The interpretation of particle size, shape, and carbon flux of marine particle images is strongly affected by the choice of particle detection algorithm

In situ imaging of particles in the ocean are rapidly establishing themselves as powerful tools to investigate the ocean carbon cycle, including the role of sinking particles for carbon sequestration via the biological carbon pump. A big challenge when analysing particles in camera images is determining the size of the particle, which is required to calculate carbon content, sinking velocity and flux. A key image processing decision is the algorithm used to decide which part of the image forms the particle and which is the background. However, this critical analysis step is often unmentioned and its effect rarely explored. Here we show that final flux estimates can easily vary by an order of magnitude when selecting different algorithms for a single dataset. We applied a range of static threshold values and 11 different algorithms (seven threshold and four edge detection algorithms) to particle profiles collected by the LISST-Holo system in two contrasting environments. Our results demonstrate that the particle detection method does not only affect estimated particle size but also particle shape. Uncertainties are likely exacerbated when different particle detection methods are mixed, e.g., when datasets from different studies or devices are merged. We conclude that there is a clear need for more transparent method descriptions and justification for particle detection algorithms, as well as for a calibration standard that allows intercomparison between different devices

Similarities between the biochemical composition of jellyfish body and mucus

Recognition of the importance of jellyfish in marine ecosystems is growing. Yet, the biochemical composition of the mucus that jellyfish constantly excrete is poorly characterized. Here we analyzed the macromolecular (proteins, lipids and carbohydrates) and elemental (carbon and nitrogen) composition of the body and mucus of five scyphozoan jellyfish species (Aurelia aurita, Chrysaora fulgida, Chrysaora pacifica, Eupilema inexpectata and Rhizostoma pulmo). We found that the relative contribution of the different macromolecules and elements in the jellyfish body and mucus was similar across all species, with protein being the major component in all samples (81 ± 4% of macromolecules; 3.6 ± 3.1% of dry weight, DW) followed by lipids (13 ± 4% of macromolecules; 0.5 ± 0.4%DW) and carbohydrates (6 ± 3% of macromolecules; 0.3 ± 0.4%DW). The energy content of the jellyfish matter ranged from 0.2 to 3.1 KJ g−1 DW. Carbon and nitrogen content was 3.7 ± 3.0 and 1.0 ± 0.8%DW, respectively. The average ratios of protein:lipid:carbohydrate and carbon:nitrogen for all samples were 14.6:2.3:1 and 3.8:1, respectively. Our study highlights the biochemical similarity between the jellyfish body and mucus and provides convenient and valuable ratios to support the integration of jellyfish into trophic and biogeochemical models

Are plankton nets a thing of the past? An assessment of in situ imaging of zooplankton for large-scale ecosystem assessment and policy decision-making

Zooplankton are fundamental to aquatic ecosystem services such as carbon and nutrient cycling. Therefore, a robust evidence base of how zooplankton respond to changes in anthropogenic pressures, such as climate change and nutrient loading, is key to implementing effective policy-making and management measures. Currently, the data on which to base this evidence, such as long time-series and large-scale datasets of zooplankton distribution and community composition, are too sparse owing to practical limitations in traditional collection and analysis methods. The advance of in situ imaging technologies that can be deployed at large scales on autonomous platforms, coupled with artificial intelligence and machine learning (AI/ML) for image analysis, promises a solution. However, whether imaging could reasonably replace physical samples, and whether AI/ML can achieve a taxonomic resolution that scientists trust, is currently unclear. We here develop a roadmap for imaging and AI/ML for future zooplankton monitoring and research based on community consensus. To do so, we determined current perceptions of the zooplankton community with a focus on their experience and trust in the new technologies. Our survey revealed a clear consensus that traditional net sampling and taxonomy must be retained, yet imaging will play an important part in the future of zooplankton monitoring and research. A period of overlapping use of imaging and physical sampling systems is needed before imaging can reasonably replace physical sampling for widespread time-series zooplankton monitoring. In addition, comprehensive improvements in AI/ML and close collaboration between zooplankton researchers and AI developers are needed for AI-based taxonomy to be trusted and fully adopted. Encouragingly, the adoption of cutting-edge technologies for zooplankton research may provide a solution to maintaining the critical taxonomic and ecological knowledge needed for future zooplankton monitoring and robust evidence-based policy decision-making

Evidence of nitrification associated with globally distributed pelagic jellyfish

Often considered detrimental to the environment and human activities, jellyfish blooms are increasing in several coastal regions worldwide. Yet, the overall effect of these outbreaks on ecosystem productivity and structure are not fully understood. Here we provide evidence for a so far unanticipated role of jellyfish in marine nitrogen cycling. Pelagic jellyfish release nitrogen as a metabolic waste product in form of ammonium. Yet, we observed high rates of nitrification (NH4+ → NO3−, 5.7–40.8 nM gWW−1 [wet weight] h−1) associated with the scyphomedusae Aurelia aurita, Chrysaora hysoscella, and Chrysaora pacifica and low rates of incomplete nitrification (NH4+ → NO2−, 1.0–2.8 nM gWW−1 h−1) associated with Chrysaora fulgida, C. hysoscella, and C. pacifica. These observations indicate that microbes living in association with these jellyfish thrive by oxidizing the readily available ammonia to nitrite and nitrate. The four studied species have a large geographic distribution and exhibit frequent population outbreaks. We show that, during such outbreaks, jellyfish‐associated release of nitrogen can provide more than 100% of the nitrogen required for primary production. These findings reveal a so far overlooked pathway when assessing pelagic nitrification rates that might be of particular relevance in nitrogen depleted surface waters and at high jellyfish population densities

Particle flux in the oceans: Challenging the steady state assumption

Atmospheric carbon dioxide levels are strongly controlled by the depth at which the organic matter that sinks out of the surface ocean is remineralized. This depth is generally estimated from particle flux profiles measured using sediment traps. Inherent in this analysis is a steady state assumption; that export from the surface does not significantly change in the time it takes material to reach the deepest trap. However, recent observations suggest that a significant fraction of material in the mesopelagic zone sinks slowly enough to bring this into doubt. We use data from a study in the North Atlantic during July/August 2009 to challenge the steady state assumption. An increase in biogenic silica flux with depth was observed which we interpret, based on vertical profiles of diatom taxonomy, as representing the remnants of the spring diatom bloom sinking slowly (<40 m d-1). We were able to reproduce this behaviour using a simple model using satellite-derived export rates and literature-derived remineralization rates. We further provide a simple equation to estimate ‘additional’ (or ‘excess’) POC supply to the dark ocean during non-steady state conditions, which is not captured by traditional sediment trap deployments. In seasonal systems, mesopelagic net organic carbon supply could be wrong by as much as 25% when assuming steady state. We conclude that the steady state assumption leads to misinterpretation of particle flux profiles when input fluxes from the upper ocean vary on the order of weeks, such as in temperate and polar regions with strong seasonal cycles in export

Copepods Boost the Production but Reduce the Carbon Export Efficiency by Diatoms

The fraction of net primary production that is exported from the euphotic zone as sinking particulate organic carbon (POC) varies notably through time and from region to region. Phytoplankton containing biominerals, such as silicified diatoms have long been associated with high export fluxes. However, recent reviews point out that the magnitude of export is not controlled by diatoms alone, but determined by the whole plankton community structure. The combined effect of phytoplankton community composition and zooplankton abundance on export flux dynamics, were explored using a set of 12 large outdoor mesocosms. All mesocosms received a daily addition of minor amounts of nitrate and phosphate, while only 6 mesocosms received silicic acid (dSi). This resulted in a dominance of diatoms and dinoflagellate in the +Si mesocosms and a dominance of dinoflagellate in the -Si mesocosms. Simultaneously, half of the mesocosms had decreased mesozooplankton populations whereas the other half were supplemented with additional zooplankton. In all mesocosms, POC fluxes were positively correlated to Si/C ratios measured in the surface community and additions of dSi globally increased the export fluxes in all treatments highlighting the role of diatoms in C export. The presence of additional copepods resulted in higher standing stocks of POC, most probably through trophic cascades. However it only resulted in higher export fluxes for the +Si mesocosms. In the +Si with copepod addition (+Si +Cops) export was dominated by large diatoms with higher Si/C ratios in sinking material than in standing stocks. During non-bloom situations, the grazing activity of copepods decrease the export efficiency in diatom dominated systems by changing the structure of the phytoplankton community and/or preventing their aggregation. However, in flagellate-dominated system, the copepods increased phytoplankton growth, aggregation and fecal pellet production, with overall higher net export not always visible in term of export efficiency

Reconciliation of the carbon budget in the ocean’s twilight zone

Photosynthesis in the surface ocean produces approximately 100 gigatonnes of organic carbon per year, of which 5 to 15 per cent is exported to the deep ocean1, 2. The rate at which the sinking carbon is converted into carbon dioxide by heterotrophic organisms at depth is important in controlling oceanic carbon storage3. It remains uncertain, however, to what extent surface ocean carbon supply meets the demand of water-column biota; the discrepancy between known carbon sources and sinks is as much as two orders of magnitude4, 5, 6, 7, 8. Here we present field measurements, respiration rate estimates and a steady-state model that allow us to balance carbon sources and sinks to within observational uncertainties at the Porcupine Abyssal Plain site in the eastern North Atlantic Ocean. We find that prokaryotes are responsible for 70 to 92 per cent of the estimated remineralization in the twilight zone (depths of 50 to 1,000 metres) despite the fact that much of the organic carbon is exported in the form of large, fast-sinking particles accessible to larger zooplankton. We suggest that this occurs because zooplankton fragment and ingest half of the fast-sinking particles, of which more than 30 per cent may be released as suspended and slowly sinking matter, stimulating the deep-ocean microbial loop. The synergy between microbes and zooplankton in the twilight zone is important to our understanding of the processes controlling the oceanic carbon sink

Controls over Ocean Mesopelagic Interior Carbon Storage (COMICS): Fieldwork, Synthesis, and Modeling Efforts

The ocean's biological carbon pump plays a central role in regulating atmospheric CO2 levels. In particular, the depth at which sinking organic carbon is broken down and respired in the mesopelagic zone is critical, with deeper remineralization resulting in greater carbon storage. Until recently, however, a balanced budget of the supply and consumption of organic carbon in the mesopelagic had not been constructed in any region of the ocean, and the processes controlling organic carbon turnover are still poorly understood. Large-scale data syntheses suggest that a wide range of factors can influence remineralization depth including upper-ocean ecological interactions, and interior dissolved oxygen concentration and temperature. However, these analyses do not provide a mechanistic understanding of remineralization, which increases the challenge of appropriately modeling the mesopelagic carbon dynamics. In light of this, the UK Natural Environment Research Council has funded a programme with this mechanistic understanding as its aim, drawing targeted fieldwork right through to implementation of a new parameterization for mesopelagic remineralization within an IPCC class global biogeochemical model. The Controls over Ocean Mesopelagic Interior Carbon Storage (COMICS) programme will deliver new insights into the processes of carbon cycling in the mesopelagic zone and how these influence ocean carbon storage. Here we outline the programme's rationale, its goals, planned fieldwork, and modeling activities, with the aim of stimulating international collaboration

Machine learning techniques to characterize functional traits of plankton from image data

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms