Nitrogen cycle feedbacks as a control on euxinia in the mid-Proterozoic ocean

Geochemical evidence invokes anoxic deep oceans until the terminal Neoproterozoic similar to 0.55 Ma, despite oxygenation of Earth's atmosphere nearly 2 Gyr earlier. Marine sediments from the intervening period suggest predominantly ferruginous (anoxic Fe(II)-rich) waters, interspersed with euxinia (anoxic H2S-rich conditions) along productive continental margins. Today, sustained biotic H2S production requires NO3- depletion because denitrifiers outcompete sulphate reducers. Thus, euxinia is rare, only occurring concurrently with (steady state) organic carbon availability when N-2-fixers dominate the production in the photic zone. Here we use a simple box model of a generic Proterozoic coastal upwelling zone to show how these feedbacks caused the mid-Proterozoic ocean to exhibit a spatial/temporal separation between two states: photic zone NO3- with denitrification in lower anoxic waters, and N-2-fixation- driven production overlying euxinia. Interchange between these states likely explains the varying H2S concentration implied by existing data, which persisted until the Neoproterozoic oxygenation event gave rise to modern marine biogeochemistry.

Spatial and inter-decadal variability in plankton abundance and composition in the Northwest Atlantic (1958-2006)

The results of Continuous Plankton Recorder sampling in the NW Atlantic between 1958 and 2006 are presented for 11 plankton taxa in eight shelf and deep ocean regions. For shelf regions, phytoplankton abundances increased in the early 1990s, mainly in winter, as the contribution of Arctic-derived freshwater to the Newfoundland (NLS) and Scotian shelves (SS) increased. Farther east, in the sub-polar gyre, phytoplankton levels increased with rising temperatures during the 1990s and 2000s. In both areas, the changes can be explained by increased stratification. The increased influx of arctic water to the NLS in the 1990s was also probably directly responsible for the increased abundances of two arctic Calanus species (C. glacialis and C. hyperboreus) and indirectly responsible for the decreased abundance of Calanus I–IV (mainly C. finmarchicus), perhaps via changes in food composition. On the SS the arctic Calanus species increased in abundance in the 2000s, likely as the result of increased transport from the Arctic via the Gulf of St Lawrence. In the deep ocean, plankton seasonal cycles changed little over the decades and increasing phytoplankton levels in the 2000s were accompanied by increases in zooplankton abundance, suggesting bottom-up control. In shelf regions, phytoplankton increases in the 1990s were in winter and Calanus I–IV appeared earlier in spring than in previous decades. Zooplankton levels generally did not change overall however, perhaps because the species examined were mainly inactive during winter.

Spatial variability of the plankton trophic interaction in the North Sea: a new feature after the early 1970s

Traditionally, marine ecosystem structure was thought to be bottom-up controlled. In recent years, a number of studies have highlighted the importance of top-down regulation. Evidence is accumulating that the type of trophic forcing varies temporally and spatially, and an integrated view – considering the interplay of both types of control – is emerging. Correlations between time series spanning several decades of the abundances of adjacent trophic levels are conventionally used to assess the type of control: bottom-up if positive or top-down if this is negative. This approach implies averaging periods which might show time-varying dynamics and therefore can hide part of this temporal variability. Using spatially referenced plankton information extracted from the Continuous Plankton Recorder, this study addresses the potential dynamic character of the trophic structure at the planktonic level in the North Sea by assessing its variation over both temporal and spatial scales. Our results show that until the early-1970s a bottom-up control characterized the base of the food web across the whole North Sea, with diatoms having a positive and homogeneous effect on zooplankton filter-feeders. Afterwards, different regional trophic dynamics were observed, in particular a negative relationship between total phytoplankton and zooplankton was detected off the west coast of Norway and the Skagerrak as opposed to a positive one in the southern reaches. Our results suggest that after the early 1970s diatoms remained the main food source for zooplankton filter-feeders east of Orkney–Shetland and off Scotland, while in the east, from the Norwegian Trench to the German Bight, filter-feeders were mainly sustained by dinoflagellates.

Nutrient regimes control phytoplankton ecophysiology in the South Atlantic

Fast Repetition Rate fluorometry (FRRf) measurements of phytoplankton photophysiology from an across-basin South Atlantic cruise (as part of the GEOTRACES programme) characterised two dominant ecophysiological regimes which were interpreted on the basis of nutrient limitation. South of the South Subtropical Convergence (SSTC) in the northern sub-Antarctic sector of the Antarctic Circumpolar Current (ACC) in the Eastern Atlantic Basin, waters are characterised by elevated chlorophyll concentrations, a dominance by larger phytoplankton cells, and low apparent photochemical efficiency (F-v/F-m). Shipboard 24 h iron (Fe) addition incubation experiments confirmed that Fe stress was primarily responsible for the low F-v/F-m, with Fe addition to these waters, either within the artificial bottle additions or naturally occurring downstream enrichment from Gough Island, significantly increasing F-v/F-m values. To the north of the SSTC at the southern boundary of the South Atlantic Gyre, phytoplankton are characterised by high values of F-v/F-m which, coupled with the low macronutrient concentrations and increased presence of picocyanobacteria, are interpreted as conditions of Fe replete, balanced macronutrient-limited growth. Spatial correlation was found between F-v/F-m and Fe: nitrate ratios, supporting the suggestion that the relative supply ratios of these two nutrients can control patterns of limitation and consequently the ecophysiology of phytoplankton in subtropical gyre and ACC regimes.

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.