These data were collected on the RV L'Astrolabe (platform code: FHZI) from 04/11/2005 to 08/11/2005 on a trip from Dumont D'Urville to Hobart. Maximum photochemical efficiency of photosystem II (PSII), also called maximum quantum yield of PSII (Fv/Fm), has become one of the most widely utilized fluorescence parameters in phytoplankton research. It represents the potential photochemical efficiency, which is the probability that the light energy captured by the photosynthetic apparatus is being utilized as photochemistry. Fv/Fm has been shown to have an instant response to variations in physical and chemical properties and is interpreted as a diagnostic of the overall health or competence of phytoplankton. Together with the absorption cross section area of PSII and chlorophyll concentration, it can be used to measure primary production (Cheah et al. 2011, Deep Sea Research). Seawater from 3 m depth was supplied continuously from the ship’s clean seawater line. FRR fluorescence yields were measured continuously at 1 minute intervals in dark-adapted state (! 15 minutes dark-adaptation) using a flash sequence consisting of a series of 100 subsaturation flashlets (1.1 μs flash duration and 2.8 μs interflash period) and a series of 20 relaxation flashlets (1.1 μs flash duration and 51.6 μs interflash period).

This dataset is derived from a comparative study evaluating six DNA extraction methods for their efficiency in recovering diatom sedimentary ancient DNA (sedaDNA) from Antarctic marine sediments. Sediment samples were collected from two sites: U1536C (Scotia Sea, West Antarctica) and KC02 (Sabrina Coast, Totten Glacier Region, East Antarctica). Each of the six extraction methods was applied to the same set of samples. Following shotgun metagenomic sequencing, the methods were assessed based on metrics such as diatom DNA recovery, average fragment length, and taxonomic diversity. The purpose of the study was to identify the optimal extraction approach for maximizing the yield and quality of diatom sedaDNA, thereby improving its utility for paleoenvironmental reconstruction.

This dataset describes the predicted occurrence of juvenile sharks around Northwest Australia, mapped over a 0.01 degree spatial grid. Juvenile sharks were mapped at two taxonomic levels: order by including all juvenile sharks sampled (all juveniles) and species by considering the three most abundant species sampled separately (grey reef (Carcharhinus amblyrhynchos), sandbar (Carcharhinus plumbeus), and whitetip reef (Triaenodon obesus) sharks). The data cover the period 2003-2013 and are derived from an analysis of count data derived from baited remote underwater videos deployed through various sampling programs. Further detail can be found in the following peer-reviewed publication: Oh, BZL, Sequeira, AMM, Meekan, MG, Ruppert, JLW and Meeuwig, JJ (2017), Predicting occurrence of juvenile shark habitat to improve conservation planning. Conservation Biology, 31: 635–645. doi:10.1111/cobi.12868 Below is a full list of species, with contributions to the total counted (%): -------------------------------------------------- Silvertip shark / Carcharhinus albimarginatus – 4.14% Grey reef shark / Carcharhinus amblyrhynchos – 28.06% Bronze whaler / Carcharhinus brachyurus – 0.18% Galapagos shark / Carcharhinus galapagensis – 0.09% Bull shark / Carcharhinus leucas – 0.18% Common-Australian blacktip shark / Carcharhinus limbatus-C.tilstoni – 1.38% Blacktip reef shark / Carcharhinus melanopterus – 1.56% Sandbar shark / Carcharhinus plumbeus – 4.78% Spot-tail shark / Carcharhinus sorrah – 0.18% Tiger shark / Galeocerdo cuvier – 2.39% Sliteye-Sharpnose shark / Loxodon macrorhinus-Rhizoprionodon spp. – 6.35% Lemon shark / Negaprion acutidens – 1.01% Whitetip reef shark / Triaenodon obesus – 18.95% Tawny shark / Nebrius ferrugineus – 0.83% Grey carpetshark / Chiloscyllium punctatum – 1.38% Taselled wobbegong / Eucrossorhinus dasypogon – 0.09% Scalloped hammerhead / Sphyrna lewini – 0.46% Great hammerhead / Sphyrna mokarran – 3.86% Zebra shark / Stegostoma fasciatum – 0.83% Sicklefin houndshark / Hemitriakis falcata – 1.01% Grey gummy shark / Mustelus ravidus – 0.28% Archived BRUVS video files used in this study are the intellectual property of multiple institutions and industry partners and are not published in this record. See credits for further information.

Phytoplankton was counted and identified from five sites over the 5-year period. Annual cycles in abundance are available (as cells mL-1), along with detailed species identification. Cell measurements and approximate geometric shape were also recorded for the calculation of biovolume (μL cell-1). Diatoms and dinoflagellates dominated the samples in terms of biomass, however, small cells were also very abundant throughout each year. The data are restricted to an integrated sample from the top 12 m of the water column. Fluorescence profiles elsewhere in this dataset can provide an indication of phytoplankton presence lower in the water column.

The Flinders CMR AUV survey was a pilot study undertaken in June 2013 as part of the National Marine Biodiversity Hub's National monitoring, evaluation and reporting theme. The aim of this theme is to develop a blueprint for the sustained monitoring of the South-east Commonwealth Marine Reserve Network. The particular aim of the survey was to contribute to an inventory of the distribution and cover of epibenthic biota in the reserve using IMOS AUV 'Sirius'. Data contained here represents a scored subset of the ~ 36,700 images collected at the Flinders CMR. Images were scored for proportion cover of visible macrobiota using 25 random points superimposed on each image. Taxon were biologically classified using CATAMI (http://catami.org/).

A supervised classification was applied to a Landsat TM5 image. This image was acquired 9:40 am, on the 27th July 2011 (5.14 am low tide at Brisbane Bar). The image classification was applied on areas of clear waters up to three metres depth and for exposed regions of Moreton Bay. Field validation data was collected at 4797 survey sites by UQ. GPS referenced field data were used as training areas for the image classification process. For this training the substrate DN signatures were extracted from the Landsat 5 TM image for field survey locations of known substrate cover, enabling a characteristic "spectral reflectance signature" to be defined for each target. The Landsat TM image, containing only those pixels in water < 3.0m deep, was then subject to minimum distance to means algorithm to group pixels with similar DN signatures (assumed to correspond to the different substrata). This process enabled each pixel to be assigned a label of either seagrass cover (0, 1-25 %, 25-50 %, 50-75 % and 75-100 %). The resulting raster data was then converted into a vector polygon file. Species information was added based on the field data and expert knowledge. Both polygon files were joined by overlaying features of remote sensing files with the EHMP field data to produce an output theme that contains the attributes and full extent of both themes. If polygons of remote sensing were within polygons of field data the assumption was made that the remote sensing polygon was showing more detail and the underlying field polygon was deleted.