Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison

Mixing models have become increasingly common tools for apportioning fluvial sediment load to various sediment sources across catchments using a wide variety of Bayesian and frequentist modeling approaches. In this study, we demonstrate how different model setups can impact upon resulting source apportionment estimates in a Bayesian framework via a one-factor-at-a-time (OFAT) sensitivity analysis. We formulate 13 versions of a mixing model, each with different error assumptions and model structural choices, and apply them to sediment geochemistry data from the River Blackwater, Norfolk, UK, to apportion suspended particulate matter (SPM) contributions from three sources (arable topsoils, road verges, and subsurface material) under base flow conditions between August 2012 and August 2013. Whilst all 13 models estimate subsurface sources to be the largest contributor of SPM (median ∼76%), comparison of apportionment estimates reveal varying degrees of sensitivity to changing priors, inclusion of covariance terms, incorporation of time-variant distributions, and methods of proportion characterization. We also demonstrate differences in apportionment results between a full and an empirical Bayesian setup, and between a Bayesian and a frequentist optimization approach. This OFAT sensitivity analysis reveals that mixing model structural choices and error assumptions can significantly impact upon sediment source apportionment results, with estimated median contributions in this study varying by up to 21% between model versions. Users of mixing models are therefore strongly advised to carefully consider and justify their choice of model structure prior to conducting sediment source apportionment investigations

Deep Exponential Families

We describe \textit{deep exponential families} (DEFs), a class of latent variable models that are inspired by the hidden structures used in deep neural networks. DEFs capture a hierarchy of dependencies between latent variables, and are easily generalized to many settings through exponential families. We perform inference using recent "black box" variational inference techniques. We then evaluate various DEFs on text and combine multiple DEFs into a model for pairwise recommendation data. In an extensive study, we show that going beyond one layer improves predictions for DEFs. We demonstrate that DEFs find interesting exploratory structure in large data sets, and give better predictive performance than state-of-the-art models

Rare coral under the genomic microscope: timing and relationships among Hawaiian Montipora

Background Evolutionary patterns of scleractinian (stony) corals are difficult to infer given the existence of few diagnostic characters and pervasive phenotypic plasticity. A previous study of Hawaiian Montipora (Scleractinia: Acroporidae) based on five partial mitochondrial and two nuclear genes revealed the existence of a species complex, grouping one of the rarest known species (M. dilatata, which is listed as Endangered by the International Union for Conservation of Nature - IUCN) with widespread corals of very different colony growth forms (M. flabellata and M. cf. turgescens). These previous results could result from a lack of resolution due to a limited number of markers, compositional heterogeneity or reflect biological processes such as incomplete lineage sorting (ILS) or introgression. Results All 13 mitochondrial protein-coding genes from 55 scleractinians (14 lineages from this study) were used to evaluate if a recent origin of the M. dilatata species complex or rate heterogeneity could be compromising phylogenetic inference. Rate heterogeneity detected in the mitochondrial data set seems to have no significant impacts on the phylogenies but clearly affects age estimates. Dating analyses show different estimations for the speciation of M. dilatata species complex depending on whether taking compositional heterogeneity into account (0.8 [0.05–2.6] Myr) or assuming rate homogeneity (0.4 [0.14–0.75] Myr). Genomic data also provided evidence of introgression among all analysed samples of the complex. RADseq data indicated that M. capitata colour morphs may have a genetic basis. Conclusions Despite the volume of data (over 60,000 SNPs), phylogenetic relationships within the M. dilatata species complex remain unresolved most likely due to a recent origin and ongoing introgression. Species delimitation with genomic data is not concordant with the current taxonomy, which does not reflect the true diversity of this group. Nominal species within the complex are either undergoing a speciation process or represent ecomorphs exhibiting phenotypic polymorphisms.info:eu-repo/semantics/publishedVersio