In search of Apis mellifera pomonella in Kazakhstan
Kamshat Temirbayeva, Aibyn Torekhanov, Ulzhan Nuralieva, Zhanar Sheralieva, Adam Tofilski
2023-08-18
Affiliations
Kamshat Temirbayeva
Kazakh Research Institute of Livestock and Fodder Production, al-Farabi
Kazakh National University, Kazakhstan
Aibyn Torekhanov
Kazakh Research Institute of Livestock and Fodder Production,
Kazakhstan
Ulzhan Nuralieva
Kazakh Research Institute of Livestock and Fodder Production,
Kazakhstan
Zhanar Sheralieva
Kazakh Research Institute of Livestock and Fodder Production,
Kazakhstan
Adam Tofilski
University of Agriculture in Krakow, Poland
Libraries
# calculations
library(geomorph) # GPA
library(shapes) # OPA
library(Morpho) # CVA
library(MASS) # LDA
# plotting and visualization
library(ggplot2) # plots
ggplot2::theme_set(theme_light())
library(ggforce) # geom_ellipse
library(ggrepel) # geom_label_repel
library(rnaturalearth) # maps
library(raster) # raster
library(ggspatial) # annotation_scale
options(digits=4) # number of digits to displayVariable names
In order to avoid mistakes it is safer to use columns names instead of indexes
p <- 19 # number of landmarks
k <- 2 # number of dimensions, in this case 2 for coordinates (x, y)
# create coordinates names used by IdentiFly
xyNames <- c("x1", "y1")
for (i in 2:p) {
xyNames <- c(xyNames, paste0("x", i))
xyNames <- c(xyNames, paste0("y", i))
}
xyNames## [1] "x1" "y1" "x2" "y2" "x3" "y3" "x4" "y4" "x5" "y5" "x6" "y6"
## [13] "x7" "y7" "x8" "y8" "x9" "y9" "x10" "y10" "x11" "y11" "x12" "y12"
## [25] "x13" "y13" "x14" "y14" "x15" "y15" "x16" "y16" "x17" "y17" "x18" "y18"
## [37] "x19" "y19"# create coordinates names used by geomorph
xy.Names <- c("1.X", "1.Y")
for (i in 2:p) {
xy.Names <- c(xy.Names, paste(i, "X", sep = "."))
xy.Names <- c(xy.Names, paste(i, "Y", sep = "."))
}
xy.Names## [1] "1.X" "1.Y" "2.X" "2.Y" "3.X" "3.Y" "4.X" "4.Y" "5.X" "5.Y"
## [11] "6.X" "6.Y" "7.X" "7.Y" "8.X" "8.Y" "9.X" "9.Y" "10.X" "10.Y"
## [21] "11.X" "11.Y" "12.X" "12.Y" "13.X" "13.Y" "14.X" "14.Y" "15.X" "15.Y"
## [31] "16.X" "16.Y" "17.X" "17.Y" "18.X" "18.Y" "19.X" "19.Y"# The number of principal components used is 2*p-4 = 34, which is equal to the
# degrees of freedom create principal components names used by prcomp
pcNames <- paste0("PC", 1:(2 * p - 4))
pcNames## [1] "PC1" "PC2" "PC3" "PC4" "PC5" "PC6" "PC7" "PC8" "PC9" "PC10"
## [11] "PC11" "PC12" "PC13" "PC14" "PC15" "PC16" "PC17" "PC18" "PC19" "PC20"
## [21] "PC21" "PC22" "PC23" "PC24" "PC25" "PC26" "PC27" "PC28" "PC29" "PC30"
## [31] "PC31" "PC32" "PC33" "PC34"geoNames <- c("latitude", "longitude")Read landmark coordinates
wings <- read.csv("https://zenodo.org/record/8128010/files/KZ-raw-coordinates.csv")
# extract sample classifier
wings$sample = substr(wings$file,1,7)
head(wings, 2)## file x1 y1 x2 y2 x3 y3 x4 y4 x5 y5 x6 y6
## 1 KZ-0001-000001_c10C.dw.png 264 274 289 271 352 375 351 301 366 190 449 378
## 2 KZ-0001-000002_c11C.dw.png 347 260 380 259 432 369 438 292 449 174 531 376
## x7 y7 x8 y8 x9 y9 x10 y10 x11 y11 x12 y12 x13 y13 x14 y14 x15 y15 x16
## 1 520 416 496 387 553 339 505 314 562 267 573 216 590 170 609 401 651 349 751
## 2 607 414 583 388 636 339 599 314 653 263 666 213 685 166 699 402 754 349 849
## y16 x17 y17 x18 y18 x19 y19 sample
## 1 298 806 286 818 245 100 379 KZ-0001
## 2 298 894 290 909 247 189 363 KZ-0001Map of sampling locations
# Read geographic coordinates
geo.data <- read.csv("https://zenodo.org/record/8128010/files/KZ-data.csv", strip.white=TRUE)
geo.data.sample <- aggregate(geo.data[geoNames], by = list(geo.data$sample), FUN = mean)
rownames(geo.data.sample) = geo.data.sample$Group.1
geo.data.sample <- subset(geo.data.sample, select = -c(Group.1))
sample.gr = geo.data[,c("sample", "group")]
sample.gr = sample.gr[!duplicated(sample.gr), ]
geo.data.sample$group = sample.gr$group
table(geo.data.sample$group)##
## carnica local bee mellifera
## 38 29 4locations = geo.data[,geoNames]
locations = locations[!duplicated(locations), ]
# Read elevation data
# In order to download the TIF file uncomment the two lines below
# download.file("https://geodata.ucdavis.edu/climate/worldclim/2_1/base/wc2.1_2.5m_elev.zip", "wc2.1_2.5m_elev.zip")
# unzip("wc2.1_2.5m_elev.zip")
elev <- raster("E:/WorldClim/wc2.1_2.5m_elev.tif")
elev.color <- colorRampPalette(c("#f7f7f7", "#f0f0f0", "#d9d9d9", "#bdbdbd", "#636363"))
x.min = 45
x.max = 90
y.min = 35
y.max = 56
e <- extent(x.min, x.max, y.min, y.max)
elev = crop(elev, e)
elev.df <- data.frame(rasterToPoints(elev))
colnames(elev.df) = c("longitude", "latitude", "altitude")
world <- ne_countries(scale = "medium", returnclass = "sf")
# Chen et al. 2016 locations
Chen = data.frame(latitude = c(43.2941, 43.3019, 43.2975, 43.3013),
longitude = c(83.5897, 83.6633, 83.7133, 83.6663),
group = "Chen et al. 2016")
# Sheppard and Meixner 2003 locations
Sheppard = data.frame(latitude = c(42.3527, 43.0203, 43.1666))
Sheppard$longitude = c(70.3775, 78.2894, 79.2943)
Sheppard$group = "Shepard and Meixner 2003"
map.coord = rbind(geo.data.sample, Chen, Sheppard)
# jitter the coordinates to show all locations
jitter.x <- jitter(map.coord$longitude, amount = 0.2)
jitter.y <- jitter(map.coord$latitude, amount = 0.2)
ggplot(data = world) +
geom_sf() +
coord_sf(xlim = c(30, 130), ylim = c(10, 60)) +
annotate("rect", xmin = x.min, xmax = x.max, ymin = y.min, ymax = y.max, alpha = 0, color= "red")
ggplot(data = world) +
geom_sf() +
geom_point(data = map.coord, aes(x = jitter.x, y = jitter.y,
colour = group, stroke = 1), size = 1) +
scale_color_manual(name ="",
values = c("red", "blue", "green", "black", "magenta"),
breaks=c('local bee', 'carnica', 'mellifera',
'Shepard and Meixner 2003','Chen et al. 2016'),
labels=c('local bees', 'A. m. carnica', 'A. m. mellifera',
'Shepard and Meixner 2003','Chen et al. 2016')) +
coord_sf(xlim = c(x.min, x.max), ylim = c(y.min, y.max), expand = FALSE) +
theme(axis.title.x=element_blank(), axis.title.y=element_blank())
ggplot(data = world) +
geom_raster(data = elev.df, aes(longitude, latitude, fill = altitude)) +
scale_fill_gradientn(colours = elev.color(100)) +
geom_sf(fill = NA) +
geom_point(data = map.coord, aes(x = jitter.x, y = jitter.y,
colour = group, stroke = 1), size = 1) +
scale_color_manual(name ="",
values = c("red", "blue", "green", "black", "magenta"),
breaks=c('local bee', 'carnica', 'mellifera',
'Shepard and Meixner 2003','Chen et al. 2016'),
labels=c('local bees', 'A. m. carnica', 'A. m. mellifera',
'Shepard and Meixner 2003','Chen et al. 2016')) +
coord_sf(xlim = c(67, 84), ylim = c(38, 47)) +
theme(axis.title.x=element_blank(), axis.title.y=element_blank()) +
annotation_scale(location = "tl", width_hint = 0.2)
GPA-alignment
# Convert from 2D array to 3D array
wings.coords <- arrayspecs(wings[xyNames], p, k)
dimnames(wings.coords)[[3]] <- wings$file
# Align the coordinates using Generalized Procrustes Analysis
GPA <- gpagen(wings.coords, print.progress = FALSE)
consensus = GPA$consensus
# # plot landmarks after alignment
# plotAllSpecimens(GPA$coords,links=links.apis,label=TRUE,
# plot.param = list(pt.bg = "black", pt.cex = 0.5,
# mean.bg = "red", mean.cex=1, link.col="red",
# txt.pos=3, txt.cex=1))
# Convert from 3D array to 2D array
wings.aligned <- two.d.array(GPA$coords)
colnames(wings.aligned) = xyNames
head(wings.aligned, 2)## x1 y1 x2 y2 x3 y3
## KZ-0001-000001_c10C.dw.png -0.2793 -0.05838 -0.2499 -0.05997 -0.1843 0.06623
## KZ-0001-000002_c11C.dw.png -0.2831 -0.06161 -0.2451 -0.06130 -0.1901 0.06756
## x4 y4 x5 y5 x6 y6
## KZ-0001-000001_c10C.dw.png -0.1798 -0.02022 -0.1538 -0.1486 -0.07131 0.07714
## KZ-0001-000002_c11C.dw.png -0.1798 -0.02076 -0.1619 -0.1560 -0.07652 0.08001
## x7 y7 x8 y8 x9 y9
## KZ-0001-000001_c10C.dw.png 0.008661 0.1269 -0.01714 0.09124 0.05306 0.03956
## KZ-0001-000002_c11C.dw.png 0.009229 0.1271 -0.01723 0.09612 0.04592 0.04210
## x10 y10 x11 y11 x12 y12
## KZ-0001-000001_c10C.dw.png -0.001055 0.006718 0.06906 -0.04379 0.08580 -0.1025
## KZ-0001-000002_c11C.dw.png 0.004462 0.011693 0.06885 -0.04458 0.08602 -0.1015
## x13 y13 x14 y14 x15 y15 x16
## KZ-0001-000001_c10C.dw.png 0.1092 -0.1549 0.1137 0.1162 0.1667 0.05872 0.2873
## KZ-0001-000002_c11C.dw.png 0.1100 -0.1548 0.1156 0.1174 0.1812 0.05884 0.2928
## y16 x17 y17 x18 y18 x19
## KZ-0001-000001_c10C.dw.png 0.006832 0.3524 -0.002972 0.3696 -0.04991 -0.4787
## KZ-0001-000002_c11C.dw.png 0.004376 0.3449 -0.002831 0.3641 -0.05164 -0.4694
## y19
## KZ-0001-000001_c10C.dw.png 0.05165
## KZ-0001-000002_c11C.dw.png 0.04988Average aligned coordinates within samples
sample.aligned <- aggregate(wings.aligned, by = list(wings$sample), FUN = mean)
rownames(sample.aligned) = sample.aligned$Group.1
sample.aligned <- subset(sample.aligned, select = -c(Group.1))
sample.aligned$group = sample.gr$group
head(sample.aligned, 2)## x1 y1 x2 y2 x3 y3 x4 y4
## KZ-0001 -0.2817 -0.06049 -0.2501 -0.06247 -0.1805 0.06655 -0.1777 -0.01958
## KZ-0002 -0.2815 -0.05963 -0.2476 -0.06147 -0.1844 0.06520 -0.1784 -0.01907
## x5 y5 x6 y6 x7 y7 x8 y8
## KZ-0001 -0.1641 -0.1508 -0.06890 0.07734 0.009142 0.1278 -0.01484 0.09266
## KZ-0002 -0.1638 -0.1471 -0.06891 0.07552 0.012188 0.1267 -0.01176 0.09120
## x9 y9 x10 y10 x11 y11 x12 y12
## KZ-0001 0.04752 0.04263 0.004954 0.009638 0.07003 -0.04236 0.08267 -0.1033
## KZ-0002 0.05041 0.04329 0.005981 0.009975 0.06791 -0.04037 0.07971 -0.1004
## x13 y13 x14 y14 x15 y15 x16 y16 x17
## KZ-0001 0.1045 -0.1567 0.1136 0.1180 0.1723 0.06210 0.2945 0.003492 0.3493
## KZ-0002 0.1010 -0.1552 0.1138 0.1174 0.1737 0.06184 0.2974 0.002025 0.3493
## y17 x18 y18 x19 y19 group
## KZ-0001 -0.002553 0.3644 -0.04886 -0.4750 0.04691 carnica
## KZ-0002 -0.002603 0.3636 -0.04937 -0.4786 0.04216 carnicaPrincipal component analysis of the samples
sample.pca <- prcomp(sample.aligned[ , xyNames])
sample.pca.scores <- as.data.frame(sample.pca$x)
sample.pca.scores$group = sample.aligned$group
# create plot labels
variance.tab <- summary(sample.pca)$importance
variance <- variance.tab["Proportion of Variance", "PC1"]
variance <- round(100 * variance, 1)
label.x <- paste0("PC1 (", variance, "%)")
variance <- variance.tab["Proportion of Variance", "PC2"]
variance <- round(100 * variance, 1)
label.y <- paste0("PC2 (", variance, "%)")
ggplot(sample.pca.scores, aes(x = PC1, y = PC2, shape = group, color = group)) +
geom_point() +
scale_shape_manual(name ="", values = c(15:17),
breaks=c('local bee', 'carnica', 'mellifera'),
labels=c('local bees', 'A. m. carnica', 'A. m. mellifera')) +
scale_color_manual(name ="",
values = c("red", "blue", "green"),
breaks=c('local bee', 'carnica', 'mellifera'),
labels=c('local bees', 'A. m. carnica', 'A. m. mellifera')) +
stat_ellipse() +
xlab(label.x) + ylab(label.y)
MANOVA
MANOVA.samples = manova(as.matrix(cbind(sample.pca.scores[, pcNames])) ~ group, sample.pca.scores)
summary(MANOVA.samples)## Df Pillai approx F num Df den Df Pr(>F)
## group 2 1.89 19 68 72 <2e-16 ***
## Residuals 68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Canonical variate analysis
# use equal prior probability for all groups
n.country <- length(unique(sample.pca.scores$group)) # number of groups
sample.cva <- CVA(sample.pca.scores[pcNames], sample.pca.scores$group,
rounds = 10000, cv = TRUE,
prior = rep(1/n.country, n.country))
sample.cva.scores <- as.data.frame(sample.cva$CVscores)
# remove unwanted spaces in variable names otherwise use `CV 1`
colnames(sample.cva.scores) <- gsub(" ", "", colnames(sample.cva.scores))
# sample.cva.scores <- cbind(sample.geo.data, sample.cva.scores)
sample.cva.scores$group = sample.aligned$group
ggplot(sample.cva.scores, aes(x = CV1, y = CV2, shape = group, color = group)) +
geom_point() +
scale_shape_manual(name ="", values = c(15:17),
breaks=c('local bee', 'carnica', 'mellifera'),
labels=c('local bees', 'A. m. carnica', 'A. m. mellifera')) +
scale_color_manual(name ="",
values = c("red", "blue", "green"),
breaks=c('local bee', 'carnica', 'mellifera'),
labels=c('local bees', 'A. m. carnica', 'A. m. mellifera')) +
stat_ellipse() 
# Confusion matrix
# Classification of the samples to groups
CVA.class <- typprobClass(sample.cva$CVscores, groups = as.factor(sample.cva.scores$group), outlier = 0)
print(CVA.class)## cross-validated classification results in frequencies
##
## carnica local bee mellifera
## carnica 38 0 0
## local bee 0 29 0
## mellifera 0 0 4
##
##
## cross-validated classification result in %
##
## carnica local bee mellifera
## carnica 100 0 0
## local bee 0 100 0
## mellifera 0 0 100
##
##
## overall classification accuracy: 100 %
##
## Kappa statistic: 1# Mahalanobis distances between groups
knitr::kable(as.data.frame(as.matrix(sample.cva$Dist$GroupdistMaha)), digits = 3)| carnica | local bee | mellifera | |
|---|---|---|---|
| carnica | 0.000 | 9.269 | 17.64 |
| local bee | 9.269 | 0.000 | 17.39 |
| mellifera | 17.643 | 17.385 | 0.00 |
# Significance of differences between groups
knitr::kable(as.data.frame(as.matrix(sample.cva$Dist$probsMaha)))| carnica | local bee | mellifera | |
|---|---|---|---|
| carnica | 0e+00 | 1e-04 | 1e-04 |
| local bee | 1e-04 | 0e+00 | 1e-04 |
| mellifera | 1e-04 | 1e-04 | 0e+00 |
Cubital index
a = ((sample.aligned$x1-sample.aligned$x2)^2+
(sample.aligned$y1-sample.aligned$y2)^2)^0.5
cubital = data.frame(a = a)
cubital$b = ((sample.aligned$x2-sample.aligned$x4)^2+
(sample.aligned$y2-sample.aligned$y4)^2)^0.5
cubital$ci = cubital$b/cubital$a
cubital$group = sample.aligned$group
aggregate(cubital$ci, by = list(cubital$group), FUN=mean)## Group.1 x
## 1 carnica 2.693
## 2 local bee 2.305
## 3 mellifera 1.720aggregate(cubital$ci, by = list(cubital$group), FUN=sd)## Group.1 x
## 1 carnica 0.2174
## 2 local bee 0.2235
## 3 mellifera 0.1246ANOVA = aov(ci ~ group, data = cubital)
summary(ANOVA)## Df Sum Sq Mean Sq F value Pr(>F)
## group 2 4.91 2.457 52.3 1.8e-14 ***
## Residuals 68 3.19 0.047
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Comparison with honey bee lineages
Read landmark coordinates from lineages dataset Nawrocka et al., (2018a) and Nawrocka et al., (2018b)
wings.lin <- read.csv("https://zenodo.org/record/7567336/files/Nawrocka_et_al2018.csv", header = TRUE)
# extract sample classifier
wings.lin$sample = substr(wings.lin$file,1,10)
wings.lin$lineage = substr(wings.lin$file,1,1)
wings.lin$subspecies = substr(wings.lin$file,1,5)
head(wings.lin, 2)## file x1 y1 x2 y2 x3 y3 x4 y4 x5 y5 x6 y6 x7 y7
## 1 A-ada-0698-01.dw.png 191 229 207 227 239 276 239 240 238 185 295 272 331 288
## 2 A-ada-0698-02.dw.png 166 243 182 241 212 295 216 258 218 204 270 294 303 312
## x8 y8 x9 y9 x10 y10 x11 y11 x12 y12 x13 y13 x14 y14 x15 y15 x16 y16 x17
## 1 319 274 341 249 325 240 344 214 342 188 348 165 372 276 392 249 438 219 460
## 2 291 297 318 274 301 263 323 240 326 214 336 192 346 302 369 276 414 249 441
## y17 x18 y18 x19 y19 sample lineage subspecies
## 1 214 461 191 118 289 A-ada-0698 A A-ada
## 2 245 446 224 91 295 A-ada-0698 A A-adaGPA-alignment of lineages dataset
Before analysis all landmark configurations have to be superimposed.
# Convert 2D array into a 3D array
wings.lin.3D <- arrayspecs(wings.lin[xyNames], p, k)
dimnames(wings.lin.3D)[[3]] <- wings.lin$file
# Align the coordinates using Generalized Procrustes Analysis
GPA.lin <- gpagen(wings.lin.3D, print.progress = FALSE)
# Convert 3D array into a 2D array - opposite to arrayspecs
wings.lin.aligned <- two.d.array(GPA.lin$coords) Average aligned coordinates within samples for lineages dataset
sample.lin.aligned <- aggregate(wings.lin.aligned, by = list(wings.lin$sample), FUN = mean)
names(sample.lin.aligned)[names(sample.lin.aligned) == "Group.1"] <- "sample" # rename column
# extract lineage code as classifier
sample.lin.aligned$lineage = substr(sample.lin.aligned$sample,1,1)
geo.data.lin <- read.csv("https://zenodo.org/record/7567336/files/Nawrocka_et_al2018-geo-data.csv", header = TRUE)Canonical variate analysis based on lineages
# Convert 2D array into a 3D array for easier analysis of unknown samples
sample.lin.3D <- arrayspecs(sample.lin.aligned[xy.Names], p, k)
dimnames(sample.lin.3D)[[3]] <- sample.lin.aligned$sample
# use equal prior probability for all groups
n.lin <- length(unique(sample.lin.aligned$lineage)) # number of groups
sample.lin.cva <- CVA(sample.lin.3D, sample.lin.aligned$lineage, rounds = 10000, cv = TRUE,
prior = rep(1/n.lin, n.lin))## singular Covariance matrix: General inverse is used. Threshold for zero eigenvalue is 1e-10sample.lin.cva.scores <- as.data.frame(sample.lin.cva$CVscores)
# remove unwanted spaces in variable names otherwise use `CV 1`
colnames(sample.lin.cva.scores) <- gsub(" ", "", colnames(sample.lin.cva.scores))
sample.lin.cva.scores <- cbind(sample.lin.aligned$lineage, sample.lin.cva.scores)
names(sample.lin.cva.scores)[1] <- "lineage" # rename column
rownames(sample.lin.cva.scores) <- sample.lin.aligned$sample
ggplot(sample.lin.cva.scores, aes(x = CV1, y = CV2, color = lineage)) +
geom_point() +
coord_fixed() +
stat_ellipse() 
Classification of the samples to lineages
# Convert 2D array into a 3D array
sample.only <- sample.aligned[xyNames]
unknown.wings <- arrayspecs(sample.only, p, k)
dimnames(unknown.wings)[[3]] <- sample.only$sample
# calculate covarience for each lineage
covariances <- lapply(unique(sample.lin.cva.scores$lineage),
function(x)
cov(sample.lin.cva.scores[sample.lin.cva.scores$lineage==x,-1]))
means <- aggregate(sample.lin.cva.scores[,names(sample.lin.cva.scores) != "lineage"],
list(sample.lin.cva.scores$lineage), FUN=mean)
rownames(means) <- means$Group.1
means <- means[,names(means) != "Group.1"]
# Projection of the samples to canonical variate space from Nawrocka et al. 2018
groups <- rownames(means)
result.list <- vector(mode = "list", length = nrow(sample.aligned))
CV.list <- vector(mode = "list", length = nrow(sample.aligned))
for (r in 1:nrow(sample.aligned)) {
# Align unknown consensus with consensus from reference samples
unknown.OPA <- procOPA(GPA.lin$consensus, unknown.wings[,,r])
unknown.aligned <- unknown.OPA$Bhat
CV.row <- predict(sample.lin.cva, unknown.aligned)
# create empty list for results
result <- numeric(length(groups))
for (i in 1:length(groups)) {
MD <- mahalanobis(CV.row, unlist(means[i, ]), as.matrix(covariances[[i]]))
result[i] <- MD
}
result.list[[r]] <- result
CV.list[[r]] <- CV.row
}
CV.tab <- do.call(rbind, CV.list)
# remove unwanted spaces in variable names otherwise use `CV 1`
colnames(CV.tab) <- gsub(" ", "", colnames(CV.tab))
CV.tab <- as.data.frame(CV.tab)
CV.tab$group <- sample.aligned$group
rownames(CV.tab) = rownames(sample.aligned)
# Black ellipses A, C, M and O indicate 95% confidence regions of reference samples from Nawrocka et al. 2018
ggplot(CV.tab, aes(x = CV1, y = CV2, shape = group, color = group))+
geom_point() +
scale_shape_manual(name ="", values = c(15:17),
breaks=c('local bee', 'carnica', 'mellifera'),
labels=c('local bees', 'A. m. carnica', 'A. m. mellifera')) +
scale_color_manual(name ="",
values = c("red", "blue", "green"),
breaks=c('local bee', 'carnica', 'mellifera'),
labels=c('local bees', 'A. m. carnica', 'A. m. mellifera')) +
stat_ellipse() +
stat_ellipse(data = subset(sample.lin.cva.scores, lineage == "A"),
mapping = aes(x = CV1, y = CV2), inherit.aes = FALSE) +
stat_ellipse(data = subset(sample.lin.cva.scores, lineage == "C"),
mapping = aes(x = CV1, y = CV2), inherit.aes = FALSE) +
stat_ellipse(data = subset(sample.lin.cva.scores, lineage == "M"),
mapping = aes(x = CV1, y = CV2), inherit.aes = FALSE) +
stat_ellipse(data = subset(sample.lin.cva.scores, lineage == "O"),
mapping = aes(x = CV1, y = CV2), inherit.aes = FALSE) +
geom_label(means,
mapping = aes(x = CV1, y = CV2, label = rownames(means)), inherit.aes = FALSE)
MD.tab <- do.call(rbind, result.list)
MD.tab <- sqrt(MD.tab)
# column names for lineages
colnames(MD.tab) <- groups
MD.tab <- as.data.frame(MD.tab)
lineage <- colnames(MD.tab)[apply(MD.tab, 1, which.min)]
# final column names
colnames(MD.tab) <- paste0("MD.",groups)
MD.tab <- cbind(MD.tab, lineage)
rownames(MD.tab) <- sample.aligned$sample
# frequencies of the lineages
table(MD.tab$lineage)##
## A C M O
## 8 49 2 12# frequencies of the lineage in countries
table(sample.aligned$group, MD.tab$lineage)##
## A C M O
## carnica 0 38 0 0
## local bee 6 11 0 12
## mellifera 2 0 2 0head(CVA.class$probs)## carnica local bee mellifera
## carnica 0.4901 3.708e-19 4.896e-77
## carnica 0.8432 3.796e-20 1.104e-64
## carnica 0.6932 1.122e-16 2.099e-63
## carnica 0.4775 2.003e-17 1.720e-59
## carnica 0.2197 9.410e-14 3.820e-71
## carnica 0.9316 6.330e-21 5.022e-69head(CVA.class$groupaffin)## [1] carnica carnica carnica carnica carnica carnica
## Levels: carnica local bee melliferahead(CVA.class$probsCV)## carnica local bee mellifera
## carnica 0.4689 6.919e-19 2.428e-77
## carnica 0.8368 7.125e-20 5.819e-64
## carnica 0.6803 1.487e-16 7.109e-63
## carnica 0.4558 3.165e-17 6.780e-60
## carnica 0.1920 5.268e-14 2.511e-70
## carnica 0.9288 1.137e-20 4.966e-68p.local = CVA.class$probs
colnames(p.local)[2] = "pomonella"
p.local = subset(p.local, )Discrimination between local bees and evilutionary lineages
# use only local bees
KZ.lin = subset(sample.aligned, group == "local bee")
# format lineage data
lin.tmp = sample.lin.aligned[, xy.Names]
rownames(lin.tmp) = sample.lin.aligned$sample
colnames(lin.tmp) = xyNames
lin.tmp$group = sample.lin.aligned$lineage
KZ.lin = rbind(KZ.lin, lin.tmp)
head(KZ.lin, 2)## x1 y1 x2 y2 x3 y3 x4 y4
## KZ-0041 -0.2784 -0.05707 -0.2361 -0.05729 -0.1949 0.06576 -0.1752 -0.01745
## KZ-0042 -0.2754 -0.05717 -0.2422 -0.05842 -0.1909 0.06448 -0.1810 -0.02003
## x5 y5 x6 y6 x7 y7 x8 y8
## KZ-0041 -0.1563 -0.1487 -0.06587 0.07741 0.010014 0.1285 -0.01531 0.09147
## KZ-0042 -0.1611 -0.1465 -0.06797 0.07672 0.008286 0.1247 -0.01707 0.09101
## x9 y9 x10 y10 x11 y11 x12 y12
## KZ-0041 0.04815 0.04019 0.007034 0.007755 0.06873 -0.04266 0.08078 -0.1048
## KZ-0042 0.05221 0.04176 0.011682 0.009496 0.06649 -0.04276 0.07897 -0.1057
## x13 y13 x14 y14 x15 y15 x16 y16 x17
## KZ-0041 0.1051 -0.1565 0.1046 0.1182 0.1716 0.05991 0.2955 0.002670 0.3516
## KZ-0042 0.0996 -0.1546 0.1059 0.1170 0.1737 0.06049 0.2979 0.003016 0.3524
## y17 x18 y18 x19 y19 group
## KZ-0041 -0.002806 0.3641 -0.05098 -0.4852 0.04637 local bee
## KZ-0042 -0.001117 0.3677 -0.04858 -0.4793 0.04619 local bee# Convert 2D array into a 3D array
wings.coords <- arrayspecs(KZ.lin[xyNames], p, k)
dimnames(wings.coords)[[3]] <- rownames(KZ.lin)
# Align the coordinates using Generalized Procrustes Analysis
GPA.KZ.lin <- gpagen(wings.coords, print.progress = FALSE)
# Convert 3D array into a 2D array - opposite to arrayspecs
KZ.lin.aligned <- two.d.array(GPA.KZ.lin$coords)
# use equal prior probability for all groups
n.gr <- length(unique(KZ.lin$group)) # number of groups
KZ.lin.cva <- CVA(KZ.lin.aligned, KZ.lin$group, rounds = 10000, cv = TRUE,
prior = rep(1/n.gr, n.gr))## singular Covariance matrix: General inverse is used. Threshold for zero eigenvalue is 1e-10# Mahalanobis distances between groups
knitr::kable(as.data.frame(as.matrix(KZ.lin.cva$Dist$GroupdistMaha)))| A | C | local bee | M | O | |
|---|---|---|---|---|---|
| A | 0.000 | 8.513 | 7.960 | 8.061 | 6.091 |
| C | 8.513 | 0.000 | 7.562 | 11.155 | 8.008 |
| local bee | 7.960 | 7.562 | 0.000 | 11.886 | 7.207 |
| M | 8.061 | 11.155 | 11.886 | 0.000 | 10.209 |
| O | 6.091 | 8.008 | 7.207 | 10.209 | 0.000 |
# Significance of differences between groups
knitr::kable(as.data.frame(as.matrix(KZ.lin.cva$Dist$probsMaha)))| A | C | local bee | M | O | |
|---|---|---|---|---|---|
| A | 0e+00 | 1e-04 | 1e-04 | 1e-04 | 1e-04 |
| C | 1e-04 | 0e+00 | 1e-04 | 1e-04 | 1e-04 |
| local bee | 1e-04 | 1e-04 | 0e+00 | 1e-04 | 1e-04 |
| M | 1e-04 | 1e-04 | 1e-04 | 0e+00 | 1e-04 |
| O | 1e-04 | 1e-04 | 1e-04 | 1e-04 | 0e+00 |
KZ.lin.scores <- as.data.frame(KZ.lin.cva$CVscores)
# remove unwanted spaces in variable names otherwise use `CV 1`
colnames(KZ.lin.scores) <- gsub(" ", "", colnames(KZ.lin.scores))
KZ.lin.scores$group <- KZ.lin$group
rownames(KZ.lin.scores) = rownames(KZ.lin)
ggplot(KZ.lin.scores, aes(x = CV1, y = CV2, shape = group, color = group)) +
geom_point() +
scale_shape_manual(name ="", values = c(15, 18, 16, 17, 3),
breaks=c('local bee', 'A', 'C', 'M', 'O'),
labels=c('local bees', 'A', 'C', 'M', 'O')) +
scale_color_manual(name ="",
values = c("red", "orange", "blue", "green", "purple"),
breaks=c('local bee', 'A', 'C', 'M', 'O'),
labels=c('local bees', 'A', 'C', 'M', 'O')) +
stat_ellipse() 
CVA.class <- typprobClass(KZ.lin.cva$CVscores, groups = as.factor(KZ.lin$group), outlier = 0)
print(CVA.class)## cross-validated classification results in frequencies
##
## A C local bee M O
## A 85 0 0 0 0
## C 0 37 0 0 0
## local bee 0 0 29 0 0
## M 0 0 0 16 0
## O 1 0 0 0 48
##
##
## cross-validated classification result in %
##
## A C local bee M O
## A 100.0000 0.0000 0.0000 0.0000 0.0000
## C 0.0000 100.0000 0.0000 0.0000 0.0000
## local bee 0.0000 0.0000 100.0000 0.0000 0.0000
## M 0.0000 0.0000 0.0000 100.0000 0.0000
## O 2.0408 0.0000 0.0000 0.0000 97.9592
##
##
## overall classification accuracy: 99.53704 %
##
## Kappa statistic: 0.99374Discrimination between local bees and subspecies from lineage O
KZ.sub = subset(sample.aligned, group == "local bee")
lin.tmp = sample.lin.aligned[, xy.Names]
rownames(lin.tmp) = sample.lin.aligned$sample
colnames(lin.tmp) = xyNames
lin.tmp$group = substr(rownames(lin.tmp),1,5)
lin.tmp = subset(lin.tmp, sample.lin.aligned$lineage == "O")
KZ.sub = rbind(KZ.sub, lin.tmp)
head(KZ.sub, 2)## x1 y1 x2 y2 x3 y3 x4 y4
## KZ-0041 -0.2784 -0.05707 -0.2361 -0.05729 -0.1949 0.06576 -0.1752 -0.01745
## KZ-0042 -0.2754 -0.05717 -0.2422 -0.05842 -0.1909 0.06448 -0.1810 -0.02003
## x5 y5 x6 y6 x7 y7 x8 y8
## KZ-0041 -0.1563 -0.1487 -0.06587 0.07741 0.010014 0.1285 -0.01531 0.09147
## KZ-0042 -0.1611 -0.1465 -0.06797 0.07672 0.008286 0.1247 -0.01707 0.09101
## x9 y9 x10 y10 x11 y11 x12 y12
## KZ-0041 0.04815 0.04019 0.007034 0.007755 0.06873 -0.04266 0.08078 -0.1048
## KZ-0042 0.05221 0.04176 0.011682 0.009496 0.06649 -0.04276 0.07897 -0.1057
## x13 y13 x14 y14 x15 y15 x16 y16 x17
## KZ-0041 0.1051 -0.1565 0.1046 0.1182 0.1716 0.05991 0.2955 0.002670 0.3516
## KZ-0042 0.0996 -0.1546 0.1059 0.1170 0.1737 0.06049 0.2979 0.003016 0.3524
## y17 x18 y18 x19 y19 group
## KZ-0041 -0.002806 0.3641 -0.05098 -0.4852 0.04637 local bee
## KZ-0042 -0.001117 0.3677 -0.04858 -0.4793 0.04619 local bee#GPA re-alignment
# Convert 2D array into a 3D array
wings.coords <- arrayspecs(KZ.sub[xyNames], p, k)
dimnames(wings.coords)[[3]] <- rownames(KZ.sub)
# Align the coordinates using Generalized Procrustes Analysis
GPA.KZ.sub <- gpagen(wings.coords, print.progress = FALSE)
# Convert 3D array into a 2D array - opposite to arrayspecs
KZ.sub.aligned <- two.d.array(GPA.KZ.sub$coords)
# use equal prior probability for all groups
n.gr <- length(unique(KZ.sub$group)) # number of groups
KZ.sub.cva <- CVA(KZ.sub.aligned, KZ.sub$group, rounds = 10000, cv = TRUE,
prior = rep(1/n.gr, n.gr))## singular Covariance matrix: General inverse is used. Threshold for zero eigenvalue is 1e-10# Mahalanobis distances between groups
knitr::kable(as.data.frame(as.matrix(KZ.sub.cva$Dist$GroupdistMaha)))| local bee | O-adm | O-ana | O-arm | O-cau | O-cyp | O-med | O-syr | |
|---|---|---|---|---|---|---|---|---|
| local bee | 0.00 | 16.022 | 13.082 | 14.600 | 13.514 | 13.737 | 14.053 | 14.839 |
| O-adm | 16.02 | 0.000 | 8.857 | 10.209 | 13.462 | 9.679 | 9.860 | 11.197 |
| O-ana | 13.08 | 8.857 | 0.000 | 7.671 | 8.303 | 8.757 | 11.542 | 9.460 |
| O-arm | 14.60 | 10.209 | 7.671 | 0.000 | 9.978 | 7.654 | 9.412 | 10.382 |
| O-cau | 13.51 | 13.462 | 8.303 | 9.978 | 0.000 | 13.674 | 14.769 | 12.357 |
| O-cyp | 13.74 | 9.679 | 8.757 | 7.654 | 13.674 | 0.000 | 7.580 | 8.340 |
| O-med | 14.05 | 9.860 | 11.542 | 9.412 | 14.769 | 7.580 | 0.000 | 9.537 |
| O-syr | 14.84 | 11.197 | 9.460 | 10.382 | 12.357 | 8.340 | 9.537 | 0.000 |
# Significance of differences between groups
knitr::kable(as.data.frame(as.matrix(KZ.sub.cva$Dist$probsMaha)))| local bee | O-adm | O-ana | O-arm | O-cau | O-cyp | O-med | O-syr | |
|---|---|---|---|---|---|---|---|---|
| local bee | 0e+00 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 |
| O-adm | 1e-04 | 0.0000 | 0.0651 | 0.0139 | 0.0001 | 0.0517 | 0.0099 | 0.0015 |
| O-ana | 1e-04 | 0.0651 | 0.0000 | 0.1248 | 0.0304 | 0.1010 | 0.0013 | 0.0144 |
| O-arm | 1e-04 | 0.0139 | 0.1248 | 0.0000 | 0.0018 | 0.1809 | 0.0108 | 0.0029 |
| O-cau | 1e-04 | 0.0001 | 0.0304 | 0.0018 | 0.0000 | 0.0001 | 0.0001 | 0.0001 |
| O-cyp | 1e-04 | 0.0517 | 0.1010 | 0.1809 | 0.0001 | 0.0000 | 0.1427 | 0.0677 |
| O-med | 1e-04 | 0.0099 | 0.0013 | 0.0108 | 0.0001 | 0.1427 | 0.0000 | 0.0032 |
| O-syr | 1e-04 | 0.0015 | 0.0144 | 0.0029 | 0.0001 | 0.0677 | 0.0032 | 0.0000 |
KZ.sub.scores <- as.data.frame(KZ.sub.cva$CVscores)
# remove unwanted spaces in variable names otherwise use `CV 1`
colnames(KZ.sub.scores) <- gsub(" ", "", colnames(KZ.sub.scores))
KZ.sub.scores$group <- KZ.sub$group
rownames(KZ.sub.scores) = rownames(KZ.sub)
ggplot(KZ.sub.scores, aes(x = CV1, y = CV2, shape = group, color = group)) +
geom_point() +
scale_shape_manual(name ="", values = c(15, 18, 16, 17, 0:3),
breaks=c('local bee', 'O-adm', 'O-ana',
'O-arm', 'O-cau',
'O-cyp', 'O-med', 'O-syr'),
labels=c('local bees', 'A. m. adami', 'A. m. anatolica',
'A. m. armeniaca', 'A. m. caucasia',
'A. m. cypria', 'A. m. meda', 'A. m. syriaca')) +
scale_color_manual(name ="",
values = c("red", "orange", "blue", "green",
"purple", "black", "magenta", "gray70"),
breaks=c('local bee', 'O-adm', 'O-ana',
'O-arm', 'O-cau',
'O-cyp', 'O-med', 'O-syr'),
labels=c('local bees', 'A. m. adami', 'A. m. anatolica',
'A. m. armeniaca', 'A. m. caucasia',
'A. m. cypria', 'A. m. meda', 'A. m. syriaca')) +
stat_ellipse() 
CVA.class <- typprobClass(KZ.sub.cva$CVscores, groups = as.factor(KZ.sub$group), outlier = 0)
print(CVA.class)## cross-validated classification results in frequencies
##
## local bee O-adm O-ana O-arm O-cau O-cyp O-med O-syr
## local bee 29 0 0 0 0 0 0 0
## O-adm 0 5 0 0 0 0 0 0
## O-ana 0 0 5 0 0 0 0 0
## O-arm 0 0 0 6 0 0 0 0
## O-cau 0 0 0 0 12 0 0 0
## O-cyp 0 0 0 0 0 4 0 0
## O-med 0 0 0 0 0 0 8 0
## O-syr 0 0 0 0 0 0 0 9
##
##
## cross-validated classification result in %
##
## local bee O-adm O-ana O-arm O-cau O-cyp O-med O-syr
## local bee 100 0 0 0 0 0 0 0
## O-adm 0 100 0 0 0 0 0 0
## O-ana 0 0 100 0 0 0 0 0
## O-arm 0 0 0 100 0 0 0 0
## O-cau 0 0 0 0 100 0 0 0
## O-cyp 0 0 0 0 0 100 0 0
## O-med 0 0 0 0 0 0 100 0
## O-syr 0 0 0 0 0 0 0 100
##
##
## overall classification accuracy: 100 %
##
## Kappa statistic: 1References
Nawrocka, A., Kandemir, İ., Fuchs, S., & Tofilski, A. (2018). Computer software for identification of honey bee subspecies and evolutionary lineages. Apidologie, 49(2), 172-184. https://doi.org/10.1007/s13592-017-0538-y
Nawrocka, A., Kandemir, İ., Fuchs, S., & Tofiilski, A. (2018). Dataset: Computer software for identification of honey bee subspecies and evolutionary lineages. Apidologie, 49, 172–184. https://doi.org/10.5281/zenodo.7567336
Information about session
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19043)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Polish_Poland.1250 LC_CTYPE=Polish_Poland.1250
## [3] LC_MONETARY=Polish_Poland.1250 LC_NUMERIC=C
## [5] LC_TIME=Polish_Poland.1250
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggspatial_1.1.6 raster_3.5-21 sp_1.5-0
## [4] rnaturalearth_0.1.0 ggrepel_0.9.1 ggforce_0.3.3
## [7] ggplot2_3.3.6 MASS_7.3-58.1 Morpho_2.9
## [10] shapes_1.2.6 geomorph_4.0.4 Matrix_1.4-1
## [13] rgl_0.109.6 RRPP_1.3.0
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.1 jsonlite_1.8.0 foreach_1.5.2
## [4] bslib_0.4.1 assertthat_0.2.1 highr_0.9
## [7] yaml_2.3.5 pillar_1.8.1 lattice_0.20-41
## [10] glue_1.6.2 digest_0.6.29 polyclip_1.10-0
## [13] colorspace_2.0-3 htmltools_0.5.3 pkgconfig_2.0.3
## [16] s2_1.1.2 Rvcg_0.21 scales_1.2.1
## [19] tweenr_1.0.2 terra_1.6-7 jpeg_0.1-9
## [22] tibble_3.1.8 proxy_0.4-27 generics_0.1.3
## [25] farver_2.1.1 cachem_1.0.6 withr_2.5.0
## [28] cli_3.3.0 magrittr_2.0.1 evaluate_0.18
## [31] fansi_1.0.3 doParallel_1.0.17 nlme_3.1-149
## [34] class_7.3-17 tools_4.0.3 minpack.lm_1.2-2
## [37] formatR_1.14 lifecycle_1.0.1 stringr_1.4.0
## [40] munsell_0.5.0 colorRamps_2.3.1 bezier_1.1.2
## [43] compiler_4.0.3 jquerylib_0.1.4 e1071_1.7-11
## [46] rlang_1.0.4 classInt_0.4-7 units_0.8-0
## [49] grid_4.0.3 iterators_1.0.14 rstudioapi_0.14
## [52] htmlwidgets_1.5.4 labeling_0.4.2 base64enc_0.1-3
## [55] rmarkdown_2.18 wk_0.6.0 gtable_0.3.1
## [58] codetools_0.2-16 DBI_1.1.3 R6_2.5.1
## [61] rgdal_1.5-32 knitr_1.40 dplyr_1.0.9
## [64] fastmap_1.1.0 utf8_1.2.2 KernSmooth_2.23-17
## [67] ape_5.4-1 stringi_1.7.8 parallel_4.0.3
## [70] Rcpp_1.0.9 vctrs_0.4.1 sf_1.0-7
## [73] rnaturalearthdata_0.1.0 scatterplot3d_0.3-42 tidyselect_1.2.0
## [76] xfun_0.31