Back to Blog
thurifera to verify the distinct character of the Algerian population in terms of the genetic breaks reported among several North African taxa. We aimed to investigate the spatial genetic structure in J. Previous genetic and morphological investigations suggested that Algerian populations are genetically more similar to European than to Moroccan populations and advocated their recognition at the variety rank. (You can view the original function with: findMethods(ecogen2gstudio).Juniperus thurifera is a key element of the forest communities in arid and semi-arid areas of the western Mediterranean. The following chunk contains code adapted from within the ecogen2gstudio function, tweaked to work for our data. This should be easy with the function EcoGenetics::ecogen2gstudio. We will also use some functions from the package gstudio, hence we import the individual-level genetic data into gstudio. The object ‘Frogs.genpop’ has 30 rows, each representing a population. However, this can create warnings later on when calculating genetic distances. Note: Alternatively, we could directly import the ecopop object into a genpop object ( adegenet) with EcoGenetics::ecopop2genpop(Frogs.ecopop). # adegenet::genind2genpop(x = Frogs.genind) # locus factor for the 43 columns of list of allele names for each locus # number of alleles per locus (range: 3-9) # // 30 populations 8 loci 43 alleles size: 18.2 Kb In addition, EcoGenetics created a table of absolute frequencies (i.e. counts) of alleles for each individual in slot A.įrogs.genpop # /// GENPOP OBJECT ///////// The summary confirms that we now have data in the S and G slots. # | slot S: | -> 413 x 2 structures > 2 structures found # | slot E: | -> 0 x 0 environmental variables # | slot G: | -> 413 x 8 loci > ploidy: 2 || codominant # | slot P: | -> 0 x 0 phenotypic variables Linking paternity to ecological variables Nested model (NMLPE) for hierarchical sampling designs A quick note on controlling for population structure in RDA Redundancy Analysis (RDA): a multivariate GEA Latent Factor Mixed Models (LFMM): a univariate GEA How does changing resolution affect these metrics? Convert conductance into effective distance Setting cost values and calculating conductance Simulated data: 2-island model with admixture Benchmarking file import and export options Run simulator using a previously defined parameter set Calculate Hanski’s index Si with source patch parameters Fit spatially varying coefficients model with package ‘spmoran’ Spatial filtering with MEM using package ‘spmoran’ Fit spatial simultaneous autoregressive error models (SAR) Fit models with spatially correlated error (GLS) with package ‘nlme’ Test regression residuals for spatial autocorrelation Assess correlation between trait and environment Estimate genetic and non-genetic variance components from a common garden experiment Specify spatial weights and calculate Moran’s I Create Mantel correlogram for genetic data Are genetic differentiation and diversity related? What determines genetic differentiation among sites? Spatial distribution of genetic structure Aggregate genetic data at population level (allele frequencies) Basic checking of markers and populations Use ‘terra’ and ‘tmap’ to display categorical map with color scheme Convert ‘SpatialPointsDataFrame’ to ‘sf’ object Sample landscape metrics within buffer around sampling locations Display raster data and overlay sampling locations, extract data View information stored in ‘genind’ object
0 Comments
Read More
Leave a Reply. |