ALBERT

Description: Although ocean warming and acidification are recognized as two major anthropogenic perturbations of today’s oceans we know very little about how marine phytoplankton may respond via evolutionary change. We tested for adaptation to ocean warming in combination with ocean acidification in the globally important phytoplankton species Emiliania huxleyi. Temperature adaptation occurred independently of ocean acidification levels. Growth rates were up to 16% higher in populations adapted for one year to warming when assayed at their upper thermal tolerance limit. Particulate inorganic (PIC) and organic (POC) carbon production was restored to values under present-day ocean conditions, owing to adaptive evolution, and were 101% and 55% higher under combined warming and acidification, respectively, than in non-adapted controls. Cells also evolved to a smaller size while they recovered their initial PIC:POC ratio even under elevated CO2. The observed changes in coccolithophore growth, calcite and biomass production, cell size and elemental composition demonstrate the importance of evolutionary processes for phytoplankton performance in a future ocean.

Description: The utilization of stationary underwater cameras is a modern and well-adapted approach to provide a continuous and cost-effective long-term solution to monitor underwater habitats of particular interest. A common goal of such monitoring systems is to gain better insight into the dynamics and condition of populations of various marine organisms, such as migratory or commercially relevant fish taxa. This paper describes a complete processing pipeline to automatically determine the abundance, type and estimate the size of biological taxa from stereoscopic video data captured by the stereo camera of a stationary Underwater Fish Observatory (UFO). A calibration of the recording system was carried out in situ and, afterward, validated using the synchronously recorded sonar data. The video data were recorded continuously for nearly one year in the Kiel Fjord, an inlet of the Baltic Sea in northern Germany. It shows underwater organisms in their natural behavior, as passive low-light cameras were used instead of active lighting to dampen attraction effects and allow for the least invasive recording possible. The recorded raw data are pre-filtered by an adaptive background estimation to extract sequences with activity, which are then processed by a deep detection network, i.e., Yolov5. This provides the location and type of organisms detected in each video frame of both cameras, which are used to calculate stereo correspondences following a basic matching scheme. In a subsequent step, the size and distance of the depicted organisms are approximated using the corner coordinates of the matched bounding boxes. The Yolov5 model employed in this study was trained on a novel dataset comprising 73,144 images and 92,899 bounding box annotations for 10 categories of marine animals. The model achieved a mean detection accuracy of 92.4%, a mean average precision (mAP) of 94.8% and an F1 score of 93%.