Most studies on buoyant microplastics in the marine environment rely on sea surface sampling. Consequently, microplastic amounts can be underestimated, as turbulence leads to vertical mixing. Models that correct for vertical mixing are based on limited data. In this study we report measurements of the depth profile of buoyant microplastics in the North Atlantic subtropical gyre, from 0 to 5 m depth. Microplastics were separated into size classes (0.5–1.5 and 1.5–5.0 mm) and types (‘fragments’ and ‘lines’), and associated with a sea state. Microplastic concentrations decreased exponentially with depth, with both sea state and particle properties affecting the steepness of the decrease. Concentrations approached zero within 5 m depth, indicating that most buoyant microplastics are present on or near the surface. Plastic rise velocities were also measured, and were found to differ significantly for different sizes and shapes. Our results suggest that (1) surface samplers such as manta trawls underestimate total buoyant microplastic amounts by a factor of 1.04–30.0 and (2) estimations of depth-integrated buoyant plastic concentrations should be done across different particle sizes and types. Our findings can assist with improving buoyant ocean plastic vertical mixing models, mass balance exercises, impact assessments and mitigation strategies.

In order to develop algorithms for sensor networks and in order to give mathematical correctness and performance proofs, models for various aspects of sensor networks are needed. This chapter presents and discusses currently used models for sensor networks. Generally, finding good models is a challenging task. On the one hand, a model should be as simple as possible such that the analysis of a given algorithm remains tractable. On the other hand, however, a model must not be too simplistic in the sense that it neglects important properties of the network. A great algorithm in theory may be inefficient or even incorrect in practice if the analysis is based on idealistic assumptions. For example, an algorithm that ignores interference may fail in practice since communication happens over a shared medium. Many models for sensor network have their origin in classic areas of theoretical computer science and applied mathematics. Since the topology of a sensor network can be regarded as a graph, the distributed algorithms community uses models from graph theory, representing nodes by vertices and wireless links by edges. Another crucial ingredient of sensor network models is geometry. Geometry comes into play as the distribution of sensor nodes in space, as well as the propagation range of wireless links, usually adheres to geometric constraints.The chapter is organized as follows. In Section 4.2, the reader will become familiar with various models for the network's connectivity. Connectivity models answer the question: Which nodes are "connected" to which other nodes and can therefore directly communicate with each other. Section 4.3 then enhances these connectivity models by adding interference aspects: Since sensor nodes communicate over a shared, wireless medium, a transmission may disturb a nearby concurrent transmission. After having studied connectivity and interference issues, we look at modeling questions related to algorithm design in Section 4.4. The reader is provided

Eddies can enhance primary as well as secondary production, creating a diverse meso-and submesoscale seascape at the eddy front which can affect the aggregation of plankton and particles. Due to the coarse resolution provided by sampling with plankton nets, our knowledge of plankton distributions at these edges is limited. We used a towed, undulating underwater imaging system to investigate the physical and biological drivers of zoo-and ichthyoplankton aggregations at the edge of a decaying mesoscale eddy (ME) in the Straits of Florida. Using a sparse Convolutional Neural Network we identified 132 million images of plankton. Larval fish and Oithona spp. copepod concentrations were significantly higher in the eddy water mass, compared to the Florida Current water mass, only four days before the ME's dissipation. Larval fish and Oithona distributions were tightly coupled, indicating potential predator-prey interactions. Larval fishes are known predators of Oithona, however, Random forests models showed that Oithona spp. and larval fish concentrations were primarily driven by variables signifying the physical footprint of the ME, such as current speed and direction. These results suggest that eddy-related advection leads to largely passive overlap between predator and prey, a positive, energy-efficient outcome for predators at the expense of prey. Eddies are ubiquitous features of the ocean, turning mechanical energy into trophic energy 1. The footprint of a mesoscale eddy can extend 100-300 km in diameter and can last for several weeks to months 2. Through their upwelling effect, cyclonic mesoscale eddies (MEs) have been shown to enhance primary 3,4 and secondary production 5-7. This enhanced productivity may increase growth 8 and survival 9 of larval fishes, which normally experience up to 99% mortality due to starvation and predation 10. Eddies may also physically retain larval fishes 11 , leading to higher larval fish concentrations inside eddies, relative to outside ambient waters, and are considered effective vectors for the transport of zoo-, and ichthyoplankton 12-14. As such, mesoscale eddies play an important role in the connectivity of holo-and meroplankton populations 15. Eddy divergence and convergence patterns in the ocean lead to a cascading flow of energy from large to small scales 16 , with turbulent frictional coupling inducing smaller anti-cyclonic eddies at the periphery of larger cyclonic eddies thereby creating a feature-and energy-rich seascape 17. Upwelling occurs in the centre of cyclonic MEs during their spin-up phase (termed a 'forced' eddy), but during the decay/spin-down phase (termed a 'free' eddy), this switches to downwelling at the core with upwelling occurring at the eddy edge 1,18. In both instances, due to its frontal character, the eddy edge is an important feature for predator-prey interactions 1. Less motile prey are often passively aggregated at the eddy edge and can be exploited by higher trophic levels and top predators

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