scholarly journals Supplementary material to "Complex interactions of in-stream DOM and nutrient spiralling unravelled by Bayesian regression analysis"

Author(s):  
Matthias Pucher ◽  
Peter Flödl ◽  
Daniel Graeber ◽  
Klaus Felsenstein ◽  
Thomas Hein ◽  
...  
2021 ◽  
Vol 18 (10) ◽  
pp. 3103-3122
Author(s):  
Matthias Pucher ◽  
Peter Flödl ◽  
Daniel Graeber ◽  
Klaus Felsenstein ◽  
Thomas Hein ◽  
...  

Abstract. Uptake and release patterns of dissolved organic matter (DOM) compounds and co-transported nutrients are entangled, and the current literature does not provide a consistent picture of the interactions between the retention processes of DOM fractions. We performed plateau addition experiments with five different complex DOM leachates in a small experimental stream impacted by diffuse agricultural pollution. The study used a wide range of DOM qualities by including leachates of cow dung, pig dung, corn leaves, leaves from trees, and whole nettle plants. We measured changes in nutrient and dissolved organic carbon (DOC) concentrations along the stream course and determined DOM fractions by fluorescence measurements and parallel factor (PARAFAC) decomposition. To assess the influences of hydrological transport processes, we used a 1D hydrodynamic model. We developed a non-linear Bayesian approach based on the nutrient spiralling concept, which we named the “interactions in nutrient spirals using Bayesian regression” (INSBIRE) approach. This approach can disentangle complex interactions of biotic and abiotic drivers of reactive solutes' uptake in multi-component DOM sources. It can show the variability of the uptake velocities and quantify their uncertainty distributions. Furthermore, previous knowledge of nutrient spiralling can be included in the model using prior probability distributions. We used INSBIRE to assess interactions of compound-specific DOM and nutrient spiralling metrics in our experiment. Bulk DOC uptake varied among sources, showing decreasing uptake velocities in the following order: corn > pig dung > leaves > nettles > cow dung. We found no correlations between bulk DOC uptake and the amounts of protein-like compounds or co-leached soluble reactive phosphorus (SRP). The fastest uptake was observed for SRP and the tryptophan-like component, while the other DOM components' uptake velocities more or less resembled that of the bulk DOC. Almost all DOM components showed a negative relationship between uptake and concentration, known as efficiency loss. Furthermore, we observed a few negative and (weak) positive interactions between the uptake and the concentration of different components, such as a decreased uptake of protein-like compounds at high concentrations of a high-molecular-weight humic-like compound. We also found an influence of the wetted width on the uptake of SRP and a microbially derived humic substance, which indicates the importance of the sediment–water interface for P and humic C cycling in the studied stream. Overall, we show that bulk DOC is a weak predictor of DOC uptake behaviour for complex DOM leachates. Individual DOM compound uptake, including co-leached nutrients, is controlled by both internal (quality-related) and external (environmental) factors within the same aquatic ecosystem. We conclude that the cycling of different C fractions and their mutual interaction with N and P uptake in streams is a complex, non-linear problem, which can only be assessed with advanced non-linear approaches, such as the presented INSBIRE approach.


2021 ◽  
Vol 13 (5) ◽  
pp. 053303
Author(s):  
Vincent Tanoe ◽  
Saul Henderson ◽  
Amir Shahirinia ◽  
Mohammad Tavakoli Bina

2016 ◽  
Vol 21 (12) ◽  
pp. 2838-2850 ◽  
Author(s):  
Celia KN Sen ◽  
G Tarcan Kumkale

With increasing mammogram rates, identifying attributes of non-attending women entails going beyond differences in demographic groups to reveal complex interactions among personality attributes. In this study, we analyzed survey data from 474 women aged 41 years and older using decision trees. By incorporating personality, religiousness, and age, we were able to correctly classify 42.9 percent of non-attenders compared to 4.4 percent with logistic regression analysis. Our findings suggest that incorporating personality and religiousness attributes may increase non-attender identification. Furthermore, the simple profile generated by decision trees provides a clear map useful for intervention planning.


Author(s):  
Abhisek Mudgal ◽  
Shauna Hallmark ◽  
Alicia Carriquiry ◽  
Konstantina Gkritza

2021 ◽  
Vol 26 (3) ◽  
Author(s):  
Muntadher Almusaedi ◽  
Ahmad Naeem Flaih

Bayesian regression analysis has great importance in recent years, especially in the Regularization method, Such as ridge, Lasso, adaptive lasso, elastic net methods, where choosing the prior distribution of the interested parameter is the main idea in the Bayesian regression analysis. By penalizing the Bayesian regression model, the variance of the estimators are reduced notable and the bias is getting smaller. The tradeoff between the bias and variance of the penalized Bayesian regression estimator consequently produce more interpretable model with more prediction accuracy. In this paper, we proposed new hierarchical model for the Bayesian quantile regression by employing the scale mixture of normals mixing with truncated gamma distribution that stated by (Li and Lin, 2010) as Laplace prior distribution. Therefore, new Gibbs sampling algorithms are introduced. A comparison has made with classical quantile regression model and with lasso quantile regression model by conducting simulations studies. Our model is comparable and gives better results.


2020 ◽  
Author(s):  
Matthias Pucher ◽  
Peter Flödl ◽  
Daniel Graeber ◽  
Klaus Felsenstein ◽  
Thomas Hein ◽  
...  

Abstract. Uptake and release patterns of dissolved organic matter (DOM) compounds and nutrients are entangled, and the current literature does not provide a consistent picture of the link between DOM composition, nutrient concentrations, and effects on their cycling. We performed two plateau addition experiments for each of five different, realistic, complex DOM leachates in a small stream, heavily enriched in nitrate but not phosphate or DOM due to diffuse agricultural pollution. By including cow and pig dung as well as corn, leaves and nettles leachates, the study used a wide range of different DOM qualities. We measured changes in nutrient concentrations and determined DOM fractions by fluorescence measurements and parallel factor (PARAFAC) decomposition. To assess influences from hydrological transport processes, we used a 1-D hydrodynamic model. We propose a non-linear Bayesian approach to the nutrient spiralling concept, the Interactions in Nutrient Spirals using BayesIan REgression (INSBIRE) approach. This approach can disentangle complex and interacting biotic and abiotic drivers in nutrient uptake metrics, show their variability and quantify their error distribution. Furthermore, previous knowledge on nutrient spiralling can be included in the model using prior probability distributions. We used INSBIRE to assess interactions of compound-specific DOM and nutrient spiralling metrics the data of our experiment. The uptake processes of different DOM fractions were linked to each other. We observed stimulating and dampening effects of DOM fractions on each other and the overall DOM uptake. We found saturation effects for dissolved organic carbon (concentration of C, DOC) uptake, as rising concentrations of a DOM fraction dampened its uptake. The degradation of a humic DOM component of terrestrial origin was stimulated by other DOM fractions, pointing to priming effects. We also found an influence of the wetted width on the uptake of soluble reactive phosphorus (SRP) and a microbially derived humic substance, which indicates the importance of the sediment-water interface for P and humic C cycling in the studied stream. Interestingly, we found no interactions between DOM uptake and nitrate or SRP concentrations, or any effect of the added DOM leachates on nitrate uptake, indicating that the increase in DOC concentrations and SRP concentrations were not sufficient to affect the relatively steady nitrate uptake during the experiments. Overall, we show that bulk DOC is a weak predictor of DOC uptake behaviour for complex DOM leachates and that individual DOM compound uptake, nitrate uptake and SRP uptake are controlled very differently within the same aquatic ecosystem. We also found effects of hydromorphology on the uptake of one humic fluorophore and SRP. We conclude that cycling of different C fractions, their interaction and interactions with N and P uptake in streams is a complex, non-linear problem, which can only be assessed with advanced non-linear approaches, such as we present with INSBIRE.


MAUSAM ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 879-886
Author(s):  
M. YEASIN ◽  
K. N. SINGH ◽  
A. LAMA ◽  
B. GURUNG

As agriculture is the backbone of the Indian economy, Government needs a reliable forecast of crop yield for planning new schemes. The most extensively used technique for forecasting crop yield is regression analysis. The significance of parameters is one of the major problems of regression analysis. Non-significant parameters lead to absurd forecast values and these forecast values are not reliable. In such cases, models need to be improved. To improve the models, we have incorporated prior knowledge through the Bayesian technique and investigate the superiority of these models under the Bayesian framework. The Bayesian technique is one of the most powerful methodologies in the modern era of statistics. We have discussed different types of prior (informative, non-informative and conjugate priors). The Markov chain Monte Carlo (MCMC) methodology has been briefly discussed for the estimation of parameters under Bayesian framework. To illustrate these models, production data of banana, mango and wheat yield data are taken under consideration. We compared the traditional regression model with the Bayesian regression model and conclusively infer that the models estimated under Bayesian framework provided superior results as compared to the models estimated under the classical approach.


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