Hongsheng Bi

Transport and coastal zooplankton communities in the northern California Current system

[1]xa0Alongshore transport was estimated from the gridded AVISO altimeter data and water level data from NOAA tide gauges (1993–2010) for the northern California Current (NCC) system. The biomass of the cold neritic copepods including Calanus marshallae, Pseudocalanus mimus and Acartia longiremis (dominants in the eastern Bering Sea, coastal Gulf of Alaska, and NCC) was estimated from a 15 year time series of zooplankton samples (1996–2010) collected biweekly at a coastal station 9 km off Newport Oregon U.S.A. The alongshore currents and the biomass of the cold neritic copepods exhibit a strong seasonal pattern and fluctuate in opposite phase: positive alongshore current (from south) leads to low biomass in winter and negative alongshore current (from north) leads to high biomass in summer. When the Pacific Decadal Oscillation (PDO) is positive, i.e., warm conditions around the northeast Pacific, there is more movement of water from the south in the NCC during winter. When the PDO is negative, there is more movement of water from the north during summer. The mean biomass of cold neritic copepods was positively correlated with the survival rate of juvenile coho salmon and cumulative transport was negatively correlated with coho salmon survival, i.e., in years when a greater portion of the source waters feeding the NCC enters from the north, the greater the salmon survival. We conclude that alongshore transport manifests PDO signals and serves as a linkage between large scale forcing to local ecosystem dynamics.

A Semi-Automated Image Analysis Procedure for In Situ Plankton Imaging Systems

Plankton imaging systems are capable of providing fine-scale observations that enhance our understanding of key physical and biological processes. However, processing the large volumes of data collected by imaging systems remains a major obstacle for their employment, and existing approaches are designed either for images acquired under laboratory controlled conditions or within clear waters. In the present study, we developed a semi-automated approach to analyze plankton taxa from images acquired by the ZOOplankton VISualization (ZOOVIS) system within turbid estuarine waters, in Chesapeake Bay. When compared to images under laboratory controlled conditions or clear waters, images from highly turbid waters are often of relatively low quality and more variable, due to the large amount of objects and nonlinear illumination within each image. We first customized a segmentation procedure to locate objects within each image and extracted them for classification. A maximally stable extremal regions algorithm was applied to segment large gelatinous zooplankton and an adaptive threshold approach was developed to segment small organisms, such as copepods. Unlike the existing approaches for images acquired from laboratory, controlled conditions or clear waters, the target objects are often the majority class, and the classification can be treated as a multi-class classification problem. We customized a two-level hierarchical classification procedure using support vector machines to classify the target objects (< 5%), and remove the non-target objects (> 95%). First, histograms of oriented gradients feature descriptors were constructed for the segmented objects. In the first step all non-target and target objects were classified into different groups: arrow-like, copepod-like, and gelatinous zooplankton. Each object was passed to a group-specific classifier to remove most non-target objects. After the object was classified, an expert or non-expert then manually removed the non-target objects that could not be removed by the procedure. The procedure was tested on 89,419 images collected in Chesapeake Bay, and results were consistent with visual counts with >80% accuracy for all three groups.