Geophysical Research Letters

Supporting Information for

Hurricanes enhance labile carbon export to the deep ocean

R. Pedrosa-Pàmies1, M.H. Conte1, 2, J.C. Weber1, R. Johnson2

1 The Ecosystems Center, Marine Biological Laboratory, Woods Hole, MA, USA

2 Institute of Ocean Science, St. Georges GE01, Bermuda [Institutional affiliations]

Contents of this file

Text S1 to S2 Figures S1 to S6 Tables S1 to S2

Additional Supporting Information (Files uploaded separately)

None

Introduction

Supporting Information Text S1 has detailed description of the study area. Text S2 is a detailed description of the data and methods.

Figure S1 is the location of the study site and track of .

Figure S2 show the temperature evolution of and chlorophyll concentration during Hurricane Nicole passage.

Figure S3 show the primary production and pigment concentrations measured in the upper water column (0-250 m) at the Bermuda Atlantic Time site in August, September and October, covering the time period from 1990-2016. This figure is to highlight the episodic phytoplankton bloom and deepening.

Figure S4 show detailed lipid biomarker concentrations in suspended particles collected 25-29 October 16, two weeks after Nicole´s passage.

Figure S5 show microscope images of particle aggregates in the 1500 m and 3200 m flux collected during the lipid flux peak associated with Hurricane Nicole.

Figure S6 is to put in evidence the area affected by major hurricanes (category 3 or higher) that passed within 300 km (black circle) of the Bermuda Time Series site (black star) from 1980 to 2016.

Table S1 and S2 contain the data of the sinking and suspended particles, respectively, discussed in this paper. Table S1 contains the fluxes of bulk constituents and lipid biomarkers in the 1500 m and 3200 m OFP sediment trap, September-December 2016. Table S2 contains concentrations of bulk constituents and lipid biomarkers in suspended particles collected through the water column in the OFP site two weeks after Hurricane Nicole passage (25-29 October 2016).

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Text S1 Study area

The Bermuda Time-Series Site is located in the oligotrophic northern Sargasso Sea gyre about 75 km southeast of the island of Bermuda (Figure 1S). Water depth is 4,500 m. Currents in the area are generally weak, with mean velocities of 5 cm s-1 at 500 m depth decreasing to <1 cm s-1 below 1500 m depth. The Site hosts several ongoing multi-decadal time-series: the Hydrostation S (since 1954) time-series of hydrographic parameters (Michaels & Knap, 1996; Phillips & Joyce, 2007; Steinberg et al., 2001), the Oceanic Flux Program (OFP, since 1978) time-series of deep ocean fluxes (Conte et al., 2001; Conte & Weber, 2014; Deuser, 1986), the Bermuda Atlantic Time-series Study (BATS, since 1988) time-series of upper ocean biogeochemistry (Lomas et al., 2013; Michaels & Knap, 1996; Steinberg et al., 2001) and previously the Bermuda Testbed Mooring (BTM, 1995- 2007, Dickey et al., 2001) time-series of upper ocean physics and optics.

The seasonal patterns in mixed layer dynamics and primary production in the area has been previously described (Lomas et al., 2013; Phillips & Joyce, 2007; Steinberg et al., 2001). Convective mixing in the late fall and winter generates a deep winter mixed layer, which reaches a maximum depth of between 150-300 m in February. With the onset of seasonal stratification in late February- early March, a short-lived phytoplankton bloom develops. Increasing solar stratification in late spring reduces vertical mixing and nutrient supply, limiting primary productivity. A shallow (<20 m), strongly stratified mixed layer is present in summer and fall, resulting in a minimum in primary production.

Significant inter-annual variability is present in upper ocean biogeochemical parameters, the seasonal cycle of production (Lomas et al., 2013; Steinberg et al., 2001), and the deep particle flux (Conte et al., 2001; Conte & Weber, 2014). Part of the non-seasonal variability arises from mesoscale physical variability, driven by eddies and fronts, which alters nutrient influx into the euphotic zone and in turn phytoplankton production (Babin et al., 2004; Conte et al., 2003; T. Dickey et al., 2001; Krause et al., 2010; McGillicuddy et al., 1998; McNeil et al., 1999; Shropshire et al., 2016; Siegel et al., 1999). Synoptic-scale weather systems, such as the passage of strong storms and hurricanes which alter upper-ocean heat loss and mixing (Jacob et al., 2000; Price, 1981) also contribute to non-seasonal variability. Dickey et al. (1998) and Black and Dickey (2008) described the upper ocean response to passage of hurricanes over the BTM site, including the generation of large inertial currents, a decrease in sea surface temperature, a deepening of the mixed layer and upwelling of nutrients stimulating phytoplankton production.

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Text S2 Extended Data and Methods

1. Satellite and hurricane data

Remote sensing products provided additional information on the surface water environment. Sea Surface Temperature (SST) data (8-day SST composites) were obtained from Moderate Resolution Imaging Spectrometer (MODIS), in orbit on the Aqua platform, using 4 km resolution level 3 binned data. 8-days mean SST data are processed and distributed by the NASA Goddard Earth Sciences (GES) Data, Information Services Center (DISC) and Ocean Biology Processing Group (OBPG). For Chlorophyll concentration (Chl) data, due data from MODIS was severely limited by gaps most probably caused by clouds, sun glint and other phenomena which disable the extraction of ocean color (Gregg et al., 1998), and there was no data available from Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Level 3, 7-days mean surface Chl concentration data were obtained from PISCES (Pelagic Interaction Scheme for Carbon and Ecosystem Studies), which is a biogeochemical model derived from the Hamburg Model of Carbon Cycle, version 5 HAMOCC5 (Aumont et al., 2015).

Hurricane position and intensity information were obtained from the National Oceanic and Atmospheric Administration (NOAA) National Hurricane Center. The dataset is a post analysis product that contains additional information not available in the operational setting, allowing the best determination of hurricane position and intensity, the so-called best track (available online at https://www.nhc.noaa.gov/data/tcr/). The translational speed was calculated using centered time differencing based on observed changes in longitude and latitude at 6-h interval.

The Saffir–Simpson hurricane wind scale (SSHWS), formerly the Saffir–Simpson hurricane scale (SSHS), classifies hurricanes into five categories distinguished by the intensities of their sustained winds. To be classified as a hurricane, a must have maximum sustained winds of at least 74 mph (33 m s-1; 64 kn; 119 km h-1, Category 1), 96 mph (43 m s-1; 83 kn; 154 km-1, Category 2), 111 mph (50 m s-1; 96 kn; 178 km h-1, Category 3), 130 mph (58 m s-1, 113kn, 208 km h-1, Category 4), and ≥157 mph (70 m s-1; 136 kn; 251 km h-1, Category 5).

The hurricane hazard index (HHI), recently introduced by Kantha (2006), provides a continuous and open-ended scaling method that overcomes some of the limitations of the traditional Saffir- Simpson Hurricane Scale (SSHS). The HHI is defined as:

2 3 (1) HHI= (Rhurr/R0) (Vmax/V0) (U0/Uh)

4 where Rhurr is the radius to hurricane force winds, Uh is the translation speed of the storm, and Vmax is the maximum velocity of tangential winds. Subscript 0 indicates reference values; R0= 96.6 km, -1 -1 V0= 33.1 m s and U0= 6.7 m s (Kantha, 2006).

The area within a 300 km radius of Bermuda that was affected by hurricane force winds during passage of hurricanes of Cat. 3 or higher (Harvey 1981, Gert 1999, Erin 2001, Fabian 2003, Ophelia 2011, Gonzalo 2014 and Nicole 2016) was calculated between 1980 and 2016 (Figure S6). The length of the hurricane tracks was multiplied by an averaged estimated radius of hurricane-force winds of 135 km, based upon the analysis of Bell et al. (2004) which found that 90% of measured hurricanes have a wind force radius reaching 135 km. This calculation results in an area of 85700 km2 for the 36-year period, corresponding to an annual average of 23,000 km2 y-1.

2. Analytical methods

Primary production analysis. Primary production estimates were based on the uptake rate of 14C from an inorganic substrate to a particulate organic form during dawn-dusk in-situ incubations (Knap et al., 1997). Approximately two hours prior to sunrise, samples were collected at 20 m intervals from surface to 140 m from a standard CTD cast. At each depth five 250 mL samples were drawn into clear polycarbonate conical flasks and subsequently spiked with 250 μL of a sodium bicarbonate solution labeled with 14C (approximate sample specific activity of 40 μCi mL- 1). Prior to sunrise four of the flasks (three clear and one dark) were deployed on a free drifting surface tethered array at each of the nominal depths, while the remaining flask was used as a blank. Recovery of the drifting array occurred approximately 30 minutes after sunset and the samples were immediately processed whereby a 50 mL aliquot was filtered onto a 25 mm GF/F filter and acidified with 1N HCl. Samples were vented for 24 hours in a fume hood to ensure complete removal of unfixed inorganic 14C, then stored in 10 mL of scintillation cocktail (Ultima Gold, Perkin &Elmer). After a minimum of two weeks, the samples were measured for radioactive counts on a liquid scintillation counter (Perkin & Elmer Tri-Carb).

Phytoplankton pigment analysis. Samples for phytoplankton pigment analyses were taken from Niskin bottles on CTD casts, and collected from 12 depths in the upper 250 m and processed using standard BATS protocols (Knap et al., 1997). At each depth 4 L of seawater was filtered onto 25 mm Whatman GF/F filters and flash frozen in liquid nitrogen. Filters were extracted in 100% acetone and analyzed by both standard fluorometric (Turner AU10) and high performance liquid chromatography (HPLC) using the method of Bidigare (1991). The HPLC analyses were performed on an Agilent 1100 series instrument. Pigment concentrations were quantified from instrument response and retention times and standardized using commercial pigment standards from the Danish Hydraulic Institute, Denmark.

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Bacterial enumeration analysis. Bacteria abundance was determined using epifluorescence microscopy techniques (Knap et al., 1997). Samples (40 mL) were collected into sterile 50 mL Falcon Tubes from standard CTD casts then preserved with 5 mL of 0.2 μm filtered formalin (VWR, histology grade) and stored at -80 °C. Prior to analysis, samples were thawed and brought back to room temperature, then 10 mL of the sample was filtered onto a black stained (using irgalan black) Nucleopore filter. After filtration, 1 mL of 6, 6-diamidino-2-phenyl dihydrochloride (5 μg mL-1, DAPI, SIGMA-Aldrich) was added to the filters, which were later mounted onto microscope slides with high viscosity immersion oil. Enumeration of the bacteria cells was achieved with an Olympus AX70 epifluorescent microscope under ultraviolet excitation at 100x magnification where at least 400 cells (10 fields) were counted for abundance.

Bulk particulate organic carbon analysis. Particulate organic carbon and nitrogen concentrations (and isotopic composition) of suspended and sinking particles were measured on a Europa 20-20 CF-IRMS interfaced with the Europa ANCA-SL elemental analyzer after acidification to remove carbonates. Prior to analysis, the suspended particle filter samples were decarbonated by fuming overnight with concentrated HCl and then dried at 60 °C. The OFP flux material (~3 mg) was pre-treated with 4% sulfurous acid (Fisher, ACS grade) to remove carbonates using a modification of the Verardo et al. (1990) method. Briefly, the sample material was placed in pre-cleaned, pre-combusted sample tins (CosTech), 50 µl of sulfurous acid was applied to dissolve carbonates and the acid evaporated at 55-75 °C. The process was repeated until there was no visible reaction under a lighted magnifier. Uncertainties on POC determinations for the flux material were <0.07% (n≥ 3) and between 0.5-25% for the suspended particles (n= 2), depending on total particle concentration on the filters (Figures 4-5).

Lipid analyses. Lipids were extracted using a modification of the method detailed in Conte et al.

(2003). Briefly, an internal standard mixture consisting of n-C21:0 fatty alcohol, n-C23:0 fatty acid, 5 - cholestane and n-C36:0 alkane was added to the samples prior to lipid extraction. Lipids were ultrasonically extracted in 2:1 CHCl3-MeOH using a cup horn (120 W, 2 min). Salts and non-lipid components were removed using the cleanup procedure of Folch et al. (1957). The lipid extracts were concentrated to just dryness using a rotoevaporator, resuspended in CHCl3, and passed through combusted, anhydrous Na2SO4 to remove residual water. The purified sample extract was transesterified using anhydrous 5 % methanolic HCl (55 °C, 12 h), following the procedure of Christie (1982). The transesterified lipid products were extracted into hexane and passed through a short bed of Na2SO4 to remove residual water. The hexane was evaporated using a Savant

SpeedVac (SC110) and the sample resuspended in CH2Cl2. Just prior to gas chromatography-mass spectrometry (GC-MS), sample extracts were transferred to GC-MS autosampler v-vials, evaporated under a stream of N2 and TMS-derivatized under N2 using 25 µL of pyridine and 25 µL of N,O-Bis (trimethylsilyl)trifluoroacetamide + 1 % Trimethylchlorosilane (BSTFA + 1 % TMCS)

(55 °C, 1 hr.). The TMS reagents were evaporated under N2 and the samples resuspended in CH2Cl2.

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The transesterified, trimethylsilyl derivatives were analyzed on an Agilent 7890A GC coupled to a 5975C MS equipped with triple-axis MS and FID detectors. An Agilent CPSil5CB low bleed MS column (60 m x 0.25 mm diameter x 0.25 µm film thickness) was used to separate the lipid compounds. The temperature programming was 50 °C (2 min hold), 10 °C/min to 150 °C, 4 °C/min to 320 °C and then held for 35 minutes. Compounds were identified by their mass spectra and were quantified from their FID response relative to the internal standard. Uncertainties in quantification of individual compounds range between 4 - 11%, based upon repeated analyses of samples having similar lipid composition.

Diagnostic lipid biomarkers in particles. We estimated the relative contributions of particulate organic carbon (POC) sources by summing the absolute and POC-normalized concentrations of key lipid biomarkers into three groups: Phytoplankton-derived POC and labile polyunsaturated fatty acids (PUFA) (PHYTO+PUFA), zooplankton-derived POC (ZOO) and bacterial-derived POC (BACT) (See Supplementary Tables 1 and 2 for abbreviation details):

(2) PHYTO+PUFA = C18 PUFA+ 20:5ω3 PUFA + 22:6ω3 PUFA+ phytosterols + alkenones+ C30 diol (3) ZOO = 18:1ω9 MUFA + 20:1ω9 MUFA + 22:1ω9 MUFA + 22:1ω11 MUFA + cholesterol + MUFAL (4) BACT = Odd/br FA+ 18:1ω7 MUFA + odd FAL + hopanoids+ hydroxy acids

Over a hundred extractable lipid compounds are present in oceanic particulate matter. Although the precise origins of many compounds remain unknown, current knowledge of taxonomic distributions and diagenetic transformations allows inferences to be made about organic matter sources and lability (i.e., ‘‘lability/freshness’’ or ease of degradability). Fatty acids (FA) are the most abundant lipid compounds. Some monounsaturated FA (MUFA), such as 18:1ω7, predominate in bacteria (Volkman, Johns, et al., 1980), while others (e.g., 18:1ω9) are enriched in zooplankton and their fecal pellets (S. G. Wakeham & Canuel, 1988). Long-chain MUFA, such as 20:1 and 22:1, are present in zooplankton wax esters (Lee et al., 2006). Odd- and branched-chain (iso- and anteiso- and mid-chain) FA (odd/br FA) are abundant in bacteria (Cho & Salton, 1966; Kaneda, 1991). C16 to C22 even chained polyunsaturated fatty acids (PUFA) with 3-6 methylene-interrupted double bonds are synthesized de novo by phytoplankton (Volkman et al., 1998). Phytoplankton classes differ in the relative abundances of the individual PUFA (Parrish, 2013; Rossi et al., 2006). PUFA are essential fatty acids and comprise a large percentage of animal storage lipids (Parrish, 2013). Animal tissues are enriched in 20:5ω3 and 22:6ω3 compounds relative to phytoplankton, due to chain elongation and desaturation of dietary PUFA (Sargent & Whittle, 1981). The presence of PUFA indicates fresh organic materials, as PUFA are rapidly degraded in organic detritus.

Sterols, and their stanol and steroidal ketone degradation products, are the second most 5,22 5,22 5,22 5,22 22 abundant lipids. The sterols norC27Δ , C27Δ , C28Δ , C29 Δ and C30Δ are major sterols in phytoplankton (Volkman, 1986) (see Supplementary Tables 1 and 2 for abbreviations). Cholesterol,

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5 C27Δ , the predominant sterol in animals, is a transformation product of dietary sterols (Harvey et 5 al., 1989; Teshima, 1971). C27Δ is also highly enriched in animal fecal material (Stuart G Wakeham & Canuel, 1986).

Fatty alcohols (FAL) are synthesized de novo by diverse phytoplankton, zooplankton and microbial sources (Volkman, Gatten, et al., 1980). Short-chain n-C12-C14 FAL have microbial, plant or animal sources (Berge et al., 1995; Sargent, 1976). Even-chained n-C16-C24 FAL are major components of zooplankton wax esters, with C16 and C18 compounds being the most prevalent (Robinson et al., 1984). Odd chained fatty alcohols are most commonly found in bacteria (Mudge et al., 2008; Parkes & Taylor, 1983).

Hopanoids (HOP), which include hopanoic acids, alcohols and ketones, are early degradation products of C35 bacteriohopanepolyols synthesized by bacteria, including some marine cyanobacteria such as Synechococcus (Rohmer et al., 1984).

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Figure S1. Map of the study region showing the Bermuda Time-Series Site (yellow circle) and track of Hurricane Nicole (4-18 October 2016). The inset shows the locations of the Oceanic Flux Program (OFP), Bermuda Atlantic Time-Series (BATS) and Hydrostation S sites. The Nicole track is colored to show the Saffir-Simpson Wind Scale: TS (tropical storm), H1-4 (category 1-4). Wind radii of hurricane force winds (64 knots), storm force winds (50 knots), and tropical storm force winds (34 knots), are also shown for Nicole’s track, with the hurricane force winds closest to the hurricane track.

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Figure S2. Maps showing the temperature evolution of sea surface temperature and chlorophyll concentration during Hurricane Nicole passage. The Bermuda Time Series site is shown as a black star. (a) Sea Surface Temperature and (b) Surface Chlorophyll (See Supplementary Information S1). Chlorophyll concentration contours (white) are 0.1 mg m-3. The Hurricane Nicole track is color coded with Saffir-Simpson Scale categories: TS (tropical storm), H1- 4 (category 1-4) when the hurricane track is within the weekly-averaged period and grey otherwise.

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Figure S3. Box-whisker plots of primary production (PP) and pigment concentrations measured in the upper water column (0-250 m) at the Bermuda Atlantic Time site in (a) August, (b) September and (c) October, covering the time period from 1990 -2016 (n ranges from 13 to 24 due BATS sample collection variability). The box defines the lower (25th percentile) and upper (75th percentile) quartiles, the center line of the box is the median, the error bars define the 5% and 95% data distribution, and the grey circles show the outliers. Each month include a vertical profile of PP and pigment concentrations after passage of (a) Category 1 in 1995 (yellow; seven days after closest approach), (b) Category 3 in 2003 (blue; 14 days after closest approach), and (c) Category 3 Hurricane Nicole in October 2016 (red; five days after closest approach). Chlorophyll a, (Chl a, photosynthetic algae pigment), Chlorophyll b (Chl b, Chlorophytes), Zeaxanthin (Zea, Cyanobacteria), 19-Butanoyloxyfucoxanthin (But, Chrysophytes). Abbreviations and taxonomic significance of pigments are from Aiken et al. (2009).

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Figure S4. Detailed lipid biomarker concentrations in suspended particles collected 25-29 October 16, two weeks after Nicole´s passage. (a) Particulate organic carbon (POC) concentrations (n= 2). (b) Total lipid concentration in suspended particles classified with diagnostic lipid biomarkers of fresh/labile phytoplankton-derived compounds and polyunsaturated fatty acids (PHYTO+PUFA, green), zooplankton-derived lipids (ZOO, orange), bacteria-derived lipids (BACT, red), and other (nonspecific source). (c) fresh/labile phytoplankton lipids and polyunsaturated fatty acids: phytosterols (see Table S1 for details), summed C18 PUFA and 20:5ω3 and 22:6ω3, C37-38 alkenones (LCK) and C30 diol). (d) zooplankton-derived lipids, ZOO: cholesterol, 18:1ω9, 20:1ω9 and 22:1ω9, 22:1ω11 monounsaturated fatty acids (MUFA), and C16-

22 monounsaturated fatty alcohols (MUFAL)). (e) Bacterial-derived lipids, BACT (odd and branched fatty acids (odd/br FA), 18:1ω7 MUFA, odd FAL, hydroxy acids and hopanoids (HOP)). Shown for reference are lipid concentration profiles in April 2015 (dark gray, typical post spring bloom conditions) and November 2015 (light gray, typical minimum production period) (Pedrosa-Pàmies et al., 2018). (Sampling depths for the three profiles differed).

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Figure S5. Microscope images of particle aggregates in the (a) 1500 m and (b) 3200 m flux collected during the lipid flux peak associated with Hurricane Nicole.

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Figure S6. Major hurricanes (cat. 3 or higher) that passed within 300 km (black circle) of the Bermuda Time Series site (black star) from 1980 to 2016. Hurricane tracks are color-coded with Saffir-Simpson Wind Scale: TD (tropical depression), TS (tropical storm), H1-4 (category 1-4). Wind radii (135 km) of hurricane force winds (64 knots) is represented within the 300 km radius.

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Table S1. Fluxes of bulk constituents and lipid biomarkers in the 1500 m and 3200 m OFP sediment trap, September-December 2016. Depth (m) 1500 1500 1500 1500 1500 1500 3200 3200 3200 3200 3200 3200 3200* Flux Flux Flux ratio Cup no. 1 2 3 4 5 ratio 1 2 3 4 5 ratio Conte et al. (2003) Start date 25 Sep 10 Oct 29 Oct 12 Nov 26 Nov Cup 2: 25 Sep 10 Oct 29 Oct 12 Nov 26 Nov Cup 3: 18-31 Dec96 End Date 10 Oct 25 Oct 12 Nov 26 Nov 10 Dec Cup 1 10 Oct 25 Oct 12 Nov 26 Nov 10 Dec Cup 1 18Nov-2Dec96 Days sampling 15 15 14 14 14 15 15 14 14 14

Particulate Organic Carbon concentration (% of dry weight) 5.26 5.85 7.49 5.59 4.75 4.75 4.53 5.68 4.91 4.74 Particulate Organic Carbon flux (mg m-2 d-1) 1.53 1.62 0.80 0.87 1.76 1.03 0.99 0.84 0.74 1.44 Total extractable lipids (% of total POC) 4.06 5.79 5.91 4.33 1.91 1.12 1.27 4.38 1.37 1.11 -2 -1 Lipid biomarker flux (µg m d ) Total extractable lipids 62.7 93.9 47.2 37.9 33.5 1.5 11.6 12.6 36.6 10.2 16.0 3.2 4.9 Total fatty acids (FA) 23.3 48.9 25.7 20.4 13.3 2.1 2.76 3.81 10.6 3.37 4.58 3.8 3.3 C12-C28 saturated even (SFA) 8.43 23.47 9.05 7.08 5.79 2.8 1.24 1.47 5.60 1.29 1.89 4.5 4.1 C14:1-C22:1 monounsaturated even (MUFA) 10.8 16.8 8.60 9.79 5.87 1.6 0.92 1.40 2.89 1.09 1.60 3.1 3.4 18:1ω9 2.71 5.79 3.51 5.40 3.53 2.1 0.29 0.43 1.01 0.36 0.58 3.5 1.9 18:1ω7 0.80 1.39 1.05 0.04 0.52 1.7 0.08 0.13 0.37 0.09 0.16 4.6 - C16-C20 polyunsaturated (PUFA) 1.09 3.08 4.57 1.38 0.61 2.8 0.16 0.21 0.54 0.16 0.20 3.4 3.4 18:5ω3 0.17 0.26 0.15 0.18 0.11 1.6 0.05 0.07 0.11 0.10 0.12 2.2 3.3 20:5ω3 0.14 0.57 0.50 0.27 0.08 4.1 0.01 0.02 0.07 0.02 0.03 6.6 4.1 22:6ω3 0.22 0.84 2.72 0.44 0.18 3.7 0.06 0.09 0.17 0.04 0.07 2.8 3.4 C12-C21 odd and saturated iso- anteiseo-branched (odd/br 3.5 3.1 FA) 3.00 5.51 3.45 2.18 1.05 1.8 0.44 0.72 1.54 0.83 0.88 Total fatty alcohols (FAL) 1.36 4.66 1.18 2.96 1.26 3.4 0.23 0.35 0.69 0.54 0.68 3.1 - C16-C22 Monounsaturated (MUFAL) 0.10 0.89 0.33 0.80 0.24 8.6 0.03 0.04 0.12 0.06 0.05 4.4 - C16- C22 Odd/branched (odd/br FAL) 0.11 0.31 0.07 0.12 0.09 2.8 0.01 0.03 0.07 0.01 0.02 12.3 - Total β+(ω-1)-hydroxy acids 0.99 1.04 0.53 0.36 0.40 1.1 0.14 0.21 0.27 0.14 0.20 1.9 6.3 Total Sterols+Stanols 16.9 17.3 9.68 4.50 5.59 1.0 2.20 1.87 16.8 1.39 3.35 7.6 7.1 Phytosterols** 2.81 5.42 1.67 1.59 2.50 1.9 1.18 1.01 9.87 0.65 1.01 8.4 7.3 5,22 norC27Δ 0.29 0.80 0.20 0.20 0.31 2.7 0.10 0.06 2.53 0.07 0.11 26.4 - 5,22 C27Δ 0.92 1.67 0.56 0.48 0.78 1.8 0.19 0.17 1.29 0.18 0.24 6.8 - 5,22 C 28Δ 0.46 1.27 0.34 0.06 0.11 2.8 0.30 0.21 3.76 0.05 0.07 12.5 - 5,22 C29Δ 0.20 0.33 0.13 0.22 0.39 1.6 0.12 0.10 0.52 0.09 0.17 4.2 5 Choles-5-en3b-ol (cholesterol) C27Δ 12.6 9.81 7.18 2.42 2.28 0.8 0.60 0.47 4.95 0.51 1.95 8.3 4.7 5 C28Δ 0.32 0.53 0.22 0.30 0.41 1.7 0.20 0.20 0.84 0.11 0.17 4.2 - 5 C29Δ 0.32 0.53 0.22 0.30 0.41 1.7 0.20 0.20 0.84 0.11 0.17 4.2 - Stanols*** 1.25 1.65 0.75 0.42 0.70 1.3 0.26 0.23 1.88 0.16 0.28 7.2 13.4 0 C27Δ 0.25 0.35 0.56 0.27 0.20 1.4 0.07 0.06 0.76 0.07 0.13 10.6 - 0 C28Δ 0.07 0.17 0.11 0.08 0.03 2.5 0.04 0.04 0.11 0.01 0.04 2.6 - 0 C29Δ 0.03 0.11 0.13 0.04 0.05 3.2 0.04 0.03 0.17 0.02 0.03 4.4 - 22 C27Δ 0.48 0.52 0.23 0.02 0.02 1.1 0.03 0.02 0.39 0.01 0.01 14.0 - 22 C28Δ 0.02 0.05 0.01 0.07 0.15 0.02 0.02 0.01 0.03 0.02 0.03 0.02 - 0 5 C27Δ /C27Δ 0.04 0.03 0.06 0.04 0.08 0.7 0.12 0.13 0.15 0.14 0.07 1.3 - 22 5,22 C27Δ /C27Δ 0.04 0.04 0.04 1.09 1.35 0.9 0.06 0.06 0.01 0.48 0.39 0.1 - Saturated 4-methyl steroidal ketones 0.57 0.68 0.24 0.10 0.38 1.2 0.01 0.01 0.12 0.02 0.08 10.9 16.3 C30 alkan-1,15-diol (C30 diol) 0.28 0.36 0.12 0.19 0.38 1.3 0.26 0.26 0.10 0.09 0.13 0.4 7.4 Hopanoids (HOP) 4.26 6.14 1.78 2.64 4.59 1.4 2.58 2.61 1.65 1.92 3.02 0.6 9.1 1-O-alkylglycerols 0.54 1.46 0.04 0.40 0.46 2.7 0.10 0.14 0.26 0.03 0.20 2.6 23.0 C37-C39 methyl ketones (alkenones) (LCK) 0.18 0.17 0.07 0.14 0.24 1.0 0.21 0.14 0.06 0.09 0.12 0.3 31.7 * 3200 m depth flux ratio from lipid fluxes from 18-31 Dec 96/18Nov-2Dec 96 from Conte et al (2003), showing pulsed export of labile carbon after an upper ocean physical feature forcing.**Phytosterols: 24- 5,22 5,22 5,22 5,22 Nor-cholesta-5,22-dien-3β-ol (C26Δ ), 27-nor-24-methylcholesta-5,22-dien-3β-ol (Cnor27Δ ), Cholesta-5,22-dien-3β -ol (C27Δ ), 24-Methylcholesta-5,22-dien-3β-ol (C28Δ ), 24-Methylcholesta-5,24(28)-dien- 5,24(28) 5 5,22 5 5,24(28) 3β-ol (C28Δ ), 24-Methylcholest-5-en-3β-ol (C28Δ ), 24-Ethylcholesta-5,22-dien-3β-ol (C29Δ ), 24-Ethylcholest-5-en-3β-ol (C29Δ ), 24-Propylcholesta-5,24(28)-dien-3β-ol (C30Δ ), 4α,23,24-Trimethyl-5a- 22 22 22 22 22 cholest-22-en-3β-ol (4αC30Δ ). ***Stanols: 24-Nor-5α-cholest-22-en-3β-ol (C26Δ ), 5α-Cholesta-22-en-3β-ol (C27Δ ), 24-Methyl-5α-cholest-22-en-3β-ol (C28Δ ), 24-Ehtyl-colesta-22-en-3β-ol (C29Δ ), 5α- 0 0 0 0 Cholestan-3β-ol (C27Δ ), 24-Methyl-colestan-3β-ol (C28Δ ), 24-Ethyl-5α-cholestan-3β-ol (C29Δ ), 4α,23S,24R-trimethyl-5α(H)-cholestan-3β-ol (4αC30Δ ).

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Table S2. Concentrations of bulk constituents and lipid biomarkers in suspended particles collected through the water column in the OFP site two weeks after Hurricane Nicole passage (25-29 October 2016). Depth (m) 30 80 100 200 250 500 700 800 850 1000 1500 1700 3200 4200 4400 Particulate Organic Carbon flux (µg L-1) 15.6 17.9 13.21 3.15 3.12 1.69 1.50 1.35 1.40 1.27 1.06 0.99 0.57 0.53 0.68 Total extractable lipids (% of total POC) 11.1 13.3 14.9 13.7 9.4 15.0 13.0 12.2 11.2 14.1 8.19 7.35 7.48 7.09 7.21 -1 Lipid biomarker concentration (ng L ) Total extractable lipids 1734 2382 1965 432 295 253 194 164 157 180 87.1 72.7 42.6 37.9 48.8 Total fatty acids (FA) 1394 1963 1616 346 228 201 153 116 131 144 63.4 50.5 30.3 28.0 36.2

C12-C28 saturated even (SFA) 571 834 666 118 78.4 77.6 57.1 44.5 46.8 50.2 14.9 11.1 5.50 4.43 5.64

C14:1-C22:1 monounsaturated even (MUFA) 273 444 398 81.7 55.3 58.4 43.2 36.7 41.6 47.6 22.7 18.3 12.3 11.9 15.7 18:1ω9 69.81 94.3 62.9 27.4 22.6 34.3 23.3 22.4 23.6 27.8 7.15 5.80 3.17 1.75 2.09 18:1ω7 50.32 40.8 23.7 10.4 7.69 5.28 4.49 2.85 3.73 4.10 3.52 2.85 2.33 2.74 3.99

C16-C20 polyunsaturated (PUFA) 452 569 474 99.4 61.2 40.5 34.2 22.6 26.3 29.9 15.0 13.3 6.14 5.37 6.63 18:3ω3 26.8 51.96 37.4 1.62 0.56 2.03 2.06 0.57 0.55 0.79 0.06 0.54 0.35 0.36 0.50 18:4ω3 40.5 96.28 104 14.8 2.89 2.43 1.63 1.30 1.38 1.57 0.56 0.00 0.00 0.00 0.00 18:5ω3 39.80 70.12 56.1 6.07 1.48 1.59 1.06 0.80 0.78 1.01 0.18 0.14 0.07 0.04 0.07 20:5ω3 55.5 66.10 55.3 18.2 12.3 7.95 6.72 4.51 5.52 6.32 3.98 3.53 1.61 1.52 1.93 22:6ω3 233 211 162 41.8 30.8 20.0 17.1 11.7 13.3 15.0 7.43 6.81 3.09 2.56 3.12

C12-C21 odd and saturated iso- anteiseo- 60.4 67.9 51.5 41.6 29.2 19.6 14.6 9.61 13.7 13.5 9.77 7.06 5.84 branched (odd/br FA) 5.99 7.90 Total fatty alcohols (FAL) 14.4 17.83 10.2 15.0 12.2 18.1 8.99 16.8 8.34 10.6 8.07 6.46 4.41 2.83 3.56

C16-C22 Monounsaturated (MUFAL) 0.74 1.14 0.39 0.39 0.69 0.72 1.09 8.31 0.69 1.03 1.74 1.76 0.77 0.71 0.97

C16- C22 Odd/branched (odd/br FAL) 0.65 0.92 0.46 0.85 0.51 1.27 0.59 0.69 0.48 0.67 0.43 0.30 0.22 0.13 0.12 Total β+(ω-1)-hydroxy acids 39.8 52.7 44.5 9.92 6.87 4.46 3.94 3.15 2.97 2.94 1.79 1.85 0.98 0.80 1.07 Total Sterols+Stanols 89.0 118 63.4 17.8 17.7 8.61 6.59 6.57 6.66 6.47 4.03 4.00 1.88 1.53 1.92 Phytosterols* 64.3 71.7 39.3 9.69 9.20 4.39 3.56 3.65 3.62 3.53 2.32 2.34 1.14 0.91 1.12 5,22 norC27Δ 7.76 6.27 3.11 0.79 0.69 0.34 0.27 0.28 0.27 0.25 0.19 0.18 0.07 0.06 0.08 5,22 C27Δ 13.6 14.0 8.84 2.81 2.55 1.27 1.01 0.99 1.10 0.93 0.50 0.47 0.19 0.15 0.18 5,22 C 28Δ 20.1 21.2 12.2 2.21 1.96 1.01 0.83 0.83 0.84 0.83 0.58 0.57 0.27 0.22 0.25 5,22 C29Δ 4.75 5.01 2.59 0.93 0.87 0.44 0.36 0.35 0.34 0.35 0.22 0.21 0.09 0.07 0.10 5 Choles-5-en3b-ol (cholesterol) C27Δ 12.9 21.4 11.4 4.34 4.53 2.15 1.60 1.54 1.77 1.66 0.83 0.84 0.36 0.28 0.37 5 C28Δ 4.59 6.70 1.41 0.70 0.97 0.26 0.27 0.34 0.25 0.32 0.22 0.21 0.12 0.09 0.12 5 C29Δ 7.85 9.38 5.08 1.15 1.22 0.64 0.47 0.51 0.48 0.52 0.37 0.43 0.27 0.20 0.25 Stanols** 9.12 19.7 10.4 3.01 3.41 1.79 1.25 1.20 1.10 1.13 0.77 0.70 0.31 0.27 0.33 0 C27Δ 2.15 4.82 1.89 0.52 0.81 0.46 0.32 0.32 0.32 0.32 0.19 0.16 0.06 0.05 0.07 0 C28Δ 0.91 1.93 0.44 0.26 0.31 0.13 0.09 0.09 0.08 0.08 0.06 0.06 0.03 0.03 0.04 0 C29Δ 1.05 2.90 2.07 0.88 0.89 0.37 0.25 0.28 0.25 0.23 0.18 0.17 0.09 0.08 0.10 22 C27Δ 1.38 1.87 1.37 0.31 0.40 0.20 0.15 0.16 0.16 0.15 0.10 0.07 0.03 0.02 0.02 22 C28Δ 2.09 3.51 2.09 0.42 0.43 0.28 0.19 0.18 0.16 0.16 0.11 0.09 0.03 0.03 0.03 22 C29Δ 1.07 1.70 0.74 0.23 0.20 0.16 0.08 0.07 0.07 0.06 0.05 0.05 0.02 0.01 0.02 0 5 C27Δ /C27Δ 0.17 0.23 0.17 0.12 0.18 0.22 0.20 0.21 0.18 0.19 0.22 0.19 0.17 0.18 0.19 22 5,22 C27Δ /C27Δ 0.10 0.13 0.15 0.11 0.16 0.16 0.15 0.17 0.15 0.17 0.19 0.14 0.14 0.17 0.11 Saturated 4-methyl steroidal ketones 2.06 2.32 1.05 0.33 0.39 0.15 0.13 0.20 0.13 0.17 0.08 0.08 0.02 0.02 0.02

C30 alkan-1,15-diol (C30 diol) 12.8 6.84 2.73 0.74 0.40 0.18 0.08 0.10 0.07 0.09 0.10 0.12 0.03 0.03 0.03 Hopanoids (HOP) 0.89 0.97 0.57 2.42 2.80 2.60 2.29 2.22 2.24 2.33 1.25 1.39 0.79 0.49 0.53 1-O-alkylglycerols 23.2 25.5 33.2 7.06 4.00 4.10 2.86 2.31 2.53 2.10 1.29 1.56 0.60 0.73 1.01

C37-C39 methyl ketones (alkenones) (LCK) 8.32 4.53 0.88 0.08 0.06 0.05 0.04 0.03 0.03 0.03 0.06 0.09 0.02 0.02 0.02 5,22 5,22 5,22 5,22 *Phytosterols: 24-Nor-cholesta-5,22-dien-3β-ol (C26Δ ), 27-nor-24-methylcholesta-5,22-dien-3β-ol (norC27Δ ), Cholesta-5,22-dien-3β -ol (C27Δ ), 24-Methylcholesta-5,22-dien-3β-ol (C28Δ ), 24- 5,24(28) 5 5,22 5 Methylcholesta-5,24(28)-dien-3β-ol (C28Δ ), 24-Methylcholest-5-en-3β-ol (C28Δ ), 24-Ethylcholesta-5,22-dien-3β-ol (C29Δ ), 24-Ethylcholest-5-en-3β-ol (C29Δ ), 24-Propylcholesta-5,24(28)-dien-3β- 5,24(28) 22 22 22 22 ol (C30Δ ), 4α,23,24-Trimethyl-5a-cholest-22-en-3β-ol (4αC30Δ ). **Stanols: 24-Nor-5α-cholest-22-en-3β-ol (C26Δ ), 5α-Cholesta-22-en-3β-ol (C27Δ ), 24-Methyl-5α-cholest-22-en-3β-ol (C28Δ ), 22 0 0 0 0 24-Ehtyl-colesta-22-en-3β-ol (C29Δ ), 5α-Cholestan-3β-ol (C27Δ ), 24-Methyl-colestan-3β-ol (C28Δ ), 24-Ethyl-5α-cholestan-3β-ol (C29Δ ), 4α,23S,24R-trimethyl-5α(H)-cholestan-3β-ol (4αC30Δ ).

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References Aiken, J., Pradhan, Y., Barlow, R., Lavender, S., Poulton, A., & Hardman-Mountford, N. (2009). Phytoplankton pigments and functional types in the Atlantic Ocean: A decadal assessment, 1995–2005. Deep Sea Research Part II: Topical Studies in Oceanography, 56(15), 899–917. https://doi.org/10.1016/J.DSR2.2008.09.017 Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., & Gehlen, M. (2015). PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies. Geoscientific Model Development, 8(8), 2465–2513. https://doi.org/10.5194/gmd-8-2465-2015 Babin, S. M., Carton, J. A., Dickey, T. D., & Wiggert, J. D. (2004). Satellite evidence of hurricane- induced phytoplankton blooms in an oceanic desert. Journal of Geophysical Research: Oceans, 109(C03043), 1–21. https://doi.org/10.1029/2003JC001938 Bell, K., Ray, P. S., Bell, K., & Ray, P. S. (2004). North Atlantic Hurricanes 1977–99: Surface Hurricane-Force Wind Radii. Monthly Weather Review, 132(5), 1167–1189. https://doi.org/10.1175/1520-0493(2004)132<1167:NAHSHW>2.0.CO;2 Berge, J.-P., Gouygou, J.-P., Dubacqt, J.-P., & Durand, P. (1995). Reassessment of lipid composition of the diatom, Skeletonema costatum. Phytochemistry, 39(5), 1017–1021. https://doi.org/10.1016/0031-9422(94)00156-N Bidigare, R. R. (1991). Analysis of algal chlorophylls and carotenoids. In D. Hurd & D. Spencer (Eds.), Marine particles: Analysis and characterization (pp. 119–123). Washington DC: American Geophysical Union. https://doi.org/10.1029/GM063p0119 Black, W. J., & Dickey, T. D. (2008). Observations and analyses of upper ocean responses to tropical storms and hurricanes in the vicinity of Bermuda. Journal of Geophysical Research: Oceans, 113(C08009), 1–25. https://doi.org/10.1029/2007JC004358 Cho, K. Y., & Salton, M. R. J. (1966). Fatty acid composition of bacterial membrane and wall lipids. Biochimica et Biophysica Acta (BBA) - Lipids and Lipid Metabolism, 116(1), 73–79. https://doi.org/10.1016/0005-2760(66)90093-2 Christie, W. W. (1982). Lipid analysis : isolation, separation, identification, and structural analysis of lipids. Pergamon Press. Conte, M. H., & Weber, J. (2014). Particle flux in the deep Sargasso Sea: the 35-year Oceanic Flux Program time series. Oceanography, 27(1), 142–147. https://doi.org/10.5670/oceanog.2014.17 Conte, M. H., Weber, J. C., & Ralph, N. (1998). Episodic particle flux in the deep Sargasso Sea. Deep Sea Research Part I: Oceanographic Research Papers, 45(11), 1819–1841. https://doi.org/10.1016/S0967-0637(98)00046-6 Conte, M. H., Ralph, N., & Ross, E. H. (2001). Seasonal and interannual variability in deep ocean particle fluxes at the Oceanic Flux Program (OFP)/Bermuda Atlantic Time Series (BATS) site in the western Sargasso Sea near Bermuda. Deep Sea Research Part II: Topical Studies in Oceanography, 48(8–9), 1471–1505. https://doi.org/10.1016/S0967- 0645(00)00150-8 Conte, M. H., Dickey, T. D., Weber, J. C., Johnson, R. J., & Knap, A. H. (2003). Transient physical forcing of pulsed export of bioreactive material to the deep Sargasso Sea. Deep Sea Research Part I: Oceanographic Research Papers, 50(10–11), 1157–1187. https://doi.org/10.1016/S0967-0637(03)00141-9 Deuser, W. G. (1986). Seasonal and interannual variations in deep-water particle fluxes in the

17

Sargasso Sea and their relation to surface hydrography. Deep Sea Research Part A. Oceanographic Research Papers, 33(2), 225–246. https://doi.org/10.1016/0198- 0149(86)90120-2 Dickey, T., Frye, D., McNeil, J., Manov, D., Nelson, N., Sigurdson, D., et al. (1998). Upper-Ocean temperature response to Hurricane Felix as measured by the Bermuda Testbed Mooring. Monthly Weather Review, 126(5), 1195–1201. https://doi.org/10.1175/1520- 0493(1998)126<1195:UOTRTH>2.0.CO;2 Dickey, T., Zedler, S., Yu, X., Doney, S. C., Frye, D., Jannasch, H., et al. (2001). Physical and biogeochemical variability from hours to years at the Bermuda Testbed Mooring site: June 1994–March 1998. Deep Sea Research Part II: Topical Studies in Oceanography, 48(8–9), 2105–2140. https://doi.org/10.1016/S0967-0645(00)00173-9 Folch, J., Lees, M., & Sloane-Stanley, G. H. (1957). A simple method for the isolation and purification of total lipides from animal tissues. The Journal of Biological Chemistry, 226(1), 497–509. Gregg, W. W., Esaias, W. E., Feldman, G. C., Frouin, R., Hooker, S. B., McClain, C. R., & Woodward, R. H. (1998). Coverage opportunities for global ocean color in a multimission era. IEEE Transactions on Geoscience and Remote Sensing, 36(5), 1620– 1627. https://doi.org/10.1109/36.718865 Harvey, H. R., O’Hara, S. C. M., Eglinton, G., & Corner, E. D. S. (1989). The comparative fate of dinosterol and cholesterol in copepod feeding: Implications for a conservative molecular biomarker in the marine water column. Organic Geochemistry, 14(6), 635– 641. https://doi.org/10.1016/0146-6380(89)90042-9 Jacob, S. D., Shay, L. K., Mariano, A. J., Black, P. G., Jacob, S. D., Shay, L. K., et al. (2000). The 3D oceanic mixed layer response to . Journal of Physical Oceanography, 30(6), 1407–1429. https://doi.org/10.1175/1520- 0485(2000)030<1407:TOMLRT>2.0.CO;2 Kaneda, T. (1991). Iso- and anteiso-fatty acids in bacteria: biosynthesis, function, and taxonomic significance. Microbiological Reviews, 55(2), 288–302. Kantha, L. (2006). Time to replace the Saffir-Simpson hurricane scale? Eos, Transactions American Geophysical Union, 87(1), 3. https://doi.org/10.1029/2006EO010003 Knap, A. H., Michaels, A. F., Steinberg, D. K., Bahr, F., Bates, N. R., Bell, S., et al. (1997). BATS Methods Manual, Version 4. Woods Hole, MA, US: U.S. JGOFS Planning Office. Retrieved from https://eprints.soton.ac.uk/361194/ Krause, J. W., Nelson, D. M., & Lomas, M. W. (2010). Production, dissolution, accumulation, and potential export of biogenic silica in a Sargasso Sea mode-water eddy. Limnology and Oceanography, 55(2), 569–579. https://doi.org/10.4319/lo.2010.55.2.0569 Lee, R., Hagen, W., & Kattner, G. (2006). Lipid storage in marine zooplankton. Marine Ecology Progress Series, 307, 273–306. https://doi.org/10.3354/meps307273 Lewis, R. W. (1967). Fatty acid composition of some marine animals from various depths. Journal of the Fisheries Research Board of Canada, 24(5), 1101–1115. https://doi.org/10.1139/f67-093 Lomas, M. W., Bates, N. R., Johnson, R. J., Knap, A. H., Steinberg, D. K., & Carlson, C. A. (2013). Two decades and counting: 24-years of sustained open ocean biogeochemical measurements in the Sargasso Sea. Deep Sea Research Part II: Topical Studies in

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Oceanography, 93, 16–32. https://doi.org/10.1016/J.DSR2.2013.01.008 McGillicuddy, D. J., Robinson, A. R., Siegel, D. A., Jannasch, H. W., Johnson, R., Dickey, T. D., et al. (1998). Influence of mesoscale eddies on new production in the Sargasso Sea. Nature, 394(6690), 263–266. https://doi.org/10.1038/28367 McNeil, J. D., Jannasch, H. W., Dickey, T., McGillicuddy, D., Brzezinski, M., & Sakamoto, C. M. (1999). New chemical, bio-optical and physical observations of upper ocean response to the passage of a mesoscale eddy off Bermuda. Journal of Geophysical Research: Oceans, 104(C7), 15537–15548. https://doi.org/10.1029/1999JC900137 Michaels, A. F., & Knap, A. H. (1996). Overview of the U.S. JGOFS Bermuda Atlantic time- series Study and the Hydrostation S program. Deep Sea Research Part II: Topical Studies in Oceanography, 43(2–3), 157–198. https://doi.org/10.1016/0967-0645(96)00004-5 Mudge, S. M., Belanger, S. E., & Nielsen, A. M. (2008). Fatty alcohols : anthropogenic and natural occurrence in the environment. Royal Society of Chemistry. Parkes, R. J., & Taylor, J. (1983). The relationship between fatty acid distributions and bacterial respiratory types in contemporary marine sediments. Estuarine, Coastal and Shelf Science, 16(2), 173–189. https://doi.org/10.1016/0272-7714(83)90139-7 Parrish, C. C. (2013). Lipids in marine ecosystems. ISRN Oceanography, 2013, 1–16. https://doi.org/10.5402/2013/604045 Pedrosa-Pàmies, R., Conte, M. H., Weber, J. C., & Johnson, R. (2018). Carbon cycling in the Sargasso Sea water column: Insights from lipid biomarkers in suspended particles. Progress in Oceanography, 168, 248–278. https://doi.org/10.1016/J.POCEAN.2018.08.005 Phillips, H. E., & Joyce, T. M. (2007). Bermuda’s tale of two time series: Hydrostation S and BATS. Journal of Physical Oceanography, 37, 554–571. https://doi.org/10.1175/JPO2997.1 Price, J. F. (1981). Upper ocean response to a hurricane. Journal of Physical Oceanography, 11(2), 153–175. https://doi.org/10.1175/1520-0485(1981)011<0153:UORTAH>2.0.CO;2 Robinson, N., Eglinton, G., Brassell, S. C., & Cranwell, P. A. (1984). Dinoflagellate origin for sedimentary 4α-methylsteroids and 5α(H)-stanols. Nature, 308(5958), 439–442. https://doi.org/10.1038/308439a0 Rohmer, M., Bouvier-Nave, P., & Ourisson, G. (1984). Distribution of hopanoid triterpenes in prokaryotes. Microbiology, 130(5), 1137–1150. https://doi.org/10.1099/00221287-130- 5-1137 Rossi, S., Sabatés, A., Latasa, M., & Reyes, E. (2006). Lipid biomarkers and trophic linkages between phytoplankton, zooplankton and anchovy (Engraulis encrasicolus) larvae in the NW Mediterranean. Journal of Plankton Research, 28(6), 551–562. https://doi.org/10.1093/plankt/fbi140 Sargent, J. R. (1976). The structure, function and metabolism of lipids in marine organisms. In D. C. Malins & J. R. Sargent (Eds.), Biochemical and biophysical perspectives in marine biology (pp. 149–212). London: Academic Press. Sargent, J. R., & Whittle, K. J. (1981). Lipids and hydrocarbons in the marine food web. Analysis of Marine ecosystems/AR Longhurst. Shropshire, T., Li, Y., & He, R. (2016). Storm impact on sea surface temperature and chlorophyll a in the Gulf of Mexico and Sargasso Sea based on daily cloud-free satellite

19

data reconstructions. Geophysical Research Letters, 43(23), 12,199–12,207. https://doi.org/10.1002/2016GL071178 Siegel, D. A., McGillicuddy, D. J., & Fields, E. A. (1999). Mesoscale eddies, satellite altimetry, and new production in the Sargasso Sea. Journal of Geophysical Research, 104(C6), 13359. https://doi.org/10.1029/1999JC900051 Steinberg, D. K., Carlson, C. A., Bates, N. R., Johnson, R. J., Michaels, A. F., & Knap, A. H. (2001). Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep Sea Research Part II: Topical Studies in Oceanography, 48(8–9), 1405–1447. https://doi.org/10.1016/S0967-0645(00)00148- X Teshima, S.-I. (1971). Bioconversion of β-sitosterol and 24-methylcholesterol to cholesterol in marine crustacea. Comparative Biochemistry and Physiology Part B: Comparative Biochemistry, 39(4), 815–822. https://doi.org/10.1016/0305-0491(71)90105-2 Verardo, D. J., Froelich, P. N., & McIntyre, A. (1990). Determination of organic carbon and nitrogen in marine sediments using the Carlo Erba NA-1500 analyzer. Deep Sea Research Part A. Oceanographic Research Papers, 37(1), 157–165. https://doi.org/10.1016/0198-0149(90)90034-S Volkman, J. K. (1986). A review of sterol markers for marine and terrigenous organic matter. Organic Geochemistry, 9(2), 83–99. https://doi.org/10.1016/0146-6380(86)90089-6 Volkman, J. K., Gatten, R. R., & Sargent, J. R. (1980). Composition and origin of milky water in the North Sea. Journal of the Marine Biological Association of the United Kingdom, 60(3), 759. https://doi.org/10.1017/S002531540004042X Volkman, J. K., Johns, R. B., Gillan, F. T., Perry, G. J., & Bavor, H. J. (1980). Microbial lipids of an intertidal sediment—I. Fatty acids and hydrocarbons. Geochimica et Cosmochimica Acta, 44(8), 1133–1143. https://doi.org/10.1016/0016-7037(80)90067-8 Volkman, J. K., Barrett, S. M., Blackburn, S. I., Mansour, M. P., Sikes, E. L., & Gelin, F. (1998). Microalgal biomarkers: A review of recent research developments. Organic Geochemistry, 29(5–7), 1163–1179. https://doi.org/10.1016/S0146-6380(98)00062-X Wakeham, S. G., & Canuel, E. A. (1986). Lipid composition of the pelagic crab Pleuroncodes planipes, its feces, and sinking particulate organic matter in the Equatorial North Pacific Ocean. Organic Geochemistry, 9(6), 331–343. https://doi.org/10.1016/0146- 6380(86)90114-2 Wakeham, S. G., & Canuel, E. A. (1988). Organic geochemistry of particulate matter in the eastern tropical North Pacific Ocean: Implications for particle dynamics. Journal of Marine Research, 46(1), 183–213. https://doi.org/10.1357/002224088785113748

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