Testing the limits to evolution: when and why does adaptation fail in response to ecological change?

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Organisms on this planet are currently experiencing unprecedented rates of habitat loss and environmental change. Climate change alone is predicted to condemn at least one in ten of all species to extinction this century unless they can adapt to deal with these changing conditions. Such a high rate of biodiversity loss is likely to have catastrophic effects on the ecological networks that we depend on for food and fuel production, for sustainable development, and ultimately for political stability. There is therefore an urgent need to understand maximum rates of evolution in response to environmental change. This will allow scientists to provide clearer guidance to policymakers and economists on what the consequences of changing climate and habitat loss are likely to be for biodiversity. At species and population margins, limits to adaptation prevent expansion into novel environments. Theoretical models predict that these limits occur either because rates of ecological change become too large relative to the movement of individuals from other populations, or because populations at species' edges lack the genetic variation necessary for adaptation. Extensive preliminary data suggest that the study of Australian rainforest fruitfly populations along repeated altitudinal transitions presents a unique opportunity to distinguish between these hypotheses, and assess which ecological and genetic factors most limit evolutionary potential in nature. The proposed research will explore how levels of genetic variation affect the point where evolution fails along similar altitudinal transitions of varying steepness. It will use field experiments to measure the strength and steepness of these selective gradients from the perspective of the flies themselves, directly estimate rates of movement of individuals, and assess genetic changes in those traits expected to be evolving along these spatial gradients. Estimates of these variables will then be related back to theoretical models to test how well these models predict the maximum rates of evolution observed in real populations. Such information will not only help to identify which species and populations are most at risk of extinction, it will also provide guidance on which strategies can be employed to maximise evolutionary responses in natural populations. This will help to reduce, or plan for, the massive rates of biodiversity loss predicted for the coming centuries.