Pyrosomes are efficient grazers that can form dense aggregations. Their clearance rates are among the highest of any zooplankton grazer, and they can rapidly repackage what they consume into thousands of fecal pellets per hour. In recent years, pyrosome swarms have been found outside of their natural geographical range; however, environmental drivers that promote these swarms are still unknown. During the austral spring of 2017 a Pyrosoma atlanticum swarm was sampled in the Tasman Sea. Depth‐stratified sampling during the day and night was used to examine the spatial and vertical distribution of P. atlanticum across three eddies. Respiration rate experiments were performed onboard to determine minimum feeding requirements for the pyrosome population. P. atlanticum was 2 orders of magnitude more abundant in the cold core eddy (CCE) compared to both warm core eddies, with maximum biomass of 360 mg WW·m−3, most likely driven by high chlorophyll a concentrations. P. atlanticum exhibited diel vertical migration and migrated to a maximum depth strata of 800–1,000 m. Active carbon transport in the CCE was 4 orders of magnitude higher than the warm core eddies. Fecal pellet production contributed to the majority (91%) of transport, and total downward carbon flux below the mixed layer was estimated at 11 mg C·m−2·d−1. When abundant, P. atlanticum swarms have the potential to play a major role in active carbon transport, comparable to fluxes for zooplankton and micronekton communities.

Zooplankton are the intermediate trophic level between phytoplankton and fish, and are an important component of carbon and nutrient cycles, accounting for a large proportion of the energy transfer to pelagic fishes and the deep ocean. Given zooplankton's importance, models need to adequately represent zooplankton dynamics. A major obstacle, though, is the lack of model assessment. Here we try and stimulate the assessment of zooplankton in models by filling three gaps. The first is that many zooplankton observationalists are unfamiliar with the biogeochemical, ecosystem, size-based and individual-based models that have zooplankton functional groups, so we describe their primary uses and how each typically represents zooplankton. The second gap is that many modelers are unaware of the zooplankton data that are available, and are unaccustomed to the different zooplankton sampling systems, so we describe the main sampling platforms and discuss their strengths and weaknesses for model assessment. Filling these gaps in our understanding of models and observations provides the necessary context to address the last gap-a blueprint for model assessment of zooplankton. We detail two ways that zooplankton biomass/abundance observations can be used to assess models: data wrangling that transforms observations to be more similar to model output; and observation models that transform model outputs to be more like observations. We hope that this review will encourage greater assessment of zooplankton in models and ultimately improve the representation of their dynamics.Keywords: plankton net, bioacoustics, optical plankton counter, Continuous Plankton Recorder, size-spectra, ecosystem model, observation model, model assessment Everett et al. Challenges for Zooplankton Model Assessment THE IMPORTANCE OF ZOOPLANKTONAll marine phyla are part of the zooplankton-either permanently as holoplankton (e.g., copepods or arrow worms) or temporarily as meroplankton (e.g., crab or fish larvae). In this review we define zooplankton as all organisms drifting in the water whose locomotive abilities are insufficient to progress against ocean currents (Lenz, 2000). Their sizes range from flagellates (about 20 µm) to siphonophores up to 30 m long. Zooplankton are the intermediate trophic level between phytoplankton and fish and are an important component of carbon and nutrient cycles in the ocean. They account for a large proportion of the energy transfer to fish on continental shelves (Marquis et al., 2011), temperate reefs (Kingsford and MacDiarmid, 1988;Champion et al., 2015), seagrass meadows (Edgar and Shaw, 1995), and coral reefs (Hamner et al., 1988;Frisch et al., 2014). Zooplankton are also key in the transfer of energy between benthic and pelagic domains (Lassalle et al., 2013). Zooplankton are responsible for transferring energy to deep water through the sinking of fecal pellets and moribund carcases (Stemmann et al., 2000;Henschke et al., 2013Henschke et al., , 2016 or through diel vertical migration (Ariza et al., 2015) and can play...

Mesoscale eddies in the south west Pacific region are prominent ocean features that represent distinctive environments for phytoplankton. Here, we examine the seasonal plankton dynamics associated with averaged cyclonic and anticyclonic eddies (CE and ACE, respectively) off eastern Australia. We do this through building seasonal climatologies of mixed layer depth (MLD) and surface chlorophyll-a for both CE and ACE by combining remotely sensed sea surface height (TOPEX/Poseidon, Envisat, Jason-1, and OSTM/Jason-2), remotely sensed ocean color (GlobColour) and in situ profiles of temperature, salinity and pressure from Argo floats. Using the CE and ACE seasonal climatologies, we assimilate the surface chlorophyll-a data into both a single (WOMBAT), and multi-phytoplankton class (EMS) biogeochemical model to investigate the level of complexity required to simulate the phytoplankton chlorophyll-a. For the two eddy types, the data assimilation showed both biogeochemical models only needed one set of parameters to represent phytoplankton but needed different parameters for zooplankton. To assess the simulated phytoplankton behavior we compared EMS model simulations with a ship-based experiment that involved incubating a winter phytoplankton community sampled from below the mixed layer under ambient and two higher light intensities with and without nutrient enrichment. By the end of the 5-day field experiment, large diatom abundance was four times greater in all treatments compared to the initial community, with a corresponding decline in pico-cyanobacteria. The experimental results were consistent with the simulated behavior in CE and ACE, where the seasonal deepening of the mixed layer during winter produced a rapid increase in large phytoplankton. Our model simulations suggest that CE off East Australia are not only characterized by a higher chlorophyll-a concentration compared to ACE, but also by a higher concentration of large phytoplankton (i.e., diatoms) due to the shallower CE mixed layer. The model simulations also suggest the zooplankton community is different in the two eddy types and this behavior needs further investigation.