Multi-decadal oceanic ecological datasets and their application in marine policy and management

Long-term biological time-series in the oceans are relatively rare. Using the two longest of these we show how the information value of such ecological time-series increases through space and time in terms of their potential policy value. We also explore the co-evolution of these oceanic biological time-series with changing marine management drivers. Lessons learnt from reviewing these sequences of observations provide valuable context for the continuation of existing time-series and perspective for the initiation of new time-series in response to rapid global change. Concluding sections call for a more integrated approach to marine observation systems and highlight the future role of ocean observations in adaptive marine management.

Sun glint estimation in marine satellite images: a comparison of results from calculation and radiative transfer modeling

The intensity and location of Sun glint in two Medium Resolution Imaging Spectrometer (MERIS) images was modeled using a radiative transfer model that includes elevation features as well as the slope of the sea surface. The results are compared to estimates made using glint flagging and correction approaches used within standard atmospheric correction processing code. The model estimate gives a glint pattern with a similar width but lower peak level than any current method, or than that estimated by a radiative transfer model with surfaces that include slope but not height. The MERIS third reprocessing recently adopted a new slope statistics model for Sun glint correction; the results show that this model is an outlier with respect to both the elevation model and other slope statistics models and we recommend that its adoption should be reviewed.

Above-water reflectance for the evaluation of adjacency effects in Earth observation data: initial results and methods comparison for near-coastal waters in the Western Channel, UK

Un-supervised hyperspectral remote-sensing reflectance data (<15 km from the shore) were collected from a moving research vessel. Two different processing methods were compared. The results were similar to concurrent Aqua-MODIS and Suomi-NPP-VIIRS satellite data.

Coccolithophore surface distributions in the North Atlantic and their modulation of the air-sea flux of CO2 from 10 years of satellite Earth observation data

Coccolithophores are the primary oceanic phytoplankton responsible for the production of calcium carbonate (CaCO3). These climatically important plankton play a key role in the oceanic carbon cycle as a major contributor of carbon to the open ocean carbonate pump (similar to 50 %) and their calcification can affect the atmosphere-to-ocean (air-sea) uptake of carbon dioxide (CO2) through increasing the seawater partial pressure of CO2 (pCO(2)). Here we document variations in the areal extent of surface blooms of the globally important coccolithophore, Emiliania huxleyi, in the North Atlantic over a 10-year period (1998-2007), using Earth observation data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). We calculate the annual mean sea surface areal coverage of E. huxleyi in the North Atlantic to be 474 000 +/- 104 000 km(2), which results in a net CaCO3 carbon (CaCO3-C) production of 0.14-1.71 Tg CaCO3-C per year. However, this surface coverage (and, thus, net production) can fluctuate inter-annually by -54/+81% about the mean value and is strongly correlated with the El Nino/Southern Oscillation (ENSO) climate oscillation index (r = 0.75, p < 0.02). Our analysis evaluates the spatial extent over which the E. huxleyi blooms in the North Atlantic can increase the pCO(2) and, thus, decrease the localised air-sea flux of atmospheric CO2. In regions where the blooms are prevalent, the average reduction in the monthly air-sea CO2 flux can reach 55%. The maximum reduction of the monthly air-sea CO2 flux in the time series is 155 %. This work suggests that the high variability, frequency and distribution of these calcifying plankton and their impact on pCO(2) should be considered if we are to fully understand the variability of the North Atlantic air-to-sea flux of CO2. We estimate that these blooms can reduce the annual N. Atlantic net sink atmospheric CO2 by between 3-28 %.

Generalized ocean color inversion model for retrieving marine inherent optical properties

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

Assessing marine plankton community structure from long-term monitoring data with multivariate autoregressive (MAR) models: a comparison of fixed station versus spatially distributed sampling data

We examined how marine plankton interaction networks, as inferred by multivariate autoregressive (MAR) analysis of time-series, differ based on data collected at a fixed sampling location (L4 station in the Western English Channel) and four similar time-series prepared by averaging Continuous Plankton Recorder (CPR) datapoints in the region surrounding the fixed station. None of the plankton community structures suggested by the MAR models generated from the CPR datasets were well correlated with the MAR model for L4, but of the four CPR models, the one most closely resembling the L4 model was that for the CPR region nearest to L4. We infer that observation error and spatial variation in plankton community dynamics influenced the model performance for the CPR datasets. A modified MAR framework in which observation error and spatial variation are explicitly incorporated could allow the analysis to better handle the diverse time-series data collected in marine environments.

Plankton indicators and ocean observing systems: support to the marine ecosystem state assessment

Ecological indicators are used extensively as tools to manage environmental resources. In the oceans, indicators of plankton can be measured using a variety of observing systems including: mooring stations, ships, autonomous floats and ocean colour remote sensing. Given the broad range of temporal and spatial sampling resolutions of these different observing systems, as well as discrepancies in measurements obtained from different sensors, the estimation and interpretation of plankton indicators can present significant challenges. To provide support to the assessment of the state of the marine ecosystem, we propose a suite of plankton indicators and subsequently classify them in an ecological framework that characterizes key attributes of the ecosystem. We present two case studies dealing with plankton indicators of biomass, size structure and phenology, estimated using the most spatially extensive and longest in situ and remote-sensing observations. Discussion of these studies illustrates how some of the challenges in estimating and interpreting plankton indicators may be addressed by using for example relative measurement thresholds, interpolation procedures and delineation of biogeochemical provinces. We demonstrate that one of the benefits attained, when analyzing a suite of plankton indicators classified in an ecological framework, is the elucidation of non-trivial changes in composition, structure and functioning of the marine ecosystem.

Frequent locations of oceanic fronts as an indicator of pelagic diversity: application to marine protected areas and renewables

Frequent locations of thermal fronts in UK shelf seas were identified using an archive of 30,000 satellite images acquired between 1999 and 2008, and applied as a proxy for pelagic diversity in the designation of Marine Protected Areas (MPAs). Networks of MPAs are required for conservation of critical marine habitats within Europe, and there are similar initiatives worldwide. Many pelagic biodiversity hotspots are related to fronts, for example cetaceans and basking sharks around the Isle of Man, Hebrides and Cornwall, and hence remote sensing can address this policy need in regions with insufficient species distribution data. This is the first study of UK Continental Shelf front locations to use a 10-year archive of full-resolution (1.1 km) AVHRR data, revealing new aspects of their spatial and seasonal variability. Frontal locations determined at sea or predicted by ocean models agreed closely with the new frequent front maps, which also identified many additional frontal zones. These front maps were among the most widely used datasets in the recommendation of UK MPAs, and would be applicable to other geographic regions and to other policy drivers such as facilitating the deployment of offshore renewable energy devices with minimal environmental impact.

On the Front Line: frontal zones as priority at-sea conservation areas for mobile marine vertebrates

1.Identifying priority areas for marine vertebrate conservation is complex because species of conservation concern are highly mobile, inhabit dynamic habitats and are difficult to monitor. 2.Many marine vertebrates are known to associate with oceanographic fronts – physical interfaces at the transition between water masses – for foraging and migration, making them important candidate sites for conservation. Here, we review associations between marine vertebrates and fronts and how they vary with scale, regional oceanography and foraging ecology. 3.Accessibility, spatiotemporal predictability and relative productivity of front-associated foraging habitats are key aspects of their ecological importance. Predictable mesoscale (10s–100s km) regions of persistent frontal activity (‘frontal zones’) are particularly significant. 4.Frontal zones are hotspots of overlap between critical habitat and spatially explicit anthropogenic threats, such as the concentration of fisheries activity. As such, they represent tractable conservation units, in which to target measures for threat mitigation. 5.Front mapping via Earth observation (EO) remote sensing facilitates identification and monitoring of these hotspots of vulnerability. Seasonal or climatological products can locate biophysical hotspots, while near-real-time front mapping augments the suite of tools supporting spatially dynamic ocean management. 6.Synthesis and applications. Frontal zones are ecologically important for mobile marine vertebrates. We surmise that relative accessibility, predictability and productivity are key biophysical characteristics of ecologically significant frontal zones in contrasting oceanographic regions. Persistent frontal zones are potential priority conservation areas for multiple marine vertebrate taxa and are easily identifiable through front mapping via EO remote sensing. These insights are useful for marine spatial planning and marine biodiversity conservation, both within Exclusive Economic Zones and in the open oceans.

Assimilation of remotely-sensed optical properties to improve marine biogeochemistry modelling

In this paper we evaluate whether the assimilation of remotely-sensed optical data into a marine ecosystem model improves the simulation of biogeochemistry in a shelf sea. A localized Ensemble Kalman filter was used to assimilate weekly diffuse light attenuation coefficient data, Kd(443) from SeaWiFs, into an ecosystem model of the western English Channel. The spatial distributions of (unassimilated) surface chlorophyll from satellite, and a multivariate time series of eighteen biogeochemical and optical variables measured in situ at one long-term monitoring site were used to evaluate the system performance for the year 2006. Assimilation reduced the root mean square error and improved the correlation with the assimilated Kd(443) observations, for both the analysis and, to a lesser extent, the forecast estimates, when compared to the reference model simulation. Improvements in the simulation of (unassimilated) ocean colour chlorophyll were less evident, and in some parts of the Channel the simulation of this data deteriorated. The estimation errors for the (unassimilated) in situ data were reduced for most variables with some exceptions, e.g. dissolved nitrogen. Importantly, the assimilation adjusted the balance of ecosystem processes by shifting the simulated food web towards the microbial loop, thus improving the estimation of some properties, e.g. total particulate carbon. Assimilation of Kd(443) outperformed a comparative chlorophyll assimilation experiment, in both the estimation of ocean colour data and in the simulation of independent in situ data. These results are related to relatively low error in Kd(443) data, and because it is a bulk optical property of marine ecosystems. Assimilation of remotely-sensed optical properties is a promising approach to improve the simulation of biogeochemical and optical variables that are relevant for ecosystem functioning and climate change studies.