sessionInfo()
## R version 4.5.0 (2025-04-11 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
## 
## Matrix products: default
##   LAPACK version 3.12.1
## 
## locale:
## [1] LC_COLLATE=English_United Kingdom.utf8 
## [2] LC_CTYPE=English_United Kingdom.utf8   
## [3] LC_MONETARY=English_United Kingdom.utf8
## [4] LC_NUMERIC=C                           
## [5] LC_TIME=English_United Kingdom.utf8    
## 
## time zone: Europe/Berlin
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] readr_2.1.6      StAMPP_1.6.3     pegas_1.4        ape_5.8-1       
##  [5] ggnewscale_0.5.2 reshape2_1.4.5   ggrepel_0.9.6    dartR_2.9.9.5   
##  [9] dartR.data_1.0.8 dplyr_1.1.4      ggplot2_4.0.2    adegenet_2.1.11 
## [13] ade4_1.7-23      vcfR_1.15.0     
## 
## loaded via a namespace (and not attached):
##   [1] Rdpack_2.6.6        gdsfmt_1.44.1       gridExtra_2.3      
##   [4] permute_0.9-10      rlang_1.2.0         magrittr_2.0.3     
##   [7] otel_0.2.0          compiler_4.5.0      mgcv_1.9-4         
##  [10] gdata_3.0.1         png_0.1-8           vctrs_0.7.3        
##  [13] maps_3.4.3          combinat_0.0-8      stringr_1.6.0      
##  [16] pkgconfig_2.0.3     crayon_1.5.3        SNPRelate_1.42.0   
##  [19] fastmap_1.2.0       promises_1.5.0      rmarkdown_2.30     
##  [22] tzdb_0.5.0          purrr_1.2.1         xfun_0.52          
##  [25] cachem_1.1.0        seqinr_4.2-36       jsonlite_2.0.0     
##  [28] later_1.4.6         terra_1.8-93        parallel_4.5.0     
##  [31] cluster_2.1.8.2     R6_2.6.1            gap.datasets_0.0.6 
##  [34] bslib_0.10.0        stringi_1.8.7       RColorBrewer_1.1-3 
##  [37] GGally_2.4.0        jquerylib_0.1.4     gdistance_1.6.5    
##  [40] iterators_1.0.14    Rcpp_1.1.0          knitr_1.51         
##  [43] fields_17.1         genetics_1.3.8.1.3  R.utils_2.13.0     
##  [46] httpuv_1.6.16       Matrix_1.7-4        splines_4.5.0      
##  [49] igraph_2.2.2        tidyselect_1.2.1    rstudioapi_0.18.0  
##  [52] dichromat_2.0-0.1   yaml_2.3.10         vegan_2.7-2        
##  [55] doParallel_1.0.17   codetools_0.2-20    lattice_0.22-9     
##  [58] tibble_3.3.1        plyr_1.8.9          shiny_1.12.1       
##  [61] withr_3.0.2         S7_0.2.1            evaluate_1.0.5     
##  [64] pinfsc50_1.3.0      mmod_1.3.3          ggstats_0.12.0     
##  [67] pillar_1.11.1       gap_1.9             foreach_1.5.2      
##  [70] generics_0.1.4      sp_2.2-1            hms_1.1.4          
##  [73] scales_1.4.0        calibrate_1.7.7     gtools_3.9.5       
##  [76] xtable_1.8-4        glue_1.8.0          tools_4.5.0        
##  [79] data.table_1.18.2.1 gsubfn_0.7          mvtnorm_1.3-3      
##  [82] dotCall64_1.2       grid_4.5.0          tidyr_1.3.2        
##  [85] rbibutils_2.4.1     nlme_3.1-168        patchwork_1.3.2    
##  [88] raster_3.6-32       RgoogleMaps_1.5.3   proto_1.0.0        
##  [91] cli_3.6.5           spam_2.11-3         viridisLite_0.4.3  
##  [94] gtable_0.3.6        R.methodsS3_1.8.2   sass_0.4.10        
##  [97] digest_0.6.37       farver_2.1.2        htmltools_0.5.8.1  
## [100] R.oo_1.27.1         lifecycle_1.0.5     dismo_1.3-16       
## [103] PopGenReport_3.1.3  mime_0.13           MASS_7.3-65

Data reading and clone correction: only populations with at least six samples are selected

dat1 <- read.vcfR("md6ct95ms200.vcf")
## Scanning file to determine attributes.
## File attributes:
##   meta lines: 10
##   header_line: 11
##   variant count: 25099
##   column count: 326
## Meta line 10 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
##   Character matrix gt rows: 25099
##   Character matrix gt cols: 326
##   skip: 0
##   nrows: 25099
##   row_num: 0
## Processed variant 1000Processed variant 2000Processed variant 3000Processed variant 4000Processed variant 5000Processed variant 6000Processed variant 7000Processed variant 8000Processed variant 9000Processed variant 10000Processed variant 11000Processed variant 12000Processed variant 13000Processed variant 14000Processed variant 15000Processed variant 16000Processed variant 17000Processed variant 18000Processed variant 19000Processed variant 20000Processed variant 21000Processed variant 22000Processed variant 23000Processed variant 24000Processed variant 25000Processed variant: 25099
## All variants processed
clones <- c(
  "DE1-2","DE1-4","DE1-7","DE1-5",
  "DE2-2","DE2-1","DE2-3","DE2-4",
  "DE3-9","DE3-10","DE3-11","DE3-5",
  "DE4-1","DE4-3","DE4-6","DE4-8",
  "DE5-1","DE5-10","DE5-3","DE5-2","DE5-4","DE5-6","DE5-7","DE5-9",
  "DE6-4","DE6-5","DE6-6","DE6-7",
  "DE7-12","DE7-6","DE7-7","DE7-10","DE7-2","DE7-17","DE7-18","DE7-19","DE7-20",
  "DE9-13",
  "DE10-1","DE10-10","DE10-11","DE10-12","DE10-2","DE10-3","DE10-5","DE10-7","DE10-8",
  "DE11-12","DE11-15","DE12-5",
  "DK-4","DK-5","DK-8","DK-9",
  "EE-5","EE-6","EE-7",
  "FI1-7-1","FI5-2","FI5-10","FI5-8",
  "LT1-9","LT2-1","LT2-2","LT2-3","LT2-4","LT2-5","LT2-6","LT2-7",
  "LT3-10","LT3-11","LT3-12","LT3-14","LT3-5","LT3-7","LT3-9",
  "LV-10","LV-12","LV-8","LV-9",
  "PL2-4","PL2-6-2",
  "PL4-14","PL4-1","PL4-3","PL4-7",
  "PL5-15","PL5-19","PL5-20","PL5-21","PL5-4",
  "PL6-10","PL6-9",
  "PL7-12","PL7-5","PL7-18",
  "PL8-17","PL8-8","PL9-4","PL9-8",
  "RU1-4","RU2-1","RU2-2","RU2-3","RU2-4","RU2-5","RU2-6",
  "SE3-4-2","SE1-1","SE1-10","SE1-4","SE1-5","SE1-6","SE1-7","SE1-9",
  "UA1-13","UA1-14","UA1-15","UA1-17",
  "UA2-1","UA2-12","UA2-18","UA2-3")

length(clones)
## [1] 123
table(colnames(dat1@gt)[-1] %in% clones)
## 
## FALSE  TRUE 
##   194   123
# Remove clones:
keep <- !colnames(dat1@gt)[-1] %in% clones
dat2 <- dat1
dat2@gt <- dat1@gt[, c(TRUE, keep)]
dat2.pre_gl <- vcfR2genlight(dat2)
## Warning in vcfR2genlight(dat2): Found 245 loci with more than two alleles.
## Objects of class genlight only support loci with two alleles.
## 245 loci will be omitted from the genlight object.
ploidy(dat2.pre_gl) <- 2
dat2.pre_gl <- gl.compliance.check(dat2.pre_gl)
## Starting gl.compliance.check 
##   Processing genlight object with SNP data
##   Warning: data include loci that are scored NA across all individuals.
##   Consider filtering using gl <- gl.filter.allna(gl)
##   The slot loc.all, which stores allele name for each locus, is empty. 
## Creating a dummy variable (A/C) to insert in this slot. 
##   Checking coding of SNPs
##     SNP data scored NA, 0, 1 or 2 confirmed
##   Checking locus metrics and flags
##   Recalculating locus metrics
##   Checking for monomorphic loci
##     Dataset contains monomorphic loci
##   Checking for loci with all missing data
##     Dataset contains loci with all missing dat
##   Checking whether individual names are unique.
##   Checking for individual metrics
##   Warning: Creating a slot for individual metrics
##   Checking for population assignments
##   Population assignments not detected, individuals assigned
##                     to a single population labelled 'pop1'
##   Spelling of coordinates checked and changed if necessary to 
##             lat/lon
## Completed: gl.compliance.check
dat2.pre_gl <- gl.filter.allna(dat2.pre_gl)
## Starting gl.filter.allna 
##   Processing genlight object with SNP data
##   Identifying and removing loci and individuals scored all 
##                 missing (NA)
##   Deleting loci that are scored as all missing (NA)
##   Deleted
##   Deleting individuals that are scored as all missing (NA)
## Completed: gl.filter.allna
dat2.pre_gl$ind.names
##   [1] "DE11-1"   "DE11-10"  "DE11-11"  "DE11-14"  "DE11-14r" "DE11-16" 
##   [7] "DE11-17"  "DE11-2"   "DE11-3"   "DE11-4"   "DE11-5"   "DE11-6"  
##  [13] "DE11-9"   "DE12-1"   "DE12-11"  "DE12-12"  "DE12-14"  "DE12-2"  
##  [19] "DE12-3"   "DE12-4"   "DE12-6"   "DE12-7"   "DE12-8"   "DE12-9"  
##  [25] "DE3-1"    "DE3-2"    "DE3-3"    "DE3-4"    "DE3-8"    "DE4-10"  
##  [31] "DE4-11"   "DE4-2"    "DE4-4"    "DE4-5"    "DE7-1"    "DE7-11"  
##  [37] "DE7-13"   "DE7-23"   "DE7-25"   "DE7-3"    "DE7-4"    "DE7-8"   
##  [43] "DE8-1"    "DE8-6"    "DE8-7"    "DE8-8"    "DE8-9"    "DE9-1"   
##  [49] "DE9-10"   "DE9-11"   "DE9-12"   "DE9-15"   "DE9-16"   "DE9-2"   
##  [55] "DE9-2r"   "DE9-4"    "DE9-6"    "DE9-7"    "DE9-9"    "FI1-1"   
##  [61] "FI1-12"   "FI1-14"   "FI1-15"   "FI1-16"   "FI1-17"   "FI1-4"   
##  [67] "FI1-5-1"  "FI1-5-2"  "FI1-6"    "FI1-7-2"  "FI1-8"    "FI2-10"  
##  [73] "FI2-13"   "FI2-15"   "FI2-17"   "FI2-19"   "FI2-20"   "FI2-3"   
##  [79] "FI2-6"    "FI3-14"   "FI3-17"   "FI3-19"   "FI3-21"   "FI3-24"  
##  [85] "FI3-3"    "FI3-5"    "FI3-9"    "FI4-1"    "FI4-13"   "FI4-16"  
##  [91] "FI4-2"    "FI4-22"   "FI4-24"   "FI4-27"   "FI4-3"    "FI4-32"  
##  [97] "FI5-1"    "FI5-12"   "FI5-4"    "FI5-5"    "FI5-7"    "FI5-9"   
## [103] "LT1-10"   "LT1-11"   "LT1-4"    "LT1-5"    "LT1-6"    "LT2-1r"  
## [109] "PL1-10"   "PL1-5"    "PL1-6"    "PL1-7"    "PL1-8"    "PL1-9"   
## [115] "PL2-10"   "PL2-2"    "PL2-3"    "PL2-5"    "PL2-6_1"  "PL4-10"  
## [121] "PL4-11"   "PL4-13"   "PL4-15"   "PL4-2"    "PL4-5"    "PL4-8"   
## [127] "PL6-1"    "PL6-13"   "PL6-3"    "PL6-4"    "PL6-5"    "PL6-6"   
## [133] "PL6-7"    "PL7-10"   "PL7-13"   "PL7-14"   "PL7-17"   "PL7-7"   
## [139] "PL7-8"    "PL7-9"    "PL8-1"    "PL8-11"   "PL8-12"   "PL8-14"  
## [145] "PL8-15"   "PL8-16"   "PL8-18"   "PL8-19"   "PL8-19-"  "PL8-2"   
## [151] "PL8-4"    "PL8-5"    "PL8-6"    "PL8-7"    "PL9-1"    "PL9-10"  
## [157] "PL9-13"   "PL9-5"    "PL9-6"    "PL9-7"    "RU1-1"    "RU1-2"   
## [163] "RU1-3"    "RU1-5"    "RU1-6"    "SE2-1"    "SE2-10"   "SE2-2"   
## [169] "SE2-3"    "SE2-6"    "SE2-8"    "SE3-1"    "SE3-10"   "SE3-2-1" 
## [175] "SE3-2-2"  "SE3-4-1"  "SE3-4-2r" "SE3-5"    "SE3-8-1"  "SE3-9"   
## [181] "UA1-1"    "UA1-10"   "UA1-12"   "UA1-16"   "UA1-16r"  "UA1-3"   
## [187] "UA1-7"    "UA2-11"   "UA2-14"   "UA2-16"   "UA2-19"   "UA2-2"   
## [193] "UA2-5"    "UA2-7"

Population statistics

Selecting only population with at least six samples

Samples <- read_delim("Coordinates_pops_min5.txt", delim = "\t", escape_double = FALSE, trim_ws = TRUE)
## Rows: 254 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (1): SampleID
## dbl (2): Latitude, Longitude
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Fst and Nei’s D between populations

keep_ids <- intersect(indNames(dat2.pre_gl), Samples$SampleID)
pop(dat2.pre_gl) <- as.factor(sub("-.*", "",  dat2.pre_gl$ind.names)) # retract the regions (Pop)
gl_filtered <- dat2.pre_gl[indNames(dat2.pre_gl) %in% keep_ids, ]


indNames(gl_filtered) <- make.names(indNames(gl_filtered), unique = TRUE)
locNames(gl_filtered) <- make.names(locNames(gl_filtered), unique = TRUE)

stampp_data <- stamppConvert(gl_filtered, type = "genlight")

fst_matrix <- stamppFst(stampp_data, nboots = 1000, percent = 95, nclusters = 9)
Nei_matrix <- stamppNeisD(stampp_data, pop = TRUE)
fst_mat <- fst_matrix$Fsts
nei_mat <- Nei_matrix
colnames(nei_mat) <- rownames(nei_mat)

all_pops<-as.vector(unique(pop(gl_filtered)))
n <- length(all_pops)
fst_full <- matrix(NA, nrow=n, ncol=n, dimnames = list(all_pops, all_pops)) # to build full FST matrix
fst_full[match(rownames(fst_mat), all_pops),match(colnames(fst_mat), all_pops)] <- fst_mat

fst_sym <- fst_full
pop_order <- rownames(fst_sym)
fst_sym[upper.tri(fst_sym)] <- t(fst_sym)[upper.tri(fst_sym)]

# FST lower
fst_tri <- fst_sym
fst_tri[upper.tri(fst_tri, diag = TRUE)] <- NA
fst_df <- melt(fst_tri, na.rm = TRUE)

# Nei upper
nei_tri <- nei_mat
nei_tri[lower.tri(nei_tri, diag = TRUE)] <- NA
nei_df <- melt(nei_tri, na.rm = TRUE)

fst_df$Var1 <- factor(fst_df$Var1, levels = all_pops)
fst_df$Var2 <- factor(fst_df$Var2, levels = all_pops)
nei_df$Var1 <- factor(nei_df$Var1, levels = all_pops)
nei_df$Var2 <- factor(nei_df$Var2, levels = all_pops)

dim(fst_sym)
## [1] 25 25
dim(nei_mat)
## [1] 25 25
all(is.na(fst_tri[upper.tri(fst_tri)]))
## [1] TRUE
all(is.na(nei_tri[lower.tri(nei_tri)]))
## [1] TRUE
ggplot() + 
  geom_tile(data = fst_df,aes(Var1, Var2, fill = value),color = "white",linewidth = 0.2) +
  geom_text(data = fst_df,aes(Var1, Var2, label = sprintf("%.2f", value)),size = 2,color = "black") +
  scale_fill_gradient(low = "white",high = "#EE7733",name = expression(F[ST]) ) +
  ggnewscale::new_scale_fill() +
  geom_tile(data = nei_df,aes(Var1, Var2, fill = value),color = "white",linewidth = 0.2 ) +
  geom_text(data = nei_df,
    aes(Var1, Var2, label = sprintf("%.2f", value*100)),size = 2,color = "black" ) +
  scale_fill_gradient(low = "white",high = "#009E73",name = "Nei’s D") +
  theme_minimal() +
  theme(
    axis.text.x = element_text(size = 9, angle = 45, hjust = 1, vjust = 1),
    axis.text.y = element_text(size = 9),
    axis.title = element_blank(),
    panel.grid = element_blank(),
    aspect.ratio = 1,
    legend.position = "right" )

Ho, He and FIS per population

HoHeFis<-gl.report.heterozygosity(gl_filtered,  method = "pop", plot.colors.pop= "#009E73")
## Starting gl.report.heterozygosity 
##   Processing genlight object with SNP data
##   Calculating Observed Heterozygosities, averaged across
##                     loci, for each population
##   Calculating Expected Heterozygosities
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the dartR package.
##   Please report the issue at <https://groups.google.com/g/dartr?pli=1>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the dartR package.
##   Please report the issue at <https://groups.google.com/g/dartr?pli=1>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

##       pop     n.Ind n.Loc n.Loc.adj polyLoc monoLoc all_NALoc       Ho     HoSD
## DE11 DE11 10.663752 24815         1    2469   22346        36 0.010221 0.051380
## DE12 DE12  8.134091 24804         1    2490   22314        47 0.013303 0.059353
## DE3   DE3  4.473222 24722         1    1619   23103       129 0.011337 0.073992
## DE4   DE4  3.952996 24636         1    1823   22813       215 0.011562 0.074870
## DE7   DE7  7.232850 24810         1    1574   23236        41 0.010463 0.064994
## DE8   DE8  3.600695 24758         1    1287   23471        93 0.011630 0.069517
## DE9   DE9  9.111859 24799         1    2131   22668        52 0.010123 0.053256
## FI1   FI1 10.565259 24824         1    3043   21781        27 0.015591 0.061184
## FI2   FI2  6.949820 24791         1    2055   22736        60 0.010909 0.062395
## FI3   FI3  7.115923 24801         1    1950   22851        50 0.011512 0.057136
## FI4   FI4  7.738354 24816         1    2591   22225        35 0.011814 0.055097
## FI5   FI5  4.750323 24736         1    1630   23106       115 0.012790 0.077499
## LT1   LT1  4.040224 24786         1    1606   23180        65 0.012123 0.073250
## PL1   PL1  5.476945 24789         1    1546   23243        62 0.010538 0.061233
## PL2   PL2  4.747097 24800         1    1291   23509        51 0.007056 0.050007
## PL4   PL4  5.523548 24800         1    1856   22944        51 0.009179 0.052472
## PL6   PL6  5.035114 24577         1    1997   22580       274 0.013756 0.073212
## PL7   PL7  6.031283 24806         1    1897   22909        45 0.012902 0.076238
## PL8   PL8 11.547263 24829         1    2139   22690        22 0.009437 0.054062
## PL9   PL9  4.450517 24776         1    1793   22983        75 0.013360 0.077079
## RU1   RU1  3.854195 24766         1    1374   23392        85 0.007449 0.051988
## SE2   SE2  5.028142 24767         1    1681   23086        84 0.012375 0.071797
## SE3   SE3  5.611286 24774         1    2130   22644        77 0.014113 0.069770
## UA1   UA1  5.259168 24760         1    1552   23208        91 0.008307 0.055447
## UA2   UA2  5.720271 24799         1    1722   23077        52 0.011429 0.067761
##          HoSE HoLCI HoHCI   Ho.adj Ho.adjSD Ho.adjSE Ho.adjLCI Ho.adjHCI
## DE11 0.000326    NA    NA 0.010221 0.051380 0.000326        NA        NA
## DE12 0.000377    NA    NA 0.013303 0.059353 0.000377        NA        NA
## DE3  0.000469    NA    NA 0.011337 0.073992 0.000471        NA        NA
## DE4  0.000475    NA    NA 0.011562 0.074870 0.000477        NA        NA
## DE7  0.000412    NA    NA 0.010463 0.064994 0.000413        NA        NA
## DE8  0.000441    NA    NA 0.011630 0.069517 0.000442        NA        NA
## DE9  0.000338    NA    NA 0.010123 0.053256 0.000338        NA        NA
## FI1  0.000388    NA    NA 0.015591 0.061184 0.000388        NA        NA
## FI2  0.000396    NA    NA 0.010909 0.062395 0.000396        NA        NA
## FI3  0.000362    NA    NA 0.011512 0.057136 0.000363        NA        NA
## FI4  0.000350    NA    NA 0.011814 0.055097 0.000350        NA        NA
## FI5  0.000492    NA    NA 0.012790 0.077499 0.000493        NA        NA
## LT1  0.000465    NA    NA 0.012123 0.073250 0.000465        NA        NA
## PL1  0.000388    NA    NA 0.010538 0.061233 0.000389        NA        NA
## PL2  0.000317    NA    NA 0.007056 0.050007 0.000318        NA        NA
## PL4  0.000333    NA    NA 0.009179 0.052472 0.000333        NA        NA
## PL6  0.000464    NA    NA 0.013756 0.073212 0.000467        NA        NA
## PL7  0.000484    NA    NA 0.012902 0.076238 0.000484        NA        NA
## PL8  0.000343    NA    NA 0.009437 0.054062 0.000343        NA        NA
## PL9  0.000489    NA    NA 0.013360 0.077079 0.000490        NA        NA
## RU1  0.000330    NA    NA 0.007449 0.051988 0.000330        NA        NA
## SE2  0.000455    NA    NA 0.012375 0.071797 0.000456        NA        NA
## SE3  0.000443    NA    NA 0.014113 0.069770 0.000443        NA        NA
## UA1  0.000352    NA    NA 0.008307 0.055447 0.000352        NA        NA
## UA2  0.000430    NA    NA 0.011429 0.067761 0.000430        NA        NA
##            He     HeSD     HeSE HeLCI HeHCI      uHe    uHeSD    uHeSE uHeLCI
## DE11 0.023611 0.084690 0.000537    NA    NA 0.024773 0.088856 0.000564     NA
## DE12 0.023181 0.082013 0.000520    NA    NA 0.024699 0.087384 0.000554     NA
## DE3  0.020687 0.086391 0.000548    NA    NA 0.023290 0.097263 0.000617     NA
## DE4  0.022870 0.090910 0.000577    NA    NA 0.026181 0.104074 0.000660     NA
## DE7  0.016680 0.072412 0.000459    NA    NA 0.017918 0.077789 0.000493     NA
## DE8  0.015909 0.074844 0.000475    NA    NA 0.018474 0.086913 0.000551     NA
## DE9  0.019191 0.075181 0.000477    NA    NA 0.020306 0.079546 0.000505     NA
## FI1  0.027842 0.090920 0.000577    NA    NA 0.029225 0.095436 0.000605     NA
## FI2  0.021903 0.082056 0.000521    NA    NA 0.023600 0.088418 0.000561     NA
## FI3  0.020287 0.079650 0.000505    NA    NA 0.021820 0.085669 0.000543     NA
## FI4  0.026604 0.089183 0.000566    NA    NA 0.028442 0.095343 0.000605     NA
## FI5  0.019410 0.081422 0.000517    NA    NA 0.021693 0.091001 0.000577     NA
## LT1  0.021351 0.087530 0.000555    NA    NA 0.024367 0.099892 0.000634     NA
## PL1  0.019167 0.081727 0.000518    NA    NA 0.021093 0.089938 0.000571     NA
## PL2  0.017263 0.079469 0.000504    NA    NA 0.019296 0.088824 0.000563     NA
## PL4  0.021631 0.084443 0.000536    NA    NA 0.023784 0.092848 0.000589     NA
## PL6  0.020869 0.083622 0.000530    NA    NA 0.023170 0.092841 0.000589     NA
## PL7  0.021918 0.084500 0.000536    NA    NA 0.023899 0.092138 0.000584     NA
## PL8  0.020466 0.080243 0.000509    NA    NA 0.021392 0.083875 0.000532     NA
## PL9  0.022419 0.087475 0.000555    NA    NA 0.025256 0.098546 0.000625     NA
## RU1  0.016620 0.074511 0.000473    NA    NA 0.019097 0.085618 0.000543     NA
## SE2  0.020044 0.082660 0.000524    NA    NA 0.022257 0.091788 0.000582     NA
## SE3  0.023977 0.088636 0.000562    NA    NA 0.026322 0.097307 0.000617     NA
## UA1  0.019152 0.082548 0.000524    NA    NA 0.021164 0.091221 0.000579     NA
## UA2  0.020172 0.081829 0.000519    NA    NA 0.022105 0.089667 0.000569     NA
##      uHeHCI   He.adj He.adjSD He.adjSE He.adjLCI He.adjHCI      FIS    FISSD
## DE11     NA 0.023611 0.084690 0.000538        NA        NA 0.438039 0.489315
## DE12     NA 0.023181 0.082013 0.000521        NA        NA 0.332977 0.465318
## DE3      NA 0.020687 0.086391 0.000549        NA        NA 0.455050 0.555171
## DE4      NA 0.022870 0.090910 0.000579        NA        NA 0.493042 0.555733
## DE7      NA 0.016680 0.072412 0.000460        NA        NA 0.351692 0.531762
## DE8      NA 0.015909 0.074844 0.000476        NA        NA 0.287973 0.494240
## DE9      NA 0.019191 0.075181 0.000477        NA        NA 0.363730 0.493123
## FI1      NA 0.027842 0.090920 0.000577        NA        NA 0.342460 0.444185
## FI2      NA 0.021903 0.082056 0.000521        NA        NA 0.444189 0.535930
## FI3      NA 0.020287 0.079650 0.000506        NA        NA 0.360944 0.463909
## FI4      NA 0.026604 0.089183 0.000566        NA        NA 0.459477 0.487905
## FI5      NA 0.019410 0.081422 0.000518        NA        NA 0.349360 0.558993
## LT1      NA 0.021351 0.087530 0.000556        NA        NA 0.410543 0.550361
## PL1      NA 0.019167 0.081727 0.000519        NA        NA 0.411041 0.504646
## PL2      NA 0.017263 0.079469 0.000505        NA        NA 0.546518 0.496753
## PL4      NA 0.021631 0.084443 0.000536        NA        NA 0.489514 0.508858
## PL6      NA 0.020869 0.083622 0.000533        NA        NA 0.289877 0.512452
## PL7      NA 0.021918 0.084500 0.000537        NA        NA 0.388009 0.557710
## PL8      NA 0.020466 0.080243 0.000509        NA        NA 0.410206 0.514376
## PL9      NA 0.022419 0.087475 0.000556        NA        NA 0.393244 0.556479
## RU1      NA 0.016620 0.074511 0.000473        NA        NA 0.523581 0.509278
## SE2      NA 0.020044 0.082660 0.000525        NA        NA 0.340093 0.538067
## SE3      NA 0.023977 0.088636 0.000563        NA        NA 0.340444 0.503658
## UA1      NA 0.019152 0.082548 0.000525        NA        NA 0.487399 0.525527
## UA2      NA 0.020172 0.081829 0.000520        NA        NA 0.395339 0.541474
##         FISSE FISLCI FISHCI
## DE11 0.003104     NA     NA
## DE12 0.002952     NA     NA
## DE3  0.003522     NA     NA
## DE4  0.003525     NA     NA
## DE7  0.003373     NA     NA
## DE8  0.003135     NA     NA
## DE9  0.003128     NA     NA
## FI1  0.002818     NA     NA
## FI2  0.003400     NA     NA
## FI3  0.002943     NA     NA
## FI4  0.003095     NA     NA
## FI5  0.003546     NA     NA
## LT1  0.003491     NA     NA
## PL1  0.003201     NA     NA
## PL2  0.003151     NA     NA
## PL4  0.003228     NA     NA
## PL6  0.003251     NA     NA
## PL7  0.003538     NA     NA
## PL8  0.003263     NA     NA
## PL9  0.003530     NA     NA
## RU1  0.003231     NA     NA
## SE2  0.003413     NA     NA
## SE3  0.003195     NA     NA
## UA1  0.003334     NA     NA
## UA2  0.003435     NA     NA
## Completed: gl.report.heterozygosity

Total Ho, He and Fis

gl_filtered_no_pops <- dat2.pre_gl[indNames(dat2.pre_gl) %in% keep_ids, ]
pop(gl_filtered_no_pops) <- factor(rep("A", nInd(gl_filtered_no_pops))) # to assign all samples to one population
gl.report.heterozygosity(gl_filtered_no_pops,  method = "pop", plot.colors.pop= "#009E73")
## Starting gl.report.heterozygosity 
##   Processing genlight object with SNP data
##   Calculating Observed Heterozygosities, averaged across
##                     loci, for each population
##   Calculating Expected Heterozygosities

##   pop    n.Ind n.Loc n.Loc.adj polyLoc monoLoc all_NALoc       Ho     HoSD
## A   A 156.1918 24851         1   16323    8528         0 0.011432 0.042549
##      HoSE HoLCI HoHCI   Ho.adj Ho.adjSD Ho.adjSE Ho.adjLCI Ho.adjHCI       He
## A 0.00027    NA    NA 0.011432 0.042549  0.00027        NA        NA 0.031328
##       HeSD     HeSE HeLCI HeHCI      uHe    uHeSD    uHeSE uHeLCI uHeHCI
## A 0.085371 0.000542    NA    NA 0.031429 0.085645 0.000543     NA     NA
##     He.adj He.adjSD He.adjSE He.adjLCI He.adjHCI      FIS    FISSD    FISSE
## A 0.031328 0.085371 0.000542        NA        NA 0.248013 0.413792 0.002625
##   FISLCI FISHCI
## A     NA     NA
## Completed: gl.report.heterozygosity