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    Dataset collected at Cape Evans, Antarctica, November 2023 as part of a long-term NIWA benthic monitoring program under the Antarctica New Zealand event number K882A. The dataset includes multiple sea-ice and seafloor hyperspectral imaging transects (10-40 meters long) coupled with normal red, green, and blue (RGB) imagery from a dual camera machine vision system. The data were acquired using the remotely operated vehicle (ROV) HIcyBot system, funded by the Australian Centre for Excellence in Antarctic Research (ACEAS). A GNSS-integrated USBL transponder equipped onto the ROV allowed every frame of the high frequency hyperspectral imager to be timestamped via GPS clock to acoustically provided underwater position and attitude. The dataset also includes hyperspectral imaging scans of sampled/retrieved organisms found at the seafloor, to support habitat mapping algorithm development (e.g., algae, urchins, sea-stars, etc.). The ROV was tested as part of an ACEAS Program 2 subcomponent that involved the design of the new under-ice hyperspectral imaging and photogrammetric payload mounted onto the HIcyBot ROV. The overarching goal of the systems was to be able to acquire information of under the sea-ice sympagic and benthic communities (e.g., biomass and photophysiology) and deliver a multi-scale array of biophysical data that can be assimilated with known information in the region and monitor fine-scale change. Through the analysis of new and existing bio-optical under-ice data, the dataset aims to ultimately envisions the delivery of new monitoring tools and algorithms that can provide support for modelling efforts and reveal complex biophysical processes under a changing Antarctic Sea ice. ***NOTE DATA TO BE EMBARGOED UNTIL 01/01/2025***

  • Ocean alkalinity enhancement (OAE) is a promising carbon removal method, but it may cause a significant perturbation of the ocean with trace metals such as Nickel (Ni). This study tested the effect of increasing Ni concentrations on phytoplankton growth and photosynthesis. The data were the growth rates of 11 phytoplankton species under different Ni concentrations (Master thesis project). The growth rates were calculated using daily fluorescence signal values measured by the fluorometer. Fv/Fm and SigmaPSII data were measured using Fast repetition rate fluorometry (FRRf). The growth rate and photo-physiological response of phytoplankton was analysed using generalised additive models (GAMs) and plotted in RStudio (R packages “mgcv” and “ggplot2”).