Supplementary Material. Appendix A.
Eric M. Wood, Ph.D - ericmwood@calstatela.edu - www.ericmwood.org
- This is an R Markdown file associated with the paper “Vertical stratification of insect abundance and species richness in an Amazonian tropical forest” (Amorim et al., Scientific Reports)
- For all queries on the data analyses presented in this document, please contact the author.
- Each code chunk for abundnace has (a) Kruskal-Wallis and multiple comparisons test, (b) means and sd, and (c) boxplots
- Code for the simulated richness data are (a) the code setup and (b) boxplot.
Diptera abundance
k1 <-kruskal.test(flies$Abundance ~ as.factor(flies$Group))
k1##
## Kruskal-Wallis rank sum test
##
## data: flies$Abundance by as.factor(flies$Group)
## Kruskal-Wallis chi-squared = 74.289, df = 4, p-value = 2.816e-15k2 <- nparcomp(Abundance ~ as.factor(Group), data = flies, type = "Tukey")##
## #------Nonparametric Multiple Comparisons for relative contrast effects-----#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Type of Contrast : Tukey
## - Confidence level: 95 %
## - Method = Logit - Transformation
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation----------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #---------------------------------------------------------------------------#
## summary(k2)##
## #------------Nonparametric Multiple Comparisons for relative contrast effects----------#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Estimation Method: Global Pseudo ranks
## - Type of Contrast : Tukey
## - Confidence Level: 95 %
## - Method = Logit - Transformation
##
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation--------------------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #-------------------------------------------------------------------------------------#
##
## #----Data Info-------------------------------------------------------------------------#
## Sample Size
## 1 Eight 867
## 2 Sixteen 867
## 3 Thirty_two 867
## 4 Twenty_Four 867
## 5 Zero 867
##
## #----Contrast--------------------------------------------------------------------------#
## Eight Sixteen Thirty_two Twenty_Four Zero
## Sixteen - Eight -1 1 0 0 0
## Thirty_two - Eight -1 0 1 0 0
## Twenty_Four - Eight -1 0 0 1 0
## Zero - Eight -1 0 0 0 1
## Thirty_two - Sixteen 0 -1 1 0 0
## Twenty_Four - Sixteen 0 -1 0 1 0
## Zero - Sixteen 0 -1 0 0 1
## Twenty_Four - Thirty_two 0 0 -1 1 0
## Zero - Thirty_two 0 0 -1 0 1
## Zero - Twenty_Four 0 0 0 -1 1
##
## #----Analysis--------------------------------------------------------------------------#
## Comparison Estimator Lower Upper Statistic p.Value
## 1 p( Eight , Sixteen ) 0.477 0.446 0.508 -1.9898429 2.707358e-01
## 2 p( Eight , Thirty_two ) 0.428 0.399 0.458 -6.5594139 1.544289e-10
## 3 p( Eight , Twenty_Four ) 0.482 0.450 0.513 -1.5933997 5.023040e-01
## 4 p( Eight , Zero ) 0.523 0.490 0.555 1.8825062 3.269358e-01
## 5 p( Sixteen , Thirty_two ) 0.450 0.421 0.479 -4.6693508 1.135792e-05
## 6 p( Sixteen , Twenty_Four ) 0.504 0.473 0.535 0.3513046 9.970241e-01
## 7 p( Sixteen , Zero ) 0.545 0.513 0.577 3.8502308 7.347897e-04
## 8 p( Thirty_two , Twenty_Four ) 0.553 0.524 0.581 4.9222782 1.100492e-05
## 9 p( Thirty_two , Zero ) 0.593 0.563 0.622 8.2577133 6.661338e-16
## 10 p( Twenty_Four , Zero ) 0.540 0.508 0.572 3.3963012 5.500490e-03
##
## #----Overall---------------------------------------------------------------------------#
## Quantile p.Value
## 1 2.720118 6.661338e-16
##
## #--------------------------------------------------------------------------------------#group_by(flies, Group) %>%
summarise(
count = n(),
mean = mean(Abundance, na.rm = TRUE),
sd = sd(Abundance, na.rm = TRUE)
)## # A tibble: 5 x 4
## Group count mean sd
## <chr> <int> <dbl> <dbl>
## 1 Eight 867 1.77 14.2
## 2 Sixteen 867 0.999 3.62
## 3 Thirty_two 867 1.11 5.74
## 4 Twenty_Four 867 2.09 11.7
## 5 Zero 867 4.19 42.1g1 <- ggbarplot(flies,
x = "Group",
y = "Abundance",
add = c("mean_se", "jitter"),
color = "Group",
fill = "Group", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.5,
order = c("Zero", "Eight", "Sixteen", "Twenty_Four", "Thirty_two"),
ylab = "Diptera abundance",
xlab = "Height categories")
g1 + coord_flip()
Tachinidae abundance
k1 <-kruskal.test(Tachinidae$Abundance ~ as.factor(Tachinidae$Group))
k1##
## Kruskal-Wallis rank sum test
##
## data: Tachinidae$Abundance by as.factor(Tachinidae$Group)
## Kruskal-Wallis chi-squared = 62.272, df = 4, p-value = 9.657e-13k2 <- nparcomp(Abundance ~ as.factor(Group), data = Tachinidae, type = "Tukey")##
## #------Nonparametric Multiple Comparisons for relative contrast effects-----#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Type of Contrast : Tukey
## - Confidence level: 95 %
## - Method = Logit - Transformation
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation----------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #---------------------------------------------------------------------------#
## summary(k2)##
## #------------Nonparametric Multiple Comparisons for relative contrast effects----------#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Estimation Method: Global Pseudo ranks
## - Type of Contrast : Tukey
## - Confidence Level: 95 %
## - Method = Logit - Transformation
##
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation--------------------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #-------------------------------------------------------------------------------------#
##
## #----Data Info-------------------------------------------------------------------------#
## Sample Size
## 1 Eight 166
## 2 Sixteen 166
## 3 Thirty_two 166
## 4 Twenty_Four 166
## 5 Zero 166
##
## #----Contrast--------------------------------------------------------------------------#
## Eight Sixteen Thirty_two Twenty_Four Zero
## Sixteen - Eight -1 1 0 0 0
## Thirty_two - Eight -1 0 1 0 0
## Twenty_Four - Eight -1 0 0 1 0
## Zero - Eight -1 0 0 0 1
## Thirty_two - Sixteen 0 -1 1 0 0
## Twenty_Four - Sixteen 0 -1 0 1 0
## Zero - Sixteen 0 -1 0 0 1
## Twenty_Four - Thirty_two 0 0 -1 1 0
## Zero - Thirty_two 0 0 -1 0 1
## Zero - Twenty_Four 0 0 0 -1 1
##
## #----Analysis--------------------------------------------------------------------------#
## Comparison Estimator Lower Upper Statistic p.Value
## 1 p( Eight , Sixteen ) 0.472 0.396 0.549 -0.9953026 8.603156e-01
## 2 p( Eight , Thirty_two ) 0.470 0.395 0.546 -1.0758403 8.214569e-01
## 3 p( Eight , Twenty_Four ) 0.381 0.315 0.452 -4.5120172 2.415471e-05
## 4 p( Eight , Zero ) 0.331 0.272 0.396 -6.8271750 2.564171e-11
## 5 p( Sixteen , Thirty_two ) 0.496 0.421 0.571 -0.1435297 9.999786e-01
## 6 p( Sixteen , Twenty_Four ) 0.406 0.339 0.476 -3.6646062 2.833029e-03
## 7 p( Sixteen , Zero ) 0.354 0.294 0.418 -6.0877798 4.336760e-09
## 8 p( Thirty_two , Twenty_Four ) 0.414 0.349 0.483 -3.4089053 4.736690e-03
## 9 p( Thirty_two , Zero ) 0.366 0.307 0.428 -5.7331110 3.668978e-08
## 10 p( Twenty_Four , Zero ) 0.453 0.401 0.507 -2.3822446 1.175740e-01
##
## #----Overall---------------------------------------------------------------------------#
## Quantile p.Value
## 1 2.73107 2.564171e-11
##
## #--------------------------------------------------------------------------------------#group_by(Tachinidae, Group) %>%
summarise(
count = n(),
mean = mean(Abundance, na.rm = TRUE),
sd = sd(Abundance, na.rm = TRUE)
)## # A tibble: 5 x 4
## Group count mean sd
## <chr> <int> <dbl> <dbl>
## 1 Eight 166 1.33 2.97
## 2 Sixteen 166 0.952 2.90
## 3 Thirty_two 166 1.60 4.74
## 4 Twenty_Four 166 0.843 4.40
## 5 Zero 166 0.295 1.57g2 <- ggbarplot(Tachinidae,
x = "Group",
y = "Abundance",
add = c("mean_se", "jitter"),
color = "Group",
fill = "Group", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.5,
order = c("Zero", "Eight", "Sixteen", "Twenty_Four", "Thirty_two"),
ylab = "Tachinidae abundance",
xlab = "Height categories")
g2 + coord_flip()
Mycetophilidae abundance
k1 <-kruskal.test(Mycetophilidae$Abundance ~ as.factor(Mycetophilidae$Group))
k1##
## Kruskal-Wallis rank sum test
##
## data: Mycetophilidae$Abundance by as.factor(Mycetophilidae$Group)
## Kruskal-Wallis chi-squared = 147.37, df = 4, p-value < 2.2e-16k2 <- nparcomp(Abundance ~ as.factor(Group), data = Mycetophilidae, type = "Tukey")##
## #------Nonparametric Multiple Comparisons for relative contrast effects-----#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Type of Contrast : Tukey
## - Confidence level: 95 %
## - Method = Logit - Transformation
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation----------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #---------------------------------------------------------------------------#
## summary(k2)##
## #------------Nonparametric Multiple Comparisons for relative contrast effects----------#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Estimation Method: Global Pseudo ranks
## - Type of Contrast : Tukey
## - Confidence Level: 95 %
## - Method = Logit - Transformation
##
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation--------------------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #-------------------------------------------------------------------------------------#
##
## #----Data Info-------------------------------------------------------------------------#
## Sample Size
## 1 Eight 103
## 2 Sixteen 103
## 3 Thirty_two 103
## 4 Twenty_Four 103
## 5 Zero 103
##
## #----Contrast--------------------------------------------------------------------------#
## Eight Sixteen Thirty_two Twenty_Four Zero
## Sixteen - Eight -1 1 0 0 0
## Thirty_two - Eight -1 0 1 0 0
## Twenty_Four - Eight -1 0 0 1 0
## Zero - Eight -1 0 0 0 1
## Thirty_two - Sixteen 0 -1 1 0 0
## Twenty_Four - Sixteen 0 -1 0 1 0
## Zero - Sixteen 0 -1 0 0 1
## Twenty_Four - Thirty_two 0 0 -1 1 0
## Zero - Thirty_two 0 0 -1 0 1
## Zero - Twenty_Four 0 0 0 -1 1
##
## #----Analysis--------------------------------------------------------------------------#
## Comparison Estimator Lower Upper Statistic p.Value
## 1 p( Eight , Sixteen ) 0.472 0.391 0.555 -0.9224584 8.990468e-01
## 2 p( Eight , Thirty_two ) 0.420 0.350 0.494 -2.9495095 2.604815e-02
## 3 p( Eight , Twenty_Four ) 0.463 0.384 0.544 -1.2550408 7.298225e-01
## 4 p( Eight , Zero ) 0.774 0.677 0.848 6.8962697 1.454703e-11
## 5 p( Sixteen , Thirty_two ) 0.445 0.378 0.515 -2.1551561 1.987147e-01
## 6 p( Sixteen , Twenty_Four ) 0.490 0.413 0.567 -0.3520344 9.985649e-01
## 7 p( Sixteen , Zero ) 0.807 0.716 0.874 7.7716850 2.731149e-14
## 8 p( Thirty_two , Twenty_Four ) 0.546 0.478 0.612 1.8474204 3.519764e-01
## 9 p( Thirty_two , Zero ) 0.844 0.757 0.904 8.3612651 3.330669e-16
## 10 p( Twenty_Four , Zero ) 0.815 0.725 0.881 7.9071744 6.550316e-15
##
## #----Overall---------------------------------------------------------------------------#
## Quantile p.Value
## 1 2.740842 3.330669e-16
##
## #--------------------------------------------------------------------------------------#group_by(Mycetophilidae, Group) %>%
summarise(
count = n(),
mean = mean(Abundance, na.rm = TRUE),
sd = sd(Abundance, na.rm = TRUE)
)## # A tibble: 5 x 4
## Group count mean sd
## <chr> <int> <dbl> <dbl>
## 1 Eight 103 0.757 2.33
## 2 Sixteen 103 0.447 1.33
## 3 Thirty_two 103 0.563 2.76
## 4 Twenty_Four 103 0.592 2.22
## 5 Zero 103 5.38 16.7g3 <- ggbarplot(Mycetophilidae,
x = "Group",
y = "Abundance",
color = "Group",
add = c("mean_se", "jitter"),
fill = "Group", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=1,
order = c("Zero", "Eight", "Sixteen", "Twenty_Four", "Thirty_two"),
ylab = "Mycetophilidae abundance",
xlab = "Height categories")
g3 + coord_flip()
***
Dolichopodidae abundance
k1 <-kruskal.test(Dolichopodidae$Abundance ~ as.factor(Dolichopodidae$Group))
k1##
## Kruskal-Wallis rank sum test
##
## data: Dolichopodidae$Abundance by as.factor(Dolichopodidae$Group)
## Kruskal-Wallis chi-squared = 14.277, df = 4, p-value = 0.006462k2 <- nparcomp(Abundance ~ as.factor(Group), data = Mycetophilidae, type = "Tukey")##
## #------Nonparametric Multiple Comparisons for relative contrast effects-----#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Type of Contrast : Tukey
## - Confidence level: 95 %
## - Method = Logit - Transformation
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation----------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #---------------------------------------------------------------------------#
## summary(k2)##
## #------------Nonparametric Multiple Comparisons for relative contrast effects----------#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Estimation Method: Global Pseudo ranks
## - Type of Contrast : Tukey
## - Confidence Level: 95 %
## - Method = Logit - Transformation
##
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation--------------------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #-------------------------------------------------------------------------------------#
##
## #----Data Info-------------------------------------------------------------------------#
## Sample Size
## 1 Eight 103
## 2 Sixteen 103
## 3 Thirty_two 103
## 4 Twenty_Four 103
## 5 Zero 103
##
## #----Contrast--------------------------------------------------------------------------#
## Eight Sixteen Thirty_two Twenty_Four Zero
## Sixteen - Eight -1 1 0 0 0
## Thirty_two - Eight -1 0 1 0 0
## Twenty_Four - Eight -1 0 0 1 0
## Zero - Eight -1 0 0 0 1
## Thirty_two - Sixteen 0 -1 1 0 0
## Twenty_Four - Sixteen 0 -1 0 1 0
## Zero - Sixteen 0 -1 0 0 1
## Twenty_Four - Thirty_two 0 0 -1 1 0
## Zero - Thirty_two 0 0 -1 0 1
## Zero - Twenty_Four 0 0 0 -1 1
##
## #----Analysis--------------------------------------------------------------------------#
## Comparison Estimator Lower Upper Statistic p.Value
## 1 p( Eight , Sixteen ) 0.472 0.391 0.555 -0.9224584 8.990468e-01
## 2 p( Eight , Thirty_two ) 0.420 0.350 0.494 -2.9495095 2.604815e-02
## 3 p( Eight , Twenty_Four ) 0.463 0.384 0.544 -1.2550408 7.298225e-01
## 4 p( Eight , Zero ) 0.774 0.677 0.848 6.8962697 1.454703e-11
## 5 p( Sixteen , Thirty_two ) 0.445 0.378 0.515 -2.1551561 1.987147e-01
## 6 p( Sixteen , Twenty_Four ) 0.490 0.413 0.567 -0.3520344 9.985649e-01
## 7 p( Sixteen , Zero ) 0.807 0.716 0.874 7.7716850 2.731149e-14
## 8 p( Thirty_two , Twenty_Four ) 0.546 0.478 0.612 1.8474204 3.519764e-01
## 9 p( Thirty_two , Zero ) 0.844 0.757 0.904 8.3612651 3.330669e-16
## 10 p( Twenty_Four , Zero ) 0.815 0.725 0.881 7.9071744 6.550316e-15
##
## #----Overall---------------------------------------------------------------------------#
## Quantile p.Value
## 1 2.740842 3.330669e-16
##
## #--------------------------------------------------------------------------------------#group_by(Dolichopodidae, Group) %>%
summarise(
count = n(),
mean = mean(Abundance, na.rm = TRUE),
sd = sd(Abundance, na.rm = TRUE)
)## # A tibble: 5 x 4
## Group count mean sd
## <chr> <int> <dbl> <dbl>
## 1 Eight 81 0.926 2.09
## 2 Sixteen 81 2.37 3.89
## 3 Thirty_two 81 2.05 6.13
## 4 Twenty_Four 81 3.94 10.8
## 5 Zero 81 2.26 8.86g4 <- ggbarplot(Dolichopodidae,
x = "Group",
y = "Abundance",
add = c("mean_se", "jitter"),
color = "Group",
fill = "Group", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.5,
order = c("Zero", "Eight", "Sixteen", "Twenty_Four", "Thirty_two"),
ylab = "Dolichopodidae abundance",
xlab = "Height categories")
g4 + coord_flip()
Tipulidae s.l. abundance
k1 <-kruskal.test(Tipulidae$Abundance ~ as.factor(Tipulidae$Group))
k1##
## Kruskal-Wallis rank sum test
##
## data: Tipulidae$Abundance by as.factor(Tipulidae$Group)
## Kruskal-Wallis chi-squared = 35.707, df = 4, p-value = 3.325e-07k2 <- nparcomp(Abundance ~ as.factor(Group), data = Mycetophilidae, type = "Tukey")##
## #------Nonparametric Multiple Comparisons for relative contrast effects-----#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Type of Contrast : Tukey
## - Confidence level: 95 %
## - Method = Logit - Transformation
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation----------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #---------------------------------------------------------------------------#
## summary(k2)##
## #------------Nonparametric Multiple Comparisons for relative contrast effects----------#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Estimation Method: Global Pseudo ranks
## - Type of Contrast : Tukey
## - Confidence Level: 95 %
## - Method = Logit - Transformation
##
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation--------------------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #-------------------------------------------------------------------------------------#
##
## #----Data Info-------------------------------------------------------------------------#
## Sample Size
## 1 Eight 103
## 2 Sixteen 103
## 3 Thirty_two 103
## 4 Twenty_Four 103
## 5 Zero 103
##
## #----Contrast--------------------------------------------------------------------------#
## Eight Sixteen Thirty_two Twenty_Four Zero
## Sixteen - Eight -1 1 0 0 0
## Thirty_two - Eight -1 0 1 0 0
## Twenty_Four - Eight -1 0 0 1 0
## Zero - Eight -1 0 0 0 1
## Thirty_two - Sixteen 0 -1 1 0 0
## Twenty_Four - Sixteen 0 -1 0 1 0
## Zero - Sixteen 0 -1 0 0 1
## Twenty_Four - Thirty_two 0 0 -1 1 0
## Zero - Thirty_two 0 0 -1 0 1
## Zero - Twenty_Four 0 0 0 -1 1
##
## #----Analysis--------------------------------------------------------------------------#
## Comparison Estimator Lower Upper Statistic p.Value
## 1 p( Eight , Sixteen ) 0.472 0.391 0.555 -0.9224584 8.990468e-01
## 2 p( Eight , Thirty_two ) 0.420 0.350 0.494 -2.9495095 2.604815e-02
## 3 p( Eight , Twenty_Four ) 0.463 0.384 0.544 -1.2550408 7.298225e-01
## 4 p( Eight , Zero ) 0.774 0.677 0.848 6.8962697 1.454703e-11
## 5 p( Sixteen , Thirty_two ) 0.445 0.378 0.515 -2.1551561 1.987147e-01
## 6 p( Sixteen , Twenty_Four ) 0.490 0.413 0.567 -0.3520344 9.985649e-01
## 7 p( Sixteen , Zero ) 0.807 0.716 0.874 7.7716850 2.731149e-14
## 8 p( Thirty_two , Twenty_Four ) 0.546 0.478 0.612 1.8474204 3.519764e-01
## 9 p( Thirty_two , Zero ) 0.844 0.757 0.904 8.3612651 3.330669e-16
## 10 p( Twenty_Four , Zero ) 0.815 0.725 0.881 7.9071744 6.550316e-15
##
## #----Overall---------------------------------------------------------------------------#
## Quantile p.Value
## 1 2.740842 3.330669e-16
##
## #--------------------------------------------------------------------------------------#group_by(Tipulidae, Group) %>%
summarise(
count = n(),
mean = mean(Abundance, na.rm = TRUE),
sd = sd(Abundance, na.rm = TRUE)
)## # A tibble: 5 x 4
## Group count mean sd
## <chr> <int> <dbl> <dbl>
## 1 Eight 78 1 2.99
## 2 Sixteen 78 0.846 4.39
## 3 Thirty_two 78 1.45 10.4
## 4 Twenty_Four 78 2.14 9.35
## 5 Zero 78 4.12 9.59g5 <- ggbarplot(Tipulidae,
x = "Group",
y = "Abundance",
add = c("mean_se", "jitter"),
color = "Group",
fill = "Group", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.5,
order = c("Zero", "Eight", "Sixteen", "Twenty_Four", "Thirty_two"),
ylab = "Tipulidae abundance",
xlab = "Height categories")
g5 + coord_flip()
Drosophilidae abundance
k1 <-kruskal.test(Drosophilidae$Abundance ~ as.factor(Drosophilidae$Group))
k1##
## Kruskal-Wallis rank sum test
##
## data: Drosophilidae$Abundance by as.factor(Drosophilidae$Group)
## Kruskal-Wallis chi-squared = 10.748, df = 4, p-value = 0.02955k2 <- nparcomp(Abundance ~ as.factor(Group), data = Mycetophilidae, type = "Tukey")##
## #------Nonparametric Multiple Comparisons for relative contrast effects-----#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Type of Contrast : Tukey
## - Confidence level: 95 %
## - Method = Logit - Transformation
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation----------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #---------------------------------------------------------------------------#
## summary(k2)##
## #------------Nonparametric Multiple Comparisons for relative contrast effects----------#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Estimation Method: Global Pseudo ranks
## - Type of Contrast : Tukey
## - Confidence Level: 95 %
## - Method = Logit - Transformation
##
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation--------------------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #-------------------------------------------------------------------------------------#
##
## #----Data Info-------------------------------------------------------------------------#
## Sample Size
## 1 Eight 103
## 2 Sixteen 103
## 3 Thirty_two 103
## 4 Twenty_Four 103
## 5 Zero 103
##
## #----Contrast--------------------------------------------------------------------------#
## Eight Sixteen Thirty_two Twenty_Four Zero
## Sixteen - Eight -1 1 0 0 0
## Thirty_two - Eight -1 0 1 0 0
## Twenty_Four - Eight -1 0 0 1 0
## Zero - Eight -1 0 0 0 1
## Thirty_two - Sixteen 0 -1 1 0 0
## Twenty_Four - Sixteen 0 -1 0 1 0
## Zero - Sixteen 0 -1 0 0 1
## Twenty_Four - Thirty_two 0 0 -1 1 0
## Zero - Thirty_two 0 0 -1 0 1
## Zero - Twenty_Four 0 0 0 -1 1
##
## #----Analysis--------------------------------------------------------------------------#
## Comparison Estimator Lower Upper Statistic p.Value
## 1 p( Eight , Sixteen ) 0.472 0.391 0.555 -0.9224584 8.990468e-01
## 2 p( Eight , Thirty_two ) 0.420 0.350 0.494 -2.9495095 2.604815e-02
## 3 p( Eight , Twenty_Four ) 0.463 0.384 0.544 -1.2550408 7.298225e-01
## 4 p( Eight , Zero ) 0.774 0.677 0.848 6.8962697 1.454703e-11
## 5 p( Sixteen , Thirty_two ) 0.445 0.378 0.515 -2.1551561 1.987147e-01
## 6 p( Sixteen , Twenty_Four ) 0.490 0.413 0.567 -0.3520344 9.985649e-01
## 7 p( Sixteen , Zero ) 0.807 0.716 0.874 7.7716850 2.731149e-14
## 8 p( Thirty_two , Twenty_Four ) 0.546 0.478 0.612 1.8474204 3.519764e-01
## 9 p( Thirty_two , Zero ) 0.844 0.757 0.904 8.3612651 3.330669e-16
## 10 p( Twenty_Four , Zero ) 0.815 0.725 0.881 7.9071744 6.550316e-15
##
## #----Overall---------------------------------------------------------------------------#
## Quantile p.Value
## 1 2.740842 3.330669e-16
##
## #--------------------------------------------------------------------------------------#group_by(Drosophilidae, Group) %>%
summarise(
count = n(),
mean = mean(Abundance, na.rm = TRUE),
sd = sd(Abundance, na.rm = TRUE)
)## # A tibble: 5 x 4
## Group count mean sd
## <chr> <int> <dbl> <dbl>
## 1 Eight 51 0.412 0.669
## 2 Sixteen 51 0.353 0.658
## 3 Thirty_two 51 0.235 1.01
## 4 Twenty_Four 51 0.667 1.31
## 5 Zero 51 0.627 1.11g6 <- ggbarplot(Drosophilidae,
x = "Group",
y = "Abundance",
add = c("mean_se", "jitter"),
color = "Group",
fill = "Group", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.5,
order = c("Zero", "Eight", "Sixteen", "Twenty_Four", "Thirty_two"),
ylab = "Drosophilidae abundance",
xlab = "Height categories")
g6 + coord_flip()
Lauxaniidae abundance
k1 <-kruskal.test(Lauxaniidae$Abundance ~ as.factor(Lauxaniidae$Group))
k1##
## Kruskal-Wallis rank sum test
##
## data: Lauxaniidae$Abundance by as.factor(Lauxaniidae$Group)
## Kruskal-Wallis chi-squared = 19.153, df = 4, p-value = 0.0007332k2 <- nparcomp(Abundance ~ as.factor(Group), data = Mycetophilidae, type = "Tukey")##
## #------Nonparametric Multiple Comparisons for relative contrast effects-----#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Type of Contrast : Tukey
## - Confidence level: 95 %
## - Method = Logit - Transformation
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation----------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #---------------------------------------------------------------------------#
## summary(k2)##
## #------------Nonparametric Multiple Comparisons for relative contrast effects----------#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Estimation Method: Global Pseudo ranks
## - Type of Contrast : Tukey
## - Confidence Level: 95 %
## - Method = Logit - Transformation
##
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation--------------------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #-------------------------------------------------------------------------------------#
##
## #----Data Info-------------------------------------------------------------------------#
## Sample Size
## 1 Eight 103
## 2 Sixteen 103
## 3 Thirty_two 103
## 4 Twenty_Four 103
## 5 Zero 103
##
## #----Contrast--------------------------------------------------------------------------#
## Eight Sixteen Thirty_two Twenty_Four Zero
## Sixteen - Eight -1 1 0 0 0
## Thirty_two - Eight -1 0 1 0 0
## Twenty_Four - Eight -1 0 0 1 0
## Zero - Eight -1 0 0 0 1
## Thirty_two - Sixteen 0 -1 1 0 0
## Twenty_Four - Sixteen 0 -1 0 1 0
## Zero - Sixteen 0 -1 0 0 1
## Twenty_Four - Thirty_two 0 0 -1 1 0
## Zero - Thirty_two 0 0 -1 0 1
## Zero - Twenty_Four 0 0 0 -1 1
##
## #----Analysis--------------------------------------------------------------------------#
## Comparison Estimator Lower Upper Statistic p.Value
## 1 p( Eight , Sixteen ) 0.472 0.391 0.555 -0.9224584 8.990468e-01
## 2 p( Eight , Thirty_two ) 0.420 0.350 0.494 -2.9495095 2.604815e-02
## 3 p( Eight , Twenty_Four ) 0.463 0.384 0.544 -1.2550408 7.298225e-01
## 4 p( Eight , Zero ) 0.774 0.677 0.848 6.8962697 1.454703e-11
## 5 p( Sixteen , Thirty_two ) 0.445 0.378 0.515 -2.1551561 1.987147e-01
## 6 p( Sixteen , Twenty_Four ) 0.490 0.413 0.567 -0.3520344 9.985649e-01
## 7 p( Sixteen , Zero ) 0.807 0.716 0.874 7.7716850 2.731149e-14
## 8 p( Thirty_two , Twenty_Four ) 0.546 0.478 0.612 1.8474204 3.519764e-01
## 9 p( Thirty_two , Zero ) 0.844 0.757 0.904 8.3612651 3.330669e-16
## 10 p( Twenty_Four , Zero ) 0.815 0.725 0.881 7.9071744 6.550316e-15
##
## #----Overall---------------------------------------------------------------------------#
## Quantile p.Value
## 1 2.740842 3.330669e-16
##
## #--------------------------------------------------------------------------------------#group_by(Lauxaniidae, Group) %>%
summarise(
count = n(),
mean = mean(Abundance, na.rm = TRUE),
sd = sd(Abundance, na.rm = TRUE)
)## # A tibble: 5 x 4
## Group count mean sd
## <chr> <int> <dbl> <dbl>
## 1 Eight 46 1.17 2.78
## 2 Sixteen 46 1.59 3.47
## 3 Thirty_two 46 0.348 1.45
## 4 Twenty_Four 46 2.57 8.09
## 5 Zero 46 0.457 0.862g7 <- ggbarplot(Lauxaniidae,
x = "Group",
y = "Abundance",
add = c("mean_se", "jitter"),
color = "Group",
fill = "Group", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.5,
order = c("Zero", "Eight", "Sixteen", "Twenty_Four", "Thirty_two"),
ylab = "Lauxaniidae abundance",
xlab = "Height categories")
g7 + coord_flip()
Phoridae abundance
k1 <-kruskal.test(Phoridae$Abundance ~ as.factor(Phoridae$Group))
k1##
## Kruskal-Wallis rank sum test
##
## data: Phoridae$Abundance by as.factor(Phoridae$Group)
## Kruskal-Wallis chi-squared = 6.1221, df = 4, p-value = 0.1902k2 <- nparcomp(Abundance ~ as.factor(Group), data = Mycetophilidae, type = "Tukey")##
## #------Nonparametric Multiple Comparisons for relative contrast effects-----#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Type of Contrast : Tukey
## - Confidence level: 95 %
## - Method = Logit - Transformation
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation----------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #---------------------------------------------------------------------------#
## summary(k2)##
## #------------Nonparametric Multiple Comparisons for relative contrast effects----------#
##
## - Alternative Hypothesis: True relative contrast effect p is not equal to 1/2
## - Estimation Method: Global Pseudo ranks
## - Type of Contrast : Tukey
## - Confidence Level: 95 %
## - Method = Logit - Transformation
##
## - Estimation Method: Pairwise rankings
##
## #---------------------------Interpretation--------------------------------------------#
## p(a,b) > 1/2 : b tends to be larger than a
## #-------------------------------------------------------------------------------------#
##
## #----Data Info-------------------------------------------------------------------------#
## Sample Size
## 1 Eight 103
## 2 Sixteen 103
## 3 Thirty_two 103
## 4 Twenty_Four 103
## 5 Zero 103
##
## #----Contrast--------------------------------------------------------------------------#
## Eight Sixteen Thirty_two Twenty_Four Zero
## Sixteen - Eight -1 1 0 0 0
## Thirty_two - Eight -1 0 1 0 0
## Twenty_Four - Eight -1 0 0 1 0
## Zero - Eight -1 0 0 0 1
## Thirty_two - Sixteen 0 -1 1 0 0
## Twenty_Four - Sixteen 0 -1 0 1 0
## Zero - Sixteen 0 -1 0 0 1
## Twenty_Four - Thirty_two 0 0 -1 1 0
## Zero - Thirty_two 0 0 -1 0 1
## Zero - Twenty_Four 0 0 0 -1 1
##
## #----Analysis--------------------------------------------------------------------------#
## Comparison Estimator Lower Upper Statistic p.Value
## 1 p( Eight , Sixteen ) 0.472 0.391 0.555 -0.9224584 8.990468e-01
## 2 p( Eight , Thirty_two ) 0.420 0.350 0.494 -2.9495095 2.604815e-02
## 3 p( Eight , Twenty_Four ) 0.463 0.384 0.544 -1.2550408 7.298225e-01
## 4 p( Eight , Zero ) 0.774 0.677 0.848 6.8962697 1.454703e-11
## 5 p( Sixteen , Thirty_two ) 0.445 0.378 0.515 -2.1551561 1.987147e-01
## 6 p( Sixteen , Twenty_Four ) 0.490 0.413 0.567 -0.3520344 9.985649e-01
## 7 p( Sixteen , Zero ) 0.807 0.716 0.874 7.7716850 2.731149e-14
## 8 p( Thirty_two , Twenty_Four ) 0.546 0.478 0.612 1.8474204 3.519764e-01
## 9 p( Thirty_two , Zero ) 0.844 0.757 0.904 8.3612651 3.330669e-16
## 10 p( Twenty_Four , Zero ) 0.815 0.725 0.881 7.9071744 6.550316e-15
##
## #----Overall---------------------------------------------------------------------------#
## Quantile p.Value
## 1 2.740842 3.330669e-16
##
## #--------------------------------------------------------------------------------------#group_by(Phoridae, Group) %>%
summarise(
count = n(),
mean = mean(Abundance, na.rm = TRUE),
sd = sd(Abundance, na.rm = TRUE)
)## # A tibble: 5 x 4
## Group count mean sd
## <chr> <int> <dbl> <dbl>
## 1 Eight 46 15.5 58.8
## 2 Sixteen 46 3.85 10.5
## 3 Thirty_two 46 5.20 15.3
## 4 Twenty_Four 46 15.7 42.6
## 5 Zero 46 43.2 176.g8 <- ggbarplot(Phoridae,
x = "Group",
y = "Abundance",
add = c("mean_se", "jitter"),
color = "Group",
fill = "Group", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.5,
order = c("Zero", "Eight", "Sixteen", "Twenty_Four", "Thirty_two"),
ylab = "Phoridae abundance",
xlab = "Height categories")
g8 + coord_flip()
Diptera richness
height0 <- rpois(1000, 347)
height8 <- rpois(1000, 303)
height16 <- rpois(1000, 278)
height24 <- rpois(1000, 288)
height32 <- rpois(1000, 171)
height0[height0 < 0] <- 0
height8[height8 < 0] <- 0
height16[height16 < 0] <- 0
height24[height24 < 0] <- 0
height32[height32 < 0] <- 0
HT_IZ <- c(height0,height8,height16,height24,height32)
Heights<-rep(seq(1,1000),5)
Groups <- c(rep("height0",1000), rep("height8",1000), rep("height16",1000), rep("height24",1000), rep("height32",1000))
AllData <- data.frame(HT_IZ,Heights,Groups)
g9 <- ggboxplot(AllData,
x = "Groups",
y = "HT_IZ",
color = "Groups",
fill = "Groups", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.8,
order = c("height0", "height8", "height16", "height24", "height32"),
ylab = "Diptera richness",
xlab = "Height categories")
g9 + coord_flip()
Tachinidae richness
height0 <- rpois(1000, 17)
height8 <- rpois(1000, 73)
height16 <- rpois(1000, 66)
height24 <- rpois(1000, 32)
height32 <- rpois(1000, 61)
HT_IZ <- c(height0,height8,height16,height24,height32)
Heights<-rep(seq(1,1000),5)
Groups <- c(rep("height0",1000), rep("height8",1000), rep("height16",1000), rep("height24",1000), rep("height32",1000))
AllData <- data.frame(HT_IZ,Heights,Groups)
g10 <- ggboxplot(AllData,
x = "Groups",
y = "HT_IZ",
color = "Groups",
fill = "Groups", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.8,
order = c("height0", "height8", "height16", "height24", "height32"),
ylab = "Tachinidae richness",
xlab = "Height categories")
g10 + coord_flip()
Mycetophilidae richness
height0 <- rpois(1000, 79)
height8 <- rpois(1000, 25)
height16 <- rpois(1000, 20)
height24 <- rpois(1000, 20)
height32 <- rpois(1000, 9)
HT_IZ <- c(height0,height8,height16,height24,height32)
Heights<-rep(seq(1,1000),5)
Groups <- c(rep("height0",1000), rep("height8",1000), rep("height16",1000), rep("height24",1000), rep("height32",1000))
AllData <- data.frame(HT_IZ,Heights,Groups)
g11 <- ggboxplot(AllData,
x = "Groups",
y = "HT_IZ",
color = "Groups",
fill = "Groups", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.8,
order = c("height0", "height8", "height16", "height24", "height32"),
ylab = "Mycetophilidae richness",
xlab = "Height categories")
g11 + coord_flip()
Dolichopodidae richness
height0 <- rpois(1000, 30)
height8 <- rpois(1000, 22)
height16 <- rpois(1000, 42)
height24 <- rpois(1000, 34)
height32 <- rpois(1000, 28)
HT_IZ <- c(height0,height8,height16,height24,height32)
Heights<-rep(seq(1,1000),5)
Groups <- c(rep("height0",1000), rep("height8",1000), rep("height16",1000), rep("height24",1000), rep("height32",1000))
AllData <- data.frame(HT_IZ,Heights,Groups)
g12 <- ggboxplot(AllData,
x = "Groups",
y = "HT_IZ",
color = "Groups",
fill = "Groups", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.8,
order = c("height0", "height8", "height16", "height24", "height32"),
ylab = "Dolichopodidae richness",
xlab = "Height categories")
g12 + coord_flip()
Tipulidae s.l. richness
height0 <- rpois(1000, 43)
height8 <- rpois(1000, 22)
height16 <- rpois(1000, 15)
height24 <- rpois(1000, 28)
height32 <- rpois(1000, 14)
HT_IZ <- c(height0,height8,height16,height24,height32)
Heights<-rep(seq(1,1000),5)
Groups <- c(rep("height0",1000), rep("height8",1000), rep("height16",1000), rep("height24",1000), rep("height32",1000))
AllData <- data.frame(HT_IZ,Heights,Groups)
g13 <- ggboxplot(AllData,
x = "Groups",
y = "HT_IZ",
color = "Groups",
fill = "Groups", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.8,
order = c("height0", "height8", "height16", "height24", "height32"),
ylab = "Tipulidae s.l. richness",
xlab = "Height categories")
g13 + coord_flip()
Drosophilidae richness
height0 <- rpois(1000, 18)
height8 <- rpois(1000, 16)
height16 <- rpois(1000, 14)
height24 <- rpois(1000, 19)
height32 <- rpois(1000, 6)
HT_IZ <- c(height0,height8,height16,height24,height32)
Heights<-rep(seq(1,1000),5)
Groups <- c(rep("height0",1000), rep("height8",1000), rep("height16",1000), rep("height24",1000), rep("height32",1000))
AllData <- data.frame(HT_IZ,Heights,Groups)
g14 <- ggboxplot(AllData,
x = "Groups",
y = "HT_IZ",
color = "Groups",
fill = "Groups", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.8,
order = c("height0", "height8", "height16", "height24", "height32"),
ylab = "Drosophilidae richness",
xlab = "Height categories")
g14 + coord_flip()
Lauxaniidae richness
height0 <- rpois(1000, 14)
height8 <- rpois(1000, 19)
height16 <- rpois(1000, 22)
height24 <- rpois(1000, 21)
height32 <- rpois(1000, 5)
HT_IZ <- c(height0,height8,height16,height24,height32)
Heights<-rep(seq(1,1000),5)
Groups <- c(rep("height0",1000), rep("height8",1000), rep("height16",1000), rep("height24",1000), rep("height32",1000))
AllData <- data.frame(HT_IZ,Heights,Groups)
g15 <- ggboxplot(AllData,
x = "Groups",
y = "HT_IZ",
color = "Groups",
fill = "Groups", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.8,
order = c("height0", "height8", "height16", "height24", "height32"),
ylab = "Lauxaniidae richness",
xlab = "Height categories")
g15 + coord_flip()
Phoridae genus richness
height0 <- rpois(1000, 19)
height8 <- rpois(1000, 27)
height16 <- rpois(1000, 19)
height24 <- rpois(1000, 22)
height32 <- rpois(1000, 16)
HT_IZ <- c(height0,height8,height16,height24,height32)
Heights<-rep(seq(1,1000),5)
Groups <- c(rep("height0",1000), rep("height8",1000), rep("height16",1000), rep("height24",1000), rep("height32",1000))
AllData <- data.frame(HT_IZ,Heights,Groups)
g16 <- ggboxplot(AllData,
x = "Groups",
y = "HT_IZ",
color = "Groups",
fill = "Groups", palette = c("#DCE319FF", "#73D055FF", "#238A8DFF", "#39568CFF", "#440154FF"), alpha=0.8,
order = c("height0", "height8", "height16", "height24", "height32"),
ylab = "Phoridae genus richness",
xlab = "Height categories")
g16 + coord_flip()