North Sea Ecosystem: Status Report

The North Sea is a dynamic large marine ecosystem which is bordered by a dense coastal population, contains a productive oil and gas province, has a dense shipping network and has one of the most productive fisheries in the world. An assessment of the state of health of the North Sea was initiated in 1987 as part of a developing series of international initiatives at Ministerial level to address concerns over the impact of these activities on the marine ecosystem. Four North Sea Ministerial Conferences (1984, 1987, 1990, 1995) and an Intermediate Ministerial Meeting (1993) have been held to date to develop a harmonized approach to the sustainable management of the North Sea. In 1988 at the request of Ministers a North Sea Task Force was established to co-ordinate work leading to the production of a Quality Status Report (QSR) on the North Sea in December 1993. In recognition of the large geographical and ecological diversity exhibited, a sub-regional approach was adopted and a total of 13 sub-regional assessment reports were produced to a common protocol. The Task Force established a five-year plan to co-ordinate research, monitoring and modelling and other special topics in the preparations for the QSR. As part of this exercise a ‘Monitoring Master Plan’ was drawn up to provide for the first time reliable spatial information on the distribution of chemical contaminants and biological effects throughout the North Sea. The Task Force was a unique structure in international collaboration with a fixed remit that ended in December 1993. It was successful in bringing together many diverse organisations with interests in the North Sea and co-ordinated to a tight timetable the production of the QSR. The experiences gained are now being applied to the whole north east Atlantic under a new OSPAR Convention and have wide application to other Large Marine Ecosystems.

Physiology, ecological niches and species distribution

Although many studies have debated the theoretical links between physiology, ecological niches and species distribution, few studies have provided evidence for a tight empirical coupling between these concepts at a macroecological scale. We used an ecophysiological model to assess the fundamental niche of a key-structural marine species. We found a close relationship between its fundamental and realized niche. The relationship remains constant at both biogeographical and decadal scales, showing that changes in environmental forcing propagate from the physiological to the macroecological level. A substantial shift in the spatial distribution is detected in the North Atlantic and projections of range shift using IPCC scenarios suggest a poleward movement of the species of one degree of latitude per decade for the 21st century. The shift in the spatial distribution of this species reveals a pronounced alteration of polar pelagic ecosystems with likely implications for lower and upper trophic levels and some biogeochemical cycles.

An interlaboratory comparison for the quantification of aqueous dimethylsulfide

An interlaboratory comparison (ILC) was conducted to evaluate the proficiency of multiple laboratories to quantify dimethylsulfide (DMS) in aqueous solution. Ten participating laboratories were each supplied with blind duplicate test solutions containing dimethylsulfoniopropionate hydrochloride (DMSP HCl) dissolved in acidified artificial seawater. The test solutions were prepared by the coordinating laboratory from a DMSP HCl reference material that was synthesized and purity certified for this purpose. A concentration range was specified for the test solutions and the participating laboratories were requested to dilute them as required for their analytical procedure, together with the addition of excess alkali under gas-tight conditions to convert the DMSP to DMS. Twenty-two DMS concentrations and their estimated expanded measurement uncertainties (95% confidence level) were received from the laboratories. With two exceptions, the within-laboratory variability was 5% or less and the between-laboratory variability was ~ 25%. The magnitude of expanded measurement uncertainties reported from all participants ranged from 1% to 33% relative to the result. The information gained from this pilot ILC indicated the need for further test sample distribution studies of this type so that participating laboratories can identify systematic errors in their analysis procedures and realistically evaluate their measurement uncertainty. The outcome of ILC studies provides insights into the comparability of data in the global surface seawater DMS database.

Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models

Aim: Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote-sensing and ensemble ecological niche models (EENMs) to identify predictable foraging habitats for a globally important population of the grey-headed albatross (GHA) Thalassarche chrysostoma. Location: Bird Island, South Georgia; Southern Atlantic Ocean. Methods: GPS and geolocation-immersion loggers were used to track at-sea movements and activity patterns of GHA over two breeding seasons (n = 55; brood-guard). Immersion frequency (landings per 10-min interval) was used to define foraging events. EENM combining Generalized Additive Models (GAM), MaxEnt, Random Forest (RF) and Boosted Regression Trees (BRT) identified the biophysical conditions characterizing the locations of foraging events, using time-matched oceanographic predictors (Sea Surface Temperature, SST; chlorophyll a, chl-a; thermal front frequency, TFreq; depth). Model performance was assessed through iterative cross-validation and extrapolative performance through cross-validation among years. Results: Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl-a > 0.5 mg m−3) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi-model ensembles was superior to that of single-algorithm models, and cross-validation among years indicated reasonable extrapolative performance. Main conclusions: EENM techniques are useful for integrating the predictions of several single-algorithm models, reducing potential bias and increasing confidence in predictions. Our analysis highlights the value of EENM for use with movement data in identifying at-sea habitats of wide-ranging marine predators, with clear implications for conservation and management.

Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models

Aim: Ecological niche modelling can provide valuable insight into species' environmental preferences and aid the identification of key habitats for populations of conservation concern. Here, we integrate biologging, satellite remote-sensing and ensemble ecological niche models (EENMs) to identify predictable foraging habitats for a globally important population of the grey-headed albatross (GHA) Thalassarche chrysostoma. Location: Bird Island, South Georgia; Southern Atlantic Ocean. Methods: GPS and geolocation-immersion loggers were used to track at-sea movements and activity patterns of GHA over two breeding seasons (n = 55; brood-guard). Immersion frequency (landings per 10-min interval) was used to define foraging events. EENM combining Generalized Additive Models (GAM), MaxEnt, Random Forest (RF) and Boosted Regression Trees (BRT) identified the biophysical conditions characterizing the locations of foraging events, using time-matched oceanographic predictors (Sea Surface Temperature, SST; chlorophyll a, chl-a; thermal front frequency, TFreq; depth). Model performance was assessed through iterative cross-validation and extrapolative performance through cross-validation among years. Results: Predictable foraging habitats identified by EENM spanned neritic (<500 m), shelf break and oceanic waters, coinciding with a set of persistent biophysical conditions characterized by particular thermal ranges (3–8 °C, 12–13 °C), elevated primary productivity (chl-a > 0.5 mg m−3) and frequent manifestation of mesoscale thermal fronts. Our results confirm previous indications that GHA exploit enhanced foraging opportunities associated with frontal systems and objectively identify the APFZ as a region of high foraging habitat suitability. Moreover, at the spatial and temporal scales investigated here, the performance of multi-model ensembles was superior to that of single-algorithm models, and cross-validation among years indicated reasonable extrapolative performance. Main conclusions: EENM techniques are useful for integrating the predictions of several single-algorithm models, reducing potential bias and increasing confidence in predictions. Our analysis highlights the value of EENM for use with movement data in identifying at-sea habitats of wide-ranging marine predators, with clear implications for conservation and management.