Evaluating the performance of taxonomic and trait-based biomonitoring approaches for fine sediment in the UK

Fine sediment is a leading cause for the decline of aquatic biodiversity globally. There is an urgent need for targeted monitoring to identify where management methods are required in order to reduce the delivery of fine sediment to aquatic environments. Existing sediment-specific biomonitoring indices and indices for general ecological health (taxonomic and trait-based) developed for use in the UK were tested in a representative set of lowland rivers in England that consisted of a gradient of fine sediment pressures (deposited and suspended, organic and inorganic). Index performance was modelled against environmental variables collected during sampling and hydrological and antecedent flow variables calculated from daily flow data. Sediment-specific indices were indicative of surface sediment deposits, whereas indices for general ecological health were more closely associated with the organic content of fine sediment. The performance of biotic indices along fine sediment gradients was predominantly dependent on hydrological variability. Functional diversity indices were poorly related to different measures of fine sediment, and further development of traits-based indices and trait databases are recommended. In summary, the results suggest that sediment-specific biomonitoring tools are suitable for evaluating fine sediment stress in UK rivers when index scores are viewed within the context of local hydrolog

Abiotic predictors of fine sediment accumulation in lowland rivers

The delivery of excessive fine sediment (particles <2 mm in diameter) to rivers can cause serious deleterious effects to aquatic ecosystems and is widely acknowledged to be one of the leading contributors to the degradation of rivers globally. Despite advances in using biological methods as a proxy, physical measures remain an important method through which fine sediment can be quantified. The aim of this study was to provide further insights into the environmental variables controlling sediment accumulation in lowland gravel bed rivers. We sampled 21 sites, during spring and autumn, selected to cover a gradient of excess fine sediment. Fine sediment was sampled using a range of methods including visual assessments, the disturbance method and suspended sediment concentrations. A range of abiotic predictors were measured during sampling, and hydrological and antecedent flow indices were derived from local flow gauging station data. The results show reach scale visual estimates of fine sediment to be significantly and highly correlated with fully quantitative estimates of total surface sediment. Multivariate regression analysis showed that flow variables (regime, antecedent and local flow characteristics) were strong predictors of deposited sediment metrics but poor predictors of suspended sediment. Organic content was shown to be relatively independent of total sediment quantity and is likely driven by other factors which influence the supply and breakdown of organic matte

A Spatial Modeling Framework to Evaluate Domestic Biofuel-Induced Potential Land Use Changes and Emissions

We present a novel bottom-up approach to estimate biofuel-induced land-use change (LUC) and resulting CO₂ emissions in the U.S. from 2010 to 2022, based on a consistent methodology across four essential components: land availability, land suitability, LUC decision-making, and induced CO₂ emissions. Using high-resolution geospatial data and modeling, we construct probabilistic assessments of county-, state-, and national-level LUC and emissions for macroeconomic scenarios. We use the Cropland Data Layer and the Protected Areas Database to characterize availability of land for biofuel crop cultivation, and the CERES-Maize and BioCro biophysical crop growth models to estimate the suitability (yield potential) of available lands for biofuel crops. For LUC decisionmaking, we use a county-level stochastic partial-equilibrium modeling framework and consider five scenarios involving annual ethanol production scaling to 15, 22, and 29 BG, respectively, in 2022, with corn providing feedstock for the first 15 BG and the remainder coming from one of two dedicated energy crops. Finally, we derive high-resolution above-ground carbon factors from the National Biomass and Carbon Data set to estimate emissions from each LUC pathway. Based on these inputs, we obtain estimates for average total LUC emissions of 6.1, 2.2, 1.0, 2.2, and 2.4 gCO2e/MJ for Corn-15 Billion gallons (BG), <i>Miscanthus × giganteus</i> (MxG)-7 BG, Switchgrass (SG)-7 BG, MxG-14 BG, and SG-14 BG scenarios, respectively

Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

<div><p>Background</p><p>A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data.</p><p>Methods and Findings</p><p>Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting.</p><p>Conclusions</p><p>Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.</p></div

Summary of UPDRS ratings.

<p>Summary of UPDRS ratings.</p

Best machine learning based classification results (according to average measures of 5-fold cross-validation).

<p>Best machine learning based classification results (according to average measures of 5-fold cross-validation).</p

Exponential growth model of PD prevalence with age.

<p>Exponential growth model of PD prevalence with age.</p

(Partial) Varplots illustrating some of the critical predictive data elements for AdaBoost classifier predicting HC vs. (PD+SWEDD).

<p>(Partial) Varplots illustrating some of the critical predictive data elements for AdaBoost classifier predicting HC vs. (PD+SWEDD).</p

Frequency plot of data elements that appear as more reliable predictors of subject diagnosis, ranked by counts using rebalanced URPRS data (see Table B in S1 File for the complete numerical results).

<p>Frequency plot of data elements that appear as more reliable predictors of subject diagnosis, ranked by counts using rebalanced URPRS data (see Table B in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0157077#pone.0157077.s001" target="_blank">S1 File</a> for the complete numerical results).</p

Gender Distributions.

<p>Gender Distributions.</p