Towards an Understanding of the Interactions between Freshwater Inflows and Phytoplankton Communities in a Subtropical Estuary in the Gulf of Mexico

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

Ecosystem under Pressure: Examining the Phytoplankton Community in the High Ballast Water Discharge Environment of Galveston Bay, Texas (USA)

With steady growth in global commerce and intensified ship traffic worldwide, comes the increased risk of invasion by non-indigenous organisms. Annually, >7000 vessels traveled across Galveston Bay, Texas from 2005-2010. These vessels discharged ~106 million metric tons of ballast water, equivalent to ~3.4% of the total volume of the Bay. A majority of these discharging vessels originated from around the Gulf of Mexico and the Caribbean Sea. By evaluating the source and frequency of inoculations from various locations, we are striving to assess the invasibility risk to Galveston Bay by way of ballast water. We identified organisms from Galveston Bay, ballast water samples and growout experiments using molecular methods. To our knowledge, this is the first utilization of molecular methods to identify the phytoplankton community within Galveston Bay. Within Galveston Bay, we identified 15 genera of dinoflagellates, 2 of which have previously gone undetected including Takayama and Woloszynskia. Thirteen ballast water samples yielded twenty genera of Protists, Fungi or Animalia from at least ten different phyla. With more than seven genera identified, dinoflagellates were the most diverse group: including the known toxin producer Pfiesteria and Scrippsiella which has not previously been detected in Galveston Bay. The most common diatoms in the ballast water samples were Actinocyclus, Ditylum, Nitzschia, Stephanopyxis and Thalassiosirales. At the termination of the growout experiments eight genera of phytoplankton were identified including: Dinophysis, Gymnodinium, Gyrodinium, Heterocapsa, Peridinium, Scrippsiella, Chaetoceros and Nitzschia. With these findings, Galveston Bay has the potential to be both a recipient and donor region of dinoflagellates. Dinoflagellates, capable of forming harmful algal blooms leading to fish and shellfish kills, are being transported to Galveston Bay via ballast water. Our results suggest that Galveston Bay is at risk for invasive species introductions via ballast water and support the idea that a monitoring system within the ports as well as the bay should be put in place. The actions would help to maintain the current health of this ecosystem and aide in preventing a negative impact in the event of successful establishment of a non-indigenous species of phytoplankton transported to Galveston Bay via ballast water

Classification of samples using (A) agglomerative cluster analysis identified clusters which were significantly different from each other.

<p>Small clusters originating from same major branch were grouped together (to increase <i>n</i> 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.</p

Similarity in (A) pigment concentration and (B) relative pigment abundance visualized in ordination space and coded according to results of the agglomerative cluster analysis (in Fig 3) showing Cluster A (red), B (blue) and C (green).

<p>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 <i>b</i> (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.</p

To understand the variation driving the <i>a posteriori</i> clustering of samples, we assessed the frequency of samples taken during specific <i>a priori</i> categories including months (A) and at specific stations (B) within each of the clusters (see Fig 3).

<p>Major clusters are represented as A (red), B (blue) and C (green).</p

Water quality parameters measured in Galveston Bay during the 2008–2009 sampling campaigns (timing shown in A with *): freshwater inflows on the Trinity River (m³ s^-1; A), rainfall (cm; B), temperature (°C; C), salinity (unitless; D), chl <i>a</i> (μg L^-1; E), F_v/F_m (relative units; F), total suspended solids (μg L^-1; G), secchi depth (m; H), and nutrient concentrations (µmol L^-1): DIN (I), DIP (J), TN (K), TP (L), which were then used to calculate ratios of DIN:DIP (M) and TN:TP (N).

<p>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).</p

Spearman’s correlation among environmental distances and (A) pigment concentration and (B) relative pigment abundance dissimilarity matrices.

<p>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.</p

Map of Galveston Bay (Texas, USA) showing the locations of the six fixed stations, the major sources of freshwater inflow – Trinity and San Jacinto Rivers – and the site of the exchange with the Gulf of Mexico (Station 1).

<p>Stations are located in the four major sub-Bays: San Jacinto Bay (Station 5), Trinity Bay (Station 6), East Bay (Station 3), and West Bay (Station 2) with Station 4 located in the middle of the Bay.</p

The relative frequency of <i>a priori</i> categories “season” and “area” within <i>a posteriori</i> derived hierarchical clusters (see Fig 3A).

<p>These factors targeted expected temporal and spatial variability in phytoplankton community structure.</p