Ecological risk assessment for Pacific salmon exposed to dimethoate in California

Abstract

A probabilistic risk assessment of the potential direct and indirect effects of acute dimethoate exposure to salmon populations of concern was conducted for 3 evolutionarily significant units (ESUs) of Pacific salmon in California. These ESUs were the Sacramento River winter-run chinook, the California Central Valley spring-run chinook, and the California Central Valley steelhead. Refined acute exposures were estimated using the Soil and Water Assessment Tool, a river basin-scale model developed to quantify the impact of land-management practices in large, complex watersheds. Both direct effects (i.e., inhibition of brain acetylcholinesterase activity) and indirect effects (i.e., altered availability of aquatic invertebrate prey) were assessed. Risk to salmon and their aquatic invertebrate prey items was determined to be de minimis. Therefore, dimethoate is not expected to have direct or indirect adverse effects on Pacific salmon in these 3 ESUs. Environ Toxicol Chem 2017;36:532-543. © 2016 SETAC.

A refined ecological risk assessment for California red‐legged frog, Delta smelt, and California tiger salamander exposed to malathion

“…Although the best available data and modeling approaches are used in the present ERA, as with any risk assessment, there are inherent uncertainties (Whitfield Aslund et al ). The refined exposure assessments assume that end‐use products of malathion are used at the maximum application rates with minimum time intervals between applications and applied using methods that would result in the highest EECs.…”

“…Acute risk curves for fish were derived by integrating the acute SSD with 96‐h EEC distributions, and for aquatic invertebrates the acute and chronic SSDs were integrated with 48‐h and 21‐d EECs, respectively. The areas under the risk curve (AUC) were then used to categorize risk as de minimis, low, intermediate, or high based on the criteria described in Moore et al (); Moore, Teed et al (); and Whitfield Aslund et al (), in which risk categories are defined as follows: If the area under the risk curve is less than the AUC associated with the curve produced by risk products (risk product = exceedance probability × magnitude of effect) of 0.25% (e.g., 5% exceedance probability of 5% or greater effect = 0.25%), then the risk was categorized as de minimis. The AUC for risk products of 0.25% is 1.75%; if the AUC was equal to or greater than 1.75%, but less than 9.82% (i.e., the AUC for risk products of 2%), then the risk was categorized as low; if the AUC was equal to or greater than 9.82%, but less than 33% (i.e., the AUC for risk products of 10%), then the risk was categorized as intermediate; and if the AUC was equal to or greater than 33%, then the risk was categorized as high. …”

“…Acute risk curves for fish were derived by integrating the acute SSD with 96-h EEC distributions, and for aquatic invertebrates the acute and chronic SSDs were integrated with 48-h and 21-d EECs, respectively. The areas under the risk curve (AUC) were then used to categorize risk as de minimis, low, intermediate, or high based on the criteria described in Moore et al (2010); Moore, Teed et al (2014);and Whitfield Aslund et al (2016), in which risk categories are defined as follows:…”

Integrating Exposure and Effect Distributions with the Ecotoxicity Risk Calculator: Case Studies with Crop Protection Products

“…Although the case studies presented in this manuscript employed SSDs to characterize hazard, the ERC can use single species exposure‐response relationships. For example, risk curves have been used to determine the probability of brain acetylcholinesterase inhibition in juvenile salmon given a defined exposure distribution of dimethoate (Whitfield‐Aslund et al 2017) and other carbamates and organophosphates (Moore and Teed 2013). Assessments such as these can characterize the probabilities of certain effects, which could then be linked to existing modeling approaches that support ecological risk assessment, such as quantitative adverse outcome pathways (Perkins et al 2019), dynamic energy budget models (Murphy et al 2018), and population models (Forbes et al 2008).…”

Ecological Risk Assessment of Organochlorine Pesticides and Polychlorinated Biphenyls in Coastal Sediments in China

“…There are many subjective factors in the process of formulating PNECs, which are determined based on the effects of small concentrations reported by a limited number of studies, and the results may not be repeatable. Joint probability curve methods can remedy this deficiency through using the linear regression of two datasets to calculate the probability that a concentration will adversely affect a specific proportion (%) of a species, and classifying the risk as minimal, low, medium, or high [ 45 , 46 , 47 ]. The formula is as follows [ 38 ]: Risk product = exceedance probability × magnitude of effect, …”