Gliding robotic fish, which is a hybrid of underwater gliders and robotic fish, is energy efficient and highly maneuverable and holds strong promise for long-duration monitoring of underwater environments. In this paper, a novel scheme is proposed for autonomously sampling multiple water columns using gliding robotic fish. The scheme exploits energy-efficient spiral-down motion to sample each water column, followed by sagittal-plane glide-up toward the direction of the next water column. Once surfacing, the robot uses Global Positioning System guidance to reach the next column location through swimming. To enhance the path-tracking performance, a two-degree-of-freedom controller involving H-infinity control is used in the spiral motion, and a sliding-mode controller is employed to regulate the yaw angle during glide-up. The sampling scheme has been implemented on a gliding robotic fish prototype, "Grace," and verified first in pool experiments and then in field experiments involving the sampling of harmful algae concentration in the Wintergreen Lake, Michigan.

Marine phytoplankton are a taxonomically and functionally diverse group of organisms that are key players in the most important biogeochemical cycles. Phytoplankton taxa show different resource utilization strategies (e.g. nutrient-uptake rates and cellular allocation) and traits. Therefore, acknowledging this diversity is crucial to understanding how elemental cycles operate, including the origin and dynamics of elemental ratios. In this paper, we focus on trait-based models as tools to study the role of phytoplankton diversity in the stoichiometric phenomenology observed in the laboratory and in the open ocean. We offer a compilation of known empirical results on stoichiometry and summarize how trait-based approaches have attempted to replicate these results. By contrasting the different ecological and evolutionary approaches available in the literature, we explore the strengths and limitations of the existing models. We thus try to identify existing gaps and challenges, and point to potential new directions that can be explored to fill these gaps. We aim to highlight the potential of including diversity explicitly in our modeling approaches, which can help us gain important knowledge about changes in local and global stoichiometric patterns.

The theories developed in ecological stoichiometry (ES) are fundamentally based on traits. Traits directly linked to cell/body stoichiometry, such as nutrient uptake and storage, as well as the associated trade-offs, have the potential to shape ecological interactions such as competition and predation within ecosystems. Further, traits that indirectly influence and are influenced by nutritional requirements, such as cell/body size and growth rate, are tightly linked to organismal stoichiometry. Despite their physiological and ecological relevance, traits are rarely explicitly integrated in the framework of ES and, currently, the major challenge is to more closely inter-connect ES with trait-based ecology (TBE). Here, we highlight four interconnected nutrient trait groups, i.e., acquisition, body stoichiometry, storage, and excretion, which alter interspecific competition in autotrophs and heterotrophs. We also identify key differences between producer-consumer interactions in aquatic and terrestrial ecosystems. For instance, our synthesis shows that, in contrast to aquatic ecosystems, traits directly influencing herbivore stoichiometry in forested ecosystems should play only a minor role in the cycling of nutrients. We furthermore describe how linking ES and TBE can help predict the ecosystem consequences of global change. The concepts we highlight here allow us to predict that increasing N:P ratios in ecosystems should shift trait dominances in communities toward species with higher optimal N:P ratios and higher P uptake affinity, while decreasing N retention and increasing P storage.

"It takes a village to finish (marine) science these days" Paraphrased from Curtis Huttenhower (the Human Microbiome project) The rapidity and complexity of climate change and its potential effects on ocean biota are challenging how ocean scientists conduct research. One way in which we can begin to better tackle these challenges is to conduct community-wide scientific studies. This study provides physiological datasets fundamental to understanding functional responses of phytoplankton growth rates to temperature. While physiological experiments are not new, our experiments were conducted in many laboratories using agreed upon protocols and 25 strains of eukaryotic and prokaryotic phytoplankton isolated across a wide range of marine environments from polar to tropical, and from nearshore waters to the open ocean. This community-wide approach provides both comprehensive and internally consistent datasets produced over considerably shorter time scales than conventional individual and often uncoordinated lab efforts. Such datasets can be used to parameterise global ocean model projections of environmental change and to provide initial insights into the magnitude of regional biogeographic change in ocean biota in the coming decades. Here, we compare our datasets with a compilation of literature data on phytoplankton growth responses to temperature. A comparison with prior published data suggests that the optimal temperatures of individual species and, to a lesser degree, thermal niches were similar across studies. However, a comparison of the maximum growth rate across studies revealed significant departures between this and previously collected datasets, which may be due to differences in the cultured isolates, temporal changes in the clonal isolates in cultures, and/or differences in culture conditions. Such methodological differences mean that using particular trait measurements from the prior literature might introduce unknown errors and bias into modelling projections. Using our community-wide approach we can reduce such protocol-driven variability in culture studies, and can begin to address more complex issues such as the effect of multiple environmental drivers on ocean biota.

File List EPAdata.txt Description The EPAdata.txt file is a tab-delimited ASCII file. The file contains data on phytoplankton species richness and environmental variables of 540 lakes and reservoirs distributed across the continental USA. The lakes and reservoirs were sampled from 1973-1975 as part of the National Eutrophication Survey conducted by the U.S. EPA. Column definitions and checksum values: Name of waterbody State Type of waterbody (lake or reservoir) Latitude, SUM = 20585.85475 Longitude, SUM = -51798.3929 Altitude (in km), SUM = 304.5463 Lake area (in km2), SUM = 25453.51 Lake depth (in m), SUM = 4945.149999 Number of sampling days, SUM = 1592 Chlorophyll concentration (in mug/L), SUM = 11718.22239 Total nitrogen (in mg/L), SUM = 705.6529752 Total phosphorus (in mg/L), SUM = 70.3631407 Temperature (in °C), SUM = 10188.35764 Secchi depth (in m), SUM = 885.1552689 Species richness (number of phytoplankton species), SUM = 15875 There are no missing values. Copyright: CC-0

Supplementary statistical methods. Copyright: CC-0

Supplemental figures showing environmental covariation and species-specific responses to environmental predictors. Copyright: CC-0