Chlorophyll and true-color maps of sediments from the intertidal zones in Winyah Bay, South Carolina, USA and List, Sylt, Germany

We used hyperspectral imaging to study short-term effects of bioturbation by lugworms (Arenicola marina) on the surficial biomass of microphytobenthos (MPB) in permeable marine sediments. Within days to weeks after the addition of a lugworm to a homogenized and recomposed sediment, the average surficial MPB biomass and its spatial heterogeneity were, respectively, 150 - 250% and 280% higher than in sediments without lugworms. The surficial sediment area impacted by a single medium-sized lugworm (~4 g wet weight) over this time-scale was at least 340 cm**2. While sediment reworking was the primary cause of the increased spatial heterogeneity, experiments with lugworm-mimics together with modeling showed that bioadvective porewater transport from depth to the sediment surface, as induced by the lugworm ventilating its burrow, was the main cause of the increased surficial MPB biomass. Although direct measurements of nutrient fluxes are lacking, our present data show that enhanced advective supply of nutrients from deeper sediment layers induced by faunal ventilation is an important mechanism that fuels high primary productivity at the surface of permeable sediments even though these systems are generally characterized by low standing stocks of nutrients and organic material.

Time-series of seagrass and land cover maps from 1972 to 2010, in South East Queensland, Australia, with links to GeoTIFFs

Long term global archives of high-moderate spatial resolution, multi-spectral satellite imagery are now readily accessible, but are not being fully utilised by management agencies due to the lack of appropriate methods to consistently produce accurate and timely management ready information. This work developed an object-based remote sensing approach to map land cover and seagrass distribution in an Australian coastal environment for a 38 year Landsat image time-series archive (1972-2010). Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery were used without in situ field data input (but still using field knowledge) to produce land and seagrass cover maps every year data were available, resulting in over 60 map products over the 38 year archive. Land cover was mapped annually using vegetation, bare ground, urban and agricultural classes. Seagrass distribution was also mapped annually, and in some years monthly, via horizontal projected foliage cover classes, sand and deep water. Land cover products were validated using aerial photography and seagrass maps were validated with field survey data, producing several measures of accuracy. An average overall accuracy of 65% and 80% was reported for seagrass and land cover products respectively, which is consistent with other studies in the area. This study is the first to show moderate spatial resolution, long term annual changes in land cover and seagrass in an Australian environment, created without the use of in situ data; and only one of a few similar studies globally. The land cover products identify several long term trends; such as significant increases in South East Queensland's urban density and extent, vegetation clearing in rural and rural-residential areas, and inter-annual variation in dry vegetation types in western South East Queensland. The seagrass cover products show that there has been a minimal overall change in seagrass extent, but that seagrass cover level distribution is extremely dynamic; evidenced by large scale migrations of higher seagrass cover levels and several sudden and significant changes in cover level. These mapping products will allow management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes.

Results of wave modelling for Moreton Bay, Southeast Queensland, and a reef lagoon off Lizard Island, Great Barrier Reef, Australia

The distribution, abundance, behaviour, and morphology of marine species is affected by spatial variability in the wave environment. Maps of wave metrics (e.g. significant wave height Hs, peak energy wave period Tp, and benthic wave orbital velocity URMS) are therefore useful for predictive ecological models of marine species and ecosystems. A number of techniques are available to generate maps of wave metrics, with varying levels of complexity in terms of input data requirements, operator knowledge, and computation time. Relatively simple "fetch-based" models are generated using geographic information system (GIS) layers of bathymetry and dominant wind speed and direction. More complex, but computationally expensive, "process-based" models are generated using numerical models such as the Simulating Waves Nearshore (SWAN) model. We generated maps of wave metrics based on both fetch-based and process-based models and asked whether predictive performance in models of benthic marine habitats differed. Predictive models of seagrass distribution for Moreton Bay, Southeast Queensland, and Lizard Island, Great Barrier Reef, Australia, were generated using maps based on each type of wave model. For Lizard Island, performance of the process-based wave maps was significantly better for describing the presence of seagrass, based on Hs, Tp, and URMS. Conversely, for the predictive model of seagrass in Moreton Bay, based on benthic light availability and Hs, there was no difference in performance using the maps of the different wave metrics. For predictive models where wave metrics are the dominant factor determining ecological processes it is recommended that process-based models be used. Our results suggest that for models where wave metrics provide secondarily useful information, either fetch- or process-based models may be equally useful.

Global Ocean Biomes: Mean and time-varying maps (NetCDF 7.8 MB)

Large-scale studies of ocean biogeochemistry and carbon cycling have often partitioned the ocean into regions along lines of latitude and longitude despite the fact that spatially more complex boundaries would be closer to the true biogeography of the ocean. Herein, we define 17 open-ocean biomes classified from four observational data sets: sea surface temperature (SST), spring/summer chlorophyll a concentrations (Chl a), ice fraction, and maximum mixed layer depth (maxMLD) on a 1° × 1° grid. By considering interannual variability for each input, we create dynamic ocean biome boundaries that shift annually between 1998 and 2010. Additionally we create a core biome map, which includes only the grid cells that do not change biome assignment across the 13 years of the time-varying biomes. These biomes can be used in future studies to distinguish large-scale ocean regions based on biogeochemical function.

Hydrological and hydrochemical parameters in mouth areas of rivers entering the Kandalaksha Bay in June and September 2000

Distributions of dissolved oxygen concentration, pH, and concentrations of dissolved silica, phosphates, strontium, calcium, fluorine, and boron in mouth areas of small rivers (Niva, Kolvitsa, Knyazhaya, and Keret') entering Kandalaksha Bay of the White Sea were studied. Strontium, calcium, fluorine, and boron showed conservative, silica and phosphates showed non-conservative behavior caused by their biological consumption.

(Table) Density and biomass of phytoplankton in Vostok Bay in August and September 2010

The natural phytoplankton was monitored by means of fluorimetric equipment in Vostok Bay of the Sea of Japan. A gradual increase in the microalgae abundance was revealed in the course of the main water current, which enters the bay and leaves it. The continuous registration of chlorophyll fluorescence at a fixed point in the bay indicates the significant microscale variation of the abundance and functional state of the phytoplankton.

Survey bottom trawl data of Micromesistius poutassou (blue whiting) in Bay of Biscay and Celtic Sea from the EVHOE time series (1997-2011)

Data were collected during various groundfish surveys carried out by IFREMER from October to December between 1997 and 2011, on the eastern continental shelf of the Bay of Biscay and in the Celtic Sea (EVHOE series). The sampling design was stratified according to latitude and depth. A 36/47 GOV trawl was used with a 20 mm mesh codend liner. Haul duration was 30 minutes at a towing speed of 4 knots. Fishing was restricted to daylight hours. Catch weights and catch numbers were recorded for all species and body size measured. The weights and numbers per haul were transformed into abundances per km**2 by considering the swept area of a standard haul (0.069 km**2).

Stomach content records of albacore (Thunnus alalunga) and bluefin tuna (Thunnus thynnus) in the North Atlantic Drift Region focusing on the Bay of Biscay between 2004 and 2011

The tuna stomach database from AZTI-Tecnalia corresponds to 7 years of sampling from 2004 to 2011. Due to the absence of continuity in the different projects dealing with the feeding ecology of tunas, the sampling could not be performed every year for both species, and no sample was collected in 2008. However, the fish stomach content record contents composition - by prey weight - of 1525 albacore caught in the Bay of Biscay and surrounding waters of the North Atlantic Drift Region in 2005 (n=397), 2006 (n=196), 2007 (n=37), 2009 (n=95), 2010 (n=566) and 2011 (n=234) ; and of 686 bluefin tunas caught in the Southeastern Bay of Biscay in 2004 (n=32), 2005 (n=36), 2006 (n=3), 2009 (n=257), 2010 (n=233) and 2011 (n=125). Samples have been obtained from scientific research surveys (using a variety of different fishing gears), from commercial fisheries catches, from individual fish voluntarily sampled by recreational fishermen and from fish accidentally stranded on coastlines. Each predator is identified by an ID and its length and wet weight are given. In case the wet weight could not be measured, it was estimated through a length-weight relationship equation and is indicated in the comment for the Predator mass column. The total weight of each prey is given, as well as the weight of each prey taxonomic group in each stomach.