Localised anthropogenic wake generates a predictable foraging hotspot for top predators

Lilian Lieber et al. examined seabird foraging around natural and man-made wakes, finding that wake from a turbine structure generates a more intense and predictable foraging hotspot for seabirds. This shows the importance of changes in physical forcing to top predators when installing or removing offshore structures

Selective foraging behavior of seabirds in small-scale slicks

Lieber L, Füchtencordsjürgen C, Hilder RL, et al. Selective foraging behavior of seabirds in small-scale slicks. Limnology and Oceanography Letters . 2022.Marine predator foraging opportunities are often driven by dynamic physical processes enhancing prey accessibility. Surface slicks are ubiquitous yet ephemeral ocean features where convergent flows accumulate flotsam, concentrating marine organisms and pollutants. Slicks can manifest on the sea surface as meandering lines and seabirds often associate with slicks. Yet, how slicks may influence the fine-scale foraging behavior of seabirds is only coarsely resolved. Here we show that seabirds selectively forage in small-scale slicks. We used aerial drone technology to track surface-foraging terns (Sternidae, 107 tracks) over evolving slicks advected by the mean flow and reshaped by localized turbulence at scales of meters and seconds. Terns were more likely to switch into high-tortuosity foraging behavior when over slicks, with plunge-dive events occurring significantly more often within slicks. As we demonstrate that terns select dynamic slicks for foraging, our approach will also lend itself to interaction studies with pollutants, plumes, and fronts

A physics-enabled flow restoration algorithm for sparse PIV and PTV measurements

The gaps and noise present in particle image velocimetry (PIV) and particle tracking velocimetry (PTV) measurements affect the accuracy of the data collected. Existing algorithms developed for the restoration of such data are only applicable to experimental measurements collected under well-prepared laboratory conditions (i.e. where the pattern of the velocity flow field is known), and the distribution, size and type of gaps and noise may be controlled by the laboratory set-up. However, in many cases, such as PIV and PTV measurements of arbitrarily turbid coastal waters, the arrangement of such conditions is not possible. When the size of gaps or the level of noise in these experimental measurements become too large, their successful restoration with existing algorithms becomes questionable. Here, we outline a new physics-enabled flow restoration algorithm (PEFRA), specially designed for the restoration of such velocity data. Implemented as a 'black box' algorithm, where no user-background in fluid dynamics is necessary, the physical structure of the flow in gappy or noisy data is able to be restored in accordance with its hydrodynamical basis. The use of this is not dependent on types of flow, types of gaps or noise in measurements. The algorithm will operate on any data time-series containing a sequence of velocity flow fields recorded by PIV or PTV. Tests with numerical flow fields established that this method is able to successfully restore corrupted PIV and PTV measurements with different levels of sparsity and noise. This assessment of the algorithm performance is extended with an example application to in situ submersible 3D-PTV measurements collected in the bottom boundary layer of the coastal ocean, where the naturally-occurring plankton and suspended sediments used as tracers causes an increase in the noise level that, without such denoising, will contaminate the measurements

Increasing the Depth of Current Understanding: Sensitivity Testing of Deep-Sea Larval Dispersal Models for Ecologists

Larval dispersal is an important ecological process of great interest to conservation and the establishment of marine protected areas. Increasing numbers of studies are turning to biophysical models to simulate dispersal patterns, including in the deep-sea, but for many ecologists unassisted by a physical oceanographer, a model can present as a black box. Sensitivity testing offers a means to test the models' abilities and limitations and is a starting point for all modelling efforts. The aim of this study is to illustrate a sensitivity testing process for the unassisted ecologist, through a deep-sea case study example, and demonstrate how sensitivity testing can be used to determine optimal model settings, assess model adequacy, and inform ecological interpretation of model outputs. Five input parameters are tested (timestep of particle simulator (TS), horizontal (HS) and vertical separation (VS) of release points, release frequency (RF), and temporal range (TR) of simulations) using a commonly employed pairing of models. The procedures used are relevant to all marine larval dispersal models. It is shown how the results of these tests can inform the future set up and interpretation of ecological studies in this area. For example, an optimal arrangement of release locations spanning a release area could be deduced; the increased depth range spanned in deep-sea studies may necessitate the stratification of dispersal simulations with different numbers of release locations at different depths; no fewer than 52 releases per year should be used unless biologically informed; three years of simulations chosen based on climatic extremes may provide results with 90% similarity to five years of simulation; and this model setup is not appropriate for simulating rare dispersal events. A step-by-step process, summarising advice on the sensitivity testing procedure, is provided to inform all future unassisted ecologists looking to run a larval dispersal simulation

Comparing Deep-Sea Larval Dispersal Models: A Cautionary Tale for Ecology and Conservation

Larval dispersal data are increasingly sought after in ecology and marine conservation, the latter often requiring information under time limited circumstances. Basic estimates of dispersal [based on average current speeds and planktonic larval duration (PLD)] are often used in these situations, usually acknowledging their oversimplified nature, but rarely with an understanding of how oversimplified those assumptions are. Larval dispersal models (LDMs) are becoming more accessible and may produce “better” dispersal predictions than estimates, but the uncertainty introduced by choosing one underlying hydrodynamic model over another is rarely discussed. This case study uses theoretical and simplified deep-sea LDMs to compare the passive predictions of dispersal as driven by two different hydrodynamic models (HYCOM and POLCOMS) and a range of informed basic estimates (based on average current speeds of 0.05, 0.1, and 0.2 m/s). The aim is to provide generalizable insight into the predictive variability introduced by (a) choosing a model over an estimate, and (b) one hydrodynamic over another. LDMs were found to be up to an order of magnitude more conservative in dispersal distance predictions than even the slowest tested estimate (0.05 m/s). The difference increased with PLD which may result in a bigger disparity for deep-sea species predictions. Although the LDMs were more spatially targeted than the estimates, the two LDM predictions were also significantly different from each other. This means that choosing one hydrodynamic model over another could result in contrasting ecological interpretations or advice for marine conservation. These results show a greater potential for hydrodynamic model variability than previously appreciated by larval dispersal ecologists and strongly advocates groundtruthing predictions before use in management. Advice is offered for improved model selection and interpretation of predictions

LISST-100 response to large particles

Particles in the marine environment vary in size from sub-micron colloids to flocculated aggregates of the order of millimetres. The LISST-100 (laser in-situ scattering and transmissometer) range of instruments (Sequoia Scientific Inc.) determine the distribution of particle sizes in-situ using laser diffraction, but are limited to specific size ranges governed by the instrument configuration. Using numerical predictions of scattering and a novel observational system to combine digital holography and the LISST-100 type-c, here we examine the response of the LISST to particles larger than the intended size range of the instrument. For spheres greater than the type-c instrument limit of 500 μm, both theory and observations indicate that the inversion of the recorded scattering into particle size distributions produces volume distributions that peak at varying sizes between 250 and 400 μm. This is caused by the principal peaks in scattering moving off the inside of the ring detectors, leading to the remaining peaks being interpreted as the principal peaks. The aliasing of larger particles as a distribution of smaller particles is also applicable to the type-b configuration of the LISST, only occurring at the 250 μm size limit instead of 500 μm. When extending the Junge particle size distribution up to varying maximum sizes, numerical tests predict an increase in volume concentration of up to 45%. For power-law distributions with gradients less than that of a Junge distribution, the contamination from large particles becomes increasingly influential over the concentration and shape of the inverted size distribution

Optically significant particle sizes in seawater

Small particles (<10  μm) are often considered to play the dominant role in controlling scattering and absorption due to their relatively large numbers, which are typically found in the ocean. Here we present an approach for quantifying the size range of particles that contribute significantly to bulk inherent optical properties. We present a numerical assessment of the variability in optically significant particle sizes for simplistic populations that conform to the assumptions of homogeneous, spherical particles, and power-law size distributions. We use numerical predictions from Mie theory to suggest minimum and maximum particle sizes required for accurate predictions and observations of ocean optics for different particle size distributions (PSDs). When considering observed ranges of PSDs, our predictions suggest the need for measurements of optical properties and particles to capture information from particle sizes between diameters of 0.05–2000 μm in order to properly constrain relationships between particles and their associated optical properties. Natural particle populations in the ocean may present more complex PSDs that could be analyzed using the method presented here to establish optically significant size classes

Methods used in this study.

<p>The study area (A) focused on Anton Dohrn Seamount (ADS) in the Rockall Trough region East of UK and Ireland. Release locations were defined based on model topography and equally spaced around the circumference of ADS at three standard depths (700m, 1000m, 1500m) with modified depths for the vertical separation test (200m and 1750m; marked with asterisk *) and increment locations shown for the horizontal separation test (coordinates given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161220#pone.0161220.s002" target="_blank">S1 File</a>). Two analysis techniques were used in this study: (B) The Autocorrelation tests are a comparison of each increment track with its corresponding baseline in terms of distance separation over time. (C) Power Analysis tests derive a linear correlation between rasters of track density, converting this into the fraction of unexplained variance metric (after Simons et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161220#pone.0161220.ref020" target="_blank">20</a>]). Bathymetry and topography data were obtained online from the GEBCO Digital Atlas published by the British Oceanographic Data Centre on behalf of IOC and IHO, 2003 (GEBCO 30 arc-second grid, <a href="http://www.gebco.net" target="_blank">www.gebco.net</a>).</p

Example horizontal profiles of U and V velocity taken from one day in HYCOM (4^th Jan 2012).

<p><b>HYCOM breaks down current velocities into directional components:</b> U velocity measures current speeds in an East (+ve)/West (-ve) direction (top three plots), and V in a North (+ve)/South (-ve) direction (bottom three plots). Anton Dohrn Seamount is marked in each depth slice with an arrow. Areas of different velocity from the background values appear as coloured patches. A comparison of areas within the dotted circles shows that profiles from 1000m have the greatest variation in current velocities (a greater number of small coloured patches). Profiles from 1500m show the least variation in velocity. Topographic contours are derived directly from HYCOM velocity data.</p