Descriptive Statistics/Descriptive-Statistics.md
In RodolfoPelinson/AtlanticForestMetacommunity: Gives the functions and data for the Analysis of Pelinson et al. 2021 "Disentangling the multiple drivers of amphibian metacommunity structure in the Atlantic Forest at multiple spatial scales"
Descriptive Statistics
Rodolfo Pelinson
13/04/2021
Here you can find the number and identity of species at all spatial
extents, localities and ecoregions. We also show here which species are
shared between ecoregions and localities.
We also show maximum, minimum, and mean values of each environmental
variable considered in all analyses at all spatial extents, localities
and ecoregions.
First we need preparare all data matrices from the main dataset. These
are prepared sourcing the “Loading_data.R” file in the Auxiliary
Scripts folder.
library(AtlanticForestMetacommunity)
source("Loading_data.R")
## Error in get(genname, envir = envir) :
## objeto 'testthat_print' não encontrado
Number of Species
Large Extent
ncol(Broad_pa_orig)
## [1] 57
Intermediate Extent
DRF
ncol(DRF_pa_orig)
## [1] 28
SSF
ncol(SSF_pa_orig)
## [1] 34
Small Extent
SSF - Santa Fé do Sul
ncol(ST_pa_orig)
## [1] 14
SSF - Icém
ncol(IC_pa_orig)
## [1] 22
SSF - Nova Itapirema
ncol(NI_pa_orig)
## [1] 21
SSF - Morro do Diabo
ncol(MD_pa_orig)
## [1] 17
SSF - Jataí
ncol(JA_pa_orig)
## [1] 15
DRF - Ubatuba
ncol(UBA_pa_orig)
## [1] 23
DRF - Bertioga
ncol(BER_pa_orig)
## [1] 17
DRF - Itanhaém
ncol(ITA_pa_orig)
## [1] 21
Species Shared
Intermediate Extent
sp_comm <- na.omit(match(colnames(DRF_pa_orig), colnames(SSF_pa_orig)))
length(sp_comm)
## [1] 5
colnames(SSF_pa_orig)[sp_comm]
## [1] "Bfa" "Dmi" "Llt" "Pcu" "Ror"
Small Extent
Santa Fé do Sul - Icém
sp_comm <- na.omit(match(colnames(ST_pa_orig), colnames(IC_pa_orig)))
length(sp_comm)
## [1] 13
colnames(IC_pa_orig)[sp_comm]
## [1] "Bal" "Dmu" "Dna" "Esp1" "Lfs" "Lpo" "Pcu" "Pma" "Pna" "Rsc"
## [11] "Sfs" "Ssi" "Tve"
Santa Fé do Sul - Nova Itapirema
sp_comm <- na.omit(match(colnames(ST_pa_orig), colnames(NI_pa_orig)))
length(sp_comm)
## [1] 13
colnames(NI_pa_orig)[sp_comm]
## [1] "Bal" "Bra" "Dna" "Esp1" "Lfs" "Lpo" "Pcu" "Pma" "Pna" "Rsc"
## [11] "Sfs" "Ssi" "Tve"
Santa Fé do Sul - Morro do Diabo
sp_comm <- na.omit(match(colnames(ST_pa_orig), colnames(MD_pa_orig)))
length(sp_comm)
## [1] 11
colnames(MD_pa_orig)[sp_comm]
## [1] "Bra" "Dna" "Esp1" "Lfs" "Lpo" "Pcu" "Pna" "Rsc" "Sfs" "Ssi"
## [11] "Tve"
Santa Fé do Sul - Jataí
sp_comm <- na.omit(match(colnames(ST_pa_orig), colnames(JA_pa_orig)))
length(sp_comm)
## [1] 7
colnames(JA_pa_orig)[sp_comm]
## [1] "Bal" "Dna" "Lfs" "Lpo" "Pcu" "Sfs" "Ssi"
Icém - Nova Itapirema
sp_comm <- na.omit(match(colnames(IC_pa_orig), colnames(NI_pa_orig)))
length(sp_comm)
## [1] 17
colnames(NI_pa_orig)[sp_comm]
## [1] "Bal" "Dmi" "Dna" "Esp1" "Esp2" "Lfs" "Llb" "Lpo" "Pcu" "Pma"
## [11] "Pmy" "Pna" "Rsc" "Sfs" "Sfu" "Ssi" "Tve"
Icém - Morro do Diabo
sp_comm <- na.omit(match(colnames(IC_pa_orig), colnames(MD_pa_orig)))
length(sp_comm)
## [1] 12
colnames(MD_pa_orig)[sp_comm]
## [1] "Dmi" "Dna" "Esp1" "Lfs" "Lpo" "Pcu" "Pna" "Rsc" "Sfs" "Sfu"
## [11] "Ssi" "Tve"
Icém - Jataí
sp_comm <- na.omit(match(colnames(IC_pa_orig), colnames(JA_pa_orig)))
length(sp_comm)
## [1] 9
colnames(JA_pa_orig)[sp_comm]
## [1] "Bal" "Dmi" "Dna" "Lfs" "Llb" "Lpo" "Pcu" "Sfs" "Ssi"
Nova Itapirema - Morro do Diabo
sp_comm <- na.omit(match(colnames(NI_pa_orig), colnames(MD_pa_orig)))
length(sp_comm)
## [1] 14
colnames(MD_pa_orig)[sp_comm]
## [1] "Bra" "Dmi" "Dna" "Esp1" "Lfs" "Llt" "Lpo" "Pcu" "Pna" "Rsc"
## [11] "Sfs" "Sfu" "Ssi" "Tve"
Nova Itapirema - Jataí
sp_comm <- na.omit(match(colnames(NI_pa_orig), colnames(JA_pa_orig)))
length(sp_comm)
## [1] 9
colnames(JA_pa_orig)[sp_comm]
## [1] "Bal" "Dmi" "Dna" "Lfs" "Llb" "Lpo" "Pcu" "Sfs" "Ssi"
Morro do Diabo - Jataí
sp_comm <- na.omit(match(colnames(MD_pa_orig), colnames(JA_pa_orig)))
length(sp_comm)
## [1] 8
colnames(JA_pa_orig)[sp_comm]
## [1] "Bfa" "Dmi" "Dna" "Lfs" "Lpo" "Pcu" "Sfs" "Ssi"
Ubatuba - Bertioga
sp_comm <- na.omit(match(colnames(UBA_pa_orig), colnames(BER_pa_orig)))
length(sp_comm)
## [1] 15
colnames(BER_pa_orig)[sp_comm]
## [1] "Aeu" "Bab" "Bfa" "Dbe" "Del" "Dmi" "Ila" "Llt" "Ror" "Sal" "Sar" "Sha"
## [13] "Spe" "Str" "Tme"
Ubatuba - Itanhaém
sp_comm <- na.omit(match(colnames(UBA_pa_orig), colnames(ITA_pa_orig)))
length(sp_comm)
## [1] 17
colnames(ITA_pa_orig)[sp_comm]
## [1] "Bab" "Bfa" "Cca" "Dbe" "Del" "Dmc" "Dmi" "Ila" "Llt" "Ror" "Sal" "Sar"
## [13] "Sha" "Sli" "Spe" "Str" "Tme"
Itanhaém - Bertioga
sp_comm <- na.omit(match(colnames(ITA_pa_orig), colnames(BER_pa_orig)))
length(sp_comm)
## [1] 15
colnames(BER_pa_orig)[sp_comm]
## [1] "Bab" "Bfa" "Bse" "Dbe" "Del" "Dmi" "Ila" "Llt" "Ror" "Sal" "Sar" "Sha"
## [13] "Spe" "Str" "Tme"
Means, maximum and minimum of predictor variables
Intermediate Extent
DRF
data.frame(DRF_min = apply(DRF_clim, 2, min), DRF_mean = apply(DRF_clim, 2, mean), DRF_max = apply(DRF_clim, 2, max))
## DRF_min DRF_mean DRF_max
## temp_Season 2194 2308.18 2603
## range_temp 143 161.42 173
## total_prec 1911 2279.84 2747
## prec_season 36 41.86 48
data.frame(DRF_min = apply(DRF_env, 2, min), DRF_mean = apply(DRF_env, 2, mean), DRF_max = apply(DRF_env, 2, max))
## DRF_min DRF_mean DRF_max
## hydroperiod 0.00 0.3600 1.0
## area 1.15 2397.0658 66803.5
## depth 0.10 0.8704 4.0
## canopy_cover 0.00 1.4800 3.0
## nvt 2.00 2.9400 3.0
## dist_to_forest 0.00 13.1200 209.0
SSF
data.frame(SSF_min = apply(SSF_clim, 2, min), SSF_mean = apply(SSF_clim, 2, mean), SSF_max = apply(SSF_clim, 2, max))
## SSF_min SSF_mean SSF_max
## temp_Season 1890 2128.65217 2598
## range_temp 182 188.97826 197
## total_prec 1174 1263.69565 1435
## prec_season 41 67.06522 78
data.frame(SSF_min = apply(SSF_env, 2, min), SSF_mean = apply(SSF_env, 2, mean), SSF_max = apply(SSF_env, 2, max))
## SSF_min SSF_mean SSF_max
## hydroperiod 0.00 0.4782609 1.0
## area 4.00 695.7586957 10000.0
## depth 0.05 0.6728261 2.4
## canopy_cover 0.00 0.3478261 3.0
## nvt 1.00 2.0000000 3.0
## dist_to_forest 0.00 177.5652174 834.0
Small Extent
Santa Fé do Sul
data.frame(ST_min = apply(ST_env, 2, min),
ST_mean = apply(ST_env, 2, mean),
ST_max = apply(ST_env, 2, max))
## ST_min ST_mean ST_max
## hydroperiod 0.0 0.37500 1
## area 50.0 270.31250 960
## depth 0.1 0.86625 2
## nvt 1.0 1.62500 2
## dist_to_forest 65.0 140.37500 230
Icém
data.frame(IC_min = apply(IC_env, 2, min),
IC_mean = apply(IC_env, 2, mean),
IC_max = apply(IC_env, 2, max))
## IC_min IC_mean IC_max
## hydroperiod 0.0 0.5833333 1.0
## area 60.0 398.4166667 800.0
## depth 0.1 0.4225000 0.7
## nvt 1.0 1.9166667 3.0
## dist_to_forest 10.0 223.9166667 551.0
Nova Itapirema
data.frame(NI_min = apply(NI_env, 2, min),
NI_mean = apply(NI_env, 2, mean),
NI_max = apply(NI_env, 2, max))
## NI_min NI_mean NI_max
## hydroperiod 0.00 0.625 1.0
## area 32.00 338.550 800.0
## depth 0.27 0.665 1.5
## nvt 1.00 1.875 2.0
## dist_to_forest 12.00 422.500 834.0
Morro do Diabo
data.frame(MD_min = apply(MD_env, 2, min),
MD_mean = apply(MD_env, 2, mean),
MD_max = apply(MD_env, 2, max))
## MD_min MD_mean MD_max
## hydroperiod 0.0 0.3750 1.0
## area 100.0 2300.2500 10000.0
## depth 0.3 0.8975 2.1
## canopy_cover 0.0 0.7500 3.0
## nvt 1.0 2.2500 3.0
## dist_to_forest 0.0 17.8750 36.0
Jataí
data.frame(JA_min = apply(JA_env, 2, min),
JA_mean = apply(JA_env, 2, mean),
JA_max = apply(JA_env, 2, max))
## JA_min JA_mean JA_max
## hydroperiod 0.00 0.400 1.0
## area 4.00 395.100 1833.0
## depth 0.05 0.645 2.4
## canopy_cover 0.00 1.000 3.0
## nvt 1.00 2.300 3.0
## dist_to_forest 0.00 83.500 214.0
Ubatuba
data.frame(UBA_min = apply(UBA_env, 2, min),
UBA_mean = apply(UBA_env, 2, mean),
UBA_max = apply(UBA_env, 2, max))
## UBA_min UBA_mean UBA_max
## hydroperiod 0.00 0.4347826 1.0
## area 1.15 3530.8978261 66803.5
## depth 0.10 0.6217391 2.0
## canopy_cover 0.00 1.9565217 3.0
## dist_to_forest 0.00 5.6521739 90.0
Bertioga
data.frame(BER_min = apply(BER_env, 2, min),
BER_mean = apply(BER_env, 2, mean),
BER_max = apply(BER_env, 2, max))
## BER_min BER_mean BER_max
## hydroperiod 0.00 0.250000 1.00
## area 70.65 1935.707500 12363.75
## depth 0.30 1.066667 2.00
## canopy_cover 0.00 1.083333 3.00
## dist_to_forest 0.00 43.833333 209.00
Itanhaém
data.frame(ITA_min = apply(ITA_env, 2, min),
ITA_mean = apply(ITA_env, 2, mean),
ITA_max = apply(ITA_env, 2, max))
## ITA_min ITA_mean ITA_max
## hydroperiod 0.00 0.3333333 1.00
## area 53.44 1027.6100000 4592.25
## depth 0.25 1.0946667 4.00
## canopy_cover 0.00 1.0666667 3.00
## nvt 2.00 2.8000000 3.00
Coefficient of Variation of predictor variables
Intermediate Extent
Int_Clim_cv <- data.frame(DRF_cv = apply(DRF_clim, 2, coef_var), SSF_cv = apply(SSF_clim, 2, coef_var))
Int_Env_cv <- data.frame(DRF_cv = apply(DRF_env, 2, coef_var), SSF_cv = apply(SSF_env, 2, coef_var))
Int_Clim_cv
## DRF_cv SSF_cv
## temp_Season 0.05215230 0.10576190
## range_temp 0.05661376 0.02396501
## total_prec 0.09938354 0.06859710
## prec_season 0.06739787 0.18438093
Int_Env_cv
## DRF_cv SSF_cv
## hydroperiod 1.34687006 1.0560073
## area 3.98121316 2.2115741
## depth 0.80900349 0.8667245
## canopy_cover 0.84413943 2.7252676
## nvt 0.08159794 0.3651484
## dist_to_forest 3.22261540 1.3173039
apply(Int_Clim_cv, 2, mean)
## DRF_cv SSF_cv
## 0.06888687 0.09567624
apply(Int_Env_cv, 2, mean)
## DRF_cv SSF_cv
## 1.714240 1.423671
Small Extent
UBA_cv <- c(apply(UBA_env, 2, coef_var), nvt = NA)
BER_cv <- c(apply(BER_env, 2, coef_var), nvt = NA)
ITA_cv <- c(apply(ITA_env, 2, coef_var), dist_to_forest = NA)
ST_cv <- c(apply(ST_env, 2, coef_var), canopy_cover = NA)
IC_cv <- c(apply(IC_env, 2, coef_var), canopy_cover = NA)
NI_cv <- c(apply(NI_env, 2, coef_var), canopy_cover = NA)
MD_cv <- c(apply(MD_env, 2, coef_var))
JA_cv <- c(apply(JA_env, 2, coef_var))
UBA_cv <- UBA_cv[order(names(UBA_cv))]
BER_cv <- BER_cv[order(names(BER_cv))]
ITA_cv <- ITA_cv[order(names(ITA_cv))]
ST_cv <- ST_cv[order(names(ST_cv))]
IC_cv <- IC_cv[order(names(IC_cv))]
NI_cv <- NI_cv[order(names(NI_cv))]
MD_cv <- MD_cv[order(names(MD_cv))]
JA_cv <- JA_cv[order(names(JA_cv))]
Sm_Env_cv <- data.frame(UBA_cv,BER_cv,ITA_cv,ST_cv,IC_cv,NI_cv,MD_cv,JA_cv)
Sm_Env_cv
## UBA_cv BER_cv ITA_cv ST_cv IC_cv NI_cv
## area 3.9205225 1.9703002 1.3393552 1.2354847 0.6800164 0.9218279
## canopy_cover 0.6255682 1.1447191 1.0310471 NA NA NA
## depth 0.8307725 0.4633208 0.8837021 0.8303653 0.4495484 0.5676852
## dist_to_forest 3.5715239 1.7326073 NA 0.4482230 0.8530787 0.9661925
## hydroperiod 1.1658005 1.8090681 1.4638501 1.3801311 0.8827348 0.8280787
## nvt NA NA 0.1478712 0.3184918 0.4697409 0.1885618
## MD_cv JA_cv
## area 1.4337193 1.4884257
## canopy_cover 1.8516402 1.4142136
## depth 0.7715253 1.2083799
## dist_to_forest 0.8586156 0.9410876
## hydroperiod 1.3801311 1.2909944
## nvt 0.3142697 0.3579446
apply(na.omit(Sm_Env_cv), 2, mean)
## UBA_cv BER_cv ITA_cv ST_cv IC_cv NI_cv MD_cv JA_cv
## 1.9723652 1.4142297 1.2289691 1.1486604 0.6707665 0.7725306 1.1951252 1.3292667
RodolfoPelinson/AtlanticForestMetacommunity documentation built on Aug. 5, 2023, 3:53 p.m.
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Descriptive Statistics
Rodolfo Pelinson 13/04/2021
Here you can find the number and identity of species at all spatial extents, localities and ecoregions. We also show here which species are shared between ecoregions and localities.
We also show maximum, minimum, and mean values of each environmental variable considered in all analyses at all spatial extents, localities and ecoregions.
First we need preparare all data matrices from the main dataset. These are prepared sourcing the “Loading_data.R” file in the Auxiliary Scripts folder.
library(AtlanticForestMetacommunity)
source("Loading_data.R")
## Error in get(genname, envir = envir) :
## objeto 'testthat_print' não encontrado
Number of Species
Large Extent
ncol(Broad_pa_orig)
## [1] 57
Intermediate Extent
DRF
ncol(DRF_pa_orig)
## [1] 28
SSF
ncol(SSF_pa_orig)
## [1] 34
Small Extent
SSF - Santa Fé do Sul
ncol(ST_pa_orig)
## [1] 14
SSF - Icém
ncol(IC_pa_orig)
## [1] 22
SSF - Nova Itapirema
ncol(NI_pa_orig)
## [1] 21
SSF - Morro do Diabo
ncol(MD_pa_orig)
## [1] 17
SSF - Jataí
ncol(JA_pa_orig)
## [1] 15
DRF - Ubatuba
ncol(UBA_pa_orig)
## [1] 23
DRF - Bertioga
ncol(BER_pa_orig)
## [1] 17
DRF - Itanhaém
ncol(ITA_pa_orig)
## [1] 21
Species Shared
Intermediate Extent
sp_comm <- na.omit(match(colnames(DRF_pa_orig), colnames(SSF_pa_orig)))
length(sp_comm)
## [1] 5
colnames(SSF_pa_orig)[sp_comm]
## [1] "Bfa" "Dmi" "Llt" "Pcu" "Ror"
Small Extent
Santa Fé do Sul - Icém
sp_comm <- na.omit(match(colnames(ST_pa_orig), colnames(IC_pa_orig)))
length(sp_comm)
## [1] 13
colnames(IC_pa_orig)[sp_comm]
## [1] "Bal" "Dmu" "Dna" "Esp1" "Lfs" "Lpo" "Pcu" "Pma" "Pna" "Rsc"
## [11] "Sfs" "Ssi" "Tve"
Santa Fé do Sul - Nova Itapirema
sp_comm <- na.omit(match(colnames(ST_pa_orig), colnames(NI_pa_orig)))
length(sp_comm)
## [1] 13
colnames(NI_pa_orig)[sp_comm]
## [1] "Bal" "Bra" "Dna" "Esp1" "Lfs" "Lpo" "Pcu" "Pma" "Pna" "Rsc"
## [11] "Sfs" "Ssi" "Tve"
Santa Fé do Sul - Morro do Diabo
sp_comm <- na.omit(match(colnames(ST_pa_orig), colnames(MD_pa_orig)))
length(sp_comm)
## [1] 11
colnames(MD_pa_orig)[sp_comm]
## [1] "Bra" "Dna" "Esp1" "Lfs" "Lpo" "Pcu" "Pna" "Rsc" "Sfs" "Ssi"
## [11] "Tve"
Santa Fé do Sul - Jataí
sp_comm <- na.omit(match(colnames(ST_pa_orig), colnames(JA_pa_orig)))
length(sp_comm)
## [1] 7
colnames(JA_pa_orig)[sp_comm]
## [1] "Bal" "Dna" "Lfs" "Lpo" "Pcu" "Sfs" "Ssi"
Icém - Nova Itapirema
sp_comm <- na.omit(match(colnames(IC_pa_orig), colnames(NI_pa_orig)))
length(sp_comm)
## [1] 17
colnames(NI_pa_orig)[sp_comm]
## [1] "Bal" "Dmi" "Dna" "Esp1" "Esp2" "Lfs" "Llb" "Lpo" "Pcu" "Pma"
## [11] "Pmy" "Pna" "Rsc" "Sfs" "Sfu" "Ssi" "Tve"
Icém - Morro do Diabo
sp_comm <- na.omit(match(colnames(IC_pa_orig), colnames(MD_pa_orig)))
length(sp_comm)
## [1] 12
colnames(MD_pa_orig)[sp_comm]
## [1] "Dmi" "Dna" "Esp1" "Lfs" "Lpo" "Pcu" "Pna" "Rsc" "Sfs" "Sfu"
## [11] "Ssi" "Tve"
Icém - Jataí
sp_comm <- na.omit(match(colnames(IC_pa_orig), colnames(JA_pa_orig)))
length(sp_comm)
## [1] 9
colnames(JA_pa_orig)[sp_comm]
## [1] "Bal" "Dmi" "Dna" "Lfs" "Llb" "Lpo" "Pcu" "Sfs" "Ssi"
Nova Itapirema - Morro do Diabo
sp_comm <- na.omit(match(colnames(NI_pa_orig), colnames(MD_pa_orig)))
length(sp_comm)
## [1] 14
colnames(MD_pa_orig)[sp_comm]
## [1] "Bra" "Dmi" "Dna" "Esp1" "Lfs" "Llt" "Lpo" "Pcu" "Pna" "Rsc"
## [11] "Sfs" "Sfu" "Ssi" "Tve"
Nova Itapirema - Jataí
sp_comm <- na.omit(match(colnames(NI_pa_orig), colnames(JA_pa_orig)))
length(sp_comm)
## [1] 9
colnames(JA_pa_orig)[sp_comm]
## [1] "Bal" "Dmi" "Dna" "Lfs" "Llb" "Lpo" "Pcu" "Sfs" "Ssi"
Morro do Diabo - Jataí
sp_comm <- na.omit(match(colnames(MD_pa_orig), colnames(JA_pa_orig)))
length(sp_comm)
## [1] 8
colnames(JA_pa_orig)[sp_comm]
## [1] "Bfa" "Dmi" "Dna" "Lfs" "Lpo" "Pcu" "Sfs" "Ssi"
Ubatuba - Bertioga
sp_comm <- na.omit(match(colnames(UBA_pa_orig), colnames(BER_pa_orig)))
length(sp_comm)
## [1] 15
colnames(BER_pa_orig)[sp_comm]
## [1] "Aeu" "Bab" "Bfa" "Dbe" "Del" "Dmi" "Ila" "Llt" "Ror" "Sal" "Sar" "Sha"
## [13] "Spe" "Str" "Tme"
Ubatuba - Itanhaém
sp_comm <- na.omit(match(colnames(UBA_pa_orig), colnames(ITA_pa_orig)))
length(sp_comm)
## [1] 17
colnames(ITA_pa_orig)[sp_comm]
## [1] "Bab" "Bfa" "Cca" "Dbe" "Del" "Dmc" "Dmi" "Ila" "Llt" "Ror" "Sal" "Sar"
## [13] "Sha" "Sli" "Spe" "Str" "Tme"
Itanhaém - Bertioga
sp_comm <- na.omit(match(colnames(ITA_pa_orig), colnames(BER_pa_orig)))
length(sp_comm)
## [1] 15
colnames(BER_pa_orig)[sp_comm]
## [1] "Bab" "Bfa" "Bse" "Dbe" "Del" "Dmi" "Ila" "Llt" "Ror" "Sal" "Sar" "Sha"
## [13] "Spe" "Str" "Tme"
Means, maximum and minimum of predictor variables
Intermediate Extent
DRF
data.frame(DRF_min = apply(DRF_clim, 2, min), DRF_mean = apply(DRF_clim, 2, mean), DRF_max = apply(DRF_clim, 2, max))
## DRF_min DRF_mean DRF_max
## temp_Season 2194 2308.18 2603
## range_temp 143 161.42 173
## total_prec 1911 2279.84 2747
## prec_season 36 41.86 48
data.frame(DRF_min = apply(DRF_env, 2, min), DRF_mean = apply(DRF_env, 2, mean), DRF_max = apply(DRF_env, 2, max))
## DRF_min DRF_mean DRF_max
## hydroperiod 0.00 0.3600 1.0
## area 1.15 2397.0658 66803.5
## depth 0.10 0.8704 4.0
## canopy_cover 0.00 1.4800 3.0
## nvt 2.00 2.9400 3.0
## dist_to_forest 0.00 13.1200 209.0
SSF
data.frame(SSF_min = apply(SSF_clim, 2, min), SSF_mean = apply(SSF_clim, 2, mean), SSF_max = apply(SSF_clim, 2, max))
## SSF_min SSF_mean SSF_max
## temp_Season 1890 2128.65217 2598
## range_temp 182 188.97826 197
## total_prec 1174 1263.69565 1435
## prec_season 41 67.06522 78
data.frame(SSF_min = apply(SSF_env, 2, min), SSF_mean = apply(SSF_env, 2, mean), SSF_max = apply(SSF_env, 2, max))
## SSF_min SSF_mean SSF_max
## hydroperiod 0.00 0.4782609 1.0
## area 4.00 695.7586957 10000.0
## depth 0.05 0.6728261 2.4
## canopy_cover 0.00 0.3478261 3.0
## nvt 1.00 2.0000000 3.0
## dist_to_forest 0.00 177.5652174 834.0
Small Extent
Santa Fé do Sul
data.frame(ST_min = apply(ST_env, 2, min),
ST_mean = apply(ST_env, 2, mean),
ST_max = apply(ST_env, 2, max))
## ST_min ST_mean ST_max
## hydroperiod 0.0 0.37500 1
## area 50.0 270.31250 960
## depth 0.1 0.86625 2
## nvt 1.0 1.62500 2
## dist_to_forest 65.0 140.37500 230
Icém
data.frame(IC_min = apply(IC_env, 2, min),
IC_mean = apply(IC_env, 2, mean),
IC_max = apply(IC_env, 2, max))
## IC_min IC_mean IC_max
## hydroperiod 0.0 0.5833333 1.0
## area 60.0 398.4166667 800.0
## depth 0.1 0.4225000 0.7
## nvt 1.0 1.9166667 3.0
## dist_to_forest 10.0 223.9166667 551.0
Nova Itapirema
data.frame(NI_min = apply(NI_env, 2, min),
NI_mean = apply(NI_env, 2, mean),
NI_max = apply(NI_env, 2, max))
## NI_min NI_mean NI_max
## hydroperiod 0.00 0.625 1.0
## area 32.00 338.550 800.0
## depth 0.27 0.665 1.5
## nvt 1.00 1.875 2.0
## dist_to_forest 12.00 422.500 834.0
Morro do Diabo
data.frame(MD_min = apply(MD_env, 2, min),
MD_mean = apply(MD_env, 2, mean),
MD_max = apply(MD_env, 2, max))
## MD_min MD_mean MD_max
## hydroperiod 0.0 0.3750 1.0
## area 100.0 2300.2500 10000.0
## depth 0.3 0.8975 2.1
## canopy_cover 0.0 0.7500 3.0
## nvt 1.0 2.2500 3.0
## dist_to_forest 0.0 17.8750 36.0
Jataí
data.frame(JA_min = apply(JA_env, 2, min),
JA_mean = apply(JA_env, 2, mean),
JA_max = apply(JA_env, 2, max))
## JA_min JA_mean JA_max
## hydroperiod 0.00 0.400 1.0
## area 4.00 395.100 1833.0
## depth 0.05 0.645 2.4
## canopy_cover 0.00 1.000 3.0
## nvt 1.00 2.300 3.0
## dist_to_forest 0.00 83.500 214.0
Ubatuba
data.frame(UBA_min = apply(UBA_env, 2, min),
UBA_mean = apply(UBA_env, 2, mean),
UBA_max = apply(UBA_env, 2, max))
## UBA_min UBA_mean UBA_max
## hydroperiod 0.00 0.4347826 1.0
## area 1.15 3530.8978261 66803.5
## depth 0.10 0.6217391 2.0
## canopy_cover 0.00 1.9565217 3.0
## dist_to_forest 0.00 5.6521739 90.0
Bertioga
data.frame(BER_min = apply(BER_env, 2, min),
BER_mean = apply(BER_env, 2, mean),
BER_max = apply(BER_env, 2, max))
## BER_min BER_mean BER_max
## hydroperiod 0.00 0.250000 1.00
## area 70.65 1935.707500 12363.75
## depth 0.30 1.066667 2.00
## canopy_cover 0.00 1.083333 3.00
## dist_to_forest 0.00 43.833333 209.00
Itanhaém
data.frame(ITA_min = apply(ITA_env, 2, min),
ITA_mean = apply(ITA_env, 2, mean),
ITA_max = apply(ITA_env, 2, max))
## ITA_min ITA_mean ITA_max
## hydroperiod 0.00 0.3333333 1.00
## area 53.44 1027.6100000 4592.25
## depth 0.25 1.0946667 4.00
## canopy_cover 0.00 1.0666667 3.00
## nvt 2.00 2.8000000 3.00
Coefficient of Variation of predictor variables
Intermediate Extent
Int_Clim_cv <- data.frame(DRF_cv = apply(DRF_clim, 2, coef_var), SSF_cv = apply(SSF_clim, 2, coef_var))
Int_Env_cv <- data.frame(DRF_cv = apply(DRF_env, 2, coef_var), SSF_cv = apply(SSF_env, 2, coef_var))
Int_Clim_cv
## DRF_cv SSF_cv
## temp_Season 0.05215230 0.10576190
## range_temp 0.05661376 0.02396501
## total_prec 0.09938354 0.06859710
## prec_season 0.06739787 0.18438093
Int_Env_cv
## DRF_cv SSF_cv
## hydroperiod 1.34687006 1.0560073
## area 3.98121316 2.2115741
## depth 0.80900349 0.8667245
## canopy_cover 0.84413943 2.7252676
## nvt 0.08159794 0.3651484
## dist_to_forest 3.22261540 1.3173039
apply(Int_Clim_cv, 2, mean)
## DRF_cv SSF_cv
## 0.06888687 0.09567624
apply(Int_Env_cv, 2, mean)
## DRF_cv SSF_cv
## 1.714240 1.423671
Small Extent
UBA_cv <- c(apply(UBA_env, 2, coef_var), nvt = NA)
BER_cv <- c(apply(BER_env, 2, coef_var), nvt = NA)
ITA_cv <- c(apply(ITA_env, 2, coef_var), dist_to_forest = NA)
ST_cv <- c(apply(ST_env, 2, coef_var), canopy_cover = NA)
IC_cv <- c(apply(IC_env, 2, coef_var), canopy_cover = NA)
NI_cv <- c(apply(NI_env, 2, coef_var), canopy_cover = NA)
MD_cv <- c(apply(MD_env, 2, coef_var))
JA_cv <- c(apply(JA_env, 2, coef_var))
UBA_cv <- UBA_cv[order(names(UBA_cv))]
BER_cv <- BER_cv[order(names(BER_cv))]
ITA_cv <- ITA_cv[order(names(ITA_cv))]
ST_cv <- ST_cv[order(names(ST_cv))]
IC_cv <- IC_cv[order(names(IC_cv))]
NI_cv <- NI_cv[order(names(NI_cv))]
MD_cv <- MD_cv[order(names(MD_cv))]
JA_cv <- JA_cv[order(names(JA_cv))]
Sm_Env_cv <- data.frame(UBA_cv,BER_cv,ITA_cv,ST_cv,IC_cv,NI_cv,MD_cv,JA_cv)
Sm_Env_cv
## UBA_cv BER_cv ITA_cv ST_cv IC_cv NI_cv
## area 3.9205225 1.9703002 1.3393552 1.2354847 0.6800164 0.9218279
## canopy_cover 0.6255682 1.1447191 1.0310471 NA NA NA
## depth 0.8307725 0.4633208 0.8837021 0.8303653 0.4495484 0.5676852
## dist_to_forest 3.5715239 1.7326073 NA 0.4482230 0.8530787 0.9661925
## hydroperiod 1.1658005 1.8090681 1.4638501 1.3801311 0.8827348 0.8280787
## nvt NA NA 0.1478712 0.3184918 0.4697409 0.1885618
## MD_cv JA_cv
## area 1.4337193 1.4884257
## canopy_cover 1.8516402 1.4142136
## depth 0.7715253 1.2083799
## dist_to_forest 0.8586156 0.9410876
## hydroperiod 1.3801311 1.2909944
## nvt 0.3142697 0.3579446
apply(na.omit(Sm_Env_cv), 2, mean)
## UBA_cv BER_cv ITA_cv ST_cv IC_cv NI_cv MD_cv JA_cv
## 1.9723652 1.4142297 1.2289691 1.1486604 0.6707665 0.7725306 1.1951252 1.3292667
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