Radiocarbon age, and sedimentation and accumulation rate of core JM05-G001 obtained during Jan Mayen cruise JM050704 to the Barents Sea

Instrumental monitoring of the climate at high northern latitudes has documented the ongoing warming of the last few decades. Climate modelling has also demonstrated that the global warming signal will be amplified in the polar region. Such temperature increases would have important implications on the ecosystem and biota of the Barents Sea. This study therefore aims to reconstruct the climatic changes of the Barents Sea based on benthic foraminifera over approximately the last 1400 years at the decadal to sub-decadal scale. Oxygen and carbon isotope analysis and benthic foraminiferal species counts indicate an overall warming trend of approximately 2.6°C through the 1400-year record. In addition, the well-documented cooling period equating to the 'Little Ice Age' is evident between c. 1650 and 1850. Most notably, a series of highly fluctuating temperatures are observed over the last century. An increase of 1.5°C is shown across this period. Thus for the first time we are able to demonstrate that the recent Arctic warming is also reflected in the oceanic micro-fauna.

Time series of ZOOSCAN images and results of zooplankton samples at Villefranche from 1966 to 2003

ZooScan with ZooProcess and Plankton Identifier (PkID) software is an integrated analysis system for acquisition and classification of digital zooplankton images from preserved zooplankton samples. Zooplankton samples are digitized by the ZooScan and processed by ZooProcess and PkID in order to detect, enumerate, measure and classify the digitized objects. Here we present a semi-automatic approach that entails automated classification of images followed by manual validation, which allows rapid and accurate classification of zooplankton and abiotic objects. We demonstrate this approach with a biweekly zooplankton time series from the Bay of Villefranche-sur-mer, France. The classification approach proposed here provides a practical compromise between a fully automatic method with varying degrees of bias and a manual but accurate classification of zooplankton. We also evaluate the appropriate number of images to include in digital learning sets and compare the accuracy of six classification algorithms. We evaluate the accuracy of the ZooScan for automated measurements of body size and present relationships between machine measures of size and C and N content of selected zooplankton taxa. We demonstrate that the ZooScan system can produce useful measures of zooplankton abundance, biomass and size spectra, for a variety of ecological studies.