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

Martínez‐Megías, Claudia; Mentzel, Sophie; Fuentes-Edfuf, Yasser; Moe, S. Jannicke; Rico, Andreu

doi:10.2139/ssrn.4306117

Cited by 4 publications

(13 citation statements)

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“…We have focused on pesticide applied to rice fields, and therefore the RICE Water Quality model (RICEWQ) was used to simulate pesticide exposure in the water of rice paddies (Karpouzas & Capri, 2006; Miao et al, 2004). The exposure data was obtained from Martínez‐Megías et al (2023), where more details about the exposure prediction can be found. The RICEWQ model is a process‐based model that, at the field level, simulates pesticide runoff specific for use in rice paddies (Martínez‐Megías et al, 2023; Williams et al, 1999).…”

Section: Methodsmentioning

confidence: 99%

“…The exposure data was obtained from Martínez‐Megías et al (2023), where more details about the exposure prediction can be found. The RICEWQ model is a process‐based model that, at the field level, simulates pesticide runoff specific for use in rice paddies (Martínez‐Megías et al, 2023; Williams et al, 1999). Thus far, it is considered to be the most suitable and reliable model for higher tier pesticide fate and exposure prediction (Daam et al, 2013; European Commission, 2003; Karpouzas & Capri, 2006; Pereira et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…The reader is referred to Williams et al (1999) for more detailed information on the model function, assumptions, and description. Validation of the RICEWQ model for this specific study area has not yet been completed (Martínez‐Megías et al, 2023), but the model has been validated in similar rice production scenarios and has provided satisfactory results (Karpouzas and Capri, 2006; Pereira et al, 2017). In the present study, we used predictions obtained from the latest version, RICEWQ 1.92 (Waterborne Environmental, 2022).…”

Section: Methodsmentioning

confidence: 99%

“…The insecticide acetamiprid, the fungicide azoxystrobin, and the herbicide MCPA were selected to show the applicability and integration of different pesticides types into the approach developed previously by Calvo et al (2021) and Rodrigo et al (2022). Acetamiprid and azoxystrobin are usually applied at the beginning of July, whereas azoxystrobin is applied at the end of July and the beginning of August (Martínez‐Megías et al, 2023).…”

Section: Methodsmentioning

confidence: 99%

“…While being used in situations where data are limited, BNs are able to incorporate various sources of information, for example, expert elicitation, other model outputs, or other information literature (Carriger et al, 2016; Carriger & Newman, 2012; Gibert et al, 2018; Hamilton & Pollino, 2012). Moreover, BNs can act as a metamodel (see Martínez‐Megías et al, 2023; Mentzel et al, 2022) allowing the incorporation of input and output data and assumptions from different models, for example, process and case‐based prediction models into a single network model. This approach can allow the display of uncertainty of all model compartments in a transparent way.…”

Section: Introductionmentioning

confidence: 99%

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Using a Bayesian Network Model to Predict Risk of Pesticides on Aquatic Community Endpoints in a Rice Field—A Southern European Case Study

Mentzel,

Martínez‐Megías,

Grung

et al. 2023

Enviro Toxic and Chemistry

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Bayesian network (BN) models are increasingly used as tools to support probabilistic environmental risk assessments (ERA), as they can better account for uncertainty compared to the simpler approaches commonly used in traditional ERA. We used BNs as meta‐models to link various sources of information in a probabilistic framework, to predict the risk of pesticides to aquatic communities under given scenarios. The research focused on rice fields surrounding a Spanish Natural Park Albufera, considering three selected pesticides: acetamiprid (insecticide), MCPA (herbicide), and azoxystrobin (fungicide). The developed BN linked the inputs and outputs of two pesticide models: a process‐based exposure model (RICEWQ), and probabilistic effects model (PERPEST) using case‐based reasoning with data from microcosm and mesocosm experiments. The model characterised risk at three levels in a hierarchy: biological endpoints (e.g., molluscs, zooplankton, insects, etc.), endpoint groups (plants, invertebrates, vertebrates, and community processes), and community. The pesticide risk to a biological endpoint was characterised as the probability of an effect for a given pesticide concentration interval. The risk to an endpoint group was calculated as the joint probability of effect on any of the endpoints in the group. Likewise, community‐level risk was calculated as the joint probability of any of the endpoint groups being affected. This approach enabled comparison of risk to endpoint groups across different pesticide types. For example, in a scenario for year 2050, the predicted risk of the insecticide to the community (40% probability of effect) was dominated by the risk to invertebrates (36% risk). In contrast, herbicide‐related risk to the community (63%) was resulting from risk to both plants (35%) and invertebrates (38%); the latter might here represent indirect effects of toxicity through the food chain. This novel approach combines the quantification of spatial variability of exposure with probabilistic risk prediction for different components of aquatic ecosystems.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Using a Bayesian Network Model to Predict Risk of Pesticides on Aquatic Community Endpoints in a Rice Field—A Southern European Case Study

Mentzel,

Martínez‐Megías,

Grung

et al. 2023

Enviro Toxic and Chemistry

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Incorporating climate projections in the environmental risk assessment of pesticides in aquatic ecosystems

Oldenkamp,

Benestad,

Hader

et al. 2023

Integr Envir Assess & Manag

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Global climate change will significantly impact the biodiversity of freshwater ecosystems, both directly and indirectly via the exacerbation of impacts from other stressors. Pesticides form a prime example of chemical stressors that are expected to synergise with climate change. Aquatic exposures to pesticides might change in magnitude due to increased runoff from agricultural fields, and in composition as application patterns will change due to changes in pest pressures and crop types. Any prospective chemical risk assessment that aims to capture the influence of climate change should properly and comprehensively account for the variabilities and uncertainties that are inherent to projections of future climate. This is only feasible if they probabilistically propagate extensive ensembles of climate model projections. However, current prospective risk assessments typically make use of process‐based models of chemical fate that do not typically allow for such high‐throughput application. Here we describe a Bayesian network model that does. It incorporates a two‐step univariate regression model based on a thirty‐day antecedent precipitation index, circumventing the need for computationally laborious mechanistic models. We show its feasibility and application potential in a case study with two pesticides in a Norwegian stream: the fungicide trifloxystrobin and herbicide clopyralid. Our analysis showed that variations in pesticide application rates as well as precipitation intensity lead to variations in in‐stream exposures. When relating to aquatic risks, the influence of these processes reduces and distributions of risk are dominated by effect‐related parameters. Predicted risks for clopyralid were negligible, but the probability of unacceptable future environmental risks due to exposure to trifloxystrobin (i.e., a risk quotient > 1) was 8‐12%. This percentage further increases to 30‐35% when a more conservative precautionary factor of 100 instead of 30 was used.

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Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach

Moe,

Brix,

Landis

et al. 2024

Integr Envir Assess & Manag

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The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This paper explores a modeling approach for integrating climate model projections into the assessment of near‐ and long‐term ecological risks, developed in collaboration with climate scientists. State‐of‐the‐art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate.

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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

Cited by 4 publications

References 0 publications

Using a Bayesian Network Model to Predict Risk of Pesticides on Aquatic Community Endpoints in a Rice Field—A Southern European Case Study

Using a Bayesian Network Model to Predict Risk of Pesticides on Aquatic Community Endpoints in a Rice Field—A Southern European Case Study

Incorporating climate projections in the environmental risk assessment of pesticides in aquatic ecosystems

Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach

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