How News Audiences Allocate Trust in the Digital Age: A Figuration Perspective

The article enriches the understanding of trust in news at a time when mass and interpersonal communication have merged in the digital sphere. We propose disentangling individual-level patterns of trust allocation (i.e., trust figurations) across journalistic media, social media, and peers to reflect the multiplicity among modern news audiences. A latent class analysis of a representative survey among German young adults revealed four figurations: traditionalists, indifferentials, optimists, and cynics. Political characteristics and education corresponded with substantial heterogeneity in individuals’ trust in news sources, their inclination to differentiate between sources, and the ways of integrating trust in journalistic and non-journalistic sources

Bayesian Hierarchical Models for Remote Assessment of Atmospheric Dust

Dust storms emerging in the Earth's major desert regions significantly influence weather processes, the CO2-cycle and the climate on a global scale. Their effects on organisms range from providing nutrition to vegetation and microbes to direct impact on human settlements, transportation and health. The detection of dust storms, the prediction of their development, and the estimation of sources are therefore of immediate interest to a wide range of scientific disciplines. Recent spatio-temporal resolution increases of remote sensing instruments have created new opportunities to understand these phenomena. The scale of the data and their inherent stochasticity, however, pose significant challenges. This thesis develops a combination of methods from statistics, image processing, and physics that paves the way for efficient probabilistic dust assessment using satellite imagery. As a first step, we propose a BHM that maps SEVIRI measurements to a predictor of the dust density. Case studies demonstrate that, as compared to linear methods, our LSM approach mitigates effects of signal intrinsic noise on further processing steps. Furthermore, an extensive cross-validation study is employed to show that LSM successfully adapts to intra-daily changes of the infrared data and yields outstanding dust detection accuracy. Physically, the dust density and its transport process are tied together by the continuity equation. A traditional approach to determine the flow field for a given density is the variational method of Horn and Schunck (HS), which simplifies the equation to compression free motion. We characterize the equation's solution as a GMRF and introduce compressible dynamics. This link between probabilistic and variational perspectives leads to applied and theoretical advances. It enables us to employ the INLA technique for computationally efficient inference and integration over hyper-parameters. The importance of allowing for compressible motion and treating the problem in a statistical manner is emphasized by simulation and case studies showing a significant reduction in errors of the estimated flow field. In addition, we demonstrate how our methodology provides uncertainty quantification, dust storm forecasts and estimation of emission sources. The thesis is concluded by examining the analytical properties of our approach. It is shown that, under mild restrictions on an underlying Sobolev space, existence and uniqueness of the compressible flow can be guaranteed on a continuous domain and a well-posed discretization exists. Lastly, our variational calculations point to an interpretation of the density as a solution to flow-parameterized SPDE naturally extending Matern fields to non-isotropy, which provides a further step towards a joint model of dust density and flow field

inlabru: an R package for Bayesian spatial modelling from ecological survey data

1. Spatial processes are central to many ecological processes, but fitting models that incorporate spatial correlation to data from ecological surveys is computationally challenging. This is particularly true of point pattern data (in which the primary data are the locations at which target species are found), but also true of gridded data, and of georeferenced samples from continuous spatial fields. 2. We describe here the R package inlabru that builds on the widely used RINLA package to provide easier access to Bayesian inference from spatial point process, spatial count, gridded, and georeferenced data, using integrated nested Laplace approximation (INLA, Rue et al., 2009). 3. The package provides methods for fitting spatial density surfaces and estimating abundance, as well as for plotting and prediction. It accommodates data that are points, counts, georeferenced samples, or distance sampling data. 4. This paper describes the main features of the package, illustrated by fitting models to the gorilla nest data contained in the package spatstat (Baddeley, & Turner, 2005), a line transect survey dataset contained in the package dsm (Miller, Rexstad, Burt, Bravington, & Hedley, 2018), and to a georeferenced sample from a simulated continuous spatial field

Spatiotemporal variation in harbor porpoise distribution and foraging across a landscape of fear

Funding information: Marine Alliance for Science and Technology for Scotland; Marine Scotland Science; University of AberdeenPeer reviewedPublisher PD

Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales

Distance sampling is a widely used method for estimating wildlife population abundance. The fact that conventional distance sampling methods are partly design-based constrains the spatial resolution at which animal density can be estimated using these methods. Estimates are usually obtained at survey stratum level. For an endangered species such as the blue whale, it is desirable to estimate density and abundance at a finer spatial scale than stratum. Temporal variation in the spatial structure is also important. We formulate the process generating distance sampling data as a thinned spatial point process and propose model-based inference using a spatial log-Gaussian Cox process. The method adopts a flexible stochastic partial differential equation (SPDE) approach to model spatial structure in density that is not accounted for by explanatory variables, and integrated nested Laplace approximation (INLA) for Bayesian inference. It allows simultaneous fitting of detection and density models and permits prediction of density at an arbitrarily fine scale. We estimate blue whale density in the Eastern Tropical Pacific Ocean from thirteen shipboard surveys conducted over 22 years. We find that higher blue whale density is associated with colder sea surface temperatures in space, and although there is some positive association between density and mean annual temperature, our estimates are consitent with no trend in density across years. Our analysis also indicates that there is substantial spatially structured variation in density that is not explained by available covariates.Comment: 33 pages 19 figure

Spatiotemporal variation in harbor porpoise distribution and foraging across a landscape of fear

Understanding spatiotemporally varying animal distributions can inform ecological understanding of species' behavior (e.g., foraging and predator/prey interactions) and support development of management and conservation measures. Data from an array of echolocation‐click detectors (C‐PODs) were analyzed using Bayesian spatiotemporal modeling to investigate spatial and temporal variation in occurrence and foraging activity of harbor porpoises (Phocoena phocoena) and how this variation was influenced by daylight and presence of bottlenose dolphins (Tursiops truncatus). The probability of occurrence of porpoises was highest on an offshore sandbank, where the proportion of detections with foraging clicks was relatively low. The porpoises' overall distribution shifted throughout the summer and autumn, likely influenced by seasonal prey availability. Probability of porpoise occurrence was lowest in areas close to the coast, where dolphin detections were highest and declined prior to dolphin detection, leading potentially to avoidance of spatiotemporal overlap between porpoises and dolphins. Increased understanding of porpoises' seasonal distribution, key foraging areas, and their relationship with competitors can shed light on management options and potential interactions with offshore industries