Data overview
##
## Pragozd Krokar Sekundarni gozd
## 1124 1129
table(dataset$locality, dataset$succession_stage)
##
## debeljak drogovnjak mladovje
## Pragozd Krokar 487 272 365
## Sekundarni gozd 326 452 351
vegan::specnumber(table(dataset$locality_eng, dataset$full_name))
## Krokar Managed
## 68 81
length(unique(dataset$full_name))
## [1] 98
sort(table(dataset$full_name), decreasing = TRUE)
##
## Lithobius pygmaeus Inermocoelotes inermis
## 225 190
## Harpactea lepida Histopona luxurians
## 171 152
## Microneta viaria Cryptops hortensis
## 132 129
## Aptinus bombarda Strigamia acuminata
## 125 113
## Comaroma simoni Cryptops parisi
## 104 102
## Pardosa alacris Tenuiphantes tenebricola
## 61 60
## Pterostichus burmeisteri Schendyla armata
## 59 50
## Amaurobius obustus Eupolybothrus tridentinus
## 42 33
## Strigamia transsilvanica Lithobius latro
## 31 29
## Schendyla tyrolensis Carabus caelatus
## 26 24
## Lithobius carinthiacus Lithobius dentatus
## 22 22
## Cychrus attenuatus Sigibius anici
## 20 20
## Clinopodes carinthiacus Cryptops rucneri
## 17 17
## Eurygeophilus pinguis Dicellophilus carniolensis
## 13 12
## Diplocephalus picinus Molops piceus
## 12 12
## Geophilus alpinus Abax ovalis
## 11 10
## Schendyla carniolensis Abax parallelepipedus
## 10 8
## Lithobius castaneus Lithobius tenebrosus
## 8 8
## Molops striolatus Nebria dahli
## 8 8
## Robertus lividus Centromerus cavernarum
## 8 7
## Dysdera adriatica Stenotaenia sorrentina
## 7 7
## Tenuiphantes flavipes Trochosa terricola
## 7 7
## Centromerus silvicola Zora nemoralis
## 6 5
## Abax carinatus Ceratinella brevis
## 4 4
## Geophilus electricus Histopona torpida
## 4 4
## Inermocoelotes anoplus Lithobius nodulipes
## 4 4
## Lithobius pelidnus Micrargus herbigradus
## 4 4
## Carabus croaticus Eupolybothrus grossipes
## 3 3
## Harpolithobius gottscheensis Licinus hoffmannseggii
## 3 3
## Lithobius validus Mermesus trilobatus
## 3 3
## Tegenaria silvestris Amaurobius fenestralis
## 3 2
## Carabus catenulatus Carabus creutzeri
## 2 2
## Clubiona terrestris Coelotes atropos
## 2 2
## Dasumia canestrinii Dima elateroides
## 2 2
## Dysdera crocata Geophilus flavus
## 2 2
## Ocypus olens Platynus scrobiculatus
## 2 2
## Pterostichus oblongopunctatus Walckenaeria simplex
## 2 2
## Aulonia albimana Carabus coriaceus
## 1 1
## Carabus irregularis Centromerus sylvaticus
## 1 1
## Centrophantes roeweri Ceratinella scabrosa
## 1 1
## Dysdera ninnii Hahnia pusilla
## 1 1
## Halyzia sedecimguttata Haplodrassus silvestris
## 1 1
## Lithobius agilis Lithobius forficatus
## 1 1
## Maso sundevalli Metellina segmentata
## 1 1
## Neon reticulatus Notiophilus biguttatus
## 1 1
## Pardosa lugubris Scotargus pilosus
## 1 1
## Segestria senoculata Stenichnus collaris
## 1 1
## Tenuiphantes mengei Trechus croaticus
## 1 1
## Walckenaeria antica Walckenaeria mitrata
## 1 1
##
## Araneae Coleoptera Geophilomorpha Lithobiomorpha
## 1021 302 296 386
## Scolopendromorpha
## 248
dataset %>%
group_by(ordo) %>%
summarize(n = length(unique(full_name)))
## # A tibble: 5 x 2
## ordo n
## <chr> <int>
## 1 Araneae 44
## 2 Coleoptera 24
## 3 Geophilomorpha 12
## 4 Lithobiomorpha 15
## 5 Scolopendromorpha 3
dataset$month <- factor(dataset$month, levels = c("October", "January", "April", "May", "August"), ordered = TRUE)
table(dataset$group, dataset$month, dataset$sampling_method)
## , , = sifter
##
##
## October January April May August
## Araneae 42 0 0 0 0
## Chilopoda 76 0 0 0 0
## Coleoptera 5 0 0 0 0
##
## , , = talne pasti
##
##
## October January April May August
## Araneae 0 6 504 327 15
## Chilopoda 0 4 36 19 32
## Coleoptera 0 0 52 41 204
##
## , , = talni vzorci
##
##
## October January April May August
## Araneae 83 44 0 0 0
## Chilopoda 422 341 0 0 0
## Coleoptera 0 0 0 0 0
dataset %>% filter(ordo %in% c("Geophilomorpha", "Lithobiomorpha", "Scolopendromorpha")) %>%
dplyr::select(full_name) %>%
unique()
## # A tibble: 30 x 1
## full_name
## <chr>
## 1 Strigamia acuminata
## 2 Eupolybothrus tridentinus
## 3 Lithobius castaneus
## 4 Lithobius dentatus
## 5 Lithobius tenebrosus
## 6 Lithobius nodulipes
## 7 Lithobius forficatus
## 8 Eupolybothrus grossipes
## 9 Lithobius pygmaeus
## 10 Lithobius latro
## # ... with 20 more rows
gospodarc <- dataset %>%
filter(locality != "Pragozd Krokar") %>%
filter(ordo %in% c("Geophilomorpha", "Lithobiomorpha", "Scolopendromorpha")) %>%
dplyr::select(full_name) %>%
unique()
krokar <- dataset %>%
filter(locality == "Pragozd Krokar") %>%
filter(ordo %in% c("Geophilomorpha", "Lithobiomorpha", "Scolopendromorpha")) %>%
dplyr::select(full_name) %>%
unique()
krokar$full_name[!(krokar$full_name %in% gospodarc$full_name)]
## [1] "Lithobius nodulipes" "Lithobius forficatus" "Lithobius validus"
## [4] "Lithobius agilis"
gospodarc$full_name[!(krokar$full_name %in% gospodarc$full_name)]
## [1] "Eupolybothrus grossipes" "Lithobius latro"
## [3] "Lithobius tenebrosus" "Schendyla carniolensis"
Analysis
Araneae
## [1] "Araneae"
## [1] "doing inext"
## site n S.obs SC f1 f2 f3 f4 f5 f6 f7 f8 f9 f10
## 1 Krokar 465 22 0.9785 10 4 1 0 0 0 0 0 0 0
## 2 Managed 556 37 0.9766 13 4 4 1 1 1 4 0 0 0
## Site Diversity Observed Estimator s.e. LCL UCL
## 1 Krokar Species richness 22.000 34.473 10.658 24.922 75.238
## 2 Krokar Shannon diversity 7.365 7.662 0.360 7.365 8.368
## 3 Krokar Simpson diversity 5.672 5.730 0.261 5.672 6.241
## 4 Managed Species richness 37.000 58.087 16.403 42.476 118.199
## 5 Managed Shannon diversity 13.097 13.823 0.718 13.097 15.231
## 6 Managed Simpson diversity 9.267 9.407 0.407 9.267 10.204
## site T U S.obs SC Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10
## 1 Krokar 28 98 22 0.8798 12 3 0 0 0 0 0 2 0 0
## 2 Managed 24 154 37 0.9053 15 5 4 2 2 0 1 2 0 0
## Site Diversity Observed Estimator s.e. LCL UCL
## 1 Krokar Species richness 22.000 45.143 19.499 27.508 119.240
## 2 Krokar Shannon diversity 12.621 15.678 2.077 12.621 19.748
## 3 Krokar Simpson diversity 9.820 10.452 1.042 9.820 12.495
## 4 Managed Species richness 37.000 58.562 15.445 43.108 113.115
## 5 Managed Shannon diversity 23.820 28.627 2.563 23.820 33.651
## 6 Managed Simpson diversity 18.243 19.745 1.693 18.243 23.062
## [1] "iNEXT results"
## Site Diversity Observed Estimator s.e. LCL UCL dataType
## 1 Krokar Species richness 22.000 34.473 10.658 24.922 75.238 Abundance
## 2 Krokar Shannon diversity 7.365 7.662 0.360 7.365 8.368 Abundance
## 3 Krokar Simpson diversity 5.672 5.730 0.261 5.672 6.241 Abundance
## 7 Krokar Species richness 22.000 45.143 19.499 27.508 119.240 Incidence
## 8 Krokar Shannon diversity 12.621 15.678 2.077 12.621 19.748 Incidence
## 9 Krokar Simpson diversity 9.820 10.452 1.042 9.820 12.495 Incidence
## 4 Managed Species richness 37.000 58.087 16.403 42.476 118.199 Abundance
## 5 Managed Shannon diversity 13.097 13.823 0.718 13.097 15.231 Abundance
## 6 Managed Simpson diversity 9.267 9.407 0.407 9.267 10.204 Abundance
## 10 Managed Species richness 37.000 58.562 15.445 43.108 113.115 Incidence
## 11 Managed Shannon diversity 23.820 28.627 2.563 23.820 33.651 Incidence
## 12 Managed Simpson diversity 18.243 19.745 1.693 18.243 23.062 Incidence
## [1] "doing boral"
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2288
## Unobserved stochastic nodes: 2578
## Total graph size: 16509
##
## Initializing model
##
## [1] "Some diagnostic plots"
## NULL
## [1] "Ordination plot"
##
## PIPING TO 2nd MVFACTOR
## START SECTION 2
## Plotting if overlay is TRUE
## FINISHED SECTION 2
## [1] "doing mvabund"
## [1] "mvabund diagnostic plot"
## Time elapsed: 0 hr 2 min 53 sec
## [1] "ANOVA results"
## Analysis of Deviance Table
##
## Model: abnd ~ forestType + succession.stage + month + forestType:succession.stage
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 33
## forestType 32 1 121.38 0.001 ***
## succession.stage 30 2 82.23 0.134
## month 27 3 265.85 0.001 ***
## forestType:succession.stage 25 2 76.28 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.
Chilopoda
superDuper(df.chilopoda, composition = FALSE)
## [1] "Geophilomorpha" "Lithobiomorpha" "Scolopendromorpha"
## [1] "doing inext"
## site n S.obs SC f1 f2 f3 f4 f5 f6 f7 f8 f9 f10
## 1 Krokar 468 27 0.9936 3 1 2 3 0 2 1 3 1 1
## 2 Managed 462 26 0.9957 2 2 3 4 0 0 2 1 0 0
## Site Diversity Observed Estimator s.e. LCL UCL
## 1 Krokar Species richness 27.000 31.490 7.179 27.494 67.834
## 2 Krokar Shannon diversity 12.895 13.339 0.755 12.895 14.818
## 3 Krokar Simpson diversity 8.254 8.384 0.628 8.254 9.615
## 4 Managed Species richness 26.000 26.998 1.867 26.090 37.045
## 5 Managed Shannon diversity 12.831 13.217 0.698 12.831 14.586
## 6 Managed Simpson diversity 8.272 8.405 0.596 8.272 9.574
## site T U S.obs SC Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10
## 1 Krokar 26 128 27 0.9621 5 2 3 2 4 5 1 3 0 1
## 2 Managed 23 131 26 0.9880 2 6 2 4 2 3 1 1 1 2
## Site Diversity Observed Estimator s.e. LCL UCL
## 1 Krokar Species richness 27.000 33.010 7.277 27.933 65.691
## 2 Krokar Shannon diversity 22.484 25.319 1.525 22.484 28.309
## 3 Krokar Simpson diversity 20.128 22.824 1.665 20.128 26.087
## 4 Managed Species richness 26.000 26.319 0.734 26.022 30.555
## 5 Managed Shannon diversity 21.340 23.224 1.205 21.340 25.587
## 6 Managed Simpson diversity 18.552 20.559 1.462 18.552 23.425
## [1] "iNEXT results"
## Site Diversity Observed Estimator s.e. LCL UCL dataType
## 1 Krokar Species richness 27.000 31.490 7.179 27.494 67.834 Abundance
## 2 Krokar Shannon diversity 12.895 13.339 0.755 12.895 14.818 Abundance
## 3 Krokar Simpson diversity 8.254 8.384 0.628 8.254 9.615 Abundance
## 7 Krokar Species richness 27.000 33.010 7.277 27.933 65.691 Incidence
## 8 Krokar Shannon diversity 22.484 25.319 1.525 22.484 28.309 Incidence
## 9 Krokar Simpson diversity 20.128 22.824 1.665 20.128 26.087 Incidence
## 4 Managed Species richness 26.000 26.998 1.867 26.090 37.045 Abundance
## 5 Managed Shannon diversity 12.831 13.217 0.698 12.831 14.586 Abundance
## 6 Managed Simpson diversity 8.272 8.405 0.596 8.272 9.574 Abundance
## 10 Managed Species richness 26.000 26.319 0.734 26.022 30.555 Incidence
## 11 Managed Shannon diversity 21.340 23.224 1.205 21.340 25.587 Incidence
## 12 Managed Simpson diversity 18.552 20.559 1.462 18.552 23.425 Incidence
## [1] "doing boral"
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 1470
## Unobserved stochastic nodes: 1698
## Total graph size: 10687
##
## Initializing model
##
## [1] "Some diagnostic plots"
## Only the first 6 ``most important'' latent variable coefficients included in biplot.
## NULL
## [1] "Ordination plot"
## Overlapping points were shifted along the y-axis to make them visible.
##
## PIPING TO 2nd MVFACTOR
## Only the variables Lithobius.pygmaeus, Cryptops.hortensis, Strigamia.acuminata, Cryptops.parisi, Schendyla.armata, Eupolybothrus.tridentinus, Strigamia.transsilvanica, Lithobius.latro, Schendyla.tyrolensis, Lithobius.carinthiacus, Lithobius.dentatus, Sigibius.anici were included in the plot
## (the variables with highest total abundance).
## START SECTION 2
## Plotting if overlay is TRUE
## using grouping variable as.factor(ftype$forestType) 7 mean values were 0 and could
## not be included in the log-plot
## using grouping variable as.factor(ftype$forestType) 7 variance values were 0 and could not
## be included in the log-plot
## FINISHED SECTION 2
## [1] "doing mvabund"
## [1] "mvabund diagnostic plot"
## Time elapsed: 0 hr 1 min 30 sec
## [1] "ANOVA results"
## Analysis of Deviance Table
##
## Model: abnd ~ forestType + succession.stage + month + forestType:succession.stage
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 31
## forestType 30 1 27.25 0.018 *
## succession.stage 28 2 43.70 0.022 *
## month 25 3 43.86 0.168
## forestType:succession.stage 23 2 39.95 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.
Coleoptera
superDuper(df.coleoptera, composition = FALSE)
## [1] "Coleoptera"
## [1] "doing inext"
## site n S.obs SC f1 f2 f3 f4 f5 f6 f7 f8 f9 f10
## 1 Krokar 191 19 0.9583 8 3 2 0 0 0 0 2 0 1
## 2 Managed 111 18 0.9466 6 4 0 1 1 1 1 1 0 0
## Site Diversity Observed Estimator s.e. LCL UCL
## 1 Krokar Species richness 19.000 29.611 10.219 21.169 70.917
## 2 Krokar Shannon diversity 5.845 6.342 0.718 5.845 7.749
## 3 Krokar Simpson diversity 3.423 3.467 0.344 3.423 4.142
## 4 Managed Species richness 18.000 22.459 4.763 18.807 42.657
## 5 Managed Shannon diversity 9.630 10.742 1.121 9.630 12.938
## 6 Managed Simpson diversity 6.649 7.009 0.880 6.649 8.734
## site T U S.obs SC Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10
## 1 Krokar 14 52 19 0.8162 10 3 1 0 2 1 1 0 0 1
## 2 Managed 15 57 18 0.8843 7 3 1 1 2 2 1 1 0 0
## Site Diversity Observed Estimator s.e. LCL UCL
## 1 Krokar Species richness 19.000 34.476 13.825 22.449 88.442
## 2 Krokar Shannon diversity 13.288 18.295 2.991 13.288 24.157
## 3 Krokar Simpson diversity 10.165 11.579 1.604 10.165 14.723
## 4 Managed Species richness 18.000 25.622 7.758 19.460 57.793
## 5 Managed Shannon diversity 13.799 17.136 2.314 13.799 21.671
## 6 Managed Simpson diversity 11.645 13.486 1.573 11.645 16.569
## [1] "iNEXT results"
## Site Diversity Observed Estimator s.e. LCL UCL dataType
## 1 Krokar Species richness 19.000 29.611 10.219 21.169 70.917 Abundance
## 2 Krokar Shannon diversity 5.845 6.342 0.718 5.845 7.749 Abundance
## 3 Krokar Simpson diversity 3.423 3.467 0.344 3.423 4.142 Abundance
## 7 Krokar Species richness 19.000 34.476 13.825 22.449 88.442 Incidence
## 8 Krokar Shannon diversity 13.288 18.295 2.991 13.288 24.157 Incidence
## 9 Krokar Simpson diversity 10.165 11.579 1.604 10.165 14.723 Incidence
## 4 Managed Species richness 18.000 22.459 4.763 18.807 42.657 Abundance
## 5 Managed Shannon diversity 9.630 10.742 1.121 9.630 12.938 Abundance
## 6 Managed Simpson diversity 6.649 7.009 0.880 6.649 8.734 Abundance
## 10 Managed Species richness 18.000 25.622 7.758 19.460 57.793 Incidence
## 11 Managed Shannon diversity 13.799 17.136 2.314 13.799 21.671 Incidence
## 12 Managed Simpson diversity 11.645 13.486 1.573 11.645 16.569 Incidence
## [1] "doing boral"
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 696
## Unobserved stochastic nodes: 857
## Total graph size: 5150
##
## Initializing model
##
## [1] "Some diagnostic plots"
## Only the first 6 ``most important'' latent variable coefficients included in biplot.
## NULL
## [1] "Ordination plot"
## Overlapping points were shifted along the y-axis to make them visible.
##
## PIPING TO 2nd MVFACTOR
## Only the variables Aptinus.bombarda, Pterostichus.burmeisteri, Carabus.caelatus, Cychrus.attenuatus, Molops.piceus, Abax.ovalis, Abax.parallelepipedus, Molops.striolatus, Nebria.dahli, Abax.carinatus, Carabus.croaticus, Licinus.hoffmannseggii were included in the plot
## (the variables with highest total abundance).
## START SECTION 2
## Plotting if overlay is TRUE
## using grouping variable as.factor(ftype$forestType) 11 mean values were 0 and could
## not be included in the log-plot
## using grouping variable as.factor(ftype$forestType) 11 variance values were 0 and could not
## be included in the log-plot
## FINISHED SECTION 2
## [1] "doing mvabund"
## [1] "mvabund diagnostic plot"
## Time elapsed: 0 hr 1 min 25 sec
## [1] "ANOVA results"
## Analysis of Deviance Table
##
## Model: abnd ~ forestType + succession.stage + month + forestType:succession.stage
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 25
## forestType 24 1 41.31 0.043 *
## succession.stage 22 2 57.95 0.169
## month 20 2 149.19 0.001 ***
## forestType:succession.stage 18 2 49.18 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.
Alltogether
superDuper(dataset, composition = FALSE)
## [1] "Coleoptera" "Geophilomorpha" "Lithobiomorpha"
## [4] "Araneae" "Scolopendromorpha"
## [1] "doing inext"
## site n S.obs SC f1 f2 f3 f4 f5 f6 f7 f8 f9 f10
## 1 Krokar 1124 68 0.9813 21 8 5 3 0 2 1 5 1 2
## 2 Managed 1129 81 0.9814 21 10 7 6 2 2 7 2 0 0
## Site Diversity Observed Estimator s.e. LCL UCL
## 1 Krokar Species richness 68.000 95.538 16.333 77.389 148.773
## 2 Krokar Shannon diversity 25.073 26.233 1.016 25.073 28.224
## 3 Krokar Simpson diversity 16.774 17.013 0.646 16.774 18.280
## 4 Managed Species richness 81.000 103.030 12.768 88.673 144.253
## 5 Managed Shannon diversity 32.339 33.970 1.159 32.339 36.242
## 6 Managed Simpson diversity 20.891 21.266 0.885 20.891 22.999
## site T U S.obs SC Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10
## 1 Krokar 29 278 68 0.9049 27 8 4 2 6 6 2 5 0 2
## 2 Managed 29 342 81 0.9326 24 14 7 7 6 5 3 4 1 2
## Site Diversity Observed Estimator s.e. LCL UCL
## 1 Krokar Species richness 68.000 111.991 23.929 84.219 187.322
## 2 Krokar Shannon diversity 46.950 57.066 3.586 50.039 64.094
## 3 Krokar Simpson diversity 37.553 41.804 2.486 37.553 46.676
## 4 Managed Species richness 81.000 100.862 10.667 88.405 134.277
## 5 Managed Shannon diversity 58.141 67.352 3.179 61.122 73.582
## 6 Managed Simpson diversity 46.711 52.116 2.908 46.711 57.817
## [1] "iNEXT results"
## Site Diversity Observed Estimator s.e. LCL UCL dataType
## 1 Krokar Species richness 68.000 95.538 16.333 77.389 148.773 Abundance
## 2 Krokar Shannon diversity 25.073 26.233 1.016 25.073 28.224 Abundance
## 3 Krokar Simpson diversity 16.774 17.013 0.646 16.774 18.280 Abundance
## 7 Krokar Species richness 68.000 111.991 23.929 84.219 187.322 Incidence
## 8 Krokar Shannon diversity 46.950 57.066 3.586 50.039 64.094 Incidence
## 9 Krokar Simpson diversity 37.553 41.804 2.486 37.553 46.676 Incidence
## 4 Managed Species richness 81.000 103.030 12.768 88.673 144.253 Abundance
## 5 Managed Shannon diversity 32.339 33.970 1.159 32.339 36.242 Abundance
## 6 Managed Simpson diversity 20.891 21.266 0.885 20.891 22.999 Abundance
## 10 Managed Species richness 81.000 100.862 10.667 88.405 134.277 Incidence
## 11 Managed Shannon diversity 58.141 67.352 3.179 61.122 73.582 Incidence
## 12 Managed Simpson diversity 46.711 52.116 2.908 46.711 57.817 Incidence
## [1] "doing boral"
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 5684
## Unobserved stochastic nodes: 6202
## Total graph size: 40629
##
## Initializing model
##
## [1] "Some diagnostic plots"
## Only the first 6 ``most important'' latent variable coefficients included in biplot.
## NULL
## [1] "Ordination plot"
## Warning: Removed 58 rows containing missing values (geom_point).
## Overlapping points were shifted along the y-axis to make them visible.
##
## PIPING TO 2nd MVFACTOR
## Only the variables Lithobius.pygmaeus, Inermocoelotes.inermis, Harpactea.lepida, Histopona.luxurians, Microneta.viaria, Cryptops.hortensis, Aptinus.bombarda, Strigamia.acuminata, Comaroma.simoni, Cryptops.parisi, Pardosa.alacris, Tenuiphantes.tenebricola were included in the plot
## (the variables with highest total abundance).
## START SECTION 2
## Plotting if overlay is TRUE
## using grouping variable as.factor(ftype$forestType) 47 mean values were 0 and could
## not be included in the log-plot
## using grouping variable as.factor(ftype$forestType) 47 variance values were 0 and could not
## be included in the log-plot
## FINISHED SECTION 2
## [1] "doing mvabund"
## [1] "mvabund diagnostic plot"
## Time elapsed: 0 hr 7 min 9 sec
## [1] "ANOVA results"
## Analysis of Deviance Table
##
## Model: abnd ~ forestType + succession.stage + month + forestType:succession.stage
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 39
## forestType 38 1 165.0 0.001 ***
## succession.stage 36 2 173.4 0.053 .
## month 35 1 225.1 0.001 ***
## forestType:succession.stage 33 2 113.8 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.
Per forest type
samokrokar <- dataset[dataset$locality_eng == "Krokar", ]
semiDuper(dataset = samokrokar, composition = FALSE)
## [1] "Coleoptera" "Geophilomorpha" "Lithobiomorpha"
## [4] "Araneae" "Scolopendromorpha"
## Overlapping points were shifted along the y-axis to make them visible.
##
## PIPING TO 2nd MVFACTOR
## Only the variables Inermocoelotes.inermis, Lithobius.pygmaeus, Harpactea.lepida, Aptinus.bombarda, Cryptops.hortensis, Cryptops.parisi, Comaroma.simoni, Histopona.luxurians, Pterostichus.burmeisteri, Microneta.viaria, Strigamia.acuminata, Amaurobius.obustus were included in the plot
## (the variables with highest total abundance).
## START SECTION 2
## Plotting if overlay is TRUE
## using grouping variable as.factor(ftype$succ) 72 mean values were 0 and could
## not be included in the log-plot
## using grouping variable as.factor(ftype$succ) 72 variance values were 0 and could not
## be included in the log-plot
## FINISHED SECTION 2
## [1] "doing mvabund"
## [1] "mvabund diagnostic plot"
## Time elapsed: 0 hr 0 min 34 sec
## [1] "ANOVA results"
## Analysis of Deviance Table
##
## Model: abnd ~ succession.stage
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 19
## succession.stage 17 2 106.9 0.109
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.
semiDuper(dataset = dataset[dataset$locality_eng != "Krokar", ], composition = FALSE)
## [1] "Coleoptera" "Lithobiomorpha" "Araneae"
## [4] "Scolopendromorpha" "Geophilomorpha"
## Overlapping points were shifted along the y-axis to make them visible.
##
## PIPING TO 2nd MVFACTOR
## Only the variables Lithobius.pygmaeus, Histopona.luxurians, Microneta.viaria, Strigamia.acuminata, Harpactea.lepida, Inermocoelotes.inermis, Pardosa.alacris, Cryptops.hortensis, Comaroma.simoni, Tenuiphantes.tenebricola, Cryptops.parisi, Aptinus.bombarda were included in the plot
## (the variables with highest total abundance).
## START SECTION 2
## Plotting if overlay is TRUE
## using grouping variable as.factor(ftype$succ) 76 mean values were 0 and could
## not be included in the log-plot
## using grouping variable as.factor(ftype$succ) 76 variance values were 0 and could not
## be included in the log-plot
## FINISHED SECTION 2
## [1] "doing mvabund"
## [1] "mvabund diagnostic plot"
## Time elapsed: 0 hr 0 min 45 sec
## [1] "ANOVA results"
## Analysis of Deviance Table
##
## Model: abnd ~ succession.stage
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 19
## succession.stage 17 2 159.5 0.049 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.
