Literature DB >> 20136742

The use of surrogate species in risk assessment: using life history data to safeguard against false negatives.

John E Banks1, Azmy S Ackleh, John D Stark.   

Abstract

The use of surrogate species is an important tool in predicting the effects of management decisions or the establishment of protective measures for endangered/threatened species. While relying on a handful of model species to predict the fate of scores of distantly related target species has been criticized, a quantitative measure linking life history traits and population predictions has been sorely missing. We derive here a closed-form expression aimed at determining conditions under which sublethal effects of a toxicant on surrogate species population outcomes will reliably predict outcomes of target (listed) species. We develop a critical threshold in fecundity reduction above which the surrogate species outcomes indicate positive population growth, while the listed species is driven to extinction. Thus we have established a means of determining conditions under which we are prone to making a "Type II" error in assessing ecological risk using surrogate species. Finally, we use the derived expression and life history data to compare outcomes from four different commonly used fish surrogate species (round goby, fathead minnow, smallmouth bass, cutthroat trout) and their target listed species (Chinook and Coho salmon). We illustrate that all four surrogate species fail to predict population outcomes for the listed species in cases of as little as 15% fecundity reduction due to toxicant exposure. Furthermore, surrogate species reliability is a function of toxicant level, so that some species are reliable at some levels but not at others. We discuss the implications of these findings, and outline further analyses that occur as a natural extension of the criteria developed here.

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Year:  2010        PMID: 20136742     DOI: 10.1111/j.1539-6924.2009.01349.x

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  10 in total

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  10 in total

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