##########################
###Bilan avancement EAG###
##########################

#Chargement des library
library(readxl)
library(dplyr)
## Warning: le package 'dplyr' a été compilé avec la version R 4.2.3
## 
## Attachement du package : 'dplyr'
## Les objets suivants sont masqués depuis 'package:stats':
## 
##     filter, lag
## Les objets suivants sont masqués depuis 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
## Warning: le package 'ggplot2' a été compilé avec la version R 4.2.3
library(tibble)
## Warning: le package 'tibble' a été compilé avec la version R 4.2.3
library(tidyr)
## Warning: le package 'tidyr' a été compilé avec la version R 4.2.3
library(table1)
## Warning: le package 'table1' a été compilé avec la version R 4.2.3
## 
## Attachement du package : 'table1'
## Les objets suivants sont masqués depuis 'package:base':
## 
##     units, units<-
library(stringr)
## Warning: le package 'stringr' a été compilé avec la version R 4.2.2
is_outlier <- function(x) {
  return(x < quantile(x, 0.25) - 1.5 * IQR(x) | x > quantile(x, 0.75) + 1.5 * IQR(x))}

#Chargement des données
Resultats_proies <- read_excel("E:/Recherche/Projet Ecologie chimique/Stage IA-IGEPP/EAG/Resultats_proies12_06.xlsx")
## Warning: Expecting numeric in M2814 / R2814C13: got '-1.#IND00'
head(Resultats_proies)
## # A tibble: 6 × 14
##   Carabe Replicat Sexe  Date  StimID `Stim. Description` Recording
##   <chr>  <chr>    <chr> <chr> <chr>  <chr>               <chr>    
## 1 P_cup  N1       M     09_04 0      solvant             Rec - 1  
## 2 P_cup  N1       M     09_04 1*     Ref                 Rec - 2  
## 3 P_cup  N1       M     09_04 0      blanc               Rec - 3  
## 4 P_cup  N1       M     09_04 0      EBF 1               Rec - 4  
## 5 P_cup  N1       M     09_04 0      EBF 10              Rec - 5  
## 6 P_cup  N1       M     09_04 0      EBF 100             Rec - 6  
## # ℹ 7 more variables: `Surface[v*s]` <dbl>, `Maximum[v]` <dbl>, `th1[s]` <dbl>,
## #   `tm[s]` <dbl>, `th2[s]` <dbl>, `Slope1[v/s]` <dbl>, `Slope2[v/s]` <chr>
str(Resultats_proies)
## tibble [3,004 × 14] (S3: tbl_df/tbl/data.frame)
##  $ Carabe           : chr [1:3004] "P_cup" "P_cup" "P_cup" "P_cup" ...
##  $ Replicat         : chr [1:3004] "N1" "N1" "N1" "N1" ...
##  $ Sexe             : chr [1:3004] "M" "M" "M" "M" ...
##  $ Date             : chr [1:3004] "09_04" "09_04" "09_04" "09_04" ...
##  $ StimID           : chr [1:3004] "0" "1*" "0" "0" ...
##  $ Stim. Description: chr [1:3004] "solvant" "Ref" "blanc" "EBF 1" ...
##  $ Recording        : chr [1:3004] "Rec - 1" "Rec - 2" "Rec - 3" "Rec - 4" ...
##  $ Surface[v*s]     : num [1:3004] -0.000058 -0.000042 -0.00003 -0.000073 -0.000057 -0.000147 -0.000052 -0.000048 -0.00008 -0.000038 ...
##  $ Maximum[v]       : num [1:3004] -0.000266 -0.000374 -0.000292 -0.000427 -0.000568 -0.00055 -0.000296 -0.00037 -0.000371 -0.000402 ...
##  $ th1[s]           : num [1:3004] 0.53 0.56 0.6 0.52 0.58 0.54 0.54 0.49 0.45 0.51 ...
##  $ tm[s]            : num [1:3004] 0.69 0.7 0.72 0.73 0.82 0.87 0.73 0.83 0.84 0.7 ...
##  $ th2[s]           : num [1:3004] 1.03 1.09 0.95 1.05 1.28 1.85 1.08 0.99 0.97 1.03 ...
##  $ Slope1[v/s]      : num [1:3004] -0.001 -0.002 -0.001 -0.002 -0.002 -0.002 -0.001 -0.001 -0.002 -0.002 ...
##  $ Slope2[v/s]      : chr [1:3004] "1E-3" "1E-3" "1E-3" "1E-3" ...
summary(Resultats_proies)
##     Carabe            Replicat             Sexe               Date          
##  Length:3004        Length:3004        Length:3004        Length:3004       
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##     StimID          Stim. Description   Recording          Surface[v*s]       
##  Length:3004        Length:3004        Length:3004        Min.   :-1.197e-03  
##  Class :character   Class :character   Class :character   1st Qu.:-1.150e-04  
##  Mode  :character   Mode  :character   Mode  :character   Median :-8.000e-05  
##                                                           Mean   :-8.789e-05  
##                                                           3rd Qu.:-4.600e-05  
##                                                           Max.   : 2.700e-05  
##                                                                               
##    Maximum[v]             th1[s]           tm[s]            th2[s]     
##  Min.   :-0.0026630   Min.   :0.0700   Min.   :0.2200   Min.   :0.610  
##  1st Qu.:-0.0006413   1st Qu.:0.4300   1st Qu.:0.6600   1st Qu.:1.000  
##  Median :-0.0005100   Median :0.4600   Median :0.7300   Median :1.050  
##  Mean   :-0.0005307   Mean   :0.4616   Mean   :0.7339   Mean   :1.082  
##  3rd Qu.:-0.0003860   3rd Qu.:0.4900   3rd Qu.:0.8000   3rd Qu.:1.120  
##  Max.   :-0.0000370   Max.   :0.7900   Max.   :3.8100   Max.   :3.980  
##                                                                        
##   Slope1[v/s]        Slope2[v/s]       
##  Min.   :-0.012000   Length:3004       
##  1st Qu.:-0.003000   Class :character  
##  Median :-0.002000   Mode  :character  
##  Mean   :-0.002221                     
##  3rd Qu.:-0.001000                     
##  Max.   : 0.001000                     
##  NA's   :1
Date='12/06/2024 - matin'

Resultats_proies$Maximum_v=Resultats_proies$`Maximum[v]`
Resultats_proies$Stim_Description=Resultats_proies$"Stim. Description"
#Moyenne des stimuli blanc et contrôle
Resultats_proies_agg=aggregate(Maximum_v ~ Stim_Description + Carabe + Replicat + Sexe , data = Resultats_proies, mean)

#Table d'avancement ####
table(Resultats_proies_agg[Resultats_proies_agg$Stim_Description == "EBF 1",]$Carabe,Resultats_proies_agg[Resultats_proies_agg$Stim_Description == "EBF 1",]$Sexe)
##        
##         F M
##   A_aen 6 6
##   A_dor 6 6
##   B_scl 5 6
##   H_dis 6 6
##   N_qua 1 1
##   N_sal 8 6
##   P_cup 7 5
##   P_ruf 1 3
# Première liste extraite de la première image
list_1 <- c("A_aen", "A_dor", "B_scl", "H_dis", "N_qua", "N_sal", "P_cup","P_ruf")
# Deuxième liste extraite de la deuxième image
list_2 <- c("N1", "N10", "N11", "N12", "N1bis", "N2", "N2bis", "N3", "N4", "N5", "N6", "N7", "N8", "N9")
# Troisième liste extraite de la troisième image
list_3_blanc <- c("C album 1", "C album 10", "C album 3", "C album 30", "CBP 1", "CBP 10", "CBP 3", "CBP 30",
            "Collemboles 1", "Collemboles 10", "Collemboles 3",# "EBF 1", "EBF 10", "EBF 100", "Ref","solvant"
            "Pucerons 1", "Pucerons 10", "Pucerons 3", "T officinale 1", "T officinale 10", 
            "T officinale 3", "T officinale 30", "V arvensis 1", "V arvensis 10", "V arvensis 3", 
            "V arvensis 30","blanc")
list_3_solvant <- c("EBF 1", "EBF 10", "EBF 100", "Ref","solvant")

# Boucles imbriquées pour les blancs
for (item1 in list_1) {
  for (item2 in list_2) {
    for (item3 in list_3_blanc) {
      # Code à exécuter dans la boucle imbriquée
      # Utiliser la forme [,] pour sélectionner les lignes correspondant aux critères
      indices <- try(which(Resultats_proies_agg$Carabe == item1 & 
                             Resultats_proies_agg$Replicat == item2 & 
                             Resultats_proies_agg$Stim_Description == item3))
      indices_ref <- try(which(Resultats_proies_agg$Carabe == item1 & 
                         Resultats_proies_agg$Replicat == item2 & 
                         Resultats_proies_agg$Stim_Description == "blanc"))
      
      # Calculer la nouvelle colonne
      try(Resultats_proies_agg[indices, "Maximum_corr"] <- Resultats_proies_agg[indices, "Maximum_v"] /  Resultats_proies_agg[indices_ref, "Maximum_v"])
    }
  }
}
#Boucle imbriquée pour les solvants
for (item1 in list_1) {
  for (item2 in list_2) {
    for (item3 in list_3_solvant) {
      # Code à exécuter dans la boucle imbriquée
      # Utiliser la forme [,] pour sélectionner les lignes correspondant aux critères
      indices <- try(which(Resultats_proies_agg$Carabe == item1 & 
                             Resultats_proies_agg$Replicat == item2 & 
                             Resultats_proies_agg$Stim_Description == item3))
      indices_ref <- try(which(Resultats_proies_agg$Carabe == item1 & 
                                 Resultats_proies_agg$Replicat == item2 & 
                                 Resultats_proies_agg$Stim_Description == "solvant"))
      
      # Calculer la nouvelle colonne
      try(Resultats_proies_agg[indices, "Maximum_corr"] <- Resultats_proies_agg[indices, "Maximum_v"] /  Resultats_proies_agg[indices_ref, "Maximum_v"])
    }
  }
}


#Ordonnons les stimuli
Resultats_proies_agg$Stim_Description<-factor(Resultats_proies_agg$Stim_Description,c("solvant","Ref","blanc",
                                                                              "EBF 1","EBF 10","EBF 100",
                                                                              "Collemboles 1","Collemboles 3", "Collemboles 10",
                                                                              "Pucerons 1", "Pucerons 3", "Pucerons 10",
                                                                              "T officinale 1","T officinale 3","T officinale 10","T officinale 30",
                                                                              "CBP 1","CBP 3","CBP 10", "CBP 30",
                                                                              "V arvensis 1", "V arvensis 3","V arvensis 10","V arvensis 30",
                                                                              "C album 1", "C album 3", "C album 10", "C album 30"))


#Poecilus cupreus ####
Resultats_proies_Pcup=Resultats_proies_agg[Resultats_proies_agg$Carabe=="P_cup",]
row.names(Resultats_proies_Pcup)=paste(Resultats_proies_Pcup$Replicat,row.names(Resultats_proies_Pcup))
Resultats_proies_PcupNA=Resultats_proies_Pcup %>% drop_na(Maximum_corr)
dat <- Resultats_proies_PcupNA %>% tibble::rownames_to_column(var="outlier") %>% group_by(Stim_Description) %>% mutate(is_outlier=ifelse(is_outlier(Maximum_corr), Maximum_corr, as.numeric(NA)))
dat$outlier[which(is.na(dat$is_outlier))] <- as.numeric(NA)

ggplot(dat, aes(x=Stim_Description, y=Maximum_corr)) + 
  geom_boxplot(aes(fill=Stim_Description))+ theme(legend.position = "none",axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  geom_hline(yintercept=1, linetype="dashed", color = "red")+ geom_text(aes(label=outlier),na.rm=TRUE, size=2.5,angle=90)+
  labs(title = "Ratio entre la réponse au stimuli et la moyenne des contrôles associé (blanc ou solvant)",
       subtitle = paste("Poecilus cupreus, N=",length(unique(dat$Replicat)),
                        " (F=",length(unique(dat[dat$Sexe=="F",]$Replicat)),
                        ", M=",length(unique(dat[dat$Sexe=="M",]$Replicat)),
                        ")"),
       caption = Date,
       x = "Stimuli",
       y="Ratio stimulus / contrôle")

dat %>% drop_na(is_outlier)
## # A tibble: 27 × 8
## # Groups:   Stim_Description [18]
##    outlier Stim_Description Carabe Replicat Sexe  Maximum_v Maximum_corr
##    <chr>   <fct>            <chr>  <chr>    <chr>     <dbl>        <dbl>
##  1 N11 283 C album 10       P_cup  N11      F     -0.000703        1.69 
##  2 N11 297 Pucerons 10      P_cup  N11      F     -0.000699        1.68 
##  3 N11 298 Pucerons 3       P_cup  N11      F     -0.000595        1.43 
##  4 N2 480  C album 3        P_cup  N2       F     -0.000316        0.899
##  5 N6 773  Pucerons 10      P_cup  N6       F     -0.000973        1.84 
##  6 N8 1039 C album 10       P_cup  N8       F     -0.000757        2.76 
##  7 N8 1041 C album 30       P_cup  N8       F     -0.00136         4.97 
##  8 N8 1042 CBP 1            P_cup  N8       F     -0.000447        1.63 
##  9 N8 1043 CBP 10           P_cup  N8       F     -0.000635        2.31 
## 10 N8 1045 CBP 30           P_cup  N8       F     -0.000703        2.56 
## # ℹ 17 more rows
## # ℹ 1 more variable: is_outlier <dbl>
#Amara aenea ####
Resultats_proies_Aaen=Resultats_proies_agg[Resultats_proies_agg$Carabe=="A_aen",]
row.names(Resultats_proies_Aaen)=paste(Resultats_proies_Aaen$Replicat,row.names(Resultats_proies_Aaen))
Resultats_proies_AaenNA=Resultats_proies_Aaen %>% drop_na(Maximum_corr)
dat <- Resultats_proies_AaenNA %>% tibble::rownames_to_column(var="outlier") %>% group_by(Stim_Description) %>% mutate(is_outlier=ifelse(is_outlier(Maximum_corr), Maximum_corr, as.numeric(NA)))
dat$outlier[which(is.na(dat$is_outlier))] <- as.numeric(NA)

ggplot(dat, aes(x=Stim_Description, y=Maximum_corr)) + 
  geom_boxplot(aes(fill=Stim_Description))+ theme(legend.position = "none",axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  geom_hline(yintercept=1, linetype="dashed", color = "red")+ geom_text(aes(label=outlier),na.rm=TRUE,size=2.5,angle=90)+
  labs(title = "Ratio entre la réponse au stimuli et la moyenne des contrôles associé (blanc ou solvant)",
       subtitle = paste("Amara aenea, N=",length(unique(dat$Replicat)),
                        " (F=",length(unique(dat[dat$Sexe=="F",]$Replicat)),
                        ", M=",length(unique(dat[dat$Sexe=="M",]$Replicat)),
                        ")"),
       caption = Date,
       x = "Stimuli",
       y="Ratio stimulus / contrôle")

dat %>% drop_na(is_outlier)
## # A tibble: 10 × 8
## # Groups:   Stim_Description [8]
##    outlier  Stim_Description Carabe Replicat Sexe  Maximum_v Maximum_corr
##    <chr>    <fct>            <chr>  <chr>    <chr>     <dbl>        <dbl>
##  1 N5 651   CBP 10           A_aen  N5       F     -0.000363        0.791
##  2 N5 667   T officinale 3   A_aen  N5       F     -0.000374        0.815
##  3 N6 723   T officinale 3   A_aen  N6       F     -0.000641        1.43 
##  4 N8 943   Ref              A_aen  N8       F     -0.00147         1.84 
##  5 N8 948   T officinale 30  A_aen  N8       F     -0.00125         1.85 
##  6 N1 1127  CBP 10           A_aen  N1       M     -0.000747        1.73 
##  7 N1 1133  EBF 1            A_aen  N1       M     -0.000492        1.40 
##  8 N1 1134  EBF 10           A_aen  N1       M     -0.000525        1.50 
##  9 N11 1354 Collemboles 1    A_aen  N11      M     -0.00066         1.38 
## 10 N12 1376 C album 3        A_aen  N12      M     -0.000657        1.20 
## # ℹ 1 more variable: is_outlier <dbl>
#Nebria salina ####
Resultats_proies_Nsal=Resultats_proies_agg[Resultats_proies_agg$Carabe=="N_sal",]
row.names(Resultats_proies_Nsal)=paste(Resultats_proies_Nsal$Replicat,row.names(Resultats_proies_Nsal))
Resultats_proies_NsalNA=Resultats_proies_Nsal %>% drop_na(Maximum_corr)
dat <- Resultats_proies_NsalNA %>% tibble::rownames_to_column(var="outlier") %>% group_by(Stim_Description) %>% mutate(is_outlier=ifelse(is_outlier(Maximum_corr), Maximum_corr, as.numeric(NA)))
dat$outlier[which(is.na(dat$is_outlier))] <- as.numeric(NA)

ggplot(dat, aes(x=Stim_Description, y=Maximum_corr)) + 
  geom_boxplot(aes(fill=Stim_Description))+ theme(legend.position = "none",axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  geom_hline(yintercept=1, linetype="dashed", color = "red")+ geom_text(aes(label=outlier),na.rm=TRUE,size=2.5,angle=90)+
  labs(title = "Ratio entre la réponse au stimuli et la moyenne des contrôles associé (blanc ou solvant)",
       subtitle = paste("Nebria salina, N=",length(unique(dat$Replicat)),
                        " (F=",length(unique(dat[dat$Sexe=="F",]$Replicat)),
                        ", M=",length(unique(dat[dat$Sexe=="M",]$Replicat)),
                        ")"),
       caption = Date,
       x = "Stimuli",
       y="Ratio stimulus / contrôle")

dat %>% drop_na(is_outlier)
## # A tibble: 13 × 8
## # Groups:   Stim_Description [9]
##    outlier Stim_Description Carabe Replicat Sexe  Maximum_v Maximum_corr
##    <chr>   <fct>            <chr>  <chr>    <chr>     <dbl>        <dbl>
##  1 N11 270 Pucerons 3       N_sal  N11      F     -0.000652        1.25 
##  2 N2 450  C album 1        N_sal  N2       F     -0.000148        0.736
##  3 N2 460  Collemboles 3    N_sal  N2       F     -0.000402        2.00 
##  4 N2 464  Pucerons 1       N_sal  N2       F     -0.000194        0.964
##  5 N2 465  Pucerons 10      N_sal  N2       F     -0.000282        1.40 
##  6 N8 1026 Pucerons 3       N_sal  N8       F     -0.000438        0.959
##  7 N3 1656 C album 3        N_sal  N3       M     -0.000698        1.40 
##  8 N3 1670 Pucerons 3       N_sal  N3       M     -0.0005          1.00 
##  9 N6 2027 Collemboles 10   N_sal  N6       M     -0.000605        1.44 
## 10 N6 2033 Pucerons 10      N_sal  N6       M     -0.000625        1.49 
## 11 N6 2034 Pucerons 3       N_sal  N6       M     -0.000544        1.30 
## 12 N6 2037 T officinale 1   N_sal  N6       M     -0.000545        1.30 
## 13 N6 2039 T officinale 3   N_sal  N6       M     -0.000668        1.59 
## # ℹ 1 more variable: is_outlier <dbl>
#Anchomenus dorsalis ####
Resultats_proiesAdor=Resultats_proies_agg[Resultats_proies_agg$Carabe=="A_dor",]
row.names(Resultats_proiesAdor)=paste(Resultats_proiesAdor$Replicat,row.names(Resultats_proiesAdor))
Resultats_proiesAdorNA=Resultats_proiesAdor %>% drop_na(Maximum_corr)
dat <- Resultats_proiesAdorNA %>% tibble::rownames_to_column(var="outlier") %>% group_by(Stim_Description) %>% mutate(is_outlier=ifelse(is_outlier(Maximum_corr), Maximum_corr, as.numeric(NA)))
dat$outlier[which(is.na(dat$is_outlier))] <- as.numeric(NA)

ggplot(dat, aes(x=Stim_Description, y=Maximum_corr)) + 
  geom_boxplot(aes(fill=Stim_Description))+ theme(legend.position = "none",axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  geom_hline(yintercept=1, linetype="dashed", color = "red")+ geom_text(aes(label=outlier),na.rm=TRUE,size=2.5,angle=90)+
  labs(title = "Ratio entre la réponse au stimuli et la moyenne des contrôles associé (blanc ou solvant)",
      subtitle = paste("Anchomenus dorsalis, N=",length(unique(dat$Replicat)),
                        " (F=",length(unique(dat[dat$Sexe=="F",]$Replicat)),
                        ", M=",length(unique(dat[dat$Sexe=="M",]$Replicat)),
                        ")"),       
       caption = Date,
       x = "Stimuli",
       y="Ratio stimulus / contrôle")

dat %>% drop_na(is_outlier)
## # A tibble: 14 × 8
## # Groups:   Stim_Description [9]
##    outlier  Stim_Description Carabe Replicat Sexe  Maximum_v Maximum_corr
##    <chr>    <fct>            <chr>  <chr>    <chr>     <dbl>        <dbl>
##  1 N2 402   Collemboles 1    A_dor  N2       F     -0.000411        0.700
##  2 N4 611   T officinale 3   A_dor  N4       F     -0.000537        0.876
##  3 N7 820   CBP 3            A_dor  N7       F     -0.00027         0.768
##  4 N7 825   EBF 1            A_dor  N7       F     -0.000561        1.54 
##  5 N7 826   EBF 10           A_dor  N7       F     -0.000724        1.99 
##  6 N7 827   EBF 100          A_dor  N7       F     -0.000814        2.24 
##  7 N1 1161  EBF 1            A_dor  N1       M     -0.00111         1.66 
##  8 N12 1423 T officinale 3   A_dor  N12      M     -0.00044         0.834
##  9 N5 1856  CBP 3            A_dor  N5       M     -0.000707        1.13 
## 10 N9 2130  C album 1        A_dor  N9       M     -0.000522        1.21 
## 11 N9 2133  C album 30       A_dor  N9       M     -0.000665        1.54 
## 12 N9 2136  CBP 3            A_dor  N9       M     -0.000548        1.27 
## 13 N9 2139  Collemboles 10   A_dor  N9       M     -0.000372        0.864
## 14 N9 2151  T officinale 3   A_dor  N9       M     -0.000564        1.31 
## # ℹ 1 more variable: is_outlier <dbl>
#Harpalus distinguendus ####
Resultats_proiesHdis=Resultats_proies_agg[Resultats_proies_agg$Carabe=="H_dis",]
row.names(Resultats_proiesHdis)=paste(Resultats_proiesHdis$Replicat,row.names(Resultats_proiesHdis))
Resultats_proiesHdisNA=Resultats_proiesHdis %>% drop_na(Maximum_corr)
dat <- Resultats_proiesHdisNA %>% tibble::rownames_to_column(var="outlier") %>% group_by(Stim_Description) %>% mutate(is_outlier=ifelse(is_outlier(Maximum_corr), Maximum_corr, as.numeric(NA)))
dat$outlier[which(is.na(dat$is_outlier))] <- as.numeric(NA)

ggplot(dat, aes(x=Stim_Description, y=Maximum_corr)) + 
  geom_boxplot(aes(fill=Stim_Description))+ theme(legend.position = "none",axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  geom_hline(yintercept=1, linetype="dashed", color = "red")+ geom_text(aes(label=outlier),na.rm=TRUE,size=2.5,angle=90)+
  labs(title = "Ratio entre la réponse au stimuli et la moyenne des contrôles associé (blanc ou solvant)",
       subtitle = paste("Harpalus distinguendus, N=",length(unique(dat$Replicat)),
                        " (F=",length(unique(dat[dat$Sexe=="F",]$Replicat)),
                        ", M=",length(unique(dat[dat$Sexe=="M",]$Replicat)),
                        ")"),  
       caption = Date,
       x = "Stimuli",
       y="Ratio stimulus / contrôle")

dat %>% drop_na(is_outlier)
## # A tibble: 17 × 8
## # Groups:   Stim_Description [13]
##    outlier Stim_Description Carabe Replicat Sexe  Maximum_v Maximum_corr
##    <chr>   <fct>            <chr>  <chr>    <chr>     <dbl>        <dbl>
##  1 N12 314 CBP 1            H_dis  N12      F     -0.000559        1.47 
##  2 N12 326 Pucerons 3       H_dis  N12      F     -0.00051         1.34 
##  3 N7 852  Collemboles 3    H_dis  N7       F     -0.00110         0.847
##  4 N7 853  EBF 1            H_dis  N7       F     -0.000867        0.561
##  5 N7 854  EBF 10           H_dis  N7       F     -0.00097         0.627
##  6 N7 858  Pucerons 3       H_dis  N7       F     -0.00108         0.835
##  7 N8 989  CBP 30           H_dis  N8       F     -0.000518        1.23 
##  8 N8 994  EBF 10           H_dis  N8       F     -0.000382        0.748
##  9 N2 1524 Collemboles 3    H_dis  N2       M     -0.000721        1.71 
## 10 N3 1626 C album 1        H_dis  N3       M     -0.000382        0.848
## 11 N3 1627 C album 10       H_dis  N3       M     -0.000329        0.731
## 12 N3 1629 C album 30       H_dis  N3       M     -0.000445        0.988
## 13 N3 1649 V arvensis 1     H_dis  N3       M     -0.000353        0.784
## 14 N4 1780 Pucerons 1       H_dis  N4       M     -0.000759        1.54 
## 15 N5 1914 Collemboles 1    H_dis  N5       M     -0.000561        1.62 
## 16 N5 1916 Collemboles 3    H_dis  N5       M     -0.000603        1.74 
## 17 N5 1919 EBF 100          H_dis  N5       M     -0.000805        2.17 
## # ℹ 1 more variable: is_outlier <dbl>
#Brachinus sclopeta ####
Resultats_proiesBscl=Resultats_proies_agg[Resultats_proies_agg$Carabe=="B_scl",]
row.names(Resultats_proiesBscl)=paste(Resultats_proiesBscl$Replicat,row.names(Resultats_proiesBscl))
Resultats_proiesBsclNA=Resultats_proiesBscl %>% drop_na(Maximum_corr)
dat <- Resultats_proiesBsclNA %>% tibble::rownames_to_column(var="outlier") %>% group_by(Stim_Description) %>% mutate(is_outlier=ifelse(is_outlier(Maximum_corr), Maximum_corr, as.numeric(NA)))
dat$outlier[which(is.na(dat$is_outlier))] <- as.numeric(NA)

ggplot(dat, aes(x=Stim_Description, y=Maximum_corr)) + 
  geom_boxplot(aes(fill=Stim_Description))+ theme(legend.position = "none",axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  geom_hline(yintercept=1, linetype="dashed", color = "red")+ geom_text(aes(label=outlier),na.rm=TRUE,size=2.5,angle=90)+
  labs(title = "Ratio entre la réponse au stimuli et la moyenne des contrôles associé (blanc ou solvant)",
       subtitle = paste("Brachinus sclopeta, N=",length(unique(dat$Replicat)),
                        " (F=",length(unique(dat[dat$Sexe=="F",]$Replicat)),
                        ", M=",length(unique(dat[dat$Sexe=="M",]$Replicat)),
                        ")"),  
       caption = Date,
       x = "Stimuli",
       y="Ratio stimulus / contrôle")

dat %>% drop_na(is_outlier)
## # A tibble: 8 × 8
## # Groups:   Stim_Description [6]
##   outlier Stim_Description Carabe Replicat Sexe  Maximum_v Maximum_corr
##   <chr>   <fct>            <chr>  <chr>    <chr>     <dbl>        <dbl>
## 1 N1 9    CBP 30           B_scl  N1       F     -0.00049          2.32
## 2 N1 11   Collemboles 10   B_scl  N1       F     -0.000419         1.98
## 3 N1 12   Collemboles 3    B_scl  N1       F     -0.000509         2.41
## 4 N1 24   T officinale 30  B_scl  N1       F     -0.000662         3.14
## 5 N1 28   V arvensis 30    B_scl  N1       F     -0.000635         3.01
## 6 N8 961  CBP 30           B_scl  N8       F     -0.000634         1.46
## 7 N3 1623 V arvensis 3     B_scl  N3       M     -0.000562         1.57
## 8 N7 2071 V arvensis 3     B_scl  N7       M     -0.000278         1.45
## # ℹ 1 more variable: is_outlier <dbl>
#Pseudoophonus rufipes ####
Resultats_proiesPruf=Resultats_proies_agg[Resultats_proies_agg$Carabe=="P_ruf",]
row.names(Resultats_proiesPruf)=paste(Resultats_proiesPruf$Replicat,row.names(Resultats_proiesPruf))
Resultats_proiesPrufNA=Resultats_proiesPruf %>% drop_na(Maximum_corr)
dat <- Resultats_proiesPrufNA %>% tibble::rownames_to_column(var="outlier") %>% group_by(Stim_Description) %>% mutate(is_outlier=ifelse(is_outlier(Maximum_corr), Maximum_corr, as.numeric(NA)))
dat$outlier[which(is.na(dat$is_outlier))] <- as.numeric(NA)

ggplot(dat, aes(x=Stim_Description, y=Maximum_corr)) + 
  geom_boxplot(aes(fill=Stim_Description))+ theme(legend.position = "none",axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  geom_hline(yintercept=1, linetype="dashed", color = "red")+ geom_text(aes(label=outlier),na.rm=TRUE,size=2.5,angle=90)+
  labs(title = "Ratio entre la réponse au stimuli et la moyenne des contrôles associé (blanc ou solvant)",
      subtitle = paste("Pseudoophonus rufipes, N=",length(unique(dat$Replicat)),
                             " (F=",length(unique(dat[dat$Sexe=="F",]$Replicat)),
                             ", M=",length(unique(dat[dat$Sexe=="M",]$Replicat)),
                             ")"),
       caption = Date,
       x = "Stimuli",
       y="Ratio stimulus / contrôle")

#Notiophilus sp. (quadristriatus?)####
Resultats_proiesNqua=Resultats_proies_agg[Resultats_proies_agg$Carabe=="N_qua",]
row.names(Resultats_proiesNqua)=paste(Resultats_proiesNqua$Replicat,row.names(Resultats_proiesNqua))
Resultats_proiesNquaNA=Resultats_proiesNqua %>% drop_na(Maximum_corr)
dat <- Resultats_proiesNquaNA %>% tibble::rownames_to_column(var="outlier") %>% group_by(Stim_Description) %>% mutate(is_outlier=ifelse(is_outlier(Maximum_corr), Maximum_corr, as.numeric(NA)))
dat$outlier[which(is.na(dat$is_outlier))] <- as.numeric(NA)
N=length(unique(dat$Replicat))

ggplot(dat, aes(x=Stim_Description, y=Maximum_corr)) + 
  geom_boxplot(aes(fill=Stim_Description))+ theme(legend.position = "none",axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  geom_hline(yintercept=1, linetype="dashed", color = "red")+ geom_text(aes(label=outlier),na.rm=TRUE,size=2.5,angle=90)+
  labs(title = "Ratio entre la réponse au stimuli et la moyenne des contrôles associé (blanc ou solvant)",
       subtitle = paste("*Notiophilus quadristriatus*, N=",length(unique(dat$Replicat)),
                        " (F=",length(unique(dat[dat$Sexe=="F",]$Replicat)),
                        ", M=",length(unique(dat[dat$Sexe=="M",]$Replicat)),
                        ")"),       
       caption = Date,
       x = "Stimuli",
       y="Ratio stimulus / contrôle")

#Verif des différences entre blancs le long du test
Resultats_proies_blanc=Resultats_proies[Resultats_proies$Stim_Description=="blanc",]
Resultats_proies_blanc_agg=aggregate(Maximum_v ~ Carabe + Replicat + Sexe , data = Resultats_proies_blanc, sd)
row.names(Resultats_proies_blanc_agg)=paste(Resultats_proies_blanc_agg$Replicat,row.names(Resultats_proies_blanc_agg))
Resultats_proies_blanc_aggNA=Resultats_proies_blanc_agg %>% drop_na(Maximum_v)
dat <- Resultats_proies_blanc_aggNA %>% tibble::rownames_to_column(var="outlier") %>% group_by(Carabe) %>% mutate(is_outlier=ifelse(is_outlier(Maximum_v), Maximum_v, as.numeric(NA)))
dat$outlier[which(is.na(dat$is_outlier))] <- as.numeric(NA)
ggplot(dat, aes(x=Carabe, y=Maximum_v)) + 
  geom_boxplot(aes(fill=Carabe))+ theme(legend.position = "none",axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
   geom_text(aes(label=outlier),na.rm=TRUE, size=2.5,angle=90)+
  labs(title = "Ecart type des blancs calculé pour chaque individu",
       subtitle = "Toutes les espèces",
       caption = Date,
       x = "Espèce de carabe",
       y="Ecart-type des blancs")

#Liste des outliers
dat %>% drop_na(is_outlier)
## # A tibble: 6 × 6
## # Groups:   Carabe [3]
##   outlier Carabe Replicat Sexe  Maximum_v is_outlier
##   <chr>   <chr>  <chr>    <chr>     <dbl>      <dbl>
## 1 N10 6   P_cup  N10      F     0.0000187  0.0000187
## 2 N4 21   A_aen  N4       F     0.0000784  0.0000784
## 3 N7 31   H_dis  N7       F     0.000348   0.000348 
## 4 N8 34   A_aen  N8       F     0.000114   0.000114 
## 5 N8 38   P_cup  N8       F     0.0000491  0.0000491
## 6 N3 61   P_cup  N3       M     0.0000867  0.0000867
#Verif des différences entre solvant le long du test
Resultats_proies_solvant=Resultats_proies[Resultats_proies$Stim_Description=="solvant",]
Resultats_proies_solvant_agg=aggregate(Maximum_v ~ Carabe + Replicat + Sexe , data = Resultats_proies_solvant, sd)
row.names(Resultats_proies_solvant_agg)=paste(Resultats_proies_solvant_agg$Replicat,row.names(Resultats_proies_solvant_agg))
Resultats_proies_solvant_aggNA=Resultats_proies_solvant_agg %>% drop_na(Maximum_v)
dat <- Resultats_proies_solvant_aggNA %>% tibble::rownames_to_column(var="outlier") %>% group_by(Carabe) %>% mutate(is_outlier=ifelse(is_outlier(Maximum_v), Maximum_v, as.numeric(NA)))
dat$outlier[which(is.na(dat$is_outlier))] <- as.numeric(NA)
ggplot(dat, aes(x=Carabe, y=Maximum_v)) + 
  geom_boxplot(aes(fill=Carabe))+ theme(legend.position = "none",axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  geom_text(aes(label=outlier),na.rm=TRUE, size=2.5,angle=90)+
  labs(title = "Ecart type des solvants calculé pour chaque individu",
       subtitle = "Toutes les espèces",
       caption = Date,
       x = "Espèce de carabe",
       y="Ecart-type des solvants")

#Liste des outliers
dat %>% drop_na(is_outlier)
## # A tibble: 5 × 6
## # Groups:   Carabe [5]
##   outlier Carabe Replicat Sexe  Maximum_v is_outlier
##   <chr>   <chr>  <chr>    <chr>     <dbl>      <dbl>
## 1 N7 31   H_dis  N7       F      0.000715   0.000715
## 2 N8 34   A_aen  N8       F      0.000163   0.000163
## 3 N10 48  N_sal  N10      M      0.000183   0.000183
## 4 N3 61   P_cup  N3       M      0.000168   0.000168
## 5 N4 66   P_ruf  N4       M      0.000231   0.000231
#Table descriptive bila, ####
Resultats_proies_agg$Stim_Description=str_replace (Resultats_proies_agg$Stim_Description, " ", "_")
Resultats_proies_agg$Stim_Description=str_replace (Resultats_proies_agg$Stim_Description, " ", "_")

Resultats_proies_agg_wide <- spread(Resultats_proies_agg[,c(1,2,3,4,6)], Stim_Description, Maximum_corr)

table1(~Ref+blanc+EBF_1+EBF_10+EBF_100+Collemboles_1+Collemboles_3+Collemboles_10+
Pucerons_1+Pucerons_3+Pucerons_10+
T_officinale_1+T_officinale_3+T_officinale_10+T_officinale_30+
CBP_1+CBP_3+CBP_10+CBP_30+
V_arvensis_1+V_arvensis_3+V_arvensis_10+V_arvensis_30+
C_album_1+C_album_3+C_album_10+C_album_30|Carabe,data=Resultats_proies_agg_wide)
A_aen
(N=12)		 A_dor
(N=12)		 B_scl
(N=11)		 H_dis
(N=12)		 N_qua
(N=2)		 N_sal
(N=14)		 P_cup
(N=12)		 P_ruf
(N=4)		 Overall
(N=79)
Ref
Mean (SD) 1.19 (0.228) 1.36 (0.363) 1.90 (0.462) 1.55 (0.435) 1.21 (0.00268) 1.57 (0.399) 1.13 (0.279) 2.27 (0.522) 1.48 (0.473)
Median [Min, Max] 1.16 [0.959, 1.84] 1.30 [0.929, 2.20] 2.05 [1.28, 2.50] 1.56 [0.962, 2.23] 1.21 [1.20, 1.21] 1.47 [1.01, 2.27] 0.993 [0.892, 1.63] 2.28 [1.78, 2.76] 1.37 [0.892, 2.76]
blanc
Mean (SD) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (0)
Median [Min, Max] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00]
EBF_1
Mean (SD) 0.941 (0.166) 1.13 (0.252) 0.972 (0.187) 0.900 (0.135) 1.64 (0.477) 0.971 (0.0936) 1.10 (0.265) 1.03 (0.305) 1.02 (0.234)
Median [Min, Max] 0.913 [0.792, 1.40] 1.11 [0.830, 1.66] 0.964 [0.757, 1.31] 0.937 [0.561, 1.03] 1.64 [1.30, 1.98] 0.938 [0.783, 1.14] 1.05 [0.732, 1.86] 1.11 [0.614, 1.28] 0.991 [0.561, 1.98]
EBF_10
Mean (SD) 1.03 (0.191) 1.42 (0.231) 1.33 (0.222) 1.09 (0.204) 1.72 (0.190) 1.16 (0.103) 1.31 (0.389) 1.24 (0.187) 1.23 (0.272)
Median [Min, Max] 0.995 [0.733, 1.50] 1.32 [1.13, 1.99] 1.35 [0.948, 1.76] 1.17 [0.627, 1.27] 1.72 [1.58, 1.85] 1.17 [0.979, 1.38] 1.18 [0.972, 2.47] 1.28 [0.998, 1.43] 1.20 [0.627, 2.47]
EBF_100
Mean (SD) 0.835 (0.200) 1.56 (0.260) 1.49 (0.334) 1.34 (0.349) 2.54 (0.102) 1.28 (0.133) 1.32 (0.389) 1.38 (0.218) 1.34 (0.401)
Median [Min, Max] 0.873 [0.568, 1.07] 1.48 [1.30, 2.24] 1.43 [1.04, 2.04] 1.40 [0.746, 2.17] 2.54 [2.47, 2.61] 1.25 [1.11, 1.57] 1.24 [0.845, 2.39] 1.39 [1.11, 1.61] 1.30 [0.568, 2.61]
Collemboles_1
Mean (SD) 1.04 (0.171) 1.02 (0.151) 1.16 (0.173) 1.19 (0.223) 1.23 (0.186) 1.04 (0.127) 1.16 (0.147) 1.19 (0.228) 1.11 (0.179)
Median [Min, Max] 1.07 [0.742, 1.38] 1.06 [0.700, 1.17] 1.14 [0.890, 1.44] 1.19 [0.779, 1.62] 1.23 [1.10, 1.37] 1.04 [0.785, 1.18] 1.16 [0.941, 1.39] 1.19 [0.981, 1.41] 1.10 [0.700, 1.62]
Collemboles_3
Mean (SD) 1.12 (0.143) 1.09 (0.0569) 1.38 (0.429) 1.27 (0.250) 1.37 (0.232) 1.19 (0.260) 1.20 (0.140) 1.35 (0.352) 1.22 (0.255)
Median [Min, Max] 1.11 [0.824, 1.31] 1.10 [0.996, 1.20] 1.35 [0.835, 2.41] 1.22 [0.847, 1.74] 1.37 [1.20, 1.53] 1.11 [0.970, 2.00] 1.21 [0.942, 1.39] 1.23 [1.08, 1.84] 1.16 [0.824, 2.41]
Collemboles_10
Mean (SD) 1.15 (0.152) 1.23 (0.170) 1.45 (0.267) 1.38 (0.278) 1.99 (0.0646) 1.19 (0.110) 1.31 (0.205) 1.44 (0.259) 1.31 (0.248)
Median [Min, Max] 1.17 [0.866, 1.34] 1.22 [0.864, 1.51] 1.41 [1.07, 1.98] 1.39 [0.964, 1.99] 1.99 [1.95, 2.04] 1.18 [1.05, 1.44] 1.36 [1.00, 1.62] 1.42 [1.19, 1.73] 1.26 [0.864, 2.04]
Pucerons_1
Mean (SD) 1.03 (0.124) 1.03 (0.130) 1.12 (0.148) 1.07 (0.186) 1.17 (0.430) 1.08 (0.0503) 1.04 (0.110) 1.17 (0.131) 1.07 (0.139)
Median [Min, Max] 1.06 [0.795, 1.23] 1.04 [0.806, 1.21] 1.17 [0.814, 1.30] 1.06 [0.815, 1.54] 1.17 [0.870, 1.48] 1.08 [0.964, 1.16] 1.04 [0.885, 1.20] 1.14 [1.04, 1.35] 1.07 [0.795, 1.54]
Pucerons_3
Mean (SD) 1.05 (0.155) 1.15 (0.202) 1.24 (0.187) 1.09 (0.129) 1.29 (0.0331) 1.11 (0.0864) 1.11 (0.249) 1.38 (0.292) 1.14 (0.190)
Median [Min, Max] 1.04 [0.808, 1.33] 1.12 [0.934, 1.58] 1.19 [0.987, 1.53] 1.09 [0.835, 1.34] 1.29 [1.26, 1.31] 1.11 [0.959, 1.30] 1.06 [0.608, 1.64] 1.29 [1.14, 1.77] 1.10 [0.608, 1.77]
Pucerons_10
Mean (SD) 1.13 (0.157) 1.30 (0.200) 1.58 (0.307) 1.30 (0.195) 1.20 (0.324) 1.21 (0.113) 1.29 (0.267) 1.41 (0.262) 1.30 (0.246)
Median [Min, Max] 1.14 [0.865, 1.34] 1.33 [0.996, 1.59] 1.69 [1.11, 1.94] 1.33 [0.937, 1.51] 1.20 [0.973, 1.43] 1.18 [1.09, 1.49] 1.25 [0.885, 1.84] 1.36 [1.18, 1.73] 1.24 [0.865, 1.94]
T_officinale_1
Mean (SD) 1.03 (0.0967) 1.00 (0.122) 1.10 (0.161) 1.09 (0.164) 1.19 (0.107) 1.08 (0.0979) 1.02 (0.134) 1.16 (0.264) 1.06 (0.140)
Median [Min, Max] 1.01 [0.869, 1.20] 1.00 [0.785, 1.25] 1.07 [0.761, 1.32] 1.05 [0.835, 1.44] 1.19 [1.11, 1.26] 1.06 [0.942, 1.30] 1.00 [0.833, 1.30] 1.13 [0.934, 1.45] 1.03 [0.761, 1.45]
T_officinale_3
Mean (SD) 1.07 (0.154) 1.01 (0.119) 1.21 (0.253) 1.22 (0.141) 1.31 (0.352) 1.19 (0.179) 1.14 (0.229) 1.39 (0.426) 1.16 (0.216)
Median [Min, Max] 1.07 [0.815, 1.43] 0.993 [0.834, 1.31] 1.25 [0.819, 1.72] 1.23 [0.979, 1.44] 1.31 [1.06, 1.55] 1.18 [0.885, 1.59] 1.08 [0.883, 1.62] 1.22 [1.10, 2.02] 1.12 [0.815, 2.02]
T_officinale_10
Mean (SD) 1.26 (0.185) 1.24 (0.184) 1.64 (0.366) 1.57 (0.277) 1.88 (0.259) 1.51 (0.186) 1.70 (0.267) 1.59 (0.280) 1.50 (0.301)
Median [Min, Max] 1.24 [0.913, 1.57] 1.18 [0.937, 1.61] 1.70 [0.932, 2.14] 1.61 [1.18, 2.11] 1.88 [1.69, 2.06] 1.48 [1.27, 1.80] 1.68 [1.37, 2.33] 1.50 [1.36, 2.00] 1.48 [0.913, 2.33]
T_officinale_30
Mean (SD) 1.41 (0.211) 1.50 (0.152) 2.04 (0.454) 1.82 (0.278) 1.68 (0.678) 1.58 (0.215) 1.68 (0.319) 1.84 (0.330) 1.67 (0.346)
Median [Min, Max] 1.39 [1.02, 1.85] 1.53 [1.26, 1.69] 1.98 [1.48, 3.14] 1.85 [1.37, 2.35] 1.68 [1.20, 2.16] 1.59 [1.22, 1.86] 1.74 [1.15, 2.20] 1.88 [1.40, 2.20] 1.66 [1.02, 3.14]
CBP_1
Mean (SD) 1.01 (0.111) 0.982 (0.0991) 1.13 (0.0907) 1.08 (0.180) 1.43 (0.497) 1.02 (0.0750) 1.11 (0.193) 1.30 (0.222) 1.08 (0.169)
Median [Min, Max] 1.04 [0.808, 1.15] 0.959 [0.810, 1.20] 1.13 [0.970, 1.24] 1.08 [0.792, 1.47] 1.43 [1.08, 1.78] 1.03 [0.907, 1.15] 1.05 [0.842, 1.63] 1.28 [1.11, 1.52] 1.06 [0.792, 1.78]
CBP_3
Mean (SD) 1.03 (0.111) 0.971 (0.132) 1.17 (0.167) 1.21 (0.256) 1.42 (0.196) 1.08 (0.138) 1.11 (0.170) 1.41 (0.207) 1.12 (0.199)
Median [Min, Max] 1.05 [0.813, 1.19] 0.937 [0.768, 1.27] 1.19 [0.848, 1.42] 1.15 [0.854, 1.58] 1.42 [1.28, 1.55] 1.05 [0.915, 1.39] 1.11 [0.825, 1.39] 1.35 [1.23, 1.71] 1.06 [0.768, 1.71]
CBP_10
Mean (SD) 1.30 (0.248) 1.18 (0.184) 1.40 (0.319) 1.40 (0.268) 1.91 (0.539) 1.26 (0.116) 1.50 (0.359) 1.56 (0.402) 1.36 (0.296)
Median [Min, Max] 1.28 [0.791, 1.73] 1.13 [0.968, 1.56] 1.30 [0.956, 1.90] 1.39 [0.987, 1.85] 1.91 [1.53, 2.29] 1.30 [1.07, 1.42] 1.42 [1.04, 2.31] 1.56 [1.07, 2.04] 1.32 [0.791, 2.31]
CBP_30
Mean (SD) 1.43 (0.299) 1.46 (0.131) 1.82 (0.211) 1.64 (0.194) 1.81 (0.581) 1.44 (0.221) 1.76 (0.326) 1.72 (0.481) 1.60 (0.296)
Median [Min, Max] 1.34 [0.904, 1.98] 1.49 [1.20, 1.65] 1.80 [1.46, 2.32] 1.65 [1.23, 1.96] 1.81 [1.40, 2.23] 1.45 [1.02, 1.71] 1.69 [1.35, 2.56] 1.65 [1.22, 2.36] 1.55 [0.904, 2.56]
V_arvensis_1
Mean (SD) 1.06 (0.121) 1.02 (0.0767) 1.10 (0.0831) 1.08 (0.124) 0.767 (0.274) 1.15 (0.164) 1.06 (0.155) 1.24 (0.0710) 1.08 (0.143)
Median [Min, Max] 1.09 [0.852, 1.22] 1.05 [0.868, 1.12] 1.11 [0.941, 1.24] 1.09 [0.784, 1.26] 0.767 [0.573, 0.961] 1.14 [0.862, 1.39] 1.03 [0.871, 1.39] 1.24 [1.15, 1.32] 1.08 [0.573, 1.39]
V_arvensis_3
Mean (SD) 1.07 (0.158) 1.01 (0.140) 1.19 (0.187) 1.23 (0.156) 1.20 (0.142) 1.27 (0.214) 1.19 (0.353) 1.20 (0.257) 1.17 (0.225)
Median [Min, Max] 1.04 [0.797, 1.35] 0.976 [0.819, 1.27] 1.18 [0.935, 1.57] 1.19 [1.01, 1.49] 1.20 [1.10, 1.30] 1.25 [0.875, 1.53] 1.14 [0.851, 2.18] 1.14 [0.951, 1.56] 1.14 [0.797, 2.18]
V_arvensis_10
Mean (SD) 1.21 (0.205) 1.21 (0.155) 1.58 (0.368) 1.56 (0.223) 2.03 (0.419) 1.41 (0.197) 1.43 (0.585) 1.37 (0.293) 1.41 (0.352)
Median [Min, Max] 1.19 [0.817, 1.55] 1.20 [1.01, 1.56] 1.56 [1.12, 2.30] 1.65 [1.09, 1.87] 2.03 [1.73, 2.32] 1.38 [1.06, 1.72] 1.28 [1.01, 3.15] 1.31 [1.10, 1.77] 1.35 [0.817, 3.15]
V_arvensis_30
Mean (SD) 1.37 (0.204) 1.33 (0.171) 1.79 (0.455) 1.65 (0.174) 2.10 (0.604) 1.40 (0.158) 1.74 (0.597) 1.54 (0.269) 1.55 (0.377)
Median [Min, Max] 1.40 [0.961, 1.64] 1.37 [1.07, 1.67] 1.68 [1.40, 3.01] 1.63 [1.42, 1.92] 2.10 [1.67, 2.52] 1.35 [1.14, 1.68] 1.53 [1.29, 3.25] 1.43 [1.36, 1.94] 1.47 [0.961, 3.25]
C_album_1
Mean (SD) 1.02 (0.102) 1.03 (0.0883) 1.01 (0.198) 1.11 (0.120) 1.32 (0.203) 1.03 (0.111) 1.12 (0.144) 1.17 (0.136) 1.07 (0.142)
Median [Min, Max] 1.04 [0.828, 1.16] 1.00 [0.911, 1.21] 1.04 [0.668, 1.23] 1.10 [0.848, 1.30] 1.32 [1.18, 1.47] 1.07 [0.736, 1.14] 1.08 [0.911, 1.43] 1.19 [0.979, 1.30] 1.07 [0.668, 1.47]
C_album_3
Mean (SD) 1.04 (0.0750) 0.978 (0.146) 1.06 (0.120) 1.12 (0.177) 0.807 (0.479) 1.13 (0.128) 1.11 (0.143) 1.10 (0.0885) 1.07 (0.153)
Median [Min, Max] 1.03 [0.922, 1.20] 0.959 [0.789, 1.24] 1.05 [0.873, 1.27] 1.12 [0.757, 1.37] 0.807 [0.468, 1.15] 1.16 [0.873, 1.40] 1.11 [0.899, 1.48] 1.08 [1.02, 1.21] 1.07 [0.468, 1.48]
C_album_10
Mean (SD) 1.01 (0.120) 1.02 (0.172) 1.16 (0.209) 1.15 (0.196) 0.934 (0.284) 1.21 (0.176) 1.24 (0.526) 1.06 (0.0975) 1.13 (0.267)
Median [Min, Max] 1.02 [0.806, 1.20] 0.996 [0.804, 1.35] 1.20 [0.715, 1.39] 1.13 [0.731, 1.45] 0.934 [0.733, 1.13] 1.20 [0.959, 1.50] 1.07 [0.819, 2.76] 1.02 [0.985, 1.20] 1.11 [0.715, 2.76]
C_album_30
Mean (SD) 1.19 (0.225) 1.14 (0.159) 1.25 (0.222) 1.34 (0.182) 1.83 (0.646) 1.43 (0.349) 1.62 (1.08) 1.18 (0.153) 1.34 (0.494)
Median [Min, Max] 1.16 [0.826, 1.59] 1.13 [0.953, 1.54] 1.22 [0.838, 1.54] 1.34 [0.988, 1.64] 1.83 [1.38, 2.29] 1.41 [0.810, 1.99] 1.29 [0.999, 4.97] 1.23 [0.956, 1.28] 1.28 [0.810, 4.97] 
table1(~Ref+blanc+EBF_1+EBF_10+EBF_100+Collemboles_1+Collemboles_3+Collemboles_10+
         Pucerons_1+Pucerons_3+Pucerons_10+
         T_officinale_1+T_officinale_3+T_officinale_10+T_officinale_30+
         CBP_1+CBP_3+CBP_10+CBP_30+
         V_arvensis_1+V_arvensis_3+V_arvensis_10+V_arvensis_30+
         C_album_1+C_album_3+C_album_10+C_album_30|Carabe*Sexe,data=Resultats_proies_agg_wide)
## Warning in .table1.internal(x = x, labels = labels, groupspan = groupspan, :
## Table has 18 columns. Are you sure this is what you want?
A_aen
A_dor
B_scl
H_dis
N_qua
N_sal
P_cup
P_ruf
Overall
F
(N=6)		 M
(N=6)		 F
(N=6)		 M
(N=6)		 F
(N=5)		 M
(N=6)		 F
(N=6)		 M
(N=6)		 F
(N=1)		 M
(N=1)		 F
(N=8)		 M
(N=6)		 F
(N=7)		 M
(N=5)		 F
(N=1)		 M
(N=3)		 F
(N=40)		 M
(N=39)
Ref
Mean (SD) 1.20 (0.317) 1.17 (0.117) 1.39 (0.454) 1.33 (0.285) 2.04 (0.331) 1.79 (0.552) 1.72 (0.336) 1.37 (0.483) 1.20 (NA) 1.21 (NA) 1.65 (0.467) 1.48 (0.298) 1.04 (0.202) 1.27 (0.334) 1.86 (NA) 2.41 (0.544) 1.49 (0.468) 1.48 (0.483)
Median [Min, Max] 1.08 [1.02, 1.84] 1.19 [0.959, 1.30] 1.27 [0.929, 2.20] 1.37 [1.00, 1.68] 2.06 [1.50, 2.39] 1.56 [1.28, 2.50] 1.76 [1.30, 2.23] 1.12 [0.962, 2.04] 1.20 [1.20, 1.20] 1.21 [1.21, 1.21] 1.68 [1.01, 2.27] 1.44 [1.03, 1.88] 0.983 [0.892, 1.48] 1.30 [0.902, 1.63] 1.86 [1.86, 1.86] 2.69 [1.78, 2.76] 1.37 [0.892, 2.39] 1.37 [0.902, 2.76]
blanc
Mean (SD) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (NA) 1.00 (NA) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (0) 1.00 (NA) 1.00 (0) 1.00 (0) 1.00 (0)
Median [Min, Max] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00] 1.00 [1.00, 1.00]
EBF_1
Mean (SD) 0.917 (0.0966) 0.964 (0.224) 1.16 (0.232) 1.11 (0.292) 1.05 (0.247) 0.909 (0.102) 0.829 (0.151) 0.972 (0.0718) 1.98 (NA) 1.30 (NA) 0.977 (0.115) 0.962 (0.0647) 1.06 (0.0881) 1.16 (0.418) 0.991 (NA) 1.04 (0.372) 1.02 (0.235) 1.02 (0.235)
Median [Min, Max] 0.907 [0.807, 1.03] 0.913 [0.792, 1.40] 1.15 [0.830, 1.54] 1.02 [0.880, 1.66] 1.12 [0.776, 1.31] 0.947 [0.757, 1.01] 0.846 [0.561, 1.01] 0.996 [0.830, 1.03] 1.98 [1.98, 1.98] 1.30 [1.30, 1.30] 0.981 [0.783, 1.14] 0.938 [0.915, 1.09] 1.03 [0.987, 1.24] 1.09 [0.732, 1.86] 0.991 [0.991, 0.991] 1.24 [0.614, 1.28] 1.01 [0.561, 1.98] 0.976 [0.614, 1.86]
EBF_10
Mean (SD) 0.983 (0.153) 1.07 (0.227) 1.51 (0.289) 1.32 (0.119) 1.33 (0.319) 1.34 (0.129) 0.969 (0.237) 1.20 (0.0500) 1.85 (NA) 1.58 (NA) 1.19 (0.121) 1.13 (0.0684) 1.22 (0.141) 1.43 (0.597) 1.21 (NA) 1.26 (0.227) 1.21 (0.283) 1.25 (0.263)
Median [Min, Max] 1.00 [0.733, 1.14] 0.995 [0.900, 1.50] 1.50 [1.13, 1.99] 1.29 [1.22, 1.56] 1.35 [0.948, 1.76] 1.32 [1.21, 1.52] 1.02 [0.627, 1.20] 1.18 [1.15, 1.27] 1.85 [1.85, 1.85] 1.58 [1.58, 1.58] 1.21 [0.979, 1.38] 1.11 [1.05, 1.23] 1.17 [1.08, 1.44] 1.25 [0.972, 2.47] 1.21 [1.21, 1.21] 1.34 [0.998, 1.43] 1.18 [0.627, 1.99] 1.23 [0.900, 2.47]
EBF_100
Mean (SD) 0.778 (0.169) 0.892 (0.228) 1.61 (0.323) 1.52 (0.199) 1.49 (0.436) 1.49 (0.266) 1.24 (0.289) 1.45 (0.395) 2.61 (NA) 2.47 (NA) 1.33 (0.141) 1.23 (0.108) 1.28 (0.170) 1.36 (0.607) 1.30 (NA) 1.40 (0.260) 1.32 (0.401) 1.36 (0.404)
Median [Min, Max] 0.811 [0.568, 0.960] 1.02 [0.586, 1.07] 1.52 [1.32, 2.24] 1.43 [1.30, 1.79] 1.43 [1.04, 2.04] 1.48 [1.13, 1.82] 1.32 [0.746, 1.51] 1.42 [1.03, 2.17] 2.61 [2.61, 2.61] 2.47 [2.47, 2.47] 1.29 [1.15, 1.57] 1.22 [1.11, 1.38] 1.26 [1.03, 1.52] 1.15 [0.845, 2.39] 1.30 [1.30, 1.30] 1.48 [1.11, 1.61] 1.28 [0.568, 2.61] 1.32 [0.586, 2.47]
Collemboles_1
Mean (SD) 1.02 (0.142) 1.05 (0.209) 1.01 (0.184) 1.03 (0.127) 1.22 (0.225) 1.11 (0.115) 1.11 (0.187) 1.26 (0.247) 1.37 (NA) 1.10 (NA) 1.05 (0.139) 1.02 (0.120) 1.21 (0.138) 1.10 (0.150) 0.981 (NA) 1.26 (0.221) 1.10 (0.179) 1.11 (0.181)
Median [Min, Max] 1.04 [0.842, 1.23] 1.07 [0.742, 1.38] 1.06 [0.700, 1.17] 1.06 [0.801, 1.16] 1.31 [0.890, 1.44] 1.09 [0.970, 1.25] 1.14 [0.779, 1.32] 1.23 [0.978, 1.62] 1.37 [1.37, 1.37] 1.10 [1.10, 1.10] 1.06 [0.785, 1.18] 1.03 [0.867, 1.16] 1.17 [1.04, 1.39] 1.11 [0.941, 1.28] 0.981 [0.981, 0.981] 1.37 [1.01, 1.41] 1.10 [0.700, 1.44] 1.09 [0.742, 1.62]
Collemboles_3
Mean (SD) 1.08 (0.144) 1.17 (0.141) 1.11 (0.0493) 1.08 (0.0630) 1.52 (0.571) 1.27 (0.271) 1.16 (0.171) 1.37 (0.285) 1.53 (NA) 1.20 (NA) 1.26 (0.311) 1.09 (0.146) 1.22 (0.148) 1.17 (0.141) 1.08 (NA) 1.44 (0.371) 1.22 (0.285) 1.21 (0.223)
Median [Min, Max] 1.10 [0.824, 1.23] 1.18 [1.00, 1.31] 1.10 [1.05, 1.20] 1.08 [0.996, 1.16] 1.42 [0.835, 2.41] 1.24 [0.863, 1.61] 1.17 [0.847, 1.35] 1.30 [1.08, 1.74] 1.53 [1.53, 1.53] 1.20 [1.20, 1.20] 1.13 [1.05, 2.00] 1.03 [0.970, 1.32] 1.20 [0.992, 1.39] 1.23 [0.942, 1.28] 1.08 [1.08, 1.08] 1.34 [1.12, 1.84] 1.17 [0.824, 2.41] 1.16 [0.863, 1.84]
Collemboles_10
Mean (SD) 1.19 (0.135) 1.12 (0.172) 1.25 (0.0796) 1.22 (0.239) 1.52 (0.329) 1.39 (0.215) 1.31 (0.208) 1.45 (0.337) 1.95 (NA) 2.04 (NA) 1.20 (0.0859) 1.18 (0.144) 1.35 (0.218) 1.24 (0.187) 1.26 (NA) 1.50 (0.280) 1.31 (0.225) 1.31 (0.273)
Median [Min, Max] 1.21 [1.01, 1.33] 1.09 [0.866, 1.34] 1.22 [1.18, 1.39] 1.23 [0.864, 1.51] 1.50 [1.07, 1.98] 1.39 [1.15, 1.77] 1.39 [0.964, 1.51] 1.43 [1.14, 1.99] 1.95 [1.95, 1.95] 2.04 [2.04, 2.04] 1.18 [1.10, 1.37] 1.15 [1.05, 1.44] 1.44 [1.02, 1.62] 1.33 [1.00, 1.40] 1.26 [1.26, 1.26] 1.59 [1.19, 1.73] 1.25 [0.964, 1.98] 1.26 [0.864, 2.04]
Pucerons_1
Mean (SD) 0.958 (0.105) 1.09 (0.110) 1.02 (0.138) 1.04 (0.134) 1.09 (0.180) 1.14 (0.130) 1.01 (0.116) 1.13 (0.235) 1.48 (NA) 0.870 (NA) 1.09 (0.0586) 1.06 (0.0320) 1.06 (0.101) 1.01 (0.128) 1.11 (NA) 1.19 (0.152) 1.05 (0.135) 1.08 (0.143)
Median [Min, Max] 0.971 [0.795, 1.10] 1.11 [0.893, 1.23] 1.03 [0.813, 1.20] 1.04 [0.806, 1.21] 1.17 [0.814, 1.28] 1.17 [0.972, 1.30] 1.05 [0.815, 1.12] 1.07 [0.882, 1.54] 1.48 [1.48, 1.48] 0.870 [0.870, 0.870] 1.11 [0.964, 1.16] 1.06 [1.01, 1.11] 1.08 [0.910, 1.16] 0.976 [0.885, 1.20] 1.11 [1.11, 1.11] 1.17 [1.04, 1.35] 1.08 [0.795, 1.48] 1.07 [0.806, 1.54]
Pucerons_3
Mean (SD) 1.04 (0.185) 1.06 (0.136) 1.20 (0.256) 1.11 (0.139) 1.20 (0.215) 1.27 (0.171) 1.10 (0.180) 1.08 (0.0620) 1.26 (NA) 1.31 (NA) 1.11 (0.0834) 1.12 (0.0981) 1.07 (0.247) 1.16 (0.272) 1.17 (NA) 1.44 (0.317) 1.12 (0.190) 1.16 (0.190)
Median [Min, Max] 1.02 [0.808, 1.33] 1.04 [0.909, 1.25] 1.18 [0.934, 1.58] 1.12 [0.940, 1.29] 1.08 [0.987, 1.43] 1.27 [1.08, 1.53] 1.08 [0.835, 1.34] 1.09 [0.998, 1.16] 1.26 [1.26, 1.26] 1.31 [1.31, 1.31] 1.11 [0.959, 1.25] 1.10 [1.00, 1.30] 1.08 [0.608, 1.43] 1.04 [1.02, 1.64] 1.17 [1.17, 1.17] 1.41 [1.14, 1.77] 1.09 [0.608, 1.58] 1.11 [0.909, 1.77]
Pucerons_10
Mean (SD) 1.08 (0.166) 1.17 (0.147) 1.35 (0.234) 1.25 (0.165) 1.47 (0.301) 1.66 (0.309) 1.25 (0.262) 1.35 (0.0953) 0.973 (NA) 1.43 (NA) 1.20 (0.0981) 1.22 (0.138) 1.32 (0.349) 1.24 (0.0929) 1.18 (NA) 1.48 (0.260) 1.26 (0.255) 1.34 (0.234)
Median [Min, Max] 1.10 [0.865, 1.32] 1.21 [0.980, 1.34] 1.41 [1.04, 1.59] 1.29 [0.996, 1.45] 1.36 [1.16, 1.85] 1.73 [1.11, 1.94] 1.33 [0.937, 1.51] 1.33 [1.23, 1.49] 0.973 [0.973, 0.973] 1.43 [1.43, 1.43] 1.17 [1.09, 1.40] 1.18 [1.10, 1.49] 1.24 [0.885, 1.84] 1.25 [1.10, 1.34] 1.18 [1.18, 1.18] 1.50 [1.21, 1.73] 1.21 [0.865, 1.85] 1.30 [0.980, 1.94]
T_officinale_1
Mean (SD) 1.00 (0.0990) 1.07 (0.0907) 0.978 (0.0849) 1.03 (0.154) 1.06 (0.0841) 1.14 (0.208) 1.06 (0.205) 1.13 (0.119) 1.26 (NA) 1.11 (NA) 1.07 (0.0797) 1.08 (0.126) 0.979 (0.109) 1.08 (0.153) 0.934 (NA) 1.24 (0.266) 1.03 (0.121) 1.10 (0.151)
Median [Min, Max] 0.997 [0.869, 1.16] 1.05 [0.966, 1.20] 0.962 [0.893, 1.10] 1.04 [0.785, 1.25] 1.05 [0.964, 1.19] 1.20 [0.761, 1.32] 1.00 [0.835, 1.44] 1.15 [0.976, 1.26] 1.26 [1.26, 1.26] 1.11 [1.11, 1.11] 1.06 [0.994, 1.24] 1.06 [0.942, 1.30] 0.993 [0.833, 1.16] 1.03 [0.944, 1.30] 0.934 [0.934, 0.934] 1.31 [0.939, 1.45] 1.02 [0.833, 1.44] 1.09 [0.761, 1.45]
T_officinale_3
Mean (SD) 1.09 (0.201) 1.04 (0.102) 0.981 (0.0576) 1.05 (0.159) 1.26 (0.309) 1.17 (0.220) 1.16 (0.139) 1.27 (0.129) 1.55 (NA) 1.06 (NA) 1.12 (0.153) 1.28 (0.181) 1.07 (0.220) 1.24 (0.227) 1.18 (NA) 1.46 (0.492) 1.12 (0.202) 1.19 (0.226)
Median [Min, Max] 1.05 [0.815, 1.43] 1.08 [0.868, 1.15] 0.989 [0.876, 1.05] 1.02 [0.834, 1.31] 1.25 [0.947, 1.72] 1.23 [0.819, 1.39] 1.20 [0.979, 1.34] 1.26 [1.11, 1.44] 1.55 [1.55, 1.55] 1.06 [1.06, 1.06] 1.14 [0.885, 1.36] 1.24 [1.09, 1.59] 1.02 [0.883, 1.44] 1.17 [1.06, 1.62] 1.18 [1.18, 1.18] 1.26 [1.10, 2.02] 1.06 [0.815, 1.72] 1.15 [0.819, 2.02]
T_officinale_10
Mean (SD) 1.26 (0.247) 1.26 (0.120) 1.21 (0.189) 1.27 (0.190) 1.68 (0.464) 1.61 (0.306) 1.54 (0.365) 1.60 (0.184) 1.69 (NA) 2.06 (NA) 1.50 (0.203) 1.52 (0.179) 1.65 (0.226) 1.77 (0.331) 1.53 (NA) 1.61 (0.339) 1.48 (0.313) 1.52 (0.290)
Median [Min, Max] 1.23 [0.913, 1.57] 1.26 [1.13, 1.42] 1.21 [0.937, 1.45] 1.18 [1.12, 1.61] 1.73 [0.932, 2.14] 1.57 [1.30, 2.05] 1.50 [1.18, 2.11] 1.66 [1.31, 1.79] 1.69 [1.69, 1.69] 2.06 [2.06, 2.06] 1.48 [1.27, 1.80] 1.53 [1.33, 1.73] 1.73 [1.37, 1.92] 1.63 [1.52, 2.33] 1.53 [1.53, 1.53] 1.48 [1.36, 2.00] 1.48 [0.913, 2.14] 1.48 [1.12, 2.33]
T_officinale_30
Mean (SD) 1.45 (0.285) 1.36 (0.113) 1.50 (0.119) 1.50 (0.192) 2.13 (0.615) 1.97 (0.311) 1.85 (0.319) 1.78 (0.257) 1.20 (NA) 2.16 (NA) 1.56 (0.237) 1.61 (0.199) 1.57 (0.375) 1.82 (0.154) 1.85 (NA) 1.84 (0.405) 1.65 (0.387) 1.70 (0.303)
Median [Min, Max] 1.42 [1.02, 1.85] 1.33 [1.24, 1.52] 1.49 [1.34, 1.68] 1.56 [1.26, 1.69] 1.98 [1.57, 3.14] 1.99 [1.48, 2.38] 1.85 [1.40, 2.35] 1.85 [1.37, 2.05] 1.20 [1.20, 1.20] 2.16 [2.16, 2.16] 1.59 [1.22, 1.86] 1.62 [1.29, 1.84] 1.42 [1.15, 2.20] 1.77 [1.69, 2.07] 1.85 [1.85, 1.85] 1.91 [1.40, 2.20] 1.59 [1.02, 3.14] 1.69 [1.24, 2.38]
CBP_1
Mean (SD) 0.961 (0.118) 1.07 (0.0803) 0.966 (0.0444) 0.998 (0.138) 1.11 (0.0997) 1.14 (0.0891) 1.11 (0.228) 1.05 (0.129) 1.78 (NA) 1.08 (NA) 1.01 (0.0798) 1.04 (0.0706) 1.13 (0.251) 1.08 (0.0762) 1.11 (NA) 1.36 (0.223) 1.07 (0.197) 1.09 (0.137)
Median [Min, Max] 0.958 [0.808, 1.11] 1.08 [0.946, 1.15] 0.959 [0.912, 1.03] 0.998 [0.810, 1.20] 1.10 [0.970, 1.24] 1.15 [1.02, 1.24] 1.07 [0.792, 1.47] 1.08 [0.859, 1.20] 1.78 [1.78, 1.78] 1.08 [1.08, 1.08] 1.02 [0.914, 1.15] 1.06 [0.907, 1.09] 1.03 [0.842, 1.63] 1.07 [0.997, 1.17] 1.11 [1.11, 1.11] 1.46 [1.11, 1.52] 1.03 [0.792, 1.78] 1.08 [0.810, 1.52]
CBP_3
Mean (SD) 1.02 (0.143) 1.05 (0.0793) 0.909 (0.0983) 1.03 (0.138) 1.11 (0.220) 1.23 (0.0973) 1.15 (0.265) 1.28 (0.254) 1.55 (NA) 1.28 (NA) 1.03 (0.104) 1.15 (0.152) 1.04 (0.166) 1.19 (0.145) 1.23 (NA) 1.47 (0.210) 1.06 (0.191) 1.18 (0.188)
Median [Min, Max] 1.02 [0.813, 1.19] 1.05 [0.944, 1.18] 0.913 [0.768, 1.06] 0.974 [0.927, 1.27] 1.03 [0.848, 1.42] 1.19 [1.14, 1.40] 1.04 [0.854, 1.57] 1.27 [0.964, 1.58] 1.55 [1.55, 1.55] 1.28 [1.28, 1.28] 1.01 [0.915, 1.18] 1.14 [0.968, 1.39] 0.985 [0.825, 1.34] 1.19 [0.987, 1.39] 1.23 [1.23, 1.23] 1.37 [1.32, 1.71] 1.03 [0.768, 1.57] 1.18 [0.927, 1.71]
CBP_10
Mean (SD) 1.25 (0.291) 1.35 (0.211) 1.12 (0.143) 1.24 (0.214) 1.37 (0.334) 1.42 (0.337) 1.28 (0.221) 1.52 (0.268) 1.53 (NA) 2.29 (NA) 1.19 (0.0945) 1.36 (0.0498) 1.46 (0.420) 1.57 (0.284) 1.47 (NA) 1.59 (0.486) 1.29 (0.274) 1.44 (0.301)
Median [Min, Max] 1.25 [0.791, 1.59] 1.31 [1.09, 1.73] 1.10 [0.968, 1.32] 1.18 [1.04, 1.56] 1.17 [1.09, 1.82] 1.40 [0.956, 1.90] 1.32 [0.987, 1.57] 1.53 [1.18, 1.85] 1.53 [1.53, 1.53] 2.29 [2.29, 2.29] 1.19 [1.07, 1.31] 1.36 [1.31, 1.42] 1.42 [1.04, 2.31] 1.42 [1.32, 1.89] 1.47 [1.47, 1.47] 1.66 [1.07, 2.04] 1.25 [0.791, 2.31] 1.37 [0.956, 2.29]
CBP_30
Mean (SD) 1.44 (0.335) 1.42 (0.290) 1.46 (0.101) 1.46 (0.166) 1.86 (0.318) 1.79 (0.0721) 1.56 (0.212) 1.71 (0.153) 1.40 (NA) 2.23 (NA) 1.36 (0.232) 1.54 (0.178) 1.78 (0.404) 1.73 (0.215) 1.77 (NA) 1.71 (0.588) 1.56 (0.316) 1.63 (0.275)
Median [Min, Max] 1.46 [0.904, 1.85] 1.29 [1.19, 1.98] 1.45 [1.31, 1.59] 1.52 [1.20, 1.65] 1.86 [1.46, 2.32] 1.77 [1.71, 1.88] 1.59 [1.23, 1.86] 1.71 [1.49, 1.96] 1.40 [1.40, 1.40] 2.23 [2.23, 2.23] 1.45 [1.02, 1.68] 1.55 [1.30, 1.71] 1.67 [1.35, 2.56] 1.71 [1.47, 1.98] 1.77 [1.77, 1.77] 1.53 [1.22, 2.36] 1.52 [0.904, 2.56] 1.65 [1.19, 2.36]
V_arvensis_1
Mean (SD) 0.990 (0.129) 1.12 (0.0747) 1.00 (0.0491) 1.04 (0.0992) 1.09 (0.0501) 1.12 (0.107) 1.09 (0.0740) 1.07 (0.168) 0.961 (NA) 0.573 (NA) 1.10 (0.150) 1.21 (0.170) 1.00 (0.0681) 1.15 (0.204) 1.15 (NA) 1.27 (0.0496) 1.05 (0.104) 1.12 (0.168)
Median [Min, Max] 0.987 [0.852, 1.14] 1.14 [1.03, 1.22] 1.00 [0.954, 1.05] 1.07 [0.868, 1.12] 1.10 [1.03, 1.14] 1.12 [0.941, 1.24] 1.12 [0.990, 1.18] 1.07 [0.784, 1.26] 0.961 [0.961, 0.961] 0.573 [0.573, 0.573] 1.10 [0.862, 1.33] 1.25 [0.979, 1.39] 1.02 [0.871, 1.07] 1.18 [0.903, 1.39] 1.15 [1.15, 1.15] 1.24 [1.23, 1.32] 1.04 [0.852, 1.33] 1.12 [0.573, 1.39]
V_arvensis_3
Mean (SD) 1.01 (0.179) 1.13 (0.126) 0.969 (0.119) 1.05 (0.157) 1.08 (0.137) 1.28 (0.183) 1.12 (0.0725) 1.34 (0.141) 1.30 (NA) 1.10 (NA) 1.21 (0.228) 1.36 (0.175) 1.22 (0.455) 1.15 (0.168) 1.14 (NA) 1.21 (0.312) 1.12 (0.244) 1.22 (0.193)
Median [Min, Max] 0.995 [0.797, 1.27] 1.10 [1.02, 1.35] 0.994 [0.819, 1.09] 0.976 [0.925, 1.27] 1.11 [0.935, 1.25] 1.21 [1.11, 1.57] 1.13 [1.01, 1.20] 1.39 [1.13, 1.49] 1.30 [1.30, 1.30] 1.10 [1.10, 1.10] 1.20 [0.875, 1.52] 1.40 [1.11, 1.53] 1.14 [0.851, 2.18] 1.15 [0.977, 1.42] 1.14 [1.14, 1.14] 1.13 [0.951, 1.56] 1.11 [0.797, 2.18] 1.17 [0.925, 1.57]
V_arvensis_10
Mean (SD) 1.15 (0.273) 1.26 (0.107) 1.18 (0.0936) 1.25 (0.204) 1.58 (0.459) 1.58 (0.319) 1.48 (0.269) 1.64 (0.149) 1.73 (NA) 2.32 (NA) 1.36 (0.232) 1.48 (0.122) 1.42 (0.776) 1.44 (0.190) 1.22 (NA) 1.43 (0.335) 1.36 (0.410) 1.46 (0.276)
Median [Min, Max] 1.13 [0.817, 1.55] 1.25 [1.14, 1.40] 1.19 [1.08, 1.33] 1.26 [1.01, 1.56] 1.55 [1.12, 2.30] 1.61 [1.13, 2.05] 1.52 [1.09, 1.77] 1.65 [1.48, 1.87] 1.73 [1.73, 1.73] 2.32 [2.32, 2.32] 1.32 [1.06, 1.72] 1.52 [1.32, 1.59] 1.10 [1.01, 3.15] 1.45 [1.20, 1.65] 1.22 [1.22, 1.22] 1.41 [1.10, 1.77] 1.25 [0.817, 3.15] 1.45 [1.01, 2.32]
V_arvensis_30
Mean (SD) 1.36 (0.232) 1.38 (0.193) 1.30 (0.151) 1.36 (0.200) 1.97 (0.613) 1.64 (0.228) 1.64 (0.201) 1.66 (0.161) 1.67 (NA) 2.52 (NA) 1.38 (0.178) 1.43 (0.137) 1.78 (0.660) 1.67 (0.563) 1.47 (NA) 1.56 (0.324) 1.56 (0.426) 1.55 (0.324)
Median [Min, Max] 1.40 [0.961, 1.61] 1.35 [1.17, 1.64] 1.37 [1.07, 1.45] 1.34 [1.11, 1.67] 1.83 [1.41, 3.01] 1.56 [1.40, 2.01] 1.57 [1.42, 1.92] 1.70 [1.42, 1.88] 1.67 [1.67, 1.67] 2.52 [2.52, 2.52] 1.31 [1.14, 1.68] 1.41 [1.26, 1.63] 1.57 [1.34, 3.25] 1.44 [1.29, 2.66] 1.47 [1.47, 1.47] 1.39 [1.36, 1.94] 1.47 [0.961, 3.25] 1.46 [1.11, 2.66]
C_album_1
Mean (SD) 1.03 (0.0942) 1.00 (0.117) 0.992 (0.0665) 1.07 (0.0966) 0.952 (0.227) 1.06 (0.176) 1.11 (0.0903) 1.11 (0.154) 1.47 (NA) 1.18 (NA) 1.00 (0.134) 1.06 (0.0714) 1.08 (0.118) 1.18 (0.169) 1.17 (NA) 1.16 (0.166) 1.05 (0.146) 1.09 (0.137)
Median [Min, Max] 1.02 [0.882, 1.16] 1.05 [0.828, 1.11] 0.985 [0.911, 1.11] 1.04 [0.956, 1.21] 0.957 [0.668, 1.18] 1.09 [0.780, 1.23] 1.08 [1.02, 1.26] 1.14 [0.848, 1.30] 1.47 [1.47, 1.47] 1.18 [1.18, 1.18] 1.05 [0.736, 1.11] 1.07 [0.935, 1.14] 1.08 [0.911, 1.31] 1.15 [1.02, 1.43] 1.17 [1.17, 1.17] 1.21 [0.979, 1.30] 1.05 [0.668, 1.47] 1.07 [0.780, 1.43]
C_album_3
Mean (SD) 1.01 (0.0656) 1.06 (0.0829) 0.963 (0.143) 0.993 (0.161) 1.05 (0.156) 1.07 (0.0959) 1.18 (0.134) 1.05 (0.202) 0.468 (NA) 1.15 (NA) 1.09 (0.113) 1.18 (0.140) 1.08 (0.0875) 1.15 (0.203) 1.02 (NA) 1.12 (0.0908) 1.05 (0.158) 1.09 (0.149)
Median [Min, Max] 1.00 [0.922, 1.10] 1.06 [0.951, 1.20] 0.963 [0.791, 1.14] 0.959 [0.789, 1.24] 1.08 [0.873, 1.27] 1.03 [0.962, 1.20] 1.22 [0.972, 1.35] 1.04 [0.757, 1.37] 0.468 [0.468, 0.468] 1.15 [1.15, 1.15] 1.15 [0.873, 1.19] 1.18 [0.993, 1.40] 1.12 [0.899, 1.16] 1.11 [0.945, 1.48] 1.02 [1.02, 1.02] 1.13 [1.03, 1.21] 1.08 [0.468, 1.35] 1.06 [0.757, 1.48]
C_album_10
Mean (SD) 0.994 (0.150) 1.03 (0.0913) 0.985 (0.116) 1.06 (0.221) 1.04 (0.239) 1.26 (0.120) 1.21 (0.137) 1.09 (0.238) 0.733 (NA) 1.13 (NA) 1.12 (0.152) 1.33 (0.127) 1.36 (0.675) 1.09 (0.158) 1.03 (NA) 1.07 (0.117) 1.11 (0.329) 1.14 (0.186)
Median [Min, Max] 0.965 [0.806, 1.20] 1.03 [0.869, 1.13] 0.991 [0.822, 1.13] 1.03 [0.804, 1.35] 1.03 [0.715, 1.35] 1.30 [1.05, 1.39] 1.18 [1.08, 1.45] 1.07 [0.731, 1.40] 0.733 [0.733, 0.733] 1.13 [1.13, 1.13] 1.11 [0.959, 1.33] 1.34 [1.13, 1.50] 1.04 [0.819, 2.76] 1.10 [0.906, 1.28] 1.03 [1.03, 1.03] 1.01 [0.985, 1.20] 1.06 [0.715, 2.76] 1.13 [0.731, 1.50]
C_album_30
Mean (SD) 1.20 (0.304) 1.18 (0.135) 1.09 (0.0882) 1.19 (0.203) 1.15 (0.245) 1.33 (0.181) 1.39 (0.175) 1.30 (0.194) 1.38 (NA) 2.29 (NA) 1.31 (0.363) 1.58 (0.284) 1.76 (1.42) 1.43 (0.316) 1.19 (NA) 1.17 (0.186) 1.33 (0.638) 1.35 (0.290)
Median [Min, Max] 1.14 [0.826, 1.59] 1.20 [0.987, 1.32] 1.12 [0.953, 1.20] 1.16 [0.991, 1.54] 1.17 [0.838, 1.44] 1.32 [1.12, 1.54] 1.36 [1.15, 1.64] 1.32 [0.988, 1.57] 1.38 [1.38, 1.38] 2.29 [2.29, 2.29] 1.36 [0.810, 1.80] 1.57 [1.28, 1.99] 1.28 [0.999, 4.97] 1.38 [1.15, 1.96] 1.19 [1.19, 1.19] 1.28 [0.956, 1.28] 1.19 [0.810, 4.97] 1.29 [0.956, 2.29]