importance value
Roeland Kindt
31/01/2022
1 Libraries needed
library(BiodiversityR)
library(ggplot2)
library(ggsci)
library(ggrepel)2 Introduction
Someone recently asked me for some additional examples of analyzing Importance Value Index (IVI) values besides the examples provided with the documentation of the BiodiversityR::importancevalue function.
Here I show how the IVI can be analyzed through ordination analysis and with rank-abundance curves.
3 IFRI example data
The same data from the International Forestry Resources and Institutions network will be used as for the documentation of the function.
data(ifri)
summary(ifri)## forest plotID species count basal
## LOT:153 LOTP004: 11 Acersacc: 64 Min. : 1.000 Min. : 78.5
## MCF:166 YSFP021: 9 Lirituli: 38 1st Qu.: 1.000 1st Qu.: 349.6
## YSF:167 YSFP034: 9 Queralba: 34 Median : 2.000 Median : 889.7
## LOTP010: 8 Ulmurubr: 27 Mean : 2.963 Mean : 1630.1
## LOTP017: 8 Fraxamer: 26 3rd Qu.: 3.000 3rd Qu.: 2114.0
## LOTP026: 8 Caryovat: 22 Max. :26.000 Max. :12767.3
## (Other):433 (Other) :275IV <- importancevalue(ifri, site='plotID', species='species', count='count',
basal='basal', factor='forest', level='YSF')
attributes(IV)## $dim
## [1] 32 7
##
## $dimnames
## $dimnames[[1]]
## [1] "Queralba" "Acersacc" "Quervelu" "Lirituli" "Fagugran" "Acerrubr"
## [7] "Querrubr" "Fraxamer" "Caryovat" "Querprin" "Juglnigr" "Sassalbi"
## [13] "Quermueh" "Pinustro" "Ulmuamer" "Caryglab" "Cornflor" "Ulmurubr"
## [19] "Fraxpenn" "Nysssylv" "Popugran" "Carylaci" "Carysp." "Tiliamer"
## [25] "Carycord" "Castpumi" "Popuhete" "Aescglab" "Quercocc" "Quermich"
## [31] "Prunsero" "Platocci"
##
## $dimnames[[2]]
## [1] "frequency" "density" "dominance"
## [4] "frequency.percent" "density.percent" "dominance.percent"
## [7] "importance.value"These results can transformed to a data.frame directly
IV <- data.frame(IV)
summary(IV)## frequency density dominance frequency.percent
## Min. :0.02778 Min. : 1.00 Min. : 116.9 Min. : 0.5988
## 1st Qu.:0.02778 1st Qu.: 1.00 1st Qu.: 605.6 1st Qu.: 0.5988
## Median :0.08333 Median : 4.00 Median : 1975.1 Median : 1.7964
## Mean :0.14497 Mean :11.38 Mean : 9086.3 Mean : 3.1250
## 3rd Qu.:0.19444 3rd Qu.:11.00 3rd Qu.: 7679.3 3rd Qu.: 4.1916
## Max. :0.75000 Max. :78.00 Max. :76368.6 Max. :16.1677
## density.percent dominance.percent importance.value
## Min. : 0.2747 Min. : 0.0402 Min. : 0.9137
## 1st Qu.: 0.2747 1st Qu.: 0.2083 1st Qu.: 1.3217
## Median : 1.0989 Median : 0.6793 Median : 3.7465
## Mean : 3.1250 Mean : 3.1250 Mean : 9.3750
## 3rd Qu.: 3.0220 3rd Qu.: 2.6411 3rd Qu.:10.1033
## Max. :21.4286 Max. :26.2651 Max. :58.8949head(IV)## frequency density dominance frequency.percent density.percent
## Queralba 0.5833333 73 76368.6 12.574850 20.054945
## Acersacc 0.7500000 78 26615.6 16.167665 21.428571
## Quervelu 0.3888889 43 61531.9 8.383234 11.813187
## Lirituli 0.2777778 14 24138.6 5.988024 3.846154
## Fagugran 0.3055556 20 14733.5 6.586826 5.494505
## Acerrubr 0.2777778 19 9772.3 5.988024 5.219780
## dominance.percent importance.value
## Queralba 26.265077 58.89487
## Acersacc 9.153772 46.75001
## Quervelu 21.162364 41.35878
## Lirituli 8.301870 18.13605
## Fagugran 5.067220 17.14855
## Acerrubr 3.360939 14.56874In the second example, importance values are calculated separately for the different types of forests
IV <- importancevalue.comp(ifri, site='plotID', species='species', count='count',
basal='basal', factor='forest')
summary(IV)## Length Class Mode
## values 3 -none- character
## LOT 252 -none- numeric
## MCF 203 -none- numeric
## YSF 224 -none- numericIV.LOT <- data.frame(Forest=rep("LOT", nrow(IV$LOT)),
species=rownames(IV$LOT),
importance.value=IV$LOT[, "importance.value"])
IV.MCF <- data.frame(Forest=rep("MCF", nrow(IV$MCF)),
species=rownames(IV$MCF),
importance.value=IV$MCF[, "importance.value"])
IV.YSF <- data.frame(Forest=rep("YSF", nrow(IV$YSF)),
species=rownames(IV$YSF),
importance.value=IV$YSF[, "importance.value"])
row.names(IV.LOT) <- row.names(IV.MCF) <- row.names(IV.YSF) <- NULL
IV2 <- rbind(IV.LOT, IV.MCF, IV.YSF)
summary(IV2)## Forest species importance.value
## Length:97 Length:97 Min. : 0.8927
## Class :character Class :character 1st Qu.: 1.5320
## Mode :character Mode :character Median : 3.8184
## Mean : 9.2783
## 3rd Qu.:12.0124
## Max. :58.8949head(IV2)## Forest species importance.value
## 1 LOT Acernegu 45.08809
## 2 LOT Acersacc 28.13683
## 3 LOT Ulmurubr 23.29686
## 4 LOT Querrubr 16.49413
## 5 LOT Fraxamer 16.13852
## 6 LOT Prunsero 16.124844 Creating community and environmental data sets
The importance values can be used as abundance values in community ecological analyses
IV.comm <- xtabs(importance.value ~ Forest + species, data=IV2)
IV.comm <- as.data.frame.matrix(IV.comm)
IV.comm## Acernegu Acerrubr Acersacc Acersp. Aescglab Carycord Caryglab Carylaci
## LOT 45.08809 15.31176 28.13683 0.000000 16.049762 3.868702 1.060137 0.000000
## MCF 0.00000 0.00000 52.96622 2.643012 1.282767 0.000000 0.000000 0.000000
## YSF 0.00000 14.56874 46.75001 0.000000 1.086830 1.284000 3.818369 1.532039
## Caryovat Carysp. Carytome Castpumi Celtocci Cerccana Cornflor Diosvirg
## LOT 5.713080 0.000000 3.244267 0.000000 2.788618 2.692711 2.982106 0.000000
## MCF 19.504672 6.382445 0.000000 0.000000 1.721652 5.055798 9.090300 1.812186
## YSF 9.958558 1.530696 0.000000 1.146673 0.000000 0.000000 3.674706 0.000000
## Fagugran Fraxamer Fraxpenn Fraxsp. Gledtria Juglnigr Junivirg Liqustyr
## LOT 0.000000 16.13852 2.421933 0.000000 1.058403 0.9984521 0.00000 0.9620102
## MCF 3.383735 12.76360 0.000000 1.329682 1.743359 7.2994729 37.22705 0.0000000
## YSF 17.148552 10.53762 2.247570 0.000000 0.000000 8.1094262 0.00000 0.0000000
## Lirituli Nysssylv Pinuresi Pinustro Pinutaed Platocci Popudelt Popugran
## LOT 11.77381 1.212119 1.576493 9.758987 1.163062 15.8052687 7.626195 0.000000
## MCF 52.61363 0.000000 0.000000 0.000000 0.000000 2.2041259 0.000000 0.000000
## YSF 18.13605 1.940307 0.000000 4.454116 0.000000 0.9137325 0.000000 1.603921
## Popuhete Prunsero Queralba Quercocc Quermich Quermueh Querprin
## LOT 0.000000 16.124836 1.852784 5.6097928 0.0000000 15.688927 0.0000000
## MCF 0.000000 1.042707 19.795049 0.9085888 0.0000000 12.012413 0.9026208
## YSF 1.111799 0.920611 58.894872 1.0164285 0.9772899 6.322651 9.0994579
## Querrubr Quersp. Quervelu Rhussp. Robipseu Rubuocci Sassalbi Tiliamer
## LOT 16.494126 0.000000 8.771996 0.000000 5.080334 0.8926819 3.570997 1.103378
## MCF 6.812041 1.686029 1.792256 1.022198 0.000000 0.0000000 19.852069 0.000000
## YSF 13.919268 0.000000 41.358784 0.000000 0.000000 0.0000000 7.541635 1.334284
## Ulmuamer Ulmurubr Unkn11
## LOT 2.035079 23.296861 2.04689
## MCF 6.908135 8.242187 0.00000
## YSF 4.072938 2.988067 0.000005 Principal components analysis after Hellinger transformation
The rest of the ordination pipeline was modified from a previous Rpub…
IV.Hellinger <- disttransform(IV.comm, method='hellinger')
Ordination.model1 <- rda(IV.Hellinger, data=IV.Hellinger, scaling="species")5.1 Plotting
plot1 <- ordiplot(Ordination.model1, scaling=1)
sites.long1 <- sites.long(plot1)
species.long1 <- species.long(plot1)
axis.long1 <- axis.long(Ordination.model1, choices=c(1, 2))Select a subset of species
species.long1$radius <- sqrt(species.long1$axis1^2 + species.long1$axis2^2)
species.threshold <- quantile(species.long1$radius, probs=0.80)
species.long2 <- species.long1[species.long1$radius >= species.threshold, ]
species.long2## axis1 axis2 labels radius
## Acernegu -0.43829682 -0.08917326 Acernegu 0.4472762
## Acerrubr -0.16530517 0.24824126 Acerrubr 0.2982441
## Fagugran 0.17440768 0.20545321 Fagugran 0.2694978
## Junivirg 0.25421426 -0.39885932 Junivirg 0.4729838
## Lirituli 0.17878529 -0.18479353 Lirituli 0.2571242
## Pinustro -0.15408506 0.12450679 Pinustro 0.1981014
## Prunsero -0.19691344 -0.04461526 Prunsero 0.2019045
## Queralba 0.27770545 0.29467244 Queralba 0.4049101
## Querprin 0.11080055 0.17514873 Querprin 0.2072531
## Quervelu 0.01428372 0.37896785 Quervelu 0.3792369Modify the theme
BioR.theme <- theme(
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid = element_blank(),
axis.line = element_line("gray25"),
text = element_text(size = 12),
axis.text = element_text(size = 10, colour = "gray25"),
axis.title = element_text(size = 14, colour = "gray25"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.key = element_blank())5.2 Final plot
plotgg1 <- ggplot() +
geom_vline(xintercept = c(0), color = "grey70", linetype = 2) +
geom_hline(yintercept = c(0), color = "grey70", linetype = 2) +
xlab(axis.long1[1, "label"]) +
ylab(axis.long1[2, "label"]) +
scale_x_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_point(data=sites.long1,
aes(x=axis1, y=axis2),
size=2, colour="blue") +
geom_text_repel(data=sites.long1,
aes(x=axis1, y=axis2, label=labels),
size=4, colour="blue") +
geom_segment(data=species.long2,
aes(x=0, y=0, xend=axis1*1.8, yend=axis2*1.8),
colour="red", alpha=0.7, size=0.7,
arrow=arrow(angle=7,
length=unit(1, "cm"),
type="open")) +
geom_text(data=species.long2,
aes(x=axis1*2.0, y=axis2*2.0, label=labels),
colour="red") +
BioR.theme +
ggsci::scale_colour_npg() +
coord_fixed(ratio=1)
plotgg1
6 Rank-abundance curves
To construct different rank-abundance curves for the different forest types, the BiodiversityR::rankabuncomp requires an environmental data set that includes the forest type as a categorical variable.
IV.env <- data.frame(forest = factor(rownames(IV.comm)))
rownames(IV.env) <- rownames(IV.comm)RA.data <- rankabuncomp(IV.comm, y=IV.env,
factor="forest", return.data=TRUE,
specnames=c(1:15),
legend=FALSE,
xlim=c(-2, 37))## Warning in qt(0.975, df = n - 1): NaNs produced
## Warning in qt(0.975, df = n - 1): NaNs produced
## Warning in qt(0.975, df = n - 1): NaNs produced
## Warning in qt(0.975, df = n - 1): NaNs produced
## Warning in qt(0.975, df = n - 1): NaNs produced
## Warning in qt(0.975, df = n - 1): NaNs produced
## Warning in qt(0.975, df = n - 1): NaNs produced
## Warning in qt(0.975, df = n - 1): NaNs produced
## Warning in qt(0.975, df = n - 1): NaNs produced
plotgg2 <- ggplot(data=RA.data, aes(x = rank, y = abundance)) +
scale_x_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_line(aes(colour=Grouping), size=1) +
geom_point(aes(colour=Grouping), size=5, alpha=0.7) +
geom_text_repel(data=subset(RA.data, labelit == TRUE),
aes(label=species),
angle=0, nudge_x=8, nudge_y=1, show.legend=FALSE) +
BioR.theme +
scale_color_brewer(palette = "Set1") +
facet_wrap(~ Grouping) +
labs(x = "rank", y = "abundance", colour = "Forest type")
plotgg2
7 Session Info
sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United Kingdom.1252
## [2] LC_CTYPE=English_United Kingdom.1252
## [3] LC_MONETARY=English_United Kingdom.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United Kingdom.1252
##
## attached base packages:
## [1] tcltk stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ggrepel_0.8.2 ggsci_2.9 ggplot2_3.3.3
## [4] BiodiversityR_2.14-1 vegan_2.5-6 lattice_0.20-41
## [7] permute_0.9-5
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-148 RColorBrewer_1.1-2 tools_4.0.2
## [4] backports_1.1.7 R6_2.4.1 rpart_4.1-15
## [7] Hmisc_4.4-0 nortest_1.0-4 DBI_1.1.0
## [10] mgcv_1.8-31 colorspace_1.4-1 nnet_7.3-14
## [13] withr_2.2.0 tidyselect_1.1.0 gridExtra_2.3
## [16] curl_4.3 compiler_4.0.2 htmlTable_2.0.0
## [19] sandwich_2.5-1 labeling_0.3 tcltk2_1.2-11
## [22] effects_4.1-4 scales_1.1.1 checkmate_2.0.0
## [25] RcmdrMisc_2.7-2 stringr_1.4.0 digest_0.6.25
## [28] foreign_0.8-80 relimp_1.0-5 minqa_1.2.4
## [31] rmarkdown_2.3 rio_0.5.16 base64enc_0.1-3
## [34] jpeg_0.1-8.1 pkgconfig_2.0.3 htmltools_0.5.1.1
## [37] Rcmdr_2.7-2 lme4_1.1-23 htmlwidgets_1.5.1
## [40] rlang_0.4.8 readxl_1.3.1 rstudioapi_0.11
## [43] farver_2.0.3 generics_0.1.0 zoo_1.8-8
## [46] acepack_1.4.1 dplyr_1.0.2 zip_2.0.4
## [49] car_3.0-8 magrittr_1.5 Formula_1.2-3
## [52] Matrix_1.2-18 Rcpp_1.0.7 munsell_0.5.0
## [55] abind_1.4-5 lifecycle_0.2.0 stringi_1.4.6
## [58] yaml_2.2.1 carData_3.0-4 MASS_7.3-51.6
## [61] grid_4.0.2 parallel_4.0.2 forcats_0.5.0
## [64] crayon_1.3.4 haven_2.3.1 splines_4.0.2
## [67] hms_0.5.3 knitr_1.28 pillar_1.4.4
## [70] boot_1.3-25 glue_1.4.1 evaluate_0.14
## [73] mitools_2.4 latticeExtra_0.6-29 data.table_1.12.8
## [76] png_0.1-7 vctrs_0.3.4 nloptr_1.2.2.1
## [79] cellranger_1.1.0 gtable_0.3.0 purrr_0.3.4
## [82] xfun_0.15 openxlsx_4.1.5 survey_4.0
## [85] e1071_1.7-3 class_7.3-17 survival_3.1-12
## [88] tibble_3.0.1 cluster_2.1.0 statmod_1.4.34
## [91] ellipsis_0.3.1