scholarly journals A Bayesian mixing model framework for quantifying temporal variation in source of sediment to lakes across broad hydrological gradients of floodplains

2021 ◽  
Author(s):  
Mitchell L. Kay ◽  
Heidi K. Swanson ◽  
Jacob Burbank ◽  
Tanner J. Owca ◽  
Lauren A. MacDonald ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Mixing Model ◽  
Model Framework ◽  
Bayesian Mixing Model

A Bayesian mixing model framework for quantifying temporal and spatial variation in source of sediment to lakes across hydrological gradients of floodplains

2021 ◽  
Author(s):  
Mitchell Kay ◽  
Heidi Swanson ◽  
Jacob Burbank ◽  
Tanner Owca ◽  
Lauren MacDonald ◽  
...  
Keyword(s):  
Mixing Model ◽  
Floodplain Lakes ◽  
Energy Conditions ◽  
Universal Method ◽  
Sediment Composition ◽  
Model Framework ◽  
Bayesian Mixing Model ◽  
The Past ◽  
Peace River ◽  
Wide Range

<p>Episodic flood events are critical for recharging water balance of floodplain lakes and maintaining their ecological integrity, yet are subject to alteration in frequency and magnitude by natural and anthropogenic processes that operate over a range of spatial and temporal scales. To evaluate roles of potential stressors, paleolimnological reconstructions are used to obtain insights into hydrological variability of dynamic floodplain lakes. However, spatial and temporal integration is often underdeveloped because different paleolimnological measurements must be applied across lakes due to the wide range of energy conditions that impart marked differences in sediment composition. Here, we use a linear discriminant analysis to identify 10 significant elemental concentrations in surveyed sediment from multiple sampling campaigns that distinguish the geochemical fingerprints of three end-member sources in lakes at the Peace-Athabasca Delta (PAD; Canada): the Athabasca River, the Peace River and local catchment runoff. Over 90% of the sediment samples were correctly classified into the original groups after cross-validation due to the distinctiveness of the three end members, which permits development of a robust Bayesian mixing model to discern the relative contributions of sediment from the three sources. We evaluate the mixing model at two adjacent lakes in the Athabasca sector of the PAD and demonstrate its effectiveness to discriminate three known hydrological phases during the past 300 years. Notably, the model infers ~60% of the sediment originated from the Peace River during the largest ice-jam flood event on record (1974), which was unrecognized by other methods. We then applied our model to sediment records from 18 lakes spanning the hydrological gradients across the 6000 km<sup>2</sup> PAD to further probe the hydrological evolution during the past ~150 years. Results demonstrate decline in frequency of flooding from both the Athabasca and Peace rivers and lake-level drawdown since the early 20<sup>th</sup> century and align remarkably well with prior interpretation of conventional paleohydrological records of individual lakes. We advocate our approach provides a universal method that can be applied across the full range of sediment composition to quantify change in source, frequency and magnitude of river floodwaters to lakes and is transferable to other dynamic floodplain landscapes where variation of sediment composition challenges efficacy of other approaches.</p>

scholarly journals Quantitative assessment of Pb sources in isotopic mixtures using a Bayesian mixing model

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Jack Longman ◽  
Daniel Veres ◽  
Vasile Ersek ◽  
Donald L. Phillips ◽  
Catherine Chauvel ◽  
...  
Keyword(s):  
Quantitative Assessment ◽  
Mixing Model ◽  
Bayesian Mixing Model

scholarly journals Source Apportionment of PM2.5 in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 512
Author(s):  
Tingting Li ◽  
Jun Li ◽  
Hongxing Jiang ◽  
Duohong Chen ◽  
Zheng Zong ◽  
...  
Keyword(s):  
Source Apportionment ◽  
Matrix Factorization ◽  
Coal Combustion ◽  
Mixing Model ◽  
Positive Matrix ◽  
Traffic Emission ◽  
Bayesian Mixing Model ◽  
Pmf Model ◽  
Aerosol Source ◽  
Biogenic Source

To accurately apportion the sources of aerosols, a combined method of positive matrix factorization (PMF) and the Bayesian mixing model was applied in this study. The PMF model was conducted to identify the sources of PM2.5 in Guangzhou. The secondary inorganic aerosol source was one of the seven main sources in Guangzhou. Based on stable isotopes of oxygen and nitrogen (δ15N-NO3− and δ18O-NO3−), the Bayesian mixing model was performed to apportion the source of NO3− to coal combustion, traffic emission and biogenic source. Then the secondary aerosol source was subdivided into three sources according to the discrepancy in source apportionment of NO3− between PMF and Bayesian mixing model results. After secondary aerosol assignment, the six main sources of PM2.5 were traffic emission (30.6%), biomass burning (23.1%), coal combustion (17.7%), ship emission (14.0%), biomass boiler (9.9%) and industrial emission (4.7%). To assess the source apportionment results, fossil/non-fossil source contributions to organic carbon (OC) and element carbon (EC) inferred from 14C measurements were compared with the corresponding results in the PMF model. The results showed that source distributions of EC matched well between those two methods, indicating that the PMF model captured the primary sources well. Probably because of the lack of organic molecular markers to identify the biogenic source of OC, the non-fossil source contribution to OC in PMF results was obviously lower than 14C results. Thus, an indicative organic molecular tracer should be used to identify the biogenic source when accurately apportioning the sources of aerosols, especially in the region with high plant coverage or intense biomass burning.

scholarly journals Using Stable Isotopes and a Bayesian Mixing Model (FRUITS) to Investigate Diet at the Early Neolithic Site of Carding Mill Bay, Scotland

Radiocarbon ◽  
2017 ◽  
Vol 59 (5) ◽  
pp. 1275-1294 ◽  
Author(s):  
Jessica M Bownes ◽  
Philippa L Ascough ◽  
Gordon T Cook ◽  
Iona Murray ◽  
Clive Bonsall
Keyword(s):  
Stable Isotope ◽  
Mixing Model ◽  
Bone Collagen ◽  
Marine Resources ◽  
Sea Spray ◽  
Early Neolithic ◽  
Human Diet ◽  
Bayesian Mixing Model ◽  
Animal Bone ◽  
Fish And Shellfish

AbstractWe present δ13C, δ15N, and δ34S measurements on archaeological human and animal bone collagen samples from a shell midden dating to the Neolithic ca. 4000–3500 cal BC, together with measurements on modern fish and shellfish. These data were used in conjunction with the Bayesian mixing model, Food Reconstruction Using Isotopic Transferred Signals (FRUITS), to reconstruct human diet at the site. We demonstrate the importance of using a geographically appropriate faunal baseline in stable isotope paleodietary studies, and suggest that Neolithic individuals at this site consumed up to ca. 21% of dietary protein from marine resources, despite stable isotope ratios that imply a wholly terrestrial diet. This marine resource consumption does not significantly shift the radiocarbon (14C) dates of these individuals, so although we must consider the use of marine resources at the site, the chronology that has previously been established is secure. The δ13C and δ15N measurements from the archaeological herbivore bone collagen indicate that it is unlikely they ate plants enriched with fertilisers such as manure or seaweed. The δ34S values reveal a sea-spray effect; therefore, in this instance, δ34S cannot be used as a dietary indicator but can be used to demonstrate the likely locality of the fauna.

scholarly journals Analyzing mixing systems using a new generation of Bayesian tracer mixing models

2018 ◽  
Author(s):  
Brian C Stock ◽  
Andrew L Jackson ◽  
Eric J Ward ◽  
Andrew C Parnell ◽  
Donald L Phillips ◽  
...  
Keyword(s):  
R Package ◽  
Mixing Model ◽  
Information Criteria ◽  
Alligator Mississippiensis ◽  
Mixing Models ◽  
Model Framework ◽  
Prior Specification ◽  
Model Study ◽  
Study Designs ◽  
New Generation

The ongoing evolution of tracer mixing models has resulted in a confusing array of software tools that differ in terms of data inputs, model assumptions, and associated analytic products. Here we introduce MixSIAR, an inclusive, rich, and flexible Bayesian tracer (e.g. stable isotope) mixing model framework implemented as an open-source R package. Using MixSIAR as a foundation, we provide guidance for the implementation of mixing model analyses. We begin by outlining the practical differences between mixture data error structure formulations and relate these error structures to common mixing model study designs in ecology. Because Bayesian mixing models afford the option to specify informative priors on source proportion contributions, we outline methods for establishing prior distributions and discuss the influence of prior specification on model outputs. We also discuss the options available for source data inputs (raw data versus summary statistics) and provide guidance for combining sources. We then describe a key advantage of MixSIAR over previous mixing model software—the ability to include fixed and random effects as covariates explaining variability in mixture proportions and calculate relative support for multiple models via information criteria. We present a case study of Alligator mississippiensis diet partitioning to demonstrate the power of this approach. Finally, we conclude with a discussion of limitations to mixing model applications. Through MixSIAR, we have consolidated the disparate array of mixing model tools into a single platform, diversified the set of available parameterizations, and provided developers a platform upon which to continue improving mixing model analyses in the future.

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