Using Bayesian change point model to enhance understanding of the shifting nutrients-phytoplankton relationship

Integrated evaluation of the impact of water diversion on water quality index and phytoplankton assemblages of eutrophic lake: A case study of Yilong Lake

Water diversion has been widely utilized to enhance lake water quality and mitigate cyanobacterial blooms. However, previous studies have mainly focused on investigating the effects of water diversion on water quality or aquatic ecological health. Consequently, there is limited research investigating the combined impact of water diversion on the water quality and the ecological health of eutrophic lakes, and whether the WQI and phytoplankton assemblages demonstrate similar patterns following water diversion. In this study, the effects of water diversion on the ecosystem health of eutrophic lakes were comprehensively evaluated based on the WQI indices and phytoplankton assemblages during the NWDP-21 and WDP-22. The results showed that the annual mean of WQI increased from 52.02 to 54.36 after water diversion, which improved the water quality of the lake, especially NH3-N and TN decreased by 58.6% and 15.2%, respectively. The phytoplankton assemblages changed significantly before and after water diversion, and we observed that the total biomass of phytoplankton decreased by 12.3% and phytoplankton diversity indices (Shannon-Wiener diversity, Pielou evenness, and Simpson index) increased by 8.6%-8.9% after water diversion, with an improvement in the connectivity and stability of the phytoplankton. Notably, enhanced adaptations of rare sub-communities for resource use in water diversion environments, and water diversion inhibited the dispersal ability of dominant functional groups, and the effects of hydrological disturbances on the structure of phytoplankton assemblage favored the ecological health of eutrophic lakes. VPA analysis further reveals that water diversion alters the drivers of phytoplankton functional group biomass and phytoplankton diversity. The results of the PLS-PM analysis clarify that water diversion indirectly impacts the total phytoplankton biomass and phytoplankton diversity primarily by modifying light availability. Significant correlations are observed between the dominant functional groups biomass and diversity indices of WQI. The trends in changes observed in water quality indices and phytoplankton following water diversion align with the evaluation of water ecological health. This study provides valuable guidance for the ecological management of the diversion project in Yilong Lake and serves as a reference for similar projects in other lakes.

Foliar Application of Selenium Associated with a Multi-Nutrient Fertilizer in Soybean: Yield, Grain Quality, and Critical Se Threshold

Selenium uptake and its content in soybean grains are affected by Se application methods. This study evaluated the impact of Se foliar application combined with a multi-nutrient fertilizer (MNF) on soybean, establishing a Se threshold to better understand the relationship between Se content in grains and yield of two genotypes (58I60 Lança and M5917). Two trials were conducted in a 4 × 2 factorial design: four Se rates (0, 10, 40, 80 g Se ha-1) and two methods of foliar Se application (Se combined or not with MNF). Foliar fertilizers were applied twice, at phenological stages of beginning of pod development and grain filling. Grain yield increased with the application of MNF, yet Se rates increased Se contents linearly up to 80 g Se ha-1, regardless of the use of MNF. Lança and M5917 genotypes had grain Se critical thresholds of 1.0 and 3.0 mg kg-1, respectively. The application of Se favored higher contents of K, P, and S in grains of genotype Lança and higher contents of Mn and Fe in grains of genotype M5917. Our findings highlight the importance of addressing different Se fertilization strategies as well as genotypic variations when assessing the effects of Se on soybean yield and grain quality.

A coupling simulation and optimization method developed for environmental-economic management of Lake watershed

Many lake basins are facing the challenge of mitigating water shortage and water pollution while maintaining economic growth. Existing planning method for water pollution control often focus on how to alleviate water pollution effectively at the lowest cost, but rarely pay attention to the dynamic feedback and synergy effects between water pollution abatement, water conservation and social economy. This article proposes a method which consists of system dynamics model (SDM), Soil and Water Assessment Tool (SWAT) and objective programming model (OPM). It could be used to create insights on basin-wide water problems from a systematic perspective. The case study on Yilong Lake Watershed evaluates the proposed measures in existing local planning, calculates the optimal scheme, and discusses issues including the uncertainty of effectiveness, choice between recycling sewage and transferring outside, and the necessity of restricting food processing industry. This method could be improved on the simulation of social and industrial economy, the simulation of water cycle, and the spatial planning.

Comparison of Bayesian, k-Nearest Neighbor and Gaussian process regression methods for quantifying uncertainty of suspended sediment concentration prediction

Suspended sediment transport in river system is a complex process influenced by many factors that their interactions lead to nonlinear and high scatter of concentration-discharge relationships. This makes the model prediction subject to high uncertainty and providing one value as the model prediction is somehow useless and cannot provide adequate information about the model accuracy and associated uncertainty. Current study compares the efficiency of Bayesian (i.e. Bayesian segmented linear regression (BSLR) and Bayesian linear model (BLR)), Gaussian Process Regression (GPR) and k-Nearest Neighbor (k-NN) in quantifying uncertainty of the suspended sediment concentration prediction in three watersheds namely Arazkoseh, Oghan and Jajrood located in Iran. Three input combinations including, contemporary discharge, slow and quick flow components and contemporary, one and two antecedent days discharge, were used. The BSLR model was able to identify threshold value, furthermore, pre-threshold and post-threshold slopes of BSLR model indicated that for Arazkoseh watershed channel and for Oghan and Jajrood watersheds, upland area are dominate sediment sources. In all three studied cases, given prediction interval width and the percent of enclosed observed data by prediction interval, k-NN model provided more reliable prediction interval. Moreover, separation stream flow into slow and quick flow components lead to improved performance of GPR and k-NN models in the studied watersheds, and the best results for Arazkoseh and Oghan watersheds were obtained when slow and quick flow components were used as the model input.

A Bayesian piecewise linear model for the detection of breakpoints in housing prices

Statistical thresholds occur when the changes in the relationships between a response and predictor variables are not linear but abrupt at some points of the predictor variable values. In this paper, we defined a piecewise-linear regression model which can detect two thresholds in the relationships via changes in slopes. We developed the corresponding Bayesian methodology for model estimation and inference by proposing prior distributions, deriving posterior distributions, and generating posterior values using Metropolis and Gibbs sampling algorithm. The parameters in our model are easy to understand, highly interpretable, and flexible to make inferences. The methodology has been applied to estimate threshold effects in housing market pricing data in two cities - Kamloops and Chilliwack - in British Columbia, Canada. Our findings revealed that the implementation of changes in the government property tax policies had threshold effects in the market price trend. The proposed model will be useful to detect threshold effects in other correlated time series data as well.

Bayesian change point quantile regression approach to enhance the understanding of shifting phytoplankton-dimethyl sulfide relationships in aquatic ecosystems

Dimethyl sulfide (DMS) serves as an anti-greenhouse gas, plays multiple roles in aquatic ecosystems, and contributes to the global sulfur cycle. The chlorophyll a (CHL, an indicator of phytoplankton biomass)-DMS relationship is critical for estimating DMS emissions from aquatic ecosystems. Importantly, recent research has identified that the CHL-DMS relationship has a breakpoint, where the relationship is positive below a CHL threshold and negative at higher CHL concentrations. Conventionally, mean regression methods are employed to characterize the CHL-DMS relationship. However, these approaches focus on the response of mean conditions and cannot illustrate responses of other parts of the DMS distribution, which could be important in order to obtain a complete view of the CHL-DMS relationship. In this study, for the first time, we proposed a novel Bayesian change point quantile regression (BCPQR) model that integrates and inherits advantages of Bayesian change point models and Bayesian quantile regression models. Our objective was to examine whether or not the BCPQR approach could enhance the understanding of shifting CHL-DMS relationships in aquatic ecosystems. We fitted BCPQR models at five regression quantiles for freshwater lakes and for seas. We found that BCPQR models could provide a relatively complete view on the CHL-DMS relationship. In particular, it quantified the upper boundary of the relationship, representing the limiting effect of CHL on DMS. Based on the results of paired parameter comparisons, we revealed the inequality of regression slopes in BCPQR models for seas, indicating that applying the mean regression method to develop the CHL-DMS relationship in seas might not be appropriate. We also confirmed relationship differences between lakes and seas at multiple regression quantiles. Further, by introducing the concept of DMS emission potential, we found that pH was not likely a key factor leading to the change of the CHL-DMS relationship in lakes. These findings cannot be revealed using piecewise linear regression. We thereby concluded that the BCPQR model does indeed enhance the understanding of shifting CHL-DMS relationships in aquatic ecosystems and is expected to benefit efforts aimed at estimating DMS emissions. Considering that shifting (threshold) relationships are not rare and that the BCPQR model can easily be adapted to different systems, the BCPQR approach is expected to have great potential for generalization in other environmental and ecological studies.

Use statistical machine learning to detect nutrient thresholds in Microcystis blooms and microcystin management

The frequency of toxin-producing cyanobacterial blooms has increased in recent decades due to nutrient enrichment and climate change. Because Microcystis blooms are related to different environmental conditions, identifying potential nutrient control targets can facilitate water quality managers to reduce the likelihood of microcystins (MCs) risk. However, complex biotic interactions and field data limitations have constrained our understanding of the nutrient-microcystin relationship. This study develops a Bayesian modelling framework with intracellular and extracellular MCs that characterize the relationships between different environmental and biological factors. This model was fit to the across-lake dataset including three bloom-plagued lakes in China and estimated the putative thresholds of total nitrogen (TN) and total phosphorus (TP). The lake-specific nutrient thresholds were estimated using Bayesian updating process. Our results suggested dual N and P reduction in controlling cyanotoxin risks. The total Microcystis biomass can be substantially suppressed by achieving the putative thresholds of TP (0.10 mg/L) in Lakes Taihu and Chaohu, but a stricter TP target (0.05 mg/L) in Dianchi Lake. To maintain MCs concentrations below 1.0 μg/L, the estimated TN threshold in three lakes was 1.8 mg/L, but the effect can be counteracted by the increase of temperature. Overall, the present approach provides an efficient way to integrate empirical knowledge into the data-driven model and is helpful for the management of water resources.