Обилия и соотношения таксонов

my_col <- function(type, div = div){ 
    # type = "coast"
    div %>% 
        select(type = {{type}}, Collembola:Oribatida_total) %>% 
        pivot_longer(names_to = "taxa", values_to = "abu", -type) %>% 
        group_by(type, taxa) %>% 
        summarise(abu = sum(abu), .groups = "drop") %>% 
        ggplot(aes(x = type, y = abu, fill = taxa))
    }

Берег

Обилия

div %>% 
    my_col(type = "coast") +
    geom_col(position = "dodge") + 
    labs(x = "Coast", fill = "", y = "Abundance raw")

Соотношения обилий

div %>% 
    my_col(type = "coast") + 
    geom_col(position = "fill") + 
    labs(x = "Coast", fill = "", y = "Abundance relative")

Уклон

Обилия

div %>% 
    my_col(type = "skew") +
    geom_col(position = "dodge") + 
    labs(x = "skew", fill = "", y = "Abundance raw")

Соотношения обилий

div %>% 
    my_col(type = "skew")  + 
    geom_col(position = "fill") + 
    labs(x = "skew", fill = "", y = "Abundance relative")

Почва

Обилия

div %>% 
    my_col(type = "soil") +
    geom_col(position = "dodge") + 
    labs(x = "soil", fill = "", y = "Abundance raw")

Соотношения обилий

div %>% 
    my_col(type = "soil") + 
    geom_col(position = "fill") + 
    labs(x = "soil", fill = "", y = "Abundance relative")

Проективное покрытие травостоя

Обилия

div %>% 
    my_col(type = "vegetation") + 
    geom_col(position = "dodge") + 
    labs(x = "Vegetation cover, %", y = "Abundance raw", fill = "")

Соотношения обилий

div %>% 
    my_col(type = "vegetation") + 
    geom_col(position = "fill") + 
    labs(x = "Vegetation cover, %", y = "Abundance relative", fill = "")

Доминантные виды растений

Обилия

div %>% 
    my_col(type = "plants.d") +
    geom_col(position = "dodge") + 
    labs(x = "Dominant plants", y = "Abundance raw", fill = "") + 
    theme(axis.text.x = element_text(angle = 30, hjust = 0.9))

Соотношения обилий

div %>% 
    my_col(type = "plants.d") + 
    geom_col(position = "fill") + 
    labs(x = "Dominant plants", y = "Abundance relative")+ 
    theme(axis.text.x = element_text(angle = 30, hjust = 0.9))

Разнообразие

Берег

div %>% 
    select(coast:mst_iH) %>% 
    pivot_longer(names_to = "v1", values_to = "vr", -c(1:7)) %>% 
    filter(!is.na(vr)) %>% 
    separate(v1, into = c("taxa", "diversity"), sep = "_", extra = "merge") %>% 
    mutate(diversity2 = case_when(
        diversity == "obs_m"  ~"4. Abundance", 
        diversity == "obs_qD" ~ "1. Observed N of sp.",
        diversity == "iH"     ~ "3. Shannon index", 
        TRUE ~ "2. Rarefied N of sp."
        ), taxa = case_when(taxa == "orb" ~ "Oribatida", TRUE ~ "Mesostigmata")) %>% 
    ggplot(aes(x = coast, y = vr, fill = taxa)) + 
    geom_boxplot() + 
    facet_wrap(~diversity2, scales = "free") +
    labs(subtitle = "Oribatida rarefied to 20, Mesostigmata to 10 individuals", 
         y = NULL, x = NULL, fill = "")

Уклон

div %>% 
    select(coast:mst_iH) %>% 
    pivot_longer(names_to = "v1", values_to = "vr", -c(1:7)) %>% 
    filter(!is.na(vr)) %>% 
    separate(v1, into = c("taxa", "diversity"), sep = "_", extra = "merge") %>% 
    mutate(diversity2 = case_when(
        diversity == "obs_m"  ~"4. Abundance", 
        diversity == "obs_qD" ~ "1. Observed N of sp.",
        diversity == "iH"     ~ "3. Shannon index", 
        TRUE ~ "2. Rarefied N of sp."
        ), taxa = case_when(taxa == "orb" ~ "Oribatida", TRUE ~ "Mesostigmata")) %>% 
    ggplot(aes(x = skew, y = vr, fill = taxa)) + 
    geom_boxplot() + 
    facet_wrap(~diversity2, scales = "free") +
    labs(subtitle = "Oribatida rarefied to 20, Mesostigmata to 10 individuals", 
         y = NULL, x = NULL, fill = "")

Почва

div %>% 
    select(coast:mst_iH) %>% 
    pivot_longer(names_to = "v1", values_to = "vr", -c(1:7)) %>% 
    filter(!is.na(vr)) %>% 
    separate(v1, into = c("taxa", "diversity"), sep = "_", extra = "merge") %>% 
    mutate(diversity2 = case_when(
        diversity == "obs_m"  ~"4. Abundance", 
        diversity == "obs_qD" ~ "1. Observed N of sp.",
        diversity == "iH"     ~ "3. Shannon index", 
        TRUE ~ "2. Rarefied N of sp."
        ), taxa = case_when(taxa == "orb" ~ "Oribatida", TRUE ~ "Mesostigmata")) %>% 
    ggplot(aes(x = soil, y = vr, fill = taxa)) + 
    geom_boxplot() + 
    facet_wrap(~diversity2, scales = "free") +
    labs(subtitle = "Oribatida rarefied to 20, Mesostigmata to 10 individuals", 
         y = NULL, x = NULL, fill = "")

Проективное покрытие растительности

div %>% 
    select(coast:mst_iH) %>% 
    pivot_longer(names_to = "v1", values_to = "vr", -c(1:7)) %>% 
    filter(!is.na(vr)) %>% 
    separate(v1, into = c("taxa", "diversity"), sep = "_", extra = "merge") %>% 
    mutate(diversity2 = case_when(
        diversity == "obs_m"  ~"4. Abundance", 
        diversity == "obs_qD" ~ "1. Observed N of sp.",
        diversity == "iH"     ~ "3. Shannon index", 
        TRUE ~ "2. Rarefied N of sp."
        ), taxa = case_when(taxa == "orb" ~ "Oribatida", TRUE ~ "Mesostigmata")) %>% 
    ggplot(aes(x = vegetation, y = vr, fill = taxa)) + 
    geom_boxplot() + 
    facet_wrap(~diversity2, scales = "free") +
    labs(subtitle = "Oribatida rarefied to 20, Mesostigmata to 10 individuals", 
         y = NULL, x = NULL, fill = "")

Доминантные виды растений

div %>% 
    select(coast:mst_iH) %>% 
    pivot_longer(names_to = "v1", values_to = "vr", -c(1:7)) %>% 
    filter(!is.na(vr)) %>% 
    separate(v1, into = c("taxa", "diversity"), sep = "_", extra = "merge") %>% 
    mutate(diversity2 = case_when(
        diversity == "obs_m"  ~"4. Abundance", 
        diversity == "obs_qD" ~ "1. Observed N of sp.",
        diversity == "iH"     ~ "3. Shannon index", 
        TRUE ~ "2. Rarefied N of sp."
        ), taxa = case_when(taxa == "orb" ~ "Oribatida", TRUE ~ "Mesostigmata")) %>% 
    ggplot(aes(x = plants.d, y = vr, fill = taxa)) + 
    geom_boxplot() + 
    facet_grid(rows = vars(diversity2),scales = "free") + 
    labs(subtitle = "Oribatida rarefied to 20, Mesostigmata to 10 individuals", 
         y = NULL, x = "Dominant plant species", fill = "") + 
    theme(axis.text.x = element_text(angle = 30, hjust = 0.9))

Струкруа населения: ординация

# dissimilarity -----------------------------------------------------------
dis2 <- list()
dis2$or.bin <- or.w %>% 
    select(!starts_with("Sw")) %>% 
    column_to_rownames("sp") %>% 
    select_if(function(a){sum(a)>0}) %>% 
    t %>% 
    as.data.frame() %>% 
    vegan::vegdist(method = "jaccard", binary = TRUE)
dis2$or.num <- or.w %>% 
    select(!starts_with("Sw")) %>% 
    column_to_rownames("sp") %>% 
    select_if(function(a){sum(a)>0}) %>% 
    t %>% 
    as.data.frame() %>% 
    vegan::vegdist(method = "bray", binary = FALSE)
dis2$ms.bin <- ms.w %>% 
    select(!starts_with("Sw")) %>% 
    column_to_rownames("sp") %>% 
    select_if(function(a){sum(a)>0}) %>% 
    t %>% 
    as.data.frame() %>% 
    vegan::vegdist(method = "jaccard", binary = TRUE)
dis2$ms.num <- ms.w %>% 
    select(!starts_with("Sw")) %>% 
    column_to_rownames("sp") %>% 
    select_if(function(a){sum(a)>0}) %>% 
    t %>% 
    as.data.frame() %>% 
    vegan::vegdist(method = "bray", binary = FALSE)

# Multidimensional --------------------------------------------------------
PCOA2 <- dis2 %>% 
    lapply(function(a){
        p <- ape::pcoa(a)
        e <- p$values$Eigenvalues
        if(min(e) < 0){
            e <- e + abs(min(e))
            e <- round(e/sum(e)*100, 1)
        } else { 
            e <- round(e/sum(e)*100, 1)
        }
        p <- tibble::tibble(id = rownames(p$vectors), 
                            axis1 = p$vectors[,1], 
                            axis2 = p$vectors[,2]) 
        list(eig = e, pc = p)
    }) %>% 
    purrr::transpose()

M2 <- PCOA2 %>% 
    pluck("pc") %>% 
    map_df(rbind, .id = "D") %>% 
    separate(D, into = c("taxa", "type")) %>% 
    left_join(div, by = "id") %>% 
    select(taxa:plants.d) %>% 
    mutate(
        axis1 = case_when(taxa == "ms" & type == "bin" ~ axis1*-1, TRUE ~ axis1),
        axis1 = case_when(taxa == "or" & type == "bin" ~ axis1*-1, TRUE ~ axis1),
        taxa = case_when(taxa == "or" ~ "Oribatida", TRUE ~ "Mesostigmata"), 
        type = case_when(type == "bin" ~ "Binary data", 
                         TRUE ~ "Numeric data")) %>% 
    filter(!is.na(coast))

Тополгия проб в многомерном пространстве

Интерактивная

plotly::ggplotly(
    PCOA2 %>% 
    pluck("eig") %>% 
    lapply(function(a)data.frame(
        ax1 = paste0(a[1], " %"), ax2 = paste0(a[2], " %"))) %>% 
    map_df(rbind, .id = "D") %>% 
    separate(D, into = c("taxa", "type")) %>% 
    mutate(
        taxa = case_when(taxa == "or" ~ "Oribatida", TRUE ~ "Mesostigmata"), 
        type = case_when(type == "bin" ~ "Binary data", 
                         TRUE ~ "Numeric data")) %>% 
        
    ggplot() + 
        geom_text(aes(x = 0.3, y = -0.4, label = ax1)) +
        geom_text(aes(x = -0.55, y = 0.3, label = ax2), angle = 90) +
        geom_point(aes(x = axis1, y = axis2, color = id), data = M2) + 
        facet_grid(rows = vars(type), cols = vars(taxa)) + 
        labs(x = NULL, y = NULL, title = "General topology") + 
        theme(legend.position = "none")
)

С %% объясненной дисперсии по осям

PCOA2 %>% 
    pluck("eig") %>% 
    lapply(function(a)data.frame(
        ax1 = paste0(a[1], " %"), ax2 = paste0(a[2], " %"))) %>% 
    map_df(rbind, .id = "D") %>% 
    separate(D, into = c("taxa", "type")) %>% 
    mutate(
        taxa = case_when(taxa == "or" ~ "Oribatida", TRUE ~ "Mesostigmata"), 
        type = case_when(type == "bin" ~ "Binary data", 
                         TRUE ~ "Numeric data")) %>% 
        
    ggplot() + 
        geom_text(aes(x = 0.3, y = -0.4, label = ax1)) +
        geom_text(aes(x = -0.55, y = 0.3, label = ax2), angle = 90) +
        geom_point(aes(x = axis1, y = axis2, color = id), data = M2) + 
        facet_grid(rows = vars(type), cols = vars(taxa)) + 
        labs(x = NULL, y = NULL, title = "General topology") + 
        theme(legend.position = "none")

Берег

ggplot(M2, aes(x = axis1, y = axis2, color = coast)) + 
    geom_point() + 
    stat_ellipse() +
    facet_grid(rows = vars(type), cols = vars(taxa)) + 
    labs(x = NULL, y = NULL)

Уклон

ggplot(M2, aes(x = axis1, y = axis2, color = skew)) + 
    geom_point() + 
    stat_ellipse() +
    facet_grid(rows = vars(type), cols = vars(taxa)) + 
    labs(x = NULL, y = NULL)

Почва

ggplot(M2, aes(x = axis1, y = axis2, color = soil)) + 
    geom_point() + 
    stat_ellipse() +
    facet_grid(rows = vars(type), cols = vars(taxa)) + 
    labs(x = NULL, y = NULL)

Доминантные виды растений

M2 %>% 
    ggplot(aes(x = axis1, y = axis2, color = plants.d)) + 
    geom_point() + 
    stat_ellipse() +
    facet_grid(rows = vars(type), cols = vars(taxa)) + 
    labs(x = NULL, y = NULL, color = "Dominant plant species")

Струкруа населения: PERMANOVA

soil - почва
coast - берег
vegetation - ПП растительности, %
plants.d - доминантные виды растений

Oribatida, состав

labs %>% 
    filter(id %in% attr(dis2$or.bin, "Labels")) %>% 
    # mutate(vegetation = as.numeric(vegetation)) %>%
    vegan::adonis2(
        dis2$or.bin ~ soil + coast + vegetation + plants.d, 
        data = ., permutations = 9999)

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
## 
## vegan::adonis2(formula = dis2$or.bin ~ soil + coast + vegetation + plants.d, data = ., permutations = 9999)
##            Df SumOfSqs      R2      F Pr(>F)    
## soil        1   0.4896 0.01978 1.8239 0.0529 .  
## coast       1   1.8373 0.07422 6.8445 0.0001 ***
## vegetation  4   4.0395 0.16319 3.7621 0.0001 ***
## plants.d    7   4.6975 0.18977 2.4999 0.0001 ***
## Residual   51  13.6902 0.55305                  
## Total      64  24.7541 1.00000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# vegan::adonis2(dis2$or.bin ~ soil + coast + plants.d + vegetation , 
#                data = filter(labs, id %in% attr(dis2$or.bin, "Labels")),
#                permutations = 9)
# vegan::adonis2(dis2$or.bin ~ soil + coast + vegetation + plants.d, 
#                data = filter(labs, id %in% attr(dis2$or.bin, "Labels")),
#                permutations = 9)
# vegan::adonis2(dis2$or.bin ~ vegetation + plants.d + soil + coast, 
#                data = filter(labs, id %in% attr(dis2$or.bin, "Labels")),
#                permutations = 9)
# vegan::adonis2(dis2$or.bin ~ plants.d + vegetation + soil + coast, 
#                data = filter(labs, id %in% attr(dis2$or.bin, "Labels")),
#                permutations = 9)

Видовой соостав орибатид в пробах на 35.3 % определяется видовым составом и проективным покрытием растений (0.1632 + 0.1898 в таблице). От почвы и берега он зависит в лучшем случае на 9.4 % (а может оказаться, что и меньше)

Oribatida, структура

vegan::adonis2(dis2$or.num ~ coast + soil + vegetation + plants.d, 
               data = filter(labs, id %in% attr(dis2$or.num, "Labels")), 
               permutations = 9999)

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
## 
## vegan::adonis2(formula = dis2$or.num ~ coast + soil + vegetation + plants.d, data = filter(labs, id %in% attr(dis2$or.num, "Labels")), permutations = 9999)
##            Df SumOfSqs      R2      F Pr(>F)    
## coast       2   1.9425 0.07091 2.8812  1e-04 ***
## vegetation  4   3.4056 0.12433 2.5257  1e-04 ***
## plants.d    7   4.8520 0.17713 2.0562  1e-04 ***
## Residual   51  17.1918 0.62762                  
## Total      64  27.3919 1.00000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Mesostigmata, состав

vegan::adonis2(dis2$ms.bin ~ coast + soil + vegetation + plants.d, 
               data = filter(labs, id %in% attr(dis2$ms.bin, "Labels")), 
               permutations = 9999)

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
## 
## vegan::adonis2(formula = dis2$ms.bin ~ coast + soil + vegetation + plants.d, data = filter(labs, id %in% attr(dis2$ms.bin, "Labels")), permutations = 9999)
##            Df SumOfSqs      R2      F Pr(>F)    
## coast       2    2.744 0.08657 4.5178  1e-04 ***
## vegetation  4    5.049 0.15929 4.1565  1e-04 ***
## plants.d    7    6.291 0.19847 2.9594  1e-04 ***
## Residual   58   17.614 0.55568                  
## Total      71   31.699 1.00000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Mesostigmata, структура

vegan::adonis2(dis2$ms.num ~ coast + soil + vegetation + plants.d, 
               data = filter(labs, id %in% attr(dis2$ms.num, "Labels")), 
               permutations = 9999)

## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 9999
## 
## vegan::adonis2(formula = dis2$ms.num ~ coast + soil + vegetation + plants.d, data = filter(labs, id %in% attr(dis2$ms.num, "Labels")), permutations = 9999)
##            Df SumOfSqs      R2      F Pr(>F)    
## coast       2    2.323 0.07181 3.4933  1e-04 ***
## vegetation  4    4.676 0.14454 3.5158  1e-04 ***
## plants.d    7    6.066 0.18752 2.6064  1e-04 ***
## Residual   58   19.283 0.59613                  
## Total      71   32.348 1.00000                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Задействованные данные

Обилие общее

formattable::formattable(div[,c(1,17:20)])

id	Collembola	Prostigmata	Mesostigmata_total	Oribatida_total
PbAe1	4	5	0	0
PbAe2	24	14	0	0
PbAe3	2	3	1	0
PbAe4	1090	15	0	0
PbAe5	475	5	1	1
PbDe1	15	0	2	0
PbDe2	9	1	2	0
PbDe3	49	1	4	0
PbDe4	24	24	28	5
PbDe5	510	5	35	2
PbPo1	13	4	4	2
PbPo2	3	3	2	0
PbPo3	534	6	47	4
PbPo4	740	21	42	11
PbPo5	430	11	10	0
PbTu1	58	170	12	14
PbTu2	91	71	21	18
PbTu3	135	220	46	23
PbTu4	162	260	7	1
PbTu5	337	93	19	19
PbTl1	82	62	1	1
PbTl2	94	49	34	3
PbTl3	61	78	11	2
PbTl4	96	355	3	3
PbTl5	512	97	13	48
SdJj1	81	92	21	122
SdJj2	137	219	21	61
SdJj3	92	88	6	2
SdJj4	3410	25	5	59
SdJj5	1090	11	5	39
SdEq1	325	224	20	39
SdEq2	137	168	6	44
SdEq3	120	822	11	4
SdEq4	1310	71	6	28
SdEq5	1480	51	11	72
SdTu1	1110	83	37	95
SdTu2	265	102	33	8
SdTu3	189	111	26	24
SdTu4	62	39	8	4
SdTu5	61	21	13	2
SdEc1	98	240	13	3
SdEc2	96	336	8	179
SdEc3	129	768	83	188
SdEc4	158	137	17	23
SdEc5	31	709	72	144
SdTa1	77	285	20	38
SdTa2	305	458	14	66
SdTa3	93	891	100	169
SdTa4	194	74	10	1
SdTa5	89	190	18	2
SdJm1	56	452	4	94
SdJm2	86	141	7	23
SdJm3	11	309	13	59
SdJm4	478	102	13	79
SdJm5	332	119	5	20
SdFn1	49	25	31	111
SdFn2	318	123	58	104
SdFn3	124	197	19	49
SdFn4	61	5	11	4
SdFn5	101	22	8	10
RsFd1	115	138	23	80
RsFd2	74	98	23	34
RsFd3	102	86	11	54
RsFd4	278	154	20	89
RsFd5	145	270	46	151
SdCJ1	0	42	2	2
SdCJ2	0	5	1	3
SdCJ3	9	33	1	55
SdCJ4	1	21	2	1
SdCJ5	0	14	1	1
SdCS1	2	37	2	33
SdCS2	13	57	2	26
SdCS3	5	75	2	37
SdCS4	2	73	7	28
SdCS5	5	97	2	29
Sw1	490	16	47	60
Sw2	10	83	1	71
Sw3	15	105	1	51
Sw4	9	43	3	33
Sw5	192	35	31	167

Разнообразие Oribatida

div %>% 
    select(id, 
           `Abundance` = orb_obs_m, 
           `Number of species` = orb_obs_qD, 
           `Rarefied number of species` = orb_d20_qD, 
           `Shannon index` = orb_iH) %>% 
    mutate_at(c("Abundance", "Number of species"), function(a){a[is.na(a)] <- 0; a}) %>% 
    formattable::formattable()

id	Abundance	Number of species	Rarefied number of species	Shannon index
PbAe1	0	0	NA	NA
PbAe2	0	0	NA	NA
PbAe3	0	0	NA	NA
PbAe4	0	0	NA	NA
PbAe5	1	1	1.000000	0.0000000
PbDe1	0	0	NA	NA
PbDe2	0	0	NA	NA
PbDe3	0	0	NA	NA
PbDe4	5	4	7.243466	1.3321790
PbDe5	1	1	1.000000	0.0000000
PbPo1	1	1	1.000000	0.0000000
PbPo2	0	0	NA	NA
PbPo3	4	4	8.318528	1.3862944
PbPo4	11	2	2.000000	0.4741393
PbPo5	0	0	NA	NA
PbTu1	14	3	3.535087	0.5091373
PbTu2	18	3	3.000000	0.6546696
PbTu3	22	3	2.909091	0.8192300
PbTu4	1	1	1.000000	0.0000000
PbTu5	18	5	5.188366	1.3784974
PbTl1	1	1	1.000000	0.0000000
PbTl2	3	2	2.333331	0.6365142
PbTl3	2	2	2.500000	0.6931472
PbTl4	2	1	1.000000	0.0000000
PbTl5	39	8	5.458073	1.3967087
SdJj1	122	2	1.999272	0.5917104
SdJj2	61	3	2.327786	0.6977461
SdJj3	2	1	1.000000	0.0000000
SdJj4	59	2	1.338983	0.0859155
SdJj5	39	2	1.999999	0.6528258
SdEq1	35	3	2.568345	0.5824257
SdEq2	42	3	2.731268	0.7270014
SdEq3	4	3	4.484966	1.0397208
SdEq4	28	3	2.697192	0.4904499
SdEq5	69	3	2.883265	0.8861363
SdTu1	95	2	1.999998	0.6837549
SdTu2	8	2	2.000000	0.6615632
SdTu3	24	3	2.831357	0.5442837
SdTu4	4	2	2.000000	0.6931472
SdTu5	2	2	2.500000	0.6931472
SdEc1	3	3	4.997970	1.0986123
SdEc2	175	7	3.314495	0.8567530
SdEc3	185	4	2.987057	0.8413431
SdEc4	22	5	4.818182	1.2200467
SdEc5	46	8	6.286534	1.7165982
SdTa1	38	2	1.958545	0.3364958
SdTa2	65	2	1.982179	0.4293230
SdTa3	167	5	2.845166	0.6565421
SdTa4	1	1	1.000000	0.0000000
SdTa5	2	1	1.000000	0.0000000
SdJm1	91	3	2.763849	0.6081338
SdJm2	21	2	2.000000	0.5982696
SdJm3	59	4	3.130517	0.7165508
SdJm4	72	2	1.999909	0.6264500
SdJm5	16	2	2.000000	0.4825776
SdFn1	111	7	5.184630	1.4255178
SdFn2	104	8	5.702130	1.7169811
SdFn3	49	8	5.808943	1.5315573
SdFn4	4	2	2.000000	0.5623351
SdFn5	10	3	3.389506	0.8018186
RsFd1	76	13	8.607866	2.1646870
RsFd2	31	10	8.509630	2.0920540
RsFd3	50	10	7.423642	1.8861403
RsFd4	85	12	7.363306	1.7505727
RsFd5	144	11	5.562843	1.4598844
SdCJ1	2	1	1.000000	0.0000000
SdCJ2	3	2	2.333331	0.6365142
SdCJ3	6	2	2.000000	0.4505612
SdCJ4	0	0	NA	NA
SdCJ5	1	1	1.000000	0.0000000
SdCS1	28	9	7.211640	1.6236078
SdCS2	26	4	3.946154	1.1330848
SdCS3	36	5	4.032558	1.0536914
SdCS4	26	4	3.722074	0.8397961
SdCS5	28	5	4.623043	1.1967007
Sw1	32	11	8.197579	1.8803036
Sw2	40	8	6.038462	1.3023528
Sw3	26	11	9.698997	2.1182723
Sw4	30	12	9.118774	1.8923784
Sw5	165	9	4.855290	1.3484927

Разнообразие Mesostigmata

div %>% 
    select(id, 
           `Abundance` = mst_obs_m, 
           `Number of species` = mst_obs_qD, 
           `Rarefied number of species` = mst_d10_qD, 
           `Shannon index` = mst_iH) %>% 
    mutate_at(c("Abundance", "Number of species"), function(a){a[is.na(a)] <- 0; a}) %>% 
    formattable::formattable()

id	Abundance	Number of species	Rarefied number of species	Shannon index
PbAe1	0	0	NA	NA
PbAe2	0	0	NA	NA
PbAe3	1	1	1.000000	0.0000000
PbAe4	0	0	NA	NA
PbAe5	1	1	1.000000	0.0000000
PbDe1	2	2	2.499924	0.6931472
PbDe2	2	2	2.499924	0.6931472
PbDe3	4	1	1.000000	0.0000000
PbDe4	28	4	3.396507	1.1163671
PbDe5	35	3	1.781513	0.3470668
PbPo1	4	3	4.233032	1.0397208
PbPo2	2	1	1.000000	0.0000000
PbPo3	47	8	4.951391	1.7421588
PbPo4	42	3	1.662021	0.3027839
PbPo5	10	3	NA	0.6390319
PbTu1	12	5	4.651515	1.4241299
PbTu2	21	7	5.180264	1.6569375
PbTu3	46	8	3.603983	1.2018225
PbTu4	7	5	6.393714	1.4750763
PbTu5	19	9	5.860562	1.8224567
PbTl1	1	1	1.000000	0.0000000
PbTl2	34	8	4.910358	1.6993617
PbTl3	11	6	5.727273	1.6726254
PbTl4	3	2	2.330729	0.6365142
PbTl5	13	4	3.538462	1.0905995
SdJj1	21	7	5.248563	1.6993255
SdJj2	21	4	3.612413	1.2372274
SdJj3	6	3	3.616410	0.8675632
SdJj4	5	4	5.934408	1.3321790
SdJj5	5	5	8.560568	1.6094379
SdEq1	20	6	4.772863	1.5536520
SdEq2	6	4	4.616410	1.3296613
SdEq3	11	5	4.636364	1.1595888
SdEq4	6	5	7.113244	1.5607104
SdEq5	11	2	1.909091	0.3046361
SdTu1	37	8	4.739365	1.6057964
SdTu2	33	6	3.843327	1.2715071
SdTu3	26	6	3.930035	1.3446254
SdTu4	8	4	4.345679	1.2554823
SdTu5	13	5	4.500000	1.4127446
SdEc1	13	4	3.765734	1.2658568
SdEc2	8	4	4.615234	1.0735428
SdEc3	83	12	5.340752	1.9535878
SdEc4	17	5	3.970588	1.2033320
SdEc5	72	11	5.117188	1.8734056
SdTa1	20	7	5.147059	1.6865700
SdTa2	14	9	7.362637	2.1065773
SdTa3	100	7	3.895717	1.4313404
SdTa4	10	5	NA	1.4184837
SdTa5	18	5	3.745098	1.0797346
SdJm1	4	4	7.150069	1.3862944
SdJm2	7	4	4.952103	1.1537419
SdJm3	13	9	7.534965	2.0981474
SdJm4	13	1	1.000000	0.0000000
SdJm5	5	3	3.694650	0.9502705
SdFn1	31	3	2.701965	0.9347570
SdFn2	58	12	4.801076	1.7007024
SdFn3	19	5	3.631579	1.0435479
SdFn4	11	4	3.909091	1.2945452
SdFn5	8	7	8.399277	1.9061547
RsFd1	23	7	4.392573	1.4288697
RsFd2	23	6	3.700883	1.1792337
RsFd3	11	5	4.727273	1.2945452
RsFd4	20	3	2.526316	0.6390319
RsFd5	46	5	2.803375	0.9507672
SdCJ1	2	2	2.499924	0.6931472
SdCJ2	1	1	1.000000	0.0000000
SdCJ3	1	1	1.000000	0.0000000
SdCJ4	2	1	1.000000	0.0000000
SdCJ5	1	1	1.000000	0.0000000
SdCS1	2	2	2.499924	0.6931472
SdCS2	2	2	2.499924	0.6931472
SdCS3	2	1	1.000000	0.0000000
SdCS4	7	1	1.000000	0.0000000
SdCS5	2	1	1.000000	0.0000000
Sw1	47	6	4.004445	1.4367296
Sw2	1	1	1.000000	0.0000000
Sw3	1	1	1.000000	0.0000000
Sw4	3	3	4.882945	1.0986123
Sw5	31	7	4.135700	1.3532343

Параметры среды

div %>% 
    select(id, coast, skew, soil, substrate, 
           `Проективное покрытие, %` = vegetation, 
           `Доминантные виды растений` = plants.d,) %>% 
    formattable::formattable()

id	coast	skew	soil	substrate	Проективное покрытие, %	Доминантные виды растений
PbAe1	pebbly	steep	sand	turf	10	Aeluropus
PbAe2	pebbly	steep	sand	turf	10	Aeluropus
PbAe3	pebbly	steep	sand	turf	10	Aeluropus
PbAe4	pebbly	steep	sand	turf	10	Aeluropus
PbAe5	pebbly	steep	sand	turf	10	Aeluropus
PbDe1	pebbly	steep	sand	turf	10	Deschampsia
PbDe2	pebbly	steep	sand	turf	10	Deschampsia
PbDe3	pebbly	steep	sand	turf	10	Deschampsia
PbDe4	pebbly	steep	sand	turf	10	Deschampsia
PbDe5	pebbly	steep	sand	turf	10	Deschampsia
PbPo1	pebbly	steep	sand	turf	10	Poa
PbPo2	pebbly	steep	sand	turf	10	Poa
PbPo3	pebbly	steep	sand	turf	10	Poa
PbPo4	pebbly	steep	sand	turf	10	Poa
PbPo5	pebbly	steep	sand	turf	10	Poa
PbTu1	pebbly	steep	sand	turf	10	Typha
PbTu2	pebbly	steep	sand	turf	10	Typha
PbTu3	pebbly	steep	sand	turf	10	Typha
PbTu4	pebbly	steep	sand	turf	10	Typha
PbTu5	pebbly	steep	sand	turf	10	Typha
PbTl1	pebbly	steep	sand	turf	10	Typha
PbTl2	pebbly	steep	sand	turf	10	Typha
PbTl3	pebbly	steep	sand	turf	10	Typha
PbTl4	pebbly	steep	sand	turf	10	Typha
PbTl5	pebbly	steep	sand	turf	10	Typha
SdJj1	sandy beach	gentle	sand	turf	70	Juncus
SdJj2	sandy beach	gentle	sand	turf	70	Juncus
SdJj3	sandy beach	gentle	sand	turf	70	Juncus
SdJj4	sandy beach	gentle	sand	turf	70	Juncus
SdJj5	sandy beach	gentle	sand	turf	70	Juncus
SdEq1	sandy beach	gentle	sand	turf	70	Equisetum
SdEq2	sandy beach	gentle	sand	turf	70	Equisetum
SdEq3	sandy beach	gentle	sand	turf	70	Equisetum
SdEq4	sandy beach	gentle	sand	turf	70	Equisetum
SdEq5	sandy beach	gentle	sand	turf	70	Equisetum
SdTu1	sandy beach	gentle	sand	turf	40	Typha
SdTu2	sandy beach	gentle	sand	turf	40	Typha
SdTu3	sandy beach	gentle	sand	turf	40	Typha
SdTu4	sandy beach	gentle	sand	turf	40	Typha
SdTu5	sandy beach	gentle	sand	turf	40	Typha
SdEc1	sandy beach	gentle	sand	turf	90	Elaeagnus
SdEc2	sandy beach	gentle	sand	turf	90	Elaeagnus
SdEc3	sandy beach	gentle	sand	turf	90	Elaeagnus
SdEc4	sandy beach	gentle	sand	turf	90	Elaeagnus
SdEc5	sandy beach	gentle	sand	turf	90	Elaeagnus
SdTa1	sandy beach	gentle	sand	turf	90	Typha
SdTa2	sandy beach	gentle	sand	turf	90	Typha
SdTa3	sandy beach	gentle	sand	turf	90	Typha
SdTa4	sandy beach	gentle	sand	turf	90	Typha
SdTa5	sandy beach	gentle	sand	turf	90	Typha
SdJm1	sandy beach	gentle	sand	turf	40	Juncus
SdJm2	sandy beach	gentle	sand	turf	40	Juncus
SdJm3	sandy beach	gentle	sand	turf	40	Juncus
SdJm4	sandy beach	gentle	sand	turf	40	Juncus
SdJm5	sandy beach	gentle	sand	turf	40	Juncus
SdFn1	sandy beach	gentle	sand	turf	100	Phragmites
SdFn2	sandy beach	gentle	sand	turf	100	Phragmites
SdFn3	sandy beach	gentle	sand	turf	100	Phragmites
SdFn4	sandy beach	gentle	sand	turf	100	Typha
SdFn5	sandy beach	gentle	sand	turf	100	Typha
RsFd1	reeds	flat	clay	turf	100	Phragmites
RsFd2	reeds	flat	clay	turf	100	Phragmites
RsFd3	reeds	flat	clay	turf	100	Phragmites
RsFd4	reeds	flat	clay	turf	100	Phragmites
RsFd5	reeds	flat	clay	turf	100	Phragmites
SdCJ1	sandy beach	flat	sand	turf	15	Convolvulus
SdCJ2	sandy beach	flat	sand	turf	15	Convolvulus
SdCJ3	sandy beach	flat	sand	turf	15	Convolvulus
SdCJ4	sandy beach	flat	sand	turf	15	Convolvulus
SdCJ5	sandy beach	flat	sand	turf	15	Convolvulus
SdCS1	sandy beach	flat	sand	turf	15	Convolvulus
SdCS2	sandy beach	flat	sand	turf	15	Convolvulus
SdCS3	sandy beach	flat	sand	turf	15	Convolvulus
SdCS4	sandy beach	flat	sand	turf	15	Convolvulus
SdCS5	sandy beach	flat	sand	turf	15	Convolvulus
Sw1	sandy beach	flat	grass remnants	debris	30	no
Sw2	sandy beach	flat	grass remnants	debris	0	no
Sw3	sandy beach	flat	grass remnants	debris	0	no
Sw4	sandy beach	flat	woody debris	debris	0	no
Sw5	pebbly	steep	woody debris	debris	0	no

Caspian mites: Samoor

Sozontov

2023-02-18

Обилия и соотношения таксонов

Берег

Обилия

Соотношения обилий

Уклон

Обилия

Соотношения обилий

Почва

Обилия

Соотношения обилий

Проективное покрытие травостоя

Обилия

Соотношения обилий

Доминантные виды растений

Обилия

Соотношения обилий

Разнообразие

Берег

Уклон

Почва

Проективное покрытие растительности

Доминантные виды растений

Струкруа населения: ординация

Тополгия проб в многомерном пространстве

Интерактивная

С %% объясненной дисперсии по осям

Берег

Уклон

Почва

Доминантные виды растений

Струкруа населения: PERMANOVA

Oribatida, состав

Oribatida, структура

Mesostigmata, состав

Mesostigmata, структура

Задействованные данные

Обилие общее

Разнообразие Oribatida

Разнообразие Mesostigmata

Параметры среды