Kaspian mites
Sozontov
2022-11-10
Загрузка
library(tidyverse)
library(vegan)
library(dendextend)
library(iNEXT)
theme_set(theme_bw() + theme(legend.position = "bottom"))taxa <- readxl::read_xlsx("Caspian data_15.09.2022_SA.xlsx", sheet = "taxa")
labs <- readxl::read_xlsx("Caspian data_15.09.2022_SA.xlsx", sheet = "samples")
df <- readxl::read_xlsx("Caspian data_15.09.2022_SA.xlsx", sheet = "main")
# 0 = 6468
# !0 = 536
dfw <- df %>%
mutate_all(as.character) %>% #equalize all columns
pivot_longer(names_to = "id", values_to = "abu", -sp) %>%
filter(sp != "Oribatida Juvenile instars", abu != "0") %>%
# temporary filter !!!
filter(!(id %in% c("SmPbAe1", "SmPbAe2", "SmPbAe3", "SmPbAe4", "SmPbAe5"))) %>%
# temporary filter !!!
left_join(select(taxa, sp, order), by = "sp") %>%
filter(order == "Oribatida") %>%
left_join(select(labs, id, distr), by = "id") %>%
filter(distr == "Samoor") %>%
select(-order, -distr) %>%
separate(col = abu, into = c("adu", "juv"), sep = "\\+", fill = "right") %>%
mutate(adu = as.numeric(adu), juv = as.numeric(juv),
juv = case_when(is.na(juv) ~ 0, TRUE ~ juv)) %>%
transmute(sp, id, abu = adu + juv) %>%
pivot_wider(names_from = id, values_from = abu, values_fill = 0)
dfl <- dfw %>% pivot_longer(names_to = "id", values_to = "abu", -sp)
dfl0 <- dfl %>%
mutate(id = substr(id, 1, nchar(id)- 1)) %>%
group_by(sp, id) %>%
summarise(abu = sum(abu), .groups = "drop")
dfw0 <- dfl0 %>%
pivot_wider(names_from = id, values_from = abu)
labs <- labs %>%
select(id, plants.d, plants.sp) %>%
unite("p", 2:3, sep = ", ") %>%
column_to_rownames("id") %>%
t %>%
as.data.frame() %>%
as.list() %>%
lapply(FUN = function(a){ # No.of plants in dominant complex
str_split(a, ", ") %>%
.[[1]] %>%
unique %>%
length
}) %>%
map_dbl(c) %>%
tibble(id = names(.), dom.comp = .) %>%
left_join(labs, ., by = "id") %>%
mutate(veg = factor(veg, ordered = TRUE),
dom.comp = factor(dom.comp, ordered = TRUE)) indval <- function(dat, clas = colnames(dat), spec = rownames(dat),
method = "APCF", rawdata = FALSE, significance = FALSE,
nboot = 999) {
# Variables description: see the function's previous version
# The function works by different ways with 0 and NA in matrix:
# 0 means abcence and involved to calculations
# NA means `no data` and skipped of calculations
# test & pre-processing unit ----------------------------------------------
dat <- as.matrix(dat)
# dat[is.na(dat)] <- 0
if(!is.numeric(dat)) {show("Your data aren't numeric"); break}
if(sum(dat, na.rm = TRUE) <= 0) {show("Your data are empty or negative"); break}
if(length(clas) != ncol(dat)) {show("Classification size error"); break}
if(length(spec) != nrow(dat)) {show("Species number error"); break}
if(!(method %in% c("APCF", "ACF", "ASF", "PCF", "PSF"))) {
show("You selected unknown type of IndVal
try one of this: APCF, ACF, ASF, #PCF, #PSF"); break}
clas[clas == "NA"] <- NA
g <- unique(clas) |> sort() |> na.omit()
# Selecting unit ----------------------------------------------------------
if(method == "APCF") {
calcA <- function(d, clas) {
res0 <- rep(NA, length(g))
names(res0) <- g
res <- rep(NA, length(g))
names(res) <- g
for(h in 1:length(g)) {
res0[h] <- mean(d[clas == g[h]], na.rm = TRUE)
}
res0[is.nan(res0)] <- NA
for(h in g){
res[[h]] <- res0[[h]]/sum(res0, na.rm = TRUE)
}
return(res)
}
calcB <- function(d, clas) {
res <- rep(NA, length(g))
names(res) <- g
d[d>0] <- 1
for(j in g)
res[[j]] <- mean(d[which(clas == j)], na.rm = TRUE)
return(res)
}
}
if(method == "ACF") {
calcA <- function(d, clas) {
res0 <- rep(NA, length(g))
names(res0) <- g
res <- rep(NA, length(g))
names(res) <- g
for(h in 1:length(g)) {
res0[h] <- mean(d[clas == g[h]], na.rm = TRUE)
}
res0[is.nan(res0)] <- NA
for(h in g){
res[[h]] <- res0[[h]]/sum(res0, na.rm = TRUE)
}
return(res)
}
calcB <- function(d, clas) {
res <- rep(NA, length(g))
names(res) <- g
for(j in g) {
e <- d[clas == j] |> na.omit()
res[[j]] <- 1 - sum(abs(e/sum(e) - 1/length(e)))*0.5
}
res[is.nan(res)] <- 0
return(res)
}
}
if(method == "ASF") {
calcA <- function(d, clas) {
res0 <- rep(NA, length(g))
names(res0) <- g
res <- rep(NA, length(g))
names(res) <- g
for(h in g) {
res0[[h]] <- mean(d[clas == h], na.rm = TRUE)
}
res0[is.nan(res0)] <- NA
for(h in g) {
res[[h]] <- (res0[[h]] - mean(res0[names(res0) != h],
na.rm = TRUE)) / max(res0, na.rm = TRUE)
}
return(res)
}
calcB <- function(d, clas) {
res <- rep(NA, length(g))
names(res) <- g
for(j in g) {
e <- d[clas == j] |> na.omit()
res[[j]] <- 1 - sum(abs(e/sum(e) - 1/length(e)))*0.5
}
res[is.nan(res)] <- 0
return(res)
}
}
# computational unit ------------------------------------------------------
A <- matrix(NA, nrow = length(spec),
ncol = length(g))
colnames(A) <- g
rownames(A) <- spec
B <- A
for(i in 1:nrow(A)) {
A[i,] <- calcA(dat[i,], clas)
B[i,] <- calcB(dat[i,], clas)
}
res <- A*B*100
res[is.nan(res)] <- NA
S <- mean(abs(res), na.rm = TRUE)
# permutational test ------------------------------------------------------
if(significance == TRUE) {
library(parallel)
library(doParallel)
library(foreach)
clas2 <- matrix(NA, ncol = length(clas), nrow = nboot)
for(i in 1:nrow(clas2)) {
clas2[i,] <- sample(clas, length(clas))
}
myCluster <- makeCluster(detectCores()-1)
registerDoParallel(myCluster)
permutated <- foreach(i = 1:nboot, .combine = 'c') %dopar% {
res2 <- matrix(NA, nrow = length(spec), ncol = length(g))
for(j in 1:nrow(res2)) {
res2[j,] <- 100 *
calcA(dat[j,], clas2[i,])
calcB(dat[j,], clas2[i,])
}
mean(abs(res2), na.rm = TRUE)
}
stopCluster(myCluster)
S2 <- (S - mean(c(S, permutated), na.rm = TRUE))/sd(c(S, permutated))
}
# Return results ----------------------------------------------------------
if(rawdata == FALSE & significance == FALSE) {return(
list(Method = method, Sharpness = S, indval = res))
}
if(rawdata == TRUE & significance == FALSE) {return(
list(Method = method, Sharpness = S, indval = res, A = A, B = B))
}
if(rawdata == FALSE & significance == TRUE) {return(
list(Method = method, Sharpness = S, Significance = S2, indval = res))
}
if(rawdata == TRUE & significance == TRUE) {return(list(Method = method,
Sharpness = S, Significance = S2, indval = res, A = A, B = B) )
}
}collapse_labels <- function(a){
paste(names(sort(table(a), decreasing = TRUE)), collapse = " or ")
}
colorise_labels <- function(a) {
n <- length(unique(a))
clr <- hcl(h = seq(15, 375, length = n + 1),
l = 65, c = 100)[1:n]
data.frame(l = unique(a), clr = clr) %>%
left_join(data.frame(l = a), ., by = "l") %>%
pull(clr)
}
count1 <- function(d) {
d <- d %>%
pivot_wider(names_from = "sp", values_from = abu, values_fill = 0)
dis <- vegan::vegdist(d[,-1])
PCOA <- ape::pcoa(dis)
tibble(d[,1],
abu = apply(d[,-1], 1, function(a){sum(a)}),
nsp = apply(d[,-1], 1, function(a){length(a[a>0])}),
shan = apply(d[,-1], 1, function(a){vegan::diversity(a, "shannon")}),
ax.1 = PCOA$vectors[,1],
ax.2 = PCOA$vectors[,2]
)
}Вопрос 5
- Связано ли видовое разнообразие почвенных клещей (Oribatida, Mesostigmata) отдельных типов берега со сложностью растительного покрова (числом гипсометрических зон, биотопов или синузий видов сосудистых растений)?
— Связано, на песчаном пляже видовое богатство выше, но надо «выровнять» по числу проб.
требуется стандартизация c iNEXT
Примечание. Применение разрежения и эксраполяции для нивелирования различий между объемами выборки на результат почти не влияет
Обозначения: abu (abundance) - Обилие shan (Shannon’ index) - мера разнообразия Шеннона nsp (number of species) - количество видов i10 - ожидаемое количество видов в пробе из 10 особей 120 - ожидаемое количество видов в пробе из 20 особей i25 - ожидаемое количество видов в пробе из 25 особей
rar1 <- dfw %>%
select(-sp) %>%
lapply(function(a){sort(a[a>0], decreasing = TRUE)}) %>%
discard(~ length(.x) < 2) %>%
iNEXT::iNEXT(., q = 0, size = c(10, 20, 25),
datatype = "abundance", nboot = 9) %>% # 999
pluck("iNextEst", "size_based") %>%
filter(m %in% c(10, 20, 25)) %>%
select(id = Assemblage, m, qD) %>%
mutate(m = paste0("i", m)) %>%
pivot_wider(names_from = m, values_from = qD)
div <- dfl %>%
pivot_wider(names_from = "sp", values_from = abu, values_fill = 0) %>%
mutate(
abu = apply(.[,-1], 1, function(a){sum(a)}),
nsp = apply(.[,-1], 1, function(a){length(a[a>0])}),
shan = apply(.[,-1], 1, function(a){vegan::diversity(a, "shannon")}),
.before = 2) %>%
left_join(labs, by = "id") %>%
left_join(rar1, by = "id") %>%
select(abu, nsp, i10, i20, i25, shan, id,
coast, skew, soil, substrate, zone, veg, dom.comp) %>%
filter(shan > 0)coast type
div %>%
pivot_longer(names_to = "diversity", values_to = "val", -c(7:ncol(.))) %>%
ggplot(aes(x = coast, y = val, fill = coast)) +
geom_boxplot() +
facet_wrap(~diversity, scales = "free") +
labs(y = NULL, x = NULL, title = "Diversity ~ coast type") +
theme(axis.text.x = element_text(angle = 15, vjust = 0.7))
Skew
div %>%
pivot_longer(names_to = "diversity", values_to = "val", -c(7:ncol(.))) %>%
ggplot(aes(x = skew, y = val, fill = skew)) +
geom_boxplot() +
facet_wrap(~diversity, scales = "free") +
labs(y = NULL, x = NULL, title = "Diversity ~ skew") +
theme(axis.text.x = element_text(angle = 15, vjust = 0.7))
soil type
div %>%
pivot_longer(names_to = "diversity", values_to = "val", -c(7:ncol(.))) %>%
ggplot(aes(x = soil, y = val, fill = soil)) +
geom_boxplot() +
facet_wrap(~diversity, scales = "free") +
labs(y = NULL, x = NULL, title = "Diversity ~ soil type") +
theme(axis.text.x = element_text(angle = 15, vjust = 0.7))
substrate type
div %>%
pivot_longer(names_to = "diversity", values_to = "val", -c(7:ncol(.))) %>%
ggplot(aes(x = substrate, y = val, fill = substrate)) +
geom_boxplot() +
facet_wrap(~diversity, scales = "free") +
labs(y = NULL, x = NULL, title = "Diversity ~ substrate type") +
theme(axis.text.x = element_text(angle = 15, vjust = 0.7))
Zone
div %>%
pivot_longer(names_to = "diversity", values_to = "val", -c(7:ncol(.))) %>%
ggplot(aes(x = zone, y = val, fill = zone)) +
geom_boxplot() +
facet_wrap(~diversity, scales = "free") +
labs(y = NULL, x = NULL, title = "Diversity ~ zone") +
theme(axis.text.x = element_text(angle = 15, vjust = 0.7))
vegetation cover
div %>%
pivot_longer(names_to = "diversity", values_to = "val", -c(7:ncol(.))) %>%
ggplot(aes(x = veg, y = val, fill = veg)) +
geom_boxplot() +
facet_wrap(~diversity, scales = "free") +
labs(y = NULL, x = NULL, title = "Diversity ~ vegetation cover") +
theme(axis.text.x = element_text(angle = 15, vjust = 0.7))
Кол-во видов в доминантном комплексе
div %>%
pivot_longer(names_to = "diversity", values_to = "val", -c(7:ncol(.))) %>%
ggplot(aes(x = dom.comp, y = val, fill = dom.comp)) +
geom_boxplot() +
facet_wrap(~diversity, scales = "free") +
labs(y = NULL, x = NULL, title = "Diversity ~ Dominant complex",
subtitle = "Кол-во видов растений в доминантном комплексе")
Вопрос 5а
Кривые разрежения
По отдельным пробам
rar2 <- dfw %>%
select(-sp) %>%
lapply(function(a){sort(a[a>0], decreasing = TRUE)}) %>%
discard(~ length(.x) < 2) %>%
iNEXT::iNEXT(., q = 0, size = seq(5, 150, by = 5), #anchor_A
datatype = "abundance", nboot = 9) %>% # 999
pluck("iNextEst", "size_based") %>%
transmute(id = Assemblage, Method, m, qD, qD.LCL, qD.UCL) %>%
as_tibble() %>%
left_join(., select(labs, id, coast, skew, soil,
substrate, zone, veg, dom.comp), by = "id")
obs2 <- rar2 %>%
filter(Method == "Observed") %>%
mutate(r = case_when(m >= 150 ~ "up", m <= 5 ~ "low", TRUE ~ "r"),
m = case_when(m < 5 ~ 5,
m > 150 ~ 150,
TRUE ~ m))
rar2.rar <- rar2 %>%
filter(Method != "Extrapolation", m <= 150, m >= 5) %>%
filter(m %in% seq(5, 150, by = 5) | Method == "Observed")
rar2.ext <- rar2 %>%
filter(Method != "Rarefaction", m <= 150, m >= 5) %>%
filter(m %in% seq(5, 150, by = 5) | Method == "Observed")Coast
ggplot(mapping = aes(x = m, y = qD, size = id, color = coast, fill = coast)) +
scale_size_manual(values = rep(1, 59)) +
geom_line(data = rar2.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar2.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 155, y = qD, label = id), size = 3.5, show.legend=FALSE,
data = summarise(group_by(rar2, id, coast), qD = max(qD), .groups = "drop")) +
geom_point(mapping = aes(x = m, y = qD, size = id, fill = coast, shape = r),
data = obs2, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(25, 21, 24)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.1, 0.86),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Skew
ggplot(mapping = aes(x = m, y = qD, size = id, color = skew, fill = skew)) +
scale_size_manual(values = rep(1, 59)) +
geom_line(data = rar2.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar2.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 155, y = qD, label = id), size = 3.5, show.legend=FALSE,
data = summarise(group_by(rar2, id, skew), qD = max(qD), .groups = "drop")) +
geom_point(mapping = aes(x = m, y = qD, size = id, fill = skew, shape = r),
data = obs2, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(25, 21, 24)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.1, 0.86),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Soil
ggplot(mapping = aes(x = m, y = qD, size = id, color = soil, fill = soil)) +
scale_size_manual(values = rep(1, 59)) +
geom_line(data = rar2.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar2.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 155, y = qD, label = id), size = 3.5, show.legend=FALSE,
data = summarise(group_by(rar2, id, soil), qD = max(qD), .groups = "drop")) +
geom_point(mapping = aes(x = m, y = qD, size = id, fill = soil, shape = r),
data = obs2, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(25, 21, 24)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.1, 0.86),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Substrate
ggplot(mapping = aes(x = m, y = qD, size = id, color = substrate, fill = substrate)) +
scale_size_manual(values = rep(1, 59)) +
geom_line(data = rar2.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar2.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 155, y = qD, label = id), size = 3.5, show.legend=FALSE,
data = summarise(group_by(rar2, id, substrate), qD = max(qD), .groups = "drop")) +
geom_point(mapping = aes(x = m, y = qD, size = id, fill = substrate, shape = r),
data = obs2, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(25, 21, 24)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.1, 0.86),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Zone
ggplot(mapping = aes(x = m, y = qD, size = id, color = zone, fill = zone)) +
scale_size_manual(values = rep(1, 59)) +
geom_line(data = rar2.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar2.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 155, y = qD, label = id), size = 3.5, show.legend=FALSE,
data = summarise(group_by(rar2, id, zone), qD = max(qD), .groups = "drop")) +
geom_point(mapping = aes(x = m, y = qD, size = id, fill = zone, shape = r),
data = obs2, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(25, 21, 24)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.1, 0.86),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Vegetation
ggplot(mapping = aes(x = m, y = qD, size = id, color = veg, fill = veg)) +
scale_size_manual(values = rep(1, 59)) +
geom_line(data = rar2.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar2.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 155, y = qD, label = id), size = 3.5, show.legend=FALSE,
data = summarise(group_by(rar2, id, veg), qD = max(qD), .groups = "drop")) +
geom_point(mapping = aes(x = m, y = qD, size = id, fill = veg, shape = r),
data = obs2, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(25, 21, 24)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.1, 0.7),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Dominant complex
ggplot(mapping = aes(x = m, y = qD, size = id, color = dom.comp, fill = dom.comp)) +
scale_size_manual(values = rep(1, 59)) +
geom_line(data = rar2.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar2.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 155, y = qD, label = id), size = 3.5, show.legend=FALSE,
data = summarise(group_by(rar2, id, dom.comp), qD = max(qD), .groups = "drop")) +
geom_point(mapping = aes(x = m, y = qD, size = id, fill = dom.comp, shape = r),
data = obs2, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(25, 21, 24)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.1, 0.7),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
По участкам (пробы объединены по 5)
rar3 <- dfw0 %>%
select(-sp) %>%
lapply(function(a){sort(a[a>0], decreasing = TRUE)}) %>%
discard(~ length(.x) < 2) %>%
iNEXT::iNEXT(., q = 0, size = seq(5, 300, by = 5), #anchor_A
datatype = "abundance", nboot = 9) %>% # 999
pluck("iNextEst", "size_based") %>%
transmute(id = Assemblage, Method, m, qD, qD.LCL, qD.UCL) %>%
as_tibble() %>%
filter(id != "SmSw")
rar3 <- labs %>%
mutate(id = substr(id, 1, nchar(id)-1)) %>%
select(id, coast, skew, soil, substrate, zone, veg, dom.comp) %>%
distinct() %>%
left_join(rar3, ., by = "id")
obs3 <- rar3 %>%
filter(Method == "Observed" | m >= 300) %>%
mutate(Method = "Observed") %>%
group_by(id, Method) %>%
summarise(qD = min(qD), m = min(m), coast = unique(coast),
skew = unique(skew), soil = unique(soil),
substrate = unique(substrate), zone = unique(zone),
veg = unique(veg), dom.comp = unique(dom.comp),
.groups = "drop") %>%
mutate(r = case_when(m == 300 ~ "up", TRUE ~ "r"))
rar3.rar <- rar3 %>%
filter(Method != "Extrapolation", m <= 300, m >= 5) %>%
filter(m %in% seq(5, 300, by = 5) | Method == "Observed")
rar3.ext <- rar3 %>%
filter(Method != "Rarefaction", m <= 300, m >= 5) %>%
filter(m %in% seq(5, 300, by = 5) | Method == "Observed")Coast
ggplot(mapping = aes(x = m, y = qD, size = id, color = coast, fill = coast)) +
geom_line(data = rar3.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar3.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 315, y = qD, label = id),
size = 3.5, show.legend=FALSE,
data = filter(rar3, m == 300)) +
geom_point(mapping = aes(shape = r),
data = obs3, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(21, 24)) +
scale_size_manual(values = rep(1, 59)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.1, 0.86),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Skew
ggplot(mapping = aes(x = m, y = qD, size = id, color = skew, fill = skew)) +
geom_line(data = rar3.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar3.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 315, y = qD, label = id),
size = 3.5, show.legend=FALSE,
data = filter(rar3, m == 300)) +
geom_point(mapping = aes(shape = r),
data = obs3, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(21, 24)) +
scale_size_manual(values = rep(1, 59)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.1, 0.86),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Soil
ggplot(mapping = aes(x = m, y = qD, size = id, color = soil, fill = soil)) +
geom_line(data = rar3.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar3.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 315, y = qD, label = id),
size = 3.5, show.legend=FALSE,
data = filter(rar3, m == 300)) +
geom_point(mapping = aes(shape = r),
data = obs3, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(21, 24)) +
scale_size_manual(values = rep(1, 59)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.1, 0.86),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Substrate
Единственная проба с дебрисом удалена
ggplot(mapping = aes(x = m, y = qD, size = id, color = substrate, fill = substrate)) +
geom_line(data = rar3.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar3.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 315, y = qD, label = id),
size = 3.5, show.legend=FALSE,
data = filter(rar3, m == 300)) +
geom_point(mapping = aes(shape = r),
data = obs3, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(21, 24)) +
scale_size_manual(values = rep(1, 59)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.1, 0.86),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Zone
ggplot(mapping = aes(x = m, y = qD, size = id, color = zone, fill = zone)) +
geom_line(data = rar3.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar3.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 315, y = qD, label = id),
size = 3.5, show.legend=FALSE,
data = filter(rar3, m == 300)) +
geom_point(mapping = aes(shape = r),
data = obs3, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(21, 24)) +
scale_size_manual(values = rep(1, 59)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.1, 0.86),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Vegetation
ggplot(mapping = aes(x = m, y = qD, size = id, color = veg, fill = veg)) +
geom_line(data = rar3.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar3.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 315, y = qD, label = id),
size = 3.5, show.legend=FALSE,
data = filter(rar3, m == 300)) +
geom_point(mapping = aes(shape = r),
data = obs3, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(21, 24)) +
scale_size_manual(values = rep(1, 59)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.07, 0.76),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Dominant complex
ggplot(mapping = aes(x = m, y = qD, size = id, color = dom.comp, fill = dom.comp)) +
geom_line(data = rar3.ext, linetype = "dotted", alpha = 0.7) +
geom_line(data = rar3.rar, alpha = 0.5) +
geom_text(mapping = aes(x = 315, y = qD, label = id),
size = 3.5, show.legend=FALSE,
data = filter(rar3, m == 300)) +
geom_point(mapping = aes(shape = r),
data = obs3, size = 3, color = "black", show.legend=FALSE,) +
scale_shape_manual(values = c(21, 24)) +
scale_size_manual(values = rep(1, 59)) +
labs(x = "individuals", y = "number of species") +
theme(legend.position = c(0.07, 0.75),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
guides(size = "none", shape = "none")
Усреднены данные
support <- function(B){
B %>%
select(-sp) %>%
lapply(function(a){sort(a[a>0], decreasing = TRUE)}) %>%
discard(~ length(.x) < 2) %>%
iNEXT::iNEXT(., q = 0, size = seq(30, 3000, by = 30), #anchor_A
datatype = "abundance", nboot = 9) %>% # 999
pluck("iNextEst", "size_based") %>%
transmute(type = Assemblage, Method, m, qD, qD.LCL, qD.UCL) %>%
as_tibble() %>%
pivot_wider(names_from = Method, values_from = "qD") %>%
pivot_longer(names_to = "Method", values_to = "qD", -c(1:4, 6)) %>%
filter(!is.na(Observed) | !is.na(qD))
}Coast
dfl %>%
left_join(select(labs, id, coast), by = "id") %>%
group_by(coast, sp) %>%
summarise(abu = sum(abu), .groups = "drop") %>%
pivot_wider(values_from = "abu", names_from = "coast") %>%
support %>%
ggplot(aes(x = m, y = qD, ymin = qD.LCL, ymax = qD.UCL,
color = type, fill = type, linetype = Method)) +
geom_ribbon(alpha = 0.2, linetype = "blank") +
geom_line(linewidth = 1.2) +
geom_point(mapping = aes(x = m, y = Observed), size = 3, shape = 22) +
scale_linetype_manual(values = c("dotted", "solid")) +
guides(linetype = "none") +
theme(legend.position = c(0.15, 0.8),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey"))+
labs(x = "individuals", y = "number of species") 
Skew
dfl %>%
left_join(select(labs, id, skew), by = "id") %>%
group_by(skew, sp) %>%
summarise(abu = sum(abu), .groups = "drop") %>%
pivot_wider(values_from = "abu", names_from = "skew") %>%
support %>%
ggplot(aes(x = m, y = qD, ymin = qD.LCL, ymax = qD.UCL,
color = type, fill = type, linetype = Method)) +
geom_ribbon(alpha = 0.2, linetype = "blank") +
geom_line(linewidth = 1.2) +
geom_point(mapping = aes(x = m, y = Observed), size = 3, shape = 22) +
scale_linetype_manual(values = c("dotted", "solid")) +
guides(linetype = "none") +
theme(legend.position = c(0.15, 0.8),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey"))+
labs(x = "individuals", y = "number of species") 
Soil
dfl %>%
left_join(select(labs, id, soil), by = "id") %>%
group_by(soil, sp) %>%
summarise(abu = sum(abu), .groups = "drop") %>%
pivot_wider(values_from = "abu", names_from = "soil") %>%
support %>%
ggplot(aes(x = m, y = qD, ymin = qD.LCL, ymax = qD.UCL,
color = type, fill = type, linetype = Method)) +
geom_ribbon(alpha = 0.2, linetype = "blank") +
geom_line(linewidth = 1.2) +
geom_point(mapping = aes(x = m, y = Observed), size = 3, shape = 22) +
scale_linetype_manual(values = c("dotted", "solid")) +
guides(linetype = "none") +
theme(legend.position = c(0.15, 0.8),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey"))+
labs(x = "individuals", y = "number of species") 
Substrate
dfl %>%
left_join(select(labs, id, substrate), by = "id") %>%
group_by(substrate, sp) %>%
summarise(abu = sum(abu), .groups = "drop") %>%
pivot_wider(values_from = "abu", names_from = "substrate") %>%
support %>%
ggplot(aes(x = m, y = qD, ymin = qD.LCL, ymax = qD.UCL,
color = type, fill = type, linetype = Method)) +
geom_ribbon(alpha = 0.2, linetype = "blank") +
geom_line(linewidth = 1.2) +
geom_point(mapping = aes(x = m, y = Observed), size = 3, shape = 22) +
scale_linetype_manual(values = c("dotted", "solid")) +
guides(linetype = "none") +
theme(legend.position = c(0.15, 0.8),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey"))+
labs(x = "individuals", y = "number of species") 
Zone
dfl %>%
left_join(select(labs, id, zone), by = "id") %>%
group_by(zone, sp) %>%
summarise(abu = sum(abu), .groups = "drop") %>%
pivot_wider(values_from = "abu", names_from = "zone") %>%
support %>%
ggplot(aes(x = m, y = qD, ymin = qD.LCL, ymax = qD.UCL,
color = type, fill = type, linetype = Method)) +
geom_ribbon(alpha = 0.2, linetype = "blank") +
geom_line(linewidth = 1.2) +
geom_point(mapping = aes(x = m, y = Observed), size = 3, shape = 22) +
scale_linetype_manual(values = c("dotted", "solid")) +
guides(linetype = "none") +
theme(legend.position = c(0.15, 0.8),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey"))+
labs(x = "individuals", y = "number of species") 
Vegetation
dfl %>%
left_join(select(labs, id, veg), by = "id") %>%
group_by(veg, sp) %>%
summarise(abu = sum(abu), .groups = "drop") %>%
pivot_wider(values_from = "abu", names_from = "veg") %>%
support %>%
mutate(type = as.numeric(type),
type = factor(type, ordered = TRUE)) %>%
ggplot(aes(x = m, y = qD, ymin = qD.LCL, ymax = qD.UCL,
color = type, fill = type, linetype = Method)) +
geom_ribbon(alpha = 0.2, linetype = "blank") +
geom_line(linewidth = 1.2) +
geom_point(mapping = aes(x = m, y = Observed), size = 3, shape = 22) +
scale_linetype_manual(values = c("dotted", "solid")) +
guides(linetype = "none") +
theme(legend.position = c(0.05, 0.7),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey"))+
labs(x = "individuals", y = "number of species") 
Dominant complex
dfl %>%
left_join(select(labs, id, dom.comp), by = "id") %>%
group_by(dom.comp, sp) %>%
summarise(abu = sum(abu), .groups = "drop") %>%
pivot_wider(values_from = "abu", names_from = "dom.comp") %>%
support %>%
mutate(type = as.numeric(type),
type = factor(type, ordered = TRUE)) %>%
ggplot(aes(x = m, y = qD, ymin = qD.LCL, ymax = qD.UCL,
color = type, fill = type, linetype = Method)) +
geom_ribbon(alpha = 0.2, linetype = "blank") +
geom_line(linewidth = 1.2) +
geom_point(mapping = aes(x = m, y = Observed), size = 3, shape = 22) +
scale_linetype_manual(values = c("dotted", "solid")) +
guides(linetype = "none") +
theme(legend.position = c(0.05, 0.7),
legend.title = element_blank(),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey"))+
labs(x = "individuals", y = "number of species") 
Вопрос 5b
Профили Хилла
s <- (1.045^seq(0,20,by=0.25)-1)*3
s <- s[s< 0.96 | s >= 1]
s <- s[s< 1.97 | s >= 2.05]
s <- sort(c(s, 1, 2))
#s <- c(seq(0, 2, by = 0.05), 2.05^((seq(1, 10, length.out = 16))^0.35))
hill <- dfw[,-1] %>%
t %>%
vegan::renyi(scales = s, hill = TRUE) %>%
rownames_to_column("id") %>%
as_tibble() %>%
pivot_longer(names_to = "ord", values_to = "H", -id) %>%
mutate(ord = as.numeric(ord), O = rep(0:(length(s)-1), 69)) %>%
left_join(select(labs, id,
coast, skew, zone, substrate, soil, veg, dom.comp), by = "id")Coast
hill %>%
group_by(ord, O, coast) %>%
summarise(HH = mean(H),
UH = mean(H)+sd(H),
LH = mean(H)-sd(H),
.groups = "drop") %>%
ggplot(aes(x = O, y = HH, ymin = LH, ymax = UH, color = coast, fill = coast)) +
geom_ribbon(alpha = 0.2, size = 0) +
geom_line(size = 1) +
scale_x_continuous(
breaks = which(s %in% c(0, 1, 2, max(s)))-1,
labels = c(0, 1, 2, Inf)) +
theme(
panel.grid.minor = element_blank(),
legend.position = c(0.8, 0.8),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
labs(x = "Order", y = "Hill numbers")
Skew
hill %>%
group_by(ord, O, skew) %>%
summarise(HH = mean(H),
UH = mean(H)+sd(H),
LH = mean(H)-sd(H),
.groups = "drop") %>%
ggplot(aes(x = O, y = HH, ymin = LH, ymax = UH, color = skew, fill = skew)) +
geom_ribbon(alpha = 0.2, size = 0) +
geom_line(size = 1) +
scale_x_continuous(
breaks = which(s %in% c(0, 1, 2, max(s)))-1,
labels = c(0, 1, 2, Inf)) +
theme(
panel.grid.minor = element_blank(),
legend.position = c(0.8, 0.8),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
labs(x = "Order", y = "Hill numbers")
Soil
hill %>%
group_by(ord, O, soil) %>%
summarise(HH = mean(H),
UH = mean(H)+sd(H),
LH = mean(H)-sd(H),
.groups = "drop") %>%
ggplot(aes(x = O, y = HH, ymin = LH, ymax = UH, color = soil, fill = soil)) +
geom_ribbon(alpha = 0.2, size = 0) +
geom_line(size = 1) +
scale_x_continuous(
breaks = which(s %in% c(0, 1, 2, max(s)))-1,
labels = c(0, 1, 2, Inf)) +
theme(
panel.grid.minor = element_blank(),
legend.position = c(0.8, 0.8),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
labs(x = "Order", y = "Hill numbers")
Substrate
hill %>%
group_by(ord, O, substrate) %>%
summarise(HH = mean(H),
UH = mean(H)+sd(H),
LH = mean(H)-sd(H),
.groups = "drop") %>%
ggplot(aes(x = O, y = HH, ymin = LH, ymax = UH,
color = substrate, fill = substrate)) +
geom_ribbon(alpha = 0.2, size = 0) +
geom_line(size = 1) +
scale_x_continuous(
breaks = which(s %in% c(0, 1, 2, max(s)))-1,
labels = c(0, 1, 2, Inf)) +
theme(
panel.grid.minor = element_blank(),
legend.position = c(0.8, 0.8),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
labs(x = "Order", y = "Hill numbers")
Zone
hill %>%
group_by(ord, O, zone) %>%
summarise(HH = mean(H),
UH = mean(H)+sd(H),
LH = mean(H)-sd(H),
.groups = "drop") %>%
ggplot(aes(x = O, y = HH, ymin = LH, ymax = UH, color = zone, fill = zone)) +
geom_ribbon(alpha = 0.2, size = 0) +
geom_line(size = 1) +
scale_x_continuous(
breaks = which(s %in% c(0, 1, 2, max(s)))-1,
labels = c(0, 1, 2, Inf)) +
theme(
panel.grid.minor = element_blank(),
legend.position = c(0.8, 0.8),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
labs(x = "Order", y = "Hill numbers")
Vegetation
hill %>%
group_by(ord, O, veg) %>%
summarise(HH = mean(H),
UH = mean(H)+sd(H),
LH = mean(H)-sd(H),
.groups = "drop") %>%
ggplot(aes(x = O, y = HH, ymin = LH, ymax = UH,
color = veg, fill = veg)) +
geom_ribbon(alpha = 0.2, size = 0) +
geom_line(size = 1) +
scale_x_continuous(
breaks = which(s %in% c(0, 1, 2, max(s)))-1,
labels = c(0, 1, 2, Inf)) +
theme(
panel.grid.minor = element_blank(),
legend.position = c(0.8, 0.7),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
labs(x = "Order", y = "Hill numbers")
Dominant complex
hill %>%
group_by(ord, O, dom.comp) %>%
summarise(HH = mean(H),
UH = mean(H)+sd(H),
LH = mean(H)-sd(H),
.groups = "drop") %>%
ggplot(aes(x = O, y = HH, ymin = LH, ymax = UH,
color = dom.comp, fill = dom.comp)) +
geom_ribbon(alpha = 0.2, size = 0) +
geom_line(size = 1) +
scale_x_continuous(
breaks = which(s %in% c(0, 1, 2, max(s)))-1,
labels = c(0, 1, 2, Inf)) +
theme(
panel.grid.minor = element_blank(),
legend.position = c(0.8, 0.7),
legend.background = element_rect(fill="white",
linewidth=0.5, linetype="solid",
colour ="grey")) +
labs(x = "Order", y = "Hill numbers")
Вопрос 6
- Есть ли виды-специалисты, приуроченные к отдельному типу берега (галечный, песчаный, тростниковый), или биотопу-синузии, или к плотным злаковым дернинам в целом?
— Есть.
Примечание. Использован IndVal - “индикаторная ценность вида”. В форме APCF, как применено здесь, он принимает значения от 0 до 100%. Максимум достигается когда вид не встречается в пробах других типов, но встречается во всех пробах данного типа, причем представлен во всех них.
Примечание 2. Для синузий нужна какая-то иерархическая классификация, чтобы такой же подход можно было применить и для этого средового фактора. Вариантов два. Сделать это на основе вашего экспертного мнения или сделать это на основе каких-либо алгоритмов, например - классифицировать не население клещей по типу проб, а пробы по населению клещей. Вариант два считаю менее предпочтительным, т.к. нахожу его несколько рекурсивным, но в зарубежные коллеги так поступают часто
dummy <- data.frame(id = colnames(dfw)[-1]) %>%
left_join(labs, by = "id")
iv <- list()
iv$coast <- dfw %>%
column_to_rownames("sp") %>%
indval(., clas = dummy$coast,
significance = FALSE) %>% #
.$indval %>%
round(., 1) %>%
as.data.frame() %>%
rownames_to_column("species")
iv$skew <- dfw %>%
column_to_rownames("sp") %>%
indval(., clas = dummy$skew,
significance = FALSE) %>% #
.$indval %>%
round(., 1) %>%
as.data.frame() %>%
rownames_to_column("species")
iv$substrate <- dfw %>%
column_to_rownames("sp") %>%
indval(., clas = dummy$substrate,
significance = FALSE) %>% #
.$indval %>%
round(., 1) %>%
as.data.frame() %>%
rownames_to_column("species")
iv$soil <- dfw %>%
column_to_rownames("sp") %>%
indval(., clas = dummy$soil,
significance = FALSE) %>% #
.$indval %>%
round(., 1) %>%
as.data.frame() %>%
rownames_to_column("species")
iv$zone <- dfw %>%
column_to_rownames("sp") %>%
indval(., clas = dummy$zone,
significance = FALSE) %>% #
.$indval %>%
round(., 1) %>%
as.data.frame() %>%
rownames_to_column("species")Coast
formattable::formattable(iv[[1]])| species | pebbly | reeds | sandy beach |
|---|---|---|---|
| Aphelacarus acarinus | 0.0 | 0.0 | 2.1 |
| Oppiella nova | 0.2 | 46.9 | 1.6 |
| Microppia minus | 1.2 | 0.0 | 13.3 |
| Punctoribates insignis | 16.9 | 0.0 | 0.2 |
| Punctoribates hexagonus | 18.8 | 0.0 | 0.0 |
| Protoribates capucinus | 0.0 | 56.1 | 0.4 |
| Zygoribatula caspica | 0.0 | 0.0 | 4.2 |
| Zygoribatula glabra (cf.) | 12.3 | 0.0 | 6.3 |
| Zygoribatula exarata | 0.0 | 0.0 | 2.1 |
| Oribatula tibialis | 0.0 | 0.0 | 2.1 |
| Oribatella caspica (cf.) | 0.2 | 7.9 | 13.3 |
| Scheloribates laevigatus (cf.) | 0.0 | 57.0 | 0.2 |
| Acrotritia ardua | 3.9 | 3.3 | 2.6 |
| Epilohmannia styriaca | 0.0 | 48.5 | 1.6 |
| Passalozetes africanus | 0.0 | 0.0 | 6.2 |
| Sphaerochthonius splendidus | 0.0 | 15.7 | 1.4 |
| Phyllozetes emmae | 0.0 | 0.0 | 4.2 |
| Austrophthiracarus duplex (cf.) | 56.1 | 0.0 | 0.0 |
| Phthiracarus globosus | 6.2 | 0.0 | 0.0 |
| Pyroppia lanceolata | 4.7 | 0.0 | 0.5 |
| Eobrachychthonius latior | 0.0 | 68.2 | 1.2 |
| Haplochthonius simplex | 0.0 | 0.0 | 6.2 |
| Trichogalumna nipponica | 11.2 | 0.0 | 1.7 |
| Galumna tarsipennata | 0.0 | 0.0 | 6.2 |
| Hydrozetes lacustris parisiensis | 0.0 | 0.0 | 2.1 |
| Zetomimus furcatus | 4.7 | 0.0 | 0.5 |
| Mesotritia nuda | 6.2 | 0.0 | 0.0 |
| Xenillus moyae | 12.5 | 0.0 | 0.0 |
| Scheloribates distinctus | 0.0 | 0.0 | 47.6 |
| Liebstadia similis | 0.0 | 0.0 | 2.1 |
| Trhypochthoniellus longisetus | 18.8 | 0.0 | 0.0 |
| Sellnickochthonius immaculatus | 2.9 | 41.3 | 17.9 |
| Sellnickochthonius suecicus | 0.0 | 0.0 | 10.4 |
| Brachychthonius bimaculatus | 0.0 | 0.0 | 12.5 |
| Liochthonius lapponicus | 0.0 | 33.2 | 0.7 |
| Ramusella (Insculptoppia) furcata | 0.0 | 53.0 | 0.5 |
| Lalmoppia sp. | 0.0 | 0.0 | 2.1 |
| Suctobelbella (Flagrosuctobelba) baloghi | 0.0 | 0.0 | 6.2 |
| Suctobelbella (Ussuribata) latirostris | 0.0 | 0.0 | 4.2 |
| Suctobelbella (Suctobelbella) subcornigera | 0.0 | 17.3 | 0.8 |
| Zygoribatula undulata | 0.2 | 0.0 | 55.5 |
| Oribatula pannonica | 6.2 | 0.0 | 0.0 |
| Hydrozetes lemnae | 2.9 | 0.0 | 1.1 |
| Latilamellobates naltschicki | 6.2 | 0.0 | 0.0 |
| Trichoribates berlesei | 8.5 | 21.5 | 0.0 |
| Eupelops plicatus | 0.0 | 0.0 | 2.1 |
| Malaconothrus monodactylus | 0.0 | 0.0 | 4.2 |
| Tectocepheus sarekensis | 0.0 | 8.5 | 4.8 |
| Pilogalumna tenuiclava | 0.0 | 0.0 | 6.2 |
| Pergalumna obvia | 0.0 | 0.0 | 6.2 |
| Brachychthonius berlesei | 0.0 | 99.8 | 0.0 |
| Galumna sp. | 0.0 | 0.0 | 2.1 |
| Punctoribates tschernovi | 0.0 | 0.0 | 2.1 |
| Xenillus tegeocranus | 0.0 | 100.0 | 0.0 |
| Oppia denticulata | 0.0 | 100.0 | 0.0 |
| Microzetorchestes emeryi | 0.0 | 20.0 | 0.0 |
| Galumna dimorpha | 0.0 | 20.0 | 0.0 |
| Ramusella clavipectinata | 0.0 | 80.0 | 0.0 |
| Banksinoma sp. | 0.0 | 20.0 | 0.0 |
Skew
formattable::formattable(iv[[2]])| species | flat | gentle | steep |
|---|---|---|---|
| Aphelacarus acarinus | 5.6 | 0.0 | 0.0 |
| Oppiella nova | 11.8 | 3.6 | 0.4 |
| Microppia minus | 40.6 | 0.0 | 0.5 |
| Punctoribates insignis | 1.3 | 0.0 | 14.5 |
| Punctoribates hexagonus | 0.0 | 0.0 | 18.8 |
| Protoribates capucinus | 33.3 | 0.0 | 0.0 |
| Zygoribatula caspica | 11.1 | 0.0 | 0.0 |
| Zygoribatula glabra (cf.) | 0.6 | 5.5 | 9.8 |
| Zygoribatula exarata | 5.6 | 0.0 | 0.0 |
| Oribatula tibialis | 5.6 | 0.0 | 0.0 |
| Oribatella caspica (cf.) | 4.9 | 17.2 | 0.2 |
| Scheloribates laevigatus (cf.) | 13.3 | 1.2 | 0.0 |
| Acrotritia ardua | 8.2 | 0.7 | 3.6 |
| Epilohmannia styriaca | 38.9 | 0.0 | 0.0 |
| Passalozetes africanus | 16.7 | 0.0 | 0.0 |
| Sphaerochthonius splendidus | 22.2 | 0.0 | 0.0 |
| Phyllozetes emmae | 11.1 | 0.0 | 0.0 |
| Austrophthiracarus duplex (cf.) | 0.0 | 0.0 | 55.8 |
| Phthiracarus globosus | 0.0 | 0.0 | 6.2 |
| Pyroppia lanceolata | 2.6 | 0.0 | 3.3 |
| Eobrachychthonius latior | 44.4 | 0.0 | 0.0 |
| Haplochthonius simplex | 16.7 | 0.0 | 0.0 |
| Trichogalumna nipponica | 0.8 | 1.1 | 9.1 |
| Galumna tarsipennata | 0.3 | 5.4 | 0.0 |
| Hydrozetes lacustris parisiensis | 5.6 | 0.0 | 0.0 |
| Zetomimus furcatus | 2.6 | 0.0 | 3.3 |
| Mesotritia nuda | 0.0 | 0.0 | 6.2 |
| Xenillus moyae | 0.0 | 0.0 | 12.5 |
| Scheloribates distinctus | 0.0 | 65.4 | 0.0 |
| Liebstadia similis | 5.6 | 0.0 | 0.0 |
| Trhypochthoniellus longisetus | 0.0 | 0.0 | 18.8 |
| Sellnickochthonius immaculatus | 12.9 | 30.5 | 3.6 |
| Sellnickochthonius suecicus | 27.8 | 0.0 | 0.0 |
| Brachychthonius bimaculatus | 24.2 | 0.4 | 0.0 |
| Liochthonius lapponicus | 5.5 | 2.9 | 0.0 |
| Ramusella (Insculptoppia) furcata | 10.1 | 2.3 | 0.0 |
| Lalmoppia sp. | 5.6 | 0.0 | 0.0 |
| Suctobelbella (Flagrosuctobelba) baloghi | 16.7 | 0.0 | 0.0 |
| Suctobelbella (Ussuribata) latirostris | 3.7 | 1.0 | 0.0 |
| Suctobelbella (Suctobelbella) subcornigera | 8.3 | 1.5 | 0.0 |
| Zygoribatula undulata | 0.0 | 73.2 | 0.1 |
| Oribatula pannonica | 0.0 | 0.0 | 6.2 |
| Hydrozetes lemnae | 0.0 | 1.8 | 2.4 |
| Latilamellobates naltschicki | 0.0 | 0.0 | 6.2 |
| Trichoribates berlesei | 2.7 | 0.1 | 13.9 |
| Eupelops plicatus | 0.0 | 2.9 | 0.0 |
| Malaconothrus monodactylus | 0.0 | 5.7 | 0.0 |
| Tectocepheus sarekensis | 0.7 | 9.9 | 0.0 |
| Pilogalumna tenuiclava | 0.0 | 8.6 | 0.0 |
| Pergalumna obvia | 0.0 | 8.6 | 0.0 |
| Brachychthonius berlesei | 27.5 | 0.0 | 0.0 |
| Galumna sp. | 0.0 | 2.9 | 0.0 |
| Punctoribates tschernovi | 0.0 | 2.9 | 0.0 |
| Xenillus tegeocranus | 27.8 | 0.0 | 0.0 |
| Oppia denticulata | 27.8 | 0.0 | 0.0 |
| Microzetorchestes emeryi | 5.6 | 0.0 | 0.0 |
| Galumna dimorpha | 5.6 | 0.0 | 0.0 |
| Ramusella clavipectinata | 22.2 | 0.0 | 0.0 |
| Banksinoma sp. | 5.6 | 0.0 | 0.0 |
Substrate
formattable::formattable(iv[[3]])| species | debris | turf |
|---|---|---|
| Aphelacarus acarinus | 20.0 | 0.0 |
| Oppiella nova | 0.0 | 12.5 |
| Microppia minus | 47.6 | 1.9 |
| Punctoribates insignis | 16.2 | 0.9 |
| Punctoribates hexagonus | 9.2 | 1.7 |
| Protoribates capucinus | 53.7 | 0.5 |
| Zygoribatula caspica | 18.6 | 0.1 |
| Zygoribatula glabra (cf.) | 39.3 | 3.8 |
| Zygoribatula exarata | 20.0 | 0.0 |
| Oribatula tibialis | 20.0 | 0.0 |
| Oribatella caspica (cf.) | 13.7 | 11.3 |
| Scheloribates laevigatus (cf.) | 0.0 | 7.8 |
| Acrotritia ardua | 97.1 | 0.1 |
| Epilohmannia styriaca | 77.4 | 0.2 |
| Passalozetes africanus | 60.0 | 0.0 |
| Sphaerochthonius splendidus | 58.3 | 0.0 |
| Phyllozetes emmae | 40.0 | 0.0 |
| Austrophthiracarus duplex (cf.) | 39.5 | 0.1 |
| Phthiracarus globosus | 20.0 | 0.0 |
| Pyroppia lanceolata | 18.6 | 0.1 |
| Eobrachychthonius latior | 56.8 | 0.4 |
| Haplochthonius simplex | 60.0 | 0.0 |
| Trichogalumna nipponica | 31.4 | 1.0 |
| Galumna tarsipennata | 6.1 | 2.2 |
| Hydrozetes lacustris parisiensis | 20.0 | 0.0 |
| Zetomimus furcatus | 18.6 | 0.1 |
| Mesotritia nuda | 20.0 | 0.0 |
| Xenillus moyae | 19.7 | 0.0 |
| Scheloribates distinctus | 0.0 | 37.5 |
| Liebstadia similis | 0.0 | 1.6 |
| Trhypochthoniellus longisetus | 0.0 | 4.7 |
| Sellnickochthonius immaculatus | 0.0 | 48.4 |
| Sellnickochthonius suecicus | 0.0 | 7.8 |
| Brachychthonius bimaculatus | 0.0 | 9.4 |
| Liochthonius lapponicus | 0.0 | 6.2 |
| Ramusella (Insculptoppia) furcata | 0.0 | 7.8 |
| Lalmoppia sp. | 0.0 | 1.6 |
| Suctobelbella (Flagrosuctobelba) baloghi | 0.0 | 4.7 |
| Suctobelbella (Ussuribata) latirostris | 0.0 | 3.1 |
| Suctobelbella (Suctobelbella) subcornigera | 0.0 | 6.2 |
| Zygoribatula undulata | 0.0 | 45.3 |
| Oribatula pannonica | 0.0 | 1.6 |
| Hydrozetes lemnae | 0.0 | 3.1 |
| Latilamellobates naltschicki | 0.0 | 1.6 |
| Trichoribates berlesei | 0.0 | 9.4 |
| Eupelops plicatus | 0.0 | 1.6 |
| Malaconothrus monodactylus | 0.0 | 3.1 |
| Tectocepheus sarekensis | 0.0 | 7.8 |
| Pilogalumna tenuiclava | 0.0 | 4.7 |
| Pergalumna obvia | 0.0 | 4.7 |
| Brachychthonius berlesei | 0.0 | 9.4 |
| Galumna sp. | 0.0 | 1.6 |
| Punctoribates tschernovi | 0.0 | 1.6 |
| Xenillus tegeocranus | 0.0 | 7.8 |
| Oppia denticulata | 0.0 | 7.8 |
| Microzetorchestes emeryi | 0.0 | 1.6 |
| Galumna dimorpha | 0.0 | 1.6 |
| Ramusella clavipectinata | 0.0 | 6.2 |
| Banksinoma sp. | 0.0 | 1.6 |
Soil
formattable::formattable(iv[[4]])| species | clay | grass remnants | sand | woody debris |
|---|---|---|---|---|
| Aphelacarus acarinus | 0.0 | 33.3 | 0.0 | 0.0 |
| Oppiella nova | 49.7 | 0.0 | 1.5 | 0.0 |
| Microppia minus | 0.0 | 7.4 | 1.1 | 66.5 |
| Punctoribates insignis | 0.0 | 0.0 | 0.5 | 45.4 |
| Punctoribates hexagonus | 0.0 | 0.0 | 1.1 | 33.1 |
| Protoribates capucinus | 26.7 | 24.7 | 0.0 | 9.3 |
| Zygoribatula caspica | 0.0 | 0.0 | 0.1 | 48.4 |
| Zygoribatula glabra (cf.) | 0.0 | 9.7 | 2.4 | 32.7 |
| Zygoribatula exarata | 0.0 | 33.3 | 0.0 | 0.0 |
| Oribatula tibialis | 0.0 | 0.0 | 0.0 | 50.0 |
| Oribatella caspica (cf.) | 6.0 | 6.7 | 5.9 | 7.5 |
| Scheloribates laevigatus (cf.) | 57.6 | 0.0 | 0.1 | 0.0 |
| Acrotritia ardua | 0.5 | 15.4 | 0.0 | 81.1 |
| Epilohmannia styriaca | 12.1 | 70.3 | 0.0 | 4.8 |
| Passalozetes africanus | 0.0 | 100.0 | 0.0 | 0.0 |
| Sphaerochthonius splendidus | 3.7 | 81.6 | 0.0 | 0.0 |
| Phyllozetes emmae | 0.0 | 66.7 | 0.0 | 0.0 |
| Austrophthiracarus duplex (cf.) | 0.0 | 0.3 | 0.1 | 49.4 |
| Phthiracarus globosus | 0.0 | 0.0 | 0.0 | 50.0 |
| Pyroppia lanceolata | 0.0 | 31.7 | 0.1 | 0.0 |
| Eobrachychthonius latior | 22.2 | 72.0 | 0.0 | 0.0 |
| Haplochthonius simplex | 0.0 | 100.0 | 0.0 | 0.0 |
| Trichogalumna nipponica | 0.0 | 0.0 | 0.5 | 89.4 |
| Galumna tarsipennata | 0.0 | 0.0 | 1.7 | 25.2 |
| Hydrozetes lacustris parisiensis | 0.0 | 0.0 | 0.0 | 50.0 |
| Zetomimus furcatus | 0.0 | 0.0 | 0.1 | 48.4 |
| Mesotritia nuda | 0.0 | 0.0 | 0.0 | 50.0 |
| Xenillus moyae | 0.0 | 0.0 | 0.0 | 49.7 |
| Scheloribates distinctus | 0.0 | 0.0 | 40.7 | 0.0 |
| Liebstadia similis | 0.0 | 0.0 | 1.7 | 0.0 |
| Trhypochthoniellus longisetus | 0.0 | 0.0 | 5.1 | 0.0 |
| Sellnickochthonius immaculatus | 48.1 | 0.0 | 18.3 | 0.0 |
| Sellnickochthonius suecicus | 0.0 | 0.0 | 8.5 | 0.0 |
| Brachychthonius bimaculatus | 0.0 | 0.0 | 10.2 | 0.0 |
| Liochthonius lapponicus | 34.3 | 0.0 | 0.5 | 0.0 |
| Ramusella (Insculptoppia) furcata | 54.2 | 0.0 | 0.3 | 0.0 |
| Lalmoppia sp. | 0.0 | 0.0 | 1.7 | 0.0 |
| Suctobelbella (Flagrosuctobelba) baloghi | 0.0 | 0.0 | 5.1 | 0.0 |
| Suctobelbella (Ussuribata) latirostris | 0.0 | 0.0 | 3.4 | 0.0 |
| Suctobelbella (Suctobelbella) subcornigera | 17.7 | 0.0 | 0.6 | 0.0 |
| Zygoribatula undulata | 0.0 | 0.0 | 49.2 | 0.0 |
| Oribatula pannonica | 0.0 | 0.0 | 1.7 | 0.0 |
| Hydrozetes lemnae | 0.0 | 0.0 | 3.4 | 0.0 |
| Latilamellobates naltschicki | 0.0 | 0.0 | 1.7 | 0.0 |
| Trichoribates berlesei | 32.2 | 0.0 | 1.3 | 0.0 |
| Eupelops plicatus | 0.0 | 0.0 | 1.7 | 0.0 |
| Malaconothrus monodactylus | 0.0 | 0.0 | 3.4 | 0.0 |
| Tectocepheus sarekensis | 9.5 | 0.0 | 3.6 | 0.0 |
| Pilogalumna tenuiclava | 0.0 | 0.0 | 5.1 | 0.0 |
| Pergalumna obvia | 0.0 | 0.0 | 5.1 | 0.0 |
| Brachychthonius berlesei | 99.8 | 0.0 | 0.0 | 0.0 |
| Galumna sp. | 0.0 | 0.0 | 1.7 | 0.0 |
| Punctoribates tschernovi | 0.0 | 0.0 | 1.7 | 0.0 |
| Xenillus tegeocranus | 100.0 | 0.0 | 0.0 | 0.0 |
| Oppia denticulata | 100.0 | 0.0 | 0.0 | 0.0 |
| Microzetorchestes emeryi | 20.0 | 0.0 | 0.0 | 0.0 |
| Galumna dimorpha | 20.0 | 0.0 | 0.0 | 0.0 |
| Ramusella clavipectinata | 80.0 | 0.0 | 0.0 | 0.0 |
| Banksinoma sp. | 20.0 | 0.0 | 0.0 | 0.0 |
Zone
formattable::formattable(iv$zone)| species | first | second | third |
|---|---|---|---|
| Aphelacarus acarinus | 2.3 | 0.0 | 0.0 |
| Oppiella nova | 2.2 | 16.1 | 0.0 |
| Microppia minus | 20.5 | 0.0 | 0.0 |
| Punctoribates insignis | 9.1 | 0.0 | 0.0 |
| Punctoribates hexagonus | 6.8 | 0.0 | 0.0 |
| Protoribates capucinus | 13.6 | 0.0 | 0.0 |
| Zygoribatula caspica | 4.5 | 0.0 | 0.0 |
| Zygoribatula glabra (cf.) | 0.9 | 0.0 | 28.3 |
| Zygoribatula exarata | 2.3 | 0.0 | 0.0 |
| Oribatula tibialis | 2.3 | 0.0 | 0.0 |
| Oribatella caspica (cf.) | 1.0 | 5.3 | 21.7 |
| Scheloribates laevigatus (cf.) | 2.1 | 0.0 | 13.8 |
| Acrotritia ardua | 5.7 | 0.0 | 11.6 |
| Epilohmannia styriaca | 15.9 | 0.0 | 0.0 |
| Passalozetes africanus | 6.8 | 0.0 | 0.0 |
| Sphaerochthonius splendidus | 9.1 | 0.0 | 0.0 |
| Phyllozetes emmae | 4.5 | 0.0 | 0.0 |
| Austrophthiracarus duplex (cf.) | 22.7 | 0.0 | 0.0 |
| Phthiracarus globosus | 2.3 | 0.0 | 0.0 |
| Pyroppia lanceolata | 4.5 | 0.0 | 0.0 |
| Eobrachychthonius latior | 18.2 | 0.0 | 0.0 |
| Haplochthonius simplex | 6.8 | 0.0 | 0.0 |
| Trichogalumna nipponica | 1.4 | 0.0 | 8.5 |
| Galumna tarsipennata | 0.0 | 13.2 | 0.0 |
| Hydrozetes lacustris parisiensis | 2.3 | 0.0 | 0.0 |
| Zetomimus furcatus | 4.5 | 0.0 | 0.0 |
| Mesotritia nuda | 2.3 | 0.0 | 0.0 |
| Xenillus moyae | 4.5 | 0.0 | 0.0 |
| Scheloribates distinctus | 3.6 | 29.8 | 19.8 |
| Liebstadia similis | 2.3 | 0.0 | 0.0 |
| Trhypochthoniellus longisetus | 6.8 | 0.0 | 0.0 |
| Sellnickochthonius immaculatus | 5.7 | 0.9 | 42.6 |
| Sellnickochthonius suecicus | 11.4 | 0.0 | 0.0 |
| Brachychthonius bimaculatus | 5.0 | 0.0 | 5.6 |
| Liochthonius lapponicus | 0.7 | 11.4 | 0.0 |
| Ramusella (Insculptoppia) furcata | 1.1 | 0.3 | 8.0 |
| Lalmoppia sp. | 2.3 | 0.0 | 0.0 |
| Suctobelbella (Flagrosuctobelba) baloghi | 6.8 | 0.0 | 0.0 |
| Suctobelbella (Ussuribata) latirostris | 0.6 | 5.0 | 0.0 |
| Suctobelbella (Suctobelbella) subcornigera | 1.2 | 0.0 | 14.9 |
| Zygoribatula undulata | 1.9 | 77.6 | 3.7 |
| Oribatula pannonica | 2.3 | 0.0 | 0.0 |
| Hydrozetes lemnae | 0.2 | 6.1 | 0.0 |
| Latilamellobates naltschicki | 2.3 | 0.0 | 0.0 |
| Trichoribates berlesei | 9.7 | 0.0 | 1.4 |
| Eupelops plicatus | 0.0 | 6.7 | 0.0 |
| Malaconothrus monodactylus | 0.0 | 13.3 | 0.0 |
| Tectocepheus sarekensis | 0.2 | 1.2 | 15.7 |
| Pilogalumna tenuiclava | 0.0 | 20.0 | 0.0 |
| Pergalumna obvia | 0.0 | 12.2 | 0.9 |
| Brachychthonius berlesei | 10.4 | 0.0 | 0.8 |
| Galumna sp. | 0.0 | 0.0 | 10.0 |
| Punctoribates tschernovi | 0.0 | 0.0 | 10.0 |
| Xenillus tegeocranus | 11.4 | 0.0 | 0.0 |
| Oppia denticulata | 11.4 | 0.0 | 0.0 |
| Microzetorchestes emeryi | 2.3 | 0.0 | 0.0 |
| Galumna dimorpha | 2.3 | 0.0 | 0.0 |
| Ramusella clavipectinata | 9.1 | 0.0 | 0.0 |
| Banksinoma sp. | 2.3 | 0.0 | 0.0 |
Вопрос 10
- Как кластеризуется население почвенных клещей (Oribatida, Mesostigmata порознь и вместе) отдельных проб? Т.е. собираются ли в кластеры выборки из отдельных биотопов-синузий, а комплексы отдельных типов берега?
— Да, должны, но все бывает… Выборки под всеми рогозами (с плотной дерниной, на песчаном и галечном пляжах) могут собраться в один кластер.
par.default <- par()
d1.bc <- vegan::vegdist(as.data.frame(t(dfw[,-1])), method = "bray", binary = FALSE)
d1.cs <- vegan::vegdist(as.data.frame(t(dfw[,-1])), method = "bray", binary = TRUE)
d0.bc <- vegan::vegdist(as.data.frame(t(dfw0[,-1])), method = "bray", binary = FALSE)
d0.cs <- vegan::vegdist(as.data.frame(t(dfw0[,-1])), method = "bray", binary = TRUE)
#general, numeric
c0.bc <- hclust(d0.bc, method = "ward.D2")
c0.bc_labs <- labs %>%
transmute(id = substr(id, 1, nchar(id)-1), plants.d, substrate, soil) %>%
left_join(tibble(id = labels(c0.bc)), ., by = "id") %>%
group_by(id) %>%
mutate_at(vars(-id), collapse_labels) %>%
ungroup() %>%
distinct() %>%
mutate(substr.color = colorise_labels(.$substrate),
soil.color = colorise_labels(.$soil))
#general, binary
c0.cs <- hclust(d0.cs, method = "ward.D2")
c0.cs_labs <- labs %>%
transmute(id = substr(id, 1, nchar(id)-1), plants.d, substrate, soil) %>%
left_join(tibble(id = labels(c0.cs)), ., by = "id") %>%
group_by(id) %>%
mutate_at(vars(-id), collapse_labels) %>%
ungroup() %>%
distinct() %>%
mutate(substr.color = colorise_labels(.$substrate),
soil.color = colorise_labels(.$soil))
#local, numeric
c1.bc <- hclust(d1.bc, method = "ward.D2")
c1.bc_labs <- labs %>%
transmute(id, plants.d, substrate, soil) %>%
left_join(tibble(id = labels(c1.bc)), ., by = "id") %>%
group_by(id) %>%
mutate_at(vars(-id), collapse_labels) %>%
ungroup() %>%
distinct() %>%
mutate(substr.color = colorise_labels(.$substrate),
soil.color = colorise_labels(.$soil))
#local, binary
c1.cs <- hclust(d1.cs, method = "ward.D2")
c1.cs_labs <- labs %>%
transmute(id, plants.d, substrate, soil) %>%
left_join(tibble(id = labels(c1.cs)), ., by = "id") %>%
group_by(id) %>%
mutate_at(vars(-id), collapse_labels) %>%
ungroup() %>%
distinct() %>%
mutate(substr.color = colorise_labels(.$substrate),
soil.color = colorise_labels(.$soil))Почва
Объединенные данные, количественные
par(mar=c(3,3,1,13))
c0.bc %>%
as.dendrogram() %>%
set("labels", paste0(c0.bc_labs$id, "_", c0.bc_labs$soil)) %>%
set("labels_colors", c0.bc_labs$soil.color) %>%
plot(horiz = TRUE)
Объединенные данные, бинарные
c0.cs %>%
as.dendrogram() %>%
set("labels", paste0(c0.cs_labs$id, "_", c0.cs_labs$soil)) %>%
set("labels_colors", c0.cs_labs$soil.color) %>%
plot(horiz = TRUE)
Дробные данные, количественные
par(mar=c(3,3,1,13), cex = 0.7)
c1.bc %>%
as.dendrogram() %>%
set("labels", paste0(c1.bc_labs$id, "_", c1.bc_labs$soil)) %>%
set("labels_colors", c1.bc_labs$soil.color) %>%
plot(horiz = TRUE)
Дробные данные, бинарные
par(mar=c(3,3,1,13), cex = 0.7)
c1.cs %>%
as.dendrogram() %>%
set("labels", paste0(c1.cs_labs$id, "_", c1.cs_labs$soil)) %>%
set("labels_colors", c1.cs_labs$soil.color) %>%
plot(horiz = TRUE)
Субстрат
Объединенные данные, количественные
c0.bc %>%
as.dendrogram() %>%
set("labels", paste0(c0.bc_labs$id, "_", c0.bc_labs$substrate)) %>%
set("labels_colors", c0.bc_labs$substr.color) %>%
plot(horiz = TRUE)
Объединенные данные, бинарные
c0.cs %>%
as.dendrogram() %>%
set("labels", paste0(c0.cs_labs$id, "_", c0.cs_labs$substrate)) %>%
set("labels_colors", c0.cs_labs$substr.color) %>%
plot(horiz = TRUE)
Дробные данные, количественные
par(mar=c(3,3,1,13), cex = 0.7)
c1.bc %>%
as.dendrogram() %>%
set("labels", paste0(c1.bc_labs$id, "_", c1.bc_labs$substrate)) %>%
set("labels_colors", c1.bc_labs$substr.color) %>%
plot(horiz = TRUE)
Дробные данные, бинарные
par(mar=c(3,3,1,13), cex = 0.7)
c1.cs %>%
as.dendrogram() %>%
set("labels", paste0(c1.cs_labs$id, "_", c1.cs_labs$substrate)) %>%
set("labels_colors", c1.cs_labs$substr.color) %>%
plot(horiz = TRUE)
Синузии
Объединенные данные, количественные
c0.bc %>%
as.dendrogram() %>%
set("labels", paste0(c0.bc_labs$id, "_", c0.bc_labs$plants.d)) %>%
plot(horiz = TRUE)
Объединенные данные, бинарные
c0.cs %>%
as.dendrogram() %>%
set("labels", paste0(c0.cs_labs$id, "_", c0.cs_labs$plants.d)) %>%
plot(horiz = TRUE)
Дробные данные, количественные
par(mar=c(3,3,1,13), cex = 0.7)
c1.bc %>%
as.dendrogram() %>%
set("labels", paste0(c1.bc_labs$id, "_", c1.bc_labs$plants.d)) %>%
plot(horiz = TRUE)
Дробные данные, бинарные
par(mar=c(3,3,1,13), cex = 0.7)
c1.cs %>%
as.dendrogram() %>%
set("labels", paste0(c1.cs_labs$id, "_", c1.cs_labs$plants.d)) %>%
plot(horiz = TRUE)
dev.off() ## null device
## 1Вопрос 10а
Примечание. Вы попросили выяснить “как кластеризуются …”, что подразумевает кластерный анализ. Но представление многомерных данных “съедает”, на мой взгляд, слишком много информации, поэтому я взял на себя смелость сделать ещё и ординацию. Тут тоже можно выделять кластеры различных уровней и порядков, но не требуется предположение о дихотомическом ветвлении в классификации. На переключаемых вкладках первым идет интерактивный график, где при наведении можно посмотреть id каждой из проб. На последующих вкладках топология точек такая же, просто они визуализированы по тому или иному фактору
my.pcoa <- function(dis, df){
pcoa1 <- ape::pcoa(dis)
eig <- pcoa1$values$Eigenvalues
if(min(eig)<0){
eig <- eig+min(eig)*-1
}
eig <- round(eig/sum(eig)*100, 1)[1:2]
df <- pcoa1$vectors %>%
data.frame() %>%
rownames_to_column("id") %>%
as_tibble %>%
select(id, Axis.1, Axis.2) %>%
left_join(df, by = "id")
list(df = df, eig = eig)
}
pcoa.plot <- function(df, eig, key, tt = NULL, st = NULL){
select(df, id, Axis.1, Axis.2, environmental = all_of(key)) %>%
ggplot(., aes(Axis.1, Axis.2, color = environmental,
shape = environmental, fill = environmental)) +
geom_point() +
labs(x = paste0("Axis 1 (", eig[1], "%)"),
y = paste0("Axis 2 (", eig[2], "%)"),
title = tt, subtitle = st)
}
d1.bc <- vegan::vegdist(as.data.frame(t(dfw[,-1])), method = "bray", binary = FALSE)
d1.cs <- vegan::vegdist(as.data.frame(t(dfw[,-1])), method = "bray", binary = TRUE)
d0.bc <- vegan::vegdist(as.data.frame(t(dfw0[,-1])), method = "bray", binary = FALSE)
d0.cs <- vegan::vegdist(as.data.frame(t(dfw0[,-1])), method = "bray", binary = TRUE)
pcoa1.bc <- my.pcoa(d1.bc, labs)
pcoa1.cs <- my.pcoa(d1.cs, labs)
pcoa0.bc <- my.pcoa(d0.bc, c0.bc_labs)
pcoa0.cs <- my.pcoa(d0.cs, c0.cs_labs)Дробные данные, количественные
Общая конфигурация (топология)
plotly::ggplotly(
ggplot(pcoa1.bc[[1]], aes(Axis.1, Axis.2, color = id)) +
geom_point() +
theme(legend.position = "none") +
labs(title = "All samples; bray-curtis similarity")
)тип берега
pcoa.plot(pcoa1.bc[[1]], pcoa1.bc[[2]], "coast", NULL,
"PCoA, bray-curtis similarity")
Субстрат
pcoa.plot(pcoa1.bc[[1]], pcoa1.bc[[2]], "substrate", NULL,
"PCoA, bray-curtis similarity")
Почва
pcoa.plot(pcoa1.bc[[1]], pcoa1.bc[[2]], "soil", NULL,
"PCoA, bray-curtis similarity")
Дробные данные, качественные
Общая конфигурация (топология)
plotly::ggplotly(
ggplot(pcoa1.cs[[1]], aes(Axis.1, Axis.2, color = id)) +
geom_point() +
theme(legend.position = "none") +
labs(title = "All samples; jaccard similarity")
)тип берега
pcoa.plot(pcoa1.cs[[1]], pcoa1.cs[[2]], "coast", NULL,
"PCoA, jaccard similarity")
Субстрат
pcoa.plot(pcoa1.cs[[1]], pcoa1.cs[[2]], "substrate", NULL,
"PCoA, jaccard similarity")
Почва
pcoa.plot(pcoa1.cs[[1]], pcoa1.cs[[2]], "soil", NULL,
"PCoA, jaccard similarity")
Объединенные данные, количественные
Общая конфигурация (топология)
plotly::ggplotly(
ggplot(pcoa0.bc[[1]], aes(Axis.1, Axis.2, color = id)) +
geom_point() +
theme(legend.position = "none") +
labs(title = "United samples; bray-curtis similarity")
)Субстрат
pcoa.plot(pcoa0.bc[[1]], pcoa0.bc[[2]], "substrate", NULL,
"PCoA, bray-curtis similarity")
Почва
pcoa.plot(pcoa0.bc[[1]], pcoa0.bc[[2]], "soil", NULL,
"PCoA, bray-curtis similarity")
Объединенные данные, качественные
Общая конфигурация (топология)
plotly::ggplotly(
ggplot(pcoa0.cs[[1]], aes(Axis.1, Axis.2, color = id)) +
geom_point() +
theme(legend.position = "none") +
labs(title = "United samples; jaccard similarity")
)Субстрат
pcoa.plot(pcoa0.cs[[1]], pcoa0.cs[[2]], "substrate", NULL,
"PCoA, jaccard similarity")
Почва
pcoa.plot(pcoa0.cs[[1]], pcoa0.cs[[2]], "soil", NULL,
"PCoA, jaccard similarity")
Вопрос 11
- Что в большей степени «вкладывает» с структуру комплекса клещей отдельного биотопа-синузии — собственно специфика биотопа-синузии или тип берега?
— Возможны варианты. В нашем случае для такого анализа могут быть использованы только две пары серий — SmPbTu и SmSdTu (Typha australis и на гальке и на песке), а также, c натяжкой, SmPbTl и SmSdTa (потому, что Typha angustifolia, Ta, была на песке в смеси с Typha laxmannii, Tl; оба вида рогоза имеют сходную экологию и морфологию - с узкими листьями). Можно попробовать сравнить и небольшие тростниковые заросли на песчаном пляже (SmSdFn) с обширными тростниковыми зарослями (SmRsFd).
Примечаение. Пока не смог разобраться, как включить более двух факторов в одну модель, поэтому пришлось пока построить две штуки. В первой склон объясняет 13.1% дисперсии, почва 9.5%, во второй - субстрат 4.7%, тип берега 9.4%. Соответственно, 63.3% дисперсии остаются необъясненными. Но уже эти соотношения можно сравнивать, интерпретируя как разный вклад факторов в формирование структуры населения. Про синузии пока не разобрался как их стандартизировать (см. примечание 2 к п. 6). В таком виде с ними работать не получится, надо как-то сгруппировать.
labs2 <- as_tibble(left_join(data.frame(id = colnames(dfw[,-1])), labs, by = "id"))
vegan::adonis2(d1.bc ~ skew + soil + substrate + coast, data = labs2, permutations = 999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## vegan::adonis2(formula = d1.bc ~ skew + soil + substrate + coast, data = labs2, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## skew 2 3.8513 0.13091 5.3308 0.001 ***
## soil 3 2.8116 0.09557 2.5945 0.001 ***
## Residual 63 22.7574 0.77353
## Total 68 29.4203 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vegan::adonis2(d1.bc ~ substrate + coast, data = labs2, permutations = 999)## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## vegan::adonis2(formula = d1.bc ~ substrate + coast, data = labs2, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## substrate 1 1.3773 0.04681 3.5422 0.001 ***
## coast 2 2.7692 0.09413 3.5610 0.001 ***
## Residual 65 25.2738 0.85906
## Total 68 29.4203 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1