New Zealand Journal of Ecology (2008) 32(1): 130-137

Managing genetic diversity in threatened populations: a New Zealand perspective

Forum Article
Ian G. Jamieson *,1
Catherine E. Grueber 1
Jon M. Waters 1
Dianne M. Gleeson 2
  1. Department of Zoology, University of Otago, PO Box 56 Dunedin, New Zealand
  2. Landcare Research, Private Bag 92170 Auckland, New Zealand
Abstract: 

Genetic diversity allows a population to adapt genetically to a changing environment or to buffer it against stochastic events such as harsh weather or disease outbreaks. Genetic diversity is therefore an important consideration in the development of management strategies for threatened populations around the world, with the possible exception of New Zealand, where species recovery programmes tend to focus on increasing population size while neglecting the maintenance of genetic diversity. Many of New Zealand’s threatened species have relatively low genetic variation and consequently may still be at risk in the long-term due to reduced resilience even if the effects of introduced predators were eliminated. The three main factors affecting genetic diversity – genetic drift, inbreeding and population subdivision – are processes that potentially impact on many of our locally threatened species, but their effects tend to occur over a considerably broader timescale than ecological effects, and as such are much more difficult to detect and ultimately to justify additional resource spending towards. Our message is that genetic management of New Zealand threatened species should not take priority over other management concerns such as controlling predators or improving habitat quality, but it needs more attention than it currently receives. We recommend that genetic diversity be a fundamental component in long-term management strategies for threatened species, and that such strategies are made explicit within the New Zealand Department of Conservation’s current species recovery plans so that the persistence of biodiversity becomes of key importance, as opposed to current approaches that seek solely to maximise representation.