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How tiny teeth and their prey shape ocean ecosystems

Posted by mmaheigan

· Friday, October 25th, 2024

It has long been suggested that diatoms, microscopic algae enclosed in silica-shells, developed these structures to defend against predators like copepods, small crustaceans that graze diatoms. Copepods evolved silica-lined teeth presumably to counteract this. But actual evidence for how this predator-prey relationship may drive natural selection and evolutionary change has been lacking.

Figure caption: Left: Copepod teeth may suffer damage when feeding on thick-shelled diatoms. The red arrows indicate damage to the copepod tooth, cracks or missing setae. When fed a large diatom, the row of spinose cusps was damaged in all analyzed teeth. Scale bar = 10 µm. Right: A Temora longicornis (ca. 750 µm) copepod tethered to a human hair using super glue, allowing for the capture of high-speed videography to quantify the fraction of cells that eaten or discarded by the copepod. The hair was kindly provided by the first author’s wife.

A recent publication in Proceedings of the National Academy of Sciences U.S.A. revealed a fascinating dynamic: Copepods that feed on diatoms may suffer significant damage to their teeth, causing them to become more selective eaters. The wear and tear on the copepod teeth were particularly pronounced when copepods consumed thick-shelled diatoms compared to “softer” prey like a dinoflagellate. By glueing copepods to human hair and filming them with a high-speed video camera, the authors found that copepods with damaged teeth were more likely to reject diatoms with thick shells than those with thin shells as prey. Shell thickness varies among and within diatom species and some can respond to copepod presence by increasing shell thickness. A thicker shell, however, may come at a cost to the cell in terms of reduced growth rate or increased sinking speed. This suggests that the evolutionary “arms race” between diatoms and copepods plays a crucial role in shaping and sustaining the diversity of these species.

Diatoms and copepods are important organisms in global biogeochemical cycles and hence understanding this microscopic interaction can help predict shifts in marine ecosystems, potentially affecting nutrient cycles and food webs that support fisheries.

Authors
Fredrik Ryderheim (Technical University of Denmark/University of Copenhagen)
Jørgen Olesen (University of Copenhagen)
Thomas Kiørboe (Technical University of Denmark)

Twitter
@fryderheim (Fredrik Ryderheim)
@OlesenCrust (Jørgen Olesen)
@Thomaskiorboe (Thomas Kiørboe)
@OceanLifeCentre (FR, TK group at DTU)
@NHM_Denmark (Natural History Museum of Denmark, JO employer)

Mixotrophs in the northern North Atlantic

Posted by mmaheigan

· Tuesday, April 16th, 2024

Mixotrophs (or mixoplankton) are now accepted as a third group of plankton alongside phytoplankton and zooplankton. Our knowledge of mixotrophs lags far behind that of the other two groups. We currently have only a limited understanding of mixotrophs’ biogeographical distribution across ocean basins, and what environmental factors are associated with their distribution.

The authors of a study recently published in Frontiers in Marine Science reviewed nearly 230,000 individual microplankton samples collected by the North Atlantic Continuous Plankton Recorder program between 1958 and 2015 and calculated the proportion of organisms that are considered mixotrophs in each sample. They classified protist species in the dataset as phytoplankton, mixotrophs, or microzooplankton (heterotrophs), based on existing literature. Taken together across seasonsin shelf waters (depth ≤ 300m), mixotrophs comprise a greater proportion of the microplankton community when nitrate is high and photosynthetically available radiation (PAR) is low (e.g. during the late fall and winter), or when nitrate is low and PAR is moderate to high (e.g. during the summer and early fall). When both nitrate and PAR are high, mixotrophs comprise less of the community compared to phytoplankton. The same pattern was found in offshore waters (depth > 300m), but the key macronutrient was phosphate rather than nitrate. The annual average proportion of mixotrophs in microplankton samples compared to phytoplankton has increased since 1958 in the offshore portion of the study region, with a notable changepoint in 1993; this increasing trend is strongest in the winter season.

This paper is useful for aquatic ecologists who want to integrate mixotrophic plankton into their understanding of marine food webs and biogeochemical cycles. Understanding mixotroph temporal and spatial distributions, as well as the environmental conditions under which they flourish, is imperative to understanding their impact on trophic transfer and biogeochemical cycling.

Authors
Karen Stamieszkin (Bigelow Laboratory for Ocean Sciences)
Nicole Millette (Virginia Institute of Marine Science)
Jessica Luo (NOAA Geophysical Fluid Dynamics Laboratory)
Elizabeth Follett (University of Liverpool)
Nick Record (Bigelow Laboratory of Ocean Science)
David Johns (Marine Biological Association)

Backstory
This work and the collaboration that made it possible was catalyzed by the Eco-DAS XII symposium, attended by Karen Stamieszkin, Nicole Millette, Jessica Luo, and Elizabeth Follett in 2016. Nicole had an idea for an analysis but lacked collaborators, just as she was ready to give up on it, Karen, Jessica, and Elizabeth expressed interest in the project. Karen, Jessica, and Elizabeth each brought a unique perspective that helped make Nicole’s original idea more practical and ensured that the analysis would come to life.

The collaboration that began with this paper lead to the OCB Mixotrophs & Mixotrophy Working Group led by Karen, Jessica, and Nicole, and a successful grant proposal to study mixotrophy awarded to Nicole and Karen by NSF’s Biological Oceanography program. This story shows the importance and power of programs that connect researchers across disciplines, especially in the early stages of their careers.

Net primary production from daily cycles of biomass using Argo floats

Posted by mmaheigan

· Thursday, August 31st, 2023

Net primary productivity is a central metric in ocean biogeochemistry that is costly and time-consuming to estimate using traditional water sampling methods. As a result, it is difficult to detect large-scale trends in ocean productivity. While satellite remote-sensing has partially solved this issue, its observations are limited to the top 10 to 40 m of the ocean and require assumptions about the depth profile of productivity.

Figure 1. (A) A map of BGC-Argo float profiles of particle backscattering where only floats deemed to contain samples from all hours of the day over its lifetime were shown. (BE) The hourly median (black points) and standard error (black vertical lines) of carbon biomass, estimated from particle backscatter. Over a 24-hour period, carbon biomass shows a net accumulation during the day (white space) and net loss during the night (gray space). This daily rhythm in biomass was used to infer gross primary productivity from a sinusoidal model fit that assumes productivity scales with light, community respiration is constant, and net community production is zero. (FI) Profiles of net primary productivity, shown with one standard error (shaded region), can be inferred from these daily cycles available for each depth and region. To estimate net primary production, we estimate gross oxygen production from gross primary (carbon) productivity assuming a photosynthetic quotient of 1.4 and that dissolved primary production is a third of total primary production. We then used an empirical ratio (equal to 2.7) to convert gross oxygen production to net primary productivity.

Our study addresses this problem by using BGC-Argo floats to estimate the in situ vertical structure of net primary productivity inferred from daily cycles of carbon biomass (Fig. 1). Although typical floats collect profiles every 5 or 10 days, it is possible to reconstruct the daily cycle in biomass by combining profiles from many floats that measure non-integer profiling frequencies (e.g., 5.2 or 10.2 days). These floats collect each subsequent profile at a different hour of the day, such that all hours of the day are about equally represented over the floats’ lifetimes. Combining enough of these floats’ profiles, the small daily variations in carbon biomass can be detected and used to infer net primary productivity. We demonstrate this for various depths, regions, and seasons.

Our approach provides low-cost, ground-truthed information throughout the water column across large expanses of the ocean and under various conditions (e.g., clouds, sea ice, or polar night), addressing some of the limitations of satellite or ship observations. We argue that the combination of BGC-Argo with satellite imagery will provide an invaluable tool for assessing large-scale trends in net primary production that may arise from climate change and other environmental purturbations.

Authors
Adam Stoer and Katja Fennel (Dalhousie University)

Dalhousie’s Marine Environmental Modelling Group: memg.ocean.dal.ca

Identifying the water mass composition of a sample has never been so easy!

Posted by mmaheigan

· Thursday, August 31st, 2023

When we collect seawater in any point of the ocean, we are collecting a mix of water masses from different origin that traveled until there keeping their salinity and temperature properties. The Atlantic Ocean is likely the most complex basin in term of water masses containing more than 15 in its depths. Some of them were “born” in the North Atlantic Ocean, others in the Southern Ocean, even in the Mediterranean Sea! And when we collect a seawater sample we can know which water masses are there, where they come from, what happened to each of them during their journey to us, what story can they tell us.

The variation of any non-conservative property (such as dissolved organic carbon or nutrients) in the deep open ocean depends on the mixing of those water masses and on the biogeochemical processes affecting it (such as heterotrophic respiration). But the effect of the water mass mixing is usually very high, so in order to study the biogeochemical processes, it is necessary to remove that effect.

On the other hand, estimating the contribution of the water masses composing a sample is useful to trace the distribution of each water mass identifying the depth of maximum water mass contribution or the depth-range where the water mass is dominant contributing > 50%. Ocean biogeochemists and microbiologists can get more out of their data estimating the impact of water mass mixing on the variability of any chemical (e.g. inorganic nutrients and dissolved organic carbon) or biological (e.g. prokaryotic heterotrophic abundance and production) property.

Knowing the contribution of each water mass to each sample was not an easy task and required expertise on the origin, circulation and mixing patterns of the water masses present in the study area. This could be even harder in very complex oceanic basin such as the deep Atlantic Ocean. The most commonly used methodology is the Optimum Multi-Parameter (OMP) analysis that was first applied by Tomczak in 1981. However, this methodology is time consuming and requires availability of a large set of quality-controlled chemical variables (e.g. nutrients, oxygen,..) together with a deep knowledge of the oceanography of the studied area. Those chemical variables are not always available or do not have the required quality by contrast to potential temperature and salinity that are high standard core variables in any cruise or database. In a recent research article, we applied multi-regression machine learning models to solve ocean water mass mixing. The models tested were trained using the solutions from OMP analyses previously applied to samples from cruises in the Atlantic Ocean. Extremely Randomized Trees algorithm yielded the highest score (R² = 0.9931; mse = 0.000227). The model allows solving the mixing of water masses in the Atlantic Ocean using potential temperature, salinity, latitude, longitude and depth. Potential temperature and salinity are the most commonly collected and curated variables in oceanography both from oceanographic cruises and autonomous vehicles (e.g. ARGO) avoiding the use of less commonly measured chemical variables which also require longer and time-consuming analyses of both the water samples and the data.

Figure 1. A16 section for the contribution of the water masses (A) AAIW5, (B) ENACW12, (C) AAIW3, (D) MW, (E) LSW, (F) ISOW, (G) CDW and (H) WSDW obtained with the Extremely Randomized Trees algorithm. Ocean Data View software (Schlitzer, 2015).

We also provide the code with instructions where any user can easily introduce the required variables (latitude, longitude, depth, temperature and salinity) of the chosen Atlantic samples and obtain the water mass proportion of each one in a fast and easy way. Actually, it would allow the user to obtain this information in real time during a cruise.

New research using other methods like OMP and its variants can be incorporated to the existing model increasing its accuracy and prediction capacity. Help us to improve the model and increase its spatial resolution!

Ocean biogeochemists and microbiologists can benefit from this tool even if they do not have a deep knowledge of the oceanography of the studied area. Identifying the water masses composition of a sample has never been so easy!

Author
Cristina Romera-Castillo (Instituto de Ciencias del Mar-CSIC, Barcelona, Spain)

Twitter: @crisrcas

Are you interested in a primer on ocean biogeochemical modeling with hands-on examples?

Posted by mmaheigan

· Thursday, May 11th, 2023

Look no further. This primer article explains what an ocean biogeochemical model is, how such a model is designed and applied, and includes easily accessible code examples. Refresh your memory on commonly used metrics for model evaluation through model-data comparison. Get introduced to the underlying rationale, mechanics, applications, and pitfalls of data assimilation for parameter optimization, state estimation, and observing system design. Peruse overviews of available community code repositories and observational databases. And tour some of the important applications of ocean biogeochemical models for carbon accounting, ocean deoxygenation and acidification studies, and fisheries yield projections. The primer also includes recommendations for best practices in ocean biogeochemical modeling and discusses current limitations and anticipated future developments and challenges. First and foremost, the article is an invitation to get involved.

Figure caption: Schematic representation of the varying level of complexity in biogeochemical models. State variables are indicated by the boxes where different colors correspond to different elemental currencies. The black arrows indicate selected biogeochemical transformations. The simplest, the nutrient–phytoplankton– zooplankton–detritus (NPZD) model, includes four state variables and one nutrient currency, often nitrogen. A typical low-complexity model includes several nutrients and nutrient currencies. Chlorophyll is omitted in the schematic, although many models have a chlorophyll state variable for each phytoplankton group to account for photoacclimation.

Authors
Katja Fennel (Dalhousie University)
Jann Paul Mattern (University of California, Santa Cruz)
Scott C. Doney (University of Virginia)
Laurent Bopp (Institute Pierre Simon Laplace)
Andrew M. Moore (University of California, Santa Cruz)
Bin Wang (Dalhousie University)
Liuqian Yu (Hong Kong University of Science and Technology)

Twitter @katjafennel @ScottDoney1 @laurent_bopp @DalhousieU @uvaevsc

Drivers of recent Chesapeake Bay warming

Posted by mmaheigan

· Friday, August 26th, 2022

Coastal water temperatures have been increasing globally with more frequent marine heat waves threatening marine life and nearshore communities reliant upon these ecosystems. Often, this warming is assumed to be uniform in space and time; however, this is not the case in the Chesapeake Bay, where warming waters play a major role in exacerbating low oxygen levels and indirectly limiting the efficacy of nutrient reduction efforts on land.

New research published in the Journal of the American Water Resources Association combined long-term observations and a hydrodynamic model to quantify the temporal and spatial variability in warming Chesapeake Bay waters, and identify the contributions of different mechanisms driving these historical temperature changes. While winter temperatures have warmed by less than a half a degree over the past 30 years, summer temperatures have warmed by nearly 1.5 °C, with similar increases at the surface and bottom. In cooler months, the atmosphere was the dominant driver of warming throughout the majority of the Bay, but oceanic warming explained more than half of the increased summer temperatures in the southern Bay nearest the Atlantic.

Figure 1: Relative contribution of different factors to warm-month Chesapeake Bay temperature change over the period 1985-2015. Percentages correspond to average main channel contributions for each component.

Warming temperatures have potentially significant implications for the future size of the Chesapeake Bay dead zone, and the marine species directly affected by these low oxygen conditions. Better quantifying warming contributions from the atmosphere, ocean, sea level, and rivers will also help constrain regional temperature projections throughout the estuary. More accurate projections of future Bay temperatures can help coastal managers better understand the potential for invasive species expansion and endemic species loss, impacts to fisheries and aquaculture, and how changes to ecosystem processes may impact coastal communities dependent on a healthy Bay.

Authors:
Kyle E. Hinson (Virginia Institute of Marine Science, William & Mary)
Marjorie A. M. Friedrichs (Virginia Institute of Marine Science, William & Mary)
Pierre St-Laurent (Virginia Institute of Marine Science, William & Mary)
Fei Da (Virginia Institute of Marine Science, William & Mary)
Raymond G. Najjar (The Pennsylvania State University)

Integrated analysis of carbon dioxide and oxygen concentrations as a quality control of ocean float data

Posted by mmaheigan

· Friday, August 26th, 2022

A recent study in Communications Earth & Environment, examined spatiotemporal patterns of the two dissolved gases CO₂ and O₂in the surface ocean, using the high-quality global dataset GLODAPv2.2020. We used surface ocean data from GLODAP to make plots of carbon dioxide and oxygen relative to saturation (CORS plots). These plots of CO₂ deviations from saturation (ΔCO₂) against oxygen deviations from saturation (ΔO₂) (Figure 1) provide detailed insight into the identity and intensity of biogeochemical processes operating in different basins.

Figure 1: Relationships between ΔCO₂ and ΔO₂ in the global ocean basins based on surface data in the GLODAPv2.2020 database. The black dashed lines are the least-squares best-fit lines to the data; unc denotes the uncertainty in the y-intercept value with 95% confidence; r is the associated Pearson correlation coefficient; n is the number of data points.

In addition, data in all basins and all seasons shares some common behaviors: (1) negative slopes of best fit lines to the data, and (2) near-zero y-intercepts of those lines. We utilized these findings to compare patterns in CORS plots from GLODAP with those from BGC-Argo float data from the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) program. Given that the float O₂ data is likely to be more accurate than the float pH data (from which the float CO₂ is calculated), CORS plots are useful for detecting questionable float CO₂ data, by comparing trends in float CORS plots (e.g. Figure 2) to trends in GLODAP CORS plots (Figure 1). As well as the immediately detected erroneous data, we discovered significant discrepancies in ΔCO₂-ΔO₂ y-intercepts compared to the global reference (i.e., GLODAPv2.2020 y-intercepts, Figure 1). The y-intercepts of 48 floats with QCed O₂ and CO₂ data (at regions south of 55°S) were on average greater by 0.36 μmol kg⁻¹ than the GLODAP-derived ones, implying the overestimations of float-based CO₂ release in the Southern Ocean.

Figure 2. CORS plots from data collected by SOCCOM floats F9096 and F9099 in the high-latitude Southern Ocean. Circles with solid edges denote data flagged as ‘good’, whereas crosses denote data flagged as ‘questionable’.

Our study demonstrates CORS plots’ ability to identify questionable data (data shown to be questionable by other QC methods) and to reveal issues with supposed ‘good’ data (i.e., quality issues not picked up by other QC methods). CORS plots use only surface data, hence this QC method complements existing methods based on analysis of deep data. As the oceanographic community becomes increasingly reliant on data collected from autonomous platforms, techniques like CORS will help diagnose data quality, and immediately detect questionable data.

Authors:
Yingxu Wu (Polar and Marine Research Institute, Jimei University, Xiamen, China; University of Southampton)
Dorothee C.E. Bakker (University of East Anglia)
Eric P. Achterberg (GEOMAR Helmholtz Centre for Ocean Research Kiel)
Amavi N. Silva (University of Southampton)
Daisy D. Pickup (University of Southampton)
Xiang Li (George Washington University)
Sue Hartman (National Oceanography Centre, Southampton)
David Stappard (University of Southampton)
Di Qi (Polar and Marine Research Institute, Jimei University, Xiamen, China)
Toby Tyrrell (University of Southampton)

How does the competition between phytoplankton and bacteria for iron alter ocean biogeochemical cycles?

Posted by mmaheigan

· Friday, August 26th, 2022

Free-living bacteria play a key role in cycling essential biogeochemical resources in the ocean, including iron, via their uptake, transformation, and release of organic matter throughout the water column. Bacteria process half of the ocean’s primary production, remineralize dissolved organic matter, and re-direct otherwise lost organic matter to higher trophic levels. For these reasons, it is crucial to understand what factors limit the growth of bacteria and how bacteria activities impact global ocean biogeochemical cycles.

In a recent study, Pham and colleagues used a global ocean ecosystem model to dive into how iron limits the growth of free-living marine bacteria, how bacteria modulate ocean iron cycling, and the consequences to marine ecosystems of the competition between bacteria and phytoplankton for iron.

Figure 1: (a) Iron limitation status of bacteria in December, January, and February (DJF) in the surface ocean. Low values (in blue color = close to zero) mean that iron is the limiting factor for the growth of bacteria; (b) Bacterial iron consumption in the upper 120m of the ocean and (c) Changes (anomalies) in export carbon production when bacteria have a high requirement for iron.

Through a series of computer simulations performed in the global ocean ecosystem model, the authors found that iron is a limiting factor for bacterial growth in iron-limited regions in the Southern Ocean, the tropical, and the subarctic Pacific due to the high iron requirement and iron uptake capability of bacteria. Bacteria act as an iron sink in the upper ocean due to their significant iron consumption, a rate comparable to phytoplankton. The competition between bacteria and phytoplankton for iron alters phytoplankton bloom dynamics, ocean carbon export, and the availability of dissolved organic carbon needed for bacterial growth. These results suggest that earth system models that omit bacteria ignore an important organism modulating biogeochemical responses of the ocean to future changes.

Authors:
Anh Le-Duy Pham (Laboratoire d’Océanographie et de Climatologie: Expérimentation et Approches Numériques (LOCEAN), IPSL, CNRS/UPMC/IRD/MNHN, Paris, France)
Olivier Aumont (Laboratoire d’Océanographie et de Climatologie: Expérimentation et Approches Numériques (LOCEAN), IPSL, CNRS/UPMC/IRD/MNHN, Paris, France)
Lavenia Ratnarajah (University of Liverpool, United Kingdom)
Alessandro Tagliabue (University of Liverpool, United Kingdom)

Powerful new tools for working with Argo data

Posted by mmaheigan

· Thursday, June 9th, 2022

No single program has been as transformative for ocean science over the past two decades as Argo: the fleet of robotic instruments that collect measurements of temperature and salinity in the upper 2 km of the ocean around the globe. The Argo program has been instrumental in revealing changes to ocean heat content, global sea level, and patterns of ice melt and precipitation. In addition, Biogeochemical Argo—the branch of the Argo program focused on floats with additional biological and chemical sensors—has recently shed light on topics such as regional patterns of carbon production and export, the magnitude of carbon dioxide air-sea flux in the Southern Ocean (thanks to the SOCCOM project), and the dynamics modulating ocean oxygen concentrations and oxygen minimum zones. While Argo data are publicly available in near-real-time via two Global Data Assembly Centers, there tends to be a steep learning curve for new users seeking to access and utilize the data.

To address this issue, a team led by scientists at NOAA’s Pacific Marine Environmental Laboratory developed a software toolbox available in two programming languages for accessing and visualizing Argo data— OneArgo-Mat for MATLAB and OneArgo-R for R. The toolbox includes functions to identify and download float data that adhere to user-defined time and space constraints, and other optional requirements like sensor type and data mode; plot float trajectories and their current positions; filter and manipulate float data based on quality flags and additional metadata; and create figures (profiles, time series, and sections) displaying physical, biological, and chemical properties measured by floats. Examples of figures created using the OneArgo-Mat toolbox are given below (Figure 1).

Figure 1. Example figures created using the OneArgo-Mat toolbox: (A) the trajectory of a float deployed in the North Atlantic from the R/V Johan Hjort in May of 2019, (B) a time series of dissolved oxygen at 80 dbars from that float, and (C) a vertical section plot of nitrate concentrations along the float track from the surface to 300 dbars. The black contour line in panel C denotes the mixed layer depth (MLD) based on a temperature criterion and the red line denotes the depth of the time series shown in panel B. The effects of seasonal phytoplankton blooms are evident in panel C, with mixed layer shoaling in the spring followed by drawdown of nitrate in the surface ocean. Panel B shows that, as the mixed layer deepens through the winter, the oxygen concentration at 80 dbars increases as a result of the oxygenated surface waters reaching that depth. The MATLAB code to download the required data and create all of these plots is shown (D).

The OneArgo-Mat and OneArgo-R toolboxes are intended for newcomers to Argo data, seasoned users, data managers, and everyone in between. For this reason, toolbox functions are equipped with options to streamline float selection, data processing, and figure creation with minimal user coding, if desired. Alternatively, the toolbox also provides rapid and straightforward access to the entire Argo database for experienced users who simply want to download up-to-date profile data for further processing and analysis. The authors hope these new tools will empower current Argo data users and entrain new users, especially as the US GO-BGC Project and US and international Argo partners move toward a global biogeochemical Argo fleet, which will create myriad new opportunities for novel studies of ocean biogeochemistry.

Authors
Jonathan Sharp – Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES) & NOAA Pacific Marine Environmental Laboratory (PMEL)
Hartmut Frenzel – CICOES & NOAA PMEL
Marin Cornec – University of Washington & NOAA PMEL
Yibin Huang – University of California Santa Cruz & NOAA PMEL
Andrea Fassbender – NOAA PMEL

Ocean Acidification drives shifts in global stoichiometry and carbon export efficiency

Posted by mmaheigan

· Friday, November 19th, 2021

Marine food webs and biogeochemical cycles react sensitively to increases in carbon dioxide (CO₂) and associated ocean acidification, but the effects are far more complex than previously thought. A comprehensive study published in Nature Climate Change by a team of researchers from GEOMAR dove deep into the impacts of ocean acidification on marine biota and biogeochemical cycling. The authors combined data from five large-scale field experiments with natural plankton communities to investigate how carbon cycling and export respond to ocean acidification.

The biological pump is a key mechanism in transferring carbon to the deep ocean and contributes significantly to the oceans’ function as a carbon sink. The carbon-to-nitrogen ratio of sinking biogenic particles, here termed (C:N_export), determines the amount of carbon that is transported from the euphotic zone to the ocean interior per unit nutrient, thereby controlling the efficiency of the biological pump. The authors demonstrate for the first time that ocean acidification can change the elemental composition of organic matter export, thereby potentially altering the biological pump and carbon sequestration in a future ocean (Figure 1).

Figure 1: Until now, the common assumption is that changes in C:N (and biogeochemistry, in general) are mainly driven by phytoplankton. In a series of in situ mesocosm experiments in different biomes (left), Taucher et al., (2020) found distinct impacts of ocean acidification on the C:N ratio of sinking organic matter (middle). By linking these observations to analysis of plankton community composition, the authors found a key role of heterotrophic processes in controlling the response of C:N to OA, particularly by altering the quality and carbon content of sinking organic matter within the biological pump (right).

Surprisingly, the observed responses were highly variable: C:N_export increased or decreased significantly with increasing CO₂, depending on the composition of species and the structure of the food web. The authors found that heterotrophic processes driven by bacteria and zooplankton play a key role in controlling the response of C:N_export to ocean acidification. This contradicts the widespread paradigm that primary producers are the principal driver of biogeochemical responses to ocean change.

Considering that such diverse pathways, by which planktonic food webs shape the elemental composition and biogeochemical cycling of organic matter, are not represented in state-of-the-art earth system models, these findings also raise the question: Are currently able to predict the large-scale consequences of ocean acidification with any certainty?

Authors:
Jan Taucher (GEOMAR, Kiel, Germany)
Tim Boxhammer (GEOMAR, Kiel, Germany)
Lennart T. Bach (University of Tasmania, Hobart, Australia)
Allanah J. Paul (GEOMAR, Kiel, Germany)
Markus Schartau (GEOMAR, Kiel, Germany)
Paul Stange (GEOMAR, Kiel, Germany)
Ulf Riebesell (GEOMAR, Kiel, Germany)

Funding for the Ocean Carbon & Biogeochemistry Project Office is provided by the National Science Foundation (NSF) and the National Aeronautics and Space Administration (NASA). The OCB Project Office is housed at the Woods Hole Oceanographic Institution.

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