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DEMOGRAPHIC UNCERTAINTY IN ECOLOGICAL RISK ASSESSMENTS. (R825347)
Citation:
Wang, G., W. D. Edge, AND J. O. Wolff. DEMOGRAPHIC UNCERTAINTY IN ECOLOGICAL RISK ASSESSMENTS. (R825347). ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Elsevier Science Ltd, New York, NY, 136(1):95-102, (2001).
Description:
We built a Ricker's model incorporating demographic stochasticity to simulate the effects of demographic uncertainty on responses of gray-tailed vole (Microtus canicaudus) populations to pesticide applications. We constructed models with mark-recapture data collected from populations in 1998 and 1999. We ran 30 simulations of a single pesticide application for small (~30 voles), medium (~50 voles), and large (~100 voles) population sizes for 1998 data. Significantly less uncertainty in detecting pesticide effects was exhibited at large population sizes. Fifty percent of the simulations for small or medium population sizes suggested no differences between control and treatments. Due to population fluctuations resulting from demographic stochasticity and small population sizes, we detected no significant differences in the simulations using 1999 data. Population sizes may affect the recovery ability of vole populations following pesticide-induced mortality. Vole population-size declines were significant for pesticide applications at large population sizes, but greater uncertainty existed in the simulations of low and medium population sizes. Our results suggested that the Quotient Method (QM), an ecological risk assessment model, should differentiate the short-term risk of a chemical to small and large populations. The QM uses the ratio of the expected environmental concentration to the median lethal dose or concentration of chemicals for a species to assess the risk of chemicals to nontarget wildlife. Our results also suggested that the QM could not predict or may underestimate the long-term extinction risk of rare or endangered wildlife species from contamination by pesticides.
Author Keywords: Demography; Microtus canicaudus; Pesticides; Population dynamics; Simulation; Stochasticity