This metadata record contains an Excel workbook of current meter data and a report derived from this data detailing an analysis of the mean and variability of the longshore component of the current using observations from four current meters, and, simple modelling of the effluent outfall using a model originally developed for shoreline discharges from the oil industry. The Excel workbook contains data from 4 of the 6 analogue Anderra current that meters were deployed in the area in front of Davis Station in early 2010. Data was not retrievable from meters CM4 and CM6. The meters were deployed at approximately 5 m below the surface. Refer to the Davis STP reports lodged under metadata record Davis_STP for current meter locations and deployment and retrieval details. Background of the Davis STP project - Refer to the Davis STP reports lodged under metadata record Davis_STP.

This study was carried out by Giulia Roncon as part of her PhD at IMAS. The study employed both archival and contemporary diving data, collected by six species of marine predators (three penguins and three seal species) from the Eastern Antarctic sector of the Southern Ocean, to clarify key questions, such as (i) are there differences and/or commonalities regarding the diving physiology and ecology of marine predators, and (ii) what are the main determinants and constrains that characterise the underwater behaviour of air-breathing vertebrates. This dataset is a compilation of data of several different studies carried out by different research teams in various locations and at various times. All TDRs were archival loggers that had to be retrieved to obtain the data. Thus, the animals had to be captured twice (deployment and retrieval). Details about the types of tags are listed in the dataset. Species used in the study were: Adelie Penguins Emperor Penguins King Penguins Fur Seals Southern Elephant Seals Weddell Seals

Two toxicity tests were conducted in the Davis station laboratories in December 2010. Tests used locally collected amphipods of the species Orchomenella pinguides. The tests were conducted by Bianca Sfiligoj, as part of her PhD research (Sfiligoj 2013), with results published in (Sfiligoj et al. 2015). Field and laboratory work was conducted under project AAS 2933, with analysis and write-up completed under AAS 4100 (both projects CI: King). Details are fully described in the published manuscript provided with this data record; file name: Sfiligoj et al 2015_Ecotoxicology.pdf. A subset of the data is also used in Candy et al. 2015 (Filename: Candy et al 2015_Ecotoxicology.pdf). Data files: Test data are provided in the .xlsx file: 'Orchomenella-Tests-Dec 2010.xlsx'. Each worksheet includes a "This worksheet provides…" description in cell A1. Laboratory notebook records are provided in the scanned file: Sfiligoj-LabBookScan-Davis10-11.pdf. In this notebook, tests are labelled LT1 and LT2 (referred to as: amphipod lentil test 1 and 2); with results recorded on pages: 1-19 and 26-28. Data associated with this record has also been presented at: - Candy SG, Sfiligoj BJ, King CK, Mondon JA (2013) Modelling interval-censored survival times in toxicological studies using generalized additive models, The International Biometric Society Australasian Region Conference 2013, Mandurah, Australia, 1-5 December 2013. - Sfiligoj BJ, King CK, Candy SG, Mondon JA (2012) Development of appropriate bioassay and statistical methods for determining survival sensitivities of Antarctic marine biota to metal exposure, 2nd Society for Environmental Toxicology and Chemistry (SETAC) Australasia Conference, Brisbane, Australia, 4-6 July 2012. - Sfiligoj BJ, King CK, Candy SG, Mondon JA (2012) Development of appropriate bioassay and statistical methods for determining survival sensitivities of Antarctic marine biota to metal exposure, Society for Environmental Toxicology and Chemistry (SETAC) World Congress, Berlin, Germany, 20-24 May 2012.

1. The Excel spreadsheet titled "1_Cape Petrel Population adjusted Estimates_Table1.xlsx is population survey count data and estimates of Cape petrels in the Vestfold islands, East Antarctica in 1974 and 2017. Numbers present the number of occupied nests in each year. Adjusted data as per ICESCAPE modelling and provides a value based on attendance of Cape petrels relative to phenology, values in brackets are the lower and upper confidence intervals based on 95% confidence. No data is where there was no survey data available; however a 0 indicates the island was searched, however no breeding birds recorded at that site. Four surveys of Cape petrel breeding populations have been conducted in the Vestfold Islands: 1972-73 (Johnstone et al 1973), 1974-75 (AAD unpublished data), 2016-17 (Louise Emmerson and Anna Lashko) and 2017-18 austral summers (Kimberley Kliska and Marcus Salton). Here we refer to breeding seasons as the year eggs were laid, which was also when surveys were conducted. For example, 1972-73 breeding season spans from October 1972 until April 1973 and is referred to as 1972; 1974/75 is referred to as 1974 and 2017/18 as 2017. In 1972, numbers of occupied nests and distribution were assessed from ground surveys across the Vestfold Islands region and Cape petrels were found only in the southern half of the Vestfold Islands. In 1974, all accessible islands in this southern region were again surveyed from the ground or sea ice for Cape petrels from Bluff Island south to the Sørsdal Glacier. In addition, the ‘Northern Islands’ (Figure 1) were opportunistically searched during seal surveys conducted from 1-8th November 1974, and no sign of breeding Cape petrels were recorded (Williams, pers. comm. 2020). The 2016 survey focussed on identifying islands with cape petrels present in the south from ground-based activities, and in the north from aerial surveys. The 2017 survey focused search effort on all the islands where breeding Cape petrels were observed in 1972 and 1974. Similar to the 1974 survey, the Northern Islands were opportunistically searched for Cape petrels during seal surveys between the 5-13th December 2017, and no Cape petrels were observed. To our knowledge, no Cape petrels have been observed in the Northern Islands. We are therefore confident that this study encompasses the entire Vestfold Islands population. To assess the status and temporal change in population numbers of Cape petrels in the Vestfold Islands, datasets from the three breeding seasons were analysed, with two complete datasets, one a combination of both the 1972 and 1974 surveys and one from the 2017 survey were used in the final analysis. Three islands surveyed in the 1972 survey were not surveyed in 1974, therefore to complete the dataset for the 1974, the counts from these three islands in 1972 (Magnetic, Turner and Gardner Islands) were used to fill data gaps in 1974. The complete dataset is referred to as the 1974 dataset. Historical count data from 1972 and 1974 seasons were obtained from Johnstone et al 1973 and the Australian Antarctic Division Davis Biology species log 1974, respectively. In the 1972 survey, breeding pairs were estimated at various locations by island name and symbol shape on hand drawn maps. These symbols indicated which side of an island Cape petrels were located. In the 1974 survey breeding pairs of Cape petrels were recorded, as counted from the sea ice or by ground searching on the 17th of November and the 17th of December 1974. Locations of breeding Cape petrels were recorded with cross marks on hand drawn maps, indicating which gully or slope on an island Cape petrels were located. To ensure consistency of survey dates, both the Davis Station log book 1974 and the personal journal of Richard Williams (the biologist who undertook the survey work in 1974) were cross checked for survey dates. In the 2017 season, the survey was conducted over three days (18th, 20th and 30th of November) at all known Cape petrel breeding colonies. At each breeding colony a combination of ground searches and/or binocular counts were conducted from a vantage point on the sea ice tens of meters perpendicular away from Cape petrel breeding areas with the aim of counting all occupied nests. Occupied nests were classified as Confirmed if a bird was present at the nest and Unconfirmed if a nest was suspected but no bird observed (i.e. bowls of small pebbles and/or large amounts of guano on rocks were indicative of nests). Counts of confirmed nests were used to represent the number of occupied nests in 2017, and were considered consistent with breeding pair estimates in historic surveys. Birds observed on ledges without guano were considered loafing rather than breeding and not included in counts. The locations of breeding colonies were recorded using a combination of geographical positioning system (GPS) locations, hand-drawn maps and photographs of breeding colonies from the vantage point where counts were conducted. To compare changes between surveys, the Vestfold Island region was divided into two sections: Northern Islands and Southern Islands. The Southern Islands were further classified into three areas labelled A, B, and C. Area A is the northern part of the Southern Islands and includes Bluff, Turner, Magnetic and Gardner Islands and the Davis Station, and has the most persistent fast ice. Area B includes Hawker and Mule Islands and is further south, with intermediate fast ice duration, and Area C includes Zolotov and Kazak Islands and is furthest south, just north of the Sørsdal Glacier, and has the earliest loss of fast ice (Figure 1).To account for potential uncertainty in the population counts, we assumed the counts were within ±10% (with 95 % confidence) of the true number present. We refer to this as ‘count repeatability’. 2. Attendance data titled "2_Attendance_CapePetrels_BluffIsland_2019-2020.csv." The attendance data is derived from images taken with a remotely deployed camera at the Bluff Island Cape petrel colony near Davis station, East Antarctica. This phenology of cape petrel at this colony was used to adjust historical and contemporary population estimates of the Cape Petrel population. The .csv file includes latitude and longitude, season, calendar time and date, and an occupied nest count from the 6th of November 2019 until the 8th of March 2020. The camera data were counted by Kimberley Kliska in June 2020 as part of a project investigating the phenology of Cape petrels in this region. 3. The dataset in folders titled "1970s polygons" and "2017 polygons revised" contains boundaries of Cape petrel nesting areas at numerous breeding sites on islands off the Vestfold Hills, Antarctica, for the purpose of assessing change in the bird’s distribution between the early 1970s and 2017 (Kliska et al. 2021 manuscript in review). Nest areas were identified in the early 1970s during three surveys over three years 1972, 73 and 74, and in 2017 during one survey that year. Details of the surveys in 1970s were presented in the ANARE SCIENTIFIC REPORTS publication N. 123 ‘The Biology of the Vestfold Hills, Antarctica’ 1972-73 summer, and in the Davis Biology Species Log 1974 (included 1973-74 summer and 1974-75 summer) (the latter by Richard Williams). Details of the survey in 2017 were presented in the Seabirds Research end-of-season field report Davis 2017-18 summer (by Kim Kliska and Marcus Salton). Polygons created from the 2017 survey are published with the AADC (Emmerson and Southwell 2020). In both periods the islands were surveyed either by ground searching an area on foot or by visualising the birds from a distance with or without binoculars, and then transcribing the area with nests onto hand drawn maps. These hand drawn maps were transcribed on to spatially projected electronic maps by Marcus Salton to represent the maximal perimeter of the cape petrel nest areas. In the 1970’s surveys, the depicted nesting areas represented locations where birds were observed sitting on or next to nests (or extensive guano deposits that were indicative of a nest). Birds that were on rocks and not associated with a nest or extensive guano deposits were considered non-breeding, and areas with extensive guano deposits without birds considered inactive nests, which were both omitted from the nesting area. The polygons that had already been created from the 2017 survey (Emmerson and Southwell 2020) were modified to match this representation of nesting area, by excluding areas within inactive nests (based on recollections of Kim Kliska and Marcus Salton). Polygons were created using R computing software version 4.0.2 (2020-06-22). The spatially projected electronic maps were derived from two shapefiles from the AADC: a coastline file (‘all_coast_poly_2003.shp’ DOI) and a contour file (‘vestfold_contours.shp’ DOI). These shapefiles were projected using Azimuthal equidistant, with the centre of the study area at latitude = -68.5785 and longitude = 77.8709 for visualisation purposes. Polygons are grouped by island. Not all islands have formal names. Therefore the number system created by Southwell (2016 a, b) for a project on Adelie penguins was adopted.

Three experiments were performed at Davis Station, East Antarctica, 77 degrees 58' E, 68 degrees 35' S to determine the effects of ocean acidification on natural assemblages of Antarctica marine microbes (bacteria, viruses, phytoplankton and protozoa). Incubation tanks (6 * 650 L minicosms) were filled on the 30/12/08, 20/01/09 and 09/02/09 with sea water that was filtered through 200 microns mesh to remove metazoan grazers. The pH of each tank was adjusted by adding calculated amounts of CO2 saturated sea water. Treatment concentrations were maintained daily and microbial communities incubated for up to 12 days. The three experiments spanned early-, mid- and late-summer, with CO2 treatments ranging from pre-industrial to post-2100. The Excel spreadsheet contains 3 tabs: Experiment 1 - Early Summer Experiment 2 - Mid Summer Experiment 3 - Late Summer Within each tab there are measurements for: pCO2, dissolved inorganic carbon, Pmax, alpha, Ek, chl a, gross primary production (14C), bacterial production (14C), cell-specific bacterial productivity, bacterial abundance, dissolved organic carbon, particulate organic carbon, heterotrophic nanoflagellates, nitrate+nitrite, phosphate, silicate, ammonium, net community production, respiration, gross primary production (O2), photosynthesis:respiration ratios. Units for each measurement supplied within. Please see the following paper for interpretation of this data: Westwood, K.J., Thomson, P.G., van den Enden, R., Maher, L., Wright, S.W., Davidson, A.T. (2018). Ocean acidification impacts primary and bacterial production in Antarctic coastal waters during austral summer. Journal of Experimental Marine Biology and Ecology 498: 46-60, doi: 10.1016/j.jembe.2017.11.003.

Synchrotron based FTIR macromolecule profiles of 5 diatom species from the AAS_4026 ocean acidification project. Data represent the peak areas for wavenumbers related to key macromolecules. For details on methods see Duncan et al. (2021) New Phytologist. Experimental design and mesocosm set up Mesocosm set up and conditions were as described previously (Deppeler et al., 2018; Hancock et al., 2018). Briefly, a near-shore, natural Antarctic microbial community was collected from an ice-free area among broken fast ice approximately 1km offshore from Davis Station, Antarctica (68° 35ʹ S, 77° 58ʹ E) on 19 November 2014. This community was incubated in 6 x 650L polyurethane tanks (mesocosms) across a gradient of fCO2 levels (343, 506, 634, 953, 1140 and 1641 μatm; denoted M1 – M6). These fCO2 levels corresponded to pH values ranging from 8.17 to 7.57. Temperature was maintained at 0.0 °C ± 0.5 °C and the mesocosms were stirred continuously by a central auger (15 r.p.m.) for gentle mixing and covered with an air-tight lid. Irradiance was initially kept low (0.8 ± 0.2 μmol photons m-2s-1), while cell physiology was left to acclimate to increasing fCO2 levels (over 5 days). When target fCO2 levels were reached in all six mesocosms, light was gradually increased (days 5-8) to 89 ± 16 μmol photons m-2s-1 on a 19 h:5 h light:dark cycle, to mimic current natural conditions. To generate the gradient in carbonate chemistry, filtered seawater saturated with CO2 was added to five of the mesocosms. Daily measurements were taken to monitor pH and dissolved inorganic carbon (DIC). For details of fCO2 manipulations, analytical procedures and calculations see Deppeler et al., (2018). Samples for physiological and macromolecular measurements in this study were taken on day 18, at the end of the incubation period (Deppeler et al., 2018). Cell volume Cell volume was determined for selected taxa from M1 and M6 via light microscopy. Cells were imaged on a calibrated microscope (Nikon Eclipse Ci-L, Japan) and length, width and height (24-77 cells per taxa) determined using ImageJ software (Schneider et al., 2012). Biovolume was then calculated according to the cell morphology and corresponding equations described by Hillebrand et al (1999). Macromolecular content by FTIR The macromolecular composition of the selected diatom taxa sampled from all six mesocosms on day 18 was determined using Synchrotron based FTIR microspectroscopy on formalin-fixed (2% v/v final concentration) cells. Measurements were made on hydrated cells and processed according to previous studies (Sackett et al. 2103; 2014; Sheehan et al. 2020). Briefly, fixed cells were loaded directly onto a micro-compression cell with a 0.3 mm thick CaF2 window. Spectral data of individual cells (between 15-49 cells per taxon per mesocosm) were collected in transmission mode, using the Infrared Microspectroscopy Beamline at the Australian Synchrotron, Melbourne, in November 2015. Spectra were acquired over the measurement range 4000− 800 cm−1 with a Vertex 80v FTIR spectrometer (Bruker Optics) in conjunction with an IR microscope (Hyperion 2000, Bruker) fitted with a mercury cadmium telluride detector cooled with liquid nitrogen. Co-added interferograms (n = 64) were collected at a wavenumber resolution of 6 cm−1s. To allow for measurements of individual cells, all measurements were made in transmission mode, using a measuring area aperture size of 5 × 5 µm. Spectral acquisition and instrument control were achieved using Opus 6.5 software (Bruker). Normalised spectra of biologically relevant regions revealed absorbance bands representative of key macromolecules were selected. Specifically, the amide II (~1540 cm-1), Free Amino Acid (~1452 cm-1), Carboxylates (~1375 cm-1), Ester carbonyl from lipids (~1745 cm-1) and Saturated Fatty Acids (~2920 cm-1) bands were selected. Infra-red spectral data were analysed using custom made scripts in R (R Development Core Team 2018). The regions of 3050-2800, 1770-1100 cm-1, which contain the major biological were selected for analysis. Spectral data were smoothed (4 pts either side) and second derivative (3rd order polynomial) transformed using the Savitzky-Golay algorithm from the prospectr package in R (Stevens and Ramirez-Lopez, 2014) and then normalised using the method of Single Normal Variate (SNV). Macromolecular content for individual taxon was estimated based on integrating the area under each assigned peak, providing metabolite content according to the Beer-Lambert Law, which assumes a direct relationship between absorbance and relative analyte concentration (Wagner et al., 2010). Integrated peak areas provide relative changes in macromolecular content between samples. Because of the differences in absorption properties of macromolecules, peak areas can only be used as relative measure within compounds.

This metadata record contains an Excel file containing total petroleum hydrocarbon data from analysis of marine sediments collected at Davis Station from December 2009 to March 2010. Refer to the Davis STP reports lodged under metadata record Davis_STP for the full Davis Sewage Treatment Project methods and result details. Davis STP - Total petroleum hydrocarbons Hydrocarbons were extracted from a 10g sub-sample of homogenised wet soil by tumbling overnight with a mixture of 10 mL of deionised water, 10 mL of dichlormethane (DCM), and 1 mL of DCM spiked with internal standards: 254 mg/L bromoeicosane; 55.2 mg/L 1,4 dichlorobenzene; 51.2 mg/L p-terphenyl; 52.2 mg/L tetracosane-d50; and 255 mg/L cyclo-octane. Samples were then centrifuged for 5 minutes at 1000 rpm, this was repeated a further 3 times to ensure complete separation of the organic and aqueous fractions. The DCM fraction was then extracted and placed into GC-vials. Extracts were analysed for total petroleum hydrocarbons (TPH) by gas chromatography using flame ionisation detection (GC-FID; Agilent 6890N with a split/splitless injector) and an auto-sampler (Agilent 7683 ALS). Separation was achieved using an SGE BP1 column (25 m x 0.22 mm ID, 0.25 µm film thickness). 1 µL of extract was injected (5:1 pulsed split) at 310° C and 17.7 psi of helium carrier gas. After 1.3 minutes, the carrier gas pressure was adjusted to maintain constant flow at 3.0 mL/min for the duration of the oven program. The oven temperature program was started at 36 °C (held for 3 minutes) and increased to 320 °C at 18 °C/min. Detector temperature was 330 °C. TPH concentrations were determined using a calibration curve, generated from standard solutions of special Antarctic blend diesel (SAB), and standard diesel. TPH was measured using the ratio of the total detector response of all hydrocarbons to the internal standard peak response. List of compounds analysed - C8-C28 individual hydrocarbon components - Naphthalene - Biomarkers (phytanes) - Total signal and area, and resolved compounds from C8 to C40, over specific ranges (e.g. C9-C18, SAB) Reporting limit - 0.3 mg.kg-1 on a dry matter basis (DMB) for individual components - 2.5-160 mg.kg-1 on a dry matter basis (DMB) for various calculated ranges Analytical uncertainty - Analytical precision: (a) 3 samples extracted and analysed in triplicate, (b) 3 extracts analysed by GC-FID in duplicate; only 1 of each set greater than RL (160): (a) RSD = 2%, (b) RSD = 0.4% - Site heterogeneity: reproducibility (RSD) of mean data from site replicate samples (mostly duplicates) was 24% (mean, SD 20%, range 4-60%, n=8) - From the limited data on reproducibility summarised above, it can be concluded that site heterogeneity contributes most to the uncertainty of the TPH data for the site locations. Background of the Davis STP project Refer to the Davis STP reports lodged under metadata record Davis_STP.

A number of toxicity tests have been conducted using the marine microgastropod, Skenella paludionoides, between the years 2006 and 2010. Tests have determined sensitivity of this species to the a range of common metals contaminants; cadmium, copper, nickel, lead and zinc. Test biota were collected from Casey and Davis, with tests conducted either at Antarctic station laboratories or in AAD Kingston laboratories (after transport of animals back to Australia). See the child records for access to the data.

The Davis Aerodrome Project (DAP) collected a range of environmental survey data over several field seasons to support a comprehensive environmental assessment of the proposed aerodrome. This data includes flora, fauna, soils, lake ecosystem, nearshore, marine, air quality and meteorological information which has been collected by a number of different methods, and extends across the current Davis Station, proposed aerodrome and supporting infrastructure footprint (Ridge Site), previous sites considered for the aerodrome (Heidemann Valley, Adams Flat), as well as locations across the Vestfold Hills away from any of the proposed developments.(this text is standard for all DAP datasets being added to the AADC). This dataset contains short-term nearshore marine current profile data collected to inform environmental assessment processes related to the Australian Antarctic Division’s DAP and Davis Masterplan projects. Eight current meter deployments were undertaken across six sites in the nearshore marine environment in the vicinity of Davis Research station. Deployment periods ranged from three days (3 x 24hr tide cycle) to two weeks. Sites were selected based on the location of previous sampling activity (CM1-5) and sites of interest to Davis Aerodrome and Davis Masterplan projects with regard to proposed future developments in the area. A second deployment was undertaken at two key sites to increase the sampling interval at each. Data was collected using a Nortec Aquadopp Profiler 1 MHz. The same instrument was used to collect current profiles at all sites. The instrument was deployed through a 40cm hole drilled through the seaice. It was suspended horizontally in the water column (mid-way between the seafloor and the under surface of the ice) by a bridal attachment and rope secured at the surface (see figure below). A 15cm fin was attached to the base of the instrument for all deployments. In shallow locations the instrument was positioned so that it could not hit the seafloor throughout the lowest tidal cycle during the deployment. The profile interval was set to record every 900 seconds (15min) for a period of 120 seconds (2min). All instrument settings and recording details are contained in the hdr files saved in each data folder. Start and end dates and times are set out in the “current meter deployment details” spreadsheet. Temporal coverage Site No. Deployment Date Retrieval Date CM1 22/10/2021 2/22/2021 CM2 16/09/2021 19/09/2021 CM2 9/10/2021 22/10/2021 CM3 3/11/2021 12/11/2021 CM5 24/11/2021 4/11/2021 ML 8/12/2021 14/12/2021 OptionA 29/09/2021 2/10/2021 OptionA 14/12/2021 20/12/2021 Spatial coverage CM2_01_20210919 68.57399536 77.96031373 OptionA_01_20211002 68.57597253 77.96121253 CM2_02_20211022 68.57399536 77.96031373 CM1_01_20211102 68.57749077 77.95758156 CM3_01_20211112 68.57276237 77.94873464 CM5_01_20211204 68.58321738 77.9180513 ML_01_20211214 -68.58381482 77.94507546 OptionA_02_20211220 68.57585945 77.96151685

Experimental Design A six-level, dose-response ocean acidification experiment was run on a natural microbial community from nearshore Antarctica, between 19th November and 7th December 2014. Seawater was collected from approximately 1 km offshore of Davis Station, Antarctica (68◦ 35’ S, 77◦ 58’ E), pre-filtered (200 μm), and transferred into six 650 L tanks (minicosms) located in a temperature-controlled shipping container. Six CO2 levels were achieved by altering the fugacity of carbon dioxide (ƒCO2) within each minicosms. The ƒCO2 was adjusted stepwise to the target concentrations for each minicosm (343, 506, 634, 953, 1140, 1641 μatm) over a five-day period using 0.2 μm filtered seawater enriched with CO2. This acclimation to CO2 was conducted at low light (0.9 ± 0.2 μmol m−2 s−1) so there was low growth of the phytoplankton. Light levels were then increased over a further two days to 90.52 ± 21.45 μmol m−2 on a 19:5 light/dark non-limiting light cycle. After this acclimation period, the microbial community was allowed to grow for 10 days (days 8-18), during which the ƒCO2 levels within each minicosm was adjusted daily to maintain the target ƒCO2 level for each minicosm, and light levels were kept constant. No nutrients were added during the experiment. For a more detailed description of minicosm set-up, lighting and carbonate chemistry see; Davidson, A. T., McKinlay, J., Westwood, K., Thomson, P. G., van den Enden, R., de Salas, M., Wright, S., Johnson, R., and Berry, K.:Enhanced CO2 concentrations change the structure of Antarctic marine microbial communities, Mar. Ecol. Prog. Ser., 552, 93-113, 2016. Deppeler, S. L., Petrou, K., Westwood, K., Pearce, I., Pascoe, P., Schulz, K. G., and Davidson, A. T. Ocean acidification effects on productivity in a coastal Antarctic marine microbial community, Biogeosciences, 15(1), 2018. Sample Collection Samples of 40-400 L were collected and sequentially size-fractionated filtered onto 293 mm biomass filters with 3.0 and 0.1 μm pore-sized polyethersulfone membrane filters (Pall XE20206 Disc 3.0 μm Versapor 293 mm and 656552 Disc 0.1 μm Supor 293 mm) using the design of the Global Ocean Sampling expedition (Rusch et al., 2007). Samples were collected on days 0 (immediately after seawater collection), 12 (mid-exponential growth) and 18 (end of experiment). On day 0, 400 L of seawater was collected from the reservoir tank (pre-filtered 200 μm), from which all the minicosms were filled, to allow characterisation of the initial community. This sample was collected from the reservoir, and not the minicosms, due to the large volume needed to collect sufficient microbial biomass on the filters. On day 12 and 18, 40 L was collected from each minicosm for filtration. The later samples were of a smaller volume due to the increase in biomass in the minicosms during the experiment, meaning less volume of water was required to gain sufficient material on the filters to perform molecular analysis. The filter membranes containing the concentrated microbial biomass were stored in 15 mL of storage buffer, flash frozen in liquid nitrogen and stored at - 80◦C. The storage buffer was freshly prepared on each sampling day with a mixture of 2.5 mM EGTA, 2.5 mM EDTA, 0.1 mM Tris-EDTA, RNA Later (0.5x house prepared), 1 mM PMSF and Protease Inhibitor Cocktail VI (Ng et al., 2010). Between samples the filtration apparatus was sequentially washed with 2 x 25 L 0.1 M NaOH, 2 x 25 L 0.07% Ca(OCl)2 and 2 x 25 L fresh water. All samples were stored and transported at -80◦C to the Australian Antarctic Division, Hobart, Australia for DNA extraction. DNA Extraction and Sequencing The DNA was extracted from half of each filter (3.0 and 0.1 μatm per sample) via the method described in Rusch et al. (2007). In short, the filters were cut into small pieces and agitated in a lysozyme and sucrose buffer for 60 minutes and underwent three freeze/thaw cycles in a Proteinase K solution. This was followed by a gentler agitation at 55◦C for 2 hrs to remove all contents from the filter membranes. DNA was then separated using buffer saturated phenol, pelleted and washed in alcohol. The final DNA pellet was dissolved and stored in a 3 M sodium acetate (pH 8.0) and 100% ethanol solution and stored at - 80◦C. The DNA was transported and stored at 4◦C to the University of Queensland, St Lucia, Australia for sequencing within two months of extraction. Eukaryotic 18S rRNA genes (V8-V9 regions) were amplified using polymerase chain reaction (PCR) with the primers V8f (5’ - AT AAC AGG TCT GTG ATG CCC T - ’3) and 1510r (5’ - CCT TCY GCA GGT TCA CCT AC - ’3) (Bradley, 2016). The 16S rRNA genes V8 region were amplified using PCR and primers 926F (5’-AAA CTY AAA KGA ATT GAC GG-3’) and 1392wR (5’-ACG GGC GGT GTG RC-3’) (Engelbrektson et al., 2010). PCR was performed using 1 or 1.5 μL of sample DNA, 2.5 μL 1x PCR buffer minus Mg+2 (Invitrogen), 0.75 μL MgCl2, 0.5 μL deoxynucleoside triphosphate (dNTPs, Invitrogen), 0.125 μL U Taq DNA Polymerase (Invitrogen), 0.625 μL of forward/reverse primer and made up to the final volume of 25 μL using molecular biology grade water. Forward and reverse primers were modified at the 5’-end to contain an Illumina overhang adaptor with P5 and i7 Nextera XT indices, respectively. The PCR thermocycling conditions were as follows: 94◦C for 3 min, 35 cycles of 94◦C for 45 sec, 55◦C for 30 sec, 7◦C for 10 min and a final extension of 72◦C for 10 min. Amplifications were performed using a Vertiti®96-well Thermocycler (Applied Biosystems) and success, amplicon size and quality was determined by gel electrophoresis. The resultant amplicons were purified using Agencourt AMPure magnetic beads (Axygen Biosciences), dual indexed using Nextera XT Index Kit (Illumina). The indexed amplicons were purified using Agencourt AMPure XP beads and quantified using PicoGreen dsDNA Quantification Kit (Invitrogen). Equal concentrations of each sample were pooled and sequenced on an Illumina MiSeq at the University of Queensland’s School for Earth and Environmental Science using 30% PhiX Control v3 (Illumina) and a MiSeq Reagent Kit v3 (600 cycle; Illumina). Bioinformatics Sequencing data and runs were merged to produced single FASTQ file for 16S and 18S rDNA per sample and imported in QIIME2 (v2019.9) (Caporaso et al., 2010). A modified version of the UPARSE analysis pipeline was used to analyse the data. Specifically, the primer sequences were removed from forward reads of the 16S rDNA and reverse complement of the 18S rDNA Illumina read pairs, and chimeras removed using UCHIME2 (Edgar, 2016). These were then trimmed to a length of 200 bp and high-quality sequences identified using USEARCH (v10.0.240) (Edgar, 2010). Duplicate sequences were removed and a set of unique operational taxonomic units (OTUs) were generated using USEARCH employing a 97% OTU similarity radius. Mitochondrial and chloroplast OTUs were classified and removed from the 16S rDNA sequence data using the BIOM tool suite (McDonald et al., 2012). Representative OTU sequences were assigned taxonomy using SILVA132 (Quast et al., 2012) and PR2 (Guillou et al., 2012) for the eukaryotic group Bacillariophyceae (diatoms). Taxonomic assignments were validated against microscopy identifications conducted on the same samples (Chapter 3, Hancock et al. 2018) as well as phylogenetic trees built in iTOL (Letunic and Bork, 2006). Residual eukaryotic chloroplast and mitochondrial sequences were removed from the 16S rDNA data. Other obvious contaminants were removed manually including: Escherichia-Shigella (16S rDNA OTU75) and Saccharomycetales (18S rDNA OTU7, 146 and 160). Escherichia-shigella was removed as this group likely represents external contamination, similarly Saccharomycetales are yeast and are obvious skin-driven contaminants. A total of 9448 OTUs were identified from the 16S rDNA reads and 232 OTUs from the 18S rDNA read data. The number of reads were rarefied to 1300 and 1200 reads per sample for the 18S and 16S rDNA datasets respectively. The following samples were removed due to lack of extracted, amplified and/or sequenced DNA, or due to low quality reads and/or low read numbers: 18S, 3.0 μm, day 18, 634 μatm ƒCO2 treatment 18S, 0.1 μm, day 12, 343 μatm or control ƒCO2 treatment 18S, 0.1 μm, day 18, 343 μatm or control ƒCO2 treatment 16S, 0.1 μm, day 18, 506 μatm ƒCO2 treatment Statistical Analysis The minicosm experiment was based on a repeated measure design, therefore due to being a dose-response experiment with no replication, no formal statistics could be undertaken on the interactions between time and ƒCO2. The richness (number of taxa) and evenness (equivalent to abundances within a sample) of the eukaryotic and prokaryotic microbial communities within each minicosm over time was estimated using three different alpha diversity indexes: observed number of OTUs (Sobs) (DeSantis et al., 2006), the Chao1 estimator of richness (Colwell et al., 2004), and Simpson’s diversity index and Berger-Parker index which account for both richness and evenness (Simpson, 1949; Berger and Parker, 1970) using QIIME2. Clustering and ordinations were performed on Bray-Curtis resemblance matrices of the rarefied, square-root transformed OTU data as per Chapter 3 (Hancock et al., 2018). In brief, hierarchical agglomerative cluster analyses were performed using group-average linkage, and significantly different clusters were determined using similarity profile permutations method (SIMPROF) (Clarke et al., 2008). Both unconstrained (non-metric multidimensional scaling, nMDS) and constrained (canonical analysis of principal coordinates, CAP) ordinations were performed using the Bray-Curtis resemblance matrixes (Kruskal, 1964a,b; Oksanen et al., 2017). The constraining variables in the CAP analysis were ƒCO2, Si, P and NOx. All cluster and ordination analyses were performed using R v.1.1.453 (R Core Team, 2016) and the add-on package Vegan v.2.5-3 (Oksanen et al., 2017). A full description of the statistical methods used for this paper is described in; Hancock, A. M., Davidson, A. T., McKinlay, J., McMinn, A., Schulz, K. G., and van den Enden, R. L. Ocean acidification changes the structure of an Antarctic coastal protistan community, Biogeosciences, 15(1), 2018.