One technique that could mitigate the impact of climate change is targeted gene flow: moving individuals with long-standing (i.e. genetically based) climate adaptations to habitat whose climate will soon match those adaptations. The challenge with this lies in first determining to which environmental variables species are most responsive. The next step is to then figure out whether geographic trait variation is due to local adaptation or phenotypic plasticity.
One way to solve this problem is to look how gene flow influences observed trait-environment relationships in a population. Populations where trait-environment relationships are weakened by high gene flow suggests there is a genetic basis to the relationship, indicative of local adaptation. Populations where that relationship is unaffected by gene flow suggests a limited genetic basis, and therefore a relationship based on phenotypic plasticity rather than local adaptation.
Data on gene flow is difficult to come by. However, new research proposes using habitat connectivity as a proxy for gene flow. Then it becomes possible to look at how connectivity affects trait-environment relationships, and to understand the degree to which different traits are based on local adaption. In this way, connectivity can be a useful measure for understanding the genetic basis behind species response to climatic drivers
This idea is tested out in the study on the Rainforest Sunskink (Lampropholis coggeri), a small lizard that inhabits the rainforests of the Wet Tropics region in northeastern Australia. By using measures of connectivity and its interaction with the environment, local adaptation is shown as a strong signal in the population for the traits that were measured. Environmental variables are also ranked according to the strength of local adaptation associated with them.
One benefit of this approach is that it has less demanding data requirements, since connectivity is usually an easier variable to measure than gene flow. It also has the potential to be applied to already existing data sets. Although it is a simplified approach to understanding complex environmental relationships, its simplicity makes it a useful starting point to link connectivity, climate, and genetics and improve management plans for adapting to climate change.
Macdonald, S. L., J. Llewellyn, and B. L. Phillips. 2018. Using connectivity to identify climatic drivers of local adaptation. Ecology Letters 21: 207-216.