title: Risk assessment considerations with regard to the potential impacts of pesticides on endangered species.
abstract: Simple, deterministic screening-level assessments that are highly conservative by design facilitate a rapid initial screening to determine whether a pesticide active ingredient has the potential to adversely affect threatened or endangered species. If a worst-case estimate of pesticide exposure is below a very conservative effects metric (e.g., the no observed effects concentration of the most sensitive tested surrogate species) then the potential risks are considered de minimis and unlikely to jeopardize the existence of a threatened or endangered species. Thus by design, such compounded layers of conservatism are intended to minimize potential Type II errors (failure to reject a false null hypothesis of de minimus risk), but correspondingly increase Type I errors (falsely reject a null hypothesis of de minimus risk). Because of the conservatism inherent in screening-level risk assessments, higher-tier scientific information and analyses that provide additional environmental realism can be applied in cases where a potential risk has been identified. This information includes community-level effects data, environmental fate and exposure data, monitoring data, geospatial location and proximity data, species biology data, and probabilistic exposure and population models. Given that the definition of "risk" includes likelihood and magnitude of effect, higher-tier risk assessments should use probabilistic techniques that more accurately and realistically characterize risk. Moreover, where possible and appropriate, risk assessments should focus on effects at the population and community levels of organization rather than the more traditional focus on the organism level. This document provides a review of some types of higher-tier data and assessment refinements available to more accurately and realistically evaluate potential risks of pesticide use to threatened and endangered species.
authors.affiliation: Syngenta Crop Protection, Greensboro, North Carolina, USA.
authors.forename: Richard A
authors.initials: RA
authors.lastname: Brain
authors.forename: R Scott
authors.initials: RS
authors.lastname: Teed
authors.forename: JiSu
authors.initials: J
authors.lastname: Bang
authors.forename: Pernille
authors.initials: P
authors.lastname: Thorbek
authors.forename: Jeff
authors.initials: J
authors.lastname: Perine
authors.forename: Natalia
authors.initials: N
authors.lastname: Peranginangin
authors.forename: Myoungwoo
authors.initials: M
authors.lastname: Kim
authors.forename: Ted
authors.initials: T
authors.lastname: Valenti
authors.forename: Wenlin
authors.initials: W
authors.lastname: Chen
authors.forename: Roger L
authors.initials: RL
authors.lastname: Breton
authors.forename: Sara I
authors.initials: SI
authors.lastname: Rodney
authors.forename: Dwayne R J
authors.initials: DR
authors.lastname: Moore
mesh_terms: D000818:Animals; D056727:Endangered Species; D004784:Environmental Monitoring; D004785:Environmental Pollutants / Q000633:toxicity*; D008962:Models, Theoretical; D010575:Pesticides / Q000633:toxicity*; D018570:Risk Assessment
chemical_list: D004785:Environmental Pollutants; D010575:Pesticides
keywords: Pesticide; Population modeling; Probabilistic risk assessment; Threatened and endangered species