COS 8-5 - Indices of population viability

Monday, August 6, 2012: 2:50 PM
D136, Oregon Convention Center
Michael A. McCarthy, School of BioSciences, The University of Melbourne, Australia, Alana L. Moore, School of Botany, The University of Melbourne, Parkville, Australia, Jochen Krauss, Department of Animal Ecology and Tropical Biology, University of W├╝rzburg, Germany and John W. Morgan, La Trobe University, Bundoora, Australia

Biodiversity monitoring programs track previous changes in abundance, but ideally they could also predict risks in the future. For example, the Convention on Biological Diversity (CBD) requires reporting about reductions in the rate of extinctions. Reporting actual extinctions, while potentially informative, is problematic because it is retrospective, whereas the convention seeks to reduce future extinctions. Conducting detailed analyses of risk for numerous species is infeasible for reporting purposes, so simple indices that reflect extinction risk are needed. Some simple indices have been proposed and heuristic properties of these have been considered to help chose among them. However, it is unclear how they relate to future extinction risk. Here we develop two indices of biodiversity based on simple models of population viability.


The first index is based on the geometric mean abundance of species, while the second is based on a more general power mean. These indices are designed to have the same data requirements as those previously considered, but they have the additional benefit of being directly related to extinction risk. Evaluating the indices using butterfly and woodland plant data show that they are correlated with local extinction rates. Applying the index based on the geometric mean to global data on changes in abundance of vertebrates suggests that the average probability of extinction of these species has increased by approximately 3% from 1970 to 2007. More generally, this study illustrates a useful way of developing ecological indices that are based on formal models with clear and testable assumptions.