Multi‐level patterns in population genetics: Variogram series detects a hidden isolation‐by‐distance‐dominated structure of Scandinavian brown bears Ursus arctos

Demographic History of the Brown Bear (Ursus arctos) on Hokkaido Island, Japan, Based on Whole-Genomic Sequence Analysis

Previous studies of the brown bear (Ursus arctos) on Hokkaido Island, Japan, have detected three geographically distinct subpopulations representing different mitochondrial lineages and shown that gene flow between subpopulations has occurred due to male-biased dispersal. In this study, we determined whole-genomic sequences for six Hokkaido brown bears and analyzed these data along with previously published genomic sequences of 17 brown bears from other parts of the world. We found that the Hokkaido population is genetically distinct from the other populations, keeping genetic diversity higher than the endangered populations in western Europe but lower than most populations on the continents. A reconstruction of historical demography showed no increase in population size for the Hokkaido population during the Eemian interglacial period (130,000–114,000 years ago). In a phylogenetic analysis of the autosomal data, the Hokkaido population formed a clade distinct from North American and European populations, showing that it has maintained genetic diversity independently from continental populations following geographical isolation on the island. This autosomal genetic similarity contrasts with the geographically separate mitochondrial lineages on Hokkaido and indicates the occurrence of male-driven gene flow between subpopulations.

Multi-level patterns of genetic structure and isolation by distance in the widespread plant Mimulus guttatus

An understanding of genetic structure is essential for answering many questions in population genetics. However, complex population dynamics and scale-dependent processes can make it difficult to detect if there are distinct genetic clusters present in natural populations. Inferring discrete population structure is particularly challenging in the presence of continuous genetic variation such as isolation by distance. Here, we use the plant species Mimulus guttatus as a case study for understanding genetic structure at three spatial scales. We use reduced-representation sequencing and marker-based genotyping to understand dispersal dynamics and to characterise genetic structure. Our results provide insight into the spatial scale of genetic structure in a widespread plant species, and demonstrate how dispersal affects spatial genetic variation at the local, regional, and range-wide scale. At a fine-spatial scale, we show dispersal is rampant with little evidence of spatial genetic structure within populations. At a regional-scale, we show continuous differentiation driven by isolation by distance over hundreds of kilometres, with broad geographic genetic clusters that span major barriers to dispersal. Across Western North America, we observe geographic genetic structure and the genetic signature of multiple postglacial recolonisation events, with historical gene flow linking isolated populations. Our genetic analyses show M. guttatus is highly dispersive and maintains large metapopulations with high intrapopulation variation. This high diversity and dispersal confounds the inference of genetic structure, with multi-level sampling and spatially-explicit analyses required to understand population history.

Multi-scale spatial genetic structure of the vector-borne pathogen 'Candidatus Phytoplasma prunorum' in orchards and in wild habitats

Inferring the dispersal processes of vector-borne plant pathogens is a great challenge because the plausible epidemiological scenarios often involve complex spread patterns at multiple scales. The spatial genetic structure of 'Candidatus Phytoplasma prunorum', responsible for European stone fruit yellows disease, was investigated by the application of a combination of statistical approaches to genotype data of the pathogen sampled from cultivated and wild compartments in three French Prunus-growing regions. This work revealed that the prevalence of the different genotypes is highly uneven both between regions and compartments. In addition, we identified a significant clustering of similar genotypes within a radius of 50 km or less, but not between nearby wild and cultivated Prunus. We also provide evidence that infected plants are transferred between production areas, and that both species of the Cacopsylla pruni complex can spread the pathogen. Altogether, this work supports a main epidemiological scenario where 'Ca. P. prunorum' is endemic in - and generally acquired from - wild Prunus by its immature psyllid vectors. The latter then migrate to shelter plants that epidemiologically connect sites less than 50 km apart by later providing infectious mature psyllids to their "migration basins". Such multi-scale studies could be useful for other pathosystems.

ResDisMapper: An r package for fine-scale mapping of resistance to dispersal

Management of biological invasions and conservation activity in the fight against habitat fragmentation both require information on how ongoing dispersal of organisms is affected by the environment. However, there are few landscape genetic computer programs that map resistance to dispersal at small spatiotemporal scales. To facilitate such analyses, we present an r package named ResDisMapper for the mapping of resistance to dispersal at small spatiotemporal scales, without the need for prior knowledge on environmental features or intensive computation. Based on the concept of isolation by distance (IBD), ResDisMapper calculates resistance using deviations of each pair of samples from the general IBD trend (IBD residuals). The IBD residuals are projected onto the studied area, which allows construction and visualization of a fine-scale map of resistance based on spatial accumulation of positive or negative IBD residuals. In this study, we tested ResDisMapper with both simulated and empirical data sets and compared its performance with two other popular landscape genetic programs. Overall, we found that ResDisMapper can map resistance with relatively high accuracy. The latest version of the package and associated documentation are available on Github (https://github.com/takfung/ResDisMapper).