration and relative abundance were significantly different between clusters. To relate these findings back to the environment, we used PERMANOVA to assess whether environmental parameters were significantly different across seasons, areas, and clusters.
We then further evaluated the relationship between the pigment and environmental data as well as functional community parameters including biomass and photosynthetic efficiency (Fv/Fm) using the Mantel test of matrix correlation [45,46]. To visualize the spatial autocorrelation, an inter-sample Euclidean distance matrix was constructed for the environmental data and plotted against the dissimilarity matrices of the concentration and relative abundance of accessory pigments. By running a Mantel test against these matrices, a Spearman’s correlation coefficient (ρ) was calculated and significance (p < 0.05) of the correlation was tested by running random permutations of the data. To assess spatial and temporal dissimilarity, month and station averages were calculated for the pigment and environmental data and their respective dissimilarity matrices were also subjected to Mantel tests. We then ran the Mantel test across all environmental variables to gain a more detailed understanding of the correlation between the phytoplankton and environmental dissimilarity.
Pigment concentration and relative pigment abundance data were then ordinated using non-metric multidimensional scaling (nMDS) following the metaMDS procedure in the “vegan” library  to produce two separate ordination maps. An unconstrained ecological interpretation of the ordinations was then performed using environmental vector fitting (envfit procedure) which allowed us to correlate the two dimensions of the nMDS solution to all the environmental parameters collected. Environmental parameters which were significantly correlated to the pigment dissimilarity matrices according to Mantel test were expressed as vectors on the nMDS plot. The length of each vector is referred to as its strength and longer vectors indicate a greater degree of correlation. Stations on the ordination map were color coded according to the cluster they were assigned to when subjected to hierarchical cluster analysis. In addition, station symbols were coded according to a respective season which was determined according to results from calculating the frequency of months within each major cluster.
Seasonality of freshwater inflows and water quality
Monthly sampling campaigns in 2008/2009 covered both high (typically spring or fall) and low (summer) freshwater inflow events (Fig 2A). Samples were collected in 19 of the 24 months (Fig 2A); when sampling campaigns were missed it was typically due to high winds which frequent the bay especially during winter/spring. There were >30% more freshwater inflows from the Trinity River into the bay in 2009 relative to 2008. Significant freshwater inflow events (freshets) are defined herein as >283 m3 s-1 . Monthly averaged flows which contributed to freshets were observed from March to May 2008 (spring), March 2009 (spring) and October to December 2009 (fall/winter). There was one false inflow event during the study period recorded immediately after Hurricane Ike made landfall on September 13th 2008; this peak reflects that drainage of surge waters back into Galveston Bay. In 2009, large monthly rainfall events (>25 cm) preceded large inflow events (Fig 2B) while in 2008 this was not the case. Temperatures were warm (October to April) and cool (May to September) (Fig 2C) and with averages of 28.6 °C (±2.5) and 18.2 °C (±3.3) respectively, characteristic of non-arid subtropical systems [47–48].
A subset of the data was used to show important spatial and temporal patterns as well as the ranges of values measured: Station 6 (adjacent to the Trinity River; green triangles), Station 5 (adjacent to the San Jacinto River; red circles) and Station 1 (closest to the Gulf of Mexico; blue diamonds).
To observe spatially sensitive as well as temporal patterns in water quality in Galveston Bay, we plotted data measured at Station 6 (adjacent to the Trinity River), Station 5 (adjacent to the San Jacinto River) and Station 1 (closest to the Gulf of Mexico). Although both Stations 5 and 6 directly receive riverine inputs, the water quality is known to be very different and so we show data collected from both these stations. Stations 2 and 3 had very similar patterns to Station 1 while Station 4 located mid-bay, had water quality parameters which were within the ranges experienced at Stations 6, 5 and 1. Salinity (presented on the unit-less practical scale) was lowest in the upper bay (Stations 5 and 6) and highest near its mouth (Station 1; Fig 2D) reflecting the estuarine gradient. Chl a did not vary significantly (p = 0.15) on temporal scales at Station 1 (Fig 2E; Table 1), with values ranging from 3.24–12 μg L-1. During 2008 at Station 5, chl a concentrations were lower during winter/spring compared to summer/fall, opposite to inflow patterns. In 2009, they essentially mirrored inflow patterns with only a few exceptions (Fig 2E). Chl a concentrations were similar in magnitude (from 0.66 to 23 μg L-1) at Stations 5 and 6, but the patterns at each station were very different.
Significant differences between the phytoplankton and environmental parameters in each of these categories are bold. Non-significant relationships are denoted using n.s and p-values for significant results are indicated as follows:
There were no trends in Fv/Fm measurements (often used as a proxy of phytoplankton health) (Fig 2F); Fv/Fm varied from 0.23–0.57 with a mean of 0.42 (±0.06). Total suspended solids were highly variable on both spatial and temporal scales at all stations varying >70-fold from 2.67 to 197 μg L-1 between 2008 and 2009 (Fig 2G). Secchi depths were generally lower at Stations 5 and 6 which mimicked each other (0.25–1.24 m; mean = 0.62 m) while those at Station 1 were generally higher (0.35–1.53 m; mean = 0.93 m) (Fig 2H). Station 4, in the middle of the bay, had secchi depths from 0.39 to 1.32 m with a mean of 0.68 m. These generally shallow Secchi depths are reflective of the particulate inputs by the two major rivers and the wind driven mixing in this shallow (2.1 m) estuary. The gradient (based on means) reflects the estuarine nature of Galveston Bay.
High freshwater discharge corresponded to increased DIN concentrations (Fig 2I) but not always DIP (Fig 2J). DIN concentrations were always < 20 μmol L-1 at Station 1 but as high as 80 μmol L-1 at Stations 5 and 6 at different times of the year. The exception was during summer when inflows were generally very low. DIP concentrations were on average 3 times lower in the lower part of the bay compared to the upper riverine influenced stations and less variable (0.72 μmol L-1 ±0.26 at Station 1 compared with 2.92 μmol L-1 ±1.79 at Stations 5 and 6). While TN concentrations were highly variable on spatial and temporal scales (Fig 2K), TP followed the same general patterns as DIP (Fig 2L). DIN:DIP ratios were generally lower than Redfield’s ratio (8.72±10.38; range 2–57) (Fig 2M). DIN:DIP ratios were on average lower at Station 5 (7.51) and Station 6 (6.36) than at Station 1 (13.23) respectively. TN:TP ratios were similar at Stations 5 and 6 but generally < 16 while those at Station 1 were more variable and higher (Fig 2N).
PERMANOVA and its univariate contrast were used to identify particular water quality parameters as being significantly different across (1) seasons (Winter/Spring, Summer/Fall, and Transitional), (2) areas (North Bay, Mid Bay, East Bay and South Bay) and/or (3) clusters (see Fig 3 below) as summarized in Table 1. With regards to the measured environmental parameters, temperature, NOX, NH4, DIN, and phytoplankton biomass were significantly different (p < 0.05) between seasons (Table 1). Salinity, NOx, TN, Pi, TP, TSS, and secchi depth measurements were significantly different (p < 0.05) between areas of the bay. Although the PERMANOVA did not identify freshwater discharge as being significantly different between the designated categorical variables of season, area or cluster (see more below), variation in nutrients, salinity, and turbidity ultimately stem from variation in the timing and magnitude of freshwater inflows.
Small clusters originating from same major branch were grouped together (to increase n values and statistical robustness) targeting major shifts in phytoplankton community structure. The major clusters are represented throughout as A (red), B (blue) and C (green). To identify differences in the structure of each major cluster, the (B) average pigment concentration and (C) relative pigment abundance (means ± standard deviation) were also calculated.
Variation in phytoplankton community structure
Cluster analysis of pigments from the 19 sampling campaigns identified six clusters (A, B1, B2, C1, C2, and C3) that were significantly different from each other (p ≤ 0.05) (Fig 3A). These clusters, based on relative concentrations of fucoxanthin, alloxanthin, peridinin, chl b and zeaxanthin, ultimately reflect shifts in phytoplankton community structure. We grouped clusters according to their association to one another on the dendrogram (i.e. cluster B consisted of B1 and B2) to increase the n value and robustness of statistical analyses, and simultaneously targeting the most dominant changes in phytoplankton community. Overall, cluster A had lower pigment concentrations when compared to clusters B and C. Fucoxanthin concentrations (1.17 ±0.45 μg L-1) were more than double zeaxanthin concentrations (0.43 ±0.34 μg L-1) and greater than all the other accessory pigments (peridinin = 0.18 ±0.21 μg L-1, alloxanthin = 0.22 ±0.15 μg L-1, chl b = 0.51 ±0.25 μg L-1) in cluster A (Fig 3B). Stations from cluster B were characterized by the greatest fucoxanthin (3.59 ±1.50 μg L-1), peridinin (0.75 ±1.10 μg L-1) and alloxanthin (0.47 ±0.32 μg L-1) concentrations among the three clusters and exhibited moderate concentrations of zeaxanthin (0.84 ±0.69 μg L-1) and chl b (0.91 ±0.57 μg L-1) (Fig 3B). This suggests a dominance of eukaryotic phytoplankton such as diatoms, dinoflagellates, and cryptophytes. Cluster C exhibited the inverse trend to cluster B with concentrations of zeaxanthin (2.95 ±1.47 μg L-1) eclipsing those of the other pigments (fucoxanthin = 1.82 ±0.96 μg L-1, peridinin = 0.44 ±0.48 μg L-1, alloxanthin = 0.35 ±0.22 μg L-1, chl b = 1.19 ±0.58 μg L-1), signifying cyanobacterial dominance (Fig 3B). This is supported by relative abundance data (Fig 3C), which shows the community composition of clusters A and B was dominated by fucoxanthin (20–25%) while zeaxanthin (25%) was most abundant in cluster C. Results indicate separation between the major clusters stem from quantitative and qualitative differences in the phytoplankton community composition.
Temporal and spatial trends among clusters
To gain an understanding of the variation driving the classification of samples across time, we assessed the frequency of samples taken during certain months within each of the clusters (Fig 4A). Our analyses show that clusters A and B were dominated by samples taken from November to April while cluster C samples were primarily from June to September. The months of May and October are unique in that they are associated with many of the samples from each of the clusters; these months also serve as transitional periods between warm and cool periods (Fig 2C), respectively. When the relative frequency of these seasonal periods within each cluster is calculated (Table 2), we found that 72.2% of the samples occurring from November to April (winter/spring) were found in Cluster A, 70.7% of the samples from June to September (summer/fall) were found in cluster C and 50.0% of samples from May and October were in cluster B (transitional). These findings suggest that temporal variability strongly influenced the grouping of samples during our study period.
Major clusters are represented as A (red), B (blue) and C (green).
These factors targeted expected temporal and spatial variability in phytoplankton community structure.
When the frequency of each station was taken into consideration, we were able to establish spatial differences among the phytoplankton community in Galveston Bay (Fig 4B; Table 2). The distribution of pigments in Stations 5 and 6 (North Bay), are similar and a majority of these samples fell in cluster A (53.3%). The northern areas of the bay exhibited low overall phytoplankton biomass with a community composition dominated by diatoms and other eukaryotic phytoplankton. Conversely, an increased number of samples in cluster C originate from Stations 1 and 2 found in the southern part of the bay (47.4%) representing an overall increase in the concentration and relative abundance of cyanobacteria in this area. Stations 3 and 4 are not similar to each other but each is comprised of a pigment profile that is different from the northern and southern stations (Table 2). Station 4 has relatively proportional amount of samples from cluster A and C (42.1 and 36.8%, respectively); this is consistent with its location in the middle of the bay and suggests this is a transitional area where marine water originating from the Gulf of Mexico and fresh water coming from the Trinity and San Jacinto Rivers mix. Station 3 is located in East Bay and likely exhibits a unique pigment profile because it is hydrographically distinct from the rest of the bay.
Phytoplankton community structure and environmental gradients
PERMANOVA and its univariate contrast were also used to identify particular pigments as being significantly different across (1) seasons, (2) areas and/or (3) clusters as summarized in Table 1. We found the concentration and relative abundance of the pigments fucoxanthin, alloxanthin, and zeaxanthin were significantly different (p < 0.05) across these three categorical variables (season, area, cluster) indicating phytoplankton groups containing these pigments within the overall community are the most sensitive to variations in time and space.
To further investigate the relationship between the environmental variables and phytoplankton in Galveston Bay, we used the Mantel test to directly correlate their respective dissimilarity matrices. Individual pigment concentration and relative pigment abundances were significantly correlated (Spearman’s ρ values = 0.36 and 0.29, respectively; p < 0.001) to environmental distances representing the overall environmental conditions (i.e., the combination of all 16 individual parameters) (Fig 5A and 5B respectively). This suggests that differences in the phytoplankton community structure increased along changing environmental gradients. To pinpoint the environmental properties that had the greatest influence on phytoplankton communities, we assessed the correlation of single environmental variable distances to the phytoplankton dissimilarities (Table 3). Our results show the dissimilarity in pigment concentrations was significantly (p < 0.05) related to differences in temperature (ρ = 0.28), Fv/Fm (ρ = 0.18), NOx (ρ = 0.18), NH4+ (ρ = 0.18), TP (ρ = 0.07), overall biomass (chl a, ρ = 0.71), DIN (ρ = 0.20), average freshwater discharge (ρ = 0.11) and maximum freshwater discharge (ρ = 0.11). When the relative abundance of pigments was tested, significant correlations (p < 0.05) included temperature (ρ = 0.30), salinity (ρ = 0.08), Fv/Fm (ρ = 0.14), NOx (ρ = 0.11), NH4+ (ρ = 0.10), Pi (ρ = 0.05), TP (ρ = 0.11), overall biomass (ρ = 0.15), DIN (ρ = 0.10), average freshwater discharge (ρ = 0.23) and maximum freshwater discharge (ρ = 0.24). Properties of freshwater inflow were found to correlate to differences in phytoplankton biomass and community composition which supports hierarchical cluster analysis and PERMANOVA results (Table 3).
Environmental distances represent Euclidean distance, and pigment dissimilarity was calculated using the Bray-Curtis index. Correlation and significance of correlation among matrices was assessed using the Mantel test.
Significant correlations are bolded, non-significant correlations are denoted using n.s., and p-values for significant results are indicated as follows:
To illustrate the interactions within our data set, we visualized the sample data in ordination space (nMDS) and projected environmental variables as vectors on the plot using environmental vector fitting. For individual pigment concentrations, we found samples from cluster A occurred on the negative side of nMDS axis 1 while those from clusters B and C lie on the positive side of this axis (Fig 6A). Dissimilarity between clusters B and C is also evident with samples from cluster B separated from cluster C along the second nMDS axis. Differences in season are also visible in Fig 6A: separation between winter/spring and summer/fall months is strong along nMDS axis 1 (circles vs. squares). The separation among transitional months (May and October) occurs on both the positive and negative side of nMDS axis 1 but samples are relatively concentrated in the second quadrant of the graph (+nMDS 1,-nMDS 2). The strength and gradient of freshwater discharge (DIS) and DIN vectors indicate that samples in the spring/winter (cluster A) exhibited decreased overall phytoplankton biomass. Samples taken during summer months exhibited overall greater zeaxanthin concentrations and accompanied by increased temperature and TP measurements (cluster C). Furthermore, Cluster B was composed mainly of transitional months (May and October; summer/fall) and increased diatom pigments and Fv/Fm. The relationship between pigment concentrations and the environment is difficult to discern because they all occur on the positive side of NMDS axis 1.
Seasons are defined as winter/spring (November-April, ○) and summer/fall (June-September, □) with two transitional months (May and October, ∆). Phytoplankton pigments included are fucoxanthin (F) for diatoms, alloxanthin (A) for cryptophytes, peridinin (P) for dinoflagellates, chl b (B) for green algae, and zeaxanthin (Z) for cyanobacteria. Non-metric multidimensional scaling coupled to environmental vector fitting shows significant environmental variables that correlate to pigment data as vectors on the plot.
When relative pigment abundance data were subjected to the same analysis; we found significant overlap between clusters A and B but not C (Fig 6B). This suggests that community composition between clusters A and B is similar and characterized by an increase in the relative abundance of fucoxanthin, supporting results of hierarchical cluster analysis (Fig 3C). On the other hand, Cluster C lies on the positive side of the first nMDS axis and its relationship to increased concentrations of zeaxanthin is shown (Fig 6B). Again, there is a clear separation between winter/spring months and summer/fall months with transitional periods distributed evenly across the first nMDS axis. There appears to be a gradient from fucoxanthin to zeaxanthin that is less apparent when pigment concentration data is subjected to nMDS analysis (Fig 6A). This may also stem from the overall low concentrations of all accessory pigments in cluster A. In addition it appears that some samples were characterized by increased peridinin concentrations indicating a short term increase in the level of this pigment (e.g., as a result of a dinoflagellate bloom). The strength and gradient of freshwater discharge, DIN, salinity and Fv/Fm vectors indicates these parameters were increased in samples characterized by increased relative abundance of fucoxanthin, peridinin and alloxanthin pigments. The vector properties of temperature, chl a, Pi, and TP indicate that these parameters were higher at stations that were dominated by cyanobacteria and green algae.
Worldwide, water regulators are faced with meeting demands of rising human populations, while maintaining critical riverine inputs of freshwater to sustain healthy estuaries and coasts, and their valuable ecosystem services [2–6,10–11]. Along with concerns of ongoing anthropogenic nutrient enrichment and human modification on hydrologic and hydrographic regimes, more recently, the impact of climate change is being considered [20–21]. Three factors are key and arguably the most important. First, droughts are predicted to increase in frequency and severity, and subsequently reduce freshwater delivery to estuaries [20,48–49]. Second, flooding may enhance nutrient loading, erosion and mobilization of land based nutrients [48,50]. Third, in most of the world’s oceans phytoplankton biomass has declined dramatically over the past century  while estuarine chl a has generally been found to be increasing [5,13,52]; both these have long term implications for fisheries. While the influence of freshwater inflows on salinity, nutrients, and turbidity in Texas estuaries have been generally described [23–24,50,53], the effects on biological components, especially phytoplankton, are less understood.
Recently Roelke et al.  defined for the first time co-occurring and opposing freshwater inflow effects on phytoplankton biomass, productivity and community composition in Galveston Bay for the period 2005–2006. In the sampling area adjacent to the San Jacinto River (equal to Station 5, Fig 1), inflows stimulated phytoplankton biomass and productivity while the opposite pattern was seen in the region near the Trinity River (equal to Station 6, Fig 1). Phytoplankton were frequently nutrient limited, responding rapidly to nutrient loading from the San Jacinto River while hydraulic displacement (and perhaps other processes such as grazing) resulted in overall lower phytoplankton biomass in the Trinity River delta. Given average inflows from the Trinity River (1977–2005) are ~3.5 times greater than the San Jacinto River , significantly greater riverine inflows negated the effect of nutrient loading on the phytoplankton in the upper northeastern sector of Galveston Bay.
Following these arguments to another station located in the mid the bay region (Station 4), it appears that phytoplankton respond to both nutrients and hydraulic activity, with both magnitude and duration of an inflow event defining which factor is more important at any one time. In the lower regions of Galveston Bay (Stations 1–3), phytoplankton biomass, productivity and nutrient concentrations are generally lower and not predictable or correlated with salinity and temperature as previously observed [24,27]; these stations are more reflective of the higher-salinity-nutrient-poorer waters frequently exchanged with the Gulf of Mexico. We observed similar such patterns in terms of biomass and productivity (not shown). Not unexpectedly, freshwater inflows have a diminishing influence on phytoplankton biomass with distance from the source. This phenomenon has been observed in other estuaries [39,54–57]. The present study and Roelke et al.  however, add that the behavior of the source (i.e., magnitude of flows) has important consequences and multiplicative effects, including co-occurring and opposing, that are influencing the phytoplankton community.
Stations 5 and 6 closest to riverine inputs (Trinity and San Jacinto Rivers) for Galveston Bay were characterized by greater biomass of diatoms and diversity of phytoplankton groups (diatoms, dinoflagellates, cryptophytes, green algae) compared to Stations (1–3) in the southern part of the bay. The latter were characterized by low overall phytoplankton biomass dominated by primarily cyanobacteria. Örnólfsdóttir et al.  found pulsed N input events preferentially favored increases in diatom biomass in the Trinity River delta (near our Station 6), and could even induce a shift from initially cyanobacteria dominated community to one of diatoms. Roelke et al.  found that cyanobacteria, haptophytes and euglenophytes did not respond to river inflows on the San Jacinto River side of the bay to the same level as diatoms. It was postulated that stimulation of these groups may not be driven by nutrient loading alone as they are able to utilize a variety of alternative nutrient acquisition strategies (i.e., diazotrophy, mixotrophy) not typically employed by diatoms. Alternatively, these groups are more sensitive to hydraulic displacement given their overall slower growth rates. Our observations of high diatom biomass in upper Galveston Bay despite the higher flows from the Trinity River compared to the San Jacinto River reflects environmental conditions, their overall faster growth rates and/or that the displacement of this phytoplankton group by inflows is low compared to their growth rates. The multivariate statistical analyses show that phytoplankton responses (biomass and community composition) are directly tied to parameters associated with freshwater inflow such as discharge (average and maximum) from the Trinity River, as well as DIN (and NOX, NH4) concentrations. These environmental parameters were found to be significantly different (p < 0.05) between sample assemblages identified using hierarchical clustering and significantly correlated (p < 0.05) to phytoplankton dissimilarity matrices. Considering the calculated flushing rates for Galveston Bay (0.08–0.035 d-1; ) are significantly lower than diatom growth rates (1–2 d-1), the ability of diatoms to accumulate in the upper region of the bay relative to other groups appears to be the result of their inherent physiological characteristics.
In the subtropical coastal Indian River Lagoon (Florida, USA), Badylak and Phlips  found that dinoflagellates, diatoms and cyanobacteria were the significant contributing phytoplankton groups to this system. It maybe that the low tidal mixing energy present there that is more favorable to dinoflagellates, which while present in Galveston Bay (present study; [18–19,58,60]) are not as abundant. It appears that dinoflagellates and cyanobacteria are favored the mid-and lower regions of Galveston Bay based on the multivariate analyses; locations where neither hydraulic displacement nor nutrient loading from freshwater inflows would have been important, but where relatively high residence times and low tidal mixing energy may contribute to their standing crops. Motile phytoplankton such as dinoflagellates and some species of cyanobacteria are selectively favored where vertical mixing energy is limited, partly because of their ability to access alternative carbon and nitrogen resources [61–62]. Chan and Hamilton  found diatoms, dinoflagellates and green algae were the main groups in the Swan River estuary, Western Australia, strongly separated spatially by flow and salinity (and temporally by season). As with the current study, diatoms occurred under a wide range of inflows while dinoflagellates were present only at very low discharges (this was attributed to group specific differences in potential growth rates). Unlike Galveston Bay, chlorophytes (green algae) were found to be important by Chan and Hamilton , but restricted to freshwater conditions. In the Swan River estuary, nutrients were found to be less important than flow and salinity in regulating phytoplankton biomass and succession while in Galveston Bay, nutrients were equally as important. The findings of Choudhury and Pal  for the Bhagirathi–Hooghly estuary in India are similar to those of Chan and Hamilton  in terms of primary driving factors and phytoplankton patterns. As in these two studies, Quigg et al.  found the switch between green algae and cyanobacteria in Moreton Bay (northeastern Australia) could mostly be explained by gradients in water clarity/turbidity. Hence, the magnitude of freshwater inflows relative to the physical properties of an estuary (e.g., size, depth) appear to be important in defining which groups of phytoplankton respond most strongly, and may explain why green algae were less important in Galveston Bay compared to other estuarine systems.
Although we highlight the role of freshwater inflows as a driver of phytoplankton structure, we must not overlook the importance of temperature (seasons) and other key variables in defining temporal patterns. Differences in temperature during the 2008–2009 sampling period correlated well with differences in the concentration and relative concentrations of pigments representing phytoplankton communities in Galveston Bay. Cluster analysis showed that winter and spring months are characterized by increased diatom, dinoflagellate and cryptophyte abundance while cyanobacterial abundance was higher during summer months in Galveston Bay, consistent with earlier studies [23,23,60] and in other estuaries [39,57]. This is partially driven by phytoplankton metabolism, with growth optima in the range 15–18°C and 20–23°C for diatoms and dinoflagellates respectively, and with cyanobacteria having higher growth optima in the 25–30°C range .
Months characterized by the dominance of diatoms (winter/spring) corresponded to periods of higher freshwater discharge and DIN concentrations. The dominance of diatoms among the phytoplankton community subsided during the summer months when freshwater discharge was also low. It can be rationalized from our findings that during summer months, a decrease in the magnitude of freshwater inflows leads to nutrient limitation, most likely as nitrogen, of the diatom community. At the same time, cyanobacteria which are able to more efficiently utilize nutrients because of their size and potential for diazotrophy, may outcompete the diatoms. Although the extent of N2 fixation in Galveston Bay has yet to be established, this deduction is supported by DIN measurements which show that inorganic nitrogen was scarce during summer/fall months and abundant during winter/spring months. These characteristics have been employed in management strategies to reduce the prevalence of cyanobacterial blooms .
Previous studies using short term bioassays in Galveston Bay found the resident phytoplankton community to be stimulated by nitrogen [23,58,60], specifically diatoms whose growth rate was enhanced by nitrate additions. It has also been argued that phosphorus also plays a role . Benthic phosphorus regeneration at the end of summer is a recurring process in the Galveston Bay estuary [23,27]; the impact on the phytoplankton community may be due to relief of summertime Pi limitation. Recent studies reveal that co-limitation by both nitrogen as nitrate and Pi is more commonly measured than either N (as nitrate) or P limitation alone in this  and other coastal systems [29,30,63].
Light limitation is known to be an important regulator of phytoplankton in estuaries and coastal systems [28,30–31,47,63], but did not appear to be an important factor in structuring the phytoplankton community in Galveston Bay in 2008 and 2009 or previously. Örnólfsdóttir et al.  found light limitation to be negligible in this system due to wind driven vertical mixing which keeps the shallow bay well mixed. We found that secchi depth and TSS were significantly different between areas of the bay but these factors ultimately did not control phytoplankton community structure (Tables 1 and 3).
Further research is needed to study the variables contributing to the succession of phytoplankton groups in Galveston Bay. Herein we identified May and October as key transitional periods where the foremost phytoplankton groups (diatoms and cyanobacteria) shift in dominance. Both historically and during the study period (2008–2009), May and the months leading up to it are characterized by the period of greatest freshwater inflow into Galveston Bay [23,47,53]. The diatom dominated spring bloom and subsequent collapse during late spring appears to be linked to changes in riverine inputs and may be important to the succession of cyanobacteria during the summer. Choudhury and Pal  also observed significant switches in the dominant phytoplankton groups (and diversity indices) timed with the summer/winter transition. Arguably, consequences of the key climate change factors being considered (drought, flooding) are going to be felt most strongly during these transitional periods, particularly in terms of implications for water quality, fisheries and health. For example, in a meta-analysis of water quality in the Dickinson Bayou watershed, a subsystem of Galveston Bay, Quigg et al.  identified shifts to elevated bacteria (fecal coliform, Escherichia coli and Enterococcus) and depressed dissolved oxygen concentrations (<3 mg l-1) during the warmer months when the water column stratified, and then a switch back in the cooler months. In years when the timing of these events was altered, fish kill events were more likely to be observed.
Food web implications
Although the details regarding trophic relationships in Galveston Bay have yet to be thoroughly understood, it is recognized that freshwater inflows play a major role in structuring food webs in this estuary . For example, Buyukates and Roelke  assessed the interactions between the phytoplankton and zooplankton community structure. It was found that pulsed inflows resulted in increased zooplankton biomass in all treatments which were accompanied by a concurrent decrease in phytoplankton biomass but an increase in species diversity. If freshwater inflows increase the abundance or variety of food sources for consumers in estuarine environments, it is reasonable to assume that the changes in the timing and magnitude of freshwater inflow events will alter the biomass of consumers and food web structure in those environments.
Grazing of phytoplankton by the benthic community in Galveston Bay may be more important than the grazing impacts associated with zooplankton. Örnólfsdóttir et al.  reported that grazers were responsible for consuming as much as 75% of daily phytoplankton production in the bay. The role of benthic communities in regulating phytoplankton may also explain some of the spatial patterns observed. For example, the Eastern oyster (Crassostrea virginica) found in the mid-region of the bay, is responsible for an estimated one-third of the state’s fishing income [35,65]. While it is understood that alterations in freshwater inflows influence the fecundity of oysters (quantity of spat as well as disease and parasite levels) ; less is known about the role of phytoplankton (biomass, community composition) (Sammy Ray, pers. comm).
Freshwater inflows have also been shown to contribute positively to the abundance and diversity of intertidal and benthic floral and faunal species assemblages in other Texas estuaries [20,23,35,47,50,53,55], but freshwater interactions among these assemblages in Galveston Bay have yet to be established. Identifying interactions between freshwater inflows and salt marsh communities is also critical as they serve as nursery habitats for important consumer organisms in this estuary. Assessing the stable isotopic abundance of carbon (δ13C) and nitrogen (δ15N) has been used to describe trophic relationships in estuarine food webs . In addition, δ15N has been used to trace anthropogenic nutrient inputs to marine systems and their eventual incorporation into food webs . Applying this powerful method to trace freshwater inflows and discern trophic relationships between food webs in Galveston Bay will undoubtedly increase our understanding of how structure and function in estuaries is driven by freshwater inflow events.
In Texas estuaries and others worldwide, scientists, engineers and resource managers have been challenged to define processes and develop freshwater management strategies which balance understanding of the current status of ecosystems against past changes and future pressures, studies such as the present one are important towards developing an understanding of the downstream ecological processes which are influenced by freshwater inflows.
This research was supported by grants from Texas Sea Grant (NA06OAR4170076), Texas Water Development Board (0904830894 and 1004831016), the Texas Commission for Environmental Quality (CE-00F208-01-0) and the Coastal Coordination Council under NOAA (NA08NOS4190458). The views expressed herein are those of the authors and do not necessarily reflect the views of the funding agencies or their staff. Many people from the Phytoplankton Dynamics Laboratory at TAMUG contributed to the successful completion of this project, Kung-Jen Liu (Calvin) from TAMU helped with nutrient analyses, and the following provided insightful and stimulating conversations: Jim Lester (HARC), Lisa Gonzalez (HARC), Ruben Solis (TWDB), Carla Guthrie (TWDB), George Guillen (UHCL) and Steven Johnston (GBEP). This paper is dedicated to Sammy Ray (1919–2013) and Charlotte Ray (1920–2014) who inspired a new generation to be passionate about all things related to freshwater inflows and the oysters they nourished. We thank the two reviewers and the editor for their careful reading of the manuscript before publication.
Conceived and designed the experiments: JLP SED DLR AQ. Performed the experiments: TB JS ASM RW HP. Analyzed the data: SD AS DLR AQ. Contributed reagents/materials/analysis tools: TB JS ASM RW AEBL HP. Wrote the paper: SD TB JS ASM RW AS AEBL HP JLP SED DLR AQ.
- 1. Olsen Y, Reinertsen H, Vadstein O, Andersen T, Gismervik I, Duarte CM, et al. (2001) Comparative analysis of food webs based on flow networks: effects of nutrient supply on structure and function of coastal plankton communities. Cont Shelf Res 21: 2043–2053.
- 2. Nixon SW, Buckely BA (2002) A strikingly rich zone—nutrient enrichment and secondary production in coastal marine ecosystems. Estuar 25: 782–796.
- 3. Flemer DA, Champ MA (2006) What is the future fate of estuaries given nutrient over-enrichment, freshwater diversion and low flows? Mar Pollut Bull 52: 247–258. pmid:16426644
- 4. Milliman JD, Farnsworth KL, Jones PD, Xu KH, Smith LC (2008) Climatic and anthropogenic factors affecting river discharge to the global ocean, 1951–2000. Glob Planet Change 62: 187–194.
- 5. Carstensen J, Sánchez-Camacho M, Duarte CM, Krause-Jensen D, Marbà N (2011) Connecting the Dots: Responses of Coastal Ecosystems to Changing Nutrient ConcentrationsEnviron Sci Technol 45: 9122–9132.
- 6. Paerl HW, Hall NS, Peierls BL, Rossignol KL (2014) Evolving Paradigms and Challenges in Estuarine and Coastal Eutrophication Dynamics in a Culturally and Climatically Stressed World. Estuar Coasts 37: 243–258.
- 7. Dagg MJ, Ammerman JW, Amon RMW, Gardner WS, Green RE, Lohrenz SE (2007) A review of water column processes influencing hypoxia in the Northern Gulf of Mexico. Estuar Coasts 30: 735–752.
- 8. Laurent A, Fennel K, Hu J, Hetland R (2012) Simulating the effects of phosphorus limitation in the Mississippi and Atchafalaya River plumes. Biogeosciences 9: 4707–4723.
- 9. Hetland RD, DiMarco SF (2008) How does the character of oxygen demand control the structure of hypoxia on the Texas-Louisiana continental shelf? J Mar Systems 70, 49–62.
- 10. Wahaab RA, Badawy MI (2004) Water Quality Assessment of the River Nile System: An Overview. Biomed Environ Sci 17: 87–100. pmid:15202868
- 11. Floehr T, Xiao H, Scholz-Starke B, Wu L, Hou J, Yin D, et al. (2013) Solution by dilution? A review of the pollution status of the Yangtze River. Environ Sci Pollut Res 20: 6934–6971.
- 12. De Jonge VN, Boiton W, D’Elia CF, Elmgren R, Welsh BL Responses to developments in eutrophication in four different North Atlantic estuarine systems. In: Dyer KR, Orth RJ, editors. Changes in Fluxes in Estuaries, Proc ECSA22/ERF Symposium, Denmark; 1994, pp. 179–196.
- 13. Cloern JE, Jassby AD (2008) Complex seasonal patterns of primary producers at the land–sea interface. Ecol Lett 11: 1294–1303. pmid:18793308
- 14. Sheppard CRC, Al-Husiani M, Al-Jamali F, Al-Yamani F, Baldwin R, Bishop J, et al. (2010) The Gulf: A young sea in decline. Mar Pollut Bull 60: 13–38. pmid:20005533
- 15. Quigg A, Al-Anasi M, Nour Al Din N, Wei C- L, Nunnally CC, Al-Ansari IS, et al. (2013) Phytoplankton along the coastal shelf of an oligotrophic hypersaline environment in a semi-enclosed marginal sea: Qatar (Arabian Gulf). Continental Shelf Res 60: 1–16.
- 16. Thronson A, Quigg A (2008) Fifty five years of fish kills in Coastal Texas. Estuar Coasts 31: 802–813.
- 17. Worm B, Barbier EB, Beaumont N, Duffy JE, Folke C, Halpern BS (2006) Impacts of biodiversity loss on ocean ecosystem services. Science 314: 787–790. pmid:17082450
- 18. Steichen J, Windham R, Brinkmeyer R, Quigg A (2012) Ballast water impacts on Galveston Bay, Texas. Bull Mar Poll 64: 779–789.
- 19. Steichen J, Schulze A, Brinkmeyer R, Quigg A (2014) All aboard! A biological survey of ballast water onboard vessels travelling the North Atlantic Ocean. Bull Mar Poll 87: 201–210.
- 20. Johns ND, Hess M, Kaderka S, McCormick L, McMahon J. Bays in peril: a forecast for freshwater flows to Texas estuaries. National Wildlife Federation, Merrifield, VA; 2004.
- 21. Richter BD, Thomas GA (2007) Restoring environmental flows by modifying dam operations. Ecol Soc 12: 12
- 22. Howarth RW, Marino R (2006) Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: evolving views over three decades. Limnol Oceanogr 51: 364–376.
- 23. Pinckney JL System-scale nutrient fluctuations in Galveston Bay, Texas (USA). In: Kromkamp JC, de Brouwer JFC, Blanchard GF, Forster RM,Creách V, editors. Functioning of Microphytobenthos in Estuaries, Royal Netherlands Academy of Arts and Sciences, Amsterdam; 2006. pp. 141–164.
- 24. Roelke DL,Li HP, Hayden NJ,Miller CJ, Davis SE, Quigg A, Buyukates Y (2013) Co-occurring and opposing freshwater inflow effects on phytoplankton biomass, productivity and community composition of Galveston Bay, USA. Mar Ecol Prog Ser 477: 61–76.
- 25. Spatharis S, Tsirtsis G, Danielidis DB, Chi TD, Mouillot D (2007) Effects of pulsed nutrient inputs on phytoplankton assemblage structure and blooms in an enclosed coastal area. Estuar Coast Shelf Sci 73: 807−815.
- 26. Mitrovic SM, Hardwick L, Dorani F (2011) Use of flow management to mitigate cyanobacterial blooms in the Lower Darling River, Australia. J Plankton Res 33: 229−241.
- 27. Santschi PH (1995) Seasonality of nutrient concentrations in Galveston Bay. Mar Environ Res 40: 337–362.
- 28. Fisher TR, Gustafson AB, Sellner K, Lacouture R, Haas LW, Wetzel RL, et al. (1999) Spatial and temporal variation of resource limitation in Chesapeake Bay. Mar Biol 133: 763–778.
- 29. Sylvan JB, Dortch Q, Nelson DM, Maier Brown AF, Morrison W, Ammerman JW (2006) Phosphorus limits phytoplankton growth on the Louisiana shelf during the period of hypoxia formation. Environ Sci Tech 40: 7548–7553.
- 30. Quigg A, Sylvan JB, Gustafson AB, Fisher TR, Tozzi S, Ammerman JW (2011) Going west: Nutrient limitation of primary production in the Northern Gulf of Mexico and the importance of the Atchafalaya River. Aqua Geochem 17: 519–544.
- 31. Malone TC, Crocker LH, Pike SE, Wendler BW (1988) Influences of river flow on the dynamics of phytoplankton production in a partially stratified estuary. Mar Ecol Prog Ser 48: 235–249.
- 32. Cloern JE (1996) Phytoplankton bloom dynamics in coastal ecosystems: a review with some general lessons from sustained investigation of San Francisco Bay. Rev Geophys 34: 127–168.
- 33. Armstrong NE, Ward GH Jr. Point Source Loading Characterization of Galveston Bay. The Galveston Bay National Estuary Program Publication GBNEP-36. The Galveston Bay National Estuary Program, Webster, TX; 1993.
- 34. Texas Water Development Board. State Water Plan for Texas: Highlights of the 2007 State Water Plan. GP-8-1, Austin, TX; 2007.
- 35. Gonzalez LA, Lester LJ. State of the bay: A characterization of the Galveston Bay ecosystem, Third Edition. Texas Commission on Environmental Quality, Galveston Bay Estuary Program. Houston, TX; 2011.
- 36. Guthrie CG, Matsumoto J, Solis RS. Analysis of the Influence of Water Plan Strategies on Inflows and Salinity in Galveston Bay. Final report to the United States Army Corps of Engineers, Contract #R0100010015. Texas Water Development Board, Austin, TX; 2012.
- 37. Pinckney JL, Millie DF, Howe KE, Paerl HW, Hurley JP (1996) Flow scintillation counting of 14C-labeled microalgal photosynthetic pigments. J Plankton Res 18: 1867–1880.
- 38. Dorado S, Rooker JR, Wissel B, Quigg A (2012) Isotope baseline shifts in pelagic food webs of the Gulf of Mexico. Mar Ecol Prog Ser 464: 37–49.
- 39. Paerl HW, Valdes LM, Pinckney JL, Piehler MF, Dyble J, Moisander PH (2003) Phytoplankton photopigments as indicators of estuarine and coastal eutrophication. Biosci 53: 953–64.
- 40. R Core Team (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. www.r-project.org/
- 41. Oksanen JF, Kindt R, Legendre L, O’Hara B, Simpson GL, Solymos P, et al. (2009) Vegan: community ecology package. R package version 1.15–2. R Foundation for Statistical Computing, Vienna. http://CRAN.R-project.org/package=vegan
- 42. Ward JH Jr (1963) Hierarchical Grouping to Optimize an Objective Function. J Am Statistical Assoc 58: 236–244.
- 43. Whitaker D, Christman M (2010) Clustsig: Significant Cluster Analysis. R package version 1.0. http://CRAN.R-project.org/package=clustsig
- 44. Anderson MJ (2005) PERMANOVA: a FORTRAN computer program for permutational multivariate analysis of variance. Department of Statistics, University of Auckland, New Zealand.
- 45. Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27: 209–220. pmid:6018555
- 46. Borcard D and Legendre P (2012) Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study. Ecol 93:1473–1481.
- 47. Quigg A, Schulze A, Booe T, Parnell A, Windham R, Trungale J. Flora and Fauna Responses to Freshwater Inflows in Galveston Bay. Final Report of the Texas Commission on Environmental Quality pursuant to US Environmental Protection Agency Grant Number: CE-00F208-01-0; 2013.
- 48. Trenberth KE, Dai A, Rasmussen RM, Parsons DB (2003) The changing character of precipitation. Bull Am Meteorol Soc 84: 1205–1217.
- 49. Wetz MS, Hutchinson E, Lunetta R, Paerl H, Taylor JC (2011) Severe droughts reduce planktonic production in estuaries with cascading effects on higher trophic levels. Limnol Oceanogr 56: 627–638.
- 50. Montagna PA, Brenner J, Gibeaut J, Morehead S Coastal Impacts. In: Schmandt J, North GR, Clarkson J, editors. The Impact of Global Warming on Texas, 2nd ed. University of Texas Press, Austin, TX; 2011. pp. 96–123.
- 51. Boyce DG, Lewis MR, Worm B (2010) Global phytoplankton decline over the past century. Nature 466: 591–596. pmid:20671703
- 52. Cloern JE, Jassby AD (2010) Patterns and Scales of Phytoplankton Variability in Estuarine–Coastal Ecosystems. Estuar Coasts 33: 230–241.
- 53. Longley WL Freshwater Inflows to Texas Bays and Estuaries: Ecological Relationships and Methods for Determination of Needs. Joint Estuarine Research Study: Texas Water Development Board and Texas Parks and Wildlife Department, Austin, TX; 1994.
- 54. Chan T, Hamilton D (2001) Effect of freshwater flow on the succession and biomass of phytoplankton in a seasonal estuary. Mar Freshwater Res 52: 869–884.
- 55. Russell MJ, Montagna PA, Kalke RD (2006) The effects of freshwater inflow on net ecosystem metabolism in Lavaca Bay, Texas. Estuar Coast Shelf Sci 68: 231−244.
- 56. Choudhury AK, Pal R (2011) Variations in seasonal phytoplankton assemblages as a response to environmental changes in the surface waters of a hypo saline coastal station along the Bhagirathi–Hooghly estuary. Environ Monit Assess 179: 531–553. pmid:21069456
- 57. Hall NS, Paerl HW, Peierls BL, Whipple AC, Rossignol KL (2013) Effects of climatic variability on phytoplankton biomass and community structure in the eutrophic, microtidal, New River Estuary, North Carolina, USA. Estuar Coastal Shelf Sci 117: 70–82.
- 58. Örnólfsdóttir EB, Lumsden SE, Pinckney JL (2004) Nutrient pulsing as a regulator of phytoplankton abundance and community composition in Galveston Bay, Texas. J Exp Mar Biol 303: 197–220.
- 59. Badylak S, Phlips EJ (2004) Spatial and temporal patterns of phytoplankton composition in a subtropical coastal lagoon, the Indian River Lagoon, Florida, USA. J Plank Res 26: 1229–1247.
- 60. Örnólfsdóttir EB, Lumsden SE, Pinckney JL (2004a) Phytoplankton community growth-rate response to nutrient pulses in a shallow turbid estuary, Galveston Bay, Texas. J Plank Res 26: 325–339.
- 61. Margelef R (1978) Life forms of phytoplankton as survival alternatives in an unstable environment. Oceanologica Acta 1: 493–509.
- 62. Smayda TJ Phytoplankton species succession. In: Morris I, editor. The physiological ecology of phytoplankton. Oxford, Blackwell. 1980, pp. 493–570.
- 63. Quigg A, Litherland S, Phillips JA, Kevekordes K (2010) Phytoplankton productivity across Moreton Bay, Australia: the impact of water quality, light and nutrients on spatial patterns. In, Proceedings of the Thirteenth International Marine Biological Workshop, The Marine Fauna and Flora of Moreton Bay, Queensland. Brisbane, Australia. Davie PJF, Phillips JA, editors. Memoirs of the Queensland Museum—Nature 54: 355–372.
- 64. Buyukates Y, Roelke DL (2005) Investigating system characteristics of a southwest Texas wetland: Nutrient and plankton dynamics of a tidal creek in the lower Nueces delta. J Appl Sci 5: 820–828.
- 65. Buzan DL, Lee W, Culbertson J, Kuhn N, Robinson L (2009) Positive relationship between freshwater Inflow and oyster abundance in Galveston Bay, Texas. Estuar Coasts 32: 206–212.