Chemical and biological data collected for surface and bottom samples from all five stations along the transect from Charlotte Harbor to offshore in southwest Florida over the four sampling periods post-hurricane Ian during 2023-2024

Website: https://www.bco-dmo.org/dataset/1003828
Data Type: Cruise Results
Version: 1
Version Date: 2026-08-07

Project
» RAPID: Collaborative Research: Unprecedented Hurricane Ian Nutrient and Organic Matter Inputs to Southwest Florida Coastal Waters (RAPID: Ian Inputs)
ContributorsAffiliationRole
Heil, CynthiaMote Marine Laboratory (Mote)Principal Investigator
Glibert, Patricia A.University of Maryland Center for Environmental Science (UMCES/HPL)Co-Principal Investigator
Hall, EmilyMote Marine Laboratory (Mote)Co-Principal Investigator
Li, MingUniversity of Maryland Center for Environmental Science (UMCES/HPL)Co-Principal Investigator
Rauch, ShannonWoods Hole Oceanographic Institution (WHOI BCO-DMO)BCO-DMO Data Manager

Abstract
This RAPID project, a collaboration between C. Heil and E. Hall (Mote Marine Laboratory) and P. Glibert and M. Li (University of Maryland Center for Environmental Science), examined the overarching hypothesis that the massive terrestrial inorganic and organic carbon and nutrient inputs to the southwest Florida coastal and shelf region from Hurricane Ian were priming this oligotrophic system for extensive phytoplankton blooms as well as significant changes in carbon chemistry and nutrient cycling that will lead to longer term regional hypoxia and anoxia. We measured a suite of water quality, carbon and nutrient parameters, as well as carbon and nutrient cycling at stations in Charlotte Harbor and the adjacent inner West Florida Shelf (WFS) system over four sampling efforts (May 2023, August 2023, September 2023, and October 2024) to examine how fast changes and recovery occurs in this system and the relationship of these hurricane inputs to hypoxia, anoxia, and ocean acidification. This dataset includes the chemical and biological data from surface and bottom samples from all five stations along the transect from Charlotte Harbor to offshore in southwest Florida over the four sampling periods post-hurricane Ian, during 2023 and 2024. Data include net community production and oxygen utilization rates, carbonate and nutrient suite and rate process measurements (including uptake and regeneration rates), as well as direct measurements of organic matter respiration to understand how Hurricane Ian impacted biogeochemistry, blooms, and dissolved oxygen of the region.


Coverage

Location: Southwest Florida coastal waters
Spatial Extent: N:26.90435 E:-82.1404 S:26.5107 W:-82.4424
Temporal Extent: 2023-05-02 - 2024-10-02

Methods & Sampling

Sampling Plan: Four sampling efforts were conducted (May 2023, August 2023, September 2023, October 2024), focusing on five stations located from Charlotte Harbor to the adjacent inner West Florida Shelf (WFS) system directly impacted by Hurricane Ian and the resulting plumes using Mote's R/V Mote (42 ft). At each station, water column CTD profiles of temperature, salinity, dissolved oxygen, and pH were made, and discrete surface and bottom water samples were collected by rosette-mounted Niskin bottles and analyzed for the parameters and rates listed in Table 1. Sample processing occurred on board or immediately after return to the laboratory. Net community production and oxygen utilization rates were calculated using observed O2 differences at hour intervals after air-sea O2 corrections (e.g., Wang et al. 2017). The balance between ecosystem respiration and production, the net community production, yielded a measure of the net autotrophic or heterotrophic ecosystem state (Hopkinson and Smith 2005). The carbonate and nutrient suite and rate process measurements (including uptake and regeneration rates) and direct measurements of organic matter respiration allowed an assessment of how nutrient availability and forms influence hypoxia and how hurricane inputs influence carbonate chemistry in this system, specifically the connection between acidification and hypoxia (e.g. O2 vs. pH relationships).

Analytical Methods: See Table 1 (supplemental file).


BCO-DMO Processing Description

- Imported the original CSV file "Heil & Hall RAPID Chemical and BIological Data -FINAL as CSV.csv" into the BCO-DMO processing system.
- Renamed columns via a series of regex substitutions to remove/replace special characters: spaces to underscores, commas removed, closing parentheses removed, opening parentheses removed, "@" replaced with "at", periods removed, "/" replaced with underscore, hyphens replaced with underscore, ">" replaced with "gt", "+" replaced with "plus". These are requirements of BCO-DMO's naming conventions.
- Split column "Date_Collected_Time_Zone" into new columns "Date_Collected" and "Time_Zone", deleting the original input column.
- Converted column "Date_Collected" from string format "%m/%d/%Y" to date type with output format "%Y-%m-%d".
- Applied find/replace to column "Total_Karenia_species" to remove commas from numeric values.
- Set data types for all columns.
- Renamed column "Tital_Dinoflagellates" to "Total_Dinoflagellates".
- Saved the final file in CSV format as "1003828_v1_chem_bio_post-ian.csv".


Problem Description

No known problems with the dataset.

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Related Publications

Ammerman, J. W. (1993). Microbial cycling of inorganic and organic phosphorus in the water column. In P. Kemp, B. Sherr, E. Sherr, and J. Cole (Eds.) Handbook of Methods in Microbial Ecology. Lewis Publ., Florida. https://isbnsearch.org/isbn/9780873715645
Methods
Chen, Y., & Li, M. (2025). Impact of Hurricane Ian (2022) on Karenia brevis Bloom on the West Florida Shelf. Geophysical Research Letters, 52(6). Portico. https://doi.org/10.1029/2024GL113500
Results
Chen, Y., Li, M., Glibert, P. M., & Heil, C. (2023). MurKy waters: Modeling the succession from r to K strategists (diatoms to dinoflagellates) following a nutrient release from a mining facility in Florida. Limnology and Oceanography, 68(10), 2288–2304. Portico. https://doi.org/10.1002/lno.12420
Results
Devillier, V. M., Hall, E. R., Lovko, V., Pierce, R., Anderson, D. M., & Lewis, K. A. (2024). Mesocosm study of PAC-modified clay effects on Karenia brevis cells and toxins, chemical dynamics, and benthic invertebrate physiology. Harmful Algae, 134, 102609. https://doi.org/10.1016/j.hal.2024.102609
Methods
Dickson, A.G., Sabine, C.L. and Christian, J.R. (Eds.) 2007. Guide to Best Practices for Ocean CO2 Measurements. PICES Special Publication 3, 191 pp https://isbnsearch.org/isbn/1-897176-07-4
Methods
Genty, B., Briantais, J.-M., & Baker, N. R. (1989). The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence. Biochimica et Biophysica Acta (BBA) - General Subjects, 990(1), 87–92. https://doi.org/10.1016/s0304-4165(89)80016-9 https://doi.org/10.1016/S0304-4165(89)80016-9
Methods
Glibert, P. M., Heil, C. A., & Li, M. (2025). More sustained, more severe blooms and shifting monthly patterns of the toxigenic dinoflagellate Karenia brevis on the West Florida Shelf. Harmful Algae, 150, 102967. https://doi.org/10.1016/j.hal.2025.102967
Results
Glibert, P. M., Heil, C. A., & Li, M. (2026). Climate shifts and anthropogenic footprints driving increased severity and duration of toxic Karenia brevis blooms in the Gulf of Mexico over the past ~50 years. Frontiers in Marine Science, 13. https://doi.org/10.3389/fmars.2026.1769349
Results
Glibert, P.M. and D.G. Capone. 1993. Mineralization and assimilation in aquatic, sediment, and wetland systems, p. 243–271. In R. Knowles and T.H. Blackburn, editors, Nitrogen isotope techniques. Academic. https://isbnsearch.org/isbn/978-0124169654
Methods
Hall, E. R., Muller, E. M., Goulet, T., Bellworthy, J., Ritchie, K. B., & Fine, M. (2018). Eutrophication may compromise the resilience of the Red Sea coral Stylophora pistillata to global change. Marine Pollution Bulletin, 131, 701–711. https://doi.org/10.1016/j.marpolbul.2018.04.067
Methods
Holm-Hansen, O., Lorenzen, C. J., Holmes, R. W., & Strickland, J. D. H. (1965). Fluorometric Determination of Chlorophyll. ICES Journal of Marine Science, 30(1), 3–15. doi:10.1093/icesjms/30.1.3
Methods
Hopkinson, C., & Smith, E. M. (2005). Estuarine respiration: An overview of benthic, pelagic, and whole system respiration. In P. A. del Giorgio & P. J. le B. Williams (Eds.), Respiration in Aquatic Ecosystems (pp. 122–146). Oxford University Press. https://isbnsearch.org/isbn/978-0198527084
Methods
Hudson, J. J., & Taylor, W. D. (1996). Measuring regeneration of dissolved phosphorus in planktonic communities. Limnology and Oceanography, 41(7), 1560–1565. Portico. https://doi.org/10.4319/lo.1996.41.7.1560
Methods
Paasche, E. (1980). Silicon content of five marine plankton diatom species measured with a rapid filter method1. Limnology and Oceanography, 25(3), 474–480. Portico. https://doi.org/10.4319/lo.1980.25.3.0474
Methods
Revilla, M., Alexander, J., & Glibert, P. M. (2005). Urea analysis in coastal waters: comparison of enzymatic and direct methods. Limnology and Oceanography: Methods, 3(7), 290–299. Portico. https://doi.org/10.4319/lom.2005.3.290
Methods
Slawyk, G., Collos, Y., & Auclair, J.-C. (1977). The use of the 13C and 15N isotopes for the simultaneous measurement of carbon and nitrogen turnover rates in marine phytoplankton1. Limnology and Oceanography, 22(5), 925–932. Portico. https://doi.org/10.4319/lo.1977.22.5.0925
Methods
Wang, K., Chen, J., Ni, X., Zeng, D., Li, D., Jin, H., Glibert, P. M., Qiu, W., & Huang, D. (2017). Real‐time monitoring of nutrients in the Changjiang Estuary reveals short‐term nutrient‐algal bloom dynamics. Journal of Geophysical Research: Oceans, 122(7), 5390–5403. Portico. https://doi.org/10.1002/2016jc012450 https://doi.org/10.1002/2016JC012450
Methods

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Related Datasets

IsRelatedTo
Heil, C., Hall, E., Glibert, P. A., Li, M. (2026) CTD data collected for surface and bottom samples from all five stations along the transect from Charlotte Harbor to offshore in southwest Florida from May to September 2023. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2026-08-06 http://lod.bco-dmo.org/id/dataset/1004016 [view at BCO-DMO]

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Parameters

ParameterDescriptionUnits
Sample_depth_surface_bottom

S=surface (Niskin bottle at surface), B=bottom (Niskin bottle 0.5 m above bottom), specific S and B depth for each station are in CTD DEPTH column

unitless
Client_ID

Funding agency

unitless
SampID

Station Identification

unitless
Latitude_1

Latitude

decimal degrees
Longitude_1

Longitude

decimal degrees
Date_Collected

Date (EDT)

unitless
Time_Zone

Time Zone

unitless
Tag_Number

Mote internal ID number

unitless
Nitrogen_Ammonia_as_N_Diss_mg_L

Ammonium

mg L-1
Nitrogen_Ammonia_as_N_Diss_uM

Ammonium

uM
Nitrogen_Nitrate_Nitrite_as_N_Diss_mg_L

Nitrate + Nitrite

mg L-1
Nitrogen_Nitrate_Nitrite_as_N_Diss_uM

Nitrate + Nitrite

uM
Nitrogen_Particulate_ug_L

Particulate Nitrogen

ug L-1
Nitrogen_Particulate_uM

Particulate Nitrogen

uM
Nitrogen_Total_Diss_mg_L

Total Dissolved Nitrogen

mg L-1
Nitrogen_Total_Diss_uM

Total Dissolved Nitrogen

uM
Phosphorus_Orthophosphate_as_P_Diss_mg_L

Orthophosphate

mg L-1
Phosphorus_Orthophosphate_as_P_Diss_uM

Orthophosphate

uM
Phosphorus_Particulate_ug_L

Particulate Phosphorus

ug L-1
Phosphorus_Particulate_uM

Particulate Phosphorus

uM
Phosphorus_Total_Diss_mg_L

Total Dissolved Phosphorus

mg L-1
Phosphorus_Total_Diss_uM

Total Dissolved Phosphorus

uM
Silica_as_SiO2_Diss_mg_L

Silica (as SiO2, dissolved)

mg L-1
Silica_as_SiO2_Diss_uM

Silica (as SiO2, dissolved)

uM
Silica_particulate_as_SiO2_ug_L

Particulate Silica

ug L-1
Silica_particulate_as_SiO2_uM

Particulate Silica

uM
Organic_Carbon_Dissolved_mg_L

dissolved organic carbon

mg L-1
Organic_Carbon_Dissolved_uM

dissolved organic carbon

uM
Carbon_Particulate_ug_L

Particulate Carbon

ug L-1
Carbon_Particulate_uM

Particulate Carbon

uM
CDOM_Abs_at_280_nm

Colored Dissolved Organic Material Absorption at 280 nm

unitless
CDOM_Abs_at_312_nm

Colored Dissolved Organic Material Absorption at 312 nm

unitless
CDOM_Abs_at_350_nm

Colored Dissolved Organic Material Absorption at 350 nm

unitless
CDOM_Abs_at_374_nm

Colored Dissolved Organic Material Absorption at 374 nm

unitless
CDOM_Abs_at_376_nm

Colored Dissolved Organic Material Absorption at 376 nm

unitless
CDOM_Abs_at_400_nm

Colored Dissolved Organic Material Absorption at 400 nm

unitless
CDOM_Abs_at_412_nm

Colored Dissolved Organic Material Absorption at 412 nm

unitless
CDOM_Abs_at_430_nm

Colored Dissolved Organic Material Absorption at 430 nm

unitless
CDOM_Abs_at_440_nm

Colored Dissolved Organic Material Absorption at 440 nm

unitless
Chlorophyll_a_free_of_pheophytin

Chlorophyll a (without phaeopigments)

ug/L
CTD_DEPTH

sample depth measured by CTD

meters (m)
CTD_DO

dissolved oxygen concentration from CTD

mg L-1
CTD_FLUOR

Fluorescence from CTD

unitless
CTD_PAR

Photosynthetically active radiation

µmol photons m-2 s-1
CTD_SAL

Salinity from CTD

PSU
CTD_TEMP

Temperature from CTD

degrees Celsius
CTD_TURB

Turbidity from CTD

NTU
Total_Karenia_species

Concentration of all Karenia species

cells L-1
Total_Phytoplankton

Concentration of all phytoplankton

cells L-1
Total_Dinoflagellates

Concentration of all dinoflagellates

cells L-1
Total_Diatoms

Concentration of all diatoms

cells L-1
Total_Other_Plankton

Concentration of all flagellates (excluding dinoflagellates)

cells L-1
Total_Cyanobacteria

Concentration of all cyanobacteria

cells L-1
Latitude_2

geographic coordinate specifying the north-south position of a point on Earth's surface

degrees
Longitude_2

angular distance of a point east or west of the Prime Meridian (0°),

degrees
Sigma_t

seawater density

kg/m3
Aragonite_saturation_state

Aragonite saturation state

unitless
Bicarbonate

Bicarbonate

µM Kg SW-1
Borate_Alkalinity

Borate Alkalinity

µM Kg SW-1
Calcite_saturation_state

Calcite saturation state

unitless
Carbon_dioxide_fugacity

effective partial pressure of CO2

µAtm
Carbonate

Carbonate

µM Kg SW-1
Dissolved_carbon_dioxide

Dissolved carbon dioxide

µM Kg SW-1
Hplus_in

Hydrogen ion concentration

unitless
Hydroxide_alkalinity

Hydroxide alkalinity

µM Kg-1
Organic_Alkalinity_calculated

Organic Alkalinity, calculated

µM Kg SW-1
Partial_pressure_of_carbon_dioxide

Partial pressure of carbon dioxide

µAtm
Revelle

ratio of instantaneous change in carbon dioxide (CO 2 ) to the change in total dissolved inorganic carbon

unitless
Total_Alkalinity

Total Alkalinity

µM Kg-1
Total_Alkalinity_calculated

Total Alkalinity, calculated

µM Kg-1
Total_Dissolved_Inorganic_Carbon

Total Dissolved Inorganic Carbon

µM Kg-1
Total_pH_calculated

Total pH, calculated

Calculated (Units)
Total_pH_measured

Total pH, measured

Calculated (Units)
BLANK_CORR_Photosynthetic_rate

blank corrected, O2 based photosynthetic rate

µmol O2 L-1 hr-1
BLANK_CORR_Respiration_rate

blank corrected, O2 based respiration rate

µmol O2 L-1 hr-1
Phosphorus_Uptake

33PO4 uptake rate of >0.7 um fraction

uM L-1 hr-1
Phosphorus_Regeneration_Bulk_whole_water

33Phosphorus Regeneration rate of whole water

uM L-1 hr-1
Phosphorus_Regeneration_gt07_um

33Phosphorus Regeneration rate of >0.7 um fraction

uM L-1 hr-1
TChl_a

HPLC Pigment -Total Chlorphyll a

ug L-1
TChl_b

HPLC Pigment -Total Chlorphyll b

ug L-1
TChl_c

HPLC Pigment -Total Chlorphyll c

ug L-1
Caro

HPLC Pigment -alphas + beta carotene

ug L-1
But_fuco

HPLC Pigment - 19′-butanoyloxyfucoxanthin

ug L-1
Hex_fuco

HPLC Pigment - 19′-hexanoyloxyfucoxanthin

ug L-1
Allo

HPLC Pigment -Alloxanthin

ug L-1
Diad

HPLC Pigment -diadinoxanthin

ug L-1
Diato

HPLC Pigment - diatoxanthin

ug L-1
Fuco

HPLC Pigment - fucoxanthin

ug L-1
Perid

HPLC Pigment -peridinin

ug L-1
Zea

HPLC Pigment - zeaxanthin

ug L-1
Chl_a

HPLC Pigment - Chlorophyll a

ug L-1
DVVChl_a

HPLC Pigment - Divinyl Chlorophyll a

ug L-1
Chlide_a

HPLC Pigment - chlorophyllide a

ug L-1
Chl_b

HPLC Pigment - Chlorophyll b

ug L-1
DVChl_b

HPLC Pigment - divinyl chlorophyll b

ug L-1
Chl_c12

HPLC Pigment - Chlorophyll c1, 2

ug L-1
Chl_c3

HPLC Pigment - Chlorophyll c3

ug L-1
Lut

HPLC Pigment - lutein

ug L-1
Neo

HPLC Pigment - neoxanthin

ug L-1
Viola

HPLC Pigment - violaxanthin

ug L-1
Phytin_a

HPLC Pigment - Phaeophytin a

ug L-1
Phide_a

HPLC Pigment - Chlorophyllide a

ug L-1
Pras

HPLC Pigment - prasinoxanthin

ug L-1
Gyr_diester

HPLC Pigment - Gyroxanthin diester

ug L-1


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Instruments

Dataset-specific Instrument Name
Bran+Luebbe/Seal AA3
Generic Instrument Name
Bran+Luebbe / SEAL Analytical AutoAnalyzer 3 (AA3) continuous-flow analyzer
Generic Instrument Description
The AutoAnalyzer 3 (AA3) is a segmented continuous-flow analyzer (continuous flow analyzer, CFA) used for automated colorimetric analysis of dissolved nutrients and other analytes in environmental, seawater, freshwater, wastewater, soil, and agricultural samples. The AA3 was originally manufactured by Bran+Luebbe and, following acquisition of the product line in 2006, has continued to be manufactured and supported by SEAL Analytical. The AA3 is the third-generation instrument in the Technicon AutoAnalyzer family and is widely used for determination of nitrate, nitrite, ammonium, phosphate, silicate, and other dissolved nutrients. See the description from the manufacturer.

Dataset-specific Instrument Name
Apollo SciTech DIC Analyzer
Generic Instrument Name
Inorganic Carbon Analyzer
Generic Instrument Description
Instruments measuring carbonate in sediments and inorganic carbon (including DIC) in the water column.

Dataset-specific Instrument Name
Niskin bottle
Generic Instrument Name
Niskin bottle
Dataset-specific Description
At each station, water column CTD profiles of temperature, salinity, dissolved oxygen, and pH were made, and discrete surface and bottom water samples were collected by rosette-mounted Niskin bottles.
Generic Instrument Description
A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends. The bottles can be attached individually on a hydrowire or deployed in 12, 24, or 36 bottle Rosette systems mounted on a frame and combined with a CTD. Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc.


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Project Information

RAPID: Collaborative Research: Unprecedented Hurricane Ian Nutrient and Organic Matter Inputs to Southwest Florida Coastal Waters (RAPID: Ian Inputs)


NSF Award Abstract:
Many coastal systems are facing the threats of continuing development and climate change, including an increased intensity of extreme weather events such as hurricanes. Such storms are now wetter than in prior years because warmer atmospheres hold more moisture. Thus, they release more rainfall and deliver more nutrients and organic matter to coastal areas. This, in turn, leads to phytoplankton blooms with potential for regional “dead zones” or areas with little or no dissolved oxygen. Hurricane Ian, a category 4 hurricane which hit southwest Florida in late September 2022, resulted in immense nutrient and organic carbon inputs to the coastal estuaries and southwest Florida shelf waters and presents an opportunity to examine the effects of a major storm disturbance on a coastal system that is under increasing coastal nutrient stress. The objective of this RAPID project is to examine carbon and nutrient cycling along a gradient of stations from the estuary to low-nutrient shelf waters to determine the rate of change and recovery in this system and the relationship of large hurricane inputs to hypoxia, anoxia, and ocean acidification. These data are being used to develop a regional model to predict the impact of future hurricanes on ocean acidification, phytoplankton blooms, and local hypoxia and anoxia. This study leverages ongoing monitoring programs funded by state and federal management agencies, as well as NSF-supported programs for undergraduate students. It provides partial support for training of postdoctoral investigators and a graduate student. Outreach will include presentations to elementary and high school students.

In September 2022, southwest Florida experienced a direct hit from category-4 Hurricane Ian, resulting in extensive watershed flooding and nutrient- and dissolved-organic-matter-laden estuarine plumes extending more than 50 miles onto the shallow oligotrophic west Florida shelf (WFS). The massive terrestrial organic carbon and nutrient inputs to the southwest Florida coastal and shelf region may be priming this oligotrophic system for extensive phytoplankton blooms, as well as significant changes in carbon chemistry and nutrient cycling that could lead to longer-term regional hypoxia and anoxia. In this RAPID project, the investigators are measuring a suite of carbon and nutrient parameters, as well as carbon and nutrient cycling, at stations in Charlotte Harbor and the adjacent inner WFS system in December 2022, February 2023, and September 2023. The objectives are to assess: 1) a timescale of system responses from shorter (weeks to months) to 1-year impacts post-hurricane; 2) how nutrient availability and forms influence hypoxia; and 3) how hurricane-driven inputs influence carbonate chemistry in this system, specifically the connection between acidification and hypoxia. The investigators are using new data collected in this study and monitoring data from regional state and federal programs in a coupled hydrodynamic-biogeochemical model to understand conditions on the WFS. Hindcast simulations and modeling experiments are assessing how large inputs of nutrients and organic matter affect phytoplankton, oxygen conditions, and carbon chemistry. Modeling experiments are exploring how storms of varying wind and precipitation intensity affect the WFS and estuaries, identifying the tipping point at which the WFS switches from episodic to persistent hypoxia.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.



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Funding

Funding SourceAward
NSF Division of Ocean Sciences (NSF OCE)

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