Strength and uncertainty of phytoplankton metrics for assessing eutrophication impacts in lakes

Phytoplankton constitutes a diverse array of short-lived organisms which derive their nutrients from the water column of lakes. These features make this community the most direct and earliest indicator of the impacts of changing nutrient conditions on lake ecosystems. It also makes them particularly suitable for measuring the success of restoration measures following reductions in nutrient loads. This paper integrates a large volume of work on a number of measures, or metrics, developed for using phytoplankton to assess the ecological status of European lakes, as required for the Water Framework Directive. It assesses the indicator strength of these metrics, specifically in relation to representing the impacts of eutrophication. It also examines how these measures vary naturally at different locations within a lake, as well as between lakes, and how much variability is associated with different replicate samples, different months within a year and between years. On the basis of this analysis, three of the strongest metrics (chlorophyll-a, phytoplankton trophic index (PTI), and cyanobacterial biovolume) are recommended for use as robust measures for assessing the ecological quality of lakes in relation to nutrient-enrichment pressures and a minimum recommended sampling frequency is provided for these three metrics

Anomalous accelerations in spacecraft flybys of the Earth

[EN] The flyby anomaly is a persistent riddle in astrodynamics. Orbital analysis in several flybys of the Earth since the Galileo spacecraft flyby of the Earth in 1990 have shown that the asymptotic post-encounter velocity exhibits a difference with the initial velocity that cannot be attributed to conventional effects. To elucidate its origin, we have developed an orbital program for analyzing the trajectory of the spacecraft in the vicinity of the perigee, including both the Sun and the Moon¿s tidal perturbations and the geopotential zonal, tesseral and sectorial harmonics provided by the EGM96 model. The magnitude and direction of the anomalous acceleration acting upon the spacecraft can be estimated from the orbital determination program by comparing with the trajectories fitted to telemetry data as provided by the mission teams. This acceleration amounts to a fraction of a mm/s2 and decays very fast with altitude. 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Using Bayesian hierarchical modelling to capture cyanobacteria dynamics in Northern European lakes

Cyanobacteria blooms in lakes and reservoirs currently threaten water security and affect the ecosystem services provided by these freshwater ecosystems, such as drinking water and recreational use. Climate change is expected to further exacerbate the situation in the future because of higher temperatures, extended droughts and nutrient enrichment, due to urbanisation and intensified agriculture. Nutrients are considered critical for the deterioration of water quality in lakes and reservoirs and responsible for the widespread increase in cyanobacterial blooms. We model the response of cyanobacteria abundance to variations in lake Total Phosphorus (TP) and Total Nitrogen (TN) concentrations, using a data set from 822 Northern European lakes. We divide lakes in ten groups based on their physico-chemical characteristics, following a modified lake typology defined for the Water Framework Directive (WFD). This classification is used in a Bayesian hierarchical linear model which employs a probabilistic approach, transforming uncertainty into probability thresholds. The hierarchical model is used to calculate probabilities of cyanobacterial concentrations exceeding risk levels for human health associated with the use of lakes for recreational activities, as defined by the World Health Organization (WHO). Different TN and TP concentration combinations result in variable probabilities to exceed pre-set thresholds. Our objective is to support lake managers in estimating acceptable nutrient concentrations and allow them to identify actions that would achieve compliance of cyanobacterial abundance risk levels with a given confidence level. © 2020 The Author(s

Machine learning approaches for predicting health risk of cyanobacterial blooms in Northern European Lakes

Cyanobacterial blooms are considered a major threat to global water security with documented impacts on lake ecosystems and public health. Given that cyanobacteria possess highly adaptive traits that favor them to prevail under different and often complicated stressor regimes, predicting their abundance is challenging. A dataset from 822 Northern European lakes is used to determine which variables better explain the variation of cyanobacteria biomass (CBB) by means of stepwise multiple linear regression. Chlorophyll-a (Chl-a) and total nitrogen (TN) provided the best modelling structure for the entire dataset, while for subsets of shallow and deep lakes, Chl-a, mean depth, TN and TN/TP explained part of the variance in CBB. Path analysis was performed and corroborated these findings. Finally, CBB was translated to a categorical variable according to risk levels for human health associated with the use of lakes for recreational activities. Several machine learning methods, namely Decision Tree, K-Nearest Neighbors, Support-vector Machine and Random Forest, were applied showing a remarkable ability to predict the risk, while Random Forest parameters were tuned and optimized, achieving a 95.81% accuracy, exceeding the performance of all other machine learning methods tested. A confusion matrix analysis is performed for all machine learning methods, identifying the potential of each method to correctly predict CBB risk levels and assessing the extent of false alarms; random forest clearly outperforms the other methods with very promising results. © 2020 by the authors

Reference data and calculators for second-generation HR-pQCT measures of the radius and tibia at anatomically standardized regions in White adults

High-resolution peripheral quantitative computed tomography (HR-pQCT) is a powerful tool to assess bone health. To determine how an individual's or population of interest's HR-pQCT outcomes compare to expected, reference data are required. This study provides reference data for HR-pQCT measures acquired in a population of White adults. PURPOSE: To provide age- and sex-specific reference data for high-resolution peripheral quantitative computed tomography (HR-pQCT) measures of the distal and diaphyseal radius and tibia acquired using a second-generation scanner and percent-of-length offsets proximal from the end of the bone. METHODS: Data were acquired in White adults (aged 18-80 years) living in the Midwest region of the USA. HR-pQCT scans were performed at the 4% distal radius, 30% diaphyseal radius, 7.3% distal tibia, and 30% diaphyseal tibia. Centile curves were fit to the data using the LMS approach. RESULTS: Scans of 867 females and 317 males were included. The fitted centile curves reveal HR-pQCT differences between ages, sexes, and sites. They also indicate differences when compared to data obtained by others using fixed length offsets. Excel-based calculators based on the current data were developed and are provided to enable computation of subject-specific percentiles, z-scores, and t-scores and to plot an individual's outcomes on the fitted curves. In addition, regression equations are provided to convert estimated failure load acquired with the conventional criteria utilized with first-generation scanners and those specifically developed for second-generation scanners. CONCLUSION: The current study provides unique data and resources. The combination of the reference data and calculators provide clinicians and investigators an ability to assess HR-pQCT outcomes in an individual or population of interest, when using the described scanning and analysis procedure. Ultimately, the expectation is these data will be expanded over time so the wealth of information HR-pQCT provides becomes increasingly interpretable and utilized

Bayesian belief networks as a meta-modelling tool in integrated river basin management -- Pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin

A Bayesian network approach is used to conduct decision analysis of nutrient abatement measures in the Morsa catchment, South Eastern Norway. The paper demonstrates the use of Bayesian networks as a meta-modelling tool in integrated river basin management (IRBM) for structuring and combining the probabilistic information available in existing cost-effectiveness studies, eutrophication models and data, non-market valuation studies and expert opinion. The Bayesian belief network is used to evaluate eutrophication mitigation costs relative to benefits, as part of the economic analysis under the EU Water Framework Directive (WFD). Pros and cons of Bayesian networks as reported in the literature are reviewed in light of the results from our Morsa catchment model. The reported advantages of Bayesian networks in promoting integrated, inter-disciplinary evaluation of uncertainty in IRBM, as well as the apparent advantages for risk communication with stakeholders, are offset in our case by the cost of obtaining reliable probabilistic data and meta-model validation procedures.