Influence diagrams as decision‐making tools for pesticide risk management

Abstract

The pesticide policy arena is filled with discussion of probabilistic approaches to assess ecological risk, however, similar discussions about implementing formal probabilistic methods in pesticide risk decision making are less common. An influence diagram approach is proposed for ecological risk-based decisions about pesticide usage. Aside from technical data, pesticide risk management relies on diverse sources, such as stakeholder opinions, to make decisions about what, how, where, and when to spray. Bayesian influence diagrams allow multiple lines of evidence, including process related information from existing data and expert judgment, in 1 inclusive decision model. In ecological risk assessments, data informally incorporated for pesticide usage decisions, such as field and laboratory effect studies along with chemical monitoring and modeling data, can be formally incorporated and expressed in linked causal diagrams. A case study is presented from the perspective of an environmental manager wishing to efficiently control pests while minimizing risk to local aquatic receptors. Exposure modeling results and toxicity studies were incorporated, and an ecological risk assessment was carried out but combined with hypothetical information on spraying efficacy and valuation of outcomes that would be necessary for making risk management decisions. The variables and their links in the influence diagram are ones that are important to a manager and can be manipulated to optimally control pests while protecting nontarget resources.

Integration of Chlorpyrifos Acetylcholinesterase Inhibition, Water Temperature, and Dissolved Oxygen Concentration into a Regional Scale Multiple Stressor Risk Assessment Estimating Risk to Chinook Salmon

“…Key attributes of BNs include their ability to incorporate a wide variety of data and to represent complex interactions such as those described by mechanistic representations of toxicity. Bayesian networks have been applied to environmental management and risk assessment (Marcot et al ; Pollino et al ; Uusitalo ; Barton et al ) to guide research and monitoring to support decision making and resource management (Nyberg et al ; Carriger and Newman ). Since 2012, the utility of the integrated BN‐RRM has been applied in numerous contexts, including contaminated sites (Hines and Landis ; Landis, Ayre et al ), emergent disease (Ayre et al ), nonindigenous species (Herring et al ), and forestry management (Ayre and Landis ).…”

Development of a Bayesian network for probabilistic risk assessment of pesticides

“…Model updates are carried out by combining prior probabilities and new data so that an update of the network posterior probabilities can take place as a response to the added observational information (Franco et al, 2016). Bayesian networks are especially useful for pesticide risk assessment and management tasks as these require characterisation of the uncertainties (Carriger and Newman (2012)). Focusing on a terrestrial species (puma), Carriger & Barron (2020) displayed a process of mapping cause-effect relations into a quantitative model.…”

Reply to Calow: In defense of science and its inclusion in decision making

“…Several decision analysis tools aid in facilitating the interaction between science and decision makers in the formulation of policy. Tools such as weight of evidence (USEPA ), multicriteria decision analysis (e.g., Linkov et al ), and influence diagrams (Carriger and Newman ) facilitate the weighing of facts and inferences to better inform policy and management options considering a range of stakeholder, regulatory, and other priorities. Wyant et al () describe how engagement among decision makers, risk assessors, and stakeholders facilitates the adaptive management process.…”