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Abstract Mesozooplankton is a very diverse group of small animals ranging in size from 0.2 to 20 mm not able to swim against ocean currents. It is a key component of pelagic ecosystems through its roles in the trophic networks and the biological carbon pump. Traditionally studied through microscopes, recent methods have been however developed to rapidly acquire large amounts of data (morphological, molecular) at the individual scale, making it possible to study mesozooplankton using a trait‐based approach. Here, combining quantitative imaging with metabarcoding time‐series data obtained in the Sargasso Sea at the Bermuda Atlantic Time‐series Study (BATS) site, we showed that organisms' transparency might be an important trait to also consider regarding mesozooplankton impact on carbon export, contrary to the common assumption that just size is the master trait directing most mesozooplankton‐linked processes. Three distinct communities were defined based on taxonomic composition, and succeeded one another throughout the study period, with changing levels of transparency among the community. A co‐occurrences' network was built from metabarcoding data revealing six groups of taxa. These were related to changes in the functioning of the ecosystem and/or in the community's morphology. The importance of Diel Vertical Migration at BATS was confirmed by the existence of a group made of taxa known to be strong migrators. Finally, we assessed if metabarcoding can provide a quantitative approach to biomass and/or abundance of certain taxa. Knowing more about mesozooplankton diversity and its impact on ecosystem functioning would allow to better represent them in biogeochemical models. 

Abstract Zooplankton contribute a major component of the vertical flux of particulate organic matter to the ocean interior by packaging consumed food and waste into large, dense fecal pellets that sink quickly. Existing methods for quantifying the contribution of fecal pellets to particulate organic matter use either visual identification or lipid biomarkers, but these methods may exclude fecal material that is not morphologically distinct, or may include zooplankton carcasses in addition to fecal pellets. Based on results from seven pairs of wild‐caught zooplankton and their fecal pellets, we assess the ability of compound‐specific isotope analysis of amino acids (CSIA‐AA) to chemically distinguish fecal pellets as an end‐member material within particulate organic matter. Nitrogen CSIA‐AA is an improvement on previous uses of bulk stable isotope ratios, which cannot distinguish between differences in baseline isotope ratios and fractionation due to metabolic processing. We suggest that the relative trophic position of zooplankton and their fecal pellets, as calculated using CSIA‐AA, can provide a metric for estimating the dietary absorption efficiency of zooplankton. Using this metric, the zooplankton examined here had widely ranging dietary absorption efficiencies, where lower dietary absorption may equate to higher proportions of fecal packaging of undigested material. The nitrogen isotope ratios of threonine and alanine statistically distinguished the zooplankton fecal pellets from literature‐derived examples of phytoplankton, zooplankton biomass, and microbially degraded organic matter. We suggest that δ¹⁵N values of threonine and alanine could be used in mixing models to quantify the contribution of fecal pellets to particulate organic matter. 

Diel rhythms are observed across taxa and are important for maintaining synchrony between the environment and organismal physiology. A striking example of this is the diel vertical migration undertaken by zooplankton, some of which, such as the 5 mm-long copepod Pleuromamma xiphias (P. xiphias), migrate hundreds of meters daily between the surface ocean and deeper waters. Some of the molecular pathways that underlie the expressed phenotype at different stages of this migration are entrained by environmental variables (e.g., day length and food availability), while others are regulated by internal clocks. We identified a series of proteomic biomarkers that vary across ocean DVM and applied them to copepods incubated in 24 h of darkness to assess circadian control. The dark-incubated copepods shared some proteomic similarities to the ocean-caught copepods (i.e., increased abundance of carbohydrate metabolism proteins at night). Shipboard-incubated copepods demonstrated a clearer distinction between night and day proteomic profiles, and more proteins were differentially abundant than in the in situ copepods, even in the absence of the photoperiod and other environmental cues. This pattern suggests that there is a canalization of rhythmic diel physiology in P. xiphias that reflects likely circadian clock control over diverse molecular pathways. 

Abstract The increasing use of image-based observing systems in marine ecosystems allows for more quantitative analysis of the ecological zonation of zooplankton. Developing methods that take advantage of these systems can provide an increasingly nuanced understanding of how morphometric characteristics (especially size) are related to distribution, abundance and ecosystem function via a wider application of allometric calculations of biogeochemical fluxes. Using MOCNESS sampling of zooplankton near the Bermuda Atlantic Time Series and a ZooSCAN/EcoTaxa pipeline, we apply a new taxonomically resolved biomass to biovolume dataset and a suite of R scripts that provide information about the relationships between zooplankter size, taxonomy, distribution, depth of migration, magnitude of migration and biogeochemical contributions (e.g. respiratory O2 consumption). The analysis pipeline provides a framework for quantitatively comparing and testing hypotheses about the distribution, migration patterns and biogeochemical impacts of mesozooplankton. Specifically, our code helps to visualize a size-based structure in the extent of vertical migration and allow for a quantification of the relative importance of non-migratory versus migratory organisms of various size classes. It additionally allows us to quantify the error associated with various methods of calculating active flux, with size-based analysis being the most important methodological choice, and taxonomic identification being the least.