Volume 7, Issue 5 p. 419-426
Letter
Open Access

An absorption-based approach to improved estimates of phytoplankton biomass and net primary production

James Fox

Corresponding Author

James Fox

Department of Microbiology, Oregon State University, Corvallis, Oregon

Correspondence: [email protected]

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Sasha J. Kramer

Sasha J. Kramer

Interdepartmental Graduate Program in Marine Science, University of California, Santa Barbara, Santa Barbara, California

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Jason R. Graff

Jason R. Graff

Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon

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Michael J. Behrenfeld

Michael J. Behrenfeld

Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon

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Emmanuel Boss

Emmanuel Boss

School of Marine Sciences, University of Maine, Orono, Maine

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Gavin Tilstone

Gavin Tilstone

Plymouth Marine Laboratory, Plymouth, UK

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Kimberly H. Halsey

Kimberly H. Halsey

Department of Microbiology, Oregon State University, Corvallis, Oregon

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First published: 04 August 2022
Citations: 3
Associate editor: Sarah Fawcett

Author Contribution Statement: JF, KHH, and MJB conceived the research question and designed the study approach. All authors contributed to field work and data collection. JF conducted the data analysis and wrote the manuscript. All authors provided input on the manuscript.

Data availability statement: All raw data files are publicly available in the NASA SeaBASS data repository (https://seabass.gsfc.nasa.gov/) or from the British Oceanographic Data Centre (www.bodc.ac.uk). Processed data, metadata, and code are available in the Zenodo data repository (https://doi.org/10.5281/zenodo.6675894).

Abstract

The abundance and productivity of phytoplankton constrain energy transfer through marine food webs and the export of organic carbon to the deep ocean. Bio-optical measurements correlate well with phytoplankton carbon (Cphyto), but the effect of taxonomic variability on this relationship is still uncertain. Here, we explore how changes in phytoplankton community composition influence the relationship between the particulate backscatter coefficient (bbp) and Cphyto and present a new approach to estimate phytoplankton biomass more accurately using bbp. We found that using a fixed scaling factor for the conversion of bbp to Cphyto could lead to the underestimation or overestimation of biomass, depending on the dominant taxonomic group in the phytoplankton community. In addition, we demonstrate how a simple ratio of absorption at two wavelengths can be used to provide a coarse approximation of phytoplankton community composition when scaling bbp to Cphyto, thereby improving the estimation of net primary production.

Scientific Significance Statement

Estimates of phytoplankton biomass are essential components of primary production models and are also required for the calculation of other useful model outputs that provide insight into nutrient cycling and carbon transfer through marine food webs. Optical proxies provide observations of phytoplankton carbon at unparalleled spatial and temporal resolution but require scaling factors that have not captured variability driven by changes in community composition or physiology. Here, we describe a new approach that improves model estimates of net primary production by mechanistically scaling optical measurements to phytoplankton carbon in a way that accounts for variation in taxonomy.

The abundance and taxonomic composition of marine phytoplankton fundamentally impact the fate of organic carbon fixed through net primary production (NPP). Environmental conditions such as light, nutrient availability, turbulence, and grazing ultimately determine phytoplankton community structure, food web dynamics, and elemental cycling. Cyanobacteria typically dominate (both numerically and as a fraction of biomass) stratified oligotrophic systems that are often associated with rapid carbon cycling driven by the microbial loop (Marañón et al. 2003; Flombaum et al. 2013). Diatoms are another taxonomic group that are widely distributed in the surface ocean (Kemp and Villareal 2018). When growth conditions in the mixed layer are improving and if nutrient levels are sufficient, the growth rate of diatoms can become temporally decoupled from grazing pressure and a large diatom-dominated bloom can occur (Behrenfeld et al. 2021). These decoupling events sometimes result in high magnitude carbon export events that can include large aggregates of cells, but this is not always the case (Martin et al. 2011; Durkin et al. 2016). As global change alters the physical and chemical nature of the ocean, it will give rise to shifts in the abundance and composition of phytoplankton communities and their role in biogeochemical cycles (Finkel et al. 2010; Henson et al. 2019).

Primary production is the product of phytoplankton growth rate and carbon biomass, but few direct measurements of phytoplankton carbon (Cphyto) exist due to the complex methods required to isolate phytoplankton cells from other components of the particulate organic carbon (POC) pool (Graff et al. 2012). Particulate backscattering (bbp) correlates well with analytical measurements of Cphyto and can also be estimated using satellite remote sensing, thereby allowing the assessment of phytoplankton biomass distribution at a global scale (Behrenfeld et al. 2005; Graff et al. 2015; Martínez-Vicente et al. 2017). After correcting for the background influence of non-algal particles, bbp is converted to Cphyto using a scaling factor determined from in situ measurements (Martinez-Vicente et al. 2013; Graff et al. 2015). Multiple scaling factors have been recommended and range in magnitude by twofold (Behrenfeld et al. 2005; Martinez-Vicente et al. 2013; Graff et al. 2015), but the factors impacting the empirical relationship between bbp and Cphyto (e.g., phytoplankton taxonomy and size) have not yet been critically evaluated over large spatial scales (Qiu et al. 2021). Here, we employ a new absorption-based method that accounts for taxonomic variability when scaling bbp to Cphyto. The new method improves estimates of NPP and can be used to coarsely evaluate phytoplankton community composition across time and space.

Methods

Data from the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES; Chase et al. 2020), the EXport Processes in the Ocean from RemoTe Sensing program (EXPORTS; Siegel et al. 2021), and the 22nd Atlantic Meridional Transect (AMT22; Graff et al. 2015) were analyzed to investigate how phytoplankton community composition influences the relationship between bio-optical measurements and biomass (Fig. 1). The collective dataset includes near-surface optical properties acquired from semi-continuous inline sampling of the ship's underway water supply, which were used to provide effective proxies for key biological parameters, and discrete samples collected for high-performance liquid chromatography (HPLC) phytoplankton pigments and flow cytometry. Although a brief overview of methods and protocols is provided below, more details on modeled parameters and instrumentation are provided in the supplemental information.

Details are in the caption following the image
Cruise tracks for the NAAMES field campaigns, the EXPORTS program in the North Pacific, and AMT22.

Chlorophyll and POC

Estimates of chlorophyll concentration were derived from the phytoplankton absorption coefficient ( a ϕ ) at 676 nm using particulate absorption [ a p λ ] red peak line height as in Boss et al. (2007; Eq. 1).
a ϕ 676 = a p 676 39 / 65 a p 650 26 / 65 a p 715 . (1)
For each cruise, values of a ϕ 676 were then calibrated against chlorophyll a concentrations measured by HPLC to provide a proxy for chlorophyll (ChlACS) specific to each region as in Boss et al. (2013). POC was estimated using particulate beam attenuation at 660 nm, cp (660), and scaling coefficients specific to the AMT22 cruise calculated in Fig. 5c of Graff et al. (2015; Eq. 2). The AMT22 values are similar to global and regional coefficients previously calculated for the North Atlantic and South Atlantic (Gardner et al. 2006; Cetinić et al. 2012).
POC = c p 660 420 + 6.4 . (2)

Phytoplankton carbon

Multiple approaches were used to calculate Cphyto. The first approach followed the methods of Graff et al. (2015), which calculates phytoplankton biomass using a scaled value of the particulate backscatter coefficient at 470 nm [bbp(470)], here we refer to these values as C b bp :
C b bp = b bp 470 12,128 + 0.59 . (3)
In a second approach, modeled phytoplankton carbon (Cmod) values were calculated using ChlACS, photosynthetically active radiation (PAR), the light attenuation coefficient (Kd), and the median growth irradiance in the surface mixed layer (Ig) as described in Fox et al. (2020):
C mod = 19 e 0.038 PAR 0.45 K d 1 + e 0.15 PAR 1 + e 3 I g Chl ACS . (4)
Hyperspectral measurements of the absorption spectrum were used to develop a simple method for scaling bbp to Cphyto that accounts for taxonomic variability. Total non-water particulate absorption, anw λ , measurements across the visible spectrum (400–700 nm) were averaged at 5 nm increments between 400 and 700 nm. A two-wavelength ratio matrix was then created for all available wavelengths before Pearson correlation coefficients and Type II linear regression were used to compare each ratio to model-based scaling factor [SFmod = Cmod/bbp(470)] values determined a priori using estimates of bbp(470) and Cmod (Fig. 2; Supporting Information Fig. S1). Comparative analyses between phytoplankton absorption ratios and SFmod showed the best correlation with the a nw (690 nm)/ a nw (580 nm) ratio (r2 = 0.56, Root Mean Square Error = 3512, scalar units = mg C m−3; Fig. 2). This ratio was used to derive a variable scaling factor to estimate phytoplankton biomass (Cvar, Eq. 5):
C var = b bp 470 0.00027 · a nw 690 a nw 580 · 12287 . (5)
Details are in the caption following the image
Scaling factors determined using modeled carbon, Cmod, and the particulate backscatter coefficient at 470 nm, bbp(470), compared with the ratio of phytoplankton absorption at 690 and 580 nm [y = (12,287)x, r2 = 0.56, RMSE = 3512]. Symbol shape and fill indicate the research cruise.

Here, Cvar was estimated using bbp after subtracting the “background” value of 0.00027 m−1 before multiplying by the scaling factor of 12,287 that varies with the absorbance ratio. The “background” component was calculated using the slope and x-axis intercept from regression analysis of the bulk community Cmodbbp relationship (Supporting Information Fig. S2) and is consistent with similar estimates made using satellite or in situ data (i.e., 0.00027 m−1 for this study compared to a value of 0.00035 m−1 determined from remotely sensed bbp vs. chl; Behrenfeld et al. 2005) and values of 0.00058 and 0.00067 m−1 derived from flow-cytometric cell volume-based estimates of carbon and bbp (Martinez-Vicente et al. 2013; Qiu et al. 2021). The value of 12,287 was taken from the slope of the regression analysis between Cmod and a nw 690 a nw 580 (Fig. 2).

Finally, phytoplankton carbon measurements derived from flow cytometry (Cfcm) were calculated using group-specific abundance and biovolume data (Graff et al. 2015). Values for the group-specific diameter estimates and biovolume-carbon conversions were taken from previous studies from the AMT program (Heywood et al. 2006; Tarran et al. 2006; Martinez-Vicente et al. 2013).

Phytoplankton taxonomy

A network-based community detection analysis was used to determine the dominant pigment-based phytoplankton groups in the community following Kramer et al. (2020). Five distinguishable groups were determined and combined into three populations roughly based on cell size: (1) cyanobacteria and haptophytes (pico-nano), (2) green algae/mixed (pico-nano), (3) diatoms and dinoflagellates (nano-micro).

Phytoplankton growth and productivity

Phytoplankton growth rate (μ) was modeled using near-surface optical measurements of ChlACS, PAR, mixed layer depth (MLD), and Kd (see Supporting Information Methods). NPP was estimated as the product of the resultant growth rates and the three different Cphyto estimates (e.g., Cmod, C b bp , and Cvar) (Eq. 6).
NPP = μ C phyto . (6)

Model-based NPP was compared to discrete measurements determined using 14C uptake incubations ranging between 10 and 24 h in duration (Tilstone et al. 2009; Fox et al. 2020; Siegel et al. 2021).

The code, data, and metadata of this study are available in the Zenodo data repository (Fox et al. 2022).

Results

Scaling particulate backscatter to phytoplankton carbon

Surface measurements of bbp(470) and Cmod correlated well for the bulk plankton community (r2 = 0.76, RMSE = 10.1 mgC m−3, n = 124; Supporting Information Fig. S2), but regression analysis showed that the scaling factor used to convert bbp to Cmod ranges from 3770 to 27,697 and is influenced by taxonomy (Fig. 3). For phytoplankton populations dominated by green algae, the scalar was 13,832 ± 1142 mg C m−3 (i.e., Cmod = bbp × 13,832), which is similar to previous estimates of 13,000 by Behrenfeld et al. (2005) and 12,128 by Graff et al. (2015). However, the scalars best fitting the diatom/dinoflagellate and cyanobacteria/haptophytes communities were 22,641 ± 1714 and 8372 ± 670, respectively, suggesting that the use of a fixed scaling factor leads to under- or overestimates of Cphyto. Accounting for taxonomic variability in this relationship will therefore yield a more accurate conversion from bbp.

Details are in the caption following the image
Modeled phytoplankton carbon, Cmod, and measurements of the particulate backscatter coefficient at 470 nm, bbp(470), for (a) cyanobacteria and haptophyte populations (r2 = 0.65, RMSE = 4.5, y = (8372)x + 3.3); (b) diatoms and dinoflagellates populations (r2 = 0.85, RMSE = 11.7, y = (22,495)x – 8.0); and (c) green algae or mixed populations (r2 = 0.92, RMSE = 2.74, y = (13,832)x + 2.0). Solid line is the linear fit for each taxonomic grouping. Points are daily means, error bars are standard deviation. Symbol shape and fill indicates which research cruise data were collected.

Latitudinal changes in phytoplankton carbon and contribution to total POC

The accuracy of the Cvar estimates was assessed using near-surface bio-optical estimates of phytoplankton carbon measured by flow cytometry (Cfcm) during AMT22. Estimates of Cvar closely matched estimates of Cfcm over a broad latitudinal gradient north of 35°S (r2 = 0.79, RMSE = 1.70 mgC m−3; Supporting Information Fig. S3a) and captured a greater amount of the variability observed in the Cfcm data than C b bp estimated using a fixed scaling factor (r2 = 0.46, RMSE = 1.14 mgC m−3; Fig. 4a).

Details are in the caption following the image
Latitudinal trends in surface (a) phytoplankton carbon estimated using bbp(470) scaled using a variable (Cvar, orange line) and fixed ( C b bp , blue line) scaling factor, flow cytometry (Cfcm, gray circles) and a model (Cmod, black crosses), and (b) phytoplankton carbon-to-POC ratios calculated using Cvar (orange line) and C b bp (blue line) during AMT22. Black circles are the total carbon of Synechococcus spp. and Prochlorococcus spp. when they contribute over 80% of the total biomass estimated by flow cytometry.

Optically-derived POC was used to evaluate the contribution of phytoplankton carbon to the total POC pool over the AMT22 track. Cvar : POC estimates generally matched well with Cfcm : POC in latitudes north of 35°S (r2 = 0.69, RMSE = 0.05; Supporting Information Fig. S3b), but the relationship deteriorated in more southerly regions (Fig. 4b; Supporting Information Fig. S3d). In the northerly latitudes, the Cvar : POC relationship was largely driven by changes in the abundance of Synechococcus spp. and Prochlorococcus spp., which often contributed over 80% of the total Cfcm. Values of Cvar effectively tracked changes in picocyanobacterial biomass, while the fixed scalar approach generally led to estimates higher than those of Cfcm in areas dominated by picocyanobacteria and showed little spatial change north of 35°S. As a result, C b bp  : POC remained relatively consistent to 35°S. South of this latitude, Synechococcus spp. and Prochlorococcus spp. decreased substantially in their relative contributions to total phytoplankton biomass (Fig. 4). The magnitude and spatial variability observed in the Cvar to POC relationship closely match trends observed in previous AMT campaigns (Martinez-Vicente et al. 2013; Graff et al. 2015).

Modeled primary production across diverse ecosystems

The above analysis implies that estimates of NPP modeled over a wide range of ecosystem states and conditions are improved by using biomass values calculated using a variable scaling factor (Cvar) compared to a fixed scaling factor ( C b bp ) (Fig. 5; Table 1). This result is most notable for the NAAMES 3 and AMT22 cruises, which sampled predominantly in oligotrophic waters where the phytoplankton communities were dominated by cyanobacteria. All approaches used to estimate Cphyto performed similarly in high biomass/high productivity conditions, but Cvar showed modest improvements over C b bp at two stations during NAAMES 2 (Fig. 5b,c). The second NAAMES cruise took place during the climax of the spring bloom when the phytoplankton community was dominated by eukaryotic phytoplankton, and small diatoms were prevalent at the two stations where the NPP estimates were most improved (Bolaños et al. 2020). Model estimates were typically higher than 14C estimates of NPP, which could be due to subsurface light levels being overestimated because of an underestimate in Kd (Xing and Boss 2021).

Details are in the caption following the image
NPP measured using carbon uptake (14C) incubations compared with modeled estimates made using (a) modeled carbon (r2 = 0.72, log(y) = 1.06 log(x) − 0.33, RMSE = 0.20), (b) phytoplankton carbon estimated following the methods of Graff et al. (2015) where the scaling factor is fixed (r2 = 0.57, log(y) = 1.10 log(x) − 0.46, RMSE = 0.25) and, (c) phytoplankton carbon estimated with the variable scaling factor using the absorption method (r2 = 0.78, log(y) = 1.02 log(x) − 0.08, RMSE = 0.18). Symbol shape indicates the cruise. Solid line in each represents the 1 : 1 line.
Table 1. Population specific daily estimates of modeled and measured depth-integrated NPP (ʃNPP). Values are group means and standard deviation apart from ΔʃNPP ( C b bp –Cvar) which is medium and range values. Units are mg C m−2 d−1.
Population ʃNPP(Cmod) ʃNPP ( C b bp ) ʃNPP (Cvar) ʃNPP (14C) ΔʃNPP ( C b bp –Cvar)
Cyanobacteria/haptophytes (model n = 79, 14C n = 48) 485 ± 239 462 ± 225 338 ± 198 293 ± 182 103; −181 − 539

Diatoms/dinoflagellates

(model n = 29, 14C n = 21)

1155 ± 494 787 ± 346 930 ± 371 1130 ± 502 −155; −363 − 183

Green algae/mixed

(model n = 10, 14C n = 4)

373 ± 305 271 ± 195 283 ± 171 327 ± 309 −6; −54 − 19

Discussion and conclusions

The overarching objectives of this study were to assess the impact of community composition on in situ bbp-based estimates of phytoplankton biomass and to develop a new method that improves Cphyto estimates by accounting for taxonomic variability in the phytoplankton community. While a relationship between bbp and Cphyto is well established (Behrenfeld et al. 2005; Martinez-Vicente et al. 2013; Graff et al. 2015), we found that community composition can significantly impact the magnitude of the scaling factor required in these calculations, which is consistent with previous observations in the oligotrophic South China Sea (Qiu et al. 2021). For populations dominated by cyanobacteria or haptophytes, our estimated scaling factor was 25% to 30% lower than the fixed values currently applied to scale in situ or satellite retrievals of bbp to Cphyto. The fixed scalars likely result in systematic overestimations of Cphyto when employed in areas dominated by picocyanobacteria, such as the permanently stratified gyres and seasonally oligotrophic regions. Conversely, the scalar best fitting diatom and dinoflagellate populations was almost 100% larger than some fixed scalar estimates (Behrenfeld et al. 2005; Graff et al. 2015). Thus, use of a fixed scalar will typically underestimate Cphyto when these phytoplankton groups are dominant, especially during high biomass bloom events. The need for alternative scaling factors could explain previously reported inaccuracies in NPP modeled using bbp, where predicted values in the western North Atlantic were higher than discrete 14C data in summer months but lower in early spring (Yang et al. 2021).

The spectral backscattering properties of laboratory grown phytoplankton cultures vary between major taxa (Whitmire et al. 2010). The group-specific scaling factors presented in this paper are consistent with both optical theory and carbon-to-volume relationships of phytoplankton species. Experimental and modeling studies show that cells <10 um can make a disproportionately large contribution to the total backscattering signal (Dall'Olmo et al. 2009; Organelli et al. 2018), but the volume-to-carbon relationship is generally consistent across a range of taxa and sizes (Menden-Deuer and Lessard 2000). As such, our results show that bbp in some populations of larger phytoplankton (nano and micro size classes) such as diatoms and dinoflagellates may require a larger scaling factor than cyanobacteria and other pico-sized phytoplankton. This relationship may not apply universally, however, and further analysis is required to improve the relationship between phytoplankton size and field measurements of bbp (Qiu et al. 2021).

The two particulate absorption wavelengths identified to best estimate Cvar, 690 and 580 nm, were somewhat surprising, but there are several plausible explanations for their selection as taxonomic indicators. Although neither wavelength is associated with absorption peaks of major diagnostic pigments, the two selected regions of the absorption spectrum could be capturing subtle differences in taxon-specific physiology (Supporting Information Fig. 4). The 690 nm wavelength is notably between the absorbance maxima of the 2nd and 1st photosystem (PS) reaction centers, which are centered at 680 and 700 nm, respectively (Falkowski & Raven, 2013). The PSII/PSI ratio varies between species, with diatoms often maintaining a higher ratio than haptophytes and green algae across a range of light intensities (Vandenhecke et al. 2015). Major taxonomic differences in the PSII/PSI ratio could therefore lead to differences in absorption in the region of spectral overlap at 690 nm and permit discrimination of communities dominated by diatoms from other groups. At 580 nm, the major pigments contributing to light absorption are phycocyanin and phycoerythrin, which are diagnostic marker pigments for red cyanobacteria, and Chl c, which is a dominant pigment in red algae (including diatoms, dinoflagellates, haptophytes, and others) (Chase et al. 2013; Luimstra et al. 2018). Small changes in absorption at 580 nm could therefore reflect shifts in phytoplankton community composition and the a nw (690)/ a nw (580) ratio may amplify small differences in both signals. A simpler explanation may be that 690 nm serves as a near-baseline for variation driven by the accessory pigments represented by 580 nm, but this idea does not explain why the ratios a nw (695)/ a nw (580) and a nw (700)/ a nw (580) yielded poorer correlations with Cmod/bbp(470) compared to a nw (690)/ a nw (580).

Accounting for phytoplankton group variability in the bbp to Cphyto relationship is one step toward more reliable estimates of phytoplankton biomass but other considerations remain, such as the effects of particle composition and the non-algal contribution to scattering (Cetinić et al. 2012). The background component of the optical backscatter signal is variable and may be a notable contributor to total bbp in oligotrophic waters (Bellacicco et al. 2018; Bellacicco et al. 2019). Correcting for this background component is particularly important in low biomass/low bbp waters where non-algal particles impact the bbp signal (Stramski et al. 2004; Organelli et al. 2018). The breakdown in relationships between Cvar and Cfcm (and Cvar : POC; Fig. 4) south of 35 S could be caused by an increased prevalence of pico- and nano-eukaryotes in the flow cytometry biomass estimation which relies on an empirical carbon-to-volume relationship. This result brings in a degree of uncertainty which is compounded by the difficulty in estimating size and volume for these pico- and nano-sized cells (Martinez-Vicente et al. 2013).

Further exploration into the influence of taxonomy on the scalar required for bbp-derived biomass will help constrain uncertainty in measurements of phytoplankton carbon and provide more accurate model estimates of NPP. Improved estimates of biomass and NPP are also important for other rate measurements, such as net community production or carbon export, that provide key metrics of marine ecosystem health and function. The method put forward here is simple but shows that absorption-based approaches warrant further exploration to obtain taxon-specific estimates of phytoplankton carbon. Currently, most platforms that measure optical oceanographic parameters at high spatial or temporal resolution (e.g., autonomous floats, gliders or satellites) do not possess the hyperspectral capabilities required for our method. Future satellite missions (e.g., NASA's Plankton, Aerosol, Cloud, ocean Ecosystem) offer the required spectral resolution however, and optical technology is progressing at such a rate that we could soon see spectral absorption sensors incorporated into global arrays of ocean profilers. Until then, easily obtainable estimates of SFmod can be used to evaluate phytoplankton group composition throughout the annual cycle using data from autonomous underwater vehicles and satellite ocean color.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to thank the captain and crew of the RSS James Cook, the RV Atlantis and the RV Revelle for their help during the AMT22, NAAMES and EXPORTS cruises. The authors would also like to thank Neil Price for kindly sharing experimental data that allowed the addition of an iron limited component in the model used to estimate NPP. This work was funded by NASA Grants 80NSSC21K0421 to JF, NNX15AE67G to EB, NNX15AF30G and 80NSSC17KO568 to MB and NNX15AE70G to KH. SJK was supported by a National Defense Science and Engineering Graduate (NDSEG) fellowship through the Office of Naval Research. GT was supported by the UK Natural Environment Research Council (NERC) National Capability funding to Plymouth Marine Laboratory for the Atlantic Meridional Transect. The Atlantic Meridional Transect is funded by the UK Natural Environment Research Council through its National Capability Long-term Single Centre Science Programme, Climate Linked Atlantic Sector Science (grant number NE/R015953/1). This study contributes to the international IMBeR project and is contribution number 371 of the AMT program.