Response of a phytoplankton community in a subtropical estuary to a pulse disturbance driven by a combo hurricane and rainfall event.

doi:10.21203/rs.3.rs-3802441/v1

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Response of a phytoplankton community in a subtropical estuary to a pulse disturbance driven by a combo hurricane and rainfall event.

https://doi.org/10.21203/rs.3.rs-3802441/v1

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Water rather than wind was the major destructive force behind Hurricane Harvey, the category 4 storm that stalled over Texas in August 2017. Herein we present a unique examination of the phytoplankton community as it responded to the changing hydrodynamic conditions in Galveston Bay. We used traditional (primary productivity, pigments, Fluorescence Induction and Relaxation System, pulse-amplitude modulation fluorometer) and modern (imagining flow cytobot [IFCB], 16S rRNA and 18S rRNA gene sequencing) approaches to characterize the phytoplankton community and their physiological response. Phytoplankton communities transitioned from pre-storm estuarine to freshwater species and then began to transition back to an estuarine community with similar but not identical structure to that present before the storm. Further, phytoplankton communities were initially displaced by the flood waters, which lowered biomass and productivity despite the high nutrient concentrations and dissolved inorganic nitrogen to phosphate ratios of ~ 16. We found for cyanobacteria, factors such as time (i.e., sampling event after the storm), temperature (°C), salinity (psu), NH₄⁺ (µM), NO₃⁻ (µM), and turbidity (Secchi and total suspended solids) were the most critical environmental factors that explained the variation in community composition relative to the major eukaryotic groups. Only factors such as time, followed by salinity, and NO₃⁻ played a crucial role for eukaryotes, with time having the most impact on diatoms. The results herein provide a greater understanding of the succession and natural variability of phytoplankton species following large flood events in subtropical estuaries, which are predicted to occur more frequently with increasing effects of climate change.

primary productivity

hurricane

pigments

photophysiology

imagining flow cytobot

community composition

Globally, hurricanes, storms, floods, and droughts are thought to be increasing in frequency at lower latitudes; these phenomena create large scale, acute disturbances for coastal aquatic terrestrial ecosystems (Valiela et al. 1998; Peierls et al. 2003; Liu et al. 2019; Philippot et al. 2021). While there remains debate on whether the frequency of hurricanes is increasing due to global warming, there is a general agreement that their intensity and associated precipitation are both on the rise (Thompson et al. 2023). Extreme flooding events tend to flush out phytoplankton (Paerl et al. 2019; Steichen et al. 2020; Clementson et al. 2021), while droughts lead to altered community composition because of shifts in water quality (Paerl et al. 2010; Quigg 2015). While it appears that in some cases phytoplankton communities can quickly return to their original composition and abundance, their resilience, or their ability to recover after a pulse disturbance requires further study (Shade et al. 2012; Huang et al. 2021). Important characteristics of resilient microbes (bacteria, archaea, phytoplankton) are thought to be dormancy, speciation rates, horizontal gene transfer, community composition, functionality (e.g., Philippot et al. 2021; Walker et al. 2022), while environmental characteristics in estuaries such as water residence times, distance from inflows and magnitude/duration of river discharge are often examined (Roelke et al. 2013; Pinckney et al. 2017; Paerl et al. 2019). It has been observed amongst bacteria that there are dominant “core” taxonomic and functional groups that remain in ecosystems despite changes induced during disturbances (Kurm et al. 2019; Walker et al. 2022); this is one form of community resilience which can be measured quantitatively. Similarly, amongst the phytoplankton, cyanobacteria and diatoms often are the first to respond after a disturbance in estuarine or marine waters (Dorado et al. 2015; Steichen et al. 2020). However, when disturbances are persistent, such as is the case with eutrophication, harmful algal bloom forming species may appear (Pinckney et al. 2001; Freeman et al. 2019; Anderson et al. 2021). Alternatively, persistent propagule pressures (i.e., number of introduction events × number of individuals per introduction event) may “allow” invasive species to take hold in a new location (e.g., Steichen et al. 2012; Briski et al, 2012) if it is no longer able to buffer the introductions.

Phytoplankton community composition exhibits large spatial and temporal heterogeneity in bays and estuaries, with the largest difference between the head (freshwater inflows entering the bay) and mouth (seaward edge), as species distributions most often correlate to hydrographic and water quality parameters. Temperate ecosystems receive highest river flow during the winter, experience greater ranges in temperature and peak biomass in spring while tropical ecosystems often have highest river flow in the summer, a more limited temperature amplitude and maximum biomass at different times depending on episodic runoff events (Glibert et al. 2006; Quigg et al. 2010; Paerl et al. 2019; Clementson et al. 2021). In sub-tropical estuaries, phytoplankton biomass peaks in the spring and to a lesser degree in the fall (Pinckney 2006; Roelke et al. 2013; Dorado et al. 2015). The most frequently studied drivers of phytoplankton community composition and biomass in estuaries and coasts are light and nutrients, which may act in opposite directions (Cloern 1996; Glibert et al. 2009; Cloern and Jassby 2010; Howarth and Marion 2006). That is, a reduction in water clarity may offset an increase in biomass driven by elevated nutrient concentrations (Peierls et al. 2003; Quigg et al. 2011; Harpole et al. 2011; Moore et al. 2013).

The occurrence of extreme floods resulting from high rainfall tropical storm events in the past 20 years has increased in frequency because of the increased moisture carrying capacity of storms, thought to be a result of the warming climate (Allan and Soden 2008; Lehmann et al. 2015; Paerl et al. 2019). During the 117 hours of Hurricane Harvey (August 25-30th, 2017), more than 1.29 × 10¹¹ m³ precipitation fell on Texas and Louisiana setting a continental US record for rainfall (Shultz and Galea 2017). Houston (the fourth largest city in the country) and the surrounding area were covered with 1043 mm³ day^− 1 rainfall from August 26–28 (van Oldenborgh et al. 2017), resulting in extensive flooding, overflowing reservoirs and overtopping dams (Shultz and Galea 2017) and alterations to the water chemistry of the bay and the adjacent offshore ocean (Chapman et al. 2023). Of the more than 13 million people directly affected by the storm, 6.5 million were impacted by 76 cm or more of rainfall during the flooding event (van Oldenborgh et al. 2017; Milliner et al. 2018). Significantly less awareness is available about the health of the ecosystems in response to this type of flooding event. Galveston Bay (Fig. 1) located south of Houston, received as much as 17 trillion m³ of freshwater during Harvey, most of which came in from the San Jacinto River (Thyng et al. 2020). Based on National Oceanic and Atmospheric Administration (NOAA) and Texas General Land Office (TGLO) buoy data, the total volume of freshwater added to Galveston Bay by rainfall and runoff during Hurricane Harvey was on the order of five times the bay volume, which was sufficient to flush all the saltwater out of the estuary leading to most of it being freshened in less than 4 days (Fig. 2). The residence times during the recovery of the bay to a normal salinity range varied from 10–20 days and up to three months, depending on location (Du et al. 2019; Thyng et al. 2020).

Our objective was to examine the impact of Hurricane Harvey on the phytoplankton community in Galveston Bay. Along the sampling transect (Fig. 1), salinity ranged from 0–5 psu even a week after the storm (Fig. 2), when salinities at the same locations and time of year would historically range 20–30 psu (Roelke et al. 2013; Dorado et al. 2015). Given the nature of responding to a storm event, we utilized both traditional (primary productivity, pigments, Fluorescence Induction and Relaxation [FIRe] System, pulse-amplitude modulation (PAM) fluorometer [PHYTO-PAM]) and modern (Imagining FlowCytobot [IFCB], 16S and 18S rRNA gene sequencing) approaches. In addition, we measured nutrients and other water quality parameters. Samples were collected along a gradient from the head of the estuary where the major industrial and urban centers are located (Houston) to the mouth of the bay which opens to the Gulf of Mexico (Fig. 1). Before Harvey the phytoplankton community was dominated by estuarine and marine diatoms and dinoflagellates which transitioned to primarily freshwater species (cyanobacteria and green algae) immediately following the flooding event (Steichen et al. 2020). In the weeks following the hurricane, a transition back to estuarine and marine phytoplankton was observed, but the composition of the community was not identical to that which was present before the hurricane.

Study Area and Sampling Design

Galveston Bay (Fig. 1) is the second largest (1,544 km²) estuary in the northern Gulf of Mexico and seventh largest estuary in the United States. It receives ~ 60% of the treated and untreated urban and industrial wastewater of Texas (Armstrong and Ward 1993). Along with the billion-dollar commercial and recreational fishery, it is home to some of the nation’s largest petrochemical and industrial complexes (Gonzalez and Lester 2011). Two major rivers (Trinity and San Jacinto) typically deliver 55% and 16% of the annual riverine discharge, respectively (Guthrie et al. 2012). Buffalo Bayou delivers 12% and smaller tributaries account for the remaining freshwater inputs. Bolivar Roads and San Luis Pass are the major exchange zones with the Gulf of Mexico. Five sampling campaigns were conducted immediately following Hurricane Harvey aboard the R/V Trident: T1 (09/04/2017), T2 (09/09/2017), T3 (09/16/2017), T4 (09/21/2017), and T5 (09/28/2017). Ten stations (H1–H10) were sampled along a transect from the San Jacinto River mouth (H1) to the mouth of Galveston Bay near the Gulf of Mexico (H10) as shown in map of Galveston Bay (Fig. 1; ESRI ArcGIS Version 10.2). Sampling focused on the outflow of the San Jacinto River rather than the Trinity River because the majority of urban and industrial flows, rainfall, land runoff, and other sources were entering the estuary from this direction (Thyng et al. 2020).

Water Quality

Modeled monthly freshwater inflows are calculated by the Texas Water Development Board (TWDB) by summing gauged and ungauged inflows to the estuary. United States Geological Survey (USGS; www.waterdata.usgs.gov) gauge locations used in the TWDB model include the Trinity River at Romayor (USGS 08066500), the San Jacinto River at Lake Houston near Sheldon (USGS 08072000) and Buffalo Bayou at Houston (USGS 08074000). Downstream of these gauges, streamflow was estimated using the sum of the Texas Rainfall-Runoff (TxRR) model data, flows diverted from streams by municipal, industrial, agricultural and other users and unconsumed flows returned to streams (Guthrie et al. 2012; TWDB 2019). Precipitation data was obtained from the NOAA National Centers for Environmental Information, with values recorded from Scholes Airport, located 9.6 km from the Texas A&M University at Galveston campus (TAMUG). The Imaging Flow-Cytobot time series station is located on the TAMUG campus (see below). The Texas Bays and Estuaries continuous water quality monitoring stations house datasonde data (https://waterdatafortexas.org/coastal). These calibrated sondes measure salinity, temperature and other parameters hourly from the middle of the water column (average water depth is 2.4 m). Salinity is measured on the unit-less practical salinity unit scale. The locations of the three stations used herein are shown in Fig. 1: Fishers Reef (F) at 29°67’N, 94°85’W, Mid-Bay (M) at 29°50’N, 94°87’W and Bolivar Roads (B) at 29°34’N, 94°78’W.

During sampling, a calibrated Hydrolab MS5 datasonde was used to measure water quality parameters at each station including temperature (°C) and salinity. Turbidity was estimated as Secchi disk depth (m) and total suspended sediment (TSS). Unfiltered surface water samples (100 mL) were collected to measure total nitrogen (TN) and total phosphorus (TP), while dissolved nutrients [nitrate (NO₃-), nitrite (NO₂-), ammonium (NH₄+), phosphate (P_i), and silicate (HSiO₃-)] were measured in the filtrate (Whatman GF/F, 0.7 µm; <130 kPa) by the Geochemical and Environmental Research Group at Texas A&M University using a Lachat QuikChem AE autoanalyzer. Dissolved inorganic nitrogen (DIN) was calculated as the sum of NO₃⁻, NO₂⁻ and NH₄⁺.

Surface water was also collected to measure total suspended sediments (TSS) and pigment analysis (see below) at all time points and all stations. For TSS, the water (100 mL) was filtered through a pre-weighed and combusted (450°C) 47 mm GF/F and dried (60°C) and then reweighed after 48 hrs. The difference in weight was used to calculate the TSS (APHA 1998).

Primary production – Light data bottle method

Following a modified light and dark bottle technique (Wetzel & Likens 2000), BOD bottles (250 mL) were filled with surface water from stations H1, H4, H7 and H10. The sampling started and finished within the same time window (10:00 h and 14:00 h) at each station to avoid bias due to diel cycles. The bottles were kept in flow-through containers on the vessel’s upper deck and filled with surface water to keep them at ambient temperatures. ‘Light’ bottles used to measure Net Primary Production were covered with 50% shade cloth while ‘dark’ bottles used for measuring community respiration were blacked out. A poisoned (5% buffered formalin) blank light and dark bottle were also included with each experiment. The amount of dissolved oxygen (DO) in the original water sample (initial) was recorded (mg O₂ L^− 1) before starting the experiments. The light, dark and poisoned bottles were incubated for at least two hours before the final DO concentration was measured. Using a photosynthetic quotient of 1.2 and respiratory quotient of 1.0, changes in DO were used estimate net productivity and community respiration (after corrections using the corresponding poisoned controls). Primary productivity was calculated by summing net productivity and community respiration from surface water samples. Values were then multiplied by Secchi depth to account the photic zone according to Wetzel and Likens (2000).

Photophysiology

A Fluorescence Induction and Relaxation (FIRe) System (Satlantic Instruments S/N 2, Halifax NS, Canada) (Gorbunov & Falkowski 2005) was used on surface samples collected from all stations. Only the information collected from the single turnover (ST) component of the transient was considered (Kolber et al. 1998; Kromkamp and Forster 2003). The minimal and maximal fluorescence yields (F_o and F_m respectively) were measured on dark acclimated samples and used to calculate the efficiency of photosystem II (PS II), which is the maximum change in the quantum yield of photochemistry (F_v/F_m) where F_v = variable fluorescence. Filtered seawater (0.7 µm) was used to correct for interference from background fluorescence (Cullen and Davis 2003) prior to calculating F_v/F_m. The functional absorption cross-section for PSII (σ_PSII; Å² quanta^− 1), the minimum turnover time of electron transfer between reaction centers (τ_PSII; µsec) and the connectivity factor defining the excitation energy transfer between individual photosynthetic units (p) were calculated. These four parameters are frequently used as physiological markers when working with natural samples (e.g., Sylvan et al. 2007; Moore et al. 2008; Suggett et al. 2009a,b; Quigg et al. 2011).

A pulse-amplitude modulation (PAM) fluorometer (PHYTO-PAM EDF, Walz) operated by the software Phyto-Win (v2.13; Walz) was used to measure chlorophyll fluorescence at multiple wavelengths (Genty et al. 1989; Schreiber 1998; Jakob et al 2005). The instrument utilizes light pulses at blue (470 nm), green (520 nm), light red (645 nm) and dark red (665 nm) to detect cyanobacteria, green algae, diatoms and dinoflagellates. Rapid light curves (RLC) were prepared with nine light steps (1, 64, 164, 264, 364, 464, 764, 1064 and 1264 µmol photons m^− 2 s^− 1) to measure the electron transfer rates and calculate the relative maximum electron transfer rate (rETR_max; µmol electrons m^− 2 s^− 1) using the following equation:

rETR_max = Yield ∙PAR ∙0.84 ∙0.5

Yield (F´_v/F´_m) was measured at a given light intensity (dimensionless), PAR is the photosynthetically active radiation (µmol electrons m^− 2 s^− 1), 0.84 is the theoretical ratio of photons absorbed by photosynthetic pigments to incident photons, and 0.5 is the theoretical ratio of photons absorbed by PSII relative to photons absorbed by all photosynthetic pigments (White and Critchley 1999). In addition, the light harvesting efficiency (α; µmol photons µmol electrons^− 1) was calculated from the initial slope of the RLC.

Pigment Analysis

For high performance liquid chromatography (HPLC) diagnostic pigment analysis, water samples (500 mL) were filtered (Whatman GF/F; 0.7 µm) under low vacuum pressure (< 130 kPa) (Jeffrey and Vesk 1997; Paerl et al. 2003; Pinckney 2006). Filters were maintained at -80°C until reverse-phase HPLC analysis using the procedures of Pinckney et al. (2001a). Samples were run by the Estuarine Ecology laboratory at the University of South Carolina (Dr. Jay Pinckney). The presence of diagnostic pigments (Jeffrey and Vesk 1997) was used as a proxy to examine the phytoplankton community. In this study, the following chlorophylls and carotenoids were detected for this purpose (and their corresponding algal groups): chlorophyll a (chl a; total biomass), chlorophyll b (chl b; green algae), peridinin (dinoflagellates), fucoxanthin (diatoms), alloxanthin (cryptophytes), and zeaxanthin (cyanobacteria). Pigment concentration data were divided by the respective chlorophyll a concentration (standardized to the maximum) and then divided by the sum of all the accessory pigments (standardized to the total) according to Dorado et al. (2012; 2015).

Imaging FlowCytobot (IFCB) Community Analysis

The IFCB captured images of nano- and microplankton (~ 10–150 µm) using flow cytometry as described by Olson and Sosik (2007) and Sosik and Olson (2007). The IFCB programmable syringe pumped a homogenized sample (5 mL) into the instrument. Between samples, a seawater blank was run and at the end of each sampling campaign, a seawater blank was run followed by a biocide cycle (5% sodium azide) to clean the instrument. The images generated by the IFCB were processed and classified according to Sosik and Olson (2007) using a supervised machine learning algorithm (Support Vector Machine). The codes can be found at: https://github.com/hsosik/ifcb-analysis. Using the images captured by the IFCB, a classifier was built with > 50 and < 500 images per class. Of the 98 categories in the classifier, only those which included cyanobacteria, chlorophytes (green algae), dinoflagellates, diatoms, cryptophytes, and raphidophytes were included in this analysis. The “miscellaneous” category, which included round cells, primarily associated with small eukaryotes, such as prasinophytes and prymnesiophytes, and cells too small to be taxonomically distinguishable by IFCB imaging were not included herein. The “unclassified” category was also excluded for the analysis; this included ciliates and other plankton which are non-photosynthetic. The trained classifier was run on the timeseries image data. The “optimum” output which has the strictest threshold had an error rate of 0.35 meaning 35% of images taken were unclassified.

16S and 18S rRNA gene Community Analysis

Samples were collected at every station and time point and filtered onto 0.2 µm polyethylsulfone membrane filters immediately upon returning to shore, and then stored at − 80°C. Nucleic acids were extracted using the MO Bio PowerSoil DNA Isolation Kit (cat. no. 128888-50) followed by PCR amplification in triplicate using Promega GoTaq Flexi DNA Polymerase according to the 16S/18S rRNA gene Illumina amplicon protocol (http://www.earthmicrobiome.org/emp-standard-protocols; Earth Microbiome project). For the 16S rRNA gene amplifications, the 515F-806R primer pair (Caporaso et al. 2011), amplifying the V4 hypervariable region, was modified to include Golay barcodes and adapters for Illumina MiSeq sequencing (Walters et al. 2016). For the 18S rRNA gene analysis, V8f-1510r primer pair, amplifying the V8V9 hypervariable region, was used (Bradley et al. 2016). Amplicons were quantified with the QuantiFluor dsDNA System (Promega), and then pooled and purified with an UltraClean PCR Clean-Up Kit (MoBio Laboratories; Carlsbad, CA, United States). For MiSeq sequencing (v2 chemistry, 2 × 250 bp), the samples were sent to the Georgia Genomics Facility (Athens, GA, United States). Sequence reads for 16S and 18S rRNA gene were processed separately using mothur v.1.39.5 following the MiSeq SOP https://www.mothur.org/wiki/MiSeq_SOP (Schloss et al. 2009; Kozich et al. 2013).

Data analysis

Given the sampling campaigns were opportunistic, we do not have replicates of any of the samples presented herein. Contour maps were created by using Surfer Software Version 20.2.218 with default settings, including gridding with the kriging algorithm. To examine patterns over time in the phytoplankton community (16S and 18S rRNA gene analysis) and water quality, partial mantel tests were performed in the R platform (v4.2.1) (http://www.r-project.org) based on the ‘vegan’ package. In order to reduce noise associated with low numbers, taxa that represented > 0.1% of the total OTU abundances were used for correlation analysis. Briefly matrices were built for pairwise taxonomic distances between the environmental variables via Euclidean distance and bacterial or eukaryotic communities via Bray–Curtis’s dissimilarity. Pearson correlation analysis was also performed in the R platform (v4.2.1).

Freshwater inflows and bay-wide water quality

The volume of freshwater discharged into Galveston Bay in August and September of 2017 was ~ 17 × 10⁹ m³ (Table S1). Of this, 7.9 × 10⁹ m^− 3 was gauged flows, 7.4 × 10⁹ m^− 3 was modeled flows, 3.1 × 10⁹ m^− 3 was diverted flows with the balance associated with returned flows. Typically, modeled flows are lower than gauged flows (Table S1), but in August 2017 these were very similar reflecting the massive inputs from rainfall across the region in addition to the riverine flows. Precipitation was 68 cm during the corresponding time-period (Table S1). Before the passage of Hurricane Harvey, typical summer salinities were observed at three data-sonde stations across the bay. From north to south, salinity was 9.42 ± (1.56), 18.68 (± 3.15) and 25.79 (± 4.54) at Fishers Reef (F), Mid-Bay (M) and Bolivar Roads (B), respectively (Fig. 2a). Between precipitation and river discharge during Hurricane Harvey, salinities dropped to < 1 across Galveston Bay (Fig. 2a) and remained so for several days, even at the station located at Bolivar Pass which is at the mouth of the bay. This freshening lasted weeks notwithstanding the gradual intrusion of Gulf of Mexico waters back into the bay. This was best observed at the Bolivar Pass station, but the effect was also pronounced at Mid-Bay (Fig. 2a). At the station closest to the Trinity River, salinities gradually increased from freshwater to brackish by the end of September, as did those at Fisher Reef (Fig. 2a). It was not until months later that salinities started to return to pre-storm levels across the entire bay (data not shown; see Du et al. 2019). Water temperature shifts were not significantly different between location and time (Fig. 2b). There was nonetheless a notable decrease in water temperatures from 32ºC prior to Hurricane Harvey, down to 24ºC after the storm’s passage. This was due to the significant precipitation that fell over the bay. As a result of estuarine mixing, temperatures started to return to normal after a month but not before there was a secondary downward temperature drop (Fig. 2b). The latter reflects the magnitude of the rainfall and the time required to flush it out completely (see also Thyng et al. 2020).

During the first sampling campaign after Hurricane Harvey, Secchi depths throughout Galveston Bay were shallow, ≤ 0.25 m (Fig. 3a). Secchi depths slowly increased over the next four sampling campaigns, up to 3 m a month after the hurricane. Similarly, after the storms passage, we found TSS was lowest near the mouth of the San Jacinto River at stations H1 to H4 (< 0.08 mg L^− 1) reflecting the hydraulic displacement which occurred due to the influx of freshwater and precipitation (Fig. 3b). TSS was highest in the middle of the bay at stations H4/H5 to H8, up to 0.14 mg L^− 1 and then lower towards the entrance of the bay (Fig. 3b). With time, TSS deceased (T3-T5, < 0.04 mg L^− 1) along he sampling transect (Fig. 3b). DIN concentrations were highest at H1 and H2, the stations nearest the San Jacinto River mouth (> 28–50 µM), with progressively lower concentrations towards Bolivar Pass (~ 6 µM), on both spatial and temporal scales (Fig. 3c). Similar trends were observed for silicate, but the opposite trend was measured for P_i (Figs. 3d and 3e). Silicate concentration ranged from 30.7–68.2 µM. P_i concentrations were lowest immediately following the flooding, 1.11 to 3.74 µM, and were observed to be increasing during the last sampling campaign in parts of the bay, up to 4.5-5 µM. The DIN:P_i ratio is often used to determine if phytoplankton were nitrogen or P_i limited. During the sampling trips, we found that immediately after Hurricane Harvey, DIN:P_i ratios were in the range of 10–15 (Fig. 3f) but as marine water re-entered the bay (Fig. 2), DIN:P_i ratios were frequently less than 2, such that phytoplankton growth was predicted to be nitrogen limited. The TN ranged from 23.3 to 115 µM with the lowest concentrations occurring near the Gulf of Mexico (Fig. 3g). TP exhibited two peaks along the cruise transect at stations H1 and H2 and stations H8 and H9 during the latter time points (T3–T5; Fig. 3h). TP concentrations are often related to the P_i concentrations in estuaries, which is reflected in the spatial and temporal distributions of these materials (Figs. 3e and 3h).

Primary production

Immediately after the storm, the net productivity in the bay was very low, from 0.06–0.46 gC m^− 2 d^− 1 (Fig. 4; blue). Given the high sediment loads introduced because of the flood waters (Table S1; Fig. 2), the communities were likely light-limited despite the elevated nutrient concentrations (Fig. 3). By T3 and T4, flooding declined and the high nutrient loads from the San Jacinto River fueled primary productivity (Fig. 4), with substantially higher values, from 0.92 to 2.19 gC m^− 2 d^− 1. In general, the highest primary productivity was measured at the mouth of the bay (Fig. 4) where there was likely a combination of ample nutrients and light availability due to increased water clarity (Fig. 3). Respiration rates also increased from the initial sampling campaign and across the bay with time after the passage of Hurricane Harvey (Fig. 4).

Photophysiology

During our first sampling campaign (T1) immediately after Hurricane Harvey, F_v/F_m was frequently below < 0.4 (Fig. 5a) with higher photosynthetic efficiencies record (~ 0.54–0.60) weeks later (T3-T5). At the stations adjacent to the Gulf of Mexico (H9-H10), F_v/F_m was variable, but generally high (> 0.50). The functional absorption cross-section for PSII (σ_PSII) was small (135–175 Å² quanta^− 1) during T1. Thereafter, during T3 to T5, σ_PSII was variable but generally larger (200–250 Å² quanta^− 1) (Fig. 5b). We observed a gradient of increasing σ_PSII values which followed a pattern of increasing light availability (i.e., deeper Secchi depths, less TSS) (Fig. 3a, 3b and 5b); this suggests an alleviation of light limitation of phytoplankton communities. We found trends in σ_PSII followed DIN and DIN:P but not those for other nutrients measured (Figs. 3 and 5). The minimum turnover time of electron transfer between reaction centers (τ_PSII) and the connectivity factor between photosynthetic units (p) provide helpful information on the physiology of the photosynthetic apparatus. τ_PSII was long (> 700 µsec) during T1 but then became faster in phytoplankton with subsequent sampling campaigns, so that it was typically 300–400 µsec (Fig. 5c). Faster τ_PSII time constants reflect more effective photosynthetic electron transfer on the acceptor side of PSII. This is also consistent with the increase in F_v/F_m and decrease in σ_PSII as well as the bay wide patterns of primary productivity (Fig. 4). The connectivity factor was higher near the head of the bay and middle (0.3–0.4) and lower towards the mouth of the bay (< 0.3) (Fig. 5d). It appears that light profiles were the major driver of τ_PSII patterns while nutrients were the major driver of p (Figs. 3 and 5).

These physiological parameters are also known to vary as a function of the phytoplankton community composition. A PHYTO-PAM was used to examine shifts in the dominant components (cyanobacteria, green algae, and diatoms/dinoflagellates). Yields (F´_v/F´_m) of > 0.4 were measured for the cyanobacteria during T1 along the transect, and progressively declined to around 0.3 by T5 (Fig. 6a). F´_v/F´_m measured for green algae varied from an average of 0.35 to 0.39 during T1 and T2, increased to 0.38 to 0.53 during T3, and back to < 0.35 during T4 and T5 (Fig. 6b). Yields were higher for chlorophytes (0.30–0.53) and generally lower for diatoms/dinoflagellates on a range of spatial and temporal scales, with F´_v/F´_m varying from ~ 0.3–0.50 (Fig. 6c). Light harvesting efficiency (a) varied from ~ 0.09–0.18 µmol photons µmol electrons^− 1 for the cyanobacteria, green algae, and diatoms/dinoflagellates (Fig. 6d - f), with patterns of lower efficiencies during T1 and T2, and then increasing with latter sampling campaigns. Patterns for light harvesting efficiency mirrored those for rETR_max both spatially and temporally (Fig. 6). We found rETR_max rates were highest during T3 for all group (43–120 µmol electrons m^− 2 s^− 1) (Fig. 6g - i), while those for T1 and T2 were generally lower and those during T4 and T5 were generally higher (> 65 µmol electrons m^− 2 s^− 1), much light the primary productivity measurements shown in Fig. 4. Similarly, there was a drop in rETR_max for all the groups during T2 which will be discussed below.

Community analysis – Pigments

We examined the trends in chlorophyll a (proxy for biomass) and biomarker (accessory) pigments (µg L^− 1). After the storms passage, we found chlorophyll a concentration were lowest near the mouth of the San Jacinto River at stations H1 to H4 (< 8 µg L^− 1) reflecting the hydraulic displacement which occurred due to the influx of freshwater and precipitation (Fig. 7; black line). Chlorophyll a concentration was highest in the middle of the bay at stations H4/H5 to H8, up to 35 µg L^− 1 and then lower towards the entrance of the bay (Fig. 7). With time, chlorophyll a concentration increased along the sampling transect ranging from 0 to 40 µg L^− 1 (Fig. 7). Zeaxanthin, a biomarker for cyanobacteria, was initially present in relatively low concentrations, but then increased in comparison to the total pigment pool (Fig. 7). The relative abundance of green algae (chlorophyll b) was often 10 to 20% of the community (Fig. 7). The pigment fucoxanthin is most often associated with diatoms which typically dominate estuarine ecosystems. This accessory pigment was often present in the highest concentrations (Fig. 7), accounting for ~ 50% of the relative abundance of all the pigments shown, to as much as 90%. After an initial decline in fucoxanthin concentrations, thought to be a result of diatoms being flushed out of the bay due to the large freshwater inflows, it again increased throughout the bay (T3). This latter shift may be a combination of diatoms re-entering the bay due to intrusion of marine waters (Fig. 2) as well as a result of their fast-growing nature. Peridinin which is associated with dinoflagellates, was elevated at stations H9 and H10 compared to H1-H8 when present, reflecting the marine nature of this group (Fig. 7). Of interest was the presence of alloxanthin, a pigment typically associated with cryptophytes. During the initial sampling campaign, they accounted for a high proportion of the accessory pigments at several stations near the head of the bay (Fig. 7); thereafter, they were present in generally low concentrations. While cyanobacteria and diatoms had the highest concentrations and relative abundances, the other biomarker pigments also changed with spatially and temporally Fig. 7).

Community analysis – IFCB

While the pigment analysis provides a broad sense of which phytoplankton groups maybe present, the IFCB provides a count of microplankton (~ 10–150 µm) allowing us to determine genus level community structure in terms of cells mL^− 1 (Fig. 8). Freshwater associated cyanobacteria were introduced into the bay with the riverine influx including Anabaena sp., Merismopedia sp., Microcystis sp., Phormidium sp. and Spirulina sp. (Fig. 8a), along with a variety of other species which we were not able to classify at the start of the sampling campaigns (i.e. unclassified). Anabaena sp. initially was the most numerically dominate species (T1; H1-H7) along with the unknowns, while Microcystis sp. cell densities were very high during T2 and T3 and particularly in the middle of the bay (Fig. 8a), and then at stations at the mouth of the bay during T4 and T5 sampling campaigns. The density of freshwater cyanobacteria decreased across the sampling campaigns. The high relative abundance of cyanobacteria in the pigment data (Fig. 7) and omics data (Steichen et al. 2020), likely reflects species that are hard to identify because of their small size or not detected using the IFCB which has a cut off of 10 µm.

Freshwater chlorophytes (and euglenoids) were also introduced with the flood waters, and then were flushed out, while marine species became more numerically prevalent after the T3 sampling campaign (Fig. 8b). The freshwater genera Coelastrum sp., Micractinium sp., Oocystis sp. and Pandorina sp., were present in highest cell densities from H1 to H7 during T1. During the later sampling trips these were absent, with the unknowns numerically dominant during T5 and Pyramimonas sp. and other species present at lower cell densities. When present, the cosmopolitan Eutreptiella sp. was the most often observed euglenoid in Galveston Bay (data not shown).

By contrast, it appears that diatoms were flushed out of the bay immediately after the storm as can be seen by the relatively low cell densities at all stations, particularly during T1 and T2 (Fig. 8c). As marine waters started to intrude back into the bay, diatoms numerically dominated the phytoplankton community detected by the IFCB (T3-T5). The relative abundance of the diatoms in the IFCB analysis ranged from 4–16% during T1 and increased up to 24–57% of the relative abundance during T5 (not shown). Diatoms were the most diverse part of the community, with 39 genera classified and an additional three groups which had common features (e.g., pennate or chains). Of the diatoms present initially, Skeletonema sp., Thalassiosira sp. and several of the unknowns were most numerical dominate during T1. Asterionellopsis glacialis, Chaetoceros sp., Ditylum sp., Entomoneis sp., Fragilaria sp., and all increased in abundance at stations 6–10 during the last three sampling campaigns. The diatoms Coscinodiscus sp., Detonula sp., and Naviucla sp., Plagiogrammopsis sp. were common throughout the sampling campaign (Fig. 8c).

During the first three sampling campaigns, dinoflagellates and cryophytes were present at very low cell densities; thereafter they contributed to a larger fraction of the community (Fig. 8d and e). During these latter sampling campaigns, Scrippsiella sp., Karenia mikimotoi, Prorocentrum gracile and Prorocentrum texanum (Fig. 8d). While some known bloom forming genera were also measured during the last two campaigns, they were present in cell densities sufficiently high to warrant concerns. These include Dinophysis ovum, Akashiwo sp., Heterocapsa rotundata, and several others. The identity of the cryptophytes present remains unknown (see also Steichen et al. 2020). The raphidophyte Chattonella sp. appeared during T3 (Fig. 8f). The raphidophytes were generally not present during the sampling campaigns while dinoflagellates and cryptophytes were always present and increased in cell densities from T1 to T5.

Environmental factor – phytoplankton community analysis – 16S and 18S rRNA gene

The relative abundance of OTU’s from the 16S rRNA (cyanobacteria only) and 18S rRNA (diatoms, dinoflagellates, cryptophytes, and green algae only) genes were compared to examine shifts in the community composition. These findings are provided in detail in Steichen et al. (2020). Briefly, the cyanobacteria and diatoms dominated at all stations, with their OTU’s accounting for the largest fraction in most samples (ranging from 45–69%) while the chlorophytes, cryptophytes and dinoflagellates when present had low relative OTU abundances, typically < 11%. Here, we used a subset of the sequence data (only cyanobacteria and eukaryotic phytoplankton) in partial mantel tests to discern the environmental factor(s) that influenced community composition across all stations and timepoints (Fig. 9, Table S2 and S3). Pearson’s correlation between the environmental factors are shown in the same figure (9), with the color and size of the squares in the correlation plot corresponding to the strength and significance of the correlation (Fig. 9, Table S4). Overall, for cyanobacteria, factors such as time (i.e., sampling event after the storm) (R = 0.547, P = 0.001), temperature (°C) (R = 0.315, P = 0.001), salinity (psu) (R = 0.241, P = 0.001), secchi depth (R = 0.165, P = 0.039), TSS (R = 0.096, P = 0.033), and NH₄⁺ (µM) (R = 0.394, P = 0.001), were the most critical environmental factors that explained the variation in community composition (Fig. 9a). However, only factors such as time (R = 0.460, P = 0.001) followed by salinity (psu) (R = 0.333, P = 0.001), and NH₄⁺ (µM) (R = 0.197, P = 0.03) played a crucial role for eukaryotic phytoplankton (Fig. 9a, Table S2). Further analysis of the eukaryotic community composition revealed time, salinity, and NH₄⁺ (µM) influenced diatoms, dinoflagellates, and chlorophytes (R ≥ 0.106, P ≤ 0.05), with time having the most impact on diatoms (R = 0.452, P = 0.001) (Fig. 9b, Table S3). Chlorophytes were also influenced by a range of other factors such as temperature (R = 0.587, P = 0.001), NO₃- (µM) (R = 0.232, P = 0.002), secchi depth (R = 0.148, P = 0.031), and TSS (R = 0.126, P = 0.006) (Fig. 9b, Table S3).

Global warming in ecosystems alters temperature, acidification, circulation, stratification, nutrient inputs as well as the frequency and severity of storms, floods and droughts (Philippot et al. 2021; Clementson et al. 2021). Phytoplankton play a critical role in global biogeochemical cycles crucial for a planet’s sustainability, as well as in trophic interactions and fisheries. Yet, they are under bombardment from urbanization and industrialization upstream and climate change and sea level rise downstream, as well as alterations to natural processes including but not limited to nutrient cycling, acidification and more (e.g., Chan and Hamilton 2001; Howarth and Marino et al. 2006; Pinckney 2006; Cloern and Jassby 2010; Shade et al. 2012; Wetz and Paerl 2008; Hu et al. 2015; Freeman et al. 2019; Huang et al. 2021). Events which alter ecosystems also impact phytoplankton community composition, diversity, and productivity. The potential of a community to cope with changes driven by global warming is affected by relative constraints on individual physiology and the degree of heterogeneity of the environment. Further, some studies have shown that near shore communities may show functional redundancy (unaltered function but change in composition) to warming, while offshore communities may exhibit sensitivity such as alterations in their function and composition (Wang et al. 2021). An understanding of phytoplankton community composition and the processes that drive its variability remains essential for understanding ecosystem functioning and response to perturbations.

There are many processes that influence phytoplankton communities in bays and estuaries, including tidal exchange, food web structure, and/or presence of invasive species. Depletion of phytoplankton biomass due to hydraulic displacement (e.g., driven by riverine discharge, storms) has been observed in natural systems including Galveston Bay (Roelke et al. 2013; Dorado et al. 2015). As a result of Hurricane Harvey, flooding occurred with an unprecedented magnitude and duration (Thyng et al. 2020), significantly altering water-chemistry (Chapman et al. 2023) and biology (Steichen et al. 2020; Walker et al. 2022; present study). It was not until the last sampling campaign, about a month after the initial hurricane that freshwater inflows were returning to pre-Harvey flow levels (Du et al. 2019). Of the other processes influencing the community, tidal exchange can be large relative to inflows in some estuaries, diluting the effects of hydraulic displacement and nutrient and sediment loading originating from river discharge (see Roelke et al. 2013), but this is less so in Galveston Bay which is microtidal (Du et al. 2019; Thyng et al. 2020). In addition, to the increased freshwater inflows, there was a significant lowering of water temperatures throughout Galveston Bay (Huang et al. 2021) and Gulf of Mexico (Trenberth et al. 2018). Vincent et al. (2012) reported that hurricanes can lower sea surface temperatures by as much as 10ºC through a variety of mechanisms. This rapid cooling of the surface mixed layer has the potential to increase annual CO₂ efflux (Bates et al. 1998), both in the bay and the nearby coastal shelf as flood waters exit and are entrained in the northwestern Gulf (Thyng et al. 2020).

When subtropical estuaries experience severe floods associated with storms, the phytoplankton community will be dramatically altered. Immediately after the storm dissipated over Galveston Bay, sampling efforts showed the lowest salinities bay wide ever reported, not surprising given the volume of rainfall and runoff introduced in just a few days which was the equivalent (and more) of the annual freshwater inflow to the bay (Thyng et al. 2020; Du et al. 2019). There was also a concurrent large pulse of nutrients introduced at the head of the bay following the storm, both from riverine and urban (runoff from impervious surfaces, wastewater treatment facility overloads, etc) sources. Concentrations of DIN, TN and TP were 11–25 µmol/L, 39–74 µmol/L, and 3.1–5.5 µmol/L, respectfully, during the first sampling campaign, and the DIN:P ratio was 7.8–10.6 while the phytoplankton biomass and primary productivity were initially very low, indicative of another influence limiting biomass. The increase in Pi concentrations with time may be due to it being released by particulate matter (remineralization) or a pulse of freshwater coming in from the Trinity River (see Steichen et al. 2020; Yan et al. 2020) for details. The lack of correlation between salinity and ammonia (data not shown) indicates this nutrient came from remineralized particulate and dissolved organic matter flushed into the bay. Other factors which impaired the response of the phytoplankton community to the initial nutrient pulse include but are not limited to (1) decreased water clarity (i.e., increased turbidity), (2) short residence time with increased flow rate, (3) temperature and carbon availability, and (4) the nutrient loading (types, forms). While the former factors did not allow the phytoplankton to take advantage of the higher nutrient concentrations (Cloern, 1996; Chan and Hamilton, 2001; Rissik et al. 2009), the latter have been correlated to significant spatiotemporal shifts in the total and relative abundance of autotrophic and heterotrophic microbes (Williams and Quigg 2019; Yan et al. 2020; Walker et al. 2022). Further, hydraulic displacement has previously been shown to impact phytoplankton biomass, productivity and community structure in this estuary (Roelke et al. 2013) as well as others (e.g., Moreton Bay, Australia in Clementson et al. 2021).

After several weeks of sampling (T3-T5 campaigns), we observed that phytoplankton populations responded to the increase in nutrients (dissolved and those that had disassociated from the particulate material) and concurrent increased light availability. High primary productivity was measured in the light/dark bottle experiments and with the PHYTO-PAM as rel_ETRmax (Figs. 4 and 6) during T3. Previous studies have shown that phytoplankton reproductive growth rates respond rapidly to nutrient loadings in Galveston Bay (Örnolfsdottir et al. 2004; Roelke et al. 2013; Dorado et al. 2015), in other western Gulf of Mexico estuaries and elsewhere (Russell & Montagna 2007; Wachnicka et al. 2022; Thompson et al. 2023). The oscillations in water temperatures, despite increasing salinities likely muted the phytoplankton response during T4 which had consistently lower net primary productivity at all stations relative to both T3 and T5 (see also Trenberth et al. 2018; Huang et al. 2021). Nonetheless, we observed a gradient of increasing primary productivity from the head to the mouth of the bay, similar in pattern to that observed prior to the storm (Dorado et al. 2015). Previous work has shown that temporally, temperature and variables associated with freshwater inflow (discharge volume, salinity, DIN and P_i) were major influences on phytoplankton dynamics, with DIN:P_i ratios suggesting that phytoplankton communities were predominately nitrogen limited which has been observed in Galveston Bay previously (Örnólfsdóttir et al. 2004a,b; Quigg 2015).

Community dynamics

As part of the rapid response, we examined alterations in structure of the phytoplankton community and their underlying physiology on both spatial and temporal scales. Each of the methods used has advantages and disadvantages and provides complimentary information to achieve a holistic view of the phytoplankton community. While the pigment and omics samples considered the entire community, the IFCB data is limited to microplankton (~ 10–150 µm). One advantage of the IFCB is that we were able to identify the major taxa (genera and some species level details) that were present following the hurricane. The IFCB data compliments the omics data set which identified many species and genera, but still has many unknowns (see Steichen et al. 2020). The disadvantage of the IFCB is that it is difficult to identify phytoplankton which are smaller than 10 µm and so it may not detect the shifts in cyanobacterial communities and other small celled species/genera.

Most cyanobacteria and green algae identified immediately after the hurricane were freshwater species introduced with the increased inflows of the San Jacinto River, surrounding bayous and creeks and runoff from land. As the tidal exchange allowed, this freshwater was pushed out of the bay (initial flooding pulse) or was mixed with the marine waters of the Gulf of Mexico in the weeks following the passage of the hurricane. The benefit of this flushing and mixing was that many of these species are known to produce harmful algal blooms (HABs) (e.g., Anabaena sp., Microcystis sp.; Badylak and Phlips 2004; McInnes and Quigg 2010; Anderson et al. 2021), often associated fish kill events were not given time to flourish as salinities increased. As with Peierls et al. (2003), we observed green algae responding to flood waters with an increase in biomass; the loss thereafter was a result of increased salinity and shifts in nitrogen sources (from NH₄⁺ to NO₃-) (Fig. 9; Supplemental Tables 2 and 3). The impacts of ammonium and nitrate on the phytoplankton community highlights the importance of nitrogen utilization dynamics amongst phytoplankton, in other words differences in both inhibition and preferred uptake of ammonium (Dortch, 1990). Therefore, the general decrease in ammonium concentration over time may have also driven the decrease in cyanobacteria and green algae along with the change in salinity caused by flushing effects of the hurricane. Similar such observations were also reported for the nearby Neuse River Estuary (Pinckney et al. 1998) and the northern Gulf of Mexico (Chakraborty and Lohrenz 2015; Wachnicka et al. 2022). Studies in Moreton Bay (Australia) also found chlorophytes were restricted to parts of the estuary with freshwater (Chan and Hamilton 2001; Glibert et al. 2006; Quigg et al. 2010; Clementson et al. 2021).

Diatoms are fast-growing taxa known to respond rapidly to nutrient inputs (Collos 1986; Pinckney et al. 1999), particularly after the passage of storms (Anglès et al. 2015; Liu et al. 2019). The cosmopolitan diatoms Thalassiosira sp., Skeletonema sp., Asterionellopsis sp. and a variety of Chaetoceros sp. were prevalent in the weeks after the hurricane. Other studies have shown that Skeletonema sp.was dominant after a hurricane where there was a nutrient injection into the ecosystem occurred (Tsuchiya et al. 2013). Anglès et al. (2015) reported that the increase of diatoms (Thalassiosira sp., Skeletonema sp., and Asterionellopsis sp.) together with their association with incoming tides indicates that conditions in nearshore waters outside the estuary were favorable for phytoplankton growth because of nutrient entrainment in the Guadalupe, Mission-Aransas and Nueces estuaries and Aransas Pass in southwest Texas. There were also spikes of several benthic diatom species (e.g., Nitzchia, Navicula, Cylindrotheca), which indicate bioturbation of the sediments into the water column; this is consistent with the idea that the storm surge also resuspended nutrients inside the estuary, which favored the rapid growth of these diatom taxa. The high concentrations of diatoms in our pigment samples was consistent with previous phytoplankton community studies in Galveston Bay (Örnólfsdóttir et al. 2004a,b; Pinckney 2006; Dorado et al. 2015) and the northern Gulf of Mexico (e.g., Lambert et al. 1999; Zhao and Quigg 2014). Annually, diatoms dominate during periods of moderate to high freshwater inflows in winter/spring while cyanobacteria dominate during summer when inflow is low (Dorado et al. 2015). Fiorendino et al. (2021) also found that diatoms dominated during upwelling and water column mixing offshore after Hurricane Harvey along the coastal zone adjacent to Galveston Bay while moderate downwelling conditions favored dinoflagellates.

As with Örnólfsdóttir et al. (2006b), we found the N input with the flood waters favored increases in diatom biomass (Fig. 9; Table S3). Roelke et al. (2013) found that the growth of cyanobacteria, dinoflagellates and euglenophytes were not stimulated by nutrient loading alone in a previous study in Galveston Bay, perhaps because they can use alternative nutrient acquisition strategies (i.e., diazotrophy, mixotrophy) which diatoms cannot. These groups are also known to be more sensitive to hydraulic displacement given their overall slower growth rates. Paerl et al. (2001) and Piehler et al. (2004) found that diatoms respond by increasing biomass if nutrients were limiting prior to a flooding event whereas dinoflagellates have a negative correlation with freshwater inflows. On the other hand, dinoflagellates, which are a seasonally important component of estuarine communities (Pinckney et al. 1998) appeared but were not present at bloom densities. The common estuarine dinoflagellates Prorocentrum spp. (P. cordatum, P. gracile, P. micans, P. texanum) and Scrippsiella sp. were often observed, but not at bloom densities. The group may not have had sufficient resources, or they may have been efficiently grazed. The most significant variables impacting the diatoms, dinoflagellates, and chlorophytes were found to be time, salinity, and NH₄⁺ (Fig. 9). This is consistent with studies in the Gulf of Mexico which found that increases in abundance of dinoflagellates, cryptophytes, chlorophytes and euglenophytes were often associated with high freshwater discharge in near shore environments (Anglès et al. 2015; Wachnicka et al. 2022; Thompson et al. 2023). Less is known about some of the other groups examined, especially the prasinophytes, euglenophytes and cryptophytes, which are fast-growing phytoplankton groups, known to respond rapidly to nutrient enrichment associated with freshwater inflow and under reduced salinity conditions (e.g., Pinckney et al. 1999; Paerl et al. 2006b, 2010).

Physiology

We also examined the underpinning physiology of the phytoplankton community in response to Hurricane Harvey. In natural communities, F_v/F_m is dependent on nutritional status (e.g., Beardall et al. 2001; Parkhill et al. 2001; Sylvan et al. 2007), ambient light environment (Suggett et al. 2001; Quigg et al. 2006), but not community composition (Suggett et al. 2009a). The depressed quantum yields measured during T1 are not associated with photoinhibition (Kolber et al. 1998; Suggett et al. 2001; Sylvan et al. 2007); they may instead be associated with the loss of biomass and community structure. Along with the increase in the photosynthetic efficiency (F_v/F_m) and size of the functional absorption cross-section for PSII (σ_PSII) from T1 to T5, there was a concurrent decrease in the minimum turnover time of electron transfer between reaction centers (τ_PSII) and the connectivity factor between photosynthetic units (p) (Fig. 5). Opposite patterns for σ_PSII and τ_PSII were also reported in the Gulf of Mexico (e.g., Sylvan et al. 2007), Gulf of Aqaba (Suggett et al. 2009b), and the Arabian (Persian) Gulf (Quigg et al. 2011). As with Liu et al. (2019) we found that phytoplankton in well-mixed waters of the bay exhibited highest F_v/F_m and low τ_PSII; they also measured areas adjacent to the shelf to find low F_v/F_m and high τ_PSII which they associated with nutrient-limited communities. Together, these physiological parameters support findings that the phytoplankton community benefitted from the pulse of nutrients increasing their photosynthesis. An assertion which is supported by both the light/bottle data experiments (Fig. 4) and PHYTO-PAM data (Fig. 6). These shifts in physiology align with findings on the genomic potential of the microbial community (Walker et al. 2022). Briefly, using KEGG classifications, they found that genes associated with common photosynthetic processes (photosystems I and II, cytochrome b6f, and photosynthetic antenna proteins) decreased and never fully recovered to pre-Harvey levels. Coupled with the decrease in photosynthetic genes were enrichments of energy pathways such as amino acid metabolism critical for making proteins and growth of cells.

Coastal development is occurring at an unprecedented pace globally so that concerns about the

susceptibility and resilience of bays and estuaries are the focus of much research. Identifying relationships between phytoplankton and river discharge, flooding, droughts and other climatological events is an ongoing effort. The response of the phytoplankton community to significant storms or extreme precipitation over a short period (days-weeks) is more understood than their long-term (years) responses to changes in our waterways associated with storms (e.g., Paerl et al. 2001; Peierls et al. 2003; Paerl et al. 2019; Thompson et al. 2023). Galveston Bay appears to be a stable estuary based on studies such as that of Pinckney et al. (2017), which reported there were no evident trends in the diversity of the phytoplankton community composition over a 10-year period (1999–2009) based on diagnostic pigment data despite numerous pulse disturbances. This is also consistent with studies which predict the changes associated with climate change will be more obvious at high latitudes before they will be detected in lower latitudes (Allan and Soden 2008; Lehmann et al. 2015). The other governing factors are frequency and intensity of coastal disturbances and extreme flooding events in a specific location which may lead to an alteration in the amount of time needed for a system to recover (Paerl et al. 2001, 2019; Rahmstorf and Coumou 2011; Philippot et al. 2021; Huang et al. 2021). Concerns arise because in some cases the impacts of larger hurricane events may last for years (Paerl et al. 1998; Peierls et al. 2003; Thompson et al. 2023). Herein, we found that dominant or common members of the phytoplankton community are the most resilient, recovering quickly (increased cell densities), while others appear intermittently as conditions become ideal for short periods of time (i.e., ample nutrients and lights), and still others are outcompeted. The complimentary data provided by these multiple analyses provides insights of both phytoplankton community structure and underlying physiological responses as water quality shifts. Nonetheless, more work needs to be done to compare and better understand the community composition as it relates to higher frequency and higher intensity flooding events. As we continue to gain a better understanding of the impacts at the base of the food web, we may be able to determine how organisms at higher trophic levels may be affected during the recovery time of these ecosystems.

ACKNOWLEDGEMENTS

We would like to thank all members of the Phytoplankton Dynamics lab that assisted with cruise plans and organization, sample collection and processing. In particular, we thank Laura Bretherton, Rachel Windham, Hernando Bacosa, Sam Setta and Jessica Hillhouse for helping to process the many samples collected. Many thanks to the crew of the TAMUG R/V Trident and to everyone from TAMUG that came along (with very short notice) to assist in every way. Ram Neupane from Texas Water Development Board provided the monthly freshwater inflows data and Evan Turner helped with the datasonde data set. This work was partly funded by the National Science Foundation—RAPID Response to JML (Award #1801367), and the GOMRI (ADDOMEx; project SA-22) and Texas Commission on Environmental Quality: Galveston Bay

Estuary Program (US EPA Grant Number: CE-00655006) awards to AQ.

Data are publicly available through the Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC) at http://data.gulfresearchinitative.org (doi:10.7266/PGC99C7D).

Conflict of Interest: The authors declare that they have no conflict of interest.

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Response of a phytoplankton community in a subtropical estuary to a pulse disturbance driven by a combo hurricane and rainfall event.

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Abstract

Figures

Introduction

Methods

Study Area and Sampling Design

Water Quality

Primary production – Light data bottle method

Photophysiology

Pigment Analysis

Imaging FlowCytobot (IFCB) Community Analysis

16S and 18S rRNA gene Community Analysis

Data analysis

Results

Freshwater inflows and bay-wide water quality

Primary production

Photophysiology

Community analysis – Pigments

Community analysis – IFCB

Environmental factor – phytoplankton community analysis – 16S and 18S rRNA gene

Discussion

Community dynamics

Physiology

Conclusions

Declarations

References

Supplementary Files

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