ESR 31:243-258 (2016)  -  DOI: https://doi.org/10.3354/esr00766

ABSTRACT: The bar for justifying the use of vertebrate animals for study is being increasingly raised, thus requiring increased rigor for species selection and study design. Although we have power analyses to provide quantitative backing for the numbers of organisms used, quantitative backing for selection of study species is not frequently employed. This can be especially important when measuring the impacts of ecosystem alteration, when study species must be chosen that are both sensitive to the alteration and of sufficient abundance for study. Just as important is providing justification for designation of surrogate species for study, especially when the species of interest is rare or of conservation concern and selection of an appropriate surrogate can have legal implications. In this study, we use a combination of GIS, a fish traits database and multivariate statistical analyses to quantitatively prioritize species for study and to determine potential study surrogate species. We provide two case studies to illustrate our quantitative, traits-based approach for designating study species and surrogate species. In the first case study, we select broadly representative fish species to understand the effects of turbine passage on adult fishes based on traits that suggest sensitivity to turbine passage. In our second case study, we present a framework for selecting a surrogate species for an endangered species. We suggest that our traits-based framework can provide quantitative backing and added justification to selection of study species while expanding the inference space of study results.

KEY WORDS: Conservation · Life history traits · Monitoring · Threatened fish

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Cite this article as: Pracheil BM, McManamay RA, Bevelhimer MS, DeRolph CR, Čada GF (2016) A traits-based approach for prioritizing species for monitoring and surrogacy selection. Endang Species Res 31:243-258. https://doi.org/10.3354/esr00766 Export citation Share:    Facebook - - linkedIn