The physiological response of picophytoplankton to temperature and its model representation

Picophytoplankton account for most of the marine (sub-)tropical phytoplankton biomass and primary productivity. The contribution to biomass among plankton functional types (PFTs) could shift with climate warming, in part as a result of different physiological responses to temperature. To model these responses, Eppley's empirical relationships have been well established. However, they have not yet been statistically validated for individual PFTs. Here, we examine the physiological response of nine strains of picophytoplankton to temperature; three strains of picoprokaryotes and six strains of picoeukaryotes. We conduct laboratory experiments at 13 temperatures between –0.5 and 33°C and measure the maximum growth rates and the chlorophyll a to carbon ratios. We then statistically validate two hypotheses formulated by Eppley in 1972: The response of maximum growth rates to temperature (1) of individual strains can be represented by an optimum function, and (2) of the whole phytoplankton group can be represented by an exponential function Eppley (1972). We also quantify the temperature-related parameters. We find that the temperature span at which growth is positive is more constrained for picoprokaryotes (13.7–27°C), than for picoeukaryotes (2.8–32.4°C). However, the modeled temperature tolerance range (ΔT) follows an unimodal function of cell size for the strains examined here. Thus, the temperature tolerance range may act in conjunction with the maximum growth rate to explain the picophytoplankton community size structure in correlation with ocean temperature. The maximum growth rates obtained by a 99th quantile regression for the group of picophytoplankton or picoprokaryotes are generally lower than the rates estimated by Eppley. However, we find temperature-dependencies (Q10) of 2.3 and of 4.9 for the two groups, respectively. Both of these values are higher than the Q10 of 1.88 estimated by Eppley and could have substantial influence on the biomass distribution in models, in particular if picoprokaryotes were considered an independent PFT. We also quantify the increase of the chlorophyll a to carbon ratios with increasing temperature due to acclimation. These parameters provide essential and validated physiological information to explore the response of marine ecosystems to a warming climate using ocean biogeochemistry models

Marine regime shifts in ocean biogeochemical models:a case study in the Gulf of Alaska

Regime shifts have been reported in many marine ecosystems, and are often expressed as an abrupt change occurring in multiple physical and biological components of the system. In the Gulf of Alaska, a regime shift in the late 1970s was observed, indicated by an abrupt increase in sea surface temperature and major shifts in the catch of many fish species. A thorough understanding of the extent and mechanisms leading to such regime shifts is challenged by data paucity in time and space. We investigate the ability of a suite of ocean biogeochemistry models of varying complexity to simulate regime shifts in the Gulf of Alaska by examining the presence of abrupt changes in time series of physical variables (sea surface temperature and mixed-layer depth), nutrients and biological variables (chlorophyll, primary productivity and plankton biomass) using change-point analysis. Our results show that some ocean biogeochemical models are capable of simulating the late 1970s shift, manifested as an abrupt increase in sea surface temperature followed by an abrupt decrease in nutrients and biological productivity. Models from low to intermediate complexity simulate an abrupt transition in the late 1970s (i.e. a significant shift from one year to the next) while the transition is smoother in higher complexity models. Our study demonstrates that ocean biogeochemical models can successfully simulate regime shifts in the Gulf of Alaska region. These models can therefore be considered useful tools to enhance our understanding of how changes in physical conditions are propagated from lower to upper trophic levels

Sensitivity of global ocean biogeochemical dynamics to ecosystem structure in a future climate

Terrestrial and oceanic ecosystem components of the Earth System models (ESMs) are key to predict the future behavior of the global carbon cycle. Ocean ecosystem models represent low complexity compared to terrestrial ecosystem models. In this study we use two ocean biogeochemical models based on the explicit representation of multiple planktonic functional types. We impose to the models the same future physical perturbation and compare the response of ecosystem dynamics, export production (EP) and ocean carbon uptake (OCU) to the same physical changes. Models comparison shows that: (1) EP changes directly translate into changes of OCU on decadal time scale, (2) the representation of ecosystem structure plays a pivotal role at linking OCU and EP, (3) OCU is highly sensitive to representation of ecosystem in the Equatorial Pacific and Southern Oceans

Testing the reconstruction of modelled particulate organic carbon from surface ecosystem components using PlankTOM12 and machine learning

Understanding the relationship between surface marine ecosystems and the export of carbon to depth by sinking organic particles is key to representing the effect of ecosystem dynamics and diversity, and their evolution under multiple stressors, on the carbon cycle and climate in models. Recent observational technologies have greatly increased the amount of data available, both for the abundance of diverse plankton groups and for the concentration and properties of particulate organic carbon in the ocean interior. Here we use synthetic model data to test the potential of using machine learning (ML) to reproduce concentrations of particulate organic carbon within the ocean interior based on surface ecosystem and environmental data. We test two machine learning methods that differ in their approaches to data-fitting, the random forest and XGBoost methods. The synthetic data are sampled from the PlankTOM12 global biogeochemical model using the time and coordinates of existing observations. We test 27 different combinations of possible drivers to reconstruct small (POCS) and large (POCL) particulate organic carbon concentrations. We show that ML can successfully be used to reproduce modelled particulate organic carbon over most of the ocean based on ecosystem and modelled environmental drivers. XGBoost showed better results compared to random forest thanks to its gradient boosting trees' architecture. The inclusion of plankton functional types (PFTs) in driver sets improved the accuracy of the model reconstruction by 58 % on average for POCS and by 22 % for POCL. Results were less robust over the equatorial Pacific and some parts of the high latitudes. For POCS reconstruction, the most important drivers were the depth level, temperature, microzooplankton and PO4, while for POCL it was the depth level, temperature, mixed-layer depth, microzooplankton, phaeocystis, PO4 and chlorophyll a averaged over the mixed-layer depth. These results suggest that it will be possible to identify linkages between surface environmental and ecosystem structure and particulate organic carbon distribution within the ocean interior using real observations and to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models

Ocean biogeochemical response to phytoplankton-light feedback in a global model

Oceanic phytoplankton, absorbing solar radiation, can influence the bio-optical properties of seawater and hence upper ocean physics. We include this process in a global ocean general circulation model (OGCM) coupled to a dynamic green ocean model (DGOM) based on multiple plankton functional types (PFT). We not only study the impact of this process on ocean physics but we also explore the biogeochemical response due to this biophysical feedback. The phytoplankton-light feedback (PLF) impacts the dynamics of the upper tropical and subtropical oceans. The change in circulation enhances both the vertical supply in the tropics and the lateral supply of nutrients from the tropics to the subtropics boosting the subtropical productivity by up to 60 gC m(-2) a(-1). Physical changes, due to the PLF, impact on light and nutrient availability causing shifts in the ocean ecosystems. In the extratropics, increased stratification favors calcifiers (by up to similar to 8%) at the expense of mixed phytoplankton. In the Southern Ocean, silicifiers increase their biomass (by up to similar to 10%) because of the combined alleviation of iron and light limitation. The PLF has a small effect globally on air-sea fluxes of carbon dioxide (CO2, 72 TmolC a(-1) outgassing) and oxygen (O-2, 46 TmolO(2) a(-1) ingassing) because changes in biogeochemical processes (primary production, biogenic calcification, and export production) highly vary regionally and can also oppose each other. From our study it emerges that the main impact of the PLF is an amplification of the seasonal cycle of physical and biogeochemical properties of the high-latitude oceans mostly driven by the amplification of the SST seasonal cycle

Assessing the uncertainties of model estimates of primary productivity in the tropical Pacific Ocean

Author Posting. © Elsevier B.V., 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 76 (2009): 113-133, doi:10.1016/j.jmarsys.2008.05.010.Depth-integrated primary productivity (PP) estimates obtained from satellite ocean color based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BOGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of ~1000 14C measurements spanning more than a decade (1983- 1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PP, specifically yielding too few low PP (< 0.2 gC m-2d-2) values; more than half of the total root-mean-squared model-data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomass-normalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s, which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140°W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison six years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill.This research was supported by a grant from the National Aeronautics and Space Agency Ocean Biology and Biogeochemistry program (NNG06GA03G), as well as by numerous other grants to the various participating investigator

Peri-operative red blood cell transfusion in neonates and infants: NEonate and Children audiT of Anaesthesia pRactice IN Europe: A prospective European multicentre observational study

BACKGROUND: Little is known about current clinical practice concerning peri-operative red blood cell transfusion in neonates and small infants. Guidelines suggest transfusions based on haemoglobin thresholds ranging from 8.5 to 12 g dl-1, distinguishing between children from birth to day 7 (week 1), from day 8 to day 14 (week 2) or from day 15 (≥week 3) onwards. OBJECTIVE: To observe peri-operative red blood cell transfusion practice according to guidelines in relation to patient outcome. DESIGN: A multicentre observational study. SETTING: The NEonate-Children sTudy of Anaesthesia pRactice IN Europe (NECTARINE) trial recruited patients up to 60 weeks' postmenstrual age undergoing anaesthesia for surgical or diagnostic procedures from 165 centres in 31 European countries between March 2016 and January 2017. PATIENTS: The data included 5609 patients undergoing 6542 procedures. Inclusion criteria was a peri-operative red blood cell transfusion. MAIN OUTCOME MEASURES: The primary endpoint was the haemoglobin level triggering a transfusion for neonates in week 1, week 2 and week 3. Secondary endpoints were transfusion volumes, 'delta haemoglobin' (preprocedure - transfusion-triggering) and 30-day and 90-day morbidity and mortality. RESULTS: Peri-operative red blood cell transfusions were recorded during 447 procedures (6.9%). The median haemoglobin levels triggering a transfusion were 9.6 [IQR 8.7 to 10.9] g dl-1 for neonates in week 1, 9.6 [7.7 to 10.4] g dl-1 in week 2 and 8.0 [7.3 to 9.0] g dl-1 in week 3. The median transfusion volume was 17.1 [11.1 to 26.4] ml kg-1 with a median delta haemoglobin of 1.8 [0.0 to 3.6] g dl-1. Thirty-day morbidity was 47.8% with an overall mortality of 11.3%. CONCLUSIONS: Results indicate lower transfusion-triggering haemoglobin thresholds in clinical practice than suggested by current guidelines. The high morbidity and mortality of this NECTARINE sub-cohort calls for investigative action and evidence-based guidelines addressing peri-operative red blood cell transfusions strategies. TRIAL REGISTRATION: ClinicalTrials.gov, identifier: NCT02350348

A comprehensive quantification of global nitrous oxide sources and sinks

Nitrous oxide (N2O), like carbon dioxide, is a long-lived greenhouse gas that accumulates in the atmosphere. Over the past 150 years, increasing atmospheric N2O concentrations have contributed to stratospheric ozone depletion1 and climate change2, with the current rate of increase estimated at 2 per cent per decade. Existing national inventories do not provide a full picture of N2O emissions, owing to their omission of natural sources and limitations in methodology for attributing anthropogenic sources. Here we present a global N2O inventory that incorporates both natural and anthropogenic sources and accounts for the interaction between nitrogen additions and the biochemical processes that control N2O emissions. We use bottom-up (inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and top-down (atmospheric inversion) approaches to provide a comprehensive quantification of global N2O sources and sinks resulting from 21 natural and human sectors between 1980 and 2016. Global N2O emissions were 17.0 (minimum–maximum estimates: 12.2–23.5) teragrams of nitrogen per year (bottom-up) and 16.9 (15.9–17.7) teragrams of nitrogen per year (top-down) between 2007 and 2016. Global human-induced emissions, which are dominated by nitrogen additions to croplands, increased by 30% over the past four decades to 7.3 (4.2–11.4) teragrams of nitrogen per year. This increase was mainly responsible for the growth in the atmospheric burden. Our findings point to growing N2O emissions in emerging economies—particularly Brazil, China and India. Analysis of process-based model estimates reveals an emerging N2O–climate feedback resulting from interactions between nitrogen additions and climate change. The recent growth in N2O emissions exceeds some of the highest projected emission scenarios3,4, underscoring the urgency to mitigate N2O emissions

A model of phytoplankton acclimation to iron-light colimitation

We developed and calibrated a dynamic model for cellular carbon, chlorophyll (Chl), and iron under iron-light colimitation. The model allows growth rate and two other state variables (Fep: C and Chl: C) to be described as functions of light intensity and the free iron concentration (Fe'). The model requires specification of the values of nine parameters. We obtained values for these parameters using published experimental results for Thalassiosira pseudonana using a combination of a random parameter initialization and a golden section search to minimize the cost function. The tuned model explained 95% of the variability in the observations of growth rate, 94% in Chl: C, and 90% in Fep : C. Although the model is applicable to both balanced and unbalanced growth conditions, data were only available for balanced growth; thus, the dynamics of state variables during unbalanced growth conditions could not be investigated. A limitation in calibrating the model was in the scarcity of suitable experimental data sets under well-defined environmental forcing. This points to the need for new experimental work on iron-limited cultures, including measurements of photosynthesis-light curves and the dynamic responses to changed Fe' and light intensity. This phytoplankton growth model provides a physiological treatment of ironlight colimitation for implementation within ocean biogeochemical models. By including both growth rate and elemental stoichiometry (e.g., Fep: C) as state variables, the model can be applied to assess both rate and yield limitation

The physiological response of picophytoplankton to temperature and its model representation

Picophytoplankton account for most of the marine (sub-)tropical phytoplankton biomass and primary productivity. The contribution to biomass among plankton functional types (PFTs) could shift with climate warming, in part as a result of different physiological responses to temperature. To model these responses, Eppley's empirical relationships have been well established. However, they have not yet been statistically validated for individual PFTs. Here, we examine the physiological response of nine strains of picophytoplankton to temperature; three strains of picoprokaryotes and six strains of picoeukaryotes. We conduct laboratory experiments at 13 temperatures between -0.5°C and 33°C and measure the maximum growth rates and the chlorophyll a to carbon ratios. We then statistically validate two hypotheses formulated by Eppley in 1972: the response of maximum growth rates to temperature (1) of individual strains can be represented by an optimum function, and (2) of the whole phytoplankton group can be represented by an exponential function. We also quantify the temperature-related parameters. We find that the temperature span at which growth is positive is more constrained for picoprokaryotes (13.7 - 27°C), than for picoeukaryotes (2.8 - 32.4°C). However, the modelled temperature tolerance range (ΔT) follows an unimodal function of cell size for the strains examined here. Thus, the temperature tolerance range may act in conjunction with the maximum growth rate to explain the picophytoplankton community size structure in correlation with ocean temperature. The maximum growth rates obtained by a 99th quantile regression for the group of picophytoplankton or picoprokaryotes are generally lower than the rates estimated by Eppley. However, we find temperature-dependencies (Q10) of 2.3 and of 4.9 for the two groups, respectively. Both of these values are higher than the Q10 of 1.88 estimated by Eppley and could have substantial influence on the biomass distribution in models, in particular if picoprokaryotes were considered an independent PFT. We also quantify the increase of the chlorophyll a to carbon ratios with increasing temperature due to acclimation. These parameters provide essential and validated physiological information to explore the response of marine ecosystems to a warming climate using ocean biogeochemistry models