Memuat Paket

Memuat paket tambahan yang digunakan.

library("vegan")
library("indicspecies")

Memuat dan Manipulasi Data

# memuat data
dm <- read.csv("data ekologi.csv")
head(dm)
##    site  env1  env2 env3 env4 env5 sp.1 sp.2 sp.3 sp.4 sp.5 sp.6 sp.7 sp.8 sp.9
## 1 site1 27.71 34.17 7.78  5.4  6.7   19    2    0    0    0   12    0    0    0
## 2 site2 28.37 34.07 7.78  5.5  5.6    3    1    0    0    0   19    0    1    0
## 3 site3 29.81 34.32 7.80  4.9  7.1    6    0    1    0    0   11    1    0    0
## 4 site4 27.27 34.37 7.84  5.0  5.5    5    0    0    0    0    0    0    0    1
## 5 site5 28.42 34.01 7.79  5.2  6.9    1    4    0    1    0    2    2    0    0
## 6 site1 29.97 33.77 7.71  5.0  6.3   30    4    0    0    0   14    0    0    0
##   sp.10 sp.11 sp.12
## 1     0     0     8
## 2     2     0     0
## 3     0     9    14
## 4     0     0     0
## 5     0     0     0
## 6     8     0     5
# indeksing
dm.bio <- dm[,7:ncol(dm)] #data biologi
head(dm.bio)
##   sp.1 sp.2 sp.3 sp.4 sp.5 sp.6 sp.7 sp.8 sp.9 sp.10 sp.11 sp.12
## 1   19    2    0    0    0   12    0    0    0     0     0     8
## 2    3    1    0    0    0   19    0    1    0     2     0     0
## 3    6    0    1    0    0   11    1    0    0     0     9    14
## 4    5    0    0    0    0    0    0    0    1     0     0     0
## 5    1    4    0    1    0    2    2    0    0     0     0     0
## 6   30    4    0    0    0   14    0    0    0     8     0     5
dm.env <- dm[,2:6] #data lingkungan
head(dm.env)
##    env1  env2 env3 env4 env5
## 1 27.71 34.17 7.78  5.4  6.7
## 2 28.37 34.07 7.78  5.5  5.6
## 3 29.81 34.32 7.80  4.9  7.1
## 4 27.27 34.37 7.84  5.0  5.5
## 5 28.42 34.01 7.79  5.2  6.9
## 6 29.97 33.77 7.71  5.0  6.3
# transformasi data biologi
dm.bio.hel <- decostand(dm.bio, method = "hellinger") #Hellinger transformasi

Analisis Data

Analysis of Similarities (ANOSIM)

ano <- anosim(dm.bio.hel, dm$site, distance = "bray", permutations = 9999)
summary(ano)
## 
## Call:
## anosim(x = dm.bio.hel, grouping = dm$site, permutations = 9999,      distance = "bray") 
## Dissimilarity: bray 
## 
## ANOSIM statistic R: 0.7348 
##       Significance: 1e-04 
## 
## Permutation: free
## Number of permutations: 9999
## 
## Upper quantiles of permutations (null model):
##   90%   95% 97.5%   99% 
## 0.167 0.227 0.286 0.357 
## 
## Dissimilarity ranks between and within classes:
##         0%   25%  50%   75% 100%  N
## Between  1 35.25 59.5 82.75  105 90
## site1    4  6.50  9.0 13.50   18  3
## site2    3 17.50 32.0 36.00   40  3
## site3   14 30.50 47.0 57.50   68  3
## site4    5  9.00 13.0 22.00   31  3
## site5    2  4.00  6.0  6.50    7  3

Berdasarkan hasil analisis, perbedan kelimpahan dan komposisi spesies antar lokasi signifikan secara statistik.

Indicator Species Analysis (ISA)

loc <- dm$site #vector

inv <- multipatt(dm.bio.hel, loc, func = "r.g", control = how(nperm = 9999)) # add alpha = 1 to show all your species
summary(inv)
## 
##  Multilevel pattern analysis
##  ---------------------------
## 
##  Association function: r.g
##  Significance level (alpha): 0.05
## 
##  Total number of species: 12
##  Selected number of species: 5 
##  Number of species associated to 1 group: 4 
##  Number of species associated to 2 groups: 1 
##  Number of species associated to 3 groups: 0 
##  Number of species associated to 4 groups: 0 
## 
##  List of species associated to each combination: 
## 
##  Group site3  #sps.  1 
##       stat p.value  
## sp.3 0.945  0.0116 *
## 
##  Group site5  #sps.  3 
##       stat p.value  
## sp.4 0.984  0.0108 *
## sp.2 0.905  0.0108 *
## sp.7 0.862  0.0108 *
## 
##  Group site1+site4  #sps.  1 
##      stat p.value   
## sp.1 0.92  0.0026 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Berdasarkan hasil analisis, terbentuk tiga kelompok, yaitu kelompok 1 (site3), kelompok 2 (site5), dan kelompok 3 (site1 dan site4). Spesies indikator pada kelompok 1 adalah sp.3, pada kelompok 2 adalah sp.2, sp.4, dan sp.7, dan pada kelompok 3 adalah sp.1.

Visualisasi pengelompokan ini dapat dilakukan dengan analisis nonmetric multidimensional scaling (NMDS).

Session Info:

## R version 4.3.2 (2023-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## time zone: Asia/Jakarta
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] indicspecies_1.7.14 vegan_2.6-4         lattice_0.21-9     
## [4] permute_0.9-7      
## 
## loaded via a namespace (and not attached):
##  [1] nlme_3.1-163      cli_3.6.2         knitr_1.45        rlang_1.1.3      
##  [5] xfun_0.42         jsonlite_1.8.8    htmltools_0.5.7   sass_0.4.8       
##  [9] rmarkdown_2.25    grid_4.3.2        evaluate_0.23     jquerylib_0.1.4  
## [13] MASS_7.3-60       fastmap_1.1.1     yaml_2.3.8        lifecycle_1.0.4  
## [17] cluster_2.1.4     compiler_4.3.2    mgcv_1.9-0        rstudioapi_0.15.0
## [21] digest_0.6.34     R6_2.5.1          parallel_4.3.2    splines_4.3.2    
## [25] bslib_0.6.1       Matrix_1.6-5      tools_4.3.2       cachem_1.0.8