Copepods are the most important metazoan grazers of phytoplankton in the sea. In order to more fully understand the flow of energy from phytoplankton through copepods (and beyond), it is necessary to know what factors modify their feeding. Contrary to the classical paradigm of passive, mechanical feeding by copepods, copepods are quite selective in their preferred prey. Some of this selectivity appears to be mediated by chemicals produced by their phytoplankton prey. Knowing the time required for selectivity to occur and the persistence of that selectivity should give us a better understanding of copepod sensory and decision-making capabilities.

Some members of the marine phytoplankton, particularly some dinoflagellates, produce toxic (or noxious) chemicals. The biosynthesis, biochemical modes of action, and structures of some of these chemicals are known, but virtually nothing is known about the natural function of these toxins. One of the likely functions is chemical defense against herbivores. Chemically-defended dinoflagellates may be selectively avoided, and preferred cells selectively ingested. If true, then chemical defense may provide an ecological advantage over co-occurring undefended cells. Otherwise, all cells may be equally rejected or accepted, thus conferring no advantage on the defended cell.

The goal of my doctoral dissertation research was to investigate the physiological and behavioral aspects of the feeding biology of Calanus pacificus vis a vis the presence of noxious dinoflagellates. In addition, I studied the significance of chemical defense to the noxious dinoflagellate Gonyaulax grindleyi.

Many highway vehicle applications require reliable, high precision navigation (error

less than meter level) while using low-cost consumer-grade inertial and global navigation

satellite systems (GNSS). The application environment causes numerous GNSS measurement

outliers. Common implementations use a single epoch Extended Kalman Filter (EKF)

combined with the Receiver Autonomous Integrity Monitoring (RAIM) for GNSS outlier

detection. However, if the linearization point of the EKF is incorrect or if the number of

residuals is too low, the outlier detection decisions may be incorrect. False alarms result in

good information not being incorporated into the state and covariance estimates. Missed

detections result in incorrect information being incorporated into the state and covariance

estimates. Either case can cause subsequent incorrect decisions, possibly causing divergence,

due to the state and covariance now being incorrect. This dissertation formulates

a sliding-window estimator containing multiple GNSS epochs, and solves the full-nonlinear

Maximum A Posteriori estimate in real-time. By leveraging the resulting window of residuals,

an improved fault detection and removal strategy is implemented. Experimental sensor

data is used to demonstrate the interval RAIM (iRAIM) performance improvement.