Measurements were collected along the Oregon and Northern California coast during the Phytoplankton UPwelling Cycle (PUPCYCLE II) expedition, onboard the R/V Sally Ride from 27 May to 11 June 2023. A key objective of the program was to examine the evolution of phytoplankton blooms in recently upwelled waters, and we specifically targeted two upwelling plumes off Cape Blanco and Cape Mendocino, which were identified by low sea surface temperature (SST <12°C; Fig. 1). Along the cruise track, temperature, salinity, and chlorophyll fluorescence were monitored by the ship's underway system, supplied by a seawater supply line with a nominal intake depth of approximately 5 m. Nitrate concentrations were measured continuously with a Seabird SUNA sensor calibrated against NaNO3 standards immediately prior to the cruise. In addition to standard oceanographic variables, the seawater supply line was also used for continuous underway measurements of phytoplankton photophysiology and ETRPSII using a fast repetition rate fluorometer (FRRF; Soliense Inc.), and NCP using a custom-built pressure in situ gas instrument (PIGI), as described in Sect. .

Daily CTD and rosette casts were conducted 1 h before local sunrise, and samples were collected at four depths targeting 1 %, 10 %, 22 %, and 46 % of surface solar irradiance levels. Photosynthesis-irradiance (PI) curves (measured with fast repetition rate fluorometry; see Sect. ), chlorophyll a (Chl), and nutrient concentrations (NO3-, Si, PO43-, Fe) were measured at all four depths. Samples collected from 1 % and 46 % irradiance levels were also incubated for 14C-based PI curves (see Sect. 2.5), with subsamples collected for high-performance liquid chromatography (HPLC)-based analysis of phytoplankton pigments.

A bench-top Soliense Inc. FRRF was configured for underway collection of chlorophyll a fluorescence transients following Sezginer et al. (2021). Samples were introduced to the measurement cuvette by an integrated peristaltic pump. The pump was used to flush the measurement cuvette for 2.5 min before isolating a sample for analysis. Following a 1 min dark period to relax short-lived non-photochemical quenching, five single-turnover fluorescence transients were collected from each sample in the dark. Each single-turnover transient included a sequence of 100 subsaturating excitation light pulses of 1.5 µs duration, with a 1 µs interval between pulses. The excitation phase is designed to induce photochemistry and gradually reduce the pool of primary electron acceptor molecules, Qa (Kolber et al., 1998). As the Qa pool is reduced, further electron exchange between PSII and Qa is prevented, closing the photochemistry pathway and causing a concurrent increase in PSII fluorescence yields. The excitation phase is followed by a relaxation sequence consisting of 127 light pulses with an initial 20 µs interval. During the relaxation phase, the interval between light pulses increases exponentially, enabling Qa reoxidation between pulses to gradually reopen the photochemistry pathway such that fluorescence yields return to their basal levels. The biophysical model described by Kolber et al. (1998) was fit to the resulting fluorescence transient to derive the maximum quantum yield of PSII (Fv/Fm), functional absorption area of PSII (σPSII), and turnover rate of the primary electron acceptor Qa (τQa).

Continuous FRRF sampling was interrupted every 12 samples (∼3h) to conduct a PI curve characterizing light-dependent changes in photophysiology and photochemistry. Across the entire cruise, we collected 91 PI curves. Each PI curve was initiated with a fresh sample and consisted of 11 light levels, increasing from 0 to 850 µmolphotonsm-2s-1. Light was supplied evenly by the five actinic LEDs within the FRRF (445, 470, 505, 530, and 590 nm) calibrated against a handheld WALZ ULM-500 PAR meter prior to deployment. At each light level, ETRPSII was calculated following Suggett and Moore (2010): 1ETRPSII=PAR×σPSII′×Fq′Fv′×6.033×10-3. In this formulation, the rate of photon delivery to the pool of PSII reaction centers (RCII) is derived as the product of photosynthetically active radiation (PAR; units µmolphotonsm-2s-1) supplied by the LED lamps, and the wavelength-specific functional absorption area of RCII (σPSII′; units of Å2PSII-1). The conversion efficiency from light to photochemical energy depends on the fraction of open RCII, measured as the dimensionless ratio between the fluorescence amplitude measured under actinic light when photochemistry is active (Fq′=Fm′-F′) and that measured under actinic light if all RCII were oxidized (Fv′=Fm′-Fo′). In practice, measuring the minimum fluorescence when the RCII pool is completely open (Fo′) is challenging, as actinic light always drives some degree of photochemistry and reduction of RCII. Alternatively, Fo′ can be derived as Fo/(Fv/Fm+Fo/Fm′), following Oxborough et al., (2012).

The prime notation (^′) refers to FRRF parameters derived under actinic light. The constant 6.033×10-3 converts PAR to units of quanta m-2s-1, and σPSII′ to units of m2RCII-1, yielding ETRPSII units of quanta s-1RCII-1. For a complete description of FRRF-derived parameters, see Schuback et al. (2021) and Berman-Frank et al. (2023).

For each light curve measured, ETRPSII was plotted against PAR and fit with the PI model of Platt et al. (1980): 2ETRPSII=Ps1-e-αPARPse-βPARPs. During the initial light-limiting part of the curve, ETRPSII increases linearly with PAR with a slope of α. As PAR increases to saturating levels, ETRPSII stabilizes at maximum levels, Pmax. The light saturation index Ek is derived as Pmax/α. When phytoplankton are affected by photoinhibition, β describes the decrease in ETRPSII at high light levels (i.e., ≫Ek). In the absence of photoinhibition, Ps=Pmax. When photoinhibition is present (β>0), Ps represents the theoretical maximum potential ETRPSII. When β>0, Pmax is derived as 3Pmax=Psαα+ββα+ββα. To evaluate ETRPSII over the cruise track, we linearly interpolated derived values of α, β, and Ps to the sampling resolution of continuous PAR measurements from the ship's meteorological tower (Biospherical Inst. QSR-240P). The mean light intensity in the mixed layer (PARin situ) was estimated by accounting for light attenuation with depth, quantified by the diffuse attenuation coefficient (Kd=ln⁡(PAR0/PARmld)/(mld-0)), where PAR0 is PAR measured at the surface and PARmld is PAR measured at the mixed layer depth (mld) (Domingues and Barbosa, 2023). For each CTD cast (n=28), mld was determined using a density difference criterion of 0.125 kgm-3, and PARmld was measured with a Biospherical QSP-200 PAR sensor mounted to the CTD rosette. Both Kd and mld were linearly interpolated to the resolution of continuous PAR measurements. Finally, in situ PAR was estimated following Domingues and Barbosa (2023) as 4PARin situ=PAR0(1-e-Kd×mld)(Kd×mld)-1. To compare ETRPSII with NCP (Sect. 2.6), we converted ETRPSII from e-RCII-1s-1 to volumetric units of mmolO2m-2d-1. This conversion requires an estimate of the chlorophyll content of RCII, which is known to vary significantly across phytoplankton in response to taxonomic and environmental influences (Greene et al., 1992; Murphy et al., 2017; Aardema et al., 2024). Following previous authors (Schuback et al., 2015; Kolber and Falkowski, 1993), we assumed a possible range of Chl to RCII ratios of 400–700, yielding upper and lower bounds of Chl-normalized ETRPSII: 5GP=ETRPSII×86400×(Chl:RCII)-1×Chl×mld×14.

To obtain volumetric units, Chl-normalized ETR was multiplied by mixed-layer Chl concentrations (mmolChlm-3mld), assuming homogenous [Chl] throughout the mixed layer. Multiplying by 86 400 converts from s-1 to d-1. Given that four charge separation events are required per O2 evolved, ETR was divided by 4 for final gross photochemistry (GP) estimates in terms of mmolO2m-2d-1.

Under excess irradiance, light supply to PSII outpaces maximum downstream electron transport rates, creating the potential for dangerous reactive oxygen species (ROS) to accumulate (Müller et al., 2001). To mitigate excess excitation, photoautotrophs have evolved a number of photoprotective mechanisms, including non-photochemical quenching (NPQ), which dissipates excitation absorbed by PSII as heat, thereby reducing PSII photochemical and fluorescence yields. Previously, NPQ has been quantified from FRRF data as Stern–Volmer quenching, defined as the relative decrease in PSII fluorescence in response to light exposure: NPQSV=Fm-Fm′Fm′. However, this formulation does not account for longer-lived NPQ mechanisms that may still be active during dark measurements following recent high light exposure. To overcome this limitation, we used the normalized Stern–Volmer parameter (NPQNSV), calculated as Fo′/Fv′ (McKew et al., 2013). For each PI curve measured, NPQNSV was plotted against PAR and fit with a single component exponential curve. Out of 91 curve fits, 95 % had R2>0.90, and 87 % had R2>0.95. In situ NPQNSV was then estimated by mapping the resulting curve fit onto in situ PAR values.

Similarly, for each PI curve the fraction of closed RCII, Fq′/Fv′, was plotted as a function of PAR and fit with a single component exponential curve, with 92 % of curves having an R2>0.90. Following the same procedure used to evaluate in situ NPQNSV, in situ Fq′/Fv′ was approximated by mapping the resulting curve fit onto in situ PAR values.

During daily station sampling, 200 mL were collected from 1 % and 46 % light level depths (approximately 50 and 10 m, respectively) into acid-washed 250 mL bottles for 14C incubations. Samples were immediately spiked with 150 µCi of H14CO3 (PerkinElmer), and inverted to homogenize the contents of the bottles. The homogenized media was then aliquoted into 20 mL borosilicate scintillation vials, which were incubated over 3 h in a custom-built photosynthetron at 7 light levels from 0 to 650 µmolphotonsm-2s-1. At the end of the incubation, the entire content of the vials was filtered onto 25 mm GF/F filters with a nominal pore size of 0.7 µm. Filters were fumed with 10 % HCl for 24 h to remove any inorganic carbon prior to measuring activity on filters with an on-board scintillation counter (Beckman LS 6500). Immediately after spiking samples, three vials were filtered for triplicate time zero measurements. Three 100 µL aliquots were also taken from the initial 200 mL sample and treated with 100 µL of 3 M NaOH to measure total 14C counts. Disintegrations per minute were converted into hourly C fixation rates according to Knap et al., (1996).

We measured NCP based on mixed layer concentrations of O2 and N2 obtained from the pressure in situ gas instrument (PIGI), following Izett and Tortell (2021) and Izett et al. (2021). This method estimates NCP from the biological oxygen saturation anomaly, ΔO2/N2′, using N2′ as an analog for Argon (Ar) to correct for physical effects on O2 saturation. In this method, net community productivity is equated to the sea–air flux of O2 as determined by the biological saturation anomaly (ΔO2/N2′) scaled by the [O2] in equilibrium with the atmosphere ([O2]sat), and the O2 piston velocity (kO2): 6NCP=kO2×ΔO2N2′×[O2]sat.

The PIGI enables cost-effective measurements of ΔO2/N2′ using an oxygen optode and a gas tension device rather than a mass spectrometer, which is more commonly used to measure ΔO2/Ar (Izett and Tortell, 2021). In this method, N2′ is derived as an approximation of Ar, using model calculations that quantify differences between Ar and N2 concentrations primarily due to solubility changes and bubble processes. A full description of the 1D model applied to estimate N2′ is available in Izett and Tortell (2021). The model uses ancillary data, including wind speed, mixed layer depth, temperature, salinity, and sea level pressure, to estimate changes in mixed layer Ar and N2 concentrations over one residence time period prior to sampling. We applied a residence time of 14 d for this region where mixed layer gas residence times are strongly influenced by the timescales of upwelling events (Austin and Barth, 2002). Ancillary datasets required for N2′ calculations were obtained from a combination of satellite observations and model products, as described by Izett and Tortell (2021). The 1D model calculations and code are available at https://github.com/rizett/O2N2_NCP_toolbox (last access: 2 February 2024) with example calculations.

Additional corrections to NCP estimates were made to account for vertical mixing fluxes, which transport low O2 water to the surface (Izett et al., 2018). Previous studies have omitted ΔO2/Ar data collected in known upwelling areas, where the assumption of limited vertical mixing fluxes on ΔO2/Ar variability is violated (e.g., Stanley et al., 2010). To address this limitation, Cassar et al. (2014) developed an approach to use surface measurements of N2O to quantify vertical transport of low O2 waters. In marine environments, there is a strong stoichiometric relationship between apparent oxygen utilization and N2O, which is produced as a by-product of subsurface oxygen-consuming N remineralization pathways (Elkins et al., 1978). These N2O production pathways are thought to be photoinhibited within the euphotic zone (Olson, 1981; Horrigan et al., 1981), so that excess N2O concentrations in the mixed layer serve as a tracer for vertical influxes of O2-depleted subsurface water. We thus used the approach of Cassar et al. (2014) and Izett et al. (2018) to correct for vertical mixing following Eq. (7): 7NCP=kO2×ΔO2N2′×[O2]sat-kN2OkO2×∂[O2]B∂[N2O]B×[N2O]B. This mixing correction uses surface measurements of the N2O biological concentration ([N2O]B), the “supply ratio” of oxygen saturation, given by the vertical gradient of biological O2 to N2O (∂[O2]B∂[N2O]B), and the ratio of N2O and O2 gas transfer velocities (kN2OkO2). Biological concentrations, indicated by the superscript “B”, are derived by isolating and removing physical solubility effects from measured gas concentrations. The surface water biological concentration of N2O, [N2O]B, was derived based on the difference between the N2O saturation anomaly ([N2O]sat) and changes in N2O solubility due to recent heat fluxes ([N2O]meas-[N2O]sat-[N2O]thermal). Heat flux effects on solubility, [N2O]thermal, were derived following Keeling and Shertz (1992), with corrections from Jin et al. (2017). Surface [N2O]meas was continuously measured from the surface seawater supply with an integrated cavity output spectroscopy (OA-ICOS) gas analyzer (Los Gatos Research, N2O/CH4 Analyzer, Model Number: 913-0055) coupled to a gas extraction module (Schuler and Tortell, 2023). The region-specific supply ratio, ∂[O2]B∂[N2O]B, was calculated by taking the slope of subsurface [O2]B plotted against subsurface [N2O]B. The compiled data across the cruise track resulted in a supply ratio of -1.6×104±0.3×104mmolO2(mmolN2O)-1, which is similar to previous measurements for the Northeast Pacific (-1.8×104; Izett et al., 2018) and global basins (-1.5×104; Cassar et al., 2014). Following Cassar et al. (2014), we assumed a constant kN2OkO2 ratio of 0.92.

We note that several recent studies have observed nitrification within the euphotic zone, challenging the assumption that N2O production is limited to subsurface waters (Grundle et al., 2013; Smith et al., 2014), and potentially leading to overestimates in our vertical mixing-corrected NCP estimates. Previous observations in the CCS reported a range of depth-integrated mixed layer nitrification rates between 0.3 and 2 mmolNH4+m-2d-1, resulting in consumption of 0.6–4 mmolO2m-2d-1 (Stephens et al., 2020). Following the approach of Izett et al. (2018) we used a range of realistic N2O:O2 stoichiometries to estimate potential upper and lower bounds of mixed layer N2O production. We determined mixed layer N2O production was likely in the range 0.09–0.23 µmolN2Om-2d-1, which would yield offsets in our final NCP estimates between 1.2 and 3.3 mmolO2m-2d-1. Total uncertainty due to sources of error in other derived parameters was determined by following Izett (2021).

Samples collected during daily productivity casts were analyzed for dissolved NO3-+NO2-, PO43-, and silicic acid concentrations. Samples of 30 mL were collected from Niskin bottles and filtered through GF/F filters using acid-washed syringes into 20 mL HDPE scintillation vials. Samples were kept frozen before analysis on a OI Analytical Flow Solutions IV auto analyzer at Wetland Biogeochemistry Analytical Services at Louisiana State University. Detection limits were 0.09 µmolL-1 for nitrate + nitrate, 0.02 µmolL-1 for phosphate, and 0.02 µmolL-1 for silicic acid. Reference standards for dissolved nutrients in seawater were used to ensure quality control.

Samples for iron analysis were collected with a rosette of Teflon-coated OTE bottles during a separate cast directly after the daily sampling cast. After recovery, OTE bottles were taken directly to a trace metal-clean sampling van where they were pressurized with filtered compressed air. Surface samples (∼3m depth) were also collected with a tow-fish system plumbed into the trace metal van, as in Bruland et al. (2005). All samples were passed through pre-cleaned 0.2 micrometer Supor membrane AcroPak capsule filters into trace metal cleaned bottles (Cutter et al., 2017). Samples were acidified to pH 1.8 with optima HCl and analyzed post-cruise with a flow injection analysis method (Lohan et al., 2006) with modifications as in Biller et al. (2013). Briefly, this method involved pre-concentrating the Fe at pH 2 with Toyopearl Chelate-650 resin and eluting into a reaction stream containing the colorimetric agent N,N-dimethyl-p-phenylenediamine dihydrochloride. The absorbance of the reaction stream was measured with a flow-through spectrophotometer. Calibration was performed with a standard addition curve, and blanks were assessed using acidified Milli-Q. Reference samples analyzed to assess accuracy compared well to consensus values: SAFe D1 0.70±0.04nmolFekg-1, n=12 compared with consensus value 0.67±0.04nmolkg-1, and GEOTRACES GSC 1.51±0.07nmolkg-1 (n=11) compared with consensus value 1.53±0.11nmolkg-1.

Water collected from near-surface Niskin bottle casts (46 % surface irradiance) during daily productivity measurements was subsampled for RNA extraction. Approximately 2.5–4 L of seawater were filtered onto 0.8 µm Pall Supor filters (142 mm) using a peristaltic pump, and then flash frozen in liquid nitrogen and stored at -80°C. RNA was extracted using the RNAqueous-4PCR kit, following manufacturer instructions with the incorporation of a bead beating step during RNA lysis. All RNA samples were sent to GENEWIZ for library preparation and sequencing with PolyA tail selection. Sequencing was performed on an Illumina HiSeq 4000 with a 2×150bp configuration. GENEWIZ provided raw paired-end read sequences for each sample.

Raw reads were trimmed using Trim Galore 0.6.10 (Martin, 2011) and quality control was determined with FastQC (Andrews, 2010). A de novo metatranscriptome assembly was conducted using rnaSPAdes 3.15.5 (Bushmanova et al., 2019) and CD-HIT-EST (Li and Godzik, 2006). Contigs were annotated using the Marine Functional Eukaryotic Reference Taxa (MarFERReT) database (Groussman et al., 2023), which provides NCBI taxonomic annotations (Federhen, 2012) and Pfam 34.0 functional annotations (Mistry et al., 2021). Samples were mapped against the MarFERReT DIAMOND sequencing aligner and its compatible BLASTX command (e-value < 1×10-6) (Buchfink et al., 2015). Trimmed samples were aligned using Salmon (Patro et al., 2017). The package tximport (Soneson et al., 2016) was used to generate a comprehensive table of read count data for each sample and each contig. Only counts taxonomically mapping to Bacillariophyta (i.e., diatoms) were included. The normalized counts for all genes were then calculated using DESeq2's median of ratios method (Love et al., 2014). Normalized counts of the low iron-inducible periplasmic protein (Fea1) (Allen et al., 2007), which show high similarities to iron starvation-induced protein 2A (ISIP2A) (Behnke and LaRoche, 2020), were used as an indicator for iron stress (Marchetti et al., 2017).

During daily productivity casts, duplicate 1 L dark Nalgene bottles were filled with water from Niskin bottles collected at 1 % and 46 % PAR level depths. Under low ambient light, samples were filtered onto 47 mm GF/F filters (Whatman, nominal pore size 0.7 µm). Filters were immediately flash frozen and stored in an onboard -80°C freezer. Samples were shipped on dry ice to the Pinckney Estuarine Ecology Photopigment Analysis Facility at the University of Southern California. There, photopigment concentrations were determined with high performance liquid chromatography following Pinckney et al. (2001). Pigment compositions are reported in Appendix B.

In addition to pigment sampling, Light microscopy was used to identify and enumerate dominant phytoplankton taxa. For microscopic cell counts, 25–50 mL subsamples preserved in Lugol's solution were concentrated by sedimentation using Utermöhl chambers for >24h (Lund et al., 1958). Cell counts of recognizable dinoflagellate and diatom genera were carried out using an Olympus CKX-31 inverted microscope in at least 10 fields of view per sample at 200× and 400× magnification.

Across the cruise track, sea surface temperatures (SSTs) ranged from 8.5 to 14 °C (Fig. 2), with strong coastal to offshore gradients (Figs. 1 and 2). The lowest SST was observed within near-shore upwelling plumes, which were associated with high salinity (>33). Along the entire cruise transect, salinity was negatively correlated with the SST (ρ=-0.73, p≪0.01), as expected for upwelling regions. Sharp hydrographic fronts were apparent along coastal to offshore transects. Moving offshore, the SST rapidly increased, while salinity dropped, changing by as much as 2° and 0.5, respectively, within a span of 5 km. These results indicate the presence localized onshore upwelling plumes, as compared to more homogenous offshore waters. Within the upwelling plumes, NO3- concentrations were elevated, reaching maximum concentrations of 20.5 µM and displaying a positive relationship with salinity (ρ=0.89, p≪0.01) and a negative relationship with SST (ρ=-0.76, p≪0.01). Off-shore, NO3- decreased to concentrations below the SUNA detection limit (∼0.3µM), highlighting the difference in nutrient availability between the oligotrophic offshore waters and productive coastal upwelling environments. Chlorophyll concentrations varied in the range 0.04–5.6 mgm-3 and exhibited a statistically significant (though weak) positive relationship with NO3- (ρ=0.30, p≪0.01).

In addition to the coastal-offshore gradient, surface water hydrography also differed between the two distinct upwelling plumes we sampled. These plumes were identified as low SST in the lees of Cape Blanco and Cape Mendocino (Fig. 1). Both plumes exhibited an upwelling signature, but the apparent intensity of upwelling (as reflected in SST, salinity, NO3-, and Chl) was significantly stronger within the northern Cape Blanco plume (Fig. 3). Most apparently, SST was several degrees cooler at Cape Blanco (median of 9.6±0.4°C) than at Cape Mendocino (median of 11.5±0.01°C). Nitrate concentrations were highly variable across both plumes, but the mean NO3- at Cape Blanco (9.4±0.8µM) was nearly twice as high as that of Cape Mendocino (5.2±0.4µM). Chlorophyll concentrations were elevated at both plumes relative to offshore waters, with a median of 1.9±0.01mgm-3 around Cape Blanco and 1.4±0.01mgm-3 at Cape Mendocino. Both these Chl concentrations are well below that which can be supported by the available NO3- concentrations (1 µMNO3- can typically yield 1 µgChlL-1), indicating that the phytoplankton blooms were likely in the early phases of development following upwelling.

Underway surface measurements were accompanied by on-station discrete sampling for NO3-, PO43-, Si, and Fe concentrations. As expected, NO3- was highly correlated with PO43- (ρ=0.99, p≪0.01) and SiO2 (ρ=0.92, p≪0.01). The ratio of NO3-:PO43- was 7.4±2.0, and less than half the expected Redfield ratio of 16. This result is consistent with observations of low NO3- : PO43- (∼2-3) in the North Pacific attributed to high subsurface denitrification rates (Tyrrell and Law, 1997). In contrast with the strong covariance observed among macronutrients, surface Fe distributions were not correlated with surface NO3- concentrations (ρ=0.35, p=0.6). At Cape Blanco, Fe concentrations varied from 1.2 to 2.0 nM, whereas Cape Mendocino concentrations were significantly lower, in the range 0.21–0.65 nM. Offshore Fe concentrations were relatively high, with a surface concentration of 0.29–0.42 nM. These waters exhibited low macronutrient concentrations. In Sect. 4.1 we explore the potential causes for different nutrient signatures in the various water masses we sampled.

Along the cruise track, phytoplankton photophysiological properties displayed spatial variability associated with hydrographic gradients, superimposed on significant diel cycles. In particular, we observed strong diel signatures in the expression of various photoprotective mechanisms. Generally, we observed decreases in the PSII functional absorption area, σPSII throughout the day, followed by recovery overnight (Fig. 4a). The maximum photochemical efficiency of PSII (Fv/Fm) similarly decreased during the day and peaked overnight. Measurements of NPQNSV displayed an inverse diel pattern to those of σPSII and Fv/Fm, reflecting adjustments to the allocation of absorbed energy between competitive photochemistry and thermal dissipation pathways. However, the magnitude of diel variability in Fv/Fm, σPSII, and NPQNSV signals displayed significant variability between subregions, as discussed below. These observations agree with previous diel cycle studies of the region (Schuback and Tortell, 2019).

Overall, photosynthetic parameters derived from semi-continuous PI curves also exhibited diel patterns that mirrored those of Fv/Fm and σPSII. The maximum, light-saturated ETRPSII (Pmax) and the light utilization efficiency under light limiting conditions (α) both peaked during the midday when in situ irradiance was highest. The light saturation level, Ek, tracked surface light availability, while the photoinhibition parameter (β) peaked during midday and decreased overnight (data not shown). As with the continuous underway data, results from these discrete PI curves match the previous diel observations of Schuback and Tortell (2019). We note, however, that there is potential for some convolution of temporal and spatial variability, as the ship spent more time offshore in the night, and onshore during the daytime. It is thus possible that some of the diel cycling partially reflects variable photophysiological signals between coastal and offshore waters.

Beyond diel signals, we also observed significant gradients in photophysiological parameters in relation to oceanographic conditions. In general, Fv/Fm, Pmax, Ek, and α displayed positive relationships with upwelling indicators, i.e., high salinity and macronutrients, and low sea surface temperature (Table 1), suggesting that vertical transport of nutrient-rich water to the surface supported high photochemical yields. In contrast, upwelling signals were associated with decreased σPSII and NPQNSV. However, despite general trends between photophysiological parameters and upwelling, there were significant differences in photophysiological properties between the Cape Blanco and Cape Mendocino upwelling plumes. At Cape Blanco, mean values of NPQNSV and σPSII were significantly lower than at Cape Mendocino, while Fv/Fm, Pmax, α, and β were all higher (Table 1). Photophysiological properties at Cape Mendocino were much closer to those observed in offshore non-upwelling waters, with mean Fv/Fm values that were lower than offshore, despite elevated macronutrient concentrations. This result, combined with low Fe concentrations at Cape Mendocino, suggests that phytoplankton at Cape Mendocino were Fe stressed despite the presence of upwelling conditions (see Discussion).

The significant differences in surface oceanographic conditions between upwelling filaments may have been driven by differences in (1) the strength and timing of upwelling at the two capes, or (2) differences in the nutrient content of the subsurface upwelling source waters. We investigated these two possibilities by examining the NOAA coastal upwelling transport index (CUTI) as a proxy for upwelling strength during and prior to the sampling period, and by evaluating nutrient depth profiles to examine the upwelling source waters at the two capes. Our analysis suggests that both factors likely contributed to the apparent differences between Cape Blanco and Cape Mendocino biogeochemistry, providing evidence that Fe and Si concentrations were particularly affected by bathymetric features that influence Fe supply.

Environmental gradients exert direct effects on phytoplankton physiology and productivity by determining the supply of essential nutrients that support cellular growth and the maintenance of photosynthetic proteins. As a cofactor in many biological redox reactions, Fe plays a particularly important role in the photosynthetic electron transport chain and nutrient uptake pathways. Several lines of evidence suggest that iron stress was a key factor shaping phytoplankton productivity and photophysiology across our study site. As noted above, there was a significant difference in Fe concentrations between Cape Blanco (high Fe) and Cape Mendocino (low Fe) associated with variability in the shelf width. The difference in Fe availability was strongly correlated with Si:NO3- (ρ=0.85, p≪0.0.1), likely reflecting excess Si uptake by iron-limited diatoms (Sarthou et al., 2005; Franck et al., 2000). Further evidence of Fe stress at Cape Mendocino was obtained from ancillary transcriptomic analysis, which demonstrated elevated expression of the Fe assimilation gene Fea1 (Appendix C), which has previously been cited as a marker of Fe stress (Allen et al., 2007). Together, these observations indicate the onset of Fe stress at sites with reduced Fe availability.

Beyond direct effects, Fe gradients indirectly influence phytoplankton physiology and productivity by driving taxonomic shifts towards species that are adapted to low Fe conditions. Although diatoms were the most dominant group across the study area, their relative contribution was significantly lower around Cape Mendocino compared to Cape Blanco (Appendix B). At Cape Mendocino, we observed a taxonomic shift towards smaller phytoplankton, including smaller diatoms and dinoflagellates. Smaller cells sizes afford larger surface area to volume ratios, facilitating nutrient uptake at lower concentrations (Sunda and Huntsman, 1997). Moreover, stations near Cape Mendocino had high abundances of Pseudo-nitzschia, whereas this genus was not observed near Cape Blanco. Pseudo-nitzschia is a well-studied diatom with a number of physiological adaptations to Fe limitation (Lampe et al., 2018). These shifts in phytoplankton assemblages towards smaller sizes and low Fe specialists suggest bottom-up environmental controls driving taxonomic composition.

Cell size and nutrient status influence the optical properties and photophysiology of phytoplankton. Large cells are prone to pigment packaging effects, which decrease Chl-specific absorption as intracellular Chl concentrations increase and surface area to volume ratios decrease. This effect causes reduced σPSII, as was observed near Cape Blanco (Table 1). Nutrient limitation, particularly for Fe, can also lead to accumulation of photoinactive or damaged RCII, which still absorb light but do not contribute to photochemistry (Roncel et al., 2016). This further drives high σPSII, which is proportional to the light harvesting complex absorption coefficient normalized by active RCII concentrations (Oxborough et al., 2012; Li et al., 2021) and low Fv/Fm, due to inactive RCII contributing to the Fm but not Fv signal (Schuback et al., 2021). Both of these commonly cited indicators of Fe stress were observed around Cape Mendocino and in some offshore regions (Table 1).

The taxonomic and nutrient-dependent effects on photophysiology described above directly impact ETRPSII (Eq. 1). Previous studies have noted higher ETRPSII among Fe-limited phytoplankton, presumably due to increased σPSII (Schuback et al., 2015). However, we observed greater ETRPSII in the relatively Fe-rich waters near Cape Blanco (Table 1), likely due to high Fq′/Fv′(PAR), which represents the proportion of open RCII at a given light level (Suggett et al., 2011). Low NPQ observed in the Cape Blanco region (Fig. 4d) likely enabled Fq′/Fv′ to remain high under high light levels. Indeed, Fq′/Fv′ measured during underway PI curves and interpolated to in situ irradiances demonstrated that Fq′/Fv′ was higher around Cape Blanco compared to Cape Mendocino (Table 1). It is well recognized that iron limitation exacerbates high light stress and NPQ (Schallenberg et al., 2020; Ryan-Keogh et al., 2020), and the high NPQ at Cape Mendocino compared to Cape Blanco provides further evidence that Cape Mendocino assemblages were affected by Fe stress. Iron limitation also impacts photosynthetic processes downstream of ETRPSII. In this study, maximum carbon fixation rates (Pmax determined during 14C PI experiments) displayed a strong correlation with Si (ρ=0.63, p≪0.01) and the ratio of Si : NO3- (ρ=0.60, p≪0.0.1) in the water column.

Overall, our results suggest that Fe availability gradients between Cape Blanco and Mendocino influenced local community composition and physiology with consequential effects on C and Si cycles. Differences in taxonomic composition, photophysiology, nutrient quotas, and productivity all serve as evidence that the community shifted toward Fe limitation in proximity to Cape Mendocino. Due to the differential sensitivity of ETRPSII, carbon fixation, and NCP to Fe availability, we hypothesized that Fe limitation would lead to a decoupling between these different PP currencies. We explore this hypothesis below with direct comparisons of ETRPSII, C-fixation, and NCP.

Measurements of PP in different “currencies” (electrons, carbon, and oxygen) reflect the rates of distinct photosynthetic processes. Comparison of these alternative productivity metrics can yield information on energy transfer efficiencies across the photosynthetic pipeline (Fig. 7). A minimum of four charge separation events are required to produce one O2 and fix one CO2, stoichiometrically linking water splitting and carbon fixation. Yet a number of nonlinear electron transport pathways can divert reducing power from carbon fixation, decoupling ETRPSII from GPP, while oxygen consumption by respiration and nonlinear electron transport pathways can further decouple ETRPSII from NCP (Fig. 7). The ratio between ETRPSII and GPP thus provides information on the magnitude of nonlinear electron transport, while the ratio between ETRPSII and NCP reflects the sum of nonlinear electron transport and respiration. In practice, interpreting the decoupling between ETR, GPP, and NCP is complicated by differences in the temporal and spatial scales of the various measurement approaches, as well as the different assumptions implicit in each method. In the following sections, we directly compare parallel productivity measurements to examine energy transfer efficiencies across photosynthetic processes, taking care to note important methodological considerations.