Life history traits and abundance can predict local colonisation and extinction rates of freshwater mussels
Article first published online: 6 MAR 2012
© 2012 Blackwell Publishing Ltd
Volume 57, Issue 5, pages 982–992, May 2012
How to Cite
VAUGHN, C. C. (2012), Life history traits and abundance can predict local colonisation and extinction rates of freshwater mussels. Freshwater Biology, 57: 982–992. doi: 10.1111/j.1365-2427.2012.02759.x
- Issue published online: 4 APR 2012
- Article first published online: 6 MAR 2012
- (Manuscript accepted 24 January 2012)
- fish host;
- freshwater mussel;
- life history traits
1. A critical need in conservation biology is to determine which species are most vulnerable to extinction. Freshwater mussels (Bivalvia: Unionacea) are one of the most imperilled faunal groups globally. Freshwater mussel larvae are ectoparasites on fish and depend on the movement of their hosts to maintain connectivity among local populations in a metapopulation.
2. I calculated local colonisation and extinction rates for 16 mussel species from 14 local populations in the Red River drainage of Oklahoma and Texas, U.S. I used general linear models and AIC comparisons to determine which mussel life history traits best predicted local colonisation and extinction rates.
3. Traits related to larval dispersal ability (host infection mode, whether a mussel species was a host generalist or specialist) were the best predictors of local colonisation.
4. Traits related to local population size (regional abundance, time spent brooding) were the best predictors of local extinction. The group of fish species used as hosts by mussels also predicted local extinction and was probably related to habitat fragmentation and host dispersal abilities.
5. Overall, local extinction rates exceeded local colonisation rates, indicating that local populations are becoming increasingly isolated and suffering an ‘extinction debt’. This study demonstrates that analysis of species traits can be used to predict local colonisation and extinction patterns and provide insight into the long-term persistence of populations.