Holistic view of the seascape dynamics and environment impact on macro-scale genetic connectivity of marine plankton populations.

BACKGROUND: Plankton seascape genomics studies have revealed different trends from large-scale weak differentiation to microscale structures. Previous studies have underlined the influence of the environment and seascape on species differentiation and adaptation. However, these studies have generally focused on a few single species, sparse molecular markers, or local scales. Here, we investigated the genomic differentiation of plankton at the macro-scale in a holistic approach using Tara Oceans metagenomic data together with a reference-free computational method. RESULTS: We reconstructed the FST-based genomic differentiation of 113 marine planktonic taxa occurring in the North and South Atlantic Oceans, Southern Ocean, and Mediterranean Sea. These taxa belong to various taxonomic clades spanning Metazoa, Chromista, Chlorophyta, Bacteria, and viruses. Globally, population genetic connectivity was significantly higher within oceanic basins and lower in bacteria and unicellular eukaryotes than in zooplankton. Using mixed linear models, we tested six abiotic factors influencing connectivity, including Lagrangian travel time, as proxies of oceanic current effects. We found that oceanic currents were the main population genetic connectivity drivers, together with temperature and salinity. Finally, we classified the 113 taxa into parameter-driven groups and showed that plankton taxa belonging to the same taxonomic rank such as phylum, class or order presented genomic differentiation driven by different environmental factors. CONCLUSION: Our results validate the isolation-by-current hypothesis for a non-negligible proportion of taxa and highlight the role of other physicochemical parameters in large-scale plankton genetic connectivity. The reference-free approach used in this study offers a new systematic framework to analyse the population genomics of non-model and undocumented marine organisms from a large-scale and holistic point of view.

A dataset of hourly sea surface temperature from drifting buoys.

A dataset of sea surface temperature (SST) estimates is generated from the temperature observations of surface drifting buoys of NOAA's Global Drifter Program. Estimates of SST at regular hourly time steps along drifter trajectories are obtained by fitting to observations a mathematical model representing simultaneously SST diurnal variability with three harmonics of the daily frequency, and SST low-frequency variability with a first degree polynomial. Subsequent estimates of non-diurnal SST, diurnal SST anomalies, and total SST as their sum, are provided with their respective standard uncertainties. This Lagrangian SST dataset has been developed to match the existing and on-going hourly dataset of position and velocity from the Global Drifter Program.

The Debiased Spatial Whittle likelihood.

We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased Spatial Whittle likelihood, makes important corrections to the well-known Whittle likelihood to account for large sources of bias caused by boundary effects and aliasing. We generalize the approach to flexibly allow for significant volumes of missing data including those with lower-dimensional substructure, and for irregular sampling boundaries. We build a theoretical framework under relatively weak assumptions which ensures consistency and asymptotic normality in numerous practical settings including missing data and non-Gaussian processes. We also extend our consistency results to multivariate processes. We provide detailed implementation guidelines which ensure the estimation procedure can be conducted in O ( n log n ) operations, where n is the number of points of the encapsulating rectangular grid, thus keeping the computational scalability of Fourier and Whittle-based methods for large data sets. We validate our procedure over a range of simulated and realworld settings, and compare with state-of-the-art alternatives, demonstrating the enduring practical appeal of Fourier-based methods, provided they are corrected by the procedures developed in this paper.

Nonparametric time series summary statistics for high-frequency accelerometry data from individuals with advanced dementia.

Accelerometry data has been widely used to measure activity and the circadian rhythm of individuals across the health sciences, in particular with people with advanced dementia. Modern accelerometers can record continuous observations on a single individual for several days at a sampling frequency of the order of one hertz. Such rich and lengthy data sets provide new opportunities for statistical insight, but also pose challenges in selecting from a wide range of possible summary statistics, and how the calculation of such statistics should be optimally tuned and implemented. In this paper, we build on existing approaches, as well as propose new summary statistics, and detail how these should be implemented with high frequency accelerometry data. We test and validate our methods on an observed data set from 26 recordings from individuals with advanced dementia and 14 recordings from individuals without dementia. We study four metrics: Interdaily stability (IS), intradaily variability (IV), the scaling exponent from detrended fluctuation analysis (DFA), and a novel nonparametric estimator which we call the proportion of variance (PoV), which calculates the strength of the circadian rhythm using spectral density estimation. We perform a detailed analysis indicating how the time series should be optimally subsampled to calculate IV, and recommend a subsampling rate of approximately 5 minutes for the dataset that has been studied. In addition, we propose the use of the DFA scaling exponent separately for daytime and nighttime, to further separate effects between individuals. We compare the relationships between all these methods and show that they effectively capture different features of the time series.