Maternally and paternally inherited molecular markers elucidate population patterns and inferred dispersal processes on a small scale within a subalpine stand of Norway spruce (Picea abies [L.] Karst.)

The within-population spatial structure of genetic diversity is shaped by demographic processes, including historical accidents such as forest perturbations. Information drawn from the analysis of the spatial distribution of genetic diversity is therefore inherently linked to demographic-historical processes that ultimately determine the fate of populations. All adult trees and saplings in a 1.4-ha plot within a mixed Norway spruce (Picea abies [L.] Karst) stand were characterised by means of chloroplast (paternally inherited) markers, and a large sub-sample of these were genotyped at mitochondrial (maternally inherited) molecular markers. These data were used to analyse the spatial distribution of genetic variation and to compare the patterns corresponding to the two marker types. The plot presented non-homogeneous local stem density in the younger cohorts, and the indirect effect of this source of variation on the spatial genetic structure was investigated. Results suggest that (i) spatially limited seed dispersal induced patchiness in genotype distribution, while pollen flow had a homogenising effect; (ii) deviations from random spatial structure were stronger in the demographically most stable portions of the stand, while they were weaker where sudden bursts of regeneration occurred; (iii) spatially overlapping adult and sapling cohorts displayed the same spatial genetic structure (stronger on stable areas, weaker in portions of the stand undergoing events of intense regeneration), which was substantiated by the influence of local demographic processes. Regeneration dynamics as modulated by demography thus influences the distribution of genetic diversity within the stand both in the younger life stages and in the adult population

Integrating very high resolution environmental proxies in genotype–environment association studies

Abstract Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non‐model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype–environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole‐genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high‐resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine‐grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations

Community genetics in the time of next generation molecular technologies

Community genetics in the time of next generation molecular technologie

Community genetics in the time of next generation molecular technologies

Community genetics in the time of next generation molecular technologie