Threshold indicators of primary production in the north-east Atlantic for assessing environmental disturbances using 21 years of satellite ocean colour

Primary production (PP) is highly sensitive to changes in the ecosystem and can be used as an early warning indicator for disturbance in the marine environment. Historic indicators of good environmental status of the north-east (NE) Atlantic and north-west (NW) European Seas suggested that daily PP should not exceed 2-3 g C m-2 d-1 during phytoplankton blooms and that annual rates should be <300 g C m-2 yr-1. We use 21 years of Copernicus Marine Service (CMEMS) Ocean Colour data from September 1997 to December 2018 to assess areas in the NE Atlantic with similar peak, climatology, phenology and annual PP values. Daily and annual thresholds of the 90th percentile (P90) of PP are defined for these areas and PP values above these thresholds indicate disturbances, both natural and anthropogenic, in the marine environment. Two case studies are used to test the validity and accuracy of these thresholds. The first is the eruption of the volcano Eyjafjallajökull, which deposited large volumes of volcanic dust (and therefore iron) into the NE Atlantic during April and May 2010. A clear signature in both PP and chlorophyll-a (Chl a) was evident from 28th April to 6th May and from 18th to 27th May 2010, when PP exceeded the PP P90 threshold for the region, which was comparatively more sensitive than Chl a P90 as an indicator of this disturbance. The second case study was for the riverine input of total nitrogen and phosphorus, along the Wadden Sea coast in the North Sea. During years when total nitrogen and phosphorus were above the climatology maximum, there was a lag signature in both PP and Chl a when PP exceeded the PP P90 threshold defined for the study area which was slightly more sensitive than Chl a P90. This technique represents an accurate means of determining disturbances in the environment both in the coastal and offshore waters in the NE Atlantic using remotely sensed ocean colour data.

Photoacclimation State of Thalassiosira weissflogii is not Affected by Changes in Optical Depth Under A Fluctuating Light Regime Simulating Deep Mixing1

Satellite-based remote sensing allows for global estimates of phytoplankton primary productivity by converting measurements of ocean color or photon absorption into units of carbon fixation. Models which perform this conversion often require an estimate of phytoplankton photoacclimation state such as the carbon to chlorophyll a ratio (C:Chl). Recently, our group developed a new photoacclimation model that can be applied to models of primary production. The model assumes that the phytoplankton photoacclimation state is not affected by periods of darkness during deep mixing beneath the photic zone, due to reduction in the plastoquinone pool in darkness and the subsequent deactivation of the signal for chlorophyll synthesis. In this study, we tested these assumptions by culturing the marine diatom Thalassiosira weissflogii under fluctuating light conditions simulating three different optical depths with progressively increasing deep mixing periods. The photoacclimation state, measured by the ratio of C:Chl, in T. weissflogii was not affected by changes in the length of simulated deep mixing periods. In addition, analysis of photosynthesis vs. irradiance (PE) curves showed that increases in optical depth caused decreases in both the maximum Chl-normalized rate of photosynthesis (P^b_max ) and in the slope of light-limited photosynthesis (α^b ), but had no effect on the half-saturation irradiance (E_k , another metric of photoacclimation). However, measurements of chlorophyll fluorescence during simulated deep mixing did not support the hypothesis that the PQ pool was reduced during dark periods. Thus, our findings support the use of the photoacclimation model for estimating primary production while suggesting the need for further research into the mechanisms controlling photoacclimation in the upper mixed layer environment of the ocean.

Variability of Net Primary Productivity and Associated Biophysical Drivers in Bahía de La Paz (Mexico)

The use of information of net primary productivity (NPP) from remote ocean color sensors is increasingly common in marine sciences. The resulting information has been used to explain variations in productivity at different spatio-temporal scales and in the presence of climate phenomena, such as the El Niño Southern Oscillation, and global warming. Satellite remote sensing data were analyzed in Bahía de La Paz (BLP), Mexico, to determine the spatio-temporal variation in NPP. In addition, in situ hydrographic data were obtained to characterize the water properties in the bay. The satellite data agree with in situ measurements, validating the satellite observations over this region. The NPP generally presented seasonal variation with maximum values in winter-spring and minimum values in summer–autumn. The variance explained by NPP from the measured variables was ranked as Chl-a < DEN < SST < PAR < WSC. The highest NPP values generally occurred when subtropical subsurface (SsStW) water was relatively shallow. Due to divergence and mixing processes, this water provided nutrients to the euphotic zone, and consequently an increase in NPP and changes in plankton biomass were observed. The annual trends of the variation in hydrographic data with respect to that of remote sensing data were similar; however, it is necessary to increase the number of data validation studies. The remote sensing and in situ measurements allowed for the main biophysical variables that modulate NPP in different time scales to be identified. The satellite-derived NPP data classifies the BLP as a high productivity zone with 432 g C m−2 year−1. The use of satellite NPP data is satisfactory and should be incorporated into marine primary productivity studies.

Phytoplankton Growth and Productivity in the Western North Atlantic: Observations of Regional Variability From the NAAMES Field Campaigns

The ability to quantify spatio-temporal variability in phytoplankton growth and productivity is essential to improving our understanding of global carbon dynamics and trophic energy flow. Satellite-based observations offered the first opportunity to estimate depth-integrated net primary production (NPP) at a global scale, but early modeling approaches could not effectively address variability in algal physiology, particularly the effects of photoacclimation on changes in cellular chlorophyll. Here, a previously developed photoacclimation model was used to derive depth-resolved estimates of phytoplankton division rate (μ) and NPP. The new approach predicts NPP values that closely match discrete measurements of 14C-based NPP and effectively captured both spatial and temporal variability observed during the four field campaigns of the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). We observed favorable growth conditions for phytoplankton throughout the annual cycle in the subtropical western North Atlantic. As a result, high rates of μ are sustained year-round resulting in a strong coupling between growth and loss processes and a more moderate spring bloom compared to the high-latitude subarctic region. Considerable light limitation was observed in the subarctic province during the winter, which resulted in divergent growth dynamics, stronger decoupling from grazing pressure and a taxonomically distinct phytoplankton community. This study demonstrates how detailed knowledge of phytoplankton division rate furthers our understanding of global carbon cycling by providing insight into the resulting influence on phytoplankton taxonomy and the loss processes that dictate the fate of fixed carbon.

Accuracy Assessment of Primary Production Models with and without Photoinhibition Using Ocean-Colour Climate Change Initiative Data in the North East Atlantic Ocean

The accuracy of three satellite models of primary production (PP) of varying complexity was assessed against 95 in situ 14C uptake measurements from the North East Atlantic Ocean (NEA). The models were run using the European Space Agency (ESA), Ocean Colour Climate Change Initiative (OC-CCI) version 3.0 data. The objectives of the study were to determine which is the most accurate PP model for the region in different provinces and seasons, what is the accuracy of the models using both high (daily) and low (weekly) temporal resolution OC-CCI data, and whether the performance of the models is improved by implementing a photoinhibition function? The Platt-Sathyendranath primary production model (PPPSM) was the most accurate over all NEA provinces and, specifically, in the Atlantic Arctic province (ARCT) and North Atlantic Drift (NADR) provinces. The implementation of a photoinhibition function in the PPPSM reduced its accuracy, especially at lower range PP. The Vertical Generalized Production Model-VGPM (PPVGPM) tended to over-estimate PP, especially in summer and in the NADR. The accuracy of PPVGPM improved with the implementation of a photoinhibition function in summer. The absorption model of primary production (PPAph), with and without photoinhibition, was the least accurate model for the NEA. Mapped images of each model showed that the PPVGPM was 150% higher in the NADR compared to PPPSM. In the North Atlantic Subtropical Gyre (NAST) province, PPAph was 355% higher than PPPSM, whereas PPVGPM was 215% higher. A sensitivity analysis indicated that chlorophyll-a (Chl a), or the absorption of phytoplankton, at 443 nm (aph (443)) caused the largest error in the estimation of PP, followed by the photosynthetic rate terms and then the irradiance functions used for each model.

Estimation of daily photosynthetically active radiation (PAR) in presence of low to high aerosol loads: application to OLCI-like satellite data

Estimation of daily photosynthetically active radiation (PAR) is of primary importance for monitoring the ocean primary production and the subsequent production of carbon by phytoplankton at global scale from remote sensing ocean color sensors. On the other hand, aerosol abundance and composition play a critical role in the modulation of PAR. In this study, an original algorithm, so-called OLCIPAR, is proposed for routinely determining the daily PAR from optical satellite sensors such as the OLCI sensor aboard Sentinel-3 (ESA). The OLCIPAR algorithm has been developed to overcome some of the limitations of the current existing methods. In particular, multiple scattering effects induced by the atmospheric layer are taken into account based on exact radiative transfer calculations. Another advantage of OLCIPAR method is to consider a great variety of aerosol models to better account for their optical variability as observed in real world conditions. The OLCIPAR algorithm was applied to the archive of MERIS data, whose sensor is similar to OLCI. The validation of the retrieved daily PAR was carried out based on comparison with the time series acquired by the BOUSSOLE oceanographic buoy moored in the Mediterranean Sea. Results show a regression slope of 1% and an accuracy within 10% which confirms the robustness of the algorithm. The comparison of OLCIPAR retrievals with the products routinely distributed by NASA shows that estimates of PAR differ by up to 20% in the subtropical Atlantic Ocean where important amounts of dust aerosols are present. The improvements brought by OLCIPAR method for deriving the daily PAR could thus permit to better assess the impact of aerosols on reduction of PAR with implications on the estimation of oceanic primary production.

Estimating Oceanic Primary Production Using Vertical Irradiance and Chlorophyll Profiles from Ocean Gliders in the North Atlantic

An autonomous underwater vehicle (Seaglider) has been used to estimate marine primary production (PP) using a combination of irradiance and fluorescence vertical profiles. This method provides estimates for depth-resolved and temporally evolving PP on fine spatial scales in the absence of ship-based calibrations. We describe techniques to correct for known issues associated with long autonomous deployments such as sensor calibration drift and fluorescence quenching. Comparisons were made between the Seaglider, stable isotope ((13)C), and satellite estimates of PP. The Seaglider-based PP estimates were comparable to both satellite estimates and stable isotope measurements.

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters.

Does presence of a mid-ocean ridge enhance biomass and biodiversity?

In contrast to generally sparse biological communities in open-ocean settings, seamounts and ridges are perceived as areas of elevated productivity and biodiversity capable of supporting commercial fisheries. We investigated the origin of this apparent biological enhancement over a segment of the North Mid-Atlantic Ridge (MAR) using sonar, corers, trawls, traps, and a remotely operated vehicle to survey habitat, biomass, and biodiversity. Satellite remote sensing provided information on flow patterns, thermal fronts, and primary production, while sediment traps measured export flux during 2007-2010. The MAR, 3,704,404 km(2) in area, accounts for 44.7% lower bathyal habitat (800-3500 m depth) in the North Atlantic and is dominated by fine soft sediment substrate (95% of area) on a series of flat terraces with intervening slopes either side of the ridge axis contributing to habitat heterogeneity. The MAR fauna comprises mainly species known from continental margins with no evidence of greater biodiversity. Primary production and export flux over the MAR were not enhanced compared with a nearby reference station over the Porcupine Abyssal Plain. Biomasses of benthic macrofauna and megafauna were similar to global averages at the same depths totalling an estimated 258.9 kt C over the entire lower bathyal north MAR. A hypothetical flat plain at 3500 m depth in place of the MAR would contain 85.6 kt C, implying an increase of 173.3 kt C attributable to the presence of the Ridge. This is approximately equal to 167 kt C of estimated pelagic biomass displaced by the volume of the MAR. There is no enhancement of biological productivity over the MAR; oceanic bathypelagic species are replaced by benthic fauna otherwise unable to survive in the mid ocean. We propose that globally sea floor elevation has no effect on deep sea biomass; pelagic plus benthic biomass is constant within a given surface productivity regime.

Economic valuation for the conservation of marine biodiversity

Policy makers are increasingly recognising the role of environmental valuation to guide and support the management and conservation of biodiversity. This paper presents a goods and services approach to determine the economic value of marine biodiversity in the UK, with the aim of clarifying the role of valuation in the management of marine biodiversity. The goods and services resulting from UK marine biodiversity are detailed, and 8 of the 13 services are valued in monetary terms. It is found that a decline in UK marine biodiversity could result in a varying, and at present unpredictable, change in the provision of goods and services, including reduced resilience and resistance to change, declining marine environmental health, reduced fisheries potential, and loss of recreational opportunities. The results suggest that this approach can facilitate biodiversity management by enabling the optimal allocation of limited management resources and through raising awareness of the importance of marine biodiversity.

Semianalytical model for the derivation of ocean color inherent optical properties: description, implementation, and performance assessment

A semianalytical approach to the problem of determining inherent optical properties from satellite and in situ ocean color data is presented. The model uses empirically derived spectral slopes between neighboring wavebands in combination with radiative transfer modeling to determine the spectral absorption (alpha) and backscatter (b(b)); these values are then further decomposed into absorption due to phytoplankton, detrital, and colored dissolved organic matter components. When compared with over 400 in situ data points the model makes good retrievals of the total absorption and backscatter across the entire spectrum, with regression slopes close to unity, little or no bias, high percentage of variance explained, and low rms errors.