library(basifoR)
library(betapart)
library(cluster)
library(corrplot)
library(dplyr)
library(entropart)
library(FactoMineR)
library(factoextra)
library(forcats)
library(GGally)
library(ggplot2)
library(gridExtra)
library(Hmisc)
library(Kendall)
library(lubridate)
library(patchwork)
library(readr)
library(scales)
library(sf)
library(stringr)
library(terra)
library(tools)
library(trend)
library(tidyverse)
library(vegan)
library(viridis)
library(reshape2)
library(knitr)
library(tidyr)soil.vege
#Libraries
SOIL
getwd()[1] "C:/Users/n1227824/OneDrive - Nottingham Trent University/R/R_ldsf.rs/scripts"Soil_Data <- read_csv("data/soil.data.csv")
head(Soil_Data) # A tibble: 6 × 30
SSN Job.No Study Site cluster plot depth_std depth_top depth_bottom pH
<chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
1 WA090… ICR-2… NTU … ENAR… 1 1 TOP NA NA 6.86
2 WA090… ICR-2… NTU … ENAR… 1 1 SUB NA NA 6.97
3 WA090… ICR-2… NTU … ENAR… 1 2 TOP NA NA 6.77
4 WA090… ICR-2… NTU … ENAR… 1 2 SUB NA NA 6.96
5 WA090… ICR-2… NTU … ENAR… 1 3 TOP NA NA 6.67
6 WA090… ICR-2… NTU … ENAR… 1 3 SUB NA NA 6.95
# ℹ 20 more variables: `SOC (%)` <dbl>, `TN (%)` <dbl>, `EC (uS/cm)` <dbl>,
# `m3.P (mg/kg)` <dbl>, `m3.Al (mg/kg)` <dbl>, `m3.B (mg/kg)` <dbl>,
# `m3.Ca (mg/kg)` <dbl>, `m3.Fe (mg/kg)` <dbl>, `m3.K (mg/kg)` <dbl>,
# `m3.Mg (mg/kg)` <dbl>, `m3.Na (mg/kg)` <dbl>, `CEC (cmolc/kg)` <dbl>,
# `ExAc (cmolc/kg)` <dbl>, PSI <dbl>, `Clay (%)` <dbl>, `Silt (%)` <dbl>,
# `Sand (%)` <dbl>, Soil.Texture.Interpretation <chr>, Interpretation <lgl>,
# ...30 <chr># Select and rename relevant columns
soil_data <- Soil_Data %>%
dplyr::select(-c(1:4, 30, 29, 28)) %>%
rename(
EC = `EC (uS/cm)`, P = `m3.P (mg/kg)`, Al = `m3.Al (mg/kg)`,
B = `m3.B (mg/kg)`, Ca = `m3.Ca (mg/kg)`, Fe = `m3.Fe (mg/kg)`,
K = `m3.K (mg/kg)`, Mg = `m3.Mg (mg/kg)`, Na = `m3.Na (mg/kg)`,
CEC = `CEC (cmolc/kg)`, ExAc = `ExAc (cmolc/kg)`, TN = `TN (%)`,
SOC = `SOC (%)`, Clay = `Clay (%)`, Sand = `Sand (%)`, Silt = `Silt (%)`)
soil_data$cluster <- as.factor(soil_data$cluster)
soil_data <- soil_data %>%
mutate(across(where(is.numeric), ~ replace_na(., 0)))
# Compute the mean for each plot within each cluster
soil_data <- soil_data %>%
group_by(cluster, plot) %>%
summarise(across(where(is.numeric), mean, na.rm = TRUE))
head (soil_data)# A tibble: 6 × 22
# Groups: cluster [1]
cluster plot depth_top depth_bottom pH SOC TN EC P Al
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 1 0 10 6.89 1.28 0.12 123. 20.1 852.
2 1 2 0 12.7 6.9 1.21 0.117 120. 24.1 860.
3 1 3 0 0 6.81 1.18 0.125 123. 41.2 850.
4 1 4 0 16.5 6.86 1.22 0.12 120. 24.6 856.
5 1 5 0 8.33 7.10 0.96 0.0967 125. 27.0 846.
6 1 6 0 11 6.6 1.07 0.12 105. 28.3 873.
# ℹ 12 more variables: B <dbl>, Ca <dbl>, Fe <dbl>, K <dbl>, Mg <dbl>,
# Na <dbl>, CEC <dbl>, ExAc <dbl>, PSI <dbl>, Clay <dbl>, Silt <dbl>,
# Sand <dbl>Soil PCA TEST
soil.data <- soil_data[,-c(1:4)]
# Perform PCA
pc.results.soil <- PCA(soil.data, scale.unit = TRUE, graph = FALSE)
# Extract eigenvalues
eigenvalues <- pc.results.soil$eig[,1]
head(eigenvalues) comp 1 comp 2 comp 3 comp 4 comp 5 comp 6
7.5175646 4.4735839 1.9603027 1.2654116 1.0108506 0.4796236 #✅ Interpetation: T#The eigenvalues represent the amount of variance captured
#by each principal component (PC).The first five components have eigenvalues
# greater than 1, suggesting they retain meaningful information.
# Correlation circle - variable contribution to PCs
fviz_pca_var(pc.results.soil, col.var = "contrib",gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"), repel = TRUE, title = "",
legend.title = list(color = "Contribution Level")) +
labs(color = "Contribution") 
#✅ Interpetation: Cumulative variance: PC1 explains 41.8% of the total
#variance. PC2 adds 24.9%, bringing the cumulative variance to 66.7%
#Variable Contributions to Principal Components (Correlation with PCs)
# Extract correlation of variables with PCs
correlations <- pc.results.soil$var$coord
print(correlations) Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
pH 0.89288765 -0.31988782 0.17780786 -0.10981891 -0.11333255
SOC 0.25929878 0.94099673 0.01981635 -0.01406242 0.08483381
TN 0.05406606 0.97294912 -0.02929538 -0.01689972 0.12645926
EC 0.59203055 0.70346608 0.10441728 0.22294499 -0.17805452
P 0.44298990 0.55029393 0.47522877 0.25119190 -0.07212982
Al -0.65468650 0.23447539 -0.52590976 0.14447660 0.40048924
B 0.84790138 0.37950736 -0.13609146 -0.12156375 0.07686535
Ca 0.98229894 0.06574666 0.04358807 0.02739976 0.05486205
Fe -0.49411596 -0.22214616 0.54602690 0.37170435 0.24363434
K 0.80130115 -0.14738001 0.16312963 -0.13036827 0.05132196
Mg 0.95378888 0.06130141 -0.09460992 0.07454720 0.01620176
Na 0.26638724 -0.60632475 0.47389439 0.34768529 -0.05952582
CEC 0.97986819 0.10274880 0.05362792 0.04832476 0.06836190
ExAc -0.51302319 0.24840672 0.08453213 0.68919397 -0.24937918
PSI 0.43833203 0.16585113 -0.64037366 0.51358318 0.17009892
Clay 0.58412722 -0.69199730 -0.27970293 0.19484506 0.12518709
Silt -0.25658099 0.35554186 0.57946615 -0.13152223 0.62080195
Sand -0.54912412 0.62501068 0.03592439 -0.16033677 -0.45380663#✅ Interpetation: PC1:These properties are strongly affected by fertilisers #especially NPK Strong positive correlations: CEC (0.98), Ca (0.98), Mg (0.95), B #(0.84), pH (0.89). High values of these components show high use of the #fertilisers and lime. Further, the soils showed strong negative correlation: Al #(-0.65), Sand (-0.54), ExAc (-0.51), Fe (-0.49). Suggests PC1 represents cation #exchange capacity (CEC) and soil fertility.
##✅ Interpetation: PC2:High PC2: Probably in conservation areas and unfarmed hilly areas (due to #organic matter accumulation).Strongest positive correlations: TN (0.97), SOC #(0.94),EC (0.70), Sand (0.62). Negative correlations Clay (-0.69), Na (-0.60) #Indicates PC2 captures organic matter and soil texture gradients.
# Extract PCA scores and cluster info
pca_scores <- data.frame(
PC1 = pc.results.soil$ind$coord[, 1], PC2 = pc.results.soil$ind$coord[, 2],
Cluster = as.factor(soil_data$cluster))
# Calculate mean PC1 and PC2 per cluster
cluster_means <- pca_scores %>%
group_by(Cluster) %>%
summarise(
Mean_PC1 = mean(PC1), Mean_PC2 = mean(PC2))
head(cluster_means)# A tibble: 6 × 3
Cluster Mean_PC1 Mean_PC2
<fct> <dbl> <dbl>
1 1 0.872 -1.67
2 2 1.02 -1.04
3 3 1.66 1.92
4 4 -2.26 0.904
5 5 -3.06 -0.350
6 6 2.72 -2.01 soil.pc<- cluster_means %>% rename(pc1= Mean_PC1, pc2= Mean_PC2)
head (soil.pc)# A tibble: 6 × 3
Cluster pc1 pc2
<fct> <dbl> <dbl>
1 1 0.872 -1.67
2 2 1.02 -1.04
3 3 1.66 1.92
4 4 -2.26 0.904
5 5 -3.06 -0.350
6 6 2.72 -2.01 PLANTS
Rangeland MC analysis Seasonal and spatial
###Meta community_R
#this is data along the 28 metre transect
# Read data -----------------------------
grass.data <- read_csv("data/rl.data.csv") %>%
mutate(across(where(is.numeric), ~ replace_na(., 0))) %>%
mutate(
date = dmy(str_trim(year.month)), Season = case_when( month(date) == 4 ~ "Wet",
month(date) == 10 ~ "Dry", TRUE ~ NA_character_ ),
Season = factor(Season, levels = c("Wet", "Dry")) ) %>%
select(-date) %>%
filter(!is.na(Season))
# Long Data
rangeland.long.data <- grass.data %>%
select(-year.month, -lifeform) %>%
pivot_longer( cols = where(is.numeric), names_to = "plot", values_to = "freq")%>%
filter(!is.na(species)) %>%
mutate(plot_num = as.numeric(gsub("p", "", plot)),
cluster = paste0("C",ceiling(plot_num / 10)) ) %>%
select(-plot_num)
head(rangeland.long.data)# A tibble: 6 × 5
species Season plot freq cluster
<chr> <fct> <chr> <dbl> <chr>
1 Aristida adoensis Hochst. ex A.Rich. Wet p1 0 C1
2 Aristida adoensis Hochst. ex A.Rich. Wet p2 0 C1
3 Aristida adoensis Hochst. ex A.Rich. Wet p3 0 C1
4 Aristida adoensis Hochst. ex A.Rich. Wet p4 0 C1
5 Aristida adoensis Hochst. ex A.Rich. Wet p5 0 C1
6 Aristida adoensis Hochst. ex A.Rich. Wet p6 0 C1 # Build MetaCommunity matrices PER SEASON (For Alpha & Diversity Profiles)------
MC.rangeland <- rangeland.long.data %>%
group_by(Season, cluster, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
split(.$Season) %>%
lapply(function(df) {
df %>%
pivot_wider( names_from = cluster, values_from = freq, values_fill = 0 ) %>%
column_to_rownames("species") %>%
filter(rowSums(across(where(is.numeric))) > 0) %>%
MetaCommunity()
})
#Metacommunity Components Summary
#Nsi (Matrix)
#Species abundance in each subcommunity (cluster).
#Rows represent species and columns represent clusters (C1–C16).
#Values correspond to the total frequencies of each species summed by season and cluster.
#Ns (Vector)
#Total abundance of each species across the entire metacommunity, representing #overall (γ) diversity at the species level.
#Ni (Vector)
#Total abundance of all species within each subcommunity (cluster), representing within-community (α) diversity.
#N (Single Value)
#Total abundance of all individuals across all species and subcommunities in the metacommunity.
#Ψsi (Matrix)
#Proportion of the total metacommunity abundance contributed by each species #within each subcommunity.This is a normalized version of the Nsi matrix and #reflects the relative contribution of species–cluster combinations to overall #metacommunity abundance.
#wi (Vector)
#Relative weight or importance of each subcommunity (cluster) within the metacommunity.
##It reflects how much each cluster contributes to the total abundance across all species.
#ps (Vector)
#Proportion of the total metacommunity abundance contributed by each species.
#This shows how dominant or rare each species is at the metacommunity (γ-diversity) level.
#nSpecies (Single Value)
#Total number of species recorded in the metacommunity across all clusters and #seasons.This corresponds to species richness at the metacommunity (γ) scale.
#nCommunities (Single Value)
#Number of subcommunities included in the analysis.
# clusters (C1–C16).
MC.rangeland$Wet
$Nsi
C1 C10 C11 C12 C13 C14 C15 C16 C2 C3 C4 C5 C6 C7 C8
Wet 0 0 0 0 1 0 8 5 2 0 0 0 0 0 0
Wet 4 0 10 0 0 0 3 3 3 11 3 1 18 0 2
Wet 5 0 1 0 25 0 1 6 4 0 6 28 14 0 5
Wet 0 0 0 0 0 6 0 0 4 1 4 0 2 0 0
Wet 0 0 0 0 0 0 0 0 0 1 0 4 4 0 0
Wet 0 0 0 0 1 15 0 0 0 0 0 0 0 0 0
Wet 1 29 32 31 3 4 0 1 0 1 5 26 1 10 0
Wet 0 24 107 116 41 58 9 75 3 32 59 38 17 179 90
Wet 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0
Wet 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wet 0 0 1 5 0 3 0 0 0 0 30 6 0 55 1
Wet 1 0 0 0 0 0 17 8 0 0 0 12 1 0 0
Wet 1 0 0 0 8 4 14 0 17 0 4 0 4 0 0
Wet 0 0 0 16 0 0 1 0 0 0 2 0 0 0 0
Wet 0 0 3 0 0 0 0 0 0 0 0 0 0 0 1
Wet 1 0 0 0 2 0 0 0 3 3 0 1 3 0 0
Wet 0 0 0 0 0 0 0 0 0 0 2 1 0 1 0
Wet 0 0 1 0 0 0 0 0 0 0 0 0 0 0 2
Wet 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Wet 12 0 0 0 0 0 2 0 3 0 0 0 0 0 0
Wet 0 0 0 0 0 0 0 0 2 25 4 4 8 0 4
Wet 0 0 0 0 0 1 0 0 0 0 0 0 0 5 0
Wet 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0
Wet 1 2 8 3 19 1 5 0 0 5 3 13 12 5 4
Wet 8 2 0 4 1 41 43 13 18 7 8 25 3 22 14
Wet 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0
Wet 82 43 39 13 81 51 33 44 3 122 0 3 24 20 22
Wet 0 0 0 0 0 3 0 2 1 0 0 0 0 0 0
Wet 65 6 6 0 23 43 3 3 27 5 9 6 7 38 4
Wet 329 310 284 103 361 387 227 348 358 323 252 229 424 211 297
Wet 2 0 0 0 0 0 2 0 2 0 3 0 0 0 0
Wet 21 0 20 0 0 0 23 20 13 0 2 0 21 0 1
Wet 20 7 0 0 4 10 0 4 0 4 14 0 1 0 0
Wet 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wet 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Wet 7 0 1 1 0 0 2 1 0 3 0 0 0 0 0
Wet 1 20 109 60 0 1 0 0 0 0 0 2 0 7 27
Wet 0 0 0 2 0 5 0 0 0 0 0 0 0 0 2
Wet 4 0 1 1 0 9 1 4 5 0 0 0 4 2 0
Wet 0 0 0 0 0 0 0 0 0 0 0 0 0 14 13
Wet 48 95 68 28 98 38 22 93 98 62 27 70 20 94 64
Wet 8 0 8 7 0 1 2 7 0 9 6 0 0 18 5
Wet 0 0 0 0 0 0 0 0 3 0 4 2 1 7 6
Wet 38 38 23 13 31 23 9 44 9 11 16 5 2 28 8
Wet 0 0 0 3 0 0 0 0 0 0 11 0 0 0 0
Wet 31 87 5 1 45 78 31 71 20 54 13 17 40 4 4
Wet 6 9 8 2 2 13 19 16 0 21 5 0 0 12 10
Wet 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0
Wet 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1
Wet 2 0 6 0 0 0 0 0 0 0 0 0 0 4 0
Wet 0 0 16 14 0 0 0 0 0 0 22 2 0 13 1
Wet 0 0 0 0 2 0 0 2 0 0 0 3 7 0 1
Wet 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Wet 21 4 15 43 28 44 26 37 10 2 42 13 25 46 26
Wet 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Wet 0 1 6 0 0 0 0 0 0 0 0 2 1 6 0
Wet 0 0 2 8 1 0 2 0 0 0 0 1 0 0 0
Wet 0 0 0 3 0 0 0 3 0 0 6 19 0 4 0
Wet 138 174 153 122 118 171 97 138 52 205 151 18 122 88 191
Wet 64 33 8 9 11 43 13 13 46 10 49 31 19 35 23
Wet 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0
Wet 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0
Wet 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
Wet 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0
Wet 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0
Wet 9 0 1 0 1 0 0 0 0 0 12 9 0 0 3
Wet 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Wet 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0
Wet 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0
Wet 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Wet 0 0 0 5 1 0 0 0 0 0 2 5 0 2 0
Wet 0 0 0 0 0 0 0 0 0 1 8 3 0 0 2
Wet 0 0 0 0 1 0 0 9 5 0 0 0 0 0 4
Wet 1 5 5 11 8 16 4 40 46 10 15 12 5 16 8
Wet 0 6 0 1 0 4 0 25 6 0 0 0 0 0 1
Wet 1023 1170 1028 1173 1235 1009 1397 1031 719 1081 1024 1340 1370 810 1044
Wet 10 0 7 1 0 0 6 4 8 0 2 3 13 1 1
Wet 0 0 0 19 0 0 0 0 5 0 0 0 0 7 1
Wet 0 0 4 2 0 0 0 0 0 1 0 0 0 2 0
Wet 6 0 0 15 0 0 0 0 0 14 0 0 0 0 0
Wet 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0
Wet 0 1 0 6 0 3 0 1 0 0 0 0 0 0 0
Wet 10 19 0 0 0 24 0 14 0 24 6 0 1 4 7
Wet 18 10 25 17 0 3 1 1 0 2 3 8 0 31 3
Wet 0 11 1 0 0 1 0 0 4 0 8 2 7 19 8
Wet 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
Wet 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Wet 0 0 2 0 0 0 0 2 0 0 0 0 0 0 0
Wet 0 18 64 9 48 3 1 9 14 17 37 28 0 106 34
Wet 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1
Wet 1 6 19 46 0 15 0 12 0 0 48 14 0 28 57
Wet 0 0 0 0 0 4 0 0 0 0 1 0 0 0 4
Wet 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0
Wet 4 1 3 5 0 0 0 30 0 19 13 0 12 0 0
Wet 0 0 0 0 1 0 0 0 0 20 1 0 0 0 0
Wet 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0
Wet 15 24 14 39 7 16 2 2 1 18 27 49 0 28 34
Wet 5 0 0 0 0 0 8 2 0 1 0 0 0 1 0
Wet 2 0 2 9 0 0 0 0 0 0 1 0 0 0 0
Wet 0 0 0 0 1 0 0 0 0 14 0 0 0 0 1
Wet 18 0 0 0 0 0 0 0 3 0 0 0 0 0 0
Wet 93 55 1 12 6 6 15 8 37 19 20 32 3 5 9
Wet 31 41 26 1 33 66 38 37 89 76 28 26 44 0 13
Wet 0 0 0 0 0 1 4 0 0 0 0 0 0 0 1
Wet 0 1 0 4 2 0 0 0 0 2 0 1 0 10 1
Wet 15 31 1 36 1 20 13 43 7 1 18 5 5 11 4
Wet 0 0 0 0 0 1 0 0 2 0 0 1 12 0 2
Wet 7 51 56 106 6 87 2 51 5 6 93 42 0 103 107
Wet 4 8 10 19 0 14 9 4 0 31 5 6 4 22 21
Wet 28 5 45 98 9 16 65 63 13 37 62 16 68 76 6
Wet 0 0 0 0 0 0 0 0 0 0 9 0 2 5 0
Wet 0 0 0 2 0 0 0 0 0 0 0 0 0 3 0
Wet 19 2 0 0 0 0 0 0 2 0 8 0 3 0 4
Wet 16 1 0 0 0 0 1 1 1 0 0 0 0 4 0
Wet 0 0 0 0 1 0 0 0 0 0 0 0 2 0 0
Wet 0 0 0 0 0 0 19 0 0 0 0 0 4 0 0
Wet 0 0 0 0 0 0 0 0 0 0 2 0 0 1 0
Wet 15 4 8 4 0 3 3 12 0 13 0 0 0 1 0
Wet 0 0 5 0 0 0 0 0 1 0 0 3 0 0 0
Wet 0 0 0 0 0 8 1 1 0 0 0 0 2 0 5
Wet 0 0 0 0 2 0 0 3 4 0 0 0 0 0 0
Wet 0 0 0 0 0 0 0 0 1 0 10 8 0 0 9
C9
Wet 0
Wet 5
Wet 3
Wet 0
Wet 0
Wet 0
Wet 0
Wet 2
Wet 0
Wet 1
Wet 0
Wet 0
Wet 1
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 19
Wet 5
Wet 0
Wet 117
Wet 0
Wet 2
Wet 366
Wet 0
Wet 3
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 1
Wet 0
Wet 0
Wet 122
Wet 4
Wet 0
Wet 33
Wet 0
Wet 14
Wet 45
Wet 0
Wet 0
Wet 0
Wet 10
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 214
Wet 6
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 18
Wet 0
Wet 1115
Wet 4
Wet 0
Wet 1
Wet 10
Wet 0
Wet 0
Wet 0
Wet 44
Wet 0
Wet 0
Wet 0
Wet 0
Wet 8
Wet 0
Wet 0
Wet 0
Wet 0
Wet 2
Wet 0
Wet 0
Wet 42
Wet 0
Wet 0
Wet 0
Wet 0
Wet 4
Wet 25
Wet 0
Wet 0
Wet 8
Wet 0
Wet 4
Wet 11
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 0
Wet 48
Wet 0
Wet 0
Wet 0
Wet 0
$Ns
Wet Wet Wet Wet Wet Wet
1.655976e+01 6.235684e+01 9.856888e+01 1.792777e+01 8.835364e+00 1.517669e+01
Wet Wet Wet Wet Wet Wet
1.425254e+02 8.432203e+02 3.987583e+00 9.702201e-01 1.017518e+02 3.906868e+01
Wet Wet Wet Wet Wet Wet
5.851009e+01 1.898686e+01 3.979954e+00 1.374318e+01 4.047338e+00 3.010546e+00
Wet Wet Wet Wet Wet Wet
1.018577e+00 1.784920e+01 4.657648e+01 5.995146e+00 1.993792e+00 9.867572e+01
Wet Wet Wet Wet Wet Wet
2.181375e+02 1.968024e+00 6.807309e+02 6.073128e+00 2.520871e+02 4.837504e+03
Wet Wet Wet Wet Wet Wet
9.707763e+00 1.260838e+02 6.249737e+01 1.277831e+01 1.965894e+00 1.475247e+01
Wet Wet Wet Wet Wet Wet
2.251252e+02 9.713335e+00 3.174792e+01 2.727275e+01 1.063432e+03 7.424589e+01
Wet Wet Wet Wet Wet Wet
2.417051e+01 3.269291e+02 1.408943e+01 5.057596e+02 1.644330e+02 1.979745e+00
Wet Wet Wet Wet Wet Wet
2.943269e+00 1.194466e+01 7.787201e+01 1.458144e+01 9.898723e-01 3.804766e+02
Wet Wet Wet Wet Wet Wet
1.992830e+00 1.594023e+01 1.400231e+01 3.530017e+01 2.126757e+03 4.231604e+02
Wet Wet Wet Wet Wet Wet
1.891460e+00 3.058503e+00 2.019314e+00 3.058503e+00 1.895447e+00 3.514138e+01
Wet Wet Wet Wet Wet Wet
9.485232e-01 5.686341e+00 3.995578e+00 1.008977e+00 1.511045e+01 1.411746e+01
Wet Wet Wet Wet Wet Wet
2.026326e+01 2.318117e+02 4.326641e+01 1.754822e+04 6.167013e+01 3.371500e+01
Wet Wet Wet Wet Wet Wet
9.909733e+00 4.408384e+01 2.905330e+00 1.072206e+01 1.052594e+02 1.638840e+02
Wet Wet Wet Wet Wet Wet
6.181120e+01 1.018577e+00 9.968958e-01 3.876791e+00 3.996750e+02 1.958860e+00
Wet Wet Wet Wet Wet Wet
2.455156e+02 8.833245e+00 1.891460e+00 8.608922e+01 2.132904e+01 2.039002e+00
Wet Wet Wet Wet Wet Wet
3.157124e+02 1.693675e+01 1.392668e+01 1.553161e+01 2.170972e+01 3.329477e+02
Wet Wet Wet Wet Wet Wet
5.917337e+02 6.030376e+00 2.098565e+01 2.164080e+02 1.803637e+01 7.204748e+02
Wet Wet Wet Wet Wet Wet
1.656330e+02 6.038420e+02 1.602565e+01 5.023441e+00 3.822005e+01 2.402765e+01
Wet Wet Wet Wet Wet Wet
2.886192e+00 2.314386e+01 3.027837e+00 1.078904e+02 9.346757e+00 1.648007e+01
Wet Wet
1.018263e+01 2.867770e+01
$Ni
C1 C10 C11 C12 C13 C14 C15 C16 C2 C3 C4 C5 C6 C7 C8 C9
2287 2354 2271 2255 2269 2377 2207 2370 1679 2326 2228 2205 2372 2226 2225 2317
$N
[1] 35968
$Psi
C1 C10 C11 C12 C13
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Wet 0.0004372540 0.0021240442 0.0022016733 0.004878049 0.0035257823
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Wet 0.4473108876 0.4970263381 0.4526640247 0.520177384 0.5442926399
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Wet 0.0135548754 0.0174171623 0.0114487010 0.000443459 0.0145438519
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Wet 0.0030607783 0.0216652506 0.0246587406 0.047006652 0.0026443367
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Wet 0.0122431132 0.0021240442 0.0198150594 0.043458980 0.0039665051
Wet 0.0000000000 0.0000000000 0.0000000000 0.000000000 0.0000000000
Wet 0.0000000000 0.0000000000 0.0000000000 0.000886918 0.0000000000
Wet 0.0083078268 0.0008496177 0.0000000000 0.000000000 0.0000000000
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C14 C15 C16 C2 C3
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Wet 0.4244846445 0.6329859538 0.4350210970 0.4282310899 0.4647463457
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Wet 0.0004206984 0.0000000000 0.0000000000 0.0011911852 0.0000000000
Wet 0.0366007573 0.0009062075 0.0215189873 0.0029779631 0.0025795357
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C4 C5 C6 C7 C8
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$Wi
C1 C10 C11 C12 C13 C14 C15 C16 C2 C3 C4
0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625
C5 C6 C7 C8 C9
0.0625 0.0625 0.0625 0.0625 0.0625
$Ps
Wet Wet Wet Wet Wet Wet
4.604027e-04 1.733676e-03 2.740460e-03 4.984366e-04 2.456451e-04 4.219499e-04
Wet Wet Wet Wet Wet Wet
3.962561e-03 2.344362e-02 1.108647e-04 2.697454e-05 2.828952e-03 1.086207e-03
Wet Wet Wet Wet Wet Wet
1.626726e-03 5.278821e-04 1.106526e-04 3.820947e-04 1.125261e-04 8.370069e-05
Wet Wet Wet Wet Wet Wet
2.831899e-05 4.962521e-04 1.294942e-03 1.666800e-04 5.543237e-05 2.743431e-03
Wet Wet Wet Wet Wet Wet
6.064765e-03 5.471598e-05 1.892601e-02 1.688481e-04 7.008648e-03 1.344947e-01
Wet Wet Wet Wet Wet Wet
2.699000e-04 3.505444e-03 1.737582e-03 3.552689e-04 5.465676e-05 4.101555e-04
Wet Wet Wet Wet Wet Wet
6.259041e-03 2.700549e-04 8.826712e-04 7.582503e-04 2.956605e-02 2.064221e-03
Wet Wet Wet Wet Wet Wet
6.720005e-04 9.089443e-03 3.917213e-04 1.406138e-02 4.571648e-03 5.504183e-05
Wet Wet Wet Wet Wet Wet
8.183021e-05 3.320913e-04 2.165036e-03 4.054004e-04 2.752092e-05 1.057820e-02
Wet Wet Wet Wet Wet Wet
5.540565e-05 4.431781e-04 3.892991e-04 9.814327e-04 5.912914e-02 1.176491e-02
Wet Wet Wet Wet Wet Wet
5.258729e-05 8.503401e-05 5.614195e-05 8.503401e-05 5.269815e-05 9.770180e-04
Wet Wet Wet Wet Wet Wet
2.637131e-05 1.580944e-04 1.110870e-04 2.805206e-05 4.201081e-04 3.925005e-04
Wet Wet Wet Wet Wet Wet
5.633692e-04 6.444942e-03 1.202914e-03 4.878842e-01 1.714583e-03 9.373610e-04
Wet Wet Wet Wet Wet Wet
2.755153e-04 1.225641e-03 8.077541e-05 2.980999e-04 2.926474e-03 4.556384e-03
Wet Wet Wet Wet Wet Wet
1.718505e-03 2.831899e-05 2.771619e-05 1.077844e-04 1.111196e-02 5.446120e-05
Wet Wet Wet Wet Wet Wet
6.825946e-03 2.455862e-04 5.258729e-05 2.393495e-03 5.930005e-04 5.668934e-05
Wet Wet Wet Wet Wet Wet
8.777590e-03 4.708838e-04 3.871963e-04 4.318173e-04 6.035844e-04 9.256775e-03
Wet Wet Wet Wet Wet Wet
1.645167e-02 1.676595e-04 5.834532e-04 6.016681e-03 5.014560e-04 2.003099e-02
Wet Wet Wet Wet Wet Wet
4.605009e-03 1.678831e-02 4.455531e-04 1.396642e-04 1.062613e-03 6.680285e-04
Wet Wet Wet Wet Wet Wet
8.024332e-05 6.434570e-04 8.418140e-05 2.999621e-03 2.598631e-04 4.581871e-04
Wet Wet
2.831024e-04 7.973115e-04
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[1] 122
$Ncommunities
[1] 16
$SampleCoverage
ZhangHuang
0.9998054
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C1 C10 C11 C12 C13 C14 C15 C16
0.9960662 0.9978770 0.9955982 0.9968978 0.9951540 0.9970558 0.9954718 0.9957824
C2 C3 C4 C5 C6 C7 C8 C9
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attr(,"class")
[1] "MetaCommunity"
$Dry
$Nsi
C1 C10 C11 C12 C13 C14 C15 C16 C2 C3 C4 C5 C6 C7 C8
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C9
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$Ns
Dry Dry Dry Dry Dry Dry
12.756250 47.941071 96.302679 13.457143 1.962500 1.962500
Dry Dry Dry Dry Dry Dry
200.175000 1251.093750 21.587500 16.681250 32.381250 71.350893
Dry Dry Dry Dry Dry Dry
16.681250 2.943750 10.092857 10.793750 0.981250 27.475000
Dry Dry Dry Dry Dry Dry
98.966071 8.831250 72.612500 123.357143 0.981250 6.868750
Dry Dry Dry Dry Dry Dry
678.324107 6.308036 159.523214 4657.713393 7.709821 129.384821
Dry Dry Dry Dry Dry Dry
23.129464 30.418750 164.850000 25.652679 26.493750 1107.971429
Dry Dry Dry Dry Dry Dry
9.812500 59.856250 33.642857 177.045536 574.031250 147.187500
Dry Dry Dry Dry Dry Dry
8.831250 1.962500 54.950000 11.775000 363.623214 3.925000
Dry Dry Dry Dry Dry Dry
0.981250 8.831250 0.981250 14.718750 1802.556250 454.458929
Dry Dry Dry Dry Dry Dry
3.925000 3.925000 0.981250 0.981250 38.268750 0.981250
Dry Dry Dry Dry Dry Dry
5.887500 3.925000 1.962500 20.606250 13.737500 20.746429
Dry Dry Dry Dry Dry Dry
212.090179 1.962500 41.633036 20225.945536 52.006250 16.681250
Dry Dry Dry Dry Dry Dry
9.812500 1.962500 1.962500 7.850000 73.593750 120.693750
Dry Dry Dry Dry Dry Dry
48.361607 0.981250 0.981250 1.962500 390.817857 169.756250
Dry Dry Dry Dry Dry Dry
2.943750 54.950000 16.681250 1.962500 264.937500 11.775000
Dry Dry Dry Dry Dry Dry
1.962500 300.683036 562.116071 6.868750 1.962500 12.756250
Dry Dry Dry Dry Dry Dry
227.650000 10.233036 770.561607 146.206250 564.779464 14.718750
Dry Dry Dry Dry Dry Dry
1.962500 0.981250 105.834821 30.418750 1.962500 21.587500
Dry Dry Dry Dry Dry Dry
0.981250 65.743750 9.251786 11.775000 10.513393 26.493750
Dry
28.876786
$Ni
C1 C10 C11 C12 C13 C14 C15 C16 C2 C3 C4 C5 C6 C7 C8 C9
2400 2400 2400 2400 2400 2400 2400 2400 1680 2400 2400 2400 2400 2400 2400 2400
$N
[1] 37680
$Psi
C1 C10 C11 C12 C13
Dry 0.0000000000 0.0008333333 0.0000000000 0.0000000000 0.0000000000
Dry 0.0016666667 0.0004166667 0.0000000000 0.0000000000 0.0025000000
Dry 0.0000000000 0.0004166667 0.0004166667 0.0000000000 0.0091666667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0008333333 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0120833333 0.0125000000 0.0187500000 0.0054166667
Dry 0.0254166667 0.0191666667 0.0983333333 0.0650000000 0.0341666667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0004166667 0.0000000000 0.0000000000
Dry 0.0004166667 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0095833333 0.0000000000 0.0000000000 0.0016666667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0066666667 0.0000000000
Dry 0.0000000000 0.0000000000 0.0012500000 0.0000000000 0.0000000000
Dry 0.0004166667 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0012500000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0016666667 0.0012500000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0008333333 0.0016666667 0.0020833333 0.0000000000 0.0054166667
Dry 0.0016666667 0.0008333333 0.0000000000 0.0012500000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0012500000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0145833333 0.0187500000 0.0220833333 0.0075000000 0.0241666667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0058333333 0.0079166667 0.0012500000 0.0000000000 0.0045833333
Dry 0.1412500000 0.1245833333 0.0770833333 0.0600000000 0.1625000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0087500000 0.0016666667 0.0141666667 0.0000000000 0.0000000000
Dry 0.0000000000 0.0033333333 0.0000000000 0.0000000000 0.0000000000
Dry 0.0050000000 0.0012500000 0.0016666667 0.0004166667 0.0000000000
Dry 0.0000000000 0.0079166667 0.0391666667 0.0179166667 0.0000000000
Dry 0.0000000000 0.0000000000 0.0004166667 0.0004166667 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0175000000 0.0545833333 0.0245833333 0.0058333333 0.0408333333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0025000000 0.0033333333 0.0029166667 0.0012500000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0154166667 0.0108333333 0.0016666667 0.0000000000 0.0000000000
Dry 0.0187500000 0.0279166667 0.0133333333 0.0020833333 0.0345833333
Dry 0.0054166667 0.0079166667 0.0037500000 0.0008333333 0.0000000000
Dry 0.0000000000 0.0033333333 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0004166667 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0008333333
Dry 0.0062500000 0.0054166667 0.0112500000 0.0112500000 0.0058333333
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0025000000
Dry 0.0479166667 0.0687500000 0.0616666667 0.0391666667 0.0387500000
Dry 0.0200000000 0.0262500000 0.0029166667 0.0004166667 0.0062500000
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0004166667 0.0016666667 0.0000000000 0.0000000000 0.0004166667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0012500000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0008333333 0.0004166667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0004166667
Dry 0.0004166667 0.0029166667 0.0016666667 0.0033333333 0.0020833333
Dry 0.0000000000 0.0008333333 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0025000000 0.0000000000 0.0012500000 0.0000000000
Dry 0.5741666667 0.4279166667 0.4962500000 0.6137500000 0.5445833333
Dry 0.0000000000 0.0008333333 0.0037500000 0.0016666667 0.0004166667
Dry 0.0000000000 0.0000000000 0.0008333333 0.0029166667 0.0000000000
Dry 0.0000000000 0.0000000000 0.0020833333 0.0008333333 0.0000000000
Dry 0.0000000000 0.0008333333 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0004166667 0.0000000000 0.0025000000 0.0000000000
Dry 0.0037500000 0.0083333333 0.0012500000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0070833333 0.0050000000 0.0083333333 0.0000000000
Dry 0.0000000000 0.0045833333 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0004166667 0.0000000000 0.0000000000
Dry 0.0016666667 0.0054166667 0.0262500000 0.0037500000 0.0166666667
Dry 0.0008333333 0.0025000000 0.0170833333 0.0116666667 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0008333333 0.0012500000 0.0008333333 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0008333333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0033333333 0.0075000000 0.0020833333 0.0083333333 0.0029166667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0004166667
Dry 0.0000000000 0.0008333333 0.0000000000 0.0000000000 0.0000000000
Dry 0.0295833333 0.0420833333 0.0004166667 0.0012500000 0.0000000000
Dry 0.0116666667 0.0066666667 0.0087500000 0.0004166667 0.0187500000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0041666667 0.0100000000 0.0000000000 0.0150000000 0.0004166667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0083333333 0.0137500000 0.0204166667 0.0566666667 0.0191666667
Dry 0.0008333333 0.0045833333 0.0041666667 0.0075000000 0.0000000000
Dry 0.0033333333 0.0083333333 0.0075000000 0.0179166667 0.0079166667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0075000000 0.0112500000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0058333333 0.0016666667 0.0000000000 0.0000000000 0.0025000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0004166667
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0033333333 0.0004166667 0.0041666667 0.0008333333 0.0004166667
Dry 0.0000000000 0.0000000000 0.0020833333 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0008333333
Dry 0.0016666667 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
C14 C15 C16 C2 C3
Dry 0.0016666667 0.0004166667 0.0020833333 0.0000000000 0.0004166667
Dry 0.0000000000 0.0004166667 0.0000000000 0.0011904762 0.0025000000
Dry 0.0000000000 0.0004166667 0.0025000000 0.0029761905 0.0008333333
Dry 0.0025000000 0.0000000000 0.0000000000 0.0023809524 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0045833333 0.0004166667 0.0004166667 0.0000000000 0.0000000000
Dry 0.0354166667 0.0012500000 0.0333333333 0.0083333333 0.0012500000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0004166667 0.0016666667 0.0000000000 0.0000000000
Dry 0.0000000000 0.0070833333 0.0000000000 0.0107142857 0.0000000000
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0017857143 0.0012500000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0004166667 0.0000000000 0.0000000000 0.0083333333 0.0000000000
Dry 0.0008333333 0.0000000000 0.0000000000 0.0011904762 0.0125000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0004166667 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0025000000 0.0120833333 0.0008333333 0.0065476190 0.0079166667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0104166667 0.0133333333 0.0275000000 0.0017857143 0.0587500000
Dry 0.0008333333 0.0000000000 0.0008333333 0.0005952381 0.0000000000
Dry 0.0195833333 0.0087500000 0.0008333333 0.0119047619 0.0000000000
Dry 0.1929166667 0.0733333333 0.1029166667 0.1940476190 0.1358333333
Dry 0.0000000000 0.0008333333 0.0000000000 0.0011904762 0.0000000000
Dry 0.0008333333 0.0075000000 0.0020833333 0.0053571429 0.0025000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0035714286 0.0000000000
Dry 0.0000000000 0.0020833333 0.0004166667 0.0000000000 0.0016666667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0033333333 0.0004166667 0.0016666667 0.0029761905 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0250000000 0.0083333333 0.0358333333 0.0613095238 0.0200000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0062500000 0.0004166667 0.0020833333 0.0000000000 0.0008333333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0017857143 0.0000000000
Dry 0.0041666667 0.0000000000 0.0054166667 0.0047619048 0.0000000000
Dry 0.0025000000 0.0095833333 0.0408333333 0.0250000000 0.0000000000
Dry 0.0058333333 0.0075000000 0.0070833333 0.0000000000 0.0091666667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0008333333
Dry 0.0000000000 0.0000000000 0.0012500000 0.0000000000 0.0020833333
Dry 0.0000000000 0.0000000000 0.0008333333 0.0000000000 0.0000000000
Dry 0.0091666667 0.0108333333 0.0154166667 0.0077380952 0.0008333333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0033333333
Dry 0.0412500000 0.0416666667 0.0537500000 0.0250000000 0.0829166667
Dry 0.0283333333 0.0041666667 0.0083333333 0.0279761905 0.0025000000
Dry 0.0008333333 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0029166667
Dry 0.0000000000 0.0000000000 0.0004166667 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0004166667 0.0004166667 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0004166667 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0004166667
Dry 0.0000000000 0.0000000000 0.0037500000 0.0029761905 0.0000000000
Dry 0.0054166667 0.0016666667 0.0145833333 0.0279761905 0.0033333333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0016666667 0.0000000000 0.0087500000 0.0005952381 0.0000000000
Dry 0.4808333333 0.7087500000 0.5129166667 0.4589285714 0.5387500000
Dry 0.0004166667 0.0025000000 0.0016666667 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0004166667 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0004166667 0.0000000000 0.0000000000
Dry 0.0004166667 0.0004166667 0.0004166667 0.0000000000 0.0020833333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0017857143 0.0000000000
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0004166667 0.0000000000 0.0000000000
Dry 0.0025000000 0.0000000000 0.0033333333 0.0101190476 0.0083333333
Dry 0.0000000000 0.0000000000 0.0025000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0129166667 0.0000000000 0.0033333333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0058333333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0141666667 0.0000000000 0.0008333333 0.0000000000 0.0116666667
Dry 0.0000000000 0.0033333333 0.0008333333 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0012500000 0.0062500000 0.0025000000 0.0130952381 0.0054166667
Dry 0.0408333333 0.0100000000 0.0050000000 0.0428571429 0.0379166667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0008333333
Dry 0.0041666667 0.0125000000 0.0212500000 0.0041666667 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0005952381 0.0000000000
Dry 0.0320833333 0.0008333333 0.0183333333 0.0017857143 0.0029166667
Dry 0.0050000000 0.0033333333 0.0025000000 0.0000000000 0.0116666667
Dry 0.0070833333 0.0245833333 0.0245833333 0.0077380952 0.0154166667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0012500000 0.0000000000 0.0079166667 0.0053571429 0.0000000000
Dry 0.0004166667 0.0004166667 0.0004166667 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0029166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0020833333 0.0087500000 0.0045833333 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0005952381 0.0000000000
Dry 0.0004166667 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0023809524 0.0000000000
Dry 0.0000000000 0.0008333333 0.0004166667 0.0000000000 0.0012500000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0005952381 0.0000000000
C4 C5 C6 C7 C8
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0004166667 0.0104166667 0.0000000000 0.0008333333
Dry 0.0025000000 0.0150000000 0.0029166667 0.0000000000 0.0029166667
Dry 0.0000000000 0.0000000000 0.0008333333 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0008333333 0.0000000000 0.0000000000
Dry 0.0145833333 0.0108333333 0.0008333333 0.0012500000 0.0033333333
Dry 0.0429166667 0.0300000000 0.0054166667 0.0825000000 0.0425000000
Dry 0.0041666667 0.0000000000 0.0000000000 0.0045833333 0.0000000000
Dry 0.0029166667 0.0020833333 0.0000000000 0.0016666667 0.0000000000
Dry 0.0004166667 0.0100000000 0.0004166667 0.0000000000 0.0004166667
Dry 0.0000000000 0.0000000000 0.0012500000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0004166667 0.0004166667 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0020833333 0.0012500000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0016666667 0.0095833333 0.0145833333 0.0000000000 0.0016666667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0033333333 0.0004166667
Dry 0.0008333333 0.0045833333 0.0029166667 0.0016666667 0.0020833333
Dry 0.0004166667 0.0025000000 0.0029166667 0.0095833333 0.0033333333
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0008333333 0.0000000000
Dry 0.0000000000 0.0008333333 0.0050000000 0.0045833333 0.0091666667
Dry 0.0000000000 0.0000000000 0.0004166667 0.0000000000 0.0000000000
Dry 0.0029166667 0.0000000000 0.0041666667 0.0000000000 0.0000000000
Dry 0.1054166667 0.1312500000 0.1800000000 0.0762500000 0.0762500000
Dry 0.0012500000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0008333333 0.0000000000 0.0104166667 0.0000000000 0.0004166667
Dry 0.0000000000 0.0016666667 0.0004166667 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0008333333 0.0000000000 0.0029166667 0.0012500000
Dry 0.0000000000 0.0000000000 0.0008333333 0.0008333333 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0058333333 0.0054166667
Dry 0.0158333333 0.0387500000 0.0191666667 0.0266666667 0.0225000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0004166667 0.0000000000 0.0000000000 0.0045833333 0.0004166667
Dry 0.0012500000 0.0029166667 0.0008333333 0.0050000000 0.0025000000
Dry 0.0029166667 0.0195833333 0.0041666667 0.0037500000 0.0025000000
Dry 0.0062500000 0.0100000000 0.0320833333 0.0075000000 0.0112500000
Dry 0.0012500000 0.0000000000 0.0000000000 0.0045833333 0.0016666667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0004166667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0079166667 0.0016666667 0.0000000000 0.0079166667 0.0020833333
Dry 0.0000000000 0.0012500000 0.0016666667 0.0000000000 0.0004166667
Dry 0.0179166667 0.0104166667 0.0125000000 0.0129166667 0.0133333333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0004166667 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0012500000 0.0016666667 0.0004166667 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0004166667 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0429166667 0.0100000000 0.0433333333 0.0354166667 0.0445833333
Dry 0.0183333333 0.0162500000 0.0079166667 0.0058333333 0.0075000000
Dry 0.0000000000 0.0000000000 0.0004166667 0.0000000000 0.0000000000
Dry 0.0000000000 0.0012500000 0.0000000000 0.0000000000 0.0004166667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0004166667
Dry 0.0000000000 0.0000000000 0.0004166667 0.0000000000 0.0000000000
Dry 0.0050000000 0.0037500000 0.0004166667 0.0000000000 0.0012500000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0012500000 0.0000000000 0.0000000000
Dry 0.0004166667 0.0004166667 0.0000000000 0.0000000000 0.0000000000
Dry 0.0004166667 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0008333333 0.0050000000 0.0004166667 0.0008333333 0.0000000000
Dry 0.0033333333 0.0012500000 0.0000000000 0.0000000000 0.0008333333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0016666667
Dry 0.0062500000 0.0054166667 0.0025000000 0.0033333333 0.0025000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0004166667 0.0000000000 0.0000000000 0.0004166667
Dry 0.5345833333 0.5079166667 0.5425000000 0.4866666667 0.6150000000
Dry 0.0004166667 0.0000000000 0.0045833333 0.0008333333 0.0033333333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0029166667 0.0004166667
Dry 0.0000000000 0.0004166667 0.0000000000 0.0008333333 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0004166667 0.0004166667 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0079166667 0.0000000000 0.0050000000 0.0000000000 0.0045833333
Dry 0.0012500000 0.0033333333 0.0000000000 0.0083333333 0.0016666667
Dry 0.0033333333 0.0000000000 0.0008333333 0.0075000000 0.0025000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0158333333 0.0241666667 0.0000000000 0.0312500000 0.0133333333
Dry 0.0087500000 0.0050000000 0.0000000000 0.0104166667 0.0133333333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0012500000
Dry 0.0016666667 0.0000000000 0.0025000000 0.0000000000 0.0000000000
Dry 0.0004166667 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0008333333 0.0000000000 0.0000000000 0.0000000000
Dry 0.0112500000 0.0233333333 0.0000000000 0.0070833333 0.0145833333
Dry 0.0000000000 0.0000000000 0.0000000000 0.0004166667 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0066666667 0.0108333333 0.0012500000 0.0020833333 0.0029166667
Dry 0.0091666667 0.0075000000 0.0300000000 0.0000000000 0.0041666667
Dry 0.0000000000 0.0025000000 0.0000000000 0.0000000000 0.0004166667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0045833333 0.0000000000
Dry 0.0008333333 0.0000000000 0.0045833333 0.0162500000 0.0016666667
Dry 0.0000000000 0.0012500000 0.0016666667 0.0000000000 0.0008333333
Dry 0.0450000000 0.0233333333 0.0000000000 0.0500000000 0.0316666667
Dry 0.0012500000 0.0025000000 0.0008333333 0.0083333333 0.0095833333
Dry 0.0270833333 0.0325000000 0.0166666667 0.0341666667 0.0016666667
Dry 0.0037500000 0.0000000000 0.0004166667 0.0020833333 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0008333333 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0037500000 0.0000000000 0.0062500000 0.0004166667 0.0012500000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0016666667 0.0000000000
Dry 0.0000000000 0.0000000000 0.0004166667 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0058333333 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0004166667 0.0000000000
Dry 0.0000000000 0.0000000000 0.0000000000 0.0004166667 0.0000000000
Dry 0.0000000000 0.0012500000 0.0000000000 0.0000000000 0.0000000000
Dry 0.0000000000 0.0000000000 0.0025000000 0.0000000000 0.0016666667
Dry 0.0000000000 0.0000000000 0.0000000000 0.0012500000 0.0000000000
Dry 0.0004166667 0.0000000000 0.0000000000 0.0037500000 0.0020833333
Dry 0.0041666667 0.0033333333 0.0000000000 0.0000000000 0.0041666667
C9
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0008333333
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0062500000
Dry 0.0004166667
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0083333333
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0008333333
Dry 0.0695833333
Dry 0.0000000000
Dry 0.0000000000
Dry 0.1441666667
Dry 0.0000000000
Dry 0.0004166667
Dry 0.0008333333
Dry 0.0004166667
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0537500000
Dry 0.0041666667
Dry 0.0004166667
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0020833333
Dry 0.0075000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0033333333
Dry 0.0008333333
Dry 0.0000000000
Dry 0.0004166667
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0883333333
Dry 0.0100000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0004166667
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0066666667
Dry 0.0000000000
Dry 0.0020833333
Dry 0.5450000000
Dry 0.0016666667
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0129166667
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0033333333
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0054166667
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0020833333
Dry 0.0050000000
Dry 0.0000000000
Dry 0.0008333333
Dry 0.0000000000
Dry 0.0016666667
Dry 0.0000000000
Dry 0.0029166667
Dry 0.0000000000
Dry 0.0033333333
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0029166667
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0000000000
Dry 0.0008333333
Dry 0.0000000000
$Wi
C1 C10 C11 C12 C13 C14 C15 C16 C2 C3 C4
0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625
C5 C6 C7 C8 C9
0.0625 0.0625 0.0625 0.0625 0.0625
$Ps
Dry Dry Dry Dry Dry Dry
3.385417e-04 1.272321e-03 2.555804e-03 3.571429e-04 5.208333e-05 5.208333e-05
Dry Dry Dry Dry Dry Dry
5.312500e-03 3.320312e-02 5.729167e-04 4.427083e-04 8.593750e-04 1.893601e-03
Dry Dry Dry Dry Dry Dry
4.427083e-04 7.812500e-05 2.678571e-04 2.864583e-04 2.604167e-05 7.291667e-04
Dry Dry Dry Dry Dry Dry
2.626488e-03 2.343750e-04 1.927083e-03 3.273810e-03 2.604167e-05 1.822917e-04
Dry Dry Dry Dry Dry Dry
1.800223e-02 1.674107e-04 4.233631e-03 1.236124e-01 2.046131e-04 3.433780e-03
Dry Dry Dry Dry Dry Dry
6.138393e-04 8.072917e-04 4.375000e-03 6.808036e-04 7.031250e-04 2.940476e-02
Dry Dry Dry Dry Dry Dry
2.604167e-04 1.588542e-03 8.928571e-04 4.698661e-03 1.523438e-02 3.906250e-03
Dry Dry Dry Dry Dry Dry
2.343750e-04 5.208333e-05 1.458333e-03 3.125000e-04 9.650298e-03 1.041667e-04
Dry Dry Dry Dry Dry Dry
2.604167e-05 2.343750e-04 2.604167e-05 3.906250e-04 4.783854e-02 1.206101e-02
Dry Dry Dry Dry Dry Dry
1.041667e-04 1.041667e-04 2.604167e-05 2.604167e-05 1.015625e-03 2.604167e-05
Dry Dry Dry Dry Dry Dry
1.562500e-04 1.041667e-04 5.208333e-05 5.468750e-04 3.645833e-04 5.505952e-04
Dry Dry Dry Dry Dry Dry
5.628720e-03 5.208333e-05 1.104911e-03 5.367820e-01 1.380208e-03 4.427083e-04
Dry Dry Dry Dry Dry Dry
2.604167e-04 5.208333e-05 5.208333e-05 2.083333e-04 1.953125e-03 3.203125e-03
Dry Dry Dry Dry Dry Dry
1.283482e-03 2.604167e-05 2.604167e-05 5.208333e-05 1.037202e-02 4.505208e-03
Dry Dry Dry Dry Dry Dry
7.812500e-05 1.458333e-03 4.427083e-04 5.208333e-05 7.031250e-03 3.125000e-04
Dry Dry Dry Dry Dry Dry
5.208333e-05 7.979911e-03 1.491815e-02 1.822917e-04 5.208333e-05 3.385417e-04
Dry Dry Dry Dry Dry Dry
6.041667e-03 2.715774e-04 2.045015e-02 3.880208e-03 1.498884e-02 3.906250e-04
Dry Dry Dry Dry Dry Dry
5.208333e-05 2.604167e-05 2.808780e-03 8.072917e-04 5.208333e-05 5.729167e-04
Dry Dry Dry Dry Dry Dry
2.604167e-05 1.744792e-03 2.455357e-04 3.125000e-04 2.790179e-04 7.031250e-04
Dry
7.663690e-04
$Nspecies
[1] 115
$Ncommunities
[1] 16
$SampleCoverage
Chao
0.9997081
$SampleCoverage.communities
C1 C10 C11 C12 C13 C14 C15 C16
0.9979177 0.9945861 0.9979170 0.9983351 0.9966677 0.9970847 0.9945844 0.9958354
C2 C3 C4 C5 C6 C7 C8 C9
0.9970259 0.9991684 0.9962514 0.9975010 0.9954191 0.9970854 0.9954177 0.9975021
attr(,"class")
[1] "MetaCommunity"summary(MC.rangeland) Length Class Mode
Wet 11 MetaCommunity list
Dry 11 MetaCommunity list###Alpha diversity_R
# Alpha diversity (q = 0, 1, 2)---------------------------
alpha.rangeland <- lapply(names(MC.rangeland), function(season) {
mc <- MC.rangeland[[season]]
q0 <- DivPart(q = 0, mc, Correction = "Best")$CommunityAlphaDiversities
q1 <- DivPart(q = 1, mc, Correction = "Best")$CommunityAlphaDiversities
q2 <- DivPart(q = 2, mc, Correction = "Best")$CommunityAlphaDiversities
tibble(
Cluster = names(q0),
q0.r = as.numeric(q0),
q1.r = as.numeric(q1),
q2.r = as.numeric(q2),
Evenness.r = q2.r / q0.r,
Season = season)}) %>%
bind_rows() %>%
mutate(Cluster = factor(Cluster, levels = paste0("C", 1:16))) %>%
arrange(Season, Cluster)
head(alpha.rangeland)# A tibble: 6 × 6
Cluster q0.r q1.r q2.r Evenness.r Season
<fct> <dbl> <dbl> <dbl> <dbl> <chr>
1 C1 37 6.00 2.81 0.0760 Dry
2 C2 40 7.90 3.88 0.0970 Dry
3 C3 36 6.14 3.11 0.0863 Dry
4 C4 50 7.85 3.27 0.0655 Dry
5 C5 48 8.49 3.54 0.0738 Dry
6 C6 51 6.39 3.01 0.0590 Dry ###Beta and Diversity profile_R
# Beta Matrix: SEASONAL Turnover (Wet vs Dry)
MC_season.rangeland <- rangeland.long.data %>%
group_by(Season, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
pivot_wider(names_from = Season, values_from = freq, values_fill = 0) %>%
column_to_rownames("species") %>%
filter(rowSums(.) > 0)
head(MC_season.rangeland) Wet Dry
Abutilon mauritianum (Jacq.) Medik. 16 13
Achyranthes aspera L. 63 48
Ageratum conyzoides L. 98 96
Ajuga integrifolia Buch.-Ham. ex D.Don 17 12
Amaranthus retroflexus L. 9 2
Amphiachyris dracunculoides (DC.) Nutt. 16 0# Beta Matrix: SPATIAL Turnover (C1 to C16)
# Matrix Columns: C1, C2, ... C16 (Species x Cluster, all seasons pooled)
MC_space.rangeland <- rangeland.long.data %>%
group_by(cluster, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
pivot_wider(names_from = cluster, values_from = freq, values_fill = 0) %>%
column_to_rownames("species") %>%
filter(rowSums(.) > 0)
head(MC_space.rangeland) C1 C10 C11 C12 C13 C14 C15 C16 C2 C3 C4
Abutilon mauritianum (Jacq.) Medik. 0 2 0 0 1 4 9 10 2 1 0
Achyranthes aspera L. 8 1 10 0 6 0 4 3 5 17 3
Ageratum conyzoides L. 5 1 2 0 47 0 2 12 9 2 12
Ajuga integrifolia Buch.-Ham. ex D.Don 0 0 0 0 0 12 0 0 8 1 4
Alysicarpus vaginalis Hochst. ex Baker 0 2 0 0 0 0 0 0 0 0 0
Amaranthus retroflexus L. 0 0 0 0 0 0 0 0 0 1 0
C5 C6 C7 C8 C9
Abutilon mauritianum (Jacq.) Medik. 0 0 0 0 0
Achyranthes aspera L. 2 43 0 4 5
Ageratum conyzoides L. 64 21 0 12 5
Ajuga integrifolia Buch.-Ham. ex D.Don 0 4 0 0 0
Alysicarpus vaginalis Hochst. ex Baker 0 0 0 0 0
Amaranthus retroflexus L. 4 6 0 0 0# Calculate Beta Diversity (using betapart)
# Beta diversity – SEASONAL effect (Comparing 2 communities: Wet and Dry)
beta.season.rangeland <- beta.multi.abund(
as.data.frame(t(MC_season.rangeland)),
index.family = "bray"
)
print("Seasonal Beta Diversity (Wet vs Dry):")[1] "Seasonal Beta Diversity (Wet vs Dry):"print(beta.season.rangeland)$beta.BRAY.BAL
[1] 0.05429827
$beta.BRAY.GRA
[1] 0.02198351
$beta.BRAY
[1] 0.07628177# Beta diversity – SPATIAL effect (Comparing 16 communities: C1-C16)
beta.space.rangeland <- beta.multi.abund(
as.data.frame(t(MC_space.rangeland)),
index.family = "bray"
)
print("Spatial Beta Diversity (Among 16 Clusters):")[1] "Spatial Beta Diversity (Among 16 Clusters):"print(beta.space.rangeland)$beta.BRAY.BAL
[1] 0.6425038
$beta.BRAY.GRA
[1] 0.02547619
$beta.BRAY
[1] 0.66798# Diversity profiles
#-----------------------------
dp.wet <- DivProfile(
q = seq(0, 2, 1),
MC = MC.rangeland[["Wet"]],
Correction = "Best",
NumberOfSimulations = 10
)dp.dry <- DivProfile(
q = seq(0, 2, 1),
MC = MC.rangeland[["Dry"]],
Correction = "Best",
NumberOfSimulations = 10
)par(mfrow = c(1, 2))
plot(dp.wet, main = "Diversity Profile – Wet Season")
plot(dp.dry, main = "Diversity Profile – Dry Season")
par(mfrow = c(1, 1))Trees MC analysis Seasonal and spatial
###Metacommunity_T
# this chunk contains data on shrubs collected at the subplot level not transect
# Read data -----------------------------
trees.data <- read_csv("data/trees.plots.csv") %>%
mutate(across(where(is.numeric), ~ replace_na(., 0))) %>%
mutate(
date = dmy(str_trim(year.month)),
Season = case_when(
month(date) == 4 ~ "Wet",
month(date) == 10 ~ "Dry",
TRUE ~ NA_character_
),
Season = factor(Season, levels = c("Wet", "Dry"))
) %>%
select(-date) %>%
filter(!is.na(Season))
# Long Data ----------------------------------------------------------
trees.long.data <- trees.data %>%
select(-year.month) %>%
pivot_longer( cols = where(is.numeric),names_to = "plot", values_to = "freq")%>%
filter(!is.na(species)) %>%
mutate(
plot_num = as.numeric(gsub("p", "", plot)),
cluster = paste0("C", ceiling(plot_num / 10)) # C1 to C16
) %>%
select(-plot_num)
# Build MetaCommunity matrices PER SEASON ----------------------------
MC.trees <- trees.long.data %>%
group_by(Season, cluster, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
split(.$Season) %>%
lapply(function(df) {
df_wide <- df %>%
pivot_wider(names_from = cluster,values_from = freq,values_fill = 0) %>%
column_to_rownames("species")
df_filtered <- df_wide %>%
filter(rowSums(across(where(is.numeric))) > 0)
df_final_matrix <- df_filtered %>%
select(where(~ is.numeric(.) && sum(.) > 0))
return(MetaCommunity(df_final_matrix)) # Create the MetaCommunity object
})
summary (MC.trees) Length Class Mode
Wet 11 MetaCommunity list
Dry 11 MetaCommunity list###Alpha diversity_T
# Alpha diversity (q = 0, 1, 2) ---------------------------------------
alpha.trees <- lapply(names(MC.trees), function(season) {
mc <- MC.trees[[season]]
q0 <- DivPart(q = 0, mc, Correction = "Best")$CommunityAlphaDiversities
q1 <- DivPart(q = 1, mc, Correction = "Best")$CommunityAlphaDiversities
q2 <- DivPart(q = 2, mc, Correction = "Best")$CommunityAlphaDiversities
tibble(
Cluster = names(q0),
q0.t = as.numeric(q0),
q1.t = as.numeric(q1),
q2.t = as.numeric(q2),
Evenness = q2.t / q0.t,
Season = season
)
}) %>%
bind_rows() %>%
mutate(Cluster = factor(Cluster, levels = paste0("C", 1:16))) %>%
arrange(Season, Cluster)
head(alpha.trees)# A tibble: 6 × 6
Cluster q0.t q1.t q2.t Evenness Season
<fct> <dbl> <dbl> <dbl> <dbl> <chr>
1 C1 12 5.46 3.99 0.333 Dry
2 C2 5 3.45 2.67 0.534 Dry
3 C3 13 6.69 5.35 0.411 Dry
4 C4 19 12.3 9 0.474 Dry
5 C5 18 13.2 10.8 0.600 Dry
6 C6 1 1 1 1 Dry ###Beta and divprofile_T
# Beta Matrix: SEASONAL Turnover (Wet vs Dry) ------------------------
MC_season.trees <- trees.long.data %>%
group_by(Season, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
pivot_wider(names_from = Season, values_from = freq,values_fill = 0) %>%
column_to_rownames("species") %>%
filter(rowSums(.) > 0)
head( MC_season.trees) Wet Dry
Afrothismia mhoroana Cheek 2 4
Ageratum conyzoides L. 1 0
Carissa edulis (Forssk.) Vahl 8 6
Celtis africana Burm.f. 2 5
Combretum molle R.Br. ex G.Don 16 22
Cordia monoica Roxb. 1 3# Beta Matrix: SPATIAL Turnover (C1 to C16) --------------------------
MC_space.trees <- trees.long.data %>%
group_by(cluster, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
pivot_wider(names_from = cluster,values_from = freq, values_fill = 0 ) %>%
column_to_rownames("species") %>%
filter(rowSums(.) > 0)
head(MC_space.trees) C1 C10 C11 C12 C13 C14 C15 C16 C2 C3 C4
Abutilon mauritianum (Jacq.) Medik. 0 0 0 0 0 0 0 0 0 0 1
Acokanthera schimperi (A.DC.) Schweinf. 0 0 0 0 0 0 0 0 0 0 2
Afrothismia mhoroana Cheek 0 0 0 0 0 0 0 0 0 1 3
Ageratum conyzoides L. 0 0 0 0 1 0 0 0 0 0 0
Apodytes dimidiata E.Mey. ex Arn. 0 0 0 0 0 0 0 0 0 0 2
Asparagus africanus Lam. 0 0 0 0 0 0 0 0 0 0 0
C5 C6 C7 C8 C9
Abutilon mauritianum (Jacq.) Medik. 0 0 0 0 0
Acokanthera schimperi (A.DC.) Schweinf. 0 0 0 0 0
Afrothismia mhoroana Cheek 0 0 1 1 0
Ageratum conyzoides L. 0 0 0 0 0
Apodytes dimidiata E.Mey. ex Arn. 0 0 2 1 0
Asparagus africanus Lam. 0 0 0 0 1# Beta diversity calculations ---------------------------------------
# Seasonal beta diversity (Wet vs Dry)
beta.season.trees <- beta.multi.abund(
as.data.frame(t(MC_season.trees)),
index.family = "bray"
)
print("Seasonal Beta Diversity (Wet vs Dry):")[1] "Seasonal Beta Diversity (Wet vs Dry):"print(beta.season.trees)$beta.BRAY.BAL
[1] 0.1455108
$beta.BRAY.GRA
[1] 0.1704743
$beta.BRAY
[1] 0.3159851# Spatial beta diversity (C1–C16)
beta.space.trees <- beta.multi.abund(
as.data.frame(t(MC_space.trees)),
index.family = "bray"
)
print("Spatial Beta Diversity (Among 16 Clusters):")[1] "Spatial Beta Diversity (Among 16 Clusters):"print(beta.space.trees)$beta.BRAY.BAL
[1] 0.7402159
$beta.BRAY.GRA
[1] 0.1247122
$beta.BRAY
[1] 0.8649281# Diversity profiles ------------------------------------------------
dp.wet <- DivProfile(
q = seq(0, 2, 1),
MC = MC.trees[["Wet"]],
Correction = "Best",
NumberOfSimulations = 10
)dp.dry <- DivProfile(
q = seq(0, 2, 1),
MC = MC.trees[["Dry"]],
Correction = "Best",
NumberOfSimulations = 10
)par(mfrow = c(1, 2))
plot(dp.wet, main = "Tree Diversity Profile – Wet Season")
plot(dp.dry, main = "Tree Diversity Profile – Dry Season")
par(mfrow = c(1, 1))Shrubs MC analysis Seasonal and spatial
###Metacommunity_S
# this chunk contains data on shrubs collected at the subplot level not transect
# Read data -----------------------------
shrubs.data <- read_csv("data/shrub.plots.csv") %>%
mutate(across(where(is.numeric), ~ replace_na(., 0))) %>%
mutate(
date = dmy(str_trim(year.month)),
Season = case_when(month(date) == 4 ~ "Wet",month(date) == 10 ~ "Dry",
TRUE ~ NA_character_),
Season = factor(Season, levels = c("Wet", "Dry"))
) %>%
select(-date) %>%
filter(!is.na(Season))
# Long Data ----------------------------------------------------------
shrubs.long.data <- shrubs.data %>%
select(-year.month) %>%
pivot_longer(cols = where(is.numeric),names_to = "plot",values_to = "freq") %>%
filter(!is.na(species)) %>%
mutate(
plot_num = as.numeric(gsub("p", "", plot)),
cluster = paste0("C", ceiling(plot_num / 10)) # C1 to C16
) %>%
select(-plot_num)
head(shrubs.long.data)# A tibble: 6 × 5
species Season plot freq cluster
<chr> <fct> <chr> <dbl> <chr>
1 Abutilon mauritianum (Jacq.) Medik. Wet p1 0 C1
2 Abutilon mauritianum (Jacq.) Medik. Wet p2 0 C1
3 Abutilon mauritianum (Jacq.) Medik. Wet p3 0 C1
4 Abutilon mauritianum (Jacq.) Medik. Wet p4 0 C1
5 Abutilon mauritianum (Jacq.) Medik. Wet p5 0 C1
6 Abutilon mauritianum (Jacq.) Medik. Wet p6 0 C1 # Build MetaCommunity matrices PER SEASON (Alpha & Diversity Profiles) ---------
MC.shrubs <- shrubs.long.data %>%
group_by(Season, cluster, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
split(.$Season) %>%
lapply(function(df) {
df %>%
pivot_wider(names_from = cluster,values_from = freq,values_fill = 0) %>%
column_to_rownames("species") %>%
filter(rowSums(across(where(is.numeric))) > 0) %>%
MetaCommunity()
})
#head (MC.shrubs)
summary (MC.shrubs) Length Class Mode
Wet 11 MetaCommunity list
Dry 11 MetaCommunity list###Alpha diversity_S
# Alpha diversity (q = 0, 1, 2) ---------------------------------------
alpha.shrubs <- lapply(names(MC.shrubs), function(season) {
mc <- MC.shrubs[[season]]
q0 <- DivPart(q = 0, mc, Correction = "Best")$CommunityAlphaDiversities
q1 <- DivPart(q = 1, mc, Correction = "Best")$CommunityAlphaDiversities
q2 <- DivPart(q = 2, mc, Correction = "Best")$CommunityAlphaDiversities
tibble(
Cluster = names(q0),
q0.s = as.numeric(q0),
q1.s = as.numeric(q1),
q2.s = as.numeric(q2),
Evenness.s = q2.s / q0.s,
Season = season
)
}) %>%
bind_rows() %>%
mutate(Cluster = factor(Cluster, levels = paste0("C", 1:16))) %>%
arrange(Season, Cluster)
head(alpha.shrubs)# A tibble: 6 × 6
Cluster q0.s q1.s q2.s Evenness.s Season
<fct> <dbl> <dbl> <dbl> <dbl> <chr>
1 C1 17 6.33 3.92 0.231 Dry
2 C2 10 6.36 4.80 0.480 Dry
3 C3 16 6.40 4.13 0.258 Dry
4 C4 21 13.5 10.7 0.510 Dry
5 C5 16 8.53 5.61 0.351 Dry
6 C6 8 3.31 2.12 0.266 Dry ###Beta and diversity profile_S
# Beta Matrix: SEASONAL Turnover (Wet vs Dry) ------------------------
MC_season.shrubs <- shrubs.long.data %>%
group_by(Season, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
pivot_wider(
names_from = Season,
values_from = freq,
values_fill = 0
) %>%
column_to_rownames("species") %>%
filter(rowSums(.) > 0)
head(MC_season.shrubs) Wet Dry
Abutilon mauritianum (Jacq.) Medik. 13 21
Achyranthes aspera L. 3 5
Ageratum conyzoides L. 4 0
Aloe vera (L.) Burm.f. 4 1
Amorpha fruticosa L. 20 24
Amphiachyris dracunculoides (DC.) Nutt. 21 1# Beta Matrix: SPATIAL Turnover (C1 to C16) --------------------------
MC_space.shrubs <- shrubs.long.data %>%
group_by(cluster, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
pivot_wider(
names_from = cluster,
values_from = freq,
values_fill = 0
) %>%
column_to_rownames("species") %>%
filter(rowSums(.) > 0)
head(MC_space.shrubs) C1 C10 C11 C12 C13 C14 C15 C16 C2 C3 C4
Abutilon mauritianum (Jacq.) Medik. 3 6 0 0 1 1 17 1 0 1 0
Achyranthes aspera L. 4 0 0 0 0 0 0 1 2 0 0
Acokanthera schimperi (A.DC.) Schweinf. 0 0 2 0 1 0 0 0 4 0 0
Ageratum conyzoides L. 0 0 0 0 0 0 0 0 0 0 0
Ajuga africana (Thunb.) Pers. 0 1 0 0 0 0 0 1 0 0 0
Aloe vera (L.) Burm.f. 0 0 5 0 0 0 0 0 0 0 0
C5 C6 C7 C8 C9
Abutilon mauritianum (Jacq.) Medik. 0 0 3 1 0
Achyranthes aspera L. 0 1 0 0 0
Acokanthera schimperi (A.DC.) Schweinf. 0 0 0 1 0
Ageratum conyzoides L. 0 0 0 0 4
Ajuga africana (Thunb.) Pers. 0 0 0 0 0
Aloe vera (L.) Burm.f. 0 0 0 0 0# Calculate Beta Diversity ------------------------------------------
# Seasonal beta diversity (Wet vs Dry)
beta.season.shrubs <- beta.multi.abund(
as.data.frame(t(MC_season.shrubs)),
index.family = "bray"
)
print("Seasonal Beta Diversity (Wet vs Dry):")[1] "Seasonal Beta Diversity (Wet vs Dry):"print(beta.season.shrubs)$beta.BRAY.BAL
[1] 0.1345339
$beta.BRAY.GRA
[1] 0.1091597
$beta.BRAY
[1] 0.2436936# Spatial beta diversity (C1–C16)
beta.space.shrubs <- beta.multi.abund(
as.data.frame(t(MC_space.shrubs)),
index.family = "bray"
)
print("Spatial Beta Diversity (Among 16 Clusters):")[1] "Spatial Beta Diversity (Among 16 Clusters):"print(beta.space.shrubs)$beta.BRAY.BAL
[1] 0.7952171
$beta.BRAY.GRA
[1] 0.06506034
$beta.BRAY
[1] 0.8602775# Diversity profiles ------------------------------------------------
dp.wet <- DivProfile(
q = seq(0, 2, 1),
MC = MC.shrubs[["Wet"]],
Correction = "Best",
NumberOfSimulations = 10
)dp.wet$MetaCommunity
[1] "MC.shrubs[[\"Wet\"]]"
$Order
[1] 0 1 2
$Biased
[1] TRUE
$Correction
[1] "None"
$Normalized
[1] TRUE
$CommunityAlphaDiversities
C1 C10 C11 C12 C13 C14 C15 C16
0 18.000000 14.000000 23.00000 22.00000 16.000000 17.000000 4.000000 12.000000
1 7.330682 6.775918 13.77304 14.05080 9.362150 9.835687 3.205350 9.504947
2 4.773720 4.541636 10.27222 10.73889 7.142857 7.414991 2.816901 8.468514
C2 C3 C4 C5 C6 C7 C8 C9
0 11.00000 10.000000 19.000000 14.000000 6.000000 20.000000 26.000000 16.000000
1 7.49846 5.031643 9.493085 8.509773 3.580809 10.923400 13.263471 5.697762
2 6.19457 3.458120 6.202858 6.191724 2.631841 7.320935 8.975155 2.972435
$CommunityAlphaEntropies
C1 C10 C11 C12 C13 C14 C15
0 17.0000000 13.0000000 22.0000000 21.0000000 15.000000 16.0000000 3.000000
1 1.9920686 1.9133748 2.6227131 2.6426790 2.236675 2.2860173 1.164821
2 0.7905198 0.7798151 0.9026501 0.9068805 0.860000 0.8651381 0.645000
C16 C2 C3 C4 C5 C6 C7
0 11.0000000 10.0000000 9.0000000 18.000000 13.0000000 5.0000000 19.0000000
1 2.2518124 2.0146977 1.6157465 2.250564 2.1412153 1.2755888 2.3909073
2 0.8819155 0.8385683 0.7108255 0.838784 0.8384941 0.6200378 0.8634054
C8 C9
0 25.0000000 15.0000000
1 2.5850137 1.7400734
2 0.8885813 0.6635755
$TotalAlphaDiversity
[1] 15.500000 7.926789 5.151637
$TotalBetaDiversity
None None None
3.225806 2.148626 2.141927
$GammaDiversity
None None None
50.00000 17.03170 11.03443
$TotalAlphaEntropy
[1] 14.5000000 2.0702480 0.8058869
$TotalBetaEntropy
None None None
34.5000000 0.7648284 0.1034876
$GammaEntropy
None None None
49.0000000 2.8350763 0.9093746
$Method
[1] "HCDT"
$TotalAlphaEntropyLow
[1] 14.0812500 2.0403344 0.7999034
$TotalAlphaEntropyHigh
[1] 14.8218750 2.1000978 0.8124197
$TotalBetaEntropyLow
[1] 32.81562500 0.74303079 0.09899595
$TotalBetaEntropyHigh
[1] 35.7562500 0.7902720 0.1087594
$GammaEntropyLow
[1] 47.1500000 2.8013745 0.9057935
$GammaEntropyHigh
[1] 50.2500000 2.8740260 0.9126506
$TotalAlphaDiversityLow
[1] 15.081250 7.693326 4.997589
$TotalAlphaDiversityHigh
[1] 15.821875 8.167025 5.331666
$TotalBetaDiversityLow
[1] 3.106532 2.102298 2.090009
$TotalBetaDiversityHigh
[1] 3.309434 2.204014 2.225674
$GammaDiversityLow
[1] 48.15000 16.46796 10.61713
$GammaDiversityHigh
[1] 51.25000 17.70882 11.44912
attr(,"class")
[1] "DivProfile"dp.dry <- DivProfile(
q = seq(0, 2, 1),
MC = MC.shrubs[["Dry"]],
Correction = "Best",
NumberOfSimulations = 10
)dp.dry$MetaCommunity
[1] "MC.shrubs[[\"Dry\"]]"
$Order
[1] 0 1 2
$Biased
[1] TRUE
$Correction
[1] "None"
$Normalized
[1] TRUE
$CommunityAlphaDiversities
C1 C10 C11 C12 C13 C14 C15
0 17.00000 16.000000 19.000000 17.000000 13.000000 20.000000 11.000000
1 6.33205 6.842407 6.980273 8.375513 6.718751 11.471191 6.667636
2 3.92002 4.380841 4.956148 5.725040 4.873799 8.772319 4.927374
C16 C2 C3 C4 C5 C6 C7 C8
0 16.000000 10.000000 16.000000 21.00000 16.000000 8.000000 24.000000 23.00000
1 7.596544 6.362427 6.403495 13.51694 8.528269 3.307686 11.728683 11.85243
2 5.365079 4.798062 4.126005 10.70170 5.610003 2.124869 7.678586 7.62523
C9
0 15.000000
1 3.178797
2 1.799464
$CommunityAlphaEntropies
C1 C10 C11 C12 C13 C14 C15
0 16.0000000 15.0000000 18.0000000 16.0000000 12.0000000 19.0000000 10.0000000
1 1.8456241 1.9231396 1.9430880 2.1253124 1.9049022 2.4398388 1.8972654
2 0.7448993 0.7717333 0.7982304 0.8253287 0.7948212 0.8860051 0.7970522
C16 C2 C3 C4 C5 C6 C7
0 15.0000000 9.0000000 15.0000000 20.0000000 15.000000 7.0000000 23.0000000
1 2.0276934 1.8504098 1.8568439 2.6039440 2.143386 1.1962489 2.4620374
2 0.8136095 0.7915825 0.7576348 0.9065569 0.821747 0.5293827 0.8697677
C8 C9
0 22.0000000 14.0000000
1 2.4725330 1.1565028
2 0.8688564 0.4442791
$TotalAlphaDiversity
[1] 16.375000 7.319545 4.471131
$TotalBetaDiversity
None None None
3.603053 2.190348 2.089168
$GammaDiversity
None None None
59.000000 16.032348 9.340945
$TotalAlphaEntropy
[1] 15.3750000 1.9905481 0.7763429
$TotalBetaEntropy
None None None
42.6250000 0.7840603 0.1166015
$GammaEntropy
None None None
58.0000000 2.7746084 0.8929444
$Method
[1] "HCDT"
$TotalAlphaEntropyLow
[1] 15.1906250 1.9649900 0.7678846
$TotalAlphaEntropyHigh
[1] 15.5718750 2.0157754 0.7815597
$TotalBetaEntropyLow
[1] 39.8750000 0.7602988 0.1101219
$TotalBetaEntropyHigh
[1] 45.3031250 0.8190078 0.1247242
$GammaEntropyLow
[1] 55.1500000 2.7498719 0.8904291
$GammaEntropyHigh
[1] 60.8000000 2.8044981 0.8946703
$TotalAlphaDiversityLow
[1] 16.190625 7.134974 4.309252
$TotalAlphaDiversityHigh
[1] 16.571875 7.506691 4.577924
$TotalBetaDiversityLow
[1] 3.450045 2.138964 2.005457
$TotalBetaDiversityHigh
[1] 3.750567 2.268465 2.161394
$GammaDiversityLow
[1] 56.150000 15.640665 9.126765
$GammaDiversityHigh
[1] 61.800000 16.518794 9.494036
attr(,"class")
[1] "DivProfile"par(mfrow = c(1, 2))
plot(dp.wet, main = "Shrub Diversity Profile – Wet Season")
plot(dp.dry, main = "Shrub Diversity Profile – Dry Season")
par(mfrow = c(1, 1))Woody (trees+shrubs)MC analysis Seasonal and spatial
###Metacommunity_W
# this chunk contains data on trees and shrubs collected at the subplot level not transect
woody.data <- read_csv("data/trees.shrubs.plots.csv") %>%
mutate(across(where(is.numeric), ~ replace_na(., 0))) %>%
mutate(
date = dmy(str_trim(year.month)),
Season = case_when(
month(date) == 4 ~ "Wet",
month(date) == 10 ~ "Dry",
TRUE ~ NA_character_
),
Season = factor(Season, levels = c("Wet", "Dry"))
) %>%
select(-date) %>%
filter(!is.na(Season))
# Long Data ----------------------------------------------------------
woody.long.data <- woody.data %>%
select(-year.month) %>%
pivot_longer(cols = where(is.numeric),names_to = "plot",values_to = "freq") %>%
filter(!is.na(species)) %>%
mutate(
plot_num = as.numeric(gsub("p", "", plot)),
cluster = paste0("C", ceiling(plot_num / 10)) # C1 to C16
) %>%
select(-plot_num)
head(woody.long.data)# A tibble: 6 × 6
lifeform species Season plot freq cluster
<chr> <chr> <fct> <chr> <dbl> <chr>
1 Shrubs Abutilon mauritianum (Jacq.) Medik. Wet p1 0 C1
2 Shrubs Abutilon mauritianum (Jacq.) Medik. Wet p2 0 C1
3 Shrubs Abutilon mauritianum (Jacq.) Medik. Wet p3 0 C1
4 Shrubs Abutilon mauritianum (Jacq.) Medik. Wet p4 0 C1
5 Shrubs Abutilon mauritianum (Jacq.) Medik. Wet p5 0 C1
6 Shrubs Abutilon mauritianum (Jacq.) Medik. Wet p6 0 C1 # Build MetaCommunity matrices PER SEASON (Alpha & Diversity Profiles) ---------
MC.woody <- woody.long.data %>%
group_by(Season, cluster, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
split(.$Season) %>%
lapply(function(df) {
df %>%
pivot_wider(names_from = cluster,values_from = freq, values_fill = 0 ) %>%
column_to_rownames("species") %>%
filter(rowSums(across(where(is.numeric))) > 0) %>%
MetaCommunity()
})
#MC.woody
summary (MC.woody) Length Class Mode
Wet 11 MetaCommunity list
Dry 11 MetaCommunity list###Alpha diversity_W
# Alpha diversity (q = 0, 1, 2) ---------------------------------------
alpha.woody <- lapply(names(MC.woody), function(season) {
mc <- MC.woody[[season]]
q0 <- DivPart(q = 0, mc, Correction = "Best")$CommunityAlphaDiversities
q1 <- DivPart(q = 1, mc, Correction = "Best")$CommunityAlphaDiversities
q2 <- DivPart(q = 2, mc, Correction = "Best")$CommunityAlphaDiversities
tibble(
Cluster = names(q0),
q0.w = as.numeric(q0),
q1.w = as.numeric(q1),
q2.w = as.numeric(q2),
Evenness.w = q2.w / q0.w,
Season = season
)
}) %>%
bind_rows() %>%
mutate(Cluster = factor(Cluster, levels = paste0("C", 1:16))) %>%
arrange(Season, Cluster)
head(alpha.woody)# A tibble: 6 × 6
Cluster q0.w q1.w q2.w Evenness.w Season
<fct> <dbl> <dbl> <dbl> <dbl> <chr>
1 C1 25 10.9 7.06 0.283 Dry
2 C2 15 9.41 6.88 0.459 Dry
3 C3 25 12.1 8.33 0.333 Dry
4 C4 39 24.8 18.9 0.485 Dry
5 C5 30 15.8 9.73 0.324 Dry
6 C6 9 3.88 2.40 0.266 Dry ###Beta and diversity profile_W
# Beta Matrix: SEASONAL Turnover (Wet vs Dry) ------------------------
MC_season.woody <- woody.long.data %>%
group_by(Season, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
pivot_wider(names_from = Season, values_from = freq,values_fill = 0) %>%
column_to_rownames("species") %>%
filter(rowSums(.) > 0)
head(MC_season.woody) Wet Dry
Abutilon mauritianum (Jacq.) Medik. 13 22
Achyranthes aspera L. 3 5
Afrothismia mhoroana Cheek 2 4
Ageratum conyzoides L. 5 0
Aloe vera (L.) Burm.f. 4 1
Amorpha fruticosa L. 20 24# Beta Matrix: SPATIAL Turnover (C1 to C16) --------------------------
MC_space.woody <- woody.long.data %>%
group_by(cluster, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
pivot_wider(names_from = cluster,values_from = freq, values_fill = 0) %>%
column_to_rownames("species") %>%
filter(rowSums(.) > 0)
head(MC_space.woody) C1 C10 C11 C12 C13 C14 C15 C16 C2 C3 C4
Abutilon mauritianum (Jacq.) Medik. 3 6 0 0 1 1 17 1 0 1 1
Achyranthes aspera L. 4 0 0 0 0 0 0 1 2 0 0
Acokanthera schimperi (A.DC.) Schweinf. 0 0 2 0 1 0 0 0 4 0 2
Afrothismia mhoroana Cheek 0 0 0 0 0 0 0 0 0 1 3
Ageratum conyzoides L. 0 0 0 0 1 0 0 0 0 0 0
Ajuga africana (Thunb.) Pers. 0 1 0 0 0 0 0 1 0 0 0
C5 C6 C7 C8 C9
Abutilon mauritianum (Jacq.) Medik. 0 0 3 1 0
Achyranthes aspera L. 0 1 0 0 0
Acokanthera schimperi (A.DC.) Schweinf. 0 0 0 1 0
Afrothismia mhoroana Cheek 0 0 1 1 0
Ageratum conyzoides L. 0 0 0 0 4
Ajuga africana (Thunb.) Pers. 0 0 0 0 0# Calculate Beta Diversity ------------------------------------------
# Seasonal beta diversity (Wet vs Dry)
beta.season.woody <- beta.multi.abund(
as.data.frame(t(MC_season.woody)),
index.family = "bray"
)
print("Seasonal Beta Diversity (Wet vs Dry):")[1] "Seasonal Beta Diversity (Wet vs Dry):"print(beta.season.woody)$beta.BRAY.BAL
[1] 0.1077348
$beta.BRAY.GRA
[1] 0.1303444
$beta.BRAY
[1] 0.2380792# Spatial beta diversity (C1–C16)
beta.space.woody <- beta.multi.abund(
as.data.frame(t(MC_space.woody)),
index.family = "bray"
)
print("Spatial Beta Diversity (Among 16 Clusters):")[1] "Spatial Beta Diversity (Among 16 Clusters):"print(beta.space.woody)$beta.BRAY.BAL
[1] 0.7918877
$beta.BRAY.GRA
[1] 0.06662991
$beta.BRAY
[1] 0.8585176# Diversity profiles ------------------------------------------------
dp.wet <- DivProfile(
q = seq(0, 2, 1),
MC = MC.woody[["Wet"]],
Correction = "Best",
NumberOfSimulations = 10
)dp.wet$MetaCommunity
[1] "MC.woody[[\"Wet\"]]"
$Order
[1] 0 1 2
$Biased
[1] TRUE
$Correction
[1] "None"
$Normalized
[1] TRUE
$CommunityAlphaDiversities
C1 C10 C11 C12 C13 C14 C15 C16
0 27.00000 18.000000 31.00000 35.00000 21.000000 22.000000 9.000000 15.00000
1 11.94882 8.563010 16.82565 20.92415 11.137991 11.896994 5.975224 11.57020
2 8.61696 5.916031 12.24644 15.03106 8.048359 8.929763 4.797357 10.07619
C2 C3 C4 C5 C6 C7 C8 C9
0 16.000000 17.000000 25.000000 21.000000 6.000000 29.00000 43.00000 23.000000
1 9.752600 8.412760 13.848811 12.782025 3.580809 16.37255 21.45174 8.600875
2 7.744033 5.581409 9.121319 9.113804 2.631841 10.73883 13.50881 4.111927
$CommunityAlphaEntropies
C1 C10 C11 C12 C13 C14 C15
0 26.0000000 17.0000000 30.0000000 34.0000000 20.0000000 21.0000000 8.0000000
1 2.4806324 2.1474518 2.8229042 3.0409042 2.4103619 2.4762858 1.7876215
2 0.8839498 0.8309677 0.9183436 0.9334711 0.8757511 0.8880149 0.7915519
C16 C2 C3 C4 C5 C6 C7
0 14.0000000 15.0000000 16.0000000 24.0000000 20.0000000 5.0000000 28.000000
1 2.4484325 2.2775339 2.1297495 2.6281994 2.5480399 1.2755888 2.795606
2 0.9007561 0.8708683 0.8208338 0.8903667 0.8902763 0.6200378 0.906880
C8 C9
0 42.0000000 22.000000
1 3.0658058 2.151864
2 0.9259742 0.756805
$TotalAlphaDiversity
[1] 22.375000 11.083266 6.971217
$TotalBetaDiversity
None None None
3.039106 2.210276 2.221920
$GammaDiversity
None None None
68.00000 24.49708 15.48948
$TotalAlphaEntropy
[1] 21.375000 2.405436 0.856553
$TotalBetaEntropy
None None None
45.62500000 0.79311753 0.07888704
$GammaEntropy
None None None
67.0000000 3.1985539 0.9354401
$Method
[1] "HCDT"
$TotalAlphaEntropyLow
[1] 20.6937500 2.3642529 0.8450975
$TotalAlphaEntropyHigh
[1] 21.9000000 2.4477427 0.8654199
$TotalBetaEntropyLow
[1] 43.52187500 0.76970764 0.07286863
$TotalBetaEntropyHigh
[1] 47.37187500 0.83652484 0.08910989
$GammaEntropyLow
[1] 64.9500000 3.1570619 0.9329327
$GammaEntropyHigh
[1] 69.0500000 3.2375285 0.9386923
$TotalAlphaDiversityLow
[1] 21.693750 10.636127 6.462126
$TotalAlphaDiversityHigh
[1] 22.90000 11.56244 7.43236
$TotalBetaDiversityLow
[1] 2.940648 2.159156 2.141278
$TotalBetaDiversityHigh
[1] 3.134930 2.308466 2.387219
$GammaDiversityLow
[1] 65.95000 23.50268 14.91061
$GammaDiversityHigh
[1] 70.05000 25.47074 16.31647
attr(,"class")
[1] "DivProfile"dp.dry <- DivProfile(
q = seq(0, 2, 1),
MC = MC.woody[["Dry"]],
Correction = "Best",
NumberOfSimulations = 10
)dp.dry$MetaCommunity
[1] "MC.woody[[\"Dry\"]]"
$Order
[1] 0 1 2
$Biased
[1] TRUE
$Correction
[1] "None"
$Normalized
[1] TRUE
$CommunityAlphaDiversities
C1 C10 C11 C12 C13 C14 C15
0 25.000000 19.000000 28.000000 27.000000 20.000000 31.00000 15.000000
1 10.911553 9.652223 10.815730 13.496577 10.280460 18.59685 8.465125
2 7.064612 7.256524 6.662709 8.203906 6.914258 14.35182 6.132075
C16 C2 C3 C4 C5 C6 C7 C8
0 27.000000 15.000000 25.000000 39.00000 30.000000 9.000000 37.00000 40.00000
1 13.106145 9.406029 12.061461 24.81599 15.807907 3.877827 19.47872 22.05772
2 8.586266 6.882722 8.331562 18.90438 9.733211 2.395010 12.06215 14.34455
C9
0 24.000000
1 5.205021
2 2.513043
$CommunityAlphaEntropies
C1 C10 C11 C12 C13 C14 C15
0 24.0000000 18.000000 27.0000000 26.0000000 19.0000000 30.0000000 14.0000000
1 2.3898222 2.267188 2.3810015 2.6024361 2.3302450 2.9229924 2.1359548
2 0.8584494 0.862193 0.8499109 0.8781068 0.8553713 0.9303224 0.8369231
C16 C2 C3 C4 C5 C6 C7
0 26.0000000 14.0000000 24.0000000 38.0000000 29.000000 8.0000000 36.000000
1 2.5730812 2.2413509 2.4900154 3.2114883 2.760510 1.3552750 2.969323
2 0.8835349 0.8547086 0.8799745 0.9471022 0.897259 0.5824653 0.917096
C8 C9
0 39.0000000 23.0000000
1 3.0936628 1.6496238
2 0.9302871 0.6020761
$TotalAlphaDiversity
[1] 25.687500 11.715036 6.572949
$TotalBetaDiversity
None None None
3.231144 2.163936 2.252801
$GammaDiversity
None None None
83.00000 25.35059 14.80755
$TotalAlphaEntropy
[1] 24.6875000 2.4608732 0.8478613
$TotalBetaEntropy
None None None
57.31250000 0.77192868 0.08460558
$GammaEntropy
None None None
82.0000000 3.2328018 0.9324669
$Method
[1] "HCDT"
$TotalAlphaEntropyLow
[1] 24.3906250 2.4215259 0.8369288
$TotalAlphaEntropyHigh
[1] 25.0500000 2.5125023 0.8614557
$TotalBetaEntropyLow
[1] 54.94687500 0.74636598 0.07372803
$TotalBetaEntropyHigh
[1] 60.26250000 0.79651994 0.09385229
$GammaEntropyLow
[1] 79.8000000 3.2019574 0.9301715
$GammaEntropyHigh
[1] 84.9000000 3.2744037 0.9357396
$TotalAlphaDiversityLow
[1] 25.390625 11.263805 6.132402
$TotalAlphaDiversityHigh
[1] 26.050000 12.336516 7.220043
$TotalBetaDiversityLow
[1] 3.125569 2.109326 2.118012
$TotalBetaDiversityHigh
[1] 3.350499 2.217812 2.359864
$GammaDiversityLow
[1] 80.80000 24.58068 14.32084
$GammaDiversityHigh
[1] 85.90000 26.42751 15.56384
attr(,"class")
[1] "DivProfile"par(mfrow = c(1, 2))
plot(dp.wet, main = "Woody Diversity Profile – Wet Season")
plot(dp.dry, main = "Woody Diversity Profile – Dry Season")
par(mfrow = c(1, 1))Species rank curves
#==================================================
# 1. Species Rank-Abundance Plots – Separate by Season
#==================================================
# Extract unique seasons
seasons <- unique(rangeland.long.data$Season)
# Create a list to store plots
rank_abundance_plots <- list()
for (s in seasons) {
# Subset data for the season
season_data <- rangeland.long.data %>%
filter(Season == s) %>%
group_by(cluster, species) %>%
summarise(freq = sum(freq), .groups = "drop") %>%
filter(freq != 0) %>%
# Calculate rank within each cluster
group_by(cluster) %>%
mutate(rank = rank(desc(freq), ties.method = "average")) %>%
ungroup()
# Generate plot for the season
p <- season_data %>%
ggplot(aes(x = rank, y = log(freq + 1))) +
geom_line(aes(group = cluster), color = "#0072B2") +
geom_point(color = "#0072B2") +
theme_bw() +
facet_wrap(~ cluster, scales = "free_x", ncol = 4) +
labs(
title = paste("Species Rank-Abundance –", s, "Season"),
x = "Species Rank",
y = "log(Abundance + 1)"
)
# Store in list
rank_abundance_plots[[s]] <- p
}
# Print plots
rank_abundance_plots[["Wet"]]
rank_abundance_plots[["Dry"]]
#==================================================
# 2. Alpha Diversity Table
#==================================================
# Use existing alpha.rangeland table
print("Final Alpha Diversity Table (alpha.rangeland):")[1] "Final Alpha Diversity Table (alpha.rangeland):"print(alpha.rangeland)# A tibble: 32 × 6
Cluster q0.r q1.r q2.r Evenness.r Season
<fct> <dbl> <dbl> <dbl> <dbl> <chr>
1 C1 37 6.00 2.81 0.0760 Dry
2 C2 40 7.90 3.88 0.0970 Dry
3 C3 36 6.14 3.11 0.0863 Dry
4 C4 50 7.85 3.27 0.0655 Dry
5 C5 48 8.49 3.54 0.0738 Dry
6 C6 51 6.39 3.01 0.0590 Dry
7 C7 53 9.51 3.89 0.0733 Dry
8 C8 56 6.23 2.56 0.0457 Dry
9 C9 36 5.44 2.99 0.0832 Dry
10 C10 62 11.2 4.72 0.0762 Dry
# ℹ 22 more rowsShrub volume,cover,biomass
#==================== DATA & SEASON ASSIGNMENT ====================#
# Load shrub data and assign Season
shrubsv <- read_csv("data/raw.data.shrub.csv") %>%
mutate(across(where(is.numeric), ~ replace_na(., 0))) %>%
mutate(
# Assuming 'year.month' format is "MM/YYYY"
date = dmy(paste0("01/", str_trim(year.month))),
Season = case_when(
month(date) == 4 ~ "Wet",
month(date) == 10 ~ "Dry",
TRUE ~ NA_character_
),
Season = factor(Season, levels = c("Wet", "Dry"))
) %>%
select(-date) %>%
filter(!is.na(Season)) # Remove rows without a valid season
# Ensure Cluster is character
shrubsv <- shrubsv %>% mutate(Cluster = as.character(Cluster))
#==================== SHRUBS: COVER & VOLUME CALCULATION ====================#
shrub_df <- shrubsv %>%
mutate(
crown_area_m2 = pi * (shlength / 2) * (shwidth / 2), # Elliptical crown area
volume_m3 = (1/3) * crown_area_m2 * shheight # Cone volume
)
#==================== SHRUBS: SUMMARY BY PLOT (WITH SEASON) ====================#
shrub_plot_summary <- shrub_df %>%
group_by(Season, Cluster, Plot) %>%
summarise(
n_shrubs = n(),
total_cover.sh = sum(crown_area_m2, na.rm = TRUE),
sd_cover.sh = sd(crown_area_m2, na.rm = TRUE),
total_volume.sh = sum(volume_m3, na.rm = TRUE),
sd_volume.sh = sd(volume_m3, na.rm = TRUE),
cover_percent.sh = (total_cover.sh / 1000) * 100, # Plot area = 1000 m²
volume_ha.sh = total_volume.sh * 10, # Convert to per ha
density_ha.sh = n_shrubs * 10, # Per ha
.groups = 'drop'
)
#==================== SHRUBS: SUMMARY BY CLUSTER (WITH SEASON) ====================#
shrub.cluster_summary <- shrub_df %>%
group_by(Season, Cluster) %>%
summarise(
n_shrubs = n(),
total_cover.sh = sum(crown_area_m2, na.rm = TRUE),
sd_cover.sh = sd(crown_area_m2, na.rm = TRUE),
total_volume.sh = sum(volume_m3, na.rm = TRUE),
sd_volume.sh = sd(volume_m3, na.rm = TRUE),
.groups = 'drop'
) %>%
# Ensure all Season x Cluster combinations exist
complete(Cluster = as.character(1:16), Season = c("Wet", "Dry"),
fill = list(n_shrubs = 0,
total_cover.sh = 0,
sd_cover.sh = 0,
total_volume.sh = 0,
sd_volume.sh = 0)) %>%
mutate(
cover_percent.sh = (total_cover.sh / 1e6) * 100, # 1 km² = 1,000,000 m²
volume_ha.sh = total_volume.sh / 100, # 1 km² = 100 ha
density_ha.sh = n_shrubs / 100
) %>%
arrange(Season, as.integer(Cluster))
# Final output selection
shrub.volume.cover.biomass <- shrub.cluster_summary %>%
select(Season, Cluster, cover_percent.sh, volume_ha.sh) %>%
rename(SH.C = cover_percent.sh, SH.V = volume_ha.sh)
print(shrub.volume.cover.biomass)# A tibble: 32 × 4
Season Cluster SH.C SH.V
<chr> <chr> <dbl> <dbl>
1 Dry 1 0.0240 1.56
2 Dry 2 0.0340 2.50
3 Dry 3 0.451 28.9
4 Dry 4 0.0447 3.22
5 Dry 5 0.0668 3.39
6 Dry 6 0.00210 0.0937
7 Dry 7 0.0398 2.18
8 Dry 8 0.0888 6.61
9 Dry 9 0.0604 3.96
10 Dry 10 0.0347 2.38
# ℹ 22 more rowsTree volume, cover, biomass
#==================== DATA & SEASON ASSIGNMENT ====================#
# Load raw tree data and assign Season
treesv <- read_csv("data/raw.data.trees.csv")%>%
mutate(across(where(is.numeric), ~ replace_na(., 0))) %>%
mutate(
# Assuming 'year.month' is in "MM/YYYY" or "DD/MM/YYYY" format
date = dmy(str_trim(year.month)),
Season = case_when(
month(date) == 4 ~ "Wet",
month(date) == 10 ~ "Dry",
TRUE ~ NA_character_
),
Season = factor(Season, levels = c("Wet", "Dry"))
) %>%
select(-date) %>%
filter(!is.na(Season)) %>% # Remove rows without a valid season
mutate(
Cluster = as.character(Cluster),
Plot = as.character(Plot)
)
treesv# A tibble: 1,614 × 9
trheight trcircumf treespecies_common subplot Cluster Plot year.month
<dbl> <dbl> <chr> <dbl> <chr> <chr> <chr>
1 6.5 3 "Vachellia drepanolobium… 1 8 2 01 April …
2 14 65 "Euclea divinorum Hiern" 2 8 2 01 April …
3 8 40 "Vachellia gerrardi (Ben… 2 8 2 01 April …
4 3 15 "Vachellia nilotica (L.)… 3 8 2 01 April …
5 5 12 "Euclea divinorum Hiern" 3 8 2 01 April …
6 6 15 "Vachellia nilotica (L.)… 4 8 2 01 April …
7 3 5 "Maytenus senegalensis (… 4 8 2 01 April …
8 6 25 "Afrothismia mhoroana Ch… 4 8 2 01 April …
9 3 2 "Grewia bicolor Juss." 1 9 2 01 April …
10 3.96 28 "Euclea divinorum Hiern" 1 9 2 01 April …
# ℹ 1,604 more rows
# ℹ 2 more variables: start <dttm>, Season <fct># Optional: full list of clusters (1–16)
all_clusters <- tibble(Cluster = as.character(1:16))
#==================== CALCULATIONS PER TREE ====================#
tree_metrics <- treesv %>%
# Calculate DBH from circumference
mutate(dbh_cm = trcircumf / pi) %>%
# Basal area (m²), approximate volume, crown cover (m²)
mutate(
basal_area_m2 = (pi * (dbh_cm / 100)^2) / 4,
volume_m3 = basal_area_m2 * trheight * 0.5, # Approximate tree volume
crown_cover_m2 = pi * ((dbh_cm / 100) / 2)^2
)
#==================== SUMMARY BY CLUSTER ====================#
cluster_area_ha <- 100
cluster_area_m2 <- 1e6 # 1 km² = 1,000,000 m²
tree_cluster_summary <- tree_metrics %>%
group_by(Season, Cluster) %>%
summarise(
total_basal_area.tr = sum(basal_area_m2, na.rm = TRUE),
total_volume.tr = sum(volume_m3, na.rm = TRUE),
total_cover.tr = sum(crown_cover_m2, na.rm = TRUE),
n_trees = n(),
.groups = 'drop'
) %>%
mutate(
basal_area_ha.tr = total_basal_area.tr / cluster_area_ha,
volume_ha.tr = total_volume.tr / cluster_area_ha,
density_ha.tr = n_trees / cluster_area_ha,
cover_percent.tr = (total_cover.tr / cluster_area_m2) * 100
) %>%
# Fill missing Cluster × Season combinations with zeros
complete(Cluster = as.character(1:16), Season = c("Wet", "Dry"),
fill = list(
total_basal_area.tr = 0,
total_volume.tr = 0,
total_cover.tr = 0,
n_trees = 0,
basal_area_ha.tr = 0,
volume_ha.tr = 0,
density_ha.tr = 0,
cover_percent.tr = 0
)) %>%
arrange(Season, as.integer(Cluster))
#==================== FINAL TABLE ====================#
tree.vol.cov.area <- tree_cluster_summary %>%
select(Season, Cluster, basal_area_ha.tr, volume_ha.tr, cover_percent.tr) %>%
rename(
TR.A = basal_area_ha.tr,
TR.V = volume_ha.tr,
TR.C = cover_percent.tr
)
# View output
print(tree.vol.cov.area)# A tibble: 32 × 5
Season Cluster TR.A TR.V TR.C
<chr> <chr> <dbl> <dbl> <dbl>
1 Dry 1 0.0133 0.0663 0.000133
2 Dry 2 0.000207 0.000633 0.00000207
3 Dry 3 0.00359 0.0296 0.0000359
4 Dry 4 0.00410 0.0141 0.0000410
5 Dry 5 0.0531 0.108 0.000531
6 Dry 6 0.0000796 0.000139 0.000000796
7 Dry 7 0.00478 0.0187 0.0000478
8 Dry 8 0.00545 0.0209 0.0000545
9 Dry 9 0.00274 0.00525 0.0000274
10 Dry 10 0.0312 0.0861 0.000312
# ℹ 22 more rowstotal_basal_area.tr-Total cross-sectional area of all tree trunks in the cluster. total_volume.tr-Total estimated volume of all trees in the cluster. total_cover.tr-Total canopy area covered by trees in the cluster. basal_area_ha.tr- Total basal area of trees standardized per hectare. volume_ha.tr-Total tree volume standardized per hectare. density_ha.tr- Number of trees per hectare in the cluster. cover_percent.tr- Percentage of the cluster area covered by tree canopy.
BIRDS POINT COUNT
birds.data <- read_csv("data/point.counts.csv")
birds.data %>%
pivot_longer(cols= !species, names_to = "clusters", values_to = "freq") %>%
#filter(rowSums(across(where(is.numeric)))!=0) %>%
pivot_wider(names_from = species, values_from = freq, values_fn = sum) %>%
replace(is.na(.), 0)# A tibble: 16 × 239
clusters `Abyssinian thrush` `African Citril` `African Dusky Flycatcher`
<chr> <dbl> <dbl> <dbl>
1 C1 0 0 0
2 C2 0 4 0
3 C3 0 0 1
4 C4 1 3 0
5 C5 1 8 0
6 C6 0 0 0
7 C7 0 3 0
8 C8 0 2 0
9 C9 1 2 0
10 C10 0 0 0
11 C11 0 3 0
12 C12 0 2 0
13 C13 0 4 0
14 C14 0 3 0
15 C15 0 0 0
16 C16 0 0 0
# ℹ 235 more variables: `African Firefinch` <dbl>,
# `African Gray-Flycatcher` <dbl>, `African Green-Pigeon` <dbl>,
# `African Hawk Eagle` <dbl>, `African Paradise-Flycatcher` <dbl>,
# `African Pied-Wagtail` <dbl>, `African Pipit` <dbl>,
# `African Stonechat` <dbl>, `African Swift` <dbl>, `African Thrush` <dbl>,
# `African Woolly-necked Stork` <dbl>, `African Yellow-Warbler` <dbl>,
# `African-Black-headed Oriole` <dbl>, `Amethyst Sunbird` <dbl>, …head (birds.data)# A tibble: 6 × 17
species C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Abyssinian … 0 0 0 1 1 0 0 0 1 0 0
2 African Cit… 0 4 0 3 8 0 3 2 2 0 3
3 African Dus… 0 0 1 0 0 0 0 0 0 0 0
4 African Fir… 1 1 0 0 1 0 1 0 1 0 0
5 African Gra… 0 0 4 0 1 0 2 1 1 2 1
6 African Gre… 1 1 0 1 0 0 1 0 0 0 0
# ℹ 5 more variables: C12 <dbl>, C13 <dbl>, C14 <dbl>, C15 <dbl>, C16 <dbl>birds.data.MC <- birds.data[, c(TRUE, colSums(birds.data[,-1]) > 0)]
birds.data.MC # A tibble: 238 × 17
species C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Abyssinian… 0 0 0 1 1 0 0 0 1 0 0
2 African Ci… 0 4 0 3 8 0 3 2 2 0 3
3 African Du… 0 0 1 0 0 0 0 0 0 0 0
4 African Fi… 1 1 0 0 1 0 1 0 1 0 0
5 African Gr… 0 0 4 0 1 0 2 1 1 2 1
6 African Gr… 1 1 0 1 0 0 1 0 0 0 0
7 African Ha… 0 0 0 0 0 0 0 0 0 0 1
8 African Pa… 0 3 8 0 0 0 1 0 0 2 0
9 African Pi… 6 18 5 9 6 1 10 7 8 11 5
10 African Pi… 1 0 3 0 2 4 1 0 2 4 2
# ℹ 228 more rows
# ℹ 5 more variables: C12 <dbl>, C13 <dbl>, C14 <dbl>, C15 <dbl>, C16 <dbl>mc.birds <- MetaCommunity(birds.data.MC)
summary(mc.birds)Meta-community (class 'MetaCommunity') made of 8490 individuals in 16
communities and 238 species.
Its sample coverage is 0.995878141051649
Community weights are:
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11
0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625
C12 C13 C14 C15 C16
0.0625 0.0625 0.0625 0.0625 0.0625
Community sample numbers of individuals are:
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16
611 535 505 673 508 451 490 565 593 614 363 416 487 500 520 659
Community sample coverages are:
C1 C2 C3 C4 C5 C6 C7 C8
0.9297145 0.9234551 0.9367824 0.9228400 0.9292655 0.9203540 0.9246729 0.9363708
C9 C10 C11 C12 C13 C14 C15 C16
0.9258805 0.9333838 0.8956499 0.9112655 0.9282659 0.9381433 0.9347629 0.9393571 mc.birds$Nsi
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15
Abyssinian thrush 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0
African Citril 0 4 0 3 8 0 3 2 2 0 3 2 4 3 0
African Dusky Flycatcher 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
African Firefinch 1 1 0 0 1 0 1 0 1 0 0 0 2 2 2
African Gray-Flycatcher 0 0 4 0 1 0 2 1 1 2 1 0 2 0 1
African Green-Pigeon 1 1 0 1 0 0 1 0 0 0 0 2 1 1 0
African Hawk Eagle 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
African Paradise-Flycatcher 0 3 8 0 0 0 1 0 0 2 0 2 1 6 4
African Pied-Wagtail 6 18 5 9 6 1 10 7 8 11 5 0 4 3 7
African Pipit 1 0 3 0 2 4 1 0 2 4 2 0 1 2 0
African Stonechat 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
African Swift 2 1 4 5 2 1 5 2 2 3 1 2 5 2 3
African Thrush 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
African Woolly-necked Stork 1 0 0 1 0 0 0 2 1 0 0 0 0 0 0
African Yellow-Warbler 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0
African-Black-headed Oriole 1 1 3 2 0 2 0 4 4 2 0 0 2 0 2
Amethyst Sunbird 7 8 3 8 8 2 7 10 7 7 0 5 7 8 4
Arrow-marked Babbler 5 4 6 0 2 0 2 0 7 4 0 3 0 2 0
Ashy Flycatcher 1 3 0 0 0 1 0 1 0 0 2 0 1 0 0
Augur Buzzard 1 1 2 3 2 3 5 4 4 2 2 0 5 3 1
Baglafecht Weaver 8 5 3 20 11 2 4 14 11 2 9 1 3 2 9
Banded Martin 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Bare-faced GoAway-bird 6 2 1 1 2 1 2 2 1 2 0 0 4 1 1
Barn Swallow 4 1 2 3 3 9 3 2 2 6 13 3 5 3 2
Black Coucal 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0
Black Cuckoshrike 0 0 3 0 0 0 1 0 1 2 1 0 0 0 0
Black Kite 0 0 0 0 0 1 0 0 0 2 0 0 0 0 0
Black-backed Puffback 0 4 5 2 4 2 7 7 7 7 1 2 4 4 8
Black-chested Snake-Eagle 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0
Black-crowned Tchagra 3 0 3 5 1 5 2 3 4 7 2 6 2 5 0
Black-headed Heron 1 0 0 0 0 0 0 1 3 1 0 0 0 1 5
Black-headed Oriole 5 3 3 1 0 0 1 4 1 3 0 0 0 0 1
Black-headed weaver 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Black-rumped waxbill 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Black-shouldered Kite 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0
Black-throated Wattle-eye 1 0 0 0 0 0 1 0 1 0 0 0 2 1 0
Blue-naped Mousebird 2 3 2 1 0 0 0 0 5 4 0 1 1 1 2
Blue-spotted Wood-Dove 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0
Booted Eagle 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Brimstone Canary 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0
Bronze Mannikin 3 1 1 2 0 3 0 1 1 1 1 0 0 3 8
Bronze Sunbird 11 5 2 9 14 3 7 9 4 6 10 10 8 10 6
Brown-crowned Tchagra 5 9 7 8 6 5 11 5 9 5 7 10 11 4 4
Brown-throated Wattle-eye 0 0 2 2 5 1 1 1 1 2 0 1 3 3 3
Brubru 0 0 0 0 0 0 0 0 0 1 0 1 0 0 2
Cape Robin-Chat 1 3 2 2 4 0 1 1 4 2 3 1 1 1 1
Cape Wagtail 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Cardinal Quelea 3 0 0 2 3 0 5 1 1 2 2 2 7 3 1
Cardinal Woodpecker 1 0 0 3 0 0 0 0 0 0 0 0 0 0 0
Cattle Egret 1 0 2 0 0 0 0 0 0 3 0 0 0 6 0
Chinspot Batis 4 4 6 1 0 3 0 4 1 4 1 1 3 2 2
Cinnamon-breasted Bunting 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Cinnamon-chested Bee-eater 0 2 1 1 1 1 3 6 0 0 2 2 4 1 0
Common Bulbul 23 32 34 52 26 12 34 31 34 31 25 26 19 22 27
Common Buzzard 1 0 0 2 0 0 1 0 0 0 1 0 1 1 1
Common Cuckoo 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0
Common Waxbill 0 4 3 3 0 7 1 1 1 3 3 1 2 3 10
Coqui Francolin 3 0 0 1 7 4 0 1 0 1 1 0 3 3 1
CRB 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
Crowned Eagle 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0
Crowned Lapwing 11 1 4 1 1 11 2 0 3 14 4 5 6 3 5
D'Arnaud's (Usambiro) Barbet 10 26 12 30 9 1 20 23 19 15 7 1 15 11 1
Dideric Cuckoo 2 0 0 0 0 1 0 0 1 1 0 0 0 1 1
Diedrik Cuckcoo 5 0 1 3 0 2 0 0 2 3 0 0 0 3 0
Egyptian Goose 0 0 1 1 1 2 0 1 0 1 1 0 1 0 2
Emerald-spotted Wood Dove 14 29 31 26 14 9 25 24 44 19 10 19 17 15 25
Eurasian Hooby 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Eurasian Hoopoe 1 0 3 2 1 1 2 0 1 0 3 2 2 1 0
European Bee-eater 3 0 0 1 1 0 1 0 0 0 0 0 0 2 1
European Honey-buzzard 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Fan-tailed Widowbird 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Fine-banded woodpecker 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0
Fischer's Sparrow-lark 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Fork-tailed Drongo 2 0 2 1 0 0 0 1 0 0 0 0 0 0 0
Gabar Goshawk 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0
Golden-breasted Bunting 0 2 5 0 1 1 0 0 1 2 3 1 0 0 0
Golden-winged Sunbird 0 1 0 1 0 0 0 2 0 0 0 0 0 0 0
Grassland Pipit 0 0 0 0 0 1 0 0 0 4 0 1 0 3 1
Gray apalis 0 0 2 0 1 1 0 1 1 1 1 1 2 0 0
Gray Crowned-Crane 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1
Gray Flycatcher 0 3 6 1 1 0 0 0 0 6 0 1 0 0 0
Gray Heron 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
Gray-backed Fiscal 0 4 0 0 0 0 1 0 0 1 0 1 0 0 0
Gray-capped Warbler 1 9 3 18 9 3 8 15 1 3 8 12 5 9 9
Gray-crested helmetshrike 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0
Gray-Crowned Crane 1 0 0 0 0 2 0 2 0 0 0 0 0 1 1
Gray-headed bushshrike 1 0 4 1 1 0 3 4 3 1 2 0 3 1 0
Great Spotted Cuckoo 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
Greater Blue-eared Starling 16 1 1 5 0 1 7 2 0 2 2 0 0 1 0
Greater Honeyguide 0 1 0 1 0 0 1 1 1 0 2 0 0 0 0
Green-backed Camaroptera 9 16 17 21 16 7 17 18 23 15 9 17 21 14 20
Green-headed Sunbird 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Green-winged Pytilia 1 5 4 1 6 0 0 4 6 1 1 4 5 2 1
Grosbeak Weaver 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Hadada Ibis 6 1 8 0 0 5 3 2 4 1 3 1 1 2 1
Hamerkop 2 0 1 1 0 0 1 0 0 0 0 1 0 1 0
Harlequin Quail 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0
Helmeted Guineafowl 0 0 0 0 1 11 1 0 0 0 0 1 3 2 3
Hildebrandt's Spurfowl 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1
Hildebrandt's Starling 18 1 2 1 0 2 2 0 1 7 0 0 1 0 0
Holub's Golden-Weaver 4 1 0 1 3 3 2 3 4 3 2 2 2 3 3
Horus Swift 3 0 0 0 0 0 0 0 0 0 0 0 0 0 3
House-Sparrow 14 12 6 13 5 0 9 13 3 8 1 0 5 9 3
Icterine Warbler 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Indigo Bird 1 0 0 2 0 0 0 0 0 0 0 0 0 1 1
Joyful Greenbul 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Kenya Rufous Sparrow 2 0 0 3 0 0 1 0 0 1 0 0 0 0 1
Klaas's Cuckoo 4 11 2 7 2 2 9 1 5 14 3 5 2 5 6
Knob-billed Duck 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
Laughing Dove 3 5 5 12 4 0 7 14 6 6 3 7 6 2 3
Lesser Gray Shrike 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0
Lesser Honeyguide 1 1 2 1 0 0 0 0 0 1 0 0 0 0 0
Lesser Masked-Weaver 5 0 0 2 2 1 2 3 1 2 1 1 4 2 2
Lesser Striped Swallow 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Lesser-Masked Weaver 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0
Levaillant's Cuckoo 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
Little Bee-eater 0 1 2 1 0 1 2 0 2 0 2 1 1 3 0
Little Grebe 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
Little Sparrowhawk 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
Little Swift 2 0 0 0 0 1 1 1 1 2 1 2 0 0 0
Lizard Buzzard 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0
Long-billed Pipit 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Malachite Kingfisher 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0
Marico Sunbird 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
Martial Eagle 0 0 0 0 1 0 1 0 1 1 0 0 1 0 0
Mosque Swallow 4 0 0 1 3 2 1 1 2 0 1 0 1 0 0
Mountain Gray Woodpecker 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0
Mourning Collared-Dove 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0
Northern Anteater-Chat 1 1 1 6 2 3 5 5 2 2 2 2 0 0 0
Northern Black-Flycather 0 0 1 2 0 0 0 0 1 0 2 0 1 0 0
Northern Fiscal 45 18 12 27 13 30 25 24 20 30 17 7 16 16 10
Northern Gray-headed Sparrow 1 0 0 1 0 1 0 0 2 1 1 0 0 0 2
Northern Wheatear 2 1 0 1 1 0 0 0 0 5 3 3 4 4 4
Northern Yellow White-eye 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Nubian Woodpecker 2 0 1 1 0 0 1 0 1 2 0 0 0 0 0
Ovambo Sparrowhawk 0 0 1 1 0 0 0 0 0 0 0 0 0 0 2
Pallid Harrier 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Pectoral-patch Cisticola 0 1 0 0 0 7 1 0 0 1 0 1 0 1 0
Pied crow 1 0 0 0 0 0 0 1 0 3 0 0 0 0 0
Pied Wheatear 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0
Pigmy Kingfisher 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Pin-tailed Whydah 8 3 2 1 0 5 3 1 1 2 2 2 1 5 1
Plain Martin 2 0 0 2 0 4 0 0 0 0 1 0 1 0 3
Plain-backed Pipit 2 1 1 0 2 7 0 0 0 16 7 5 3 15 0
Purple Grenadier 10 20 16 6 16 15 2 5 22 15 6 8 15 17 9
Purple-banded Sunbird 0 2 1 1 1 2 0 0 0 1 0 1 0 0 0
Rattling Cisticola 20 22 9 21 16 24 21 35 19 31 13 12 24 21 25
Red-backed Scrub-Robin 0 2 6 5 3 0 2 7 7 3 1 3 0 0 3
Red-backed Shrike 0 0 0 2 1 0 0 0 1 0 2 1 1 0 0
Red-billed Firefinch 7 2 5 7 2 3 3 2 1 2 1 1 3 4 10
Red-Billed Oxpecker 10 7 6 8 2 5 2 4 6 13 1 2 3 4 0
Red-billed Quelea 0 0 0 2 8 1 2 0 0 2 0 1 3 2 10
Red-capped Lark 0 1 0 0 3 3 0 0 0 2 1 1 1 6 1
Red-chested Cuckoo 1 0 0 0 0 1 0 0 0 0 0 0 0 1 2
Red-cowled Widowbird 0 2 1 0 1 0 2 0 2 4 2 0 2 3 5
Red-eyed Dove 28 11 20 17 19 13 9 9 24 23 4 12 10 12 31
Red-Faced Crombec 0 2 2 0 2 0 0 1 0 0 0 0 1 1 3
Red-fronted Barbet 0 6 5 7 0 1 9 5 3 1 3 2 2 3 1
Red-fronted Tinkerbird 1 3 4 5 7 0 9 8 3 1 1 3 2 3 2
Red-headed Weaver 3 0 1 0 2 1 1 2 1 3 0 3 1 2 2
Red-necked Spurfowl 0 0 0 0 0 0 3 0 0 0 0 0 0 3 0
Red-rumped Swallow 2 1 1 2 0 0 4 1 2 1 0 0 0 0 0
Red-tailed Shrike 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Red-throated Pipit 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0
Red-throated Wryneck 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0
Reichenow's Seedeater 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0
Ring-necked Dove 39 27 23 30 23 22 19 24 33 32 10 18 21 16 14
Rosy-throated Longclaw 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0
Rufous Sparrow 1 1 0 2 0 2 1 1 0 2 0 0 0 0 0
Rufous-naped Lark 9 6 0 1 18 20 2 3 4 2 12 10 15 13 1
Rufous-necked Wryneck 0 0 0 0 0 0 2 0 1 0 1 0 0 0 0
Rufous-tailed Weaver 0 1 0 0 0 1 0 0 0 3 0 0 0 0 0
Rüppell's Starling 4 0 0 1 0 0 0 0 0 1 0 0 0 0 0
Sacred Ibis 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0
Scaly Spurfowl 0 1 0 2 0 0 1 2 1 0 1 1 0 1 0
Scaly-throated Honeyguide 0 0 2 1 0 0 0 0 0 0 0 1 0 0 0
Scarlet-chested Sunbird 7 1 2 0 1 6 2 1 1 3 1 2 2 5 2
Schalow's Turaco 1 0 1 3 0 0 1 1 3 0 0 0 0 0 0
Silverbird 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Silver-breasted Bushshrike 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
Slate-colored Boubou 20 26 17 26 20 16 15 25 30 10 5 21 13 19 14
Sooty Chat 1 0 0 0 0 0 1 0 0 1 0 0 1 1 0
Southern Ground-Hornbill 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
Speckled Mousebird 7 15 14 13 7 2 6 9 12 11 15 10 13 11 8
Speckled Pigeon 2 4 3 8 2 0 6 0 5 2 3 4 0 0 2
Spectacled Weaver 0 0 1 1 3 1 0 1 0 0 0 0 1 0 2
Speke's Weaver 1 3 0 2 0 0 2 1 0 0 0 0 0 0 1
Spot-flanked Barbet 1 1 0 2 1 1 3 2 1 1 2 1 0 2 0
Spotted Flycatcher 0 2 0 0 0 0 0 0 1 1 0 0 0 0 1
Steppe Eagle 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
Stout Cisticola 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0
Straw-tailed Whydah 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0
Streaky Seedeater 1 3 0 6 8 0 2 4 5 1 2 1 3 2 1
Strout Cisticola 0 0 0 0 0 10 0 0 0 0 0 0 0 0 4
Sulphur-breasted Bushshrike 0 2 1 5 1 5 1 4 1 1 0 3 1 0 4
Superb Starling 13 2 0 2 3 10 0 0 1 10 1 0 2 0 0
Swahili Sparrow 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0
Tambourine Dove 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0
Tawny Eagle 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0
Tawny-flanked Prinia 7 10 11 14 17 6 8 13 15 8 3 11 7 4 19
Thick-billed Seedeater 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0
Tropical Boubou 16 12 17 24 15 5 12 23 24 8 9 23 6 12 22
unknown egret 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Variable Sunbird 0 5 4 5 2 6 3 3 6 6 7 6 7 5 5
Village Weaver 6 2 2 14 10 1 5 10 3 6 2 3 6 6 14
Violet-backed Starling 3 0 6 0 0 0 0 0 0 4 0 0 0 0 3
Vitelline Masked-Weaver 3 1 0 2 1 0 0 1 0 0 0 0 0 0 2
Wattled Lapwing 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
Wattled Starling 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0
Western Cattle Egret 7 0 1 0 0 4 0 0 2 0 0 0 1 2 2
Western Yellow Wagtail 0 0 0 1 0 3 1 0 0 0 0 0 2 3 0
Whinchat 1 0 0 0 0 5 0 0 0 0 1 2 0 1 0
White-bellied Canary 5 10 12 9 5 0 2 8 7 8 3 8 5 10 10
White-bellied Tit 0 0 1 0 0 0 0 3 0 1 0 0 0 0 1
White-browed Coucal 1 1 0 1 0 2 2 6 5 0 0 1 0 4 3
White-browed Robin-Chat 10 4 10 4 6 4 5 6 3 7 2 9 13 8 8
White-browed Scrub-Robin 2 4 4 2 5 1 1 3 5 3 2 3 3 1 2
White-browed Sparrow-Weaver 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0
White-eyed Slaty-Flycatcher 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1
White-fronted Bee-eater 0 0 1 0 0 2 0 0 0 0 0 0 1 1 5
White-headed Barbet 0 0 0 0 2 0 0 0 0 0 0 2 0 0 0
White-rumped Swallow 1 0 1 0 0 1 0 0 1 1 0 0 0 0 0
White-rumped Swift 1 0 3 2 1 3 1 1 3 2 1 2 1 0 3
White-winged Widowbird 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
Willow Warbler 1 0 0 1 0 2 1 3 2 3 0 0 1 0 5
Winding Cisticola 0 0 0 0 0 0 1 1 1 2 0 1 0 0 0
Wire-tailed Swallow 2 3 0 1 1 1 0 0 1 1 3 0 4 3 0
Yellow Bishop 12 9 5 15 19 4 8 10 12 9 9 6 16 13 3
Yellow-billed Egret 1 0 0 0 0 0 0 0 0 2 1 1 0 0 1
Yellow-billed Oxpecker 0 0 0 1 1 0 0 0 1 0 1 1 0 0 0
Yellow-breasted Apalis 2 0 4 5 0 0 6 4 0 1 0 5 3 1 5
Yellow-fronted Canary 6 5 8 14 12 12 10 9 6 8 13 10 12 10 3
Yellow-mantled Widowbird 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0
Yellow-necked Spurfowl 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Yellow-rumped Tinkerbird 0 0 2 0 1 0 1 2 1 0 0 0 1 1 1
Yellow-spotted Bush Sparrow 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
Yellow-throated Longclaw 2 1 1 1 5 12 0 1 5 2 5 5 3 5 3
Zittling Cisticola 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0
C16
Abyssinian thrush 0
African Citril 0
African Dusky Flycatcher 0
African Firefinch 1
African Gray-Flycatcher 1
African Green-Pigeon 1
African Hawk Eagle 0
African Paradise-Flycatcher 2
African Pied-Wagtail 4
African Pipit 0
African Stonechat 0
African Swift 0
African Thrush 0
African Woolly-necked Stork 1
African Yellow-Warbler 0
African-Black-headed Oriole 1
Amethyst Sunbird 5
Arrow-marked Babbler 3
Ashy Flycatcher 0
Augur Buzzard 1
Baglafecht Weaver 1
Banded Martin 0
Bare-faced GoAway-bird 1
Barn Swallow 2
Black Coucal 0
Black Cuckoshrike 3
Black Kite 0
Black-backed Puffback 11
Black-chested Snake-Eagle 0
Black-crowned Tchagra 3
Black-headed Heron 6
Black-headed Oriole 1
Black-headed weaver 0
Black-rumped waxbill 0
Black-shouldered Kite 0
Black-throated Wattle-eye 0
Blue-naped Mousebird 3
Blue-spotted Wood-Dove 0
Booted Eagle 0
Brimstone Canary 0
Bronze Mannikin 6
Bronze Sunbird 9
Brown-crowned Tchagra 8
Brown-throated Wattle-eye 2
Brubru 1
Cape Robin-Chat 1
Cape Wagtail 0
Cardinal Quelea 0
Cardinal Woodpecker 0
Cattle Egret 0
Chinspot Batis 8
Cinnamon-breasted Bunting 0
Cinnamon-chested Bee-eater 2
Common Bulbul 61
Common Buzzard 0
Common Cuckoo 0
Common Waxbill 2
Coqui Francolin 0
CRB 0
Crowned Eagle 0
Crowned Lapwing 4
D'Arnaud's (Usambiro) Barbet 8
Dideric Cuckoo 5
Diedrik Cuckcoo 0
Egyptian Goose 3
Emerald-spotted Wood Dove 36
Eurasian Hooby 0
Eurasian Hoopoe 0
European Bee-eater 1
European Honey-buzzard 1
Fan-tailed Widowbird 0
Fine-banded woodpecker 0
Fischer's Sparrow-lark 0
Fork-tailed Drongo 0
Gabar Goshawk 0
Golden-breasted Bunting 1
Golden-winged Sunbird 1
Grassland Pipit 0
Gray apalis 0
Gray Crowned-Crane 1
Gray Flycatcher 9
Gray Heron 1
Gray-backed Fiscal 0
Gray-capped Warbler 5
Gray-crested helmetshrike 0
Gray-Crowned Crane 0
Gray-headed bushshrike 3
Great Spotted Cuckoo 0
Greater Blue-eared Starling 2
Greater Honeyguide 0
Green-backed Camaroptera 31
Green-headed Sunbird 1
Green-winged Pytilia 2
Grosbeak Weaver 1
Hadada Ibis 3
Hamerkop 0
Harlequin Quail 0
Helmeted Guineafowl 0
Hildebrandt's Spurfowl 0
Hildebrandt's Starling 0
Holub's Golden-Weaver 8
Horus Swift 0
House-Sparrow 3
Icterine Warbler 0
Indigo Bird 0
Joyful Greenbul 1
Kenya Rufous Sparrow 0
Klaas's Cuckoo 14
Knob-billed Duck 0
Laughing Dove 4
Lesser Gray Shrike 0
Lesser Honeyguide 0
Lesser Masked-Weaver 0
Lesser Striped Swallow 0
Lesser-Masked Weaver 0
Levaillant's Cuckoo 0
Little Bee-eater 0
Little Grebe 0
Little Sparrowhawk 0
Little Swift 1
Lizard Buzzard 0
Long-billed Pipit 0
Malachite Kingfisher 0
Marico Sunbird 0
Martial Eagle 0
Mosque Swallow 0
Mountain Gray Woodpecker 0
Mourning Collared-Dove 0
Northern Anteater-Chat 2
Northern Black-Flycather 0
Northern Fiscal 18
Northern Gray-headed Sparrow 0
Northern Wheatear 0
Northern Yellow White-eye 1
Nubian Woodpecker 1
Ovambo Sparrowhawk 1
Pallid Harrier 0
Pectoral-patch Cisticola 0
Pied crow 0
Pied Wheatear 0
Pigmy Kingfisher 1
Pin-tailed Whydah 0
Plain Martin 0
Plain-backed Pipit 1
Purple Grenadier 13
Purple-banded Sunbird 0
Rattling Cisticola 41
Red-backed Scrub-Robin 1
Red-backed Shrike 0
Red-billed Firefinch 2
Red-Billed Oxpecker 0
Red-billed Quelea 0
Red-capped Lark 2
Red-chested Cuckoo 1
Red-cowled Widowbird 1
Red-eyed Dove 19
Red-Faced Crombec 0
Red-fronted Barbet 6
Red-fronted Tinkerbird 6
Red-headed Weaver 3
Red-necked Spurfowl 0
Red-rumped Swallow 1
Red-tailed Shrike 1
Red-throated Pipit 0
Red-throated Wryneck 0
Reichenow's Seedeater 0
Ring-necked Dove 38
Rosy-throated Longclaw 0
Rufous Sparrow 0
Rufous-naped Lark 4
Rufous-necked Wryneck 0
Rufous-tailed Weaver 0
Rüppell's Starling 0
Sacred Ibis 0
Scaly Spurfowl 0
Scaly-throated Honeyguide 0
Scarlet-chested Sunbird 2
Schalow's Turaco 0
Silverbird 0
Silver-breasted Bushshrike 0
Slate-colored Boubou 38
Sooty Chat 0
Southern Ground-Hornbill 0
Speckled Mousebird 16
Speckled Pigeon 1
Spectacled Weaver 0
Speke's Weaver 0
Spot-flanked Barbet 4
Spotted Flycatcher 1
Steppe Eagle 0
Stout Cisticola 0
Straw-tailed Whydah 0
Streaky Seedeater 3
Strout Cisticola 1
Sulphur-breasted Bushshrike 7
Superb Starling 1
Swahili Sparrow 0
Tambourine Dove 0
Tawny Eagle 0
Tawny-flanked Prinia 22
Thick-billed Seedeater 0
Tropical Boubou 21
unknown egret 0
Variable Sunbird 6
Village Weaver 1
Violet-backed Starling 0
Vitelline Masked-Weaver 1
Wattled Lapwing 0
Wattled Starling 0
Western Cattle Egret 1
Western Yellow Wagtail 0
Whinchat 0
White-bellied Canary 11
White-bellied Tit 0
White-browed Coucal 3
White-browed Robin-Chat 9
White-browed Scrub-Robin 6
White-browed Sparrow-Weaver 0
White-eyed Slaty-Flycatcher 0
White-fronted Bee-eater 5
White-headed Barbet 0
White-rumped Swallow 0
White-rumped Swift 2
White-winged Widowbird 0
Willow Warbler 6
Winding Cisticola 0
Wire-tailed Swallow 0
Yellow Bishop 3
Yellow-billed Egret 1
Yellow-billed Oxpecker 0
Yellow-breasted Apalis 7
Yellow-fronted Canary 7
Yellow-mantled Widowbird 0
Yellow-necked Spurfowl 0
Yellow-rumped Tinkerbird 1
Yellow-spotted Bush Sparrow 0
Yellow-throated Longclaw 7
Zittling Cisticola 0
$Ns
Abyssinian thrush African Citril
2.7277992 36.0840813
African Dusky Flycatcher African Firefinch
1.0507426 12.0302566
African Gray-Flycatcher African Green-Pigeon
16.4422829 9.2387393
African Hawk Eagle African Paradise-Flycatcher
1.4617769 29.8930223
African Pied-Wagtail African Pipit
102.1398307 23.2809746
African Stonechat African Swift
0.9391593 40.4592332
African Thrush African Woolly-necked Stork
0.9391593 5.2352310
African Yellow-Warbler African-Black-headed Oriole
2.3200783 23.0320384
Amethyst Sunbird Arrow-marked Babbler
94.0705592 36.9541594
Ashy Flycatcher Augur Buzzard
9.9727648 39.8055190
Baglafecht Weaver Banded Martin
103.5340311 0.8684534
Bare-faced GoAway-bird Barn Swallow
26.2117475 69.8696035
Black Coucal Black Cuckoshrike
2.5125194 10.7357392
Black Kite Black-backed Puffback
2.9049723 73.4336016
Black-chested Snake-Eagle Black-crowned Tchagra
3.1321441 51.7169437
Black-headed Heron Black-headed Oriole
16.3508633 21.4110291
Black-headed weaver Black-rumped waxbill
1.0445374 0.9918224
Black-shouldered Kite Black-throated Wattle-eye
3.0852996 6.0865841
Blue-naped Mousebird Blue-spotted Wood-Dove
23.4160464 1.8780263
Booted Eagle Brimstone Canary
0.9918224 4.0771220
Bronze Mannikin Bronze Sunbird
30.0928318 126.7317800
Brown-crowned Tchagra Brown-throated Wattle-eye
117.8051221 27.1226416
Brubru Cape Robin-Chat
4.9858136 28.6675260
Cape Wagtail Cardinal Quelea
1.0507426 33.5986731
Cardinal Woodpecker Cattle Egret
3.2337951 11.9300688
Chinspot Batis Cinnamon-breasted Bunting
42.7829526 1.4617769
Cinnamon-chested Bee-eater Common Bulbul
27.4322006 483.2875711
Common Buzzard Common Cuckoo
8.1612946 2.2502241
Common Waxbill Coqui Francolin
46.0687372 26.1498437
CRB Crowned Eagle
1.0507426 2.3200783
Crowned Lapwing D'Arnaud's (Usambiro) Barbet
76.7410273 201.5225134
Dideric Cuckoo Diedrik Cuckcoo
10.7801525 17.6774646
Egyptian Goose Emerald-spotted Wood Dove
14.0480139 352.4032534
Eurasian Hooby Eurasian Hoopoe
0.9918224 21.0561163
European Bee-eater European Honey-buzzard
9.4693828 1.8256300
Fan-tailed Widowbird Fine-banded woodpecker
1.0204327 3.9030188
Fischer's Sparrow-lark Fork-tailed Drongo
0.9391593 5.5659984
Gabar Goshawk Golden-breasted Bunting
2.0450055 18.5477506
Golden-winged Sunbird Grassland Pipit
4.4637855 10.1131161
Gray apalis Gray Crowned-Crane
11.9372344 4.9318854
Gray Flycatcher Gray Heron
24.8204843 2.8460627
Gray-backed Fiscal Gray-capped Warbler
7.1899488 121.5174570
Gray-crested helmetshrike Gray-Crowned Crane
2.7202426 7.1815588
Gray-headed bushshrike Great Spotted Cuckoo
27.1275563 1.0895791
Greater Blue-eared Starling Greater Honeyguide
37.8389012 7.6207054
Green-backed Camaroptera Green-headed Sunbird
269.8605251 0.8051973
Green-winged Pytilia Grosbeak Weaver
43.8411046 0.8051973
Hadada Ibis Hamerkop
42.3707299 6.9957956
Harlequin Quail Helmeted Guineafowl
2.3531042 24.7975948
Hildebrandt's Spurfowl Hildebrandt's Starling
4.8538266 32.0667000
Holub's Golden-Weaver Horus Swift
43.4129539 5.6666581
House-Sparrow Icterine Warbler
99.3283534 0.9391593
Indigo Bird Joyful Greenbul
4.5270305 0.8051973
Kenya Rufous Sparrow Klaas's Cuckoo
7.0697994 89.3488026
Knob-billed Duck Laughing Dove
2.0816827 85.9966147
Lesser Gray Shrike Lesser Honeyguide
5.3723333 5.6144183
Lesser Masked-Weaver Lesser Striped Swallow
28.0503164 0.8642101
Lesser-Masked Weaver Levaillant's Cuckoo
3.8617230 1.4617769
Little Bee-eater Little Grebe
17.4861759 0.9391593
Little Sparrowhawk Little Swift
1.0612500 12.3768168
Lizard Buzzard Long-billed Pipit
2.2502241 0.8642101
Malachite Kingfisher Marico Sunbird
3.7120010 0.9918224
Martial Eagle Mosque Swallow
4.9760492 16.1120295
Mountain Gray Woodpecker Mourning Collared-Dove
2.8631778 3.1336122
Northern Anteater-Chat Northern Black-Flycather
33.9738495 7.5355844
Northern Fiscal Northern Gray-headed Sparrow
326.0309962 8.9899341
Northern Wheatear Northern Yellow White-eye
29.7797644 0.8051973
Nubian Woodpecker Ovambo Sparrowhawk
8.0874367 4.6852525
Pallid Harrier Pectoral-patch Cisticola
0.8642101 13.5115963
Pied crow Pied Wheatear
4.4002429 2.2210895
Pigmy Kingfisher Pin-tailed Whydah
0.8051973 38.3978027
Plain Martin Plain-backed Pipit
13.6326636 64.5345996
Purple Grenadier Purple-banded Sunbird
196.9464928 9.3602273
Rattling Cisticola Red-backed Scrub-Robin
350.8263624 42.1151067
Red-backed Shrike Red-billed Firefinch
8.8049200 54.2704472
Red-Billed Oxpecker Red-billed Quelea
70.2638269 31.8750873
Red-capped Lark Red-chested Cuckoo
22.2087352 5.9523181
Red-cowled Widowbird Red-eyed Dove
25.6850331 256.0541208
Red-Faced Crombec Red-fronted Barbet
12.3254912 54.0418906
Red-fronted Tinkerbird Red-headed Weaver
57.6323470 24.8655599
Red-necked Spurfowl Red-rumped Swallow
6.4324745 14.0861945
Red-tailed Shrike Red-throated Pipit
0.8051973 1.9254601
Red-throated Wryneck Reichenow's Seedeater
1.5768945 1.8780263
Ring-necked Dove Rosy-throated Longclaw
380.9987878 2.7202426
Rufous Sparrow Rufous-naped Lark
9.5407622 131.8570384
Rufous-necked Wryneck Rufous-tailed Weaver
4.5224077 4.7610048
Rüppell's Starling Sacred Ibis
5.1264708 2.8097672
Scaly Spurfowl Scaly-throated Honeyguide
10.2233259 4.1654733
Scarlet-chested Sunbird Schalow's Turaco
39.0182781 8.9910486
Silverbird Silver-breasted Bushshrike
0.8684534 2.1225000
Slate-colored Boubou Sooty Chat
309.7504992 4.9664007
Southern Ground-Hornbill Speckled Mousebird
2.3651199 172.3425111
Speckled Pigeon Spectacled Weaver
42.2865753 10.2189579
Speke's Weaver Spot-flanked Barbet
9.5462235 22.0867111
Spotted Flycatcher Steppe Eagle
5.5682994 0.8642101
Stout Cisticola Straw-tailed Whydah
1.9836449 2.3531042
Streaky Seedeater Strout Cisticola
41.2179957 16.6524491
Sulphur-breasted Bushshrike Superb Starling
35.1368049 43.7326140
Swahili Sparrow Tambourine Dove
3.1236704 1.7896290
Tawny Eagle Tawny-flanked Prinia
2.4535993 172.0094051
Thick-billed Seedeater Tropical Boubou
2.0890748 248.2426605
unknown egret Variable Sunbird
0.8684534 79.6253898
Village Weaver Violet-backed Starling
89.3782812 15.4279540
Vitelline Masked-Weaver Wattled Lapwing
10.0038363 1.0445374
Wattled Starling Western Cattle Egret
2.5926303 19.6838952
Western Yellow Wagtail Whinchat
10.7639198 11.8253225
White-bellied Canary White-bellied Tit
111.7561369 5.7528632
White-browed Coucal White-browed Robin-Chat
28.2741026 109.5380678
White-browed Scrub-Robin White-browed Sparrow-Weaver
46.8026017 3.0083683
White-eyed Slaty-Flycatcher White-fronted Bee-eater
3.0629977 14.6828256
White-headed Barbet White-rumped Swallow
4.6401565 4.8547726
White-rumped Swift White-winged Widowbird
26.3808307 0.7884473
Willow Warbler Winding Cisticola
23.3155763 5.9208430
Wire-tailed Swallow Yellow Bishop
21.4083322 155.0668477
Yellow-billed Egret Yellow-billed Oxpecker
7.1598212 5.4651169
Yellow-breasted Apalis Yellow-fronted Canary
42.4466453 151.9748270
Yellow-mantled Widowbird Yellow-necked Spurfowl
2.0639881 3.4738134
Yellow-rumped Tinkerbird Yellow-spotted Bush Sparrow
10.9785228 1.0204327
Yellow-throated Longclaw Zittling Cisticola
62.0101380 1.8780263
$Ni
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16
611 535 505 673 508 451 490 565 593 614 363 416 487 500 520 659
$N
[1] 8490
$Psi
C1 C2 C3 C4
Abyssinian thrush 0.000000000 0.000000000 0.000000000 0.001485884
African Citril 0.000000000 0.007476636 0.000000000 0.004457652
African Dusky Flycatcher 0.000000000 0.000000000 0.001980198 0.000000000
African Firefinch 0.001636661 0.001869159 0.000000000 0.000000000
African Gray-Flycatcher 0.000000000 0.000000000 0.007920792 0.000000000
African Green-Pigeon 0.001636661 0.001869159 0.000000000 0.001485884
African Hawk Eagle 0.000000000 0.000000000 0.000000000 0.000000000
African Paradise-Flycatcher 0.000000000 0.005607477 0.015841584 0.000000000
African Pied-Wagtail 0.009819967 0.033644860 0.009900990 0.013372957
African Pipit 0.001636661 0.000000000 0.005940594 0.000000000
African Stonechat 0.000000000 0.000000000 0.000000000 0.000000000
African Swift 0.003273322 0.001869159 0.007920792 0.007429421
African Thrush 0.000000000 0.000000000 0.000000000 0.000000000
African Woolly-necked Stork 0.001636661 0.000000000 0.000000000 0.001485884
African Yellow-Warbler 0.000000000 0.000000000 0.000000000 0.000000000
African-Black-headed Oriole 0.001636661 0.001869159 0.005940594 0.002971768
Amethyst Sunbird 0.011456628 0.014953271 0.005940594 0.011887073
Arrow-marked Babbler 0.008183306 0.007476636 0.011881188 0.000000000
Ashy Flycatcher 0.001636661 0.005607477 0.000000000 0.000000000
Augur Buzzard 0.001636661 0.001869159 0.003960396 0.004457652
Baglafecht Weaver 0.013093290 0.009345794 0.005940594 0.029717682
Banded Martin 0.001636661 0.000000000 0.000000000 0.000000000
Bare-faced GoAway-bird 0.009819967 0.003738318 0.001980198 0.001485884
Barn Swallow 0.006546645 0.001869159 0.003960396 0.004457652
Black Coucal 0.000000000 0.000000000 0.001980198 0.000000000
Black Cuckoshrike 0.000000000 0.000000000 0.005940594 0.000000000
Black Kite 0.000000000 0.000000000 0.000000000 0.000000000
Black-backed Puffback 0.000000000 0.007476636 0.009900990 0.002971768
Black-chested Snake-Eagle 0.000000000 0.001869159 0.001980198 0.000000000
Black-crowned Tchagra 0.004909984 0.000000000 0.005940594 0.007429421
Black-headed Heron 0.001636661 0.000000000 0.000000000 0.000000000
Black-headed Oriole 0.008183306 0.005607477 0.005940594 0.001485884
Black-headed weaver 0.000000000 0.000000000 0.000000000 0.000000000
Black-rumped waxbill 0.000000000 0.001869159 0.000000000 0.000000000
Black-shouldered Kite 0.000000000 0.000000000 0.000000000 0.000000000
Black-throated Wattle-eye 0.001636661 0.000000000 0.000000000 0.000000000
Blue-naped Mousebird 0.003273322 0.005607477 0.003960396 0.001485884
Blue-spotted Wood-Dove 0.000000000 0.000000000 0.000000000 0.001485884
Booted Eagle 0.000000000 0.001869159 0.000000000 0.000000000
Brimstone Canary 0.000000000 0.001869159 0.000000000 0.000000000
Bronze Mannikin 0.004909984 0.001869159 0.001980198 0.002971768
Bronze Sunbird 0.018003273 0.009345794 0.003960396 0.013372957
Brown-crowned Tchagra 0.008183306 0.016822430 0.013861386 0.011887073
Brown-throated Wattle-eye 0.000000000 0.000000000 0.003960396 0.002971768
Brubru 0.000000000 0.000000000 0.000000000 0.000000000
Cape Robin-Chat 0.001636661 0.005607477 0.003960396 0.002971768
Cape Wagtail 0.000000000 0.000000000 0.001980198 0.000000000
Cardinal Quelea 0.004909984 0.000000000 0.000000000 0.002971768
Cardinal Woodpecker 0.001636661 0.000000000 0.000000000 0.004457652
Cattle Egret 0.001636661 0.000000000 0.003960396 0.000000000
Chinspot Batis 0.006546645 0.007476636 0.011881188 0.001485884
Cinnamon-breasted Bunting 0.000000000 0.000000000 0.000000000 0.000000000
Cinnamon-chested Bee-eater 0.000000000 0.003738318 0.001980198 0.001485884
Common Bulbul 0.037643208 0.059813084 0.067326733 0.077265973
Common Buzzard 0.001636661 0.000000000 0.000000000 0.002971768
Common Cuckoo 0.000000000 0.000000000 0.000000000 0.001485884
Common Waxbill 0.000000000 0.007476636 0.005940594 0.004457652
Coqui Francolin 0.004909984 0.000000000 0.000000000 0.001485884
CRB 0.000000000 0.000000000 0.001980198 0.000000000
Crowned Eagle 0.000000000 0.000000000 0.000000000 0.000000000
Crowned Lapwing 0.018003273 0.001869159 0.007920792 0.001485884
D'Arnaud's (Usambiro) Barbet 0.016366612 0.048598131 0.023762376 0.044576523
Dideric Cuckoo 0.003273322 0.000000000 0.000000000 0.000000000
Diedrik Cuckcoo 0.008183306 0.000000000 0.001980198 0.004457652
Egyptian Goose 0.000000000 0.000000000 0.001980198 0.001485884
Emerald-spotted Wood Dove 0.022913257 0.054205607 0.061386139 0.038632987
Eurasian Hooby 0.000000000 0.001869159 0.000000000 0.000000000
Eurasian Hoopoe 0.001636661 0.000000000 0.005940594 0.002971768
European Bee-eater 0.004909984 0.000000000 0.000000000 0.001485884
European Honey-buzzard 0.000000000 0.000000000 0.000000000 0.000000000
Fan-tailed Widowbird 0.000000000 0.000000000 0.000000000 0.000000000
Fine-banded woodpecker 0.001636661 0.000000000 0.000000000 0.000000000
Fischer's Sparrow-lark 0.000000000 0.000000000 0.000000000 0.000000000
Fork-tailed Drongo 0.003273322 0.000000000 0.003960396 0.001485884
Gabar Goshawk 0.001636661 0.000000000 0.000000000 0.000000000
Golden-breasted Bunting 0.000000000 0.003738318 0.009900990 0.000000000
Golden-winged Sunbird 0.000000000 0.001869159 0.000000000 0.001485884
Grassland Pipit 0.000000000 0.000000000 0.000000000 0.000000000
Gray apalis 0.000000000 0.000000000 0.003960396 0.000000000
Gray Crowned-Crane 0.001636661 0.000000000 0.000000000 0.000000000
Gray Flycatcher 0.000000000 0.005607477 0.011881188 0.001485884
Gray Heron 0.000000000 0.000000000 0.000000000 0.000000000
Gray-backed Fiscal 0.000000000 0.007476636 0.000000000 0.000000000
Gray-capped Warbler 0.001636661 0.016822430 0.005940594 0.026745914
Gray-crested helmetshrike 0.000000000 0.001869159 0.000000000 0.000000000
Gray-Crowned Crane 0.001636661 0.000000000 0.000000000 0.000000000
Gray-headed bushshrike 0.001636661 0.000000000 0.007920792 0.001485884
Great Spotted Cuckoo 0.000000000 0.000000000 0.000000000 0.000000000
Greater Blue-eared Starling 0.026186579 0.001869159 0.001980198 0.007429421
Greater Honeyguide 0.000000000 0.001869159 0.000000000 0.001485884
Green-backed Camaroptera 0.014729951 0.029906542 0.033663366 0.031203566
Green-headed Sunbird 0.000000000 0.000000000 0.000000000 0.000000000
Green-winged Pytilia 0.001636661 0.009345794 0.007920792 0.001485884
Grosbeak Weaver 0.000000000 0.000000000 0.000000000 0.000000000
Hadada Ibis 0.009819967 0.001869159 0.015841584 0.000000000
Hamerkop 0.003273322 0.000000000 0.001980198 0.001485884
Harlequin Quail 0.000000000 0.000000000 0.000000000 0.000000000
Helmeted Guineafowl 0.000000000 0.000000000 0.000000000 0.000000000
Hildebrandt's Spurfowl 0.000000000 0.000000000 0.000000000 0.001485884
Hildebrandt's Starling 0.029459902 0.001869159 0.003960396 0.001485884
Holub's Golden-Weaver 0.006546645 0.001869159 0.000000000 0.001485884
Horus Swift 0.004909984 0.000000000 0.000000000 0.000000000
House-Sparrow 0.022913257 0.022429907 0.011881188 0.019316493
Icterine Warbler 0.000000000 0.000000000 0.000000000 0.000000000
Indigo Bird 0.001636661 0.000000000 0.000000000 0.002971768
Joyful Greenbul 0.000000000 0.000000000 0.000000000 0.000000000
Kenya Rufous Sparrow 0.003273322 0.000000000 0.000000000 0.004457652
Klaas's Cuckoo 0.006546645 0.020560748 0.003960396 0.010401189
Knob-billed Duck 0.000000000 0.000000000 0.000000000 0.000000000
Laughing Dove 0.004909984 0.009345794 0.009900990 0.017830609
Lesser Gray Shrike 0.000000000 0.000000000 0.001980198 0.001485884
Lesser Honeyguide 0.001636661 0.001869159 0.003960396 0.001485884
Lesser Masked-Weaver 0.008183306 0.000000000 0.000000000 0.002971768
Lesser Striped Swallow 0.000000000 0.000000000 0.000000000 0.000000000
Lesser-Masked Weaver 0.000000000 0.000000000 0.000000000 0.001485884
Levaillant's Cuckoo 0.000000000 0.000000000 0.000000000 0.000000000
Little Bee-eater 0.000000000 0.001869159 0.003960396 0.001485884
Little Grebe 0.000000000 0.000000000 0.000000000 0.000000000
Little Sparrowhawk 0.000000000 0.000000000 0.000000000 0.000000000
Little Swift 0.003273322 0.000000000 0.000000000 0.000000000
Lizard Buzzard 0.000000000 0.000000000 0.000000000 0.001485884
Long-billed Pipit 0.000000000 0.000000000 0.000000000 0.000000000
Malachite Kingfisher 0.000000000 0.000000000 0.000000000 0.001485884
Marico Sunbird 0.000000000 0.001869159 0.000000000 0.000000000
Martial Eagle 0.000000000 0.000000000 0.000000000 0.000000000
Mosque Swallow 0.006546645 0.000000000 0.000000000 0.001485884
Mountain Gray Woodpecker 0.000000000 0.001869159 0.000000000 0.001485884
Mourning Collared-Dove 0.000000000 0.000000000 0.000000000 0.000000000
Northern Anteater-Chat 0.001636661 0.001869159 0.001980198 0.008915305
Northern Black-Flycather 0.000000000 0.000000000 0.001980198 0.002971768
Northern Fiscal 0.073649755 0.033644860 0.023762376 0.040118871
Northern Gray-headed Sparrow 0.001636661 0.000000000 0.000000000 0.001485884
Northern Wheatear 0.003273322 0.001869159 0.000000000 0.001485884
Northern Yellow White-eye 0.000000000 0.000000000 0.000000000 0.000000000
Nubian Woodpecker 0.003273322 0.000000000 0.001980198 0.001485884
Ovambo Sparrowhawk 0.000000000 0.000000000 0.001980198 0.001485884
Pallid Harrier 0.000000000 0.000000000 0.000000000 0.000000000
Pectoral-patch Cisticola 0.000000000 0.001869159 0.000000000 0.000000000
Pied crow 0.001636661 0.000000000 0.000000000 0.000000000
Pied Wheatear 0.000000000 0.000000000 0.000000000 0.000000000
Pigmy Kingfisher 0.000000000 0.000000000 0.000000000 0.000000000
Pin-tailed Whydah 0.013093290 0.005607477 0.003960396 0.001485884
Plain Martin 0.003273322 0.000000000 0.000000000 0.002971768
Plain-backed Pipit 0.003273322 0.001869159 0.001980198 0.000000000
Purple Grenadier 0.016366612 0.037383178 0.031683168 0.008915305
Purple-banded Sunbird 0.000000000 0.003738318 0.001980198 0.001485884
Rattling Cisticola 0.032733224 0.041121495 0.017821782 0.031203566
Red-backed Scrub-Robin 0.000000000 0.003738318 0.011881188 0.007429421
Red-backed Shrike 0.000000000 0.000000000 0.000000000 0.002971768
Red-billed Firefinch 0.011456628 0.003738318 0.009900990 0.010401189
Red-Billed Oxpecker 0.016366612 0.013084112 0.011881188 0.011887073
Red-billed Quelea 0.000000000 0.000000000 0.000000000 0.002971768
Red-capped Lark 0.000000000 0.001869159 0.000000000 0.000000000
Red-chested Cuckoo 0.001636661 0.000000000 0.000000000 0.000000000
Red-cowled Widowbird 0.000000000 0.003738318 0.001980198 0.000000000
Red-eyed Dove 0.045826514 0.020560748 0.039603960 0.025260030
Red-Faced Crombec 0.000000000 0.003738318 0.003960396 0.000000000
Red-fronted Barbet 0.000000000 0.011214953 0.009900990 0.010401189
Red-fronted Tinkerbird 0.001636661 0.005607477 0.007920792 0.007429421
Red-headed Weaver 0.004909984 0.000000000 0.001980198 0.000000000
Red-necked Spurfowl 0.000000000 0.000000000 0.000000000 0.000000000
Red-rumped Swallow 0.003273322 0.001869159 0.001980198 0.002971768
Red-tailed Shrike 0.000000000 0.000000000 0.000000000 0.000000000
Red-throated Pipit 0.000000000 0.000000000 0.000000000 0.000000000
Red-throated Wryneck 0.000000000 0.000000000 0.000000000 0.002971768
Reichenow's Seedeater 0.000000000 0.000000000 0.000000000 0.001485884
Ring-necked Dove 0.063829787 0.050467290 0.045544554 0.044576523
Rosy-throated Longclaw 0.000000000 0.001869159 0.000000000 0.000000000
Rufous Sparrow 0.001636661 0.001869159 0.000000000 0.002971768
Rufous-naped Lark 0.014729951 0.011214953 0.000000000 0.001485884
Rufous-necked Wryneck 0.000000000 0.000000000 0.000000000 0.000000000
Rufous-tailed Weaver 0.000000000 0.001869159 0.000000000 0.000000000
Rüppell's Starling 0.006546645 0.000000000 0.000000000 0.001485884
Sacred Ibis 0.000000000 0.000000000 0.001980198 0.000000000
Scaly Spurfowl 0.000000000 0.001869159 0.000000000 0.002971768
Scaly-throated Honeyguide 0.000000000 0.000000000 0.003960396 0.001485884
Scarlet-chested Sunbird 0.011456628 0.001869159 0.003960396 0.000000000
Schalow's Turaco 0.001636661 0.000000000 0.001980198 0.004457652
Silverbird 0.001636661 0.000000000 0.000000000 0.000000000
Silver-breasted Bushshrike 0.000000000 0.000000000 0.000000000 0.000000000
Slate-colored Boubou 0.032733224 0.048598131 0.033663366 0.038632987
Sooty Chat 0.001636661 0.000000000 0.000000000 0.000000000
Southern Ground-Hornbill 0.000000000 0.000000000 0.000000000 0.000000000
Speckled Mousebird 0.011456628 0.028037383 0.027722772 0.019316493
Speckled Pigeon 0.003273322 0.007476636 0.005940594 0.011887073
Spectacled Weaver 0.000000000 0.000000000 0.001980198 0.001485884
Speke's Weaver 0.001636661 0.005607477 0.000000000 0.002971768
Spot-flanked Barbet 0.001636661 0.001869159 0.000000000 0.002971768
Spotted Flycatcher 0.000000000 0.003738318 0.000000000 0.000000000
Steppe Eagle 0.000000000 0.000000000 0.000000000 0.000000000
Stout Cisticola 0.000000000 0.003738318 0.000000000 0.000000000
Straw-tailed Whydah 0.000000000 0.000000000 0.000000000 0.000000000
Streaky Seedeater 0.001636661 0.005607477 0.000000000 0.008915305
Strout Cisticola 0.000000000 0.000000000 0.000000000 0.000000000
Sulphur-breasted Bushshrike 0.000000000 0.003738318 0.001980198 0.007429421
Superb Starling 0.021276596 0.003738318 0.000000000 0.002971768
Swahili Sparrow 0.000000000 0.000000000 0.000000000 0.000000000
Tambourine Dove 0.000000000 0.000000000 0.000000000 0.000000000
Tawny Eagle 0.000000000 0.001869159 0.000000000 0.000000000
Tawny-flanked Prinia 0.011456628 0.018691589 0.021782178 0.020802377
Thick-billed Seedeater 0.000000000 0.000000000 0.000000000 0.000000000
Tropical Boubou 0.026186579 0.022429907 0.033663366 0.035661218
unknown egret 0.001636661 0.000000000 0.000000000 0.000000000
Variable Sunbird 0.000000000 0.009345794 0.007920792 0.007429421
Village Weaver 0.009819967 0.003738318 0.003960396 0.020802377
Violet-backed Starling 0.004909984 0.000000000 0.011881188 0.000000000
Vitelline Masked-Weaver 0.004909984 0.001869159 0.000000000 0.002971768
Wattled Lapwing 0.000000000 0.000000000 0.000000000 0.000000000
Wattled Starling 0.000000000 0.000000000 0.000000000 0.000000000
Western Cattle Egret 0.011456628 0.000000000 0.001980198 0.000000000
Western Yellow Wagtail 0.000000000 0.000000000 0.000000000 0.001485884
Whinchat 0.001636661 0.000000000 0.000000000 0.000000000
White-bellied Canary 0.008183306 0.018691589 0.023762376 0.013372957
White-bellied Tit 0.000000000 0.000000000 0.001980198 0.000000000
White-browed Coucal 0.001636661 0.001869159 0.000000000 0.001485884
White-browed Robin-Chat 0.016366612 0.007476636 0.019801980 0.005943536
White-browed Scrub-Robin 0.003273322 0.007476636 0.007920792 0.002971768
White-browed Sparrow-Weaver 0.000000000 0.000000000 0.000000000 0.000000000
White-eyed Slaty-Flycatcher 0.000000000 0.001869159 0.001980198 0.000000000
White-fronted Bee-eater 0.000000000 0.000000000 0.001980198 0.000000000
White-headed Barbet 0.000000000 0.000000000 0.000000000 0.000000000
White-rumped Swallow 0.001636661 0.000000000 0.001980198 0.000000000
White-rumped Swift 0.001636661 0.000000000 0.005940594 0.002971768
White-winged Widowbird 0.000000000 0.000000000 0.000000000 0.001485884
Willow Warbler 0.001636661 0.000000000 0.000000000 0.001485884
Winding Cisticola 0.000000000 0.000000000 0.000000000 0.000000000
Wire-tailed Swallow 0.003273322 0.005607477 0.000000000 0.001485884
Yellow Bishop 0.019639935 0.016822430 0.009900990 0.022288262
Yellow-billed Egret 0.001636661 0.000000000 0.000000000 0.000000000
Yellow-billed Oxpecker 0.000000000 0.000000000 0.000000000 0.001485884
Yellow-breasted Apalis 0.003273322 0.000000000 0.007920792 0.007429421
Yellow-fronted Canary 0.009819967 0.009345794 0.015841584 0.020802377
Yellow-mantled Widowbird 0.000000000 0.000000000 0.000000000 0.001485884
Yellow-necked Spurfowl 0.006546645 0.000000000 0.000000000 0.000000000
Yellow-rumped Tinkerbird 0.000000000 0.000000000 0.003960396 0.000000000
Yellow-spotted Bush Sparrow 0.000000000 0.000000000 0.000000000 0.000000000
Yellow-throated Longclaw 0.003273322 0.001869159 0.001980198 0.001485884
Zittling Cisticola 0.000000000 0.000000000 0.000000000 0.001485884
C5 C6 C7 C8
Abyssinian thrush 0.001968504 0.000000000 0.000000000 0.000000000
African Citril 0.015748031 0.000000000 0.006122449 0.003539823
African Dusky Flycatcher 0.000000000 0.000000000 0.000000000 0.000000000
African Firefinch 0.001968504 0.000000000 0.002040816 0.000000000
African Gray-Flycatcher 0.001968504 0.000000000 0.004081633 0.001769912
African Green-Pigeon 0.000000000 0.000000000 0.002040816 0.000000000
African Hawk Eagle 0.000000000 0.000000000 0.000000000 0.000000000
African Paradise-Flycatcher 0.000000000 0.000000000 0.002040816 0.000000000
African Pied-Wagtail 0.011811024 0.002217295 0.020408163 0.012389381
African Pipit 0.003937008 0.008869180 0.002040816 0.000000000
African Stonechat 0.000000000 0.000000000 0.000000000 0.001769912
African Swift 0.003937008 0.002217295 0.010204082 0.003539823
African Thrush 0.000000000 0.000000000 0.000000000 0.001769912
African Woolly-necked Stork 0.000000000 0.000000000 0.000000000 0.003539823
African Yellow-Warbler 0.001968504 0.000000000 0.000000000 0.000000000
African-Black-headed Oriole 0.000000000 0.004434590 0.000000000 0.007079646
Amethyst Sunbird 0.015748031 0.004434590 0.014285714 0.017699115
Arrow-marked Babbler 0.003937008 0.000000000 0.004081633 0.000000000
Ashy Flycatcher 0.000000000 0.002217295 0.000000000 0.001769912
Augur Buzzard 0.003937008 0.006651885 0.010204082 0.007079646
Baglafecht Weaver 0.021653543 0.004434590 0.008163265 0.024778761
Banded Martin 0.000000000 0.000000000 0.000000000 0.000000000
Bare-faced GoAway-bird 0.003937008 0.002217295 0.004081633 0.003539823
Barn Swallow 0.005905512 0.019955654 0.006122449 0.003539823
Black Coucal 0.000000000 0.000000000 0.000000000 0.000000000
Black Cuckoshrike 0.000000000 0.000000000 0.002040816 0.000000000
Black Kite 0.000000000 0.002217295 0.000000000 0.000000000
Black-backed Puffback 0.007874016 0.004434590 0.014285714 0.012389381
Black-chested Snake-Eagle 0.000000000 0.000000000 0.000000000 0.000000000
Black-crowned Tchagra 0.001968504 0.011086475 0.004081633 0.005309735
Black-headed Heron 0.000000000 0.000000000 0.000000000 0.001769912
Black-headed Oriole 0.000000000 0.000000000 0.002040816 0.007079646
Black-headed weaver 0.001968504 0.000000000 0.000000000 0.000000000
Black-rumped waxbill 0.000000000 0.000000000 0.000000000 0.000000000
Black-shouldered Kite 0.001968504 0.002217295 0.000000000 0.000000000
Black-throated Wattle-eye 0.000000000 0.000000000 0.002040816 0.000000000
Blue-naped Mousebird 0.000000000 0.000000000 0.000000000 0.000000000
Blue-spotted Wood-Dove 0.000000000 0.000000000 0.000000000 0.000000000
Booted Eagle 0.000000000 0.000000000 0.000000000 0.000000000
Brimstone Canary 0.001968504 0.002217295 0.000000000 0.000000000
Bronze Mannikin 0.000000000 0.006651885 0.000000000 0.001769912
Bronze Sunbird 0.027559055 0.006651885 0.014285714 0.015929204
Brown-crowned Tchagra 0.011811024 0.011086475 0.022448980 0.008849558
Brown-throated Wattle-eye 0.009842520 0.002217295 0.002040816 0.001769912
Brubru 0.000000000 0.000000000 0.000000000 0.000000000
Cape Robin-Chat 0.007874016 0.000000000 0.002040816 0.001769912
Cape Wagtail 0.000000000 0.000000000 0.000000000 0.000000000
Cardinal Quelea 0.005905512 0.000000000 0.010204082 0.001769912
Cardinal Woodpecker 0.000000000 0.000000000 0.000000000 0.000000000
Cattle Egret 0.000000000 0.000000000 0.000000000 0.000000000
Chinspot Batis 0.000000000 0.006651885 0.000000000 0.007079646
Cinnamon-breasted Bunting 0.000000000 0.000000000 0.000000000 0.000000000
Cinnamon-chested Bee-eater 0.001968504 0.002217295 0.006122449 0.010619469
Common Bulbul 0.051181102 0.026607539 0.069387755 0.054867257
Common Buzzard 0.000000000 0.000000000 0.002040816 0.000000000
Common Cuckoo 0.000000000 0.000000000 0.000000000 0.000000000
Common Waxbill 0.000000000 0.015521064 0.002040816 0.001769912
Coqui Francolin 0.013779528 0.008869180 0.000000000 0.001769912
CRB 0.000000000 0.000000000 0.000000000 0.000000000
Crowned Eagle 0.001968504 0.000000000 0.000000000 0.000000000
Crowned Lapwing 0.001968504 0.024390244 0.004081633 0.000000000
D'Arnaud's (Usambiro) Barbet 0.017716535 0.002217295 0.040816327 0.040707965
Dideric Cuckoo 0.000000000 0.002217295 0.000000000 0.000000000
Diedrik Cuckcoo 0.000000000 0.004434590 0.000000000 0.000000000
Egyptian Goose 0.001968504 0.004434590 0.000000000 0.001769912
Emerald-spotted Wood Dove 0.027559055 0.019955654 0.051020408 0.042477876
Eurasian Hooby 0.000000000 0.000000000 0.000000000 0.000000000
Eurasian Hoopoe 0.001968504 0.002217295 0.004081633 0.000000000
European Bee-eater 0.001968504 0.000000000 0.002040816 0.000000000
European Honey-buzzard 0.000000000 0.000000000 0.000000000 0.000000000
Fan-tailed Widowbird 0.000000000 0.000000000 0.000000000 0.000000000
Fine-banded woodpecker 0.000000000 0.000000000 0.000000000 0.000000000
Fischer's Sparrow-lark 0.000000000 0.000000000 0.000000000 0.001769912
Fork-tailed Drongo 0.000000000 0.000000000 0.000000000 0.001769912
Gabar Goshawk 0.000000000 0.002217295 0.000000000 0.000000000
Golden-breasted Bunting 0.001968504 0.002217295 0.000000000 0.000000000
Golden-winged Sunbird 0.000000000 0.000000000 0.000000000 0.003539823
Grassland Pipit 0.000000000 0.002217295 0.000000000 0.000000000
Gray apalis 0.001968504 0.002217295 0.000000000 0.001769912
Gray Crowned-Crane 0.000000000 0.002217295 0.000000000 0.000000000
Gray Flycatcher 0.001968504 0.000000000 0.000000000 0.000000000
Gray Heron 0.000000000 0.000000000 0.000000000 0.000000000
Gray-backed Fiscal 0.000000000 0.000000000 0.002040816 0.000000000
Gray-capped Warbler 0.017716535 0.006651885 0.016326531 0.026548673
Gray-crested helmetshrike 0.000000000 0.000000000 0.000000000 0.000000000
Gray-Crowned Crane 0.000000000 0.004434590 0.000000000 0.003539823
Gray-headed bushshrike 0.001968504 0.000000000 0.006122449 0.007079646
Great Spotted Cuckoo 0.000000000 0.000000000 0.000000000 0.000000000
Greater Blue-eared Starling 0.000000000 0.002217295 0.014285714 0.003539823
Greater Honeyguide 0.000000000 0.000000000 0.002040816 0.001769912
Green-backed Camaroptera 0.031496063 0.015521064 0.034693878 0.031858407
Green-headed Sunbird 0.000000000 0.000000000 0.000000000 0.000000000
Green-winged Pytilia 0.011811024 0.000000000 0.000000000 0.007079646
Grosbeak Weaver 0.000000000 0.000000000 0.000000000 0.000000000
Hadada Ibis 0.000000000 0.011086475 0.006122449 0.003539823
Hamerkop 0.000000000 0.000000000 0.002040816 0.000000000
Harlequin Quail 0.000000000 0.004434590 0.000000000 0.000000000
Helmeted Guineafowl 0.001968504 0.024390244 0.002040816 0.000000000
Hildebrandt's Spurfowl 0.001968504 0.000000000 0.000000000 0.001769912
Hildebrandt's Starling 0.000000000 0.004434590 0.004081633 0.000000000
Holub's Golden-Weaver 0.005905512 0.006651885 0.004081633 0.005309735
Horus Swift 0.000000000 0.000000000 0.000000000 0.000000000
House-Sparrow 0.009842520 0.000000000 0.018367347 0.023008850
Icterine Warbler 0.000000000 0.000000000 0.000000000 0.001769912
Indigo Bird 0.000000000 0.000000000 0.000000000 0.000000000
Joyful Greenbul 0.000000000 0.000000000 0.000000000 0.000000000
Kenya Rufous Sparrow 0.000000000 0.000000000 0.002040816 0.000000000
Klaas's Cuckoo 0.003937008 0.004434590 0.018367347 0.001769912
Knob-billed Duck 0.000000000 0.000000000 0.000000000 0.000000000
Laughing Dove 0.007874016 0.000000000 0.014285714 0.024778761
Lesser Gray Shrike 0.000000000 0.002217295 0.000000000 0.000000000
Lesser Honeyguide 0.000000000 0.000000000 0.000000000 0.000000000
Lesser Masked-Weaver 0.003937008 0.002217295 0.004081633 0.005309735
Lesser Striped Swallow 0.000000000 0.000000000 0.000000000 0.000000000
Lesser-Masked Weaver 0.001968504 0.000000000 0.000000000 0.001769912
Levaillant's Cuckoo 0.000000000 0.000000000 0.000000000 0.000000000
Little Bee-eater 0.000000000 0.002217295 0.004081633 0.000000000
Little Grebe 0.000000000 0.000000000 0.000000000 0.001769912
Little Sparrowhawk 0.000000000 0.000000000 0.000000000 0.000000000
Little Swift 0.000000000 0.002217295 0.002040816 0.001769912
Lizard Buzzard 0.000000000 0.000000000 0.000000000 0.000000000
Long-billed Pipit 0.000000000 0.000000000 0.000000000 0.000000000
Malachite Kingfisher 0.000000000 0.000000000 0.000000000 0.000000000
Marico Sunbird 0.000000000 0.000000000 0.000000000 0.000000000
Martial Eagle 0.001968504 0.000000000 0.002040816 0.000000000
Mosque Swallow 0.005905512 0.004434590 0.002040816 0.001769912
Mountain Gray Woodpecker 0.000000000 0.000000000 0.002040816 0.000000000
Mourning Collared-Dove 0.005905512 0.000000000 0.000000000 0.000000000
Northern Anteater-Chat 0.003937008 0.006651885 0.010204082 0.008849558
Northern Black-Flycather 0.000000000 0.000000000 0.000000000 0.000000000
Northern Fiscal 0.025590551 0.066518847 0.051020408 0.042477876
Northern Gray-headed Sparrow 0.000000000 0.002217295 0.000000000 0.000000000
Northern Wheatear 0.001968504 0.000000000 0.000000000 0.000000000
Northern Yellow White-eye 0.000000000 0.000000000 0.000000000 0.000000000
Nubian Woodpecker 0.000000000 0.000000000 0.002040816 0.000000000
Ovambo Sparrowhawk 0.000000000 0.000000000 0.000000000 0.000000000
Pallid Harrier 0.000000000 0.000000000 0.000000000 0.000000000
Pectoral-patch Cisticola 0.000000000 0.015521064 0.002040816 0.000000000
Pied crow 0.000000000 0.000000000 0.000000000 0.001769912
Pied Wheatear 0.001968504 0.002217295 0.000000000 0.000000000
Pigmy Kingfisher 0.000000000 0.000000000 0.000000000 0.000000000
Pin-tailed Whydah 0.000000000 0.011086475 0.006122449 0.001769912
Plain Martin 0.000000000 0.008869180 0.000000000 0.000000000
Plain-backed Pipit 0.003937008 0.015521064 0.000000000 0.000000000
Purple Grenadier 0.031496063 0.033259424 0.004081633 0.008849558
Purple-banded Sunbird 0.001968504 0.004434590 0.000000000 0.000000000
Rattling Cisticola 0.031496063 0.053215078 0.042857143 0.061946903
Red-backed Scrub-Robin 0.005905512 0.000000000 0.004081633 0.012389381
Red-backed Shrike 0.001968504 0.000000000 0.000000000 0.000000000
Red-billed Firefinch 0.003937008 0.006651885 0.006122449 0.003539823
Red-Billed Oxpecker 0.003937008 0.011086475 0.004081633 0.007079646
Red-billed Quelea 0.015748031 0.002217295 0.004081633 0.000000000
Red-capped Lark 0.005905512 0.006651885 0.000000000 0.000000000
Red-chested Cuckoo 0.000000000 0.002217295 0.000000000 0.000000000
Red-cowled Widowbird 0.001968504 0.000000000 0.004081633 0.000000000
Red-eyed Dove 0.037401575 0.028824834 0.018367347 0.015929204
Red-Faced Crombec 0.003937008 0.000000000 0.000000000 0.001769912
Red-fronted Barbet 0.000000000 0.002217295 0.018367347 0.008849558
Red-fronted Tinkerbird 0.013779528 0.000000000 0.018367347 0.014159292
Red-headed Weaver 0.003937008 0.002217295 0.002040816 0.003539823
Red-necked Spurfowl 0.000000000 0.000000000 0.006122449 0.000000000
Red-rumped Swallow 0.000000000 0.000000000 0.008163265 0.001769912
Red-tailed Shrike 0.000000000 0.000000000 0.000000000 0.000000000
Red-throated Pipit 0.000000000 0.000000000 0.000000000 0.000000000
Red-throated Wryneck 0.000000000 0.000000000 0.000000000 0.000000000
Reichenow's Seedeater 0.000000000 0.000000000 0.000000000 0.000000000
Ring-necked Dove 0.045275591 0.048780488 0.038775510 0.042477876
Rosy-throated Longclaw 0.000000000 0.000000000 0.000000000 0.000000000
Rufous Sparrow 0.000000000 0.004434590 0.002040816 0.001769912
Rufous-naped Lark 0.035433071 0.044345898 0.004081633 0.005309735
Rufous-necked Wryneck 0.000000000 0.000000000 0.004081633 0.000000000
Rufous-tailed Weaver 0.000000000 0.002217295 0.000000000 0.000000000
Rüppell's Starling 0.000000000 0.000000000 0.000000000 0.000000000
Sacred Ibis 0.000000000 0.000000000 0.000000000 0.000000000
Scaly Spurfowl 0.000000000 0.000000000 0.002040816 0.003539823
Scaly-throated Honeyguide 0.000000000 0.000000000 0.000000000 0.000000000
Scarlet-chested Sunbird 0.001968504 0.013303769 0.004081633 0.001769912
Schalow's Turaco 0.000000000 0.000000000 0.002040816 0.001769912
Silverbird 0.000000000 0.000000000 0.000000000 0.000000000
Silver-breasted Bushshrike 0.000000000 0.000000000 0.000000000 0.000000000
Slate-colored Boubou 0.039370079 0.035476718 0.030612245 0.044247788
Sooty Chat 0.000000000 0.000000000 0.002040816 0.000000000
Southern Ground-Hornbill 0.000000000 0.000000000 0.000000000 0.000000000
Speckled Mousebird 0.013779528 0.004434590 0.012244898 0.015929204
Speckled Pigeon 0.003937008 0.000000000 0.012244898 0.000000000
Spectacled Weaver 0.005905512 0.002217295 0.000000000 0.001769912
Speke's Weaver 0.000000000 0.000000000 0.004081633 0.001769912
Spot-flanked Barbet 0.001968504 0.002217295 0.006122449 0.003539823
Spotted Flycatcher 0.000000000 0.000000000 0.000000000 0.000000000
Steppe Eagle 0.000000000 0.000000000 0.000000000 0.000000000
Stout Cisticola 0.000000000 0.000000000 0.000000000 0.000000000
Straw-tailed Whydah 0.000000000 0.004434590 0.000000000 0.000000000
Streaky Seedeater 0.015748031 0.000000000 0.004081633 0.007079646
Strout Cisticola 0.000000000 0.022172949 0.000000000 0.000000000
Sulphur-breasted Bushshrike 0.001968504 0.011086475 0.002040816 0.007079646
Superb Starling 0.005905512 0.022172949 0.000000000 0.000000000
Swahili Sparrow 0.000000000 0.002217295 0.002040816 0.000000000
Tambourine Dove 0.000000000 0.000000000 0.000000000 0.000000000
Tawny Eagle 0.000000000 0.000000000 0.000000000 0.000000000
Tawny-flanked Prinia 0.033464567 0.013303769 0.016326531 0.023008850
Thick-billed Seedeater 0.003937008 0.000000000 0.000000000 0.000000000
Tropical Boubou 0.029527559 0.011086475 0.024489796 0.040707965
unknown egret 0.000000000 0.000000000 0.000000000 0.000000000
Variable Sunbird 0.003937008 0.013303769 0.006122449 0.005309735
Village Weaver 0.019685039 0.002217295 0.010204082 0.017699115
Violet-backed Starling 0.000000000 0.000000000 0.000000000 0.000000000
Vitelline Masked-Weaver 0.001968504 0.000000000 0.000000000 0.001769912
Wattled Lapwing 0.001968504 0.000000000 0.000000000 0.000000000
Wattled Starling 0.000000000 0.000000000 0.000000000 0.000000000
Western Cattle Egret 0.000000000 0.008869180 0.000000000 0.000000000
Western Yellow Wagtail 0.000000000 0.006651885 0.002040816 0.000000000
Whinchat 0.000000000 0.011086475 0.000000000 0.000000000
White-bellied Canary 0.009842520 0.000000000 0.004081633 0.014159292
White-bellied Tit 0.000000000 0.000000000 0.000000000 0.005309735
White-browed Coucal 0.000000000 0.004434590 0.004081633 0.010619469
White-browed Robin-Chat 0.011811024 0.008869180 0.010204082 0.010619469
White-browed Scrub-Robin 0.009842520 0.002217295 0.002040816 0.005309735
White-browed Sparrow-Weaver 0.000000000 0.000000000 0.002040816 0.000000000
White-eyed Slaty-Flycatcher 0.000000000 0.000000000 0.000000000 0.000000000
White-fronted Bee-eater 0.000000000 0.004434590 0.000000000 0.000000000
White-headed Barbet 0.003937008 0.000000000 0.000000000 0.000000000
White-rumped Swallow 0.000000000 0.002217295 0.000000000 0.000000000
White-rumped Swift 0.001968504 0.006651885 0.002040816 0.001769912
White-winged Widowbird 0.000000000 0.000000000 0.000000000 0.000000000
Willow Warbler 0.000000000 0.004434590 0.002040816 0.005309735
Winding Cisticola 0.000000000 0.000000000 0.002040816 0.001769912
Wire-tailed Swallow 0.001968504 0.002217295 0.000000000 0.000000000
Yellow Bishop 0.037401575 0.008869180 0.016326531 0.017699115
Yellow-billed Egret 0.000000000 0.000000000 0.000000000 0.000000000
Yellow-billed Oxpecker 0.001968504 0.000000000 0.000000000 0.000000000
Yellow-breasted Apalis 0.000000000 0.000000000 0.012244898 0.007079646
Yellow-fronted Canary 0.023622047 0.026607539 0.020408163 0.015929204
Yellow-mantled Widowbird 0.000000000 0.000000000 0.000000000 0.000000000
Yellow-necked Spurfowl 0.000000000 0.000000000 0.000000000 0.000000000
Yellow-rumped Tinkerbird 0.001968504 0.000000000 0.002040816 0.003539823
Yellow-spotted Bush Sparrow 0.000000000 0.000000000 0.000000000 0.000000000
Yellow-throated Longclaw 0.009842520 0.026607539 0.000000000 0.001769912
Zittling Cisticola 0.000000000 0.000000000 0.000000000 0.000000000
C9 C10 C11 C12
Abyssinian thrush 0.001686341 0.000000000 0.000000000 0.000000000
African Citril 0.003372681 0.000000000 0.008264463 0.004807692
African Dusky Flycatcher 0.000000000 0.000000000 0.000000000 0.000000000
African Firefinch 0.001686341 0.000000000 0.000000000 0.000000000
African Gray-Flycatcher 0.001686341 0.003257329 0.002754821 0.000000000
African Green-Pigeon 0.000000000 0.000000000 0.000000000 0.004807692
African Hawk Eagle 0.000000000 0.000000000 0.002754821 0.000000000
African Paradise-Flycatcher 0.000000000 0.003257329 0.000000000 0.004807692
African Pied-Wagtail 0.013490725 0.017915309 0.013774105 0.000000000
African Pipit 0.003372681 0.006514658 0.005509642 0.000000000
African Stonechat 0.000000000 0.000000000 0.000000000 0.000000000
African Swift 0.003372681 0.004885993 0.002754821 0.004807692
African Thrush 0.000000000 0.000000000 0.000000000 0.000000000
African Woolly-necked Stork 0.001686341 0.000000000 0.000000000 0.000000000
African Yellow-Warbler 0.000000000 0.000000000 0.000000000 0.002403846
African-Black-headed Oriole 0.006745363 0.003257329 0.000000000 0.000000000
Amethyst Sunbird 0.011804384 0.011400651 0.000000000 0.012019231
Arrow-marked Babbler 0.011804384 0.006514658 0.000000000 0.007211538
Ashy Flycatcher 0.000000000 0.000000000 0.005509642 0.000000000
Augur Buzzard 0.006745363 0.003257329 0.005509642 0.000000000
Baglafecht Weaver 0.018549747 0.003257329 0.024793388 0.002403846
Banded Martin 0.000000000 0.000000000 0.000000000 0.000000000
Bare-faced GoAway-bird 0.001686341 0.003257329 0.000000000 0.000000000
Barn Swallow 0.003372681 0.009771987 0.035812672 0.007211538
Black Coucal 0.000000000 0.000000000 0.002754821 0.000000000
Black Cuckoshrike 0.001686341 0.003257329 0.002754821 0.000000000
Black Kite 0.000000000 0.003257329 0.000000000 0.000000000
Black-backed Puffback 0.011804384 0.011400651 0.002754821 0.004807692
Black-chested Snake-Eagle 0.000000000 0.000000000 0.000000000 0.000000000
Black-crowned Tchagra 0.006745363 0.011400651 0.005509642 0.014423077
Black-headed Heron 0.005059022 0.001628664 0.000000000 0.000000000
Black-headed Oriole 0.001686341 0.004885993 0.000000000 0.000000000
Black-headed weaver 0.000000000 0.000000000 0.000000000 0.000000000
Black-rumped waxbill 0.000000000 0.000000000 0.000000000 0.000000000
Black-shouldered Kite 0.000000000 0.001628664 0.000000000 0.000000000
Black-throated Wattle-eye 0.001686341 0.000000000 0.000000000 0.000000000
Blue-naped Mousebird 0.008431703 0.006514658 0.000000000 0.002403846
Blue-spotted Wood-Dove 0.000000000 0.000000000 0.000000000 0.000000000
Booted Eagle 0.000000000 0.000000000 0.000000000 0.000000000
Brimstone Canary 0.000000000 0.001628664 0.000000000 0.000000000
Bronze Mannikin 0.001686341 0.001628664 0.002754821 0.000000000
Bronze Sunbird 0.006745363 0.009771987 0.027548209 0.024038462
Brown-crowned Tchagra 0.015177066 0.008143322 0.019283747 0.024038462
Brown-throated Wattle-eye 0.001686341 0.003257329 0.000000000 0.002403846
Brubru 0.000000000 0.001628664 0.000000000 0.002403846
Cape Robin-Chat 0.006745363 0.003257329 0.008264463 0.002403846
Cape Wagtail 0.000000000 0.000000000 0.000000000 0.000000000
Cardinal Quelea 0.001686341 0.003257329 0.005509642 0.004807692
Cardinal Woodpecker 0.000000000 0.000000000 0.000000000 0.000000000
Cattle Egret 0.000000000 0.004885993 0.000000000 0.000000000
Chinspot Batis 0.001686341 0.006514658 0.002754821 0.002403846
Cinnamon-breasted Bunting 0.000000000 0.000000000 0.002754821 0.000000000
Cinnamon-chested Bee-eater 0.000000000 0.000000000 0.005509642 0.004807692
Common Bulbul 0.057335582 0.050488599 0.068870523 0.062500000
Common Buzzard 0.000000000 0.000000000 0.002754821 0.000000000
Common Cuckoo 0.000000000 0.000000000 0.002754821 0.000000000
Common Waxbill 0.001686341 0.004885993 0.008264463 0.002403846
Coqui Francolin 0.000000000 0.001628664 0.002754821 0.000000000
CRB 0.000000000 0.000000000 0.000000000 0.000000000
Crowned Eagle 0.000000000 0.000000000 0.000000000 0.002403846
Crowned Lapwing 0.005059022 0.022801303 0.011019284 0.012019231
D'Arnaud's (Usambiro) Barbet 0.032040472 0.024429967 0.019283747 0.002403846
Dideric Cuckoo 0.001686341 0.001628664 0.000000000 0.000000000
Diedrik Cuckcoo 0.003372681 0.004885993 0.000000000 0.000000000
Egyptian Goose 0.000000000 0.001628664 0.002754821 0.000000000
Emerald-spotted Wood Dove 0.074198988 0.030944625 0.027548209 0.045673077
Eurasian Hooby 0.000000000 0.000000000 0.000000000 0.000000000
Eurasian Hoopoe 0.001686341 0.000000000 0.008264463 0.004807692
European Bee-eater 0.000000000 0.000000000 0.000000000 0.000000000
European Honey-buzzard 0.000000000 0.000000000 0.000000000 0.000000000
Fan-tailed Widowbird 0.000000000 0.000000000 0.000000000 0.000000000
Fine-banded woodpecker 0.001686341 0.001628664 0.000000000 0.002403846
Fischer's Sparrow-lark 0.000000000 0.000000000 0.000000000 0.000000000
Fork-tailed Drongo 0.000000000 0.000000000 0.000000000 0.000000000
Gabar Goshawk 0.000000000 0.000000000 0.000000000 0.000000000
Golden-breasted Bunting 0.001686341 0.003257329 0.008264463 0.002403846
Golden-winged Sunbird 0.000000000 0.000000000 0.000000000 0.000000000
Grassland Pipit 0.000000000 0.006514658 0.000000000 0.002403846
Gray apalis 0.001686341 0.001628664 0.002754821 0.002403846
Gray Crowned-Crane 0.000000000 0.000000000 0.000000000 0.000000000
Gray Flycatcher 0.000000000 0.009771987 0.000000000 0.002403846
Gray Heron 0.000000000 0.000000000 0.000000000 0.000000000
Gray-backed Fiscal 0.000000000 0.001628664 0.000000000 0.002403846
Gray-capped Warbler 0.001686341 0.004885993 0.022038567 0.028846154
Gray-crested helmetshrike 0.000000000 0.003257329 0.000000000 0.000000000
Gray-Crowned Crane 0.000000000 0.000000000 0.000000000 0.000000000
Gray-headed bushshrike 0.005059022 0.001628664 0.005509642 0.000000000
Great Spotted Cuckoo 0.000000000 0.000000000 0.000000000 0.000000000
Greater Blue-eared Starling 0.000000000 0.003257329 0.005509642 0.000000000
Greater Honeyguide 0.001686341 0.000000000 0.005509642 0.000000000
Green-backed Camaroptera 0.038785835 0.024429967 0.024793388 0.040865385
Green-headed Sunbird 0.000000000 0.000000000 0.000000000 0.000000000
Green-winged Pytilia 0.010118044 0.001628664 0.002754821 0.009615385
Grosbeak Weaver 0.000000000 0.000000000 0.000000000 0.000000000
Hadada Ibis 0.006745363 0.001628664 0.008264463 0.002403846
Hamerkop 0.000000000 0.000000000 0.000000000 0.002403846
Harlequin Quail 0.000000000 0.000000000 0.000000000 0.000000000
Helmeted Guineafowl 0.000000000 0.000000000 0.000000000 0.002403846
Hildebrandt's Spurfowl 0.000000000 0.000000000 0.000000000 0.000000000
Hildebrandt's Starling 0.001686341 0.011400651 0.000000000 0.000000000
Holub's Golden-Weaver 0.006745363 0.004885993 0.005509642 0.004807692
Horus Swift 0.000000000 0.000000000 0.000000000 0.000000000
House-Sparrow 0.005059022 0.013029316 0.002754821 0.000000000
Icterine Warbler 0.000000000 0.000000000 0.000000000 0.000000000
Indigo Bird 0.000000000 0.000000000 0.000000000 0.000000000
Joyful Greenbul 0.000000000 0.000000000 0.000000000 0.000000000
Kenya Rufous Sparrow 0.000000000 0.001628664 0.000000000 0.000000000
Klaas's Cuckoo 0.008431703 0.022801303 0.008264463 0.012019231
Knob-billed Duck 0.000000000 0.000000000 0.000000000 0.000000000
Laughing Dove 0.010118044 0.009771987 0.008264463 0.016826923
Lesser Gray Shrike 0.001686341 0.000000000 0.002754821 0.000000000
Lesser Honeyguide 0.000000000 0.001628664 0.000000000 0.000000000
Lesser Masked-Weaver 0.001686341 0.003257329 0.002754821 0.002403846
Lesser Striped Swallow 0.000000000 0.001628664 0.000000000 0.000000000
Lesser-Masked Weaver 0.000000000 0.000000000 0.000000000 0.000000000
Levaillant's Cuckoo 0.000000000 0.000000000 0.002754821 0.000000000
Little Bee-eater 0.003372681 0.000000000 0.005509642 0.002403846
Little Grebe 0.000000000 0.000000000 0.000000000 0.000000000
Little Sparrowhawk 0.000000000 0.000000000 0.000000000 0.000000000
Little Swift 0.001686341 0.003257329 0.002754821 0.004807692
Lizard Buzzard 0.000000000 0.000000000 0.002754821 0.000000000
Long-billed Pipit 0.000000000 0.001628664 0.000000000 0.000000000
Malachite Kingfisher 0.000000000 0.000000000 0.005509642 0.000000000
Marico Sunbird 0.000000000 0.000000000 0.000000000 0.000000000
Martial Eagle 0.001686341 0.001628664 0.000000000 0.000000000
Mosque Swallow 0.003372681 0.000000000 0.002754821 0.000000000
Mountain Gray Woodpecker 0.000000000 0.000000000 0.000000000 0.000000000
Mourning Collared-Dove 0.000000000 0.000000000 0.000000000 0.000000000
Northern Anteater-Chat 0.003372681 0.003257329 0.005509642 0.004807692
Northern Black-Flycather 0.001686341 0.000000000 0.005509642 0.000000000
Northern Fiscal 0.033726813 0.048859935 0.046831956 0.016826923
Northern Gray-headed Sparrow 0.003372681 0.001628664 0.002754821 0.000000000
Northern Wheatear 0.000000000 0.008143322 0.008264463 0.007211538
Northern Yellow White-eye 0.000000000 0.000000000 0.000000000 0.000000000
Nubian Woodpecker 0.001686341 0.003257329 0.000000000 0.000000000
Ovambo Sparrowhawk 0.000000000 0.000000000 0.000000000 0.000000000
Pallid Harrier 0.000000000 0.001628664 0.000000000 0.000000000
Pectoral-patch Cisticola 0.000000000 0.001628664 0.000000000 0.002403846
Pied crow 0.000000000 0.004885993 0.000000000 0.000000000
Pied Wheatear 0.000000000 0.000000000 0.000000000 0.000000000
Pigmy Kingfisher 0.000000000 0.000000000 0.000000000 0.000000000
Pin-tailed Whydah 0.001686341 0.003257329 0.005509642 0.004807692
Plain Martin 0.000000000 0.000000000 0.002754821 0.000000000
Plain-backed Pipit 0.000000000 0.026058632 0.019283747 0.012019231
Purple Grenadier 0.037099494 0.024429967 0.016528926 0.019230769
Purple-banded Sunbird 0.000000000 0.001628664 0.000000000 0.002403846
Rattling Cisticola 0.032040472 0.050488599 0.035812672 0.028846154
Red-backed Scrub-Robin 0.011804384 0.004885993 0.002754821 0.007211538
Red-backed Shrike 0.001686341 0.000000000 0.005509642 0.002403846
Red-billed Firefinch 0.001686341 0.003257329 0.002754821 0.002403846
Red-Billed Oxpecker 0.010118044 0.021172638 0.002754821 0.004807692
Red-billed Quelea 0.000000000 0.003257329 0.000000000 0.002403846
Red-capped Lark 0.000000000 0.003257329 0.002754821 0.002403846
Red-chested Cuckoo 0.000000000 0.000000000 0.000000000 0.000000000
Red-cowled Widowbird 0.003372681 0.006514658 0.005509642 0.000000000
Red-eyed Dove 0.040472175 0.037459283 0.011019284 0.028846154
Red-Faced Crombec 0.000000000 0.000000000 0.000000000 0.000000000
Red-fronted Barbet 0.005059022 0.001628664 0.008264463 0.004807692
Red-fronted Tinkerbird 0.005059022 0.001628664 0.002754821 0.007211538
Red-headed Weaver 0.001686341 0.004885993 0.000000000 0.007211538
Red-necked Spurfowl 0.000000000 0.000000000 0.000000000 0.000000000
Red-rumped Swallow 0.003372681 0.001628664 0.000000000 0.000000000
Red-tailed Shrike 0.000000000 0.000000000 0.000000000 0.000000000
Red-throated Pipit 0.000000000 0.001628664 0.000000000 0.000000000
Red-throated Wryneck 0.000000000 0.000000000 0.000000000 0.000000000
Reichenow's Seedeater 0.000000000 0.000000000 0.000000000 0.000000000
Ring-necked Dove 0.055649241 0.052117264 0.027548209 0.043269231
Rosy-throated Longclaw 0.000000000 0.003257329 0.000000000 0.000000000
Rufous Sparrow 0.000000000 0.003257329 0.000000000 0.000000000
Rufous-naped Lark 0.006745363 0.003257329 0.033057851 0.024038462
Rufous-necked Wryneck 0.001686341 0.000000000 0.002754821 0.000000000
Rufous-tailed Weaver 0.000000000 0.004885993 0.000000000 0.000000000
Rüppell's Starling 0.000000000 0.001628664 0.000000000 0.000000000
Sacred Ibis 0.001686341 0.001628664 0.000000000 0.000000000
Scaly Spurfowl 0.001686341 0.000000000 0.002754821 0.002403846
Scaly-throated Honeyguide 0.000000000 0.000000000 0.000000000 0.002403846
Scarlet-chested Sunbird 0.001686341 0.004885993 0.002754821 0.004807692
Schalow's Turaco 0.005059022 0.000000000 0.000000000 0.000000000
Silverbird 0.000000000 0.000000000 0.000000000 0.000000000
Silver-breasted Bushshrike 0.000000000 0.000000000 0.000000000 0.000000000
Slate-colored Boubou 0.050590219 0.016286645 0.013774105 0.050480769
Sooty Chat 0.000000000 0.001628664 0.000000000 0.000000000
Southern Ground-Hornbill 0.000000000 0.000000000 0.000000000 0.002403846
Speckled Mousebird 0.020236088 0.017915309 0.041322314 0.024038462
Speckled Pigeon 0.008431703 0.003257329 0.008264463 0.009615385
Spectacled Weaver 0.000000000 0.000000000 0.000000000 0.000000000
Speke's Weaver 0.000000000 0.000000000 0.000000000 0.000000000
Spot-flanked Barbet 0.001686341 0.001628664 0.005509642 0.002403846
Spotted Flycatcher 0.001686341 0.001628664 0.000000000 0.000000000
Steppe Eagle 0.000000000 0.001628664 0.000000000 0.000000000
Stout Cisticola 0.000000000 0.000000000 0.000000000 0.000000000
Straw-tailed Whydah 0.000000000 0.000000000 0.000000000 0.000000000
Streaky Seedeater 0.008431703 0.001628664 0.005509642 0.002403846
Strout Cisticola 0.000000000 0.000000000 0.000000000 0.000000000
Sulphur-breasted Bushshrike 0.001686341 0.001628664 0.000000000 0.007211538
Superb Starling 0.001686341 0.016286645 0.002754821 0.000000000
Swahili Sparrow 0.000000000 0.001628664 0.000000000 0.000000000
Tambourine Dove 0.003372681 0.000000000 0.000000000 0.000000000
Tawny Eagle 0.000000000 0.000000000 0.002754821 0.000000000
Tawny-flanked Prinia 0.025295110 0.013029316 0.008264463 0.026442308
Thick-billed Seedeater 0.000000000 0.000000000 0.000000000 0.000000000
Tropical Boubou 0.040472175 0.013029316 0.024793388 0.055288462
unknown egret 0.000000000 0.000000000 0.000000000 0.000000000
Variable Sunbird 0.010118044 0.009771987 0.019283747 0.014423077
Village Weaver 0.005059022 0.009771987 0.005509642 0.007211538
Violet-backed Starling 0.000000000 0.006514658 0.000000000 0.000000000
Vitelline Masked-Weaver 0.000000000 0.000000000 0.000000000 0.000000000
Wattled Lapwing 0.000000000 0.000000000 0.000000000 0.000000000
Wattled Starling 0.000000000 0.004885993 0.000000000 0.000000000
Western Cattle Egret 0.003372681 0.000000000 0.000000000 0.000000000
Western Yellow Wagtail 0.000000000 0.000000000 0.000000000 0.000000000
Whinchat 0.000000000 0.000000000 0.002754821 0.004807692
White-bellied Canary 0.011804384 0.013029316 0.008264463 0.019230769
White-bellied Tit 0.000000000 0.001628664 0.000000000 0.000000000
White-browed Coucal 0.008431703 0.000000000 0.000000000 0.002403846
White-browed Robin-Chat 0.005059022 0.011400651 0.005509642 0.021634615
White-browed Scrub-Robin 0.008431703 0.004885993 0.005509642 0.007211538
White-browed Sparrow-Weaver 0.000000000 0.001628664 0.000000000 0.000000000
White-eyed Slaty-Flycatcher 0.000000000 0.000000000 0.000000000 0.000000000
White-fronted Bee-eater 0.000000000 0.000000000 0.000000000 0.000000000
White-headed Barbet 0.000000000 0.000000000 0.000000000 0.004807692
White-rumped Swallow 0.001686341 0.001628664 0.000000000 0.000000000
White-rumped Swift 0.005059022 0.003257329 0.002754821 0.004807692
White-winged Widowbird 0.000000000 0.000000000 0.000000000 0.000000000
Willow Warbler 0.003372681 0.004885993 0.000000000 0.000000000
Winding Cisticola 0.001686341 0.003257329 0.000000000 0.002403846
Wire-tailed Swallow 0.001686341 0.001628664 0.008264463 0.000000000
Yellow Bishop 0.020236088 0.014657980 0.024793388 0.014423077
Yellow-billed Egret 0.000000000 0.003257329 0.002754821 0.002403846
Yellow-billed Oxpecker 0.001686341 0.000000000 0.002754821 0.002403846
Yellow-breasted Apalis 0.000000000 0.001628664 0.000000000 0.012019231
Yellow-fronted Canary 0.010118044 0.013029316 0.035812672 0.024038462
Yellow-mantled Widowbird 0.000000000 0.000000000 0.000000000 0.002403846
Yellow-necked Spurfowl 0.000000000 0.000000000 0.000000000 0.000000000
Yellow-rumped Tinkerbird 0.001686341 0.000000000 0.000000000 0.000000000
Yellow-spotted Bush Sparrow 0.000000000 0.000000000 0.000000000 0.000000000
Yellow-throated Longclaw 0.008431703 0.003257329 0.013774105 0.012019231
Zittling Cisticola 0.000000000 0.000000000 0.000000000 0.000000000
C13 C14 C15 C16
Abyssinian thrush 0.000000000 0.000 0.000000000 0.000000000
African Citril 0.008213552 0.006 0.000000000 0.000000000
African Dusky Flycatcher 0.000000000 0.000 0.000000000 0.000000000
African Firefinch 0.004106776 0.004 0.003846154 0.001517451
African Gray-Flycatcher 0.004106776 0.000 0.001923077 0.001517451
African Green-Pigeon 0.002053388 0.002 0.000000000 0.001517451
African Hawk Eagle 0.000000000 0.000 0.000000000 0.000000000
African Paradise-Flycatcher 0.002053388 0.012 0.007692308 0.003034901
African Pied-Wagtail 0.008213552 0.006 0.013461538 0.006069803
African Pipit 0.002053388 0.004 0.000000000 0.000000000
African Stonechat 0.000000000 0.000 0.000000000 0.000000000
African Swift 0.010266940 0.004 0.005769231 0.000000000
African Thrush 0.000000000 0.000 0.000000000 0.000000000
African Woolly-necked Stork 0.000000000 0.000 0.000000000 0.001517451
African Yellow-Warbler 0.000000000 0.000 0.000000000 0.000000000
African-Black-headed Oriole 0.004106776 0.000 0.003846154 0.001517451
Amethyst Sunbird 0.014373717 0.016 0.007692308 0.007587253
Arrow-marked Babbler 0.000000000 0.004 0.000000000 0.004552352
Ashy Flycatcher 0.002053388 0.000 0.000000000 0.000000000
Augur Buzzard 0.010266940 0.006 0.001923077 0.001517451
Baglafecht Weaver 0.006160164 0.004 0.017307692 0.001517451
Banded Martin 0.000000000 0.000 0.000000000 0.000000000
Bare-faced GoAway-bird 0.008213552 0.002 0.001923077 0.001517451
Barn Swallow 0.010266940 0.006 0.003846154 0.003034901
Black Coucal 0.000000000 0.000 0.000000000 0.000000000
Black Cuckoshrike 0.000000000 0.000 0.000000000 0.004552352
Black Kite 0.000000000 0.000 0.000000000 0.000000000
Black-backed Puffback 0.008213552 0.008 0.015384615 0.016691958
Black-chested Snake-Eagle 0.002053388 0.000 0.000000000 0.000000000
Black-crowned Tchagra 0.004106776 0.010 0.000000000 0.004552352
Black-headed Heron 0.000000000 0.002 0.009615385 0.009104704
Black-headed Oriole 0.000000000 0.000 0.001923077 0.001517451
Black-headed weaver 0.000000000 0.000 0.000000000 0.000000000
Black-rumped waxbill 0.000000000 0.000 0.000000000 0.000000000
Black-shouldered Kite 0.000000000 0.000 0.000000000 0.000000000
Black-throated Wattle-eye 0.004106776 0.002 0.000000000 0.000000000
Blue-naped Mousebird 0.002053388 0.002 0.003846154 0.004552352
Blue-spotted Wood-Dove 0.002053388 0.000 0.000000000 0.000000000
Booted Eagle 0.000000000 0.000 0.000000000 0.000000000
Brimstone Canary 0.000000000 0.000 0.000000000 0.000000000
Bronze Mannikin 0.000000000 0.006 0.015384615 0.009104704
Bronze Sunbird 0.016427105 0.020 0.011538462 0.013657056
Brown-crowned Tchagra 0.022587269 0.008 0.007692308 0.012139605
Brown-throated Wattle-eye 0.006160164 0.006 0.005769231 0.003034901
Brubru 0.000000000 0.000 0.003846154 0.001517451
Cape Robin-Chat 0.002053388 0.002 0.001923077 0.001517451
Cape Wagtail 0.000000000 0.000 0.000000000 0.000000000
Cardinal Quelea 0.014373717 0.006 0.001923077 0.000000000
Cardinal Woodpecker 0.000000000 0.000 0.000000000 0.000000000
Cattle Egret 0.000000000 0.012 0.000000000 0.000000000
Chinspot Batis 0.006160164 0.004 0.003846154 0.012139605
Cinnamon-breasted Bunting 0.000000000 0.000 0.000000000 0.000000000
Cinnamon-chested Bee-eater 0.008213552 0.002 0.000000000 0.003034901
Common Bulbul 0.039014374 0.044 0.051923077 0.092564492
Common Buzzard 0.002053388 0.002 0.001923077 0.000000000
Common Cuckoo 0.000000000 0.000 0.000000000 0.000000000
Common Waxbill 0.004106776 0.006 0.019230769 0.003034901
Coqui Francolin 0.006160164 0.006 0.001923077 0.000000000
CRB 0.000000000 0.000 0.000000000 0.000000000
Crowned Eagle 0.000000000 0.000 0.000000000 0.000000000
Crowned Lapwing 0.012320329 0.006 0.009615385 0.006069803
D'Arnaud's (Usambiro) Barbet 0.030800821 0.022 0.001923077 0.012139605
Dideric Cuckoo 0.000000000 0.002 0.001923077 0.007587253
Diedrik Cuckcoo 0.000000000 0.006 0.000000000 0.000000000
Egyptian Goose 0.002053388 0.000 0.003846154 0.004552352
Emerald-spotted Wood Dove 0.034907598 0.030 0.048076923 0.054628225
Eurasian Hooby 0.000000000 0.000 0.000000000 0.000000000
Eurasian Hoopoe 0.004106776 0.002 0.000000000 0.000000000
European Bee-eater 0.000000000 0.004 0.001923077 0.001517451
European Honey-buzzard 0.000000000 0.000 0.001923077 0.001517451
Fan-tailed Widowbird 0.000000000 0.000 0.001923077 0.000000000
Fine-banded woodpecker 0.000000000 0.000 0.000000000 0.000000000
Fischer's Sparrow-lark 0.000000000 0.000 0.000000000 0.000000000
Fork-tailed Drongo 0.000000000 0.000 0.000000000 0.000000000
Gabar Goshawk 0.000000000 0.000 0.000000000 0.000000000
Golden-breasted Bunting 0.000000000 0.000 0.000000000 0.001517451
Golden-winged Sunbird 0.000000000 0.000 0.000000000 0.001517451
Grassland Pipit 0.000000000 0.006 0.001923077 0.000000000
Gray apalis 0.004106776 0.000 0.000000000 0.000000000
Gray Crowned-Crane 0.000000000 0.002 0.001923077 0.001517451
Gray Flycatcher 0.000000000 0.000 0.000000000 0.013657056
Gray Heron 0.000000000 0.000 0.003846154 0.001517451
Gray-backed Fiscal 0.000000000 0.000 0.000000000 0.000000000
Gray-capped Warbler 0.010266940 0.018 0.017307692 0.007587253
Gray-crested helmetshrike 0.000000000 0.000 0.000000000 0.000000000
Gray-Crowned Crane 0.000000000 0.002 0.001923077 0.000000000
Gray-headed bushshrike 0.006160164 0.002 0.000000000 0.004552352
Great Spotted Cuckoo 0.002053388 0.000 0.000000000 0.000000000
Greater Blue-eared Starling 0.000000000 0.002 0.000000000 0.003034901
Greater Honeyguide 0.000000000 0.000 0.000000000 0.000000000
Green-backed Camaroptera 0.043121150 0.028 0.038461538 0.047040971
Green-headed Sunbird 0.000000000 0.000 0.000000000 0.001517451
Green-winged Pytilia 0.010266940 0.004 0.001923077 0.003034901
Grosbeak Weaver 0.000000000 0.000 0.000000000 0.001517451
Hadada Ibis 0.002053388 0.004 0.001923077 0.004552352
Hamerkop 0.000000000 0.002 0.000000000 0.000000000
Harlequin Quail 0.000000000 0.000 0.000000000 0.000000000
Helmeted Guineafowl 0.006160164 0.004 0.005769231 0.000000000
Hildebrandt's Spurfowl 0.000000000 0.002 0.001923077 0.000000000
Hildebrandt's Starling 0.002053388 0.000 0.000000000 0.000000000
Holub's Golden-Weaver 0.004106776 0.006 0.005769231 0.012139605
Horus Swift 0.000000000 0.000 0.005769231 0.000000000
House-Sparrow 0.010266940 0.018 0.005769231 0.004552352
Icterine Warbler 0.000000000 0.000 0.000000000 0.000000000
Indigo Bird 0.000000000 0.002 0.001923077 0.000000000
Joyful Greenbul 0.000000000 0.000 0.000000000 0.001517451
Kenya Rufous Sparrow 0.000000000 0.000 0.001923077 0.000000000
Klaas's Cuckoo 0.004106776 0.010 0.011538462 0.021244310
Knob-billed Duck 0.000000000 0.002 0.001923077 0.000000000
Laughing Dove 0.012320329 0.004 0.005769231 0.006069803
Lesser Gray Shrike 0.000000000 0.000 0.000000000 0.000000000
Lesser Honeyguide 0.000000000 0.000 0.000000000 0.000000000
Lesser Masked-Weaver 0.008213552 0.004 0.003846154 0.000000000
Lesser Striped Swallow 0.000000000 0.000 0.000000000 0.000000000
Lesser-Masked Weaver 0.002053388 0.000 0.000000000 0.000000000
Levaillant's Cuckoo 0.000000000 0.000 0.000000000 0.000000000
Little Bee-eater 0.002053388 0.006 0.000000000 0.000000000
Little Grebe 0.000000000 0.000 0.000000000 0.000000000
Little Sparrowhawk 0.000000000 0.002 0.000000000 0.000000000
Little Swift 0.000000000 0.000 0.000000000 0.001517451
Lizard Buzzard 0.000000000 0.000 0.000000000 0.000000000
Long-billed Pipit 0.000000000 0.000 0.000000000 0.000000000
Malachite Kingfisher 0.000000000 0.000 0.000000000 0.000000000
Marico Sunbird 0.000000000 0.000 0.000000000 0.000000000
Martial Eagle 0.002053388 0.000 0.000000000 0.000000000
Mosque Swallow 0.002053388 0.000 0.000000000 0.000000000
Mountain Gray Woodpecker 0.000000000 0.000 0.000000000 0.000000000
Mourning Collared-Dove 0.000000000 0.000 0.000000000 0.000000000
Northern Anteater-Chat 0.000000000 0.000 0.000000000 0.003034901
Northern Black-Flycather 0.002053388 0.000 0.000000000 0.000000000
Northern Fiscal 0.032854209 0.032 0.019230769 0.027314112
Northern Gray-headed Sparrow 0.000000000 0.000 0.003846154 0.000000000
Northern Wheatear 0.008213552 0.008 0.007692308 0.000000000
Northern Yellow White-eye 0.000000000 0.000 0.000000000 0.001517451
Nubian Woodpecker 0.000000000 0.000 0.000000000 0.001517451
Ovambo Sparrowhawk 0.000000000 0.000 0.003846154 0.001517451
Pallid Harrier 0.000000000 0.000 0.000000000 0.000000000
Pectoral-patch Cisticola 0.000000000 0.002 0.000000000 0.000000000
Pied crow 0.000000000 0.000 0.000000000 0.000000000
Pied Wheatear 0.000000000 0.000 0.000000000 0.000000000
Pigmy Kingfisher 0.000000000 0.000 0.000000000 0.001517451
Pin-tailed Whydah 0.002053388 0.010 0.001923077 0.000000000
Plain Martin 0.002053388 0.000 0.005769231 0.000000000
Plain-backed Pipit 0.006160164 0.030 0.000000000 0.001517451
Purple Grenadier 0.030800821 0.034 0.017307692 0.019726859
Purple-banded Sunbird 0.000000000 0.000 0.000000000 0.000000000
Rattling Cisticola 0.049281314 0.042 0.048076923 0.062215478
Red-backed Scrub-Robin 0.000000000 0.000 0.005769231 0.001517451
Red-backed Shrike 0.002053388 0.000 0.000000000 0.000000000
Red-billed Firefinch 0.006160164 0.008 0.019230769 0.003034901
Red-Billed Oxpecker 0.006160164 0.008 0.000000000 0.000000000
Red-billed Quelea 0.006160164 0.004 0.019230769 0.000000000
Red-capped Lark 0.002053388 0.012 0.001923077 0.003034901
Red-chested Cuckoo 0.000000000 0.002 0.003846154 0.001517451
Red-cowled Widowbird 0.004106776 0.006 0.009615385 0.001517451
Red-eyed Dove 0.020533881 0.024 0.059615385 0.028831563
Red-Faced Crombec 0.002053388 0.002 0.005769231 0.000000000
Red-fronted Barbet 0.004106776 0.006 0.001923077 0.009104704
Red-fronted Tinkerbird 0.004106776 0.006 0.003846154 0.009104704
Red-headed Weaver 0.002053388 0.004 0.003846154 0.004552352
Red-necked Spurfowl 0.000000000 0.006 0.000000000 0.000000000
Red-rumped Swallow 0.000000000 0.000 0.000000000 0.001517451
Red-tailed Shrike 0.000000000 0.000 0.000000000 0.001517451
Red-throated Pipit 0.000000000 0.002 0.000000000 0.000000000
Red-throated Wryneck 0.000000000 0.000 0.000000000 0.000000000
Reichenow's Seedeater 0.002053388 0.000 0.000000000 0.000000000
Ring-necked Dove 0.043121150 0.032 0.026923077 0.057663126
Rosy-throated Longclaw 0.000000000 0.000 0.000000000 0.000000000
Rufous Sparrow 0.000000000 0.000 0.000000000 0.000000000
Rufous-naped Lark 0.030800821 0.026 0.001923077 0.006069803
Rufous-necked Wryneck 0.000000000 0.000 0.000000000 0.000000000
Rufous-tailed Weaver 0.000000000 0.000 0.000000000 0.000000000
Rüppell's Starling 0.000000000 0.000 0.000000000 0.000000000
Sacred Ibis 0.000000000 0.000 0.000000000 0.000000000
Scaly Spurfowl 0.000000000 0.002 0.000000000 0.000000000
Scaly-throated Honeyguide 0.000000000 0.000 0.000000000 0.000000000
Scarlet-chested Sunbird 0.004106776 0.010 0.003846154 0.003034901
Schalow's Turaco 0.000000000 0.000 0.000000000 0.000000000
Silverbird 0.000000000 0.000 0.000000000 0.000000000
Silver-breasted Bushshrike 0.000000000 0.004 0.000000000 0.000000000
Slate-colored Boubou 0.026694045 0.038 0.026923077 0.057663126
Sooty Chat 0.002053388 0.002 0.000000000 0.000000000
Southern Ground-Hornbill 0.002053388 0.000 0.000000000 0.000000000
Speckled Mousebird 0.026694045 0.022 0.015384615 0.024279211
Speckled Pigeon 0.000000000 0.000 0.003846154 0.001517451
Spectacled Weaver 0.002053388 0.000 0.003846154 0.000000000
Speke's Weaver 0.000000000 0.000 0.001923077 0.000000000
Spot-flanked Barbet 0.000000000 0.004 0.000000000 0.006069803
Spotted Flycatcher 0.000000000 0.000 0.001923077 0.001517451
Steppe Eagle 0.000000000 0.000 0.000000000 0.000000000
Stout Cisticola 0.000000000 0.000 0.000000000 0.000000000
Straw-tailed Whydah 0.000000000 0.000 0.000000000 0.000000000
Streaky Seedeater 0.006160164 0.004 0.001923077 0.004552352
Strout Cisticola 0.000000000 0.000 0.007692308 0.001517451
Sulphur-breasted Bushshrike 0.002053388 0.000 0.007692308 0.010622155
Superb Starling 0.004106776 0.000 0.000000000 0.001517451
Swahili Sparrow 0.000000000 0.000 0.000000000 0.000000000
Tambourine Dove 0.000000000 0.000 0.000000000 0.000000000
Tawny Eagle 0.000000000 0.000 0.000000000 0.000000000
Tawny-flanked Prinia 0.014373717 0.008 0.036538462 0.033383915
Thick-billed Seedeater 0.000000000 0.000 0.000000000 0.000000000
Tropical Boubou 0.012320329 0.024 0.042307692 0.031866464
unknown egret 0.000000000 0.000 0.000000000 0.000000000
Variable Sunbird 0.014373717 0.010 0.009615385 0.009104704
Village Weaver 0.012320329 0.012 0.026923077 0.001517451
Violet-backed Starling 0.000000000 0.000 0.005769231 0.000000000
Vitelline Masked-Weaver 0.000000000 0.000 0.003846154 0.001517451
Wattled Lapwing 0.000000000 0.000 0.000000000 0.000000000
Wattled Starling 0.000000000 0.000 0.000000000 0.000000000
Western Cattle Egret 0.002053388 0.004 0.003846154 0.001517451
Western Yellow Wagtail 0.004106776 0.006 0.000000000 0.000000000
Whinchat 0.000000000 0.002 0.000000000 0.000000000
White-bellied Canary 0.010266940 0.020 0.019230769 0.016691958
White-bellied Tit 0.000000000 0.000 0.001923077 0.000000000
White-browed Coucal 0.000000000 0.008 0.005769231 0.004552352
White-browed Robin-Chat 0.026694045 0.016 0.015384615 0.013657056
White-browed Scrub-Robin 0.006160164 0.002 0.003846154 0.009104704
White-browed Sparrow-Weaver 0.000000000 0.002 0.000000000 0.000000000
White-eyed Slaty-Flycatcher 0.000000000 0.000 0.001923077 0.000000000
White-fronted Bee-eater 0.002053388 0.002 0.009615385 0.007587253
White-headed Barbet 0.000000000 0.000 0.000000000 0.000000000
White-rumped Swallow 0.000000000 0.000 0.000000000 0.000000000
White-rumped Swift 0.002053388 0.000 0.005769231 0.003034901
White-winged Widowbird 0.000000000 0.000 0.000000000 0.000000000
Willow Warbler 0.002053388 0.000 0.009615385 0.009104704
Winding Cisticola 0.000000000 0.000 0.000000000 0.000000000
Wire-tailed Swallow 0.008213552 0.006 0.000000000 0.000000000
Yellow Bishop 0.032854209 0.026 0.005769231 0.004552352
Yellow-billed Egret 0.000000000 0.000 0.001923077 0.001517451
Yellow-billed Oxpecker 0.000000000 0.000 0.000000000 0.000000000
Yellow-breasted Apalis 0.006160164 0.002 0.009615385 0.010622155
Yellow-fronted Canary 0.024640657 0.020 0.005769231 0.010622155
Yellow-mantled Widowbird 0.000000000 0.000 0.000000000 0.000000000
Yellow-necked Spurfowl 0.000000000 0.000 0.000000000 0.000000000
Yellow-rumped Tinkerbird 0.002053388 0.002 0.001923077 0.001517451
Yellow-spotted Bush Sparrow 0.000000000 0.000 0.001923077 0.000000000
Yellow-throated Longclaw 0.006160164 0.010 0.005769231 0.010622155
Zittling Cisticola 0.002053388 0.000 0.000000000 0.000000000
$Wi
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11
0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625 0.0625
C12 C13 C14 C15 C16
0.0625 0.0625 0.0625 0.0625 0.0625
$Ps
Abyssinian thrush African Citril
3.212955e-04 4.250186e-03
African Dusky Flycatcher African Firefinch
1.237624e-04 1.416991e-03
African Gray-Flycatcher African Green-Pigeon
1.936665e-03 1.088191e-03
African Hawk Eagle African Paradise-Flycatcher
1.721763e-04 3.520968e-03
African Pied-Wagtail African Pipit
1.203060e-02 2.742164e-03
African Stonechat African Swift
1.106195e-04 4.765516e-03
African Thrush African Woolly-necked Stork
1.106195e-04 6.166350e-04
African Yellow-Warbler African-Black-headed Oriole
2.732719e-04 2.712843e-03
Amethyst Sunbird Arrow-marked Babbler
1.108016e-02 4.352669e-03
Ashy Flycatcher Augur Buzzard
1.174648e-03 4.688518e-03
Baglafecht Weaver Banded Martin
1.219482e-02 1.022913e-04
Bare-faced GoAway-bird Barn Swallow
3.087367e-03 8.229635e-03
Black Coucal Black Cuckoshrike
2.959387e-04 1.264516e-03
Black Kite Black-backed Puffback
3.421640e-04 8.649423e-03
Black-chested Snake-Eagle Black-crowned Tchagra
3.689216e-04 6.091513e-03
Black-headed Heron Black-headed Oriole
1.925897e-03 2.521912e-03
Black-headed weaver Black-rumped waxbill
1.230315e-04 1.168224e-04
Black-shouldered Kite Black-throated Wattle-eye
3.634040e-04 7.169121e-04
Blue-naped Mousebird Blue-spotted Wood-Dove
2.758074e-03 2.212045e-04
Booted Eagle Brimstone Canary
1.168224e-04 4.802264e-04
Bronze Mannikin Bronze Sunbird
3.544503e-03 1.492718e-02
Brown-crowned Tchagra Brown-throated Wattle-eye
1.387575e-02 3.194657e-03
Brubru Cape Robin-Chat
5.872572e-04 3.376623e-03
Cape Wagtail Cardinal Quelea
1.237624e-04 3.957441e-03
Cardinal Woodpecker Cattle Egret
3.808946e-04 1.405191e-03
Chinspot Batis Cinnamon-breasted Bunting
5.039217e-03 1.721763e-04
Cinnamon-chested Bee-eater Common Bulbul
3.231119e-03 5.692433e-02
Common Buzzard Common Cuckoo
9.612832e-04 2.650441e-04
Common Waxbill Coqui Francolin
5.426235e-03 3.080076e-03
CRB Crowned Eagle
1.237624e-04 2.732719e-04
Crowned Lapwing D'Arnaud's (Usambiro) Barbet
9.038990e-03 2.373646e-02
Dideric Cuckoo Diedrik Cuckcoo
1.269747e-03 2.082151e-03
Egyptian Goose Emerald-spotted Wood Dove
1.654654e-03 4.150804e-02
Eurasian Hooby Eurasian Hoopoe
1.168224e-04 2.480108e-03
European Bee-eater European Honey-buzzard
1.115357e-03 2.150330e-04
Fan-tailed Widowbird Fine-banded woodpecker
1.201923e-04 4.597195e-04
Fischer's Sparrow-lark Fork-tailed Drongo
1.106195e-04 6.555946e-04
Gabar Goshawk Golden-breasted Bunting
2.408723e-04 2.184658e-03
Golden-winged Sunbird Grassland Pipit
5.257698e-04 1.191180e-03
Gray apalis Gray Crowned-Crane
1.406035e-03 5.809052e-04
Gray Flycatcher Gray Heron
2.923496e-03 3.352253e-04
Gray-backed Fiscal Gray-capped Warbler
8.468727e-04 1.431301e-02
Gray-crested helmetshrike Gray-Crowned Crane
3.204055e-04 8.458844e-04
Gray-headed bushshrike Great Spotted Cuckoo
3.195236e-03 1.283368e-04
Greater Blue-eared Starling Greater Honeyguide
4.456879e-03 8.976096e-04
Green-backed Camaroptera Green-headed Sunbird
3.178569e-02 9.484067e-05
Green-winged Pytilia Grosbeak Weaver
5.163852e-03 9.484067e-05
Hadada Ibis Hamerkop
4.990663e-03 8.240042e-04
Harlequin Quail Helmeted Guineafowl
2.771619e-04 2.920800e-03
Hildebrandt's Spurfowl Hildebrandt's Starling
5.717110e-04 3.776996e-03
Holub's Golden-Weaver Horus Swift
5.113422e-03 6.674509e-04
House-Sparrow Icterine Warbler
1.169945e-02 1.106195e-04
Indigo Bird Joyful Greenbul
5.332191e-04 9.484067e-05
Kenya Rufous Sparrow Klaas's Cuckoo
8.327208e-04 1.052401e-02
Knob-billed Duck Laughing Dove
2.451923e-04 1.012917e-02
Lesser Gray Shrike Lesser Honeyguide
6.327837e-04 6.612978e-04
Lesser Masked-Weaver Lesser Striped Swallow
3.303924e-03 1.017915e-04
Lesser-Masked Weaver Levaillant's Cuckoo
4.548555e-04 1.721763e-04
Little Bee-eater Little Grebe
2.059620e-03 1.106195e-04
Little Sparrowhawk Little Swift
1.250000e-04 1.457811e-03
Lizard Buzzard Long-billed Pipit
2.650441e-04 1.017915e-04
Malachite Kingfisher Marico Sunbird
4.372204e-04 1.168224e-04
Martial Eagle Mosque Swallow
5.861071e-04 1.897766e-03
Mountain Gray Woodpecker Mourning Collared-Dove
3.372412e-04 3.690945e-04
Northern Anteater-Chat Northern Black-Flycather
4.001631e-03 8.875836e-04
Northern Fiscal Northern Gray-headed Sparrow
3.840177e-02 1.058885e-03
Northern Wheatear Northern Yellow White-eye
3.507628e-03 9.484067e-05
Nubian Woodpecker Ovambo Sparrowhawk
9.525838e-04 5.518554e-04
Pallid Harrier Pectoral-patch Cisticola
1.017915e-04 1.591472e-03
Pied crow Pied Wheatear
5.182854e-04 2.616124e-04
Pigmy Kingfisher Pin-tailed Whydah
9.484067e-05 4.522709e-03
Plain Martin Plain-backed Pipit
1.605732e-03 7.601248e-03
Purple Grenadier Purple-banded Sunbird
2.319747e-02 1.102500e-03
Rattling Cisticola Red-backed Scrub-Robin
4.132230e-02 4.960554e-03
Red-backed Shrike Red-billed Firefinch
1.037093e-03 6.392279e-03
Red-Billed Oxpecker Red-billed Quelea
8.276069e-03 3.754427e-03
Red-capped Lark Red-chested Cuckoo
2.615870e-03 7.010975e-04
Red-cowled Widowbird Red-eyed Dove
3.025328e-03 3.015950e-02
Red-Faced Crombec Red-fronted Barbet
1.451766e-03 6.365358e-03
Red-fronted Tinkerbird Red-headed Weaver
6.788262e-03 2.928806e-03
Red-necked Spurfowl Red-rumped Swallow
7.576531e-04 1.659151e-03
Red-tailed Shrike Red-throated Pipit
9.484067e-05 2.267915e-04
Red-throated Wryneck Reichenow's Seedeater
1.857355e-04 2.212045e-04
Ring-necked Dove Rosy-throated Longclaw
4.487618e-02 3.204055e-04
Rufous Sparrow Rufous-naped Lark
1.123765e-03 1.553086e-02
Rufous-necked Wryneck Rufous-tailed Weaver
5.326746e-04 5.607780e-04
Rüppell's Starling Sacred Ibis
6.038246e-04 3.309502e-04
Scaly Spurfowl Scaly-throated Honeyguide
1.204161e-03 4.906329e-04
Scarlet-chested Sunbird Schalow's Turaco
4.595792e-03 1.059016e-03
Silverbird Silver-breasted Bushshrike
1.022913e-04 2.500000e-04
Slate-colored Boubou Sooty Chat
3.648416e-02 5.849706e-04
Southern Ground-Hornbill Speckled Mousebird
2.785771e-04 2.029947e-02
Speckled Pigeon Spectacled Weaver
4.980751e-03 1.203646e-03
Speke's Weaver Spot-flanked Barbet
1.124408e-03 2.601497e-03
Spotted Flycatcher Steppe Eagle
6.558657e-04 1.017915e-04
Stout Cisticola Straw-tailed Whydah
2.336449e-04 2.771619e-04
Streaky Seedeater Strout Cisticola
4.854888e-03 1.961419e-03
Sulphur-breasted Bushshrike Superb Starling
4.138611e-03 5.151073e-03
Swahili Sparrow Tambourine Dove
3.679235e-04 2.107926e-04
Tawny Eagle Tawny-flanked Prinia
2.889987e-04 2.026024e-02
Thick-billed Seedeater Tropical Boubou
2.460630e-04 2.923942e-02
unknown egret Variable Sunbird
1.022913e-04 9.378727e-03
Village Weaver Violet-backed Starling
1.052748e-02 1.817191e-03
Vitelline Masked-Weaver Wattled Lapwing
1.178308e-03 1.230315e-04
Wattled Starling Western Cattle Egret
3.053746e-04 2.318480e-03
Western Yellow Wagtail Whinchat
1.267835e-03 1.392853e-03
White-bellied Canary White-bellied Tit
1.316327e-02 6.776046e-04
White-browed Coucal White-browed Robin-Chat
3.330283e-03 1.290201e-02
White-browed Scrub-Robin White-browed Sparrow-Weaver
5.512674e-03 3.543426e-04
White-eyed Slaty-Flycatcher White-fronted Bee-eater
3.607771e-04 1.729426e-03
White-headed Barbet White-rumped Swallow
5.465438e-04 5.718225e-04
White-rumped Swift White-winged Widowbird
3.107283e-03 9.286776e-05
Willow Warbler Winding Cisticola
2.746240e-03 6.973902e-04
Wire-tailed Swallow Yellow Bishop
2.521594e-03 1.826465e-02
Yellow-billed Egret Yellow-billed Oxpecker
8.433241e-04 6.437122e-04
Yellow-breasted Apalis Yellow-fronted Canary
4.999605e-03 1.790045e-02
Yellow-mantled Widowbird Yellow-necked Spurfowl
2.431081e-04 4.091653e-04
Yellow-rumped Tinkerbird Yellow-spotted Bush Sparrow
1.293112e-03 1.201923e-04
Yellow-throated Longclaw Zittling Cisticola
7.303903e-03 2.212045e-04
$Nspecies
[1] 238
$Ncommunities
[1] 16
$SampleCoverage
ZhangHuang
0.9958781
$SampleCoverage.communities
C1 C2 C3 C4 C5 C6 C7 C8
0.9297145 0.9234551 0.9367824 0.9228400 0.9292655 0.9203540 0.9246729 0.9363708
C9 C10 C11 C12 C13 C14 C15 C16
0.9258805 0.9333838 0.8956499 0.9112655 0.9282659 0.9381433 0.9347629 0.9393571
attr(,"class")
[1] "MetaCommunity"#Alpha diversity
alpha.birds.0 <-DivPart(q=0, mc.birds,Correction = "Best" )
summary (alpha.birds.0)HCDT diversity partitioning of order 0 of metaCommunity mc.birds
Alpha diversity of communities:
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16
117 104 105 125 100 102 105 101 110 129 99 95 105 108 108 106
Total alpha diversity of the communities:
[1] 107.4375
Beta diversity of the communities:
None
2.215241
Gamma diversity of the metacommunity:
None
238 alpha.birds.1 <-DivPart(q=1, mc.birds,Correction = "Best" )
summary (alpha.birds.1)HCDT diversity partitioning of order 1 of metaCommunity mc.birds
Alpha diversity of communities:
C1 C2 C3 C4 C5 C6 C7 C8
64.47629 56.16150 62.67563 61.64511 59.54253 61.58561 60.66394 56.03549
C9 C10 C11 C12 C13 C14 C15 C16
55.35520 72.98342 62.08340 55.97694 64.93713 69.74119 63.03563 50.41178
Total alpha diversity of the communities:
[1] 60.8398
Beta diversity of the communities:
None
1.361156
Gamma diversity of the metacommunity:
None
82.81248 alpha.birds.2 <-DivPart(q=2, mc.birds,Correction = "Best" )
summary (alpha.birds.2)HCDT diversity partitioning of order 2 of metaCommunity mc.birds
Alpha diversity of communities:
C1 C2 C3 C4 C5 C6 C7 C8
41.16452 37.64632 41.62314 39.24521 43.54776 41.91243 40.46175 38.42843
C9 C10 C11 C12 C13 C14 C15 C16
34.93780 47.18348 42.93548 38.80179 46.51285 51.22951 42.10526 30.27825
Total alpha diversity of the communities:
[1] 40.54773
Beta diversity of the communities:
None
1.207687
Gamma diversity of the metacommunity:
None
48.96898 data.frame(alpha.birds.0$CommunityAlphaDiversities,alpha.birds.1$CommunityAlphaDiversitie,alpha.birds.2$CommunityAlphaDiversities)->alpha.birds
alpha.birds alpha.birds.0.CommunityAlphaDiversities
C1 117
C2 104
C3 105
C4 125
C5 100
C6 102
C7 105
C8 101
C9 110
C10 129
C11 99
C12 95
C13 105
C14 108
C15 108
C16 106
alpha.birds.1.CommunityAlphaDiversitie
C1 64.47629
C2 56.16150
C3 62.67563
C4 61.64511
C5 59.54253
C6 61.58561
C7 60.66394
C8 56.03549
C9 55.35520
C10 72.98342
C11 62.08340
C12 55.97694
C13 64.93713
C14 69.74119
C15 63.03563
C16 50.41178
alpha.birds.2.CommunityAlphaDiversities
C1 41.16452
C2 37.64632
C3 41.62314
C4 39.24521
C5 43.54776
C6 41.91243
C7 40.46175
C8 38.42843
C9 34.93780
C10 47.18348
C11 42.93548
C12 38.80179
C13 46.51285
C14 51.22951
C15 42.10526
C16 30.27825alpha.birds%>%
rename("q0b"="alpha.birds.0.CommunityAlphaDiversities",
"q1b"="alpha.birds.1.CommunityAlphaDiversitie" ,
"q2b"="alpha.birds.2.CommunityAlphaDiversities") %>%
mutate(D.eveb=q2b/q0b)->alpha.birds2
alpha.birds2 q0b q1b q2b D.eveb
C1 117 64.47629 41.16452 0.3518335
C2 104 56.16150 37.64632 0.3619839
C3 105 62.67563 41.62314 0.3964109
C4 125 61.64511 39.24521 0.3139617
C5 100 59.54253 43.54776 0.4354776
C6 102 61.58561 41.91243 0.4109061
C7 105 60.66394 40.46175 0.3853500
C8 101 56.03549 38.42843 0.3804795
C9 110 55.35520 34.93780 0.3176164
C10 129 72.98342 47.18348 0.3657634
C11 99 62.08340 42.93548 0.4336918
C12 95 55.97694 38.80179 0.4084399
C13 105 64.93713 46.51285 0.4429795
C14 108 69.74119 51.22951 0.4743473
C15 108 63.03563 42.10526 0.3898635
C16 106 50.41178 30.27825 0.2856439REMOTE SENSING
# ================================
rs.data.monthly <- read_csv("data/S2_CHIRPS_Per_Tile_Monthly_2019_2025.csv")%>%
select(BSI, NDVI, SAVI, year, month, precip, fid) %>%
mutate(across(c(BSI, NDVI, SAVI), ~ na_if(., -9999)))
indices <- c("NDVI", "SAVI", "BSI")
rs.data.monthly# A tibble: 1,344 × 7
BSI NDVI SAVI year month precip fid
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0.0840 0.456 0.684 2019 1 35.1 15
2 0.0114 0.587 0.880 2019 1 35.1 10
3 -0.0691 0.660 0.990 2019 1 34.6 9
4 -0.0248 0.603 0.905 2019 1 34.5 7
5 0.173 0.370 0.554 2019 1 35.1 6
6 0.107 0.467 0.700 2019 1 35.2 1
7 -0.0709 0.667 1.00 2019 1 34.5 3
8 -0.0364 0.614 0.921 2019 1 33.5 8
9 0.0885 0.479 0.718 2019 1 34.5 16
10 0.130 0.455 0.683 2019 1 35.5 14
# ℹ 1,334 more rowsmean.rs.data.monthly <- rs.data.monthly %>%
group_by( fid) %>%
summarise(
mean_BSI = mean(BSI, na.rm = TRUE),
mean_NDVI = mean(NDVI, na.rm = TRUE),
mean_SAVI = mean(SAVI, na.rm = TRUE),
.groups = 'drop' # This drops the grouping after summarising
)
# Print the new summarized data frame
print(mean.rs.data.monthly)# A tibble: 16 × 4
fid mean_BSI mean_NDVI mean_SAVI
<dbl> <dbl> <dbl> <dbl>
1 1 -0.0192 0.470 0.705
2 2 -0.0424 0.488 0.732
3 3 -0.0502 0.502 0.754
4 4 -0.0403 0.484 0.726
5 5 0.00204 0.440 0.660
6 6 -0.00302 0.448 0.672
7 7 -0.0249 0.469 0.703
8 8 -0.0303 0.474 0.711
9 9 -0.0439 0.496 0.743
10 10 -0.0373 0.491 0.736
11 11 -0.0469 0.493 0.739
12 12 -0.00657 0.454 0.681
13 13 -0.00306 0.446 0.669
14 14 -0.0162 0.468 0.702
15 15 -0.0409 0.481 0.721
16 16 -0.0266 0.476 0.713write.csv(mean.rs.data.monthly, "mean.rs.data.monthly.csv", row.names = FALSE)
# ================================
# Mann-Kendall & Sen’s slope (Monthly only)
# ================================
mk_results_monthly <- lapply(indices, function(idx) {
df <- na.omit(data.frame(values = rs.data.monthly[[idx]],
years = rs.data.monthly$year + (rs.data.monthly$month - 1)/12))
mk <- MannKendall(df$values)
sen <- sens.slope(df$values)
data.frame(
Index = idx,
Tau = mk$tau,
Pval = mk$sl,
SenSlope = sen$estimates
)
}) %>% bind_rows()
# Add significance stars
add_stars <- function(p) {
if (p <= 0.001) return("***")
else if (p <= 0.01) return("**")
else if (p <= 0.05) return("*")
else return("")
}
mk_results_monthly$Signif <- sapply(mk_results_monthly$Pval, add_stars)
# Publication-ready table
kable(mk_results_monthly, caption = "Mann-Kendall Test with Sen’s slope estimates (Monthly, 2019–2025)")| Index | Tau | Pval | SenSlope | Signif | |
|---|---|---|---|---|---|
| Sen’s slope…1 | NDVI | -0.4337198 | 0.0000000 | -0.0002269 | *** |
| Sen’s slope…2 | SAVI | -0.4337024 | 0.0000000 | -0.0003402 | *** |
| Sen’s slope…3 | BSI | -0.0290817 | 0.1211612 | -0.0000103 |
# ================================
# Correlation Analysis (Monthly)
# ================================
cor_monthly <- cor(rs.data.monthly[, indices], use = "pairwise.complete.obs")
print(cor_monthly) NDVI SAVI BSI
NDVI 1.000000 1.0000000 -0.6311220
SAVI 1.000000 1.0000000 -0.6311825
BSI -0.631122 -0.6311825 1.0000000# Heatmap
melted_cor <- melt(cor_monthly)
ggplot(melted_cor, aes(x = Var1, y = Var2, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1, 1)) +
theme_minimal(base_size = 13) +
theme(panel.grid = element_blank()) +
labs(title = "Correlation between NDVI, SAVI, BSI (Monthly)")
# ================================
# Monthly Trend Plots
# ================================
plot_trend <- function(df, time_col, value_col, index_name) {
sen <- sens.slope(df[[value_col]])
ggplot(df, aes(x = !!sym(time_col), y = !!sym(value_col))) +
geom_point(color = "blue", alpha = 0.6) +
geom_smooth(method = "lm", se = FALSE, color = "red", linetype = "dashed") +
labs(title = paste(index_name, "- Monthly Trend"),
x = "Time (Years)", y = index_name,
subtitle = paste0("Sen's slope = ", signif(sen$estimates, 3))) +
theme_minimal(base_size = 13) +
theme(panel.grid = element_blank())
}
for (idx in indices) {
df <- na.omit(data.frame(
time = rs.data.monthly$year + (rs.data.monthly$month - 1)/12,
value = rs.data.monthly[[idx]]
))
print(plot_trend(df, "time", "value", idx))
}
# ================================
# Rainfall-Adjusted Residual Trends (RESTREND, Monthly only)
# ================================
compute_residuals <- function(df, index_col, precip_col = "precip") {
df_clean <- na.omit(df[, c(index_col, precip_col)])
if (nrow(df_clean) < 3) return(rep(NA, nrow(df))) # not enough data
lm_fit <- lm(df_clean[[index_col]] ~ df_clean[[precip_col]])
residuals_full <- rep(NA, nrow(df))
residuals_full[!is.na(df[[index_col]]) & !is.na(df[[precip_col]])] <- resid(lm_fit)
return(residuals_full)
}
# Compute residuals
rs.data.monthly$resid_NDVI <- compute_residuals(rs.data.monthly, "NDVI")
rs.data.monthly$resid_SAVI <- compute_residuals(rs.data.monthly, "SAVI")
rs.data.monthly$resid_BSI <- compute_residuals(rs.data.monthly, "BSI")
# MK + Sen’s slope on residuals
indices_resid <- c("resid_NDVI", "resid_SAVI", "resid_BSI")
mk_resid_results <- lapply(indices_resid, function(idx) {
df <- na.omit(data.frame(values = rs.data.monthly[[idx]],
time = rs.data.monthly$year + (rs.data.monthly$month-1)/12))
mk <- MannKendall(df$values)
sen <- sens.slope(df$values)
data.frame(
Index = gsub("resid_", "", idx),
Tau = mk$tau,
Pval = mk$sl,
SenSlope = sen$estimates,
Significance = add_stars(mk$sl)
)
}) %>% bind_rows()
# Residuals results table
kable(mk_resid_results, caption = "Rainfall-Adjusted Residual Trend (RESTREND, Monthly, 2019–2025)")| Index | Tau | Pval | SenSlope | Significance | |
|---|---|---|---|---|---|
| Sen’s slope…1 | NDVI | -0.4114406 | 0.000000 | -0.0002173 | *** |
| Sen’s slope…2 | SAVI | -0.4114131 | 0.000000 | -0.0003258 | *** |
| Sen’s slope…3 | BSI | -0.0408482 | 0.029477 | -0.0000144 | * |
# ================================
# Combined Monthly Trend Plot (NDVI, SAVI, BSI) with Sen's slope
# ================================
monthly_long <- rs.data.monthly %>%
mutate(time = year + (month - 1)/12) %>%
select(time, NDVI, SAVI, BSI) %>%
pivot_longer(cols = c(NDVI, SAVI, BSI), names_to = "Index", values_to = "Value") %>%
na.omit()
monthly_long$Index <- factor(monthly_long$Index, levels = c("NDVI", "SAVI", "BSI"))
sen_slopes <- monthly_long %>%
group_by(Index) %>%
summarise(SenSlope = sens.slope(Value)$estimates, .groups = "drop")
ggplot(monthly_long, aes(x = time, y = Value)) +
geom_line(color = "black", alpha = 0.5) +
geom_smooth(method = "lm", se = FALSE, color = "red", linetype = "dashed") +
facet_wrap(~Index, scales = "free_y", ncol = 2) +
labs(x = "Time (Year)", y = "Index Value") +
theme_minimal(base_size = 11) +
theme(panel.grid = element_blank()) +
geom_text(data = sen_slopes,
aes(x = max(monthly_long$time),
y = min(monthly_long$Value),
label = paste0("Sen (Dashed red) = ", signif(SenSlope, 3))),
inherit.aes = FALSE,
hjust = 1.1, vjust = -0.5, size = 3, color = "black")
# ================================
# Prepare Data
# ================================
monthly_long <- rs.data.monthly %>%
mutate(time = year + (month - 1)/12) %>%
select(time, NDVI, SAVI, BSI) %>%
pivot_longer(cols = c(NDVI, SAVI, BSI), names_to = "Index", values_to = "Value") %>%
na.omit()
monthly_long$Index <- factor(monthly_long$Index, levels = c("NDVI", "SAVI", "BSI"))
# ================================
# Sen's slope estimates
# ================================
sen_slopes <- monthly_long %>%
group_by(Index) %>%
summarise(SenSlope = sens.slope(Value)$estimates, .groups = "drop") %>%
mutate(Label = paste0(Index, " (Sen = ", signif(SenSlope, 3), ")"))
# ================================
# Faceted Plot (NDVI, SAVI, BSI separately)
# ================================
facet_plot <- ggplot(monthly_long, aes(x = time, y = Value)) +
geom_line(color = "black", alpha = 0.5) +
geom_smooth(method = "lm", se = FALSE, color = "red", linetype = "dashed") +
facet_wrap(~Index, scales = "free_y", ncol = 2) +
labs(x = "Time (Year)", y = "Index Value") +
theme_minimal(base_size = 11) +
theme(panel.grid = element_blank()) +
geom_text(data = sen_slopes,
aes(x = max(monthly_long$time),
y = min(monthly_long$Value),
label = paste0("Sen (Dashed red) = ", signif(SenSlope, 3))),
inherit.aes = FALSE,
hjust = 1.1, vjust = -0.5, size = 3, color = "black")
print(facet_plot)
# ================================
# ================================
# Combined Plot (NDVI, SAVI, BSI together, slopes in legend, smooth by index)
# ================================
monthly_long_labeled <- monthly_long %>%
left_join(sen_slopes %>% select(Index, Label), by = "Index")
combined_plot <- ggplot(monthly_long_labeled, aes(x = time, y = Value, color = Label)) +
geom_line( alpha = 0.8,size = 1) +
geom_smooth(aes(color = Label), method = "lm", se = FALSE, linetype = "dashed") +
scale_color_manual(values = c("darkgreen", "darkblue", "darkred")) +
labs(
title = "",
x = "Time (Year)", y = "Index Value",
color = "" # legend works as subheading
) +
theme_minimal(base_size = 11) +
theme(
panel.grid = element_blank(),
legend.position = "top",
legend.direction = "horizontal",
legend.box = "horizontal",
legend.spacing.x = unit(0.5, "cm")
) +
scale_x_continuous(breaks = 2019:2025, labels = 2019:2025)
print(combined_plot)
#(title = "Monthly Trends of NDVI, SAVI, and BSI (2019–2025)",
#x = "Time (Year)", y = "Index Value",
#subtitle = "Dashed red = linear trend with Sen's slope values")
# ================================
# Residual Trend Plots (RESTREND) with Sen slopes
# ================================
resid_long <- rs.data.monthly %>%
mutate(time = year + (month - 1)/12) %>%
select(time, resid_NDVI, resid_SAVI, resid_BSI) %>%
pivot_longer(cols = c(resid_NDVI, resid_SAVI, resid_BSI),
names_to = "Index", values_to = "Residual") %>%
na.omit()
# Clean index labels
resid_long$Index <- factor(
resid_long$Index,
levels = c("resid_NDVI", "resid_SAVI", "resid_BSI"),
labels = c("NDVI (Residual)", "SAVI (Residual)", "BSI (Residual)")
)
# Sen's slope for residuals (used in legend)
sen_resid_slopes <- resid_long %>%
group_by(Index) %>%
summarise(SenSlope = sens.slope(Residual)$estimates, .groups = "drop") %>%
mutate(Label = paste0(Index, " (Sen = ", signif(SenSlope, 3), ")"))
# Merge labels back
resid_long_labeled <- resid_long %>%
left_join(sen_resid_slopes %>% select(Index, Label), by = "Index")
# --- Combined residual plot with Sen slopes
resid_plot <- ggplot(resid_long_labeled, aes(x = time, y = Residual, color = Label)) +
geom_line(size = 1, alpha = 0.8) +
geom_smooth(aes(color = Label), method = "lm", se = FALSE, linetype = "dashed", size = 0.9) +
scale_color_manual(values = c("darkgreen", "darkblue", "darkred")) +
labs(
title = "",
x = "Time (Year)", y = "Residual Index Value",
color = "" # legend works as subheading
) +
theme_minimal(base_size = 11) +
theme(
panel.grid = element_blank(),
legend.position = "top",
legend.direction = "horizontal",
legend.title = element_text(face = "bold"),
legend.box = "horizontal",
legend.spacing.x = unit(0.5, "cm")
) +
scale_x_continuous(breaks = 2019:2025, labels = 2019:2025)
print(resid_plot)
Remote sensing spatial analysis by tile
# ================================
# Spatial Analysis by Tile (16 tiles of 2.5x2.5km each)
# ================================
# Check unique tiles
tiles <- unique(rs.data.monthly$fid)
cat("Number of tiles:", length(tiles), "\n")Number of tiles: 16 cat("Tile IDs:", sort(tiles), "\n")Tile IDs: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # ================================
# Mann-Kendall & Sen's Slope per Tile
# ================================
tile_mk_results <- lapply(tiles, function(tile_id) {
tile_data <- rs.data.monthly %>% filter(fid == tile_id)
tile_results <- lapply(indices, function(idx) {
df <- na.omit(data.frame(values = tile_data[[idx]],
time = tile_data$year + (tile_data$month - 1)/12))
if (nrow(df) < 3) {
return(data.frame(
Tile = tile_id,
Index = idx,
Tau = NA,
Pval = NA,
SenSlope = NA,
N_Obs = nrow(df),
Trend = "Insufficient data"
))
}
mk <- MannKendall(df$values)
sen <- sens.slope(df$values)
# Determine trend direction and significance
trend_direction <- ifelse(sen$estimates > 0, "Increasing",
ifelse(sen$estimates < 0, "Decreasing", "No trend"))
trend_signif <- ifelse(mk$sl <= 0.05, "Significant", "Not significant")
trend_category <- ifelse(mk$sl > 0.05, "No trend",
ifelse(sen$estimates > 0, "Increasing", "Decreasing"))
data.frame(
Tile = tile_id,
Index = idx,
Tau = mk$tau,
Pval = mk$sl,
SenSlope = sen$estimates,
N_Obs = nrow(df),
Trend_Direction = trend_direction,
Trend_Significance = trend_signif,
Trend_Category = trend_category
)
}) %>% bind_rows()
return(tile_results)
}) %>% bind_rows()
# Add significance stars
tile_mk_results$Signif <- sapply(tile_mk_results$Pval, function(p) {
if (is.na(p)) return("")
if (p <= 0.001) return("***")
else if (p <= 0.01) return("**")
else if (p <= 0.05) return("*")
else return("")
})
# Display results table
kable(tile_mk_results, caption = "Mann-Kendall Test with Sen's Slope by Tile (Monthly, 2019–2025)")| Tile | Index | Tau | Pval | SenSlope | N_Obs | Trend_Direction | Trend_Significance | Trend_Category | Signif | |
|---|---|---|---|---|---|---|---|---|---|---|
| Sen’s slope…1 | 15 | NDVI | -0.4819864 | 0.0000000 | -0.0036635 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…2 | 15 | SAVI | -0.4819864 | 0.0000000 | -0.0054941 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…3 | 15 | BSI | 0.0107108 | 0.8922431 | 0.0000427 | 79 | Increasing | Not significant | No trend | |
| Sen’s slope…4 | 10 | NDVI | -0.4625122 | 0.0000000 | -0.0038153 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…5 | 10 | SAVI | -0.4625122 | 0.0000000 | -0.0057185 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…6 | 10 | BSI | 0.0133074 | 0.8655348 | 0.0000766 | 79 | Increasing | Not significant | No trend | |
| Sen’s slope…7 | 9 | NDVI | -0.4553716 | 0.0000000 | -0.0039241 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…8 | 9 | SAVI | -0.4553716 | 0.0000000 | -0.0058848 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…9 | 9 | BSI | 0.0418695 | 0.5879111 | 0.0002130 | 79 | Increasing | Not significant | No trend | |
| Sen’s slope…10 | 7 | NDVI | -0.5040571 | 0.0000000 | -0.0038183 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…11 | 7 | SAVI | -0.5040571 | 0.0000000 | -0.0057269 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…12 | 7 | BSI | -0.0035703 | 0.9662330 | -0.0000158 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…13 | 6 | NDVI | -0.3378773 | 0.0000107 | -0.0027521 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…14 | 6 | SAVI | -0.3378773 | 0.0000107 | -0.0041267 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…15 | 6 | BSI | -0.1963648 | 0.0105601 | -0.0011173 | 79 | Decreasing | Significant | Decreasing | * |
| Sen’s slope…16 | 1 | NDVI | -0.3995456 | 0.0000002 | -0.0032879 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…17 | 1 | SAVI | -0.3995456 | 0.0000002 | -0.0049304 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…18 | 1 | BSI | -0.0710808 | 0.3560776 | -0.0003594 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…19 | 3 | NDVI | -0.4281078 | 0.0000000 | -0.0037655 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…20 | 3 | SAVI | -0.4281078 | 0.0000000 | -0.0056467 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…21 | 3 | BSI | 0.0139565 | 0.8588803 | 0.0001261 | 79 | Increasing | Not significant | No trend | |
| Sen’s slope…22 | 8 | NDVI | -0.4969166 | 0.0000000 | -0.0037730 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…23 | 8 | SAVI | -0.4969166 | 0.0000000 | -0.0056592 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…24 | 8 | BSI | 0.0009737 | 0.9932446 | 0.0000009 | 79 | Increasing | Not significant | No trend | |
| Sen’s slope…25 | 16 | NDVI | -0.3993671 | 0.0000002 | -0.0031801 | 80 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…26 | 16 | SAVI | -0.3993671 | 0.0000002 | -0.0047687 | 80 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…27 | 16 | BSI | -0.1050633 | 0.1690715 | -0.0006343 | 80 | Decreasing | Not significant | No trend | |
| Sen’s slope…28 | 14 | NDVI | -0.3755274 | 0.0000010 | -0.0031063 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…29 | 14 | SAVI | -0.3755274 | 0.0000010 | -0.0046577 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…30 | 14 | BSI | -0.0905550 | 0.2392496 | -0.0006675 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…31 | 2 | NDVI | -0.4307043 | 0.0000000 | -0.0037510 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…32 | 2 | SAVI | -0.4307043 | 0.0000000 | -0.0056246 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…33 | 2 | BSI | 0.0042194 | 0.9594851 | 0.0000241 | 79 | Increasing | Not significant | No trend | |
| Sen’s slope…34 | 4 | NDVI | -0.4930218 | 0.0000000 | -0.0039117 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…35 | 4 | SAVI | -0.4930218 | 0.0000000 | -0.0058661 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…36 | 4 | BSI | 0.0483609 | 0.5309659 | 0.0002672 | 79 | Increasing | Not significant | No trend | |
| Sen’s slope…37 | 11 | NDVI | -0.4373417 | 0.0000000 | -0.0036986 | 80 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…38 | 11 | SAVI | -0.4373417 | 0.0000000 | -0.0055463 | 80 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…39 | 11 | BSI | -0.0056962 | 0.9436928 | -0.0000578 | 80 | Decreasing | Not significant | No trend | |
| Sen’s slope…40 | 12 | NDVI | -0.4066861 | 0.0000001 | -0.0032824 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…41 | 12 | SAVI | -0.4066861 | 0.0000001 | -0.0049218 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…42 | 12 | BSI | -0.1002921 | 0.1922799 | -0.0005700 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…43 | 13 | NDVI | -0.4203181 | 0.0000000 | -0.0035241 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…44 | 13 | SAVI | -0.4203181 | 0.0000000 | -0.0052848 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…45 | 13 | BSI | -0.0529049 | 0.4928401 | -0.0003826 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…46 | 5 | NDVI | -0.4690036 | 0.0000000 | -0.0038403 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…47 | 5 | SAVI | -0.4690036 | 0.0000000 | -0.0057586 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…48 | 5 | BSI | -0.0269393 | 0.7284917 | -0.0000964 | 79 | Decreasing | Not significant | No trend |
# ================================
# Summary of Trends by Tile
# ================================
trend_summary <- tile_mk_results %>%
group_by(Index, Trend_Category) %>%
summarise(
Count = n(),
Percentage = round(n() / length(tiles) * 100, 1),
.groups = 'drop'
) %>%
arrange(Index, Trend_Category)
kable(trend_summary, caption = "Summary of Trend Categories by Index Across Tiles")| Index | Trend_Category | Count | Percentage |
|---|---|---|---|
| BSI | Decreasing | 1 | 6.2 |
| BSI | No trend | 15 | 93.8 |
| NDVI | Decreasing | 16 | 100.0 |
| SAVI | Decreasing | 16 | 100.0 |
# ================================
# RESTREND Analysis per Tile
# ================================
tile_restrand_results <- lapply(tiles, function(tile_id) {
tile_data <- rs.data.monthly %>% filter(fid == tile_id)
tile_results <- lapply(indices_resid, function(idx) {
df <- na.omit(data.frame(values = tile_data[[idx]],
time = tile_data$year + (tile_data$month - 1)/12))
if (nrow(df) < 3) {
return(data.frame(
Tile = tile_id,
Index = gsub("resid_", "", idx),
Tau = NA,
Pval = NA,
SenSlope = NA,
N_Obs = nrow(df),
Trend = "Insufficient data"
))
}
mk <- MannKendall(df$values)
sen <- sens.slope(df$values)
# Determine trend direction and significance
trend_direction <- ifelse(sen$estimates > 0, "Increasing",
ifelse(sen$estimates < 0, "Decreasing", "No trend"))
trend_signif <- ifelse(mk$sl <= 0.05, "Significant", "Not significant")
trend_category <- ifelse(mk$sl > 0.05, "No trend",
ifelse(sen$estimates > 0, "Increasing", "Decreasing"))
data.frame(
Tile = tile_id,
Index = gsub("resid_", "", idx),
Tau = mk$tau,
Pval = mk$sl,
SenSlope = sen$estimates,
N_Obs = nrow(df),
Trend_Direction = trend_direction,
Trend_Significance = trend_signif,
Trend_Category = trend_category
)
}) %>% bind_rows()
return(tile_results)
}) %>% bind_rows()
# Add significance stars
tile_restrand_results$Signif <- sapply(tile_restrand_results$Pval, function(p) {
if (is.na(p)) return("")
if (p <= 0.001) return("***")
else if (p <= 0.01) return("**")
else if (p <= 0.05) return("*")
else return("")
})
# Display RESTREND results table
kable(tile_restrand_results, caption = "RESTREND Analysis by Tile (Monthly, 2019–2025)")| Tile | Index | Tau | Pval | SenSlope | N_Obs | Trend_Direction | Trend_Significance | Trend_Category | Signif | |
|---|---|---|---|---|---|---|---|---|---|---|
| Sen’s slope…1 | 15 | NDVI | -0.4605648 | 0.0000000 | -0.0034746 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…2 | 15 | SAVI | -0.4605648 | 0.0000000 | -0.0052099 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…3 | 15 | BSI | -0.0035703 | 0.9662330 | -0.0000131 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…4 | 10 | NDVI | -0.4417397 | 0.0000000 | -0.0036502 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…5 | 10 | SAVI | -0.4417397 | 0.0000000 | -0.0054746 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…6 | 10 | BSI | 0.0029211 | 0.9729836 | 0.0000124 | 79 | Increasing | Not significant | No trend | |
| Sen’s slope…7 | 9 | NDVI | -0.4287569 | 0.0000000 | -0.0037443 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…8 | 9 | SAVI | -0.4281078 | 0.0000000 | -0.0056153 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…9 | 9 | BSI | 0.0301850 | 0.6969316 | 0.0001400 | 79 | Increasing | Not significant | No trend | |
| Sen’s slope…10 | 7 | NDVI | -0.4761441 | 0.0000000 | -0.0036577 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…11 | 7 | SAVI | -0.4761441 | 0.0000000 | -0.0054849 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…12 | 7 | BSI | -0.0223953 | 0.7734492 | -0.0001138 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…13 | 6 | NDVI | -0.3138591 | 0.0000433 | -0.0026105 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…14 | 6 | SAVI | -0.3132100 | 0.0000449 | -0.0039137 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…15 | 6 | BSI | -0.2074002 | 0.0069160 | -0.0011839 | 79 | Decreasing | Significant | Decreasing | ** |
| Sen’s slope…16 | 1 | NDVI | -0.3722817 | 0.0000012 | -0.0031145 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…17 | 1 | SAVI | -0.3722817 | 0.0000012 | -0.0046706 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…18 | 1 | BSI | -0.0847128 | 0.2710427 | -0.0004762 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…19 | 3 | NDVI | -0.4047387 | 0.0000001 | -0.0036202 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…20 | 3 | SAVI | -0.4047387 | 0.0000001 | -0.0054289 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…21 | 3 | BSI | 0.0022720 | 0.9797360 | 0.0000334 | 79 | Increasing | Not significant | No trend | |
| Sen’s slope…22 | 8 | NDVI | -0.4741967 | 0.0000000 | -0.0036464 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…23 | 8 | SAVI | -0.4741967 | 0.0000000 | -0.0054679 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…24 | 8 | BSI | -0.0139565 | 0.8588803 | -0.0000597 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…25 | 16 | NDVI | -0.3759493 | 0.0000008 | -0.0030128 | 80 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…26 | 16 | SAVI | -0.3759493 | 0.0000008 | -0.0045177 | 80 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…27 | 16 | BSI | -0.1139240 | 0.1358240 | -0.0007208 | 80 | Decreasing | Not significant | No trend | |
| Sen’s slope…28 | 14 | NDVI | -0.3508601 | 0.0000048 | -0.0029185 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…29 | 14 | SAVI | -0.3508601 | 0.0000048 | -0.0043761 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…30 | 14 | BSI | -0.0983447 | 0.2010857 | -0.0006980 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…31 | 2 | NDVI | -0.4099318 | 0.0000001 | -0.0035660 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…32 | 2 | SAVI | -0.4092827 | 0.0000001 | -0.0053475 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…33 | 2 | BSI | -0.0048685 | 0.9527398 | -0.0000288 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…34 | 4 | NDVI | -0.4703018 | 0.0000000 | -0.0037747 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…35 | 4 | SAVI | -0.4703018 | 0.0000000 | -0.0056605 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…36 | 4 | BSI | 0.0353781 | 0.6475279 | 0.0001700 | 79 | Increasing | Not significant | No trend | |
| Sen’s slope…37 | 11 | NDVI | -0.4183544 | 0.0000000 | -0.0035528 | 80 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…38 | 11 | SAVI | -0.4183544 | 0.0000000 | -0.0053281 | 80 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…39 | 11 | BSI | -0.0202532 | 0.7935190 | -0.0001263 | 80 | Decreasing | Not significant | No trend | |
| Sen’s slope…40 | 12 | NDVI | -0.3859137 | 0.0000005 | -0.0031461 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…41 | 12 | SAVI | -0.3846154 | 0.0000005 | -0.0047176 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…42 | 12 | BSI | -0.1080818 | 0.1598834 | -0.0006042 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…43 | 13 | NDVI | -0.3988965 | 0.0000002 | -0.0033579 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…44 | 13 | SAVI | -0.3988965 | 0.0000002 | -0.0050351 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…45 | 13 | BSI | -0.0632911 | 0.4114953 | -0.0004217 | 79 | Decreasing | Not significant | No trend | |
| Sen’s slope…46 | 5 | NDVI | -0.4456345 | 0.0000000 | -0.0037186 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…47 | 5 | SAVI | -0.4456345 | 0.0000000 | -0.0055756 | 79 | Decreasing | Significant | Decreasing | *** |
| Sen’s slope…48 | 5 | BSI | -0.0373255 | 0.6293805 | -0.0001685 | 79 | Decreasing | Not significant | No trend |
# ================================
# RESTREND Summary
# ================================
restrand_summary <- tile_restrand_results %>%
group_by(Index, Trend_Category) %>%
summarise(
Count = n(),
Percentage = round(n() / length(tiles) * 100, 1),
.groups = 'drop'
) %>%
arrange(Index, Trend_Category)
kable(restrand_summary, caption = "Summary of RESTREND Trend Categories by Index Across Tiles")| Index | Trend_Category | Count | Percentage |
|---|---|---|---|
| BSI | Decreasing | 1 | 6.2 |
| BSI | No trend | 15 | 93.8 |
| NDVI | Decreasing | 16 | 100.0 |
| SAVI | Decreasing | 16 | 100.0 |
# ================================
# Tile-level Time Series Plots with Sen's Slope
# ================================
# Function to create tile-specific plots with Sen's slope
plot_tile_timeseries <- function(tile_id) {
tile_data <- rs.data.monthly %>% filter(fid == tile_id)
# Convert to long format for original indices
tile_long <- tile_data %>%
mutate(time = year + (month - 1)/12) %>%
select(time, NDVI, SAVI, BSI) %>%
pivot_longer(cols = c(NDVI, SAVI, BSI), names_to = "Index", values_to = "Value") %>%
na.omit()
# Calculate Sen's slope for each index in this tile
sen_slopes_tile <- tile_long %>%
group_by(Index) %>%
summarise(
SenSlope = ifelse(n() >= 3, sens.slope(Value)$estimates, NA),
.groups = 'drop'
) %>%
mutate(Label = paste0(Index, " (Sen = ", ifelse(is.na(SenSlope), "NA", signif(SenSlope, 3)), ")"))
if(nrow(tile_long) > 2) {
# Plot 1: Original indices with Sen's slope
p1 <- ggplot(tile_long, aes(x = time, y = Value, color = Index)) +
geom_line(alpha = 0.7, size = 0.8) +
geom_point(alpha = 0.5, size = 1) +
geom_smooth(method = "lm", se = FALSE, linetype = "dashed", size = 1.2) +
scale_color_manual(values = c("NDVI" = "darkgreen", "SAVI" = "darkblue", "BSI" = "darkred")) +
labs(title = paste("Tile", tile_id, "- Monthly Time Series with Sen's Slope"),
x = "Time (Year)", y = "Index Value",
subtitle = "Dashed lines show Sen's slope trend") +
theme_minimal() +
theme(legend.position = "top",
panel.grid.minor = element_blank()) +
scale_x_continuous(breaks = 2019:2025)
print(p1)
# Plot 2: Residuals with Sen's slope
tile_resid_long <- tile_data %>%
mutate(time = year + (month - 1)/12) %>%
select(time, resid_NDVI, resid_SAVI, resid_BSI) %>%
pivot_longer(cols = c(resid_NDVI, resid_SAVI, resid_BSI),
names_to = "Index", values_to = "Residual") %>%
na.omit()
if(nrow(tile_resid_long) > 2) {
tile_resid_long$Index <- gsub("resid_", "", tile_resid_long$Index)
# Calculate Sen's slope for residuals
sen_resid_slopes <- tile_resid_long %>%
group_by(Index) %>%
summarise(
SenSlope = ifelse(n() >= 3, sens.slope(Residual)$estimates, NA),
.groups = 'drop'
) %>%
mutate(Label = paste0(Index, " (Sen = ", ifelse(is.na(SenSlope), "NA", signif(SenSlope, 3)), ")"))
p2 <- ggplot(tile_resid_long, aes(x = time, y = Residual, color = Index)) +
geom_line(alpha = 0.7, size = 0.8) +
geom_point(alpha = 0.5, size = 1) +
geom_smooth(method = "lm", se = FALSE, linetype = "dashed", size = 1.2) +
scale_color_manual(values = c("NDVI" = "darkgreen", "SAVI" = "darkblue", "BSI" = "darkred")) +
labs(title = paste("Tile", tile_id, "- RESTREND Residuals with Sen's Slope"),
x = "Time (Year)", y = "Residual Value",
subtitle = "Dashed lines show Sen's slope trend (rainfall-adjusted)") +
theme_minimal() +
theme(legend.position = "top",
panel.grid.minor = element_blank()) +
scale_x_continuous(breaks = 2019:2025)
print(p2)
}
}
}
# Generate plots for all tiles
for(tile_id in sort(tiles)) {
plot_tile_timeseries(tile_id)
}
# ================================
# Spatial Variation Heatmaps
# ================================
# Create heatmap of Sen's slopes across tiles
ggplot(tile_mk_results, aes(x = factor(Tile), y = Index, fill = SenSlope)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "blue", high = "green", mid = "white",
midpoint = 0, name = "Sen's Slope") +
geom_text(aes(label = Signif), size = 3, vjust = 1) +
labs(title = "",
x = "Tile ID", y = "Index") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# RESTREND heatmap
ggplot(tile_restrand_results, aes(x = factor(Tile), y = Index, fill = SenSlope)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "blue", high = "green", mid = "white",
midpoint = 0, name = "Sen's Slope (residual)") +
geom_text(aes(label = Signif), size = 3, vjust = 1) +
labs(title = "",
x = "Tile ID", y = "Index") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# ================================
# Final Summary Statistics
# ================================
cat("\n=== SPATIAL ANALYSIS SUMMARY ===\n")
=== SPATIAL ANALYSIS SUMMARY ===cat("Area of Interest: 100 km² divided into", length(tiles), "tiles (2.5x2.5km each)\n\n")Area of Interest: 100 km² divided into 16 tiles (2.5x2.5km each)# Original trends summary
cat("TREND ANALYSIS SUMMARY:\n")TREND ANALYSIS SUMMARY:for(idx in indices) {
idx_data <- tile_mk_results %>% filter(Index == idx & !is.na(Trend_Category))
if(nrow(idx_data) > 0) {
increasing <- sum(idx_data$Trend_Category == "Increasing")
decreasing <- sum(idx_data$Trend_Category == "Decreasing")
no_trend <- sum(idx_data$Trend_Category == "No trend")
cat("-", idx, ": Increasing =", increasing, "Decreasing =", decreasing,
"No trend =", no_trend, "\n")
}
}- NDVI : Increasing = 0 Decreasing = 16 No trend = 0
- SAVI : Increasing = 0 Decreasing = 16 No trend = 0
- BSI : Increasing = 0 Decreasing = 1 No trend = 15 # RESTREND summary
cat("\nRESTREND ANALYSIS SUMMARY:\n")
RESTREND ANALYSIS SUMMARY:for(idx in unique(tile_restrand_results$Index)) {
idx_data <- tile_restrand_results %>% filter(Index == idx & !is.na(Trend_Category))
if(nrow(idx_data) > 0) {
increasing <- sum(idx_data$Trend_Category == "Increasing")
decreasing <- sum(idx_data$Trend_Category == "Decreasing")
no_trend <- sum(idx_data$Trend_Category == "No trend")
cat("-", idx, ": Increasing =", increasing, "Decreasing =", decreasing,
"No trend =", no_trend, "\n")
}
}- NDVI : Increasing = 0 Decreasing = 16 No trend = 0
- SAVI : Increasing = 0 Decreasing = 16 No trend = 0
- BSI : Increasing = 0 Decreasing = 1 No trend = 15 cat("\nSpatial analysis completed for", length(tiles), "tiles.\n")
Spatial analysis completed for 16 tiles.rs.data.monthly # A tibble: 1,344 × 10
BSI NDVI SAVI year month precip fid resid_NDVI resid_SAVI resid_BSI
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0.0840 0.456 0.684 2019 1 35.1 15 -0.0183 -0.0275 0.111
2 0.0114 0.587 0.880 2019 1 35.1 10 0.113 0.169 0.0386
3 -0.0691 0.660 0.990 2019 1 34.6 9 0.185 0.278 -0.0420
4 -0.0248 0.603 0.905 2019 1 34.5 7 0.129 0.193 0.00227
5 0.173 0.370 0.554 2019 1 35.1 6 -0.105 -0.157 0.200
6 0.107 0.467 0.700 2019 1 35.2 1 -0.00775 -0.0117 0.134
7 -0.0709 0.667 1.00 2019 1 34.5 3 0.193 0.289 -0.0438
8 -0.0364 0.614 0.921 2019 1 33.5 8 0.140 0.210 -0.00928
9 0.0885 0.479 0.718 2019 1 34.5 16 0.00416 0.00621 0.116
10 0.130 0.455 0.683 2019 1 35.5 14 -0.0191 -0.0286 0.157
# ℹ 1,334 more rowswrite.csv(rs.data.monthly, "rs_data_monthly.csv", row.names = FALSE)
tile_restrand_results Tile Index Tau Pval SenSlope N_Obs
Sen's slope...1 15 NDVI -0.460564762 1.938778e-09 -3.474611e-03 79
Sen's slope...2 15 SAVI -0.460564762 1.938778e-09 -5.209917e-03 79
Sen's slope...3 15 BSI -0.003570269 9.662330e-01 -1.311369e-05 79
Sen's slope...4 10 NDVI -0.441739708 8.545870e-09 -3.650179e-03 79
Sen's slope...5 10 SAVI -0.441739708 8.545870e-09 -5.474568e-03 79
Sen's slope...6 10 BSI 0.002921130 9.729836e-01 1.241231e-05 79
Sen's slope...7 9 NDVI -0.428756893 2.297138e-08 -3.744254e-03 79
Sen's slope...8 9 SAVI -0.428107768 2.411795e-08 -5.615327e-03 79
Sen's slope...9 9 BSI 0.030185005 6.969316e-01 1.400117e-04 79
Sen's slope...10 7 NDVI -0.476144105 5.433131e-10 -3.657714e-03 79
Sen's slope...11 7 SAVI -0.476144105 5.433131e-10 -5.484906e-03 79
Sen's slope...12 7 BSI -0.022395326 7.734492e-01 -1.137884e-04 79
Sen's slope...13 6 NDVI -0.313859135 4.325049e-05 -2.610517e-03 79
Sen's slope...14 6 SAVI -0.313210011 4.485672e-05 -3.913701e-03 79
Sen's slope...15 6 BSI -0.207400188 6.916015e-03 -1.183946e-03 79
Sen's slope...16 1 NDVI -0.372281730 1.225990e-06 -3.114453e-03 79
Sen's slope...17 1 SAVI -0.372281730 1.225990e-06 -4.670577e-03 79
Sen's slope...18 1 BSI -0.084712759 2.710427e-01 -4.762299e-04 79
Sen's slope...19 3 NDVI -0.404738724 1.329568e-07 -3.620215e-03 79
Sen's slope...20 3 SAVI -0.404738724 1.329568e-07 -5.428937e-03 79
Sen's slope...21 3 BSI 0.002271990 9.797360e-01 3.344390e-05 79
Sen's slope...22 8 NDVI -0.474196702 6.383642e-10 -3.646437e-03 79
Sen's slope...23 8 SAVI -0.474196702 6.383642e-10 -5.467859e-03 79
Sen's slope...24 8 BSI -0.013956508 8.588803e-01 -5.968247e-05 79
Sen's slope...25 16 NDVI -0.375949323 8.156534e-07 -3.012779e-03 80
Sen's slope...26 16 SAVI -0.375949323 8.156534e-07 -4.517668e-03 80
Sen's slope...27 16 BSI -0.113924041 1.358240e-01 -7.208273e-04 80
Sen's slope...28 14 NDVI -0.350860119 4.831119e-06 -2.918490e-03 79
Sen's slope...29 14 SAVI -0.350860119 4.831119e-06 -4.376086e-03 79
Sen's slope...30 14 BSI -0.098344691 2.010857e-01 -6.980313e-04 79
Sen's slope...31 2 NDVI -0.409931839 9.169547e-08 -3.566024e-03 79
Sen's slope...32 2 SAVI -0.409282714 9.607805e-08 -5.347533e-03 79
Sen's slope...33 2 BSI -0.004868549 9.527398e-01 -2.881242e-05 79
Sen's slope...34 4 NDVI -0.470301837 8.795935e-10 -3.774693e-03 79
Sen's slope...35 4 SAVI -0.470301837 8.795935e-10 -5.660463e-03 79
Sen's slope...36 4 BSI 0.035378125 6.475279e-01 1.699538e-04 79
Sen's slope...37 11 NDVI -0.418354392 4.057988e-08 -3.552767e-03 80
Sen's slope...38 11 SAVI -0.418354392 4.057988e-08 -5.328076e-03 80
Sen's slope...39 11 BSI -0.020253163 7.935190e-01 -1.262529e-04 80
Sen's slope...40 12 NDVI -0.385913670 4.925558e-07 -3.146075e-03 79
Sen's slope...41 12 SAVI -0.384615391 5.379527e-07 -4.717577e-03 79
Sen's slope...42 12 BSI -0.108081795 1.598834e-01 -6.041627e-04 79
Sen's slope...43 13 NDVI -0.398896456 2.008783e-07 -3.357886e-03 79
Sen's slope...44 13 SAVI -0.398896456 2.008783e-07 -5.035106e-03 79
Sen's slope...45 13 BSI -0.063291140 4.114953e-01 -4.216984e-04 79
Sen's slope...46 5 NDVI -0.445634544 6.317741e-09 -3.718566e-03 79
Sen's slope...47 5 SAVI -0.445634544 6.317741e-09 -5.575613e-03 79
Sen's slope...48 5 BSI -0.037325542 6.293805e-01 -1.685265e-04 79
Trend_Direction Trend_Significance Trend_Category Signif
Sen's slope...1 Decreasing Significant Decreasing ***
Sen's slope...2 Decreasing Significant Decreasing ***
Sen's slope...3 Decreasing Not significant No trend
Sen's slope...4 Decreasing Significant Decreasing ***
Sen's slope...5 Decreasing Significant Decreasing ***
Sen's slope...6 Increasing Not significant No trend
Sen's slope...7 Decreasing Significant Decreasing ***
Sen's slope...8 Decreasing Significant Decreasing ***
Sen's slope...9 Increasing Not significant No trend
Sen's slope...10 Decreasing Significant Decreasing ***
Sen's slope...11 Decreasing Significant Decreasing ***
Sen's slope...12 Decreasing Not significant No trend
Sen's slope...13 Decreasing Significant Decreasing ***
Sen's slope...14 Decreasing Significant Decreasing ***
Sen's slope...15 Decreasing Significant Decreasing **
Sen's slope...16 Decreasing Significant Decreasing ***
Sen's slope...17 Decreasing Significant Decreasing ***
Sen's slope...18 Decreasing Not significant No trend
Sen's slope...19 Decreasing Significant Decreasing ***
Sen's slope...20 Decreasing Significant Decreasing ***
Sen's slope...21 Increasing Not significant No trend
Sen's slope...22 Decreasing Significant Decreasing ***
Sen's slope...23 Decreasing Significant Decreasing ***
Sen's slope...24 Decreasing Not significant No trend
Sen's slope...25 Decreasing Significant Decreasing ***
Sen's slope...26 Decreasing Significant Decreasing ***
Sen's slope...27 Decreasing Not significant No trend
Sen's slope...28 Decreasing Significant Decreasing ***
Sen's slope...29 Decreasing Significant Decreasing ***
Sen's slope...30 Decreasing Not significant No trend
Sen's slope...31 Decreasing Significant Decreasing ***
Sen's slope...32 Decreasing Significant Decreasing ***
Sen's slope...33 Decreasing Not significant No trend
Sen's slope...34 Decreasing Significant Decreasing ***
Sen's slope...35 Decreasing Significant Decreasing ***
Sen's slope...36 Increasing Not significant No trend
Sen's slope...37 Decreasing Significant Decreasing ***
Sen's slope...38 Decreasing Significant Decreasing ***
Sen's slope...39 Decreasing Not significant No trend
Sen's slope...40 Decreasing Significant Decreasing ***
Sen's slope...41 Decreasing Significant Decreasing ***
Sen's slope...42 Decreasing Not significant No trend
Sen's slope...43 Decreasing Significant Decreasing ***
Sen's slope...44 Decreasing Significant Decreasing ***
Sen's slope...45 Decreasing Not significant No trend
Sen's slope...46 Decreasing Significant Decreasing ***
Sen's slope...47 Decreasing Significant Decreasing ***
Sen's slope...48 Decreasing Not significant No trend write.csv(tile_restrand_results, "tile_restrand_results.csv", row.names = FALSE)
tile_mk_results Tile Index Tau Pval SenSlope N_Obs
Sen's slope...1 15 NDVI -0.4819863737 3.336988e-10 -3.663525e-03 79
Sen's slope...2 15 SAVI -0.4819863737 3.336988e-10 -5.494090e-03 79
Sen's slope...3 15 BSI 0.0107108084 8.922431e-01 4.269100e-05 79
Sen's slope...4 10 NDVI -0.4625121653 1.657394e-09 -3.815345e-03 79
Sen's slope...5 10 SAVI -0.4625121653 1.657394e-09 -5.718455e-03 79
Sen's slope...6 10 BSI 0.0133073675 8.655348e-01 7.658556e-05 79
Sen's slope...7 9 NDVI -0.4553716183 2.936270e-09 -3.924132e-03 79
Sen's slope...8 9 SAVI -0.4553716183 2.936270e-09 -5.884791e-03 79
Sen's slope...9 9 BSI 0.0418695211 5.879111e-01 2.130393e-04 79
Sen's slope...10 7 NDVI -0.5040571094 5.027462e-11 -3.818334e-03 79
Sen's slope...11 7 SAVI -0.5040571094 5.027462e-11 -5.726949e-03 79
Sen's slope...12 7 BSI -0.0035702693 9.662330e-01 -1.575851e-05 79
Sen's slope...13 6 NDVI -0.3378773034 1.069326e-05 -2.752098e-03 79
Sen's slope...14 6 SAVI -0.3378773034 1.069326e-05 -4.126662e-03 79
Sen's slope...15 6 BSI -0.1963648200 1.056010e-02 -1.117259e-03 79
Sen's slope...16 1 NDVI -0.3995456100 1.919288e-07 -3.287894e-03 79
Sen's slope...17 1 SAVI -0.3995456100 1.919288e-07 -4.930385e-03 79
Sen's slope...18 1 BSI -0.0710808188 3.560776e-01 -3.594342e-04 79
Sen's slope...19 3 NDVI -0.4281077683 2.411795e-08 -3.765464e-03 79
Sen's slope...20 3 SAVI -0.4281077683 2.411795e-08 -5.646665e-03 79
Sen's slope...21 3 BSI 0.0139565077 8.588803e-01 1.260858e-04 79
Sen's slope...22 8 NDVI -0.4969165921 9.357197e-11 -3.772977e-03 79
Sen's slope...23 8 SAVI -0.4969165921 9.357197e-11 -5.659169e-03 79
Sen's slope...24 8 BSI 0.0009737099 9.932446e-01 9.413165e-07 79
Sen's slope...25 16 NDVI -0.3993670642 1.614233e-07 -3.180074e-03 80
Sen's slope...26 16 SAVI -0.3993670642 1.614233e-07 -4.768730e-03 80
Sen's slope...27 16 BSI -0.1050632820 1.690715e-01 -6.343020e-04 80
Sen's slope...28 14 NDVI -0.3755274117 9.894560e-07 -3.106270e-03 79
Sen's slope...29 14 SAVI -0.3755274117 9.894560e-07 -4.657696e-03 79
Sen's slope...30 14 BSI -0.0905550122 2.392496e-01 -6.674956e-04 79
Sen's slope...31 2 NDVI -0.4307043254 1.984020e-08 -3.750980e-03 79
Sen's slope...32 2 SAVI -0.4307043254 1.984020e-08 -5.624643e-03 79
Sen's slope...33 2 BSI 0.0042194091 9.594851e-01 2.414509e-05 79
Sen's slope...34 4 NDVI -0.4930217564 1.308439e-10 -3.911652e-03 79
Sen's slope...35 4 SAVI -0.4930217564 1.308439e-10 -5.866121e-03 79
Sen's slope...36 4 BSI 0.0483609214 5.309659e-01 2.671597e-04 79
Sen's slope...37 11 NDVI -0.4373417497 9.603391e-09 -3.698569e-03 80
Sen's slope...38 11 SAVI -0.4373417497 9.603391e-09 -5.546264e-03 80
Sen's slope...39 11 BSI -0.0056962022 9.436928e-01 -5.783165e-05 80
Sen's slope...40 12 NDVI -0.4066861272 1.157245e-07 -3.282383e-03 79
Sen's slope...41 12 SAVI -0.4066861272 1.157245e-07 -4.921812e-03 79
Sen's slope...42 12 BSI -0.1002921164 1.922799e-01 -5.700032e-04 79
Sen's slope...43 13 NDVI -0.4203180671 4.303485e-08 -3.524146e-03 79
Sen's slope...44 13 SAVI -0.4203180671 4.303485e-08 -5.284818e-03 79
Sen's slope...45 13 BSI -0.0529049002 4.928401e-01 -3.826302e-04 79
Sen's slope...46 5 NDVI -0.4690035582 9.782369e-10 -3.840267e-03 79
Sen's slope...47 5 SAVI -0.4690035582 9.782369e-10 -5.758570e-03 79
Sen's slope...48 5 BSI -0.0269393045 7.284917e-01 -9.643943e-05 79
Trend_Direction Trend_Significance Trend_Category Signif
Sen's slope...1 Decreasing Significant Decreasing ***
Sen's slope...2 Decreasing Significant Decreasing ***
Sen's slope...3 Increasing Not significant No trend
Sen's slope...4 Decreasing Significant Decreasing ***
Sen's slope...5 Decreasing Significant Decreasing ***
Sen's slope...6 Increasing Not significant No trend
Sen's slope...7 Decreasing Significant Decreasing ***
Sen's slope...8 Decreasing Significant Decreasing ***
Sen's slope...9 Increasing Not significant No trend
Sen's slope...10 Decreasing Significant Decreasing ***
Sen's slope...11 Decreasing Significant Decreasing ***
Sen's slope...12 Decreasing Not significant No trend
Sen's slope...13 Decreasing Significant Decreasing ***
Sen's slope...14 Decreasing Significant Decreasing ***
Sen's slope...15 Decreasing Significant Decreasing *
Sen's slope...16 Decreasing Significant Decreasing ***
Sen's slope...17 Decreasing Significant Decreasing ***
Sen's slope...18 Decreasing Not significant No trend
Sen's slope...19 Decreasing Significant Decreasing ***
Sen's slope...20 Decreasing Significant Decreasing ***
Sen's slope...21 Increasing Not significant No trend
Sen's slope...22 Decreasing Significant Decreasing ***
Sen's slope...23 Decreasing Significant Decreasing ***
Sen's slope...24 Increasing Not significant No trend
Sen's slope...25 Decreasing Significant Decreasing ***
Sen's slope...26 Decreasing Significant Decreasing ***
Sen's slope...27 Decreasing Not significant No trend
Sen's slope...28 Decreasing Significant Decreasing ***
Sen's slope...29 Decreasing Significant Decreasing ***
Sen's slope...30 Decreasing Not significant No trend
Sen's slope...31 Decreasing Significant Decreasing ***
Sen's slope...32 Decreasing Significant Decreasing ***
Sen's slope...33 Increasing Not significant No trend
Sen's slope...34 Decreasing Significant Decreasing ***
Sen's slope...35 Decreasing Significant Decreasing ***
Sen's slope...36 Increasing Not significant No trend
Sen's slope...37 Decreasing Significant Decreasing ***
Sen's slope...38 Decreasing Significant Decreasing ***
Sen's slope...39 Decreasing Not significant No trend
Sen's slope...40 Decreasing Significant Decreasing ***
Sen's slope...41 Decreasing Significant Decreasing ***
Sen's slope...42 Decreasing Not significant No trend
Sen's slope...43 Decreasing Significant Decreasing ***
Sen's slope...44 Decreasing Significant Decreasing ***
Sen's slope...45 Decreasing Not significant No trend
Sen's slope...46 Decreasing Significant Decreasing ***
Sen's slope...47 Decreasing Significant Decreasing ***
Sen's slope...48 Decreasing Not significant No trend write.csv(tile_mk_results, "tile_mk_results.csv", row.names = FALSE)ACOUSTICS
#acoustic indices
acousticindex <- read_csv("data/acousticindex.csv")
acousticindex <- acousticindex %>%
mutate(
month = sub(" .*", "", FOLDER),
year = sub(".*?(\\d{4}).*", "\\1", FOLDER),
cluster = sub(".*Cluster (\\d+).*", "\\1", FOLDER),
season = case_when(
month == "January" ~ "Dry",
month == "April" ~ "Wet",
month == "June" ~ "Dry",
month == "October" ~ "Wet",
TRUE ~ NA_character_ # For any other months
)
) %>%
select(month, year, season, cluster, SH, NDSI, ACI, ADI, AEI, BI)
acousticindex# A tibble: 25,386 × 10
month year season cluster SH NDSI ACI ADI AEI BI
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
1 April 2024 Wet 10 0.860 -0.0681 153. 2.28 0.110618 77.2
2 April 2024 Wet 10 0.861 0.220 152. 2.28 0.120012 75.8
3 April 2024 Wet 10 0.805 0.657 155. 2.26 0.15303 75.3
4 April 2024 Wet 10 0.862 0.547 153. 2.29 0.097994 71.9
5 April 2024 Wet 10 0.774 0.864 151. 2.02 0.366955 91.7
6 April 2024 Wet 10 0.802 0.715 155. 2.25 0.171262 85.1
7 April 2024 Wet 10 0.803 0.718 153. 2.19 0.243794 90.1
8 April 2024 Wet 10 0.770 0.743 151. 2.00 0.378752 93.7
9 April 2024 Wet 10 0.771 0.785 152. 2.04 0.364778 93.8
10 April 2024 Wet 10 0.771 0.812 151. 2.02 0.357468 101.
# ℹ 25,376 more rows# Calculate mean values by cluster
acoustic_cluster_means <- acousticindex %>%
group_by(cluster) %>%
summarise(
SH. = mean(SH, na.rm = TRUE),
NDSI. = mean(NDSI, na.rm = TRUE),
ACI. = mean(ACI, na.rm = TRUE),
ADI. = mean(ADI, na.rm = TRUE),
AEI. = mean(as.numeric(AEI), na.rm = TRUE),
BI. = mean(BI, na.rm = TRUE),
.groups = 'drop'
)
acoustic_cluster_means# A tibble: 16 × 7
cluster SH. NDSI. ACI. ADI. AEI. BI.
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 0.727 0.434 160. 1.77 0.436 66.0
2 10 0.805 0.672 158. 2.16 0.181 72.2
3 11 0.816 0.621 166. 2.12 0.224 38.0
4 12 0.841 0.786 160. 2.23 0.103 43.6
5 13 0.846 0.758 163. 2.20 0.132 45.5
6 14 0.829 0.771 160. 2.17 0.184 58.8
7 15 0.794 0.786 167. 2.10 0.252 64.9
8 16 0.818 0.806 157. 2.16 0.210 62.9
9 2 0.797 0.697 161. 2.11 0.260 51.8
10 3 0.810 0.623 157. 2.16 0.192 51.6
11 4 0.804 0.777 158. 2.12 0.234 62.7
12 5 0.787 0.429 158. 1.90 0.352 40.9
13 6 0.780 0.821 159. 2.03 0.291 72.0
14 7 0.793 0.641 157. 2.09 0.275 66.2
15 8 0.811 0.631 159. 2.15 0.204 50.0
16 9 0.763 0.843 158. 2.03 0.326 92.4acoustic_cluster_means %>%
mutate(cluster=factor(cluster)) %>%
mutate(cluster=fct_relevel(cluster,c("1","2", "3", "4", "5","6","7","8","9","10","11","12","13","14","15","16"))) %>%
arrange(cluster)->acoustic_cluster_means
acoustic_cluster_means# A tibble: 16 × 7
cluster SH. NDSI. ACI. ADI. AEI. BI.
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 0.727 0.434 160. 1.77 0.436 66.0
2 2 0.797 0.697 161. 2.11 0.260 51.8
3 3 0.810 0.623 157. 2.16 0.192 51.6
4 4 0.804 0.777 158. 2.12 0.234 62.7
5 5 0.787 0.429 158. 1.90 0.352 40.9
6 6 0.780 0.821 159. 2.03 0.291 72.0
7 7 0.793 0.641 157. 2.09 0.275 66.2
8 8 0.811 0.631 159. 2.15 0.204 50.0
9 9 0.763 0.843 158. 2.03 0.326 92.4
10 10 0.805 0.672 158. 2.16 0.181 72.2
11 11 0.816 0.621 166. 2.12 0.224 38.0
12 12 0.841 0.786 160. 2.23 0.103 43.6
13 13 0.846 0.758 163. 2.20 0.132 45.5
14 14 0.829 0.771 160. 2.17 0.184 58.8
15 15 0.794 0.786 167. 2.10 0.252 64.9
16 16 0.818 0.806 157. 2.16 0.210 62.9# To get means by both cluster AND season:
cluster_season_means <- acousticindex %>%
group_by(cluster, season) %>%
summarise(across(c(SH, NDSI, ACI, ADI, AEI, BI), mean, na.rm = TRUE))
cluster_season_means# A tibble: 32 × 8
# Groups: cluster [16]
cluster season SH NDSI ACI ADI AEI BI
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 Dry 0.782 0.709 159. 2.07 NA 73.8
2 1 Wet 0.719 0.394 161. 1.73 NA 64.9
3 10 Dry 0.798 0.770 159. 2.08 NA 74.7
4 10 Wet 0.807 0.653 158. 2.18 NA 71.7
5 11 Dry 0.853 0.714 181. 2.05 NA 29.5
6 11 Wet 0.805 0.594 162. 2.13 NA 40.5
7 12 Dry 0.846 0.871 160. 2.23 NA 47.6
8 12 Wet 0.839 0.756 160. 2.23 NA 42.2
9 13 Dry 0.864 0.865 168. 2.25 NA 57.3
10 13 Wet 0.841 0.728 162. 2.19 NA 42.1
# ℹ 22 more rows