Population modeling for pesticide risk assessment of threatened species—A case study of a terrestrial plant, Boltonia decurrens. (2nd September 2016)

• Record Type: Journal Article
• Authors: Schmolke, Amelie; Brain, Richard; Thorbek, Pernille; Perkins, Daniel; Forbes, Valery
• Is Part Of: Environmental toxicology and chemistry. Volume 36:Number 2(2017:Feb.)
• Page Start: 480
• Page End: 491
• Publication Date: 2016-09-02
• Subjects: Population model -- Individual‐based model -- Boltonia decurrens -- Endangered Species Act -- Pesticide risk assessment; Pollution -- Environmental aspects -- Periodicals; Environmental chemistry -- Periodicals; 615.902
• DOI: 10.1002/etc.3576
• Languages: English
• ISSNs: 0730-7268

Abstract

Abstract: Although population models are recognized as necessary tools in the ecological risk assessment of pesticides, particularly for species listed under the Endangered Species Act, their application in this context is currently limited to very few cases. The authors developed a detailed, individual‐based population model for a threatened plant species, the decurrent false aster ( Boltonia decurrens ), for application in pesticide risk assessment. Floods and competition with other plant species are known factors that drive the species' population dynamics and were included in the model approach. The authors use the model to compare the population‐level effects of 5 toxicity surrogates applied to B. decurrens under varying environmental conditions. The model results suggest that the environmental conditions under which herbicide applications occur may have a higher impact on populations than organism‐level sensitivities to an herbicide within a realistic range. Indirect effects may be as important as the direct effects of herbicide applications by shifting competition strength if competing species have different sensitivities to the herbicide. The model approach provides a case study for population‐level risk assessments of listed species. Population‐level effects of herbicides can be assessed in a realistic and species‐specific context, and uncertainties can be addressed explicitly. The authors discuss how their approach can inform the future development and application of modeling for population‐level risk assessments of listed species, and ecological risk assessment in general. Environ Toxicol Chem 2017;36:480–491. © 2016 SETAC