A3 Wolmar

Assessing population coherence and connectivity: some methods and caveats


Mike Bruford


Genetic tools have been championed as providing novel and sensitive ways to detect the otherwise cryptic movements of organisms, and their genes, in the context of landscape connectivity. A variety of statistical approaches have been developed to detect migrants and their descendants within populations of conservation concern. These methods have taken advantage of increasingly powerful molecular approaches, initially from microsatellite DNA profiling, now to whole genome resequencing1. The transition from standard molecular ecology techniques to population genomics not only allows the demographic consequences of migration and gene-flow to be better inferred, but the fitness consequences may become predictable too.

Inferring movement between populations that are genetically very similar (possibly because they recently lost contact, or because gene-flow is reciprocal) has been traditionally challenging using a handful of DNA profiling markers, but the resolution of this approach will be transformed by the application of genome-wide datasets. Recent and ongoing studies, using whole genome resequencing of species benefitting from well-annotated reference genomes, have been able to shed light on likely chromosome and finer scale effects of immigration and for managed translocation/gene-flow, which potentially puts individual fitness-defining genomic variants in the hands of conservation managers.

The conservation implications of this transformation in our ability to resolve connectivity and population coherence remains, however, unclear. For instance, when assessing the likely benefits of gene-flow, whether natural or by assisted translocation, the balance between genome-wide versus locus-specific management has yet to be fully assessed, especially in the context of environmental heterogeneity. One possible solution is the use of so-called landscape genomics tools that simultaneously evaluate genomic and large-scale environmental datasets2 but these methods can produce conflicting results and/or different rankings and priorities. I will discuss these problems and methods in the context of ongoing conservation initiatives in Europe and at a global level, with the aim of proposing research avenues and policy discussions that could be relevant to advancing this exciting new field.

1. Caroll EL, Bruford MW, et al (2018) Genetic and genomic monitoring with minimally invasive sampling methods. Evolutionary Applications. In press.

2. Stucki S, Orozco-terWengel P, et al (2017) High performance computation of landscape genomic models including local indicators of spatial association. Molecular Ecology Resources 17: 1072-1089.