ALBERT

Description: Atlantic herring (Clupea harengus) has a complex population structure and displays a variety of reproductive strategies. Differences in reproductive strategies among herring populations are linked to their time of spawning, as well as to their reproductive investment which can be an indicator for migratory vs. stationary behavior. These differences are reflected in the number of oocytes (fecundity) and the size of the oocytes prior spawning. We studied potential mixing of herring with different reproductive strategies during the spring spawning season on a coastal spawning ground. It has been hypothesized that both spring and autumn spawning herring co-occur on this specific spawning ground. Therefore, we investigated the reproductive traits oocyte size, fecundity, fertilization success as well as length of the hatching larvae during the spring spawning season from February to April. We used a set of 11 single nucleotide polymorphism markers (SNPs), which are associated with spawning season, to genetically identify autumn and spring spawning herring. Reproductive traits were investigated separately within these genetically distinct spawning types. Furthermore, we used multivariate analyses to identify groups with potentially different reproductive strategies within the genetic spring spawners. Our results indicate that mixing between ripe spring and autumn spawners occurs on the spawning ground during spring, with ripe autumn spawners being generally smaller but having larger oocytes than spring spawners. Within spring spawners, we found large variability in reproductive traits. A following multivariate cluster analysis indicated two groups with different reproductive investment. Comparisons with other herring populations along the Norwegian coastline suggest that the high variability can be explained by the co-occurrence of groups with different reproductive investments potentially resulting from stationary or migratory behavior. Fertilization success and the length of the hatching larvae decreased with progression of the spawning season, with strong inter-individual variation, supporting our findings. Incorporating such complex population dynamics into management strategies of this species will be essential to build its future population resilience.

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%.