In oceanography, there has been a growing emphasis on coastal regions, partially because of their inherent complexity, as well as the increasing acknowledgment of anthropogenic impacts. To improve understanding and characterization of coastal dynamics, there has been significant effort devoted to the development of autonomous systems that sample the ocean on relevant scales. Autonomous underwater vehicles (AUVs) are especially well suited for studies of the coastal ocean because they are able to provide near-synoptic spatial observations. These sampling platforms are beginning to transition from the engineering groups that developed and continue to improve them to the science user. With this transition comes novel applications of these vehicles to address new questions in coastal oceanography. Here, the relatively mature Remote Environmental Monitoring Units (REMUS) AUV system is described and assessed. Analysis of data, based on 37 missions and nearly 800 km of in-water operation, shows that the vehicle's navigational error estimates were consistently less than 10 m, and error estimates of mission duration, distance, velocity, and power usage, once the vehicle was properly ballasted, were below 10%. An example of the transition to science is demonstrated in an experiment conducted in 2002 in Monterey Bay, California, where the vehicle was used to quantify critical horizontal length scales of variability. Length scales on the order of tens to hundreds of meters were found for the region within 25 km of the coastline, which has significant implications for designing proper sampling approaches and parameterizing model domains. Results also demonstrate the overall utility of the REMUS vehicle for use by coastal oceanographers.

Abstractincreasing in magnitude and frequency around the globe, causing extensive economic and Blooms of toxic algae are environmental impacts. On the west coast of Florida, blooms of the toxic dinoflagellate Karenia brevis (Davis) have been documented annually for the last 30 years causing respiratory irritation in humans, fish kills, and toxin bioaccumulation in shellfish beds. As a result, methods need to be established to monitor and predict bloom formation and transport to mitigate their harmful effects on the surrounding ecosystems and local communities. In the past, monitoring and mitigation efforts have relied on visual confirmation of water discoloration, fish kills, and laborious cell counts, but recently satellite remote sensing has been used to track harmful algal blooms (HABs) along the Florida coast. Unfortunately satellite ocean color is limited by cloud cover, lack of detection below one optical depth, and revisit frequency, all of which can lead to extended periods without data. To address these shortcomings, an optical phytoplankton discriminator (OPD) was developed to detect K. brevis cells in mixed phytoplankton assemblages. The OPD was integrated into autonomous underwater vehicle (AUV) platforms to gather spatially and temporally relevant data that can be used in collaboration with satellite imagery to provide a 3D picture of bloom dynamics over time. In January 2005, a Remote Environmental Monitoring UnitS (REMUS) AUV with an OPD payload was deployed on the west coast of Florida to retrieve a similarity index (SI), which indicates when K. brevis dominates the phytoplankton community. SI was used to monitor a K. brevis bloom in relation to temperature, salinity, chlorophyll, and ocean currents. Current speed, SI, temperature, salinity, and chlorophyll a from the AUV were used to quantify a 1 km displacement of the K. brevis bloom front that was observed over the deployment period. The ability to monitor short term bloom movement will improve monitoring and predictive efforts that are used to provide warnings for local tourism and fishing industries. In addition, understanding the fine scale environmental conditions associated with bloom formation will increase our ability to predict the location and timing of K. brevis bloom formation. This study demonstrates the use of one autonomous platform and provides evidence that a nested array of AUVs and moorings equipped with new sensors, combined with remote sensing, can provide an early warning and monitoring system to reduce the impact of HABs.

a b s t r a c tEcosystem function will in large part be determined by functional groups present in biological communities. The simplest distinction with respect to functional groups of an ecosystem is the differentiation between primary and secondary producers. A challenge thus far has been to examine these groups simultaneously with sufficient temporal and spatial resolution for observations to be relevant to the scales of change in coastal oceans. This study takes advantage of general differences in the bioluminescence flash kinetics between planktonic dinoflagellates and zooplankton to measure relative abundances of the two groups within the same-time space volume. This novel approach for distinguishing these general classifications using a single sensor is validated using fluorescence data and exclusion experiments. The approach is then applied to data collected from an autonomous underwater vehicle surveying 4500 km in Monterey Bay and San Luis Obispo Bay, CA during the summers of [2002][2003][2004]. The approach also reveals that identifying trophic interaction between the two planktonic communities may also be possible.

Patchiness or spatial variability is ubiquitous in marine systems. With increasing anthropogenic impacts to coastal resources and coastal systems being disproportionately large contributors to ocean productivity, identifying the spatial scales of this patchiness, particularly in coastal waters, is of critical importance to understand coastal ecosystem dynamics. The current work focuses on fine scale structure in three coastal regions. More specifically, we utilize variogram analyses to identify sub-kilometer scales of variability in biological and physical parameters measured by an autonomous underwater vehicle (AUV) in the Mid-Atlantic Bight, Monterey Bay, and in San Luis Obispo Bay between 2001 and 2004. Critical scales of variability in density, turbidity, fluorescence, and bioluminescence are examined as a function of depth and distance offshore. Furthermore, the effects of undersampling are assessed using predictive error analysis. Results indicate the presence of scales of variability ranging from 10s to 100s of meters and provide valuable insight for sampling design and resource allocation for future studies.

[1] The high variability in physical, biological, and chemical properties in coastal waters have limited our ability to sample the appropriate timescale and space scale to resolve physical forcing of the ecosystem. To improve our understanding, a multiplatform adaptive sampling program at the Long-term Ecosystem Observatory (LEO-15) off the coast of New Jersey examined the relationship between episodic summertime upwelling and downwelling events and the corresponding dynamics in bulk phytoplankton biomass and community structure. Inherent and apparent optical properties were concurrently measured to evaluate the use of optics to improve future sampling coverage in coastal regions. Results indicate peak chlorophyll biomass tracked the maximum density gradient and that increasing surface phytoplankton biomass was associated with decreasing stratification offshore over time. Diatoms dominated the study site; however, significant shifts in cyanobacteria and dinoflagellate communities were observed. Dinoflagellate and cyanobacteria communities responded inversely to episodic events, with cyanobacteria being favored during intense downwelling. Differences in phytoplankton absorption properties significantly changed the corresponding in water inherent optical properties, allowing for characterization of the community structure from measurements of above water hyperspectral reflectance.