Bathymetric map of Heron Reef, Australia, derived from airborne hyperspectral data at 1 m resolution

A simple method for efficient inversion of arbitrary radiative transfer models for image analysis is presented. The method operates by representing the shape of the function that maps model parameters to spectral reflectance by an adaptive look-up tree (ALUT) that evenly distributes the discretization error of tabulated reflectances in spectral space. A post-processing step organizes the data into a binary space partitioning tree that facilitates an efficient inversion search algorithm. In an example shallow water remote sensing application, the method performs faster than an implementation of previously published methodology and has the same accuracy in bathymetric retrievals. The method has no user configuration parameters requiring expert knowledge and minimizes the number of forward model runs required, making it highly suitable for routine operational implementation of image analysis methods. For the research community, straightforward and robust inversion allows research to focus on improving the radiative transfer models themselves without the added complication of devising an inversion strategy.

Growth increments of the recent brachiopod Magellania venosa mechanically marked in Paso Comau and Comau Fjord, Chile, 2011/2012

Magellania venosa, the largest recent brachiopod, occurs in clusters and banks in population densities of up to 416 ind/m**2 in Comau Fjord, Northern Chilean fjord region. Below 15 m, it co-occurs with the mytilid Aulacomya atra and it dominates the benthic community below 20 m. To determine the question of why M. venosa is a successful competitor, the in situ growth rate of the brachiopod was studied and its overall growth performance compared with that of other brachiopods and mussels. The growth in length was measured between February 2011 and March 2012 after mechanical tagging and calcein staining. Settlement and juvenile growth were determined from recruitment tiles installed in 2009 and from subsequent photocensus. Growth of M. venosa is best described by the general von Bertalanffy growth function, with a maximum shell length (Linf) of 71.53 mm and a Brody growth constant (K) of 0.336/year. The overall growth performance (OGP index = 5.1) is the highest recorded for a rynchonelliform brachiopod and in the range of that for Mytilus chilensis (4.8-5.27), but lower than that of A. atra (5.74). The maximal individual production (PInd) is 0.29 g AFDM/ind/year at 42 mm shell length and annual production ranges from 1.28 to 89.25 g AFDM/year/m**2 (1-57% of that of A. atra in the respective fjords). The high shell growth rate of M. venosa, together with its high overall growth performance may explain the locally high population density of this brachiopod in Comau Fjord. However, the production per biomass of the population (P/B-ratio) is low (0.535) and M. venosa may play only a minor role in the food chain. Settling dynamics indicates that M. venosa is a pioneer species with low juvenile mortality. The coexistence of the brachiopod and bivalve suggests that brachiopod survival is affected by neither the presence of potential brachiopod predators nor that of space competitors (i.e. mytilids).

Seawater carbonate chemistry and encrusting algal communities during a mesocosm experiment, 2007

Owing to anthropogenic emissions, atmospheric concentrations of carbon dioxide could almost double between 2006 and 2100 according to business-as-usual carbon dioxide emission scenarios. Because the ocean absorbs carbon dioxide from the atmosphere, increasing atmospheric carbon dioxide concentrations will lead to increasing dissolved inorganic carbon and carbon dioxide in surface ocean waters, and hence acidification and lower carbonate saturation states. As a consequence, it has been suggested that marine calcifying organisms, for example corals, coralline algae, molluscs and foraminifera, will have difficulties producing their skeletons and shells at current rates, with potentially severe implications for marine ecosystems, including coral reefs. Here we report a seven-week experiment exploring the effects of ocean acidification on crustose coralline algae, a cosmopolitan group of calcifying algae that is ecologically important in most shallowwater habitats. Six outdoor mesocosms were continuously supplied with sea water from the adjacent reef and manipulated to simulate conditions of either ambient or elevated seawater carbon dioxide concentrations. The recruitment rate and growth of crustose coralline algae were severely inhibited in the elevated carbon dioxide mesocosms. Our findings suggest that ocean acidification due to human activities could cause significant change to benthic community structure in shallow-warm-water carbonate ecosystems.

Multi-temporal mapping of seagrass cover, species and biomass of the Eastern Banks, Moreton Bay, Australia, with links to shapefiles

The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100 m**2) scales. However, landscape scale (> 100 km**2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142 km**2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.

Seawater carbonate chemistry and biological parameters during experiments with white sea bass Atractoscion nobilis, 2009

A large fraction of the carbon dioxide added to the atmosphere by human activity enters the sea, causing ocean acidification. We show that otoliths (aragonite ear bones) of young fish grown under high CO2 (low pH) conditions are larger than normal, contrary to expectation. We hypothesize that CO2 moves freely through the epithelium around the otoliths in young fish, accelerating otolith growth while the local pH is controlled. This is the converse of the effect commonly reported for structural biominerals.

Abundance and Biomass from heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus in the eastern Mediteranean Sea in October 2008 during SES_GR2

The HCMR_SES_LAGRANGIAN_GR2_ MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the North Aegean Sea during October 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column as influenced by the BSW. Heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus abundance: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Heterotrophic Nanoflagellate abundance: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6?m and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Ciliate abundance: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Heterotrophic bacteria, Synechococcus, Prochlorococcus bacteria: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Abundance data were converted into C biomass using 250 fgC cell-1 (Kana & Glibert 1987) for Synechococcus, 50 fgC cell-1 (Campbell et al. 1994) for Prochlorococcus and 20fgC cell-1 (Lee & Fuhrman 1987) for heterotrophic bacteria. Heterotrophic Nanoflagellate biomass: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6µm and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Abundance data were converted into C biomass using 183 fgC µm**3 (Caron et al. 1995). Ciliate biomass: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Ciliate cell sizes were measured and converted into cell volumes using appropriate geometric formulae using image analysis. For biomass estimation, the conversion factor 190 fgC µm**3 was used (Putt and Stoecker 1989).

Abundance and Biomass from heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus in the Aegean Sea in April 2008 during SES_GR1

The HCMR_SES_LAGRANGIAN_GR1_ MICROBIAL PARAMETERS dataset is based on samples collected in the framework of the project SESAME, in the North Aegean Sea during April 2008. The objectives were to measure the standing stocks and calculate the production of the microbial compartment of the food web, describe the vertical distribution pattern and characterize its structure and function through the water column as influenced by the BSW. Heterotrophic bacteria, Synechococcus, Prochlorococcus and Virus abundance: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Heterotrophic Nanoflagellate abundance: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6µm and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Ciliate abundance: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Heterotrophic bacteria, Synechococcus, Prochlorococcus biomass: Subsamples for virus, heterotrophic bacteria and cyanobacteria (Synechococcus spp. and Prochlorococcus spp.) counting were analyzed using a FACSCalibur (Becton Dickinson) flow cytometer equipped with a standard laser (488 nm) and filter set and using deionized water as sheath fluid. Fluorescent beads with a diameter of 0.97 µm (Polysciences) were added to each sample as an internal standard, and all parameters were normalized to the beads and expressed as relative units. SYBRGreen I stain (Molecular Probe) was used to stain viral and heterotrophic bacterial DNA. Viruses were counted according to (Brussaard 1984). In order to avoid bulk consentrations of viruses samples we dilluted to Tris-EDTA (pH=8,0) buffer to a final sollution of 1/5 to 1/100. Total abundance and nucleid content classes were calculated using the Paint-A-Gate software (Becton Dickinson). Abundance data were converted into C biomass using 250 fgC cell-1 (Kana & Glibert 1987) for Synechococcus, 50 fgC cell-1 (Campbell et al. 1994) for Prochlorococcus and 20fgC cell-1 (Lee & Fuhrman 1987) for heterotrophic bacteria. Heterotrophic Nanoflagellate biomass: Subsamples (30-150 ml) were concentrated on 25mm black polycarbonate filters of porosity 0.6µm and stained with DAPI for 10 min (Porter and Feig 1980). Under epifluorescence microscopy heterotrophic nanoflagellates (HNAN) were distinguished using UV and blue excitation and enumerated. Nanoflagellates were classified in size categories and the biovolume was calculated. Abundance data were converted into C biomass using 183 fgC µm**3 (Caron et al. 1995). Ciliate biomass: For ciliate identification and enumeration, 100-3000 ml samples were left for 24h-4d for sedimentation and then observed under an inverted microscope. Ciliates were counted, distinguished into size-classes and major taxonomic groups and identified down to genus or species level where possible (Pitta et al. 2005). Ciliate cell sizes were measured and converted into cell volumes using appropriate geometric formulae using image analysis. For biomass estimation, the conversion factor 190 fgC µm**3 was used (Putt and Stoecker 1989).