Biology subjects

Moe, S. J.

Publications and source records attributed to Moe, S. J..

5 recordsLinked to original sources

bioRxiv2026

Chemical Mixtures in River Basins: Combining Additive and Independent Effects with Probabilistic Risk Modelling

Chemical mixtures and potential cocktail effects in aquatic ecosystems are recognised as a threat for river basins world-wide. Probabilistic risk approaches are becoming more common in environmental risk assessment, and offer new opportunities for metodological challenges such as of mixture risk characterisation. The Concentration Addition (CA) concept is commonly used in lower-tier risk assessment (e.g., sum of risk quotients), as a pragmatic and protective method. However, the alternative Independent Action (IA) concept can easily be implemented in probabilistic risk calculation (e.g., joint probability of threshold exceedances). We have developed a multi-level probabilistic model for integrating these two concepts, formulated as an object-oriented Bayesian network (BN). First, probabilistic risk quotients (RQ) are calculated for individual substances, as probability distributions of environmental concentrations divided by a threshold environmental value. Next, the CA concept is applied within groups of substances by summing the RQ distributions. Finally, the IA concept is applied across the different substance groups, assuming independent modes of action, to combine RQ distributions by joint probability calculation ("OR" expressions). Predicted exposure concentrations were obtained from the ENCORE fate model, a process-based model for simulation of chemicals in river basins across Europe. Here we present a pilot study focusing on a subset of the substances (15 pesticides) and river basins (in Belgium), as a proof-of-concept. The purpose of this pilot study was to demonstrate a novel probabilistic approach to mixture risk characterisation, by combining the CA and IA concepts in a multi-level BN. The results were consistent across scenarios as well as with literature, with CA-based risk characterisations being slightly higher the IA-based. The combined CA+IA-based risk represents a reasonable compromise. Sensitivity analysis of the BN can provide an effective ranking of the risk-driving substances and groups, to support chemical prioritisation and risk managment. Key pointsO_LIA multi-level Bayesian network (BN) was developed for probabilistic calculation of environmental risk from chemical mixtures, based on risk quotients (RQ) calculated for individual substances, with a selection of 15 pesticides in Belgium as a pilot study. C_LIO_LIPredicted environmental concentrations (PEC) are obtained from the ENCORE exposure model; a process-based model which can simulate transport, fate and concentration of >1000 substances (pesticides, pharmaceuticals, etc.) in rivers subcatchments across Europe based on chemical use and emission. C_LIO_LIThe BNs risk calculation uses substance groups (Fungicides, Herbicides and Insecticides) to combine two classical mixture concepts: (1) Concentration Addition: sum of RQs within groups, followed by (2) Independent Action: joint probability of threshold exceedance for one or more groups. C_LIO_LIThe resulting rankings of risk-driving substances by the BN for this pilot study are robust across scenarios, suggesting a potential for expanding this generic BN approach to different mixtures with higher numbers of substances and groups, and to larger regions of Europe. C_LI

ecology↗

bioRxiv2022

A Bayesian network approach to assess the influence of climate change and pesticide use practices on the ecological risks of pesticides in a protected Mediterranean wetland

Pollution by agricultural pesticides is one of the most important pressures affecting Mediterranean coastal wetlands. Pesticide risks are expected to be influenced by climate change, which will result in an increase of temperatures and a decrease in annual precipitation rates in this region. On the other hand, pesticide dosages are expected to change given the increase in pest resistance and the implementation of environmental policies like the European Farm-to-Fork strategy, which aims for a 50% reduction in pesticide usage by 2030. The influence of climate change and pesticide use practices on the ecological risks of pesticides needs to be evaluated making use of realistic environmental scenarios. This study aimed to assess how different climate change and pesticide use practices affect the ecological risks of pesticides in the Albufera Natural Park (Valencia, Spain), a protected Mediterranean coastal wetland. We performed a probabilistic risk assessment for nine pesticides applied in rice production using scenarios comprised of three climatic regimes (the 2008 record, and projections for 2050 and 2100), three pesticide application regimes (the recommended dose, and 50% increase and 50% decrease), and their combinations. The scenarios were used to simulate pesticide exposure concentrations in the water column of the rice paddies using the RICEWQ model. Pesticide effects were characterized using acute and chronic Species Sensitivity Distributions built with laboratory toxicity data for aquatic organisms. Risk quotients were calculated as probability distributions making use of a Bayesian network approach, and best fitting distributions for the calculated exposure data and the SSDs. Our results show that future climate projections will influence exposure concentrations for some of the studied pesticides, yielding higher dissipation and lower exposure in scenarios dominated by an increase of temperatures, and higher exposure for scenarios in which heavy precipitation events occur after pesticide application. Our case study shows that pesticides such as azoxystrobin, difenoconazole and MCPA are posing high ecological risks for aquatic organisms, and that the implementation of the Farm-to-Fork strategy is crucial to reduce them, although will need additional measures to eliminate them.

ecology↗

bioRxiv2022

Using a Bayesian network model to predict effects of pesticides on aquatic community endpoints in a rice field - A southern European case study

In recent years, Bayesian network (BN) models have become more popular as a tool to support probabilistic environmental risk assessments (ERA). They can better account for and communicate uncertainty compared to the deterministic approaches currently used in traditional ERA. In this study, we used the BN as a meta-model to predict the potential effect of various pesticides on different biological levels in the aquatic ecosystem. The meta-model links the inputs and outputs of a process-based exposure model (RICEWQ), that is run with various scenarios combination built on meteorological, hydrological, and agricultural scenarios, and a probabilistic case-based effect model (PERPEST), which bases its prediction on a database of microcosm and mesocosm experiments. The research focused on the pesticide exposure in rice fields surrounding a Spanish Natural Park, considering three selected pesticides for this case study: acetamiprid (insecticide), MCPA (herbicide), and azoxystrobin (fungicide). For each of the pesticide types, the developed BN model enabled the prediction of their effects on biological endpoints, endpoint groups, and community in an aquatic ecosystem. Also, it enables comparison between the different pesticide types, their effects on endpoint groups and community. While directly linking future scenarios of climate and agricultural practice to the exposure concentration and indirectly linking them to the effect on biological endpoints as well as community. In summary, azoxystrobin and MCPA seem to have a higher predicted risk for the community with at least one of the biological endpoint being effected compared to acetamiprid. Generally, the developed approach facilitates the communication of uncertainties associated with the predicted effect on different biological levels of the aquatic ecosystem. This transparency in all model components can aid risk management and decision making.

ecology↗

bioRxiv2019

Development of a hybrid Bayesian network model for predicting acute fish toxicity using multiple lines of evidence

A Bayesian network was developed for predicting the acute toxicity intervals of chemical substances to fish, based on information on fish embryo toxicity (FET) in combination with other information. This model can support the use of FET data in a Weight-of-Evidence (WOE) approach for replacing the use of juvenile fish. The BN predicted correct toxicity intervals for 69%-80% of the tested substances. The model was most sensitive to components quantified by toxicity data, and least sensitive to components quantified by expert knowledge. The model is publicly available through a web interface. Further development of this model should include additional lines of evidence, refinement of the discretisation, and training with a larger dataset for weighting of the lines of evidence. A refined version of this model can be a useful tool for predicting acute fish toxicity, and a contribution to more quantitative WOE approaches for ecotoxicology and environmental assessment more generally.\n\nHighlightsO_LIA Bayesian network (BN) was developed to predict the toxicity of chemicals to fish\nC_LIO_LIThe BN uses fish embryo toxicity data in a quantitative weight-of-evidence approach\nC_LIO_LIThe BN integrates physical, chemical and toxicological properties of chemicals\nC_LIO_LICorrect toxicity intervals were predicted for 69-80% of test cases\nC_LIO_LIThe BN is publicly available for demonstration and testing through a web interface\nC_LI

pharmacology and toxicology↗

bioRxiv2019

A 50-year series of mark-recapture data of large-sized brown trout (Salmo trutta) from Lake Mjosa, Norway

BackgroundLong-term data from marked animals provide a wealth of opportunities for studies with high relevance to both basic ecological understanding and successful management in a changing world. The key strength of such data is that they allow to quantify individual variation in vital rates (e.g. survival, growth, reproduction) and then link it mechanistically to dynamics at the population level. However, maintaining the collection of individual-based data over long time periods comes with large logistic efforts and costs, and studies spanning over decades are therefore rare. This is the case particularly for migratory aquatic species, many of which are in decline despite their high ecological, cultural, and economical value. New informationThis paper describes two unique publicly available time series of individual-based data originating from a 51-year mark-recapture study of a land-locked population of large-sized migratory brown trout (Salmo trutta) in Norway: the Hunder trout. In the period 1966-2015, nearly 14,000 adult Hunder trout have been captured and individually marked during their spawning migration from Lake Mjosa to the river Gubrandsdalsl[a]gen. Almost a third of those individuals were later recaptured alive during a later spawning run and/or captured by fishermen and reported dead or alive. This has resulted in the first data series: a mark-recapture-recovery dataset spanning half a century and more than 18,000 capture records. The second data series consists of additional data on juvenile and adult growth and life-history schedules from half of the marked individuals, obtained by means of scale sample analysis. The two datasets offer a rare long-term perspective on individuals and population dynamics and provide unique opportunities to gain insights into questions surrounding management, conservation, and restoration of migratory salmonid populations and freshwater ecosystems.

ecology↗