R/syntable.R
syntable.RdSynoptic tables are a tool for the visualization and interpretation of previously defined plant species groups (clusters), e.g. from cluster analysis, classification methods or pre-defined categories, e.g. spatial distribution units. They help to determine characteristic patterning of species occurrences in plant communities by calculating cluster-wise percentage or absolute frequencies, mean/median cover values, fidelity (phi) or differential species character.
syntable function calculates an unordered synoptic table for plant community analysis, using
an input species-sample data frame and a vector of cluster identity input.
The unordered output table can be sorted automatically with synsort function
in this package.
syntable(matrix, cluster, abund = "percentage", type = "percfreq", digits = 0)Species matrix or data frame with species in columns and samples in rows. Missing values (NA) will be transformed to 0. If non-numeric abundance values are present, the matrix will be transformed to presence/absence with all non-zero values defined as 1. Species and sample names must be defined as column- and row names, respectively.
Integer or character vector/factor with classification cluster identity. Ensure matching order of cluster identity and samples in matrix for correct allocation of cluster numbers to samples.
Type of abundances. Define whether input species matrix or data frame is percentage cover (abund = "percentage", default)
or presence/absence data (abund = "pa", with values 0/1). You may use function cov2per to transform
cover-abundance values from different scales into percentage cover.
Type of synoptic table output type = c("percfreq", "totalfreq", "mean",
"median", "diffspec", "phi"). See Details.
Integer indicating the number of decimal places to be displayed in result tables (default 0)
The function returns an (invisible) list of result components.
$syntable unordered synoptic table for given species and clusters
$samplesize total number of samples per cluster
Additionally for differential species character calculation:
$onlydiff Synoptic table only with differential species
$others List of non-differential species
$differentials Lists differential species for each cluster
For synoptic table calculation, six types are available.
type = "percfreq" Creates a percentage frequency table (default)
type = "totalfreq" Creates an absolute frequency table
type = "mean" Calculates mean of species values given in matrix per cluster
type = "median" Calculates median of species values given in matrix per
cluster
type = "diffspec" Calculates differential character of species according to
Tsiripidis et al. 2009, with resulting character p = positive, n = negative, pn = positive-
negative or no differential character (-). Consider that differential character is always
restricted to some and not necessarily all of the other units, thus considering percentage
frequency is essential for correct interpretation of the diagnostic species character.
This calculation needs at least three groups.
type = "phi" Calculates fidelity measure phi (algorithm basing on Sokal & Rohlf
1995, Bruelheide 2000). Values are ranging between -1 and 1 with high values near 1 indicating
high fidelity.
For sorting the output synoptic table, use synsort function, providing several
options.
Bruelheide, H. (2000): A new measure of fidelity and its application to defining species groups. Journal of Vegetation Science 11: 167-178. doi:10.2307/3236796
Chytry, M., Tichy, L., Holt, J., Botta-Dukat, Z. (2002): Determination of diagnostic species with statistical fidelity measures. Journal of Vegetation Science 13: 79-90. doi:10.1111/j.1654-1103.2002.tb02025.x
Sokal, R.R. & Rohlf, F.J. (1995): Biometry. 3rd edition Freemann, New York.
Tsiripidis, I., Bergmeier, E., Fotiadis, G. & Dimopoulos, P. (2009): A new algorithm for the determination of differential taxa. Journal of Vegetation Science 20: 233-240. doi:10.1111/j.1654-1103.2009.05273.x
## Synoptic table of Scheden vegetation data
library(cluster)
pam1 <- pam(schedenveg, 4) # PAM clustering with 4 clusters output
## 1) Unordered synoptic percentage frequency table
percfreq <- syntable(schedenveg, pam1$clustering, abund = "percentage",
type = "percfreq")
percfreq # view results
#> $syntable
#> 1 2 3 4
#> AceCamp 0 10 0 0
#> AchMill 0 30 55 25
#> AgrEupa 0 0 36 0
#> AjuGene 0 0 9 0
#> AjuRept 0 0 0 25
#> AllVine 0 20 0 25
#> AloPrat 67 50 0 0
#> AntDioi 0 0 9 0
#> AntOdor 33 60 9 50
#> AntSylv 0 20 0 0
#> AntVuln 0 0 18 0
#> AraThal 0 0 0 25
#> AreSerp 0 0 9 0
#> ArrElat 67 100 55 75
#> AstGlyc 0 0 9 0
#> BelPere 0 20 0 0
#> BetPend 0 0 9 0
#> BriMedi 0 20 82 0
#> BroErec 33 100 100 75
#> BroHord 67 20 9 25
#> BroSter 0 10 0 25
#> CalSepi 0 10 0 0
#> CamGlom 0 10 0 0
#> CarPrat 33 0 0 25
#> CarCary 0 10 45 0
#> CarFlac 0 10 73 0
#> CarBetu 0 10 18 0
#> CenJace 0 10 36 0
#> CenScab 0 0 27 0
#> CenEryt 0 0 9 0
#> CerArve 0 0 0 25
#> CerGlom 33 30 18 50
#> CerHolo 33 80 9 75
#> CirAcau 0 10 55 0
#> CirArve 33 0 0 0
#> CirVulg 33 0 0 25
#> ConArve 0 40 18 25
#> CorSang 0 10 18 0
#> CorAvel 0 10 0 0
#> CraLaev 0 20 27 0
#> CraMono 0 0 9 0
#> CraSpec 0 0 27 0
#> CreBien 0 40 27 50
#> CynCris 33 30 9 25
#> DacGlom 100 100 64 100
#> DauCaro 67 20 36 25
#> EupCypa 0 0 27 0
#> EupSpec 0 0 9 0
#> FesOvin 0 10 36 0
#> FesPrat 100 60 27 75
#> FesRubr 67 70 27 100
#> FraVesc 0 0 0 25
#> FraViri 0 10 55 0
#> FraExce 0 10 0 0
#> GalAlbu 100 90 64 100
#> GalPumi 0 0 9 0
#> GalVeru 0 0 45 0
#> GenTinc 0 10 27 0
#> GerDiss 0 20 0 0
#> GerMoll 0 10 0 0
#> GeuUrba 0 0 0 25
#> GleHede 67 30 27 0
#> GymCono 0 0 18 0
#> HelNumm 0 0 9 0
#> HelPube 67 40 27 50
#> HerSpho 0 20 27 0
#> HieMuro 0 0 18 0
#> HipComo 0 0 55 0
#> HolLana 100 70 36 75
#> HypMacu 0 0 9 0
#> HypPerf 0 0 45 75
#> JunComm 0 0 9 0
#> KnaArve 33 70 73 50
#> KoePyra 0 0 18 0
#> LatPrat 33 40 18 50
#> LeoAutu 0 0 9 0
#> LeoHisp 0 0 27 0
#> LeuIrcu 33 40 73 50
#> LinCath 0 0 36 25
#> LisOvat 0 0 18 0
#> LolPere 67 80 27 0
#> LotCorn 0 30 55 50
#> LuzCamp 0 40 45 25
#> LuzMult 0 0 0 25
#> LysNumm 0 0 9 0
#> MedFalc 0 0 9 0
#> MedLupu 33 50 73 25
#> MyoArve 33 20 0 50
#> OnoVici 0 0 9 0
#> OnoRepe 0 10 27 0
#> OphInse 0 0 9 0
#> OrcMasc 0 0 9 0
#> PhlPrat 67 10 9 0
#> PilOffi 0 10 36 0
#> PimMajo 0 10 0 50
#> PimSaxi 0 50 55 50
#> PinSpec 0 0 9 0
#> PlaLaet 0 0 9 0
#> PlaLanc 67 100 100 100
#> PlaMajo 0 10 9 0
#> PlaMedi 0 0 55 25
#> PoaAngu 0 50 18 25
#> PoaPrat 33 90 64 75
#> PoaTriv 100 70 18 75
#> PolComo 0 0 45 0
#> PotAnse 0 0 9 0
#> PotRept 0 10 0 0
#> PotVern 0 0 36 0
#> PriVeri 0 60 73 75
#> PruGran 0 0 9 0
#> PruAviu 0 20 55 0
#> PruSpin 0 0 45 0
#> QueRobu 0 30 27 0
#> QueSpec 0 0 18 0
#> RanAcri 67 30 27 75
#> RanBulb 33 80 100 25
#> RanRepe 33 10 0 0
#> RanSpec 0 10 0 0
#> RhiMino 33 40 27 25
#> RosCani 0 0 9 0
#> RosSpec 0 30 18 25
#> RubFrut 0 0 0 25
#> RubIdae 0 0 9 0
#> RumAcet 33 90 36 100
#> RumObtu 0 0 9 0
#> SalPrat 0 10 27 0
#> SanMino 0 20 91 50
#> ScaColu 0 0 27 0
#> SedSexa 0 0 9 0
#> SenJaco 0 10 36 0
#> SilNuta 0 10 18 0
#> SilPusi 0 0 9 0
#> SteGram 0 10 0 0
#> TarEryt 0 10 0 0
#> TarRude 67 70 55 75
#> ThlPerf 0 0 9 25
#> ThyPule 0 0 27 0
#> TraPrat 0 40 27 25
#> TriCamp 0 0 9 0
#> TriDubi 0 50 36 50
#> TriPrat 67 70 64 75
#> TriRepe 67 50 27 50
#> TriFlav 100 100 82 75
#> UrtDioi 0 0 0 25
#> ValCari 0 0 0 25
#> ValLocu 0 20 9 25
#> VerArve 33 30 18 25
#> VerCham 67 40 36 100
#> VerHede 33 0 0 0
#> VerTeuc 0 0 27 0
#> VibOpul 0 0 9 0
#> VicAngu 0 40 45 50
#> VicCrac 0 20 18 0
#> VicSepi 0 20 18 50
#> VioHirt 0 20 55 50
#>
#> $samplesize
#> 1 2 3 4
#> 3 10 11 4
#>
## 2) Differential species analysis
differential <- syntable(schedenveg, pam1$clustering, abund = "percentage",
type = "diffspec")
#>
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# show complete table with differential character of species
differential$syntable
#> 1 2 3 4
#> AceCamp - - - -
#> AchMill n p p p
#> AgrEupa n n p n
#> AjuGene - - - -
#> AjuRept n n n p
#> AllVine n p n p
#> AloPrat p p n n
#> AntDioi - - - -
#> AntOdor - p n p
#> AntSylv n p n n
#> AntVuln - - - -
#> AraThal n n n p
#> AreSerp - - - -
#> ArrElat - - - -
#> AstGlyc - - - -
#> BelPere n p n n
#> BetPend - - - -
#> BriMedi n pn p n
#> BroErec n p p -
#> BroHord p n n -
#> BroSter n - n p
#> CalSepi - - - -
#> CamGlom - - - -
#> CarPrat p n n p
#> CarCary n n p n
#> CarFlac n n p n
#> CarBetu - - - -
#> CenJace n - p n
#> CenScab n n p n
#> CenEryt - - - -
#> CerArve n n n p
#> CerGlom - - - -
#> CerHolo - p n p
#> CirAcau n n p n
#> CirArve p n n n
#> CirVulg p n n p
#> ConArve n p - p
#> CorSang - - - -
#> CorAvel - - - -
#> CraLaev n p p n
#> CraMono - - - -
#> CraSpec n n p n
#> CreBien n p p p
#> CynCris - - - -
#> DacGlom - - - -
#> DauCaro p n - -
#> EupCypa n n p n
#> EupSpec - - - -
#> FesOvin n - p n
#> FesPrat p - n p
#> FesRubr - - n p
#> FraVesc n n n p
#> FraViri n n p n
#> FraExce - - - -
#> GalAlbu - - - -
#> GalPumi - - - -
#> GalVeru n n p n
#> GenTinc n - p n
#> GerDiss n p n n
#> GerMoll - - - -
#> GeuUrba n n n p
#> GleHede p p p n
#> GymCono - - - -
#> HelNumm - - - -
#> HelPube - - - -
#> HerSpho n p p n
#> HieMuro - - - -
#> HipComo n n p n
#> HolLana p - n -
#> HypMacu - - - -
#> HypPerf n n p p
#> JunComm - - - -
#> KnaArve - - - -
#> KoePyra - - - -
#> LatPrat - - - -
#> LeoAutu - - - -
#> LeoHisp n n p n
#> LeuIrcu - - - -
#> LinCath n n p p
#> LisOvat - - - -
#> LolPere p p p n
#> LotCorn n p p p
#> LuzCamp n p p p
#> LuzMult n n n p
#> LysNumm - - - -
#> MedFalc - - - -
#> MedLupu - - p n
#> MyoArve p p n p
#> OnoVici - - - -
#> OnoRepe n - p n
#> OphInse - - - -
#> OrcMasc - - - -
#> PhlPrat p n n n
#> PilOffi n - p n
#> PimMajo n n n p
#> PimSaxi n p p p
#> PinSpec - - - -
#> PlaLaet - - - -
#> PlaLanc - - - -
#> PlaMajo - - - -
#> PlaMedi n n p p
#> PoaAngu n p - p
#> PoaPrat n p - -
#> PoaTriv p p n p
#> PolComo n n p n
#> PotAnse - - - -
#> PotRept - - - -
#> PotVern n n p n
#> PriVeri n p p p
#> PruGran - - - -
#> PruAviu n p p n
#> PruSpin n n p n
#> QueRobu n p p n
#> QueSpec - - - -
#> RanAcri - - n p
#> RanBulb n p p n
#> RanRepe p - n n
#> RanSpec - - - -
#> RhiMino - - - -
#> RosCani - - - -
#> RosSpec n p - p
#> RubFrut n n n p
#> RubIdae - - - -
#> RumAcet n p n p
#> RumObtu - - - -
#> SalPrat n - p n
#> SanMino n pn p p
#> ScaColu n n p n
#> SedSexa - - - -
#> SenJaco n - p n
#> SilNuta - - - -
#> SilPusi - - - -
#> SteGram - - - -
#> TarEryt - - - -
#> TarRude - - - -
#> ThlPerf n n - p
#> ThyPule n n p n
#> TraPrat n p p p
#> TriCamp - - - -
#> TriDubi n p p p
#> TriPrat - - - -
#> TriRepe - - - -
#> TriFlav - - - -
#> UrtDioi n n n p
#> ValCari n n n p
#> ValLocu n p - p
#> VerArve - - - -
#> VerCham - n n p
#> VerHede p n n n
#> VerTeuc n n p n
#> VibOpul - - - -
#> VicAngu n p p p
#> VicCrac n p - n
#> VicSepi n p - p
#> VioHirt n p p p
# list differential species for second cluster
differential$differentials[2]
#> $`2`
#> $`2`$`positive diff`
#> [1] "AchMill" "AllVine" "AloPrat" "AntOdor" "AntSylv" "BelPere" "BroErec"
#> [8] "CerHolo" "ConArve" "CraLaev" "CreBien" "GerDiss" "GleHede" "HerSpho"
#> [15] "LolPere" "LotCorn" "LuzCamp" "MyoArve" "PimSaxi" "PoaAngu" "PoaPrat"
#> [22] "PoaTriv" "PriVeri" "PruAviu" "QueRobu" "RanBulb" "RosSpec" "RumAcet"
#> [29] "TraPrat" "TriDubi" "ValLocu" "VicAngu" "VicCrac" "VicSepi" "VioHirt"
#>
#> $`2`$`negative diff`
#> [1] "AgrEupa" "AjuRept" "AraThal" "BroHord" "CarPrat" "CarCary" "CarFlac"
#> [8] "CenScab" "CerArve" "CirAcau" "CirArve" "CirVulg" "CraSpec" "DauCaro"
#> [15] "EupCypa" "FraVesc" "FraViri" "GalVeru" "GeuUrba" "HipComo" "HypPerf"
#> [22] "LeoHisp" "LinCath" "LuzMult" "PhlPrat" "PimMajo" "PlaMedi" "PolComo"
#> [29] "PotVern" "PruSpin" "RubFrut" "ScaColu" "ThlPerf" "ThyPule" "UrtDioi"
#> [36] "ValCari" "VerCham" "VerHede" "VerTeuc"
#>
#> $`2`$`positive/negative diff`
#> [1] "BriMedi" "SanMino"
#>
#>
## 3) Synoptic table with phi fidelity
phitable <- syntable(schedenveg, pam1$clustering, abund = "percentage",
type = "phi")
phitable
#> $syntable
#> 1 2 3 4
#> AceCamp -0.066666667 0.25819889 -0.15480679 -0.07856742
#> AchMill -0.258198890 -0.08888889 0.31613380 -0.09128709
#> AgrEupa -0.141421356 -0.30429031 0.50751922 -0.16666667
#> AjuGene -0.066666667 -0.14344383 0.23924685 -0.07856742
#> AjuRept -0.066666667 -0.14344383 -0.15480679 0.47140452
#> AllVine -0.120000000 0.22377237 -0.27865222 0.18856181
#> AloPrat 0.333333333 0.43033148 -0.46442036 -0.23570226
#> AntDioi -0.066666667 -0.14344383 0.23924685 -0.07856742
#> AntOdor -0.017213259 0.37777778 -0.44694779 0.12171612
#> AntSylv -0.096076892 0.37210420 -0.22310033 -0.11322770
#> AntVuln -0.096076892 -0.20672456 0.34479141 -0.11322770
#> AraThal -0.066666667 -0.14344383 -0.15480679 0.47140452
#> AreSerp -0.066666667 -0.14344383 0.23924685 -0.07856742
#> ArrElat -0.066666667 0.43033148 -0.37998030 0.00000000
#> AstGlyc -0.066666667 -0.14344383 0.23924685 -0.07856742
#> BelPere -0.096076892 0.37210420 -0.22310033 -0.11322770
#> BetPend -0.066666667 -0.14344383 0.23924685 -0.07856742
#> BriMedi -0.278652218 -0.29433147 0.70053476 -0.32839479
#> BroErec -0.626666667 0.25819889 0.27865222 -0.18856181
#> BroHord 0.381914370 -0.02594996 -0.24186656 0.03553345
#> BroSter -0.096076892 0.08268982 -0.22310033 0.28306926
#> CalSepi -0.066666667 0.25819889 -0.15480679 -0.07856742
#> CamGlom -0.066666667 0.25819889 -0.15480679 -0.07856742
#> CarPrat 0.352281938 -0.20672456 -0.22310033 0.28306926
#> CarCary -0.180906807 -0.20759972 0.47100330 -0.21320072
#> CarFlac -0.238415824 -0.35339254 0.69902250 -0.28097574
#> CarBetu -0.120000000 -0.01721326 0.19421215 -0.14142136
#> CenJace -0.161514571 -0.15291057 0.38869158 -0.19034675
#> CenScab -0.120000000 -0.25819889 0.43064434 -0.14142136
#> CenEryt -0.066666667 -0.14344383 0.23924685 -0.07856742
#> CerArve -0.066666667 -0.14344383 -0.15480679 0.47140452
#> CerGlom 0.036514837 0.02357023 -0.18499892 0.19364917
#> CerHolo -0.090958805 0.50173488 -0.60222632 0.23388214
#> CirAcau -0.200000000 -0.25819889 0.54886043 -0.23570226
#> CirArve 0.555555556 -0.14344383 -0.15480679 -0.07856742
#> CirVulg 0.352281938 -0.20672456 -0.22310033 0.28306926
#> ConArve -0.200000000 0.25819889 -0.12666010 0.00000000
#> CorSang -0.120000000 -0.01721326 0.19421215 -0.14142136
#> CorAvel -0.066666667 0.25819889 -0.15480679 -0.07856742
#> CraLaev -0.161514571 0.04170288 0.19775536 -0.19034675
#> CraMono -0.066666667 -0.14344383 0.23924685 -0.07856742
#> CraSpec -0.120000000 -0.25819889 0.43064434 -0.14142136
#> CreBien -0.238415824 0.12539735 -0.08388270 0.15609764
#> CynCris 0.100503782 0.15569979 -0.24186656 0.03553345
#> DacGlom 0.141421356 0.30429031 -0.50751922 0.16666667
#> DauCaro 0.256076256 -0.19379591 0.07269834 -0.06243905
#> EupCypa -0.120000000 -0.25819889 0.43064434 -0.14142136
#> EupSpec -0.066666667 -0.14344383 0.23924685 -0.07856742
#> FesOvin -0.161514571 -0.15291057 0.38869158 -0.19034675
#> FesPrat 0.322490310 0.09607689 -0.42417680 0.17541160
#> FesRubr 0.066666667 0.19364917 -0.48553038 0.35355339
#> FraVesc -0.066666667 -0.14344383 -0.15480679 0.47140452
#> FraViri -0.200000000 -0.25819889 0.54886043 -0.23570226
#> FraExce -0.066666667 0.25819889 -0.15480679 -0.07856742
#> GalAlbu 0.161514571 0.15291057 -0.38869158 0.19034675
#> GalPumi -0.066666667 -0.14344383 0.23924685 -0.07856742
#> GalVeru -0.161514571 -0.34752402 0.57962779 -0.19034675
#> GenTinc -0.141421356 -0.09128709 0.29854072 -0.16666667
#> GerDiss -0.096076892 0.37210420 -0.22310033 -0.11322770
#> GerMoll -0.066666667 0.25819889 -0.15480679 -0.07856742
#> GeuUrba -0.066666667 -0.14344383 -0.15480679 0.47140452
#> GleHede 0.292118697 0.02357023 -0.02312486 -0.25819889
#> GymCono -0.096076892 -0.20672456 0.34479141 -0.11322770
#> HelNumm -0.066666667 -0.14344383 0.23924685 -0.07856742
#> HelPube 0.194212152 0.01090117 -0.19786096 0.08956222
#> HerSpho -0.161514571 0.04170288 0.19775536 -0.19034675
#> HieMuro -0.096076892 -0.20672456 0.34479141 -0.11322770
#> HipComo -0.180906807 -0.38924947 0.64922077 -0.21320072
#> HolLana 0.278652218 0.14171515 -0.40106952 0.11941629
#> HypMacu -0.066666667 -0.14344383 0.23924685 -0.07856742
#> HypPerf -0.219089023 -0.47140452 0.30062324 0.41957320
#> JunComm -0.066666667 -0.14344383 0.23924685 -0.07856742
#> KnaArve -0.223772371 0.08888889 0.14171515 -0.12171612
#> KoePyra -0.096076892 -0.20672456 0.34479141 -0.11322770
#> LatPrat 0.008830216 0.12539735 -0.24046374 0.15609764
#> LeoAutu -0.066666667 -0.14344383 0.23924685 -0.07856742
#> LeoHisp -0.120000000 -0.25819889 0.43064434 -0.14142136
#> LeuIrcu -0.140572699 -0.20282899 0.30896829 -0.02923527
#> LinCath -0.161514571 -0.34752402 0.38869158 0.07613870
#> LisOvat -0.096076892 -0.20672456 0.34479141 -0.11322770
#> LolPere 0.140572699 0.50173488 -0.30896829 -0.38005848
#> LotCorn -0.278652218 -0.14171515 0.25133690 0.08956222
#> LuzCamp -0.258198890 0.06666667 0.16351749 -0.09128709
#> LuzMult -0.066666667 -0.14344383 -0.15480679 0.47140452
#> LysNumm -0.066666667 -0.14344383 0.23924685 -0.07856742
#> MedFalc -0.066666667 -0.14344383 0.23924685 -0.07856742
#> MedLupu -0.140572699 -0.05337605 0.30896829 -0.23388214
#> MyoArve 0.139979295 0.04170288 -0.37505328 0.34262414
#> OnoVici -0.066666667 -0.14344383 0.23924685 -0.07856742
#> OnoRepe -0.141421356 -0.09128709 0.29854072 -0.16666667
#> OphInse -0.066666667 -0.14344383 0.23924685 -0.07856742
#> OrcMasc -0.066666667 -0.14344383 0.23924685 -0.07856742
#> PhlPrat 0.518544973 -0.09128709 -0.11941629 -0.16666667
#> PilOffi -0.161514571 -0.15291057 0.38869158 -0.19034675
#> PimMajo -0.120000000 -0.01721326 -0.27865222 0.51854497
#> PimSaxi -0.322490310 0.05337605 0.13091876 0.02923527
#> PinSpec -0.066666667 -0.14344383 0.23924685 -0.07856742
#> PlaLaet -0.066666667 -0.14344383 0.23924685 -0.07856742
#> PlaLanc -0.555555556 0.14344383 0.15480679 0.07856742
#> PlaMajo -0.096076892 0.08268982 0.06084554 -0.11322770
#> PlaMedi -0.200000000 -0.43033148 0.54886043 0.00000000
#> PoaAngu -0.219089023 0.35355339 -0.18499892 -0.03227486
#> PoaPrat -0.292118697 0.30641294 -0.13874919 0.03227486
#> PoaTriv 0.322490310 0.24552984 -0.57080582 0.17541160
#> PolComo -0.161514571 -0.34752402 0.57962779 -0.19034675
#> PotAnse -0.066666667 -0.14344383 0.23924685 -0.07856742
#> PotRept -0.066666667 0.25819889 -0.15480679 -0.07856742
#> PotVern -0.141421356 -0.30429031 0.50751922 -0.16666667
#> PriVeri -0.430644338 -0.01090117 0.19786096 0.11941629
#> PruGran -0.066666667 -0.14344383 0.23924685 -0.07856742
#> PruAviu -0.219089023 -0.14142136 0.46249729 -0.25819889
#> PruSpin -0.161514571 -0.34752402 0.57962779 -0.19034675
#> QueRobu -0.180906807 0.15569979 0.11456837 -0.21320072
#> QueSpec -0.096076892 -0.20672456 0.34479141 -0.11322770
#> RanAcri 0.194212152 -0.14171515 -0.19786096 0.29854072
#> RanBulb -0.333333333 0.08606630 0.46442036 -0.47140452
#> RanRepe 0.352281938 0.08268982 -0.22310033 -0.11322770
#> RanSpec -0.066666667 0.25819889 -0.15480679 -0.07856742
#> RhiMino 0.008830216 0.12539735 -0.08388270 -0.06243905
#> RosCani -0.066666667 -0.14344383 0.23924685 -0.07856742
#> RosSpec -0.180906807 0.15569979 -0.06364909 0.03553345
#> RubFrut -0.066666667 -0.14344383 -0.15480679 0.47140452
#> RubIdae -0.066666667 -0.14344383 0.23924685 -0.07856742
#> RumAcet -0.223772371 0.40000000 -0.46875012 0.30429031
#> RumObtu -0.066666667 -0.14344383 0.23924685 -0.07856742
#> SalPrat -0.141421356 -0.09128709 0.29854072 -0.16666667
#> SanMino -0.346410162 -0.44721360 0.65814518 0.00000000
#> ScaColu -0.120000000 -0.25819889 0.43064434 -0.14142136
#> SedSexa -0.066666667 -0.14344383 0.23924685 -0.07856742
#> SenJaco -0.161514571 -0.15291057 0.38869158 -0.19034675
#> SilNuta -0.120000000 -0.01721326 0.19421215 -0.14142136
#> SilPusi -0.066666667 -0.14344383 0.23924685 -0.07856742
#> SteGram -0.066666667 0.25819889 -0.15480679 -0.07856742
#> TarEryt -0.066666667 0.25819889 -0.15480679 -0.07856742
#> TarRude 0.017213259 0.08888889 -0.16351749 0.09128709
#> ThlPerf -0.096076892 -0.20672456 0.06084554 0.28306926
#> ThyPule -0.120000000 -0.25819889 0.43064434 -0.14142136
#> TraPrat -0.219089023 0.18856181 -0.02312486 -0.03227486
#> TriCamp -0.066666667 -0.14344383 0.23924685 -0.07856742
#> TriDubi -0.278652218 0.16351749 -0.04812834 0.08956222
#> TriPrat -0.008830216 0.03419928 -0.07269834 0.06243905
#> TriRepe 0.166666667 0.10758287 -0.25332020 0.05892557
#> TriFlav 0.120000000 0.25819889 -0.19421215 -0.18856181
#> UrtDioi -0.066666667 -0.14344383 -0.15480679 0.47140452
#> ValCari -0.066666667 -0.14344383 -0.15480679 0.47140452
#> ValLocu -0.141421356 0.12171612 -0.11941629 0.12500000
#> VerArve 0.066666667 0.08606630 -0.12666010 0.00000000
#> VerCham 0.115470054 -0.14907120 -0.21938173 0.40824829
#> VerHede 0.555555556 -0.14344383 -0.15480679 -0.07856742
#> VerTeuc -0.120000000 -0.25819889 0.43064434 -0.14142136
#> VibOpul -0.066666667 -0.14344383 0.23924685 -0.07856742
#> VicAngu -0.278652218 0.01090117 0.10160428 0.08956222
#> VicCrac -0.141421356 0.12171612 0.08956222 -0.16666667
#> VicSepi -0.180906807 -0.02594996 -0.06364909 0.28426762
#> VioHirt -0.258198890 -0.24444444 0.31613380 0.12171612
#>
#> $samplesize
#> 1 2 3 4
#> 3 10 11 4
#>
## 4) Synoptic percentage frequency table based on historical classification from 1997
percfreq <- syntable(schedenveg, schedenenv$comm, abund = "percentage",
type = "percfreq")
percfreq
#> $syntable
#> A GK
#> AceCamp 6 0
#> AchMill 28 50
#> AgrEupa 6 30
#> AjuGene 0 10
#> AjuRept 6 0
#> AllVine 17 0
#> AloPrat 39 0
#> AntDioi 0 10
#> AntOdor 50 10
#> AntSylv 11 0
#> AntVuln 0 20
#> AraThal 6 0
#> AreSerp 0 10
#> ArrElat 83 60
#> AstGlyc 0 10
#> BelPere 11 0
#> BetPend 0 10
#> BriMedi 17 80
#> BroErec 83 100
#> BroHord 28 10
#> BroSter 11 0
#> CalSepi 6 0
#> CamGlom 0 10
#> CarPrat 11 0
#> CarCary 0 60
#> CarFlac 6 80
#> CarBetu 6 20
#> CenJace 6 40
#> CenScab 0 30
#> CenEryt 0 10
#> CerArve 6 0
#> CerGlom 33 20
#> CerHolo 61 20
#> CirAcau 0 70
#> CirArve 6 0
#> CirVulg 11 0
#> ConArve 28 20
#> CorSang 0 30
#> CorAvel 6 0
#> CraLaev 6 40
#> CraMono 0 10
#> CraSpec 0 30
#> CreBien 44 10
#> CynCris 28 10
#> DacGlom 100 60
#> DauCaro 28 40
#> EupCypa 0 30
#> EupSpec 0 10
#> FesOvin 0 50
#> FesPrat 67 30
#> FesRubr 67 40
#> FraVesc 6 0
#> FraViri 6 60
#> FraExce 6 0
#> GalAlbu 89 70
#> GalPumi 0 10
#> GalVeru 11 30
#> GenTinc 0 40
#> GerDiss 11 0
#> GerMoll 6 0
#> GeuUrba 6 0
#> GleHede 33 20
#> GymCono 0 20
#> HelNumm 0 10
#> HelPube 50 20
#> HerSpho 17 20
#> HieMuro 0 20
#> HipComo 0 60
#> HolLana 72 40
#> HypMacu 0 10
#> HypPerf 22 40
#> JunComm 0 10
#> KnaArve 56 80
#> KoePyra 0 20
#> LatPrat 39 20
#> LeoAutu 6 0
#> LeoHisp 0 30
#> LeuIrcu 39 80
#> LinCath 11 30
#> LisOvat 0 20
#> LolPere 61 20
#> LotCorn 22 70
#> LuzCamp 28 50
#> LuzMult 6 0
#> LysNumm 0 10
#> MedFalc 0 10
#> MedLupu 33 90
#> MyoArve 28 0
#> OnoVici 0 10
#> OnoRepe 0 40
#> OphInse 0 10
#> OrcMasc 0 10
#> PhlPrat 22 0
#> PilOffi 0 50
#> PimMajo 17 0
#> PimSaxi 33 70
#> PinSpec 0 10
#> PlaLaet 6 0
#> PlaLanc 94 100
#> PlaMajo 11 0
#> PlaMedi 11 50
#> PoaAngu 28 30
#> PoaPrat 72 70
#> PoaTriv 83 0
#> PolComo 0 50
#> PotAnse 0 10
#> PotRept 6 0
#> PotVern 0 40
#> PriVeri 50 80
#> PruGran 0 10
#> PruAviu 6 70
#> PruSpin 0 50
#> QueRobu 17 30
#> QueSpec 0 20
#> RanAcri 56 10
#> RanBulb 61 100
#> RanRepe 11 0
#> RanSpec 6 0
#> RhiMino 28 40
#> RosCani 0 10
#> RosSpec 17 30
#> RubFrut 6 0
#> RubIdae 0 10
#> RumAcet 78 40
#> RumObtu 0 10
#> SalPrat 0 40
#> SanMino 28 90
#> ScaColu 0 30
#> SedSexa 0 10
#> SenJaco 0 50
#> SilNuta 0 30
#> SilPusi 6 0
#> SteGram 6 0
#> TarEryt 6 0
#> TarRude 72 50
#> ThlPerf 6 10
#> ThyPule 0 30
#> TraPrat 39 10
#> TriCamp 0 10
#> TriDubi 44 30
#> TriPrat 78 50
#> TriRepe 67 0
#> TriFlav 94 80
#> UrtDioi 6 0
#> ValCari 6 0
#> ValLocu 17 10
#> VerArve 28 20
#> VerCham 61 30
#> VerHede 6 0
#> VerTeuc 0 30
#> VibOpul 0 10
#> VicAngu 28 60
#> VicCrac 11 20
#> VicSepi 28 10
#> VioHirt 22 60
#>
#> $samplesize
#> A GK
#> 18 10
#>