Growth and fatty acid composition of pikeperch (Sander lucioperca L., 1758) larvae under altered feeding protocol including the copepod Apocyclops panamensis (Marsh, 1913).

Alternative live feeds for small and sensitive fish early life stages such as pikeperch (Sander lucioperca L., 1758) can improve the larval quantity, quality and performance in aquaculture. Therefore, this study evaluated the cyclopoid copepod Apocyclops panamensis (Marsh, 1913) as live feed for pikeperch larviculture from day 11 post hatch (dph) in two independent experiments. In both experiments, pikeperch larvae had the highest specific growth rate (SGR) when they fed on Brachionus plicatilis until dph 11 and A. panamensis until dph 16-18. SGR was related to a decrease in total fatty acids (FAs), saturated FAs and monounsaturated FAs in pikeperch larvae, indicating their use as energy for growth. Within the polyunsaturated FAs, docosahexaenoic acid (DHA) increased in larvae fed with A. panamensis and coincided with the highest SGR suggesting that DHA is accumulated in larvae as structural FA. Our study demonstrated a suitable pikeperch larval fatty acid composition for growth after feeding A. panamensis compared with Artemia sp. from dph 11 until dph 16 and previously fed with B. plicatilis. Moreover, it highlighted the importance of the dietary PUFAs in pikeperch rearing, specifically of linoleic acid (LA) from dph 4 until dph 11 and of DHA from dph 11 onwards.

Image-based Visualization of Large Volumetric Data Using Moments.

We present a novel image-based representation to interactively visualize large and arbitrarily structured volumetric data. This image-based representation is created from a fixed view and models the scalar densities along each viewing ray. Then, any transfer function can be applied and changed interactively to visualize the data. In detail, we transform the density in each pixel to the Fourier basis and store Fourier coefficients of a bounded signal, i.e. bounded trigonometric moments. To keep this image-based representation compact, we adaptively determine the number of moments in each pixel and present a novel coding and quantization strategy. Additionally, we perform spatial and temporal interpolation of our image representation and discuss the visualization of introduced uncertainties. Moreover, we use our representation to add single scattering illumination. Lastly, we achieve accurate results even with changes in the view configuration. We evaluate our approach on two large volume datasets and a time-dependent SPH dataset.

Visual Analysis of Large Multivariate Scattered Data using Clustering and Probabilistic Summaries.

Rapidly growing data sizes of scientific simulations pose significant challenges for interactive visualization and analysis techniques. In this work, we propose a compact probabilistic representation to interactively visualize large scattered datasets. In contrast to previous approaches that represent blocks of volumetric data using probability distributions, we model clusters of arbitrarily structured multivariate data. In detail, we discuss how to efficiently represent and store a high-dimensional distribution for each cluster. We observe that it suffices to consider low-dimensional marginal distributions for two or three data dimensions at a time to employ common visual analysis techniques. Based on this observation, we represent high-dimensional distributions by combinations of low-dimensional Gaussian mixture models. We discuss the application of common interactive visual analysis techniques to this representation. In particular, we investigate several frequency-based views, such as density plots in 1D and 2D, density-based parallel coordinates, and a time histogram. We visualize the uncertainty introduced by the representation, discuss a level-of-detail mechanism, and explicitly visualize outliers. Furthermore, we propose a spatial visualization by splatting anisotropic 3D Gaussians for which we derive a closed-form solution. Lastly, we describe the application of brushing and linking to this clustered representation. Our evaluation on several large, real-world datasets demonstrates the scaling of our approach.

Void-and-Cluster Sampling of Large Scattered Data and Trajectories.

We propose a data reduction technique for scattered data based on statistical sampling. Our void-and-cluster sampling technique finds a representative subset that is optimally distributed in the spatial domain with respect to the blue noise property. In addition, it can adapt to a given density function, which we use to sample regions of high complexity in the multivariate value domain more densely. Moreover, our sampling technique implicitly defines an ordering on the samples that enables progressive data loading and a continuous level-of-detail representation. We extend our technique to sample time-dependent trajectories, for example pathlines in a time interval, using an efficient and iterative approach. Furthermore, we introduce a local and continuous error measure to quantify how well a set of samples represents the original dataset. We apply this error measure during sampling to guide the number of samples that are taken. Finally, we use this error measure and other quantities to evaluate the quality, performance, and scalability of our algorithm.

Fast-slow life history is correlated with individual differences in movements and prey selection in an aquatic predator in the wild.

Fast and slow life histories are proposed to covary with consistent individual differences in behaviour, but little is known whether it holds in the wild, where individuals experience natural fluctuations of the environment. We investigated whether individual differences in behaviour, such as movement traits and prey selection, are linked to variation in life-history traits in Eurasian perch (Perca fluviatilis) in the wild. Using high-resolution acoustic telemetry, we collected the positional data of fish in a whole natural lake and estimated individual movement traits by fitting a two-state correlated random walk model. Prey selection was inferred from stable isotope analysis using scale samples. Life-history traits were estimated by fitting a biphasic growth model to an individual growth trajectory back-calculated from scale samples. Life-history traits were correlated with behavioural traits such as movements and prey selection. Individuals with higher reproductive effort were found to switch more frequently between active and inactive modes and show greater reliance on prey from pelagic pathways (indicated by lower δ13 C). Further, individuals with faster juvenile growth were found to stay active for a longer time during the adult stage. Our results demonstrate the link between individual behavioural differences and fast-slow life-history traits under ecologically relevant conditions.