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"

knitr::opts_chunk$set(echo = TRUE)

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")

devtools::load_all()
source("C:/Users/rodol/OneDrive/Trabalho/Papers/Analysis/AtlanticForestMetacommunity/Auxiliary Scripts/Loading_data.R")

Number of Species

Large Extent

ncol(Broad_pa_orig)

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Intermediate Extent

DRF

ncol(DRF_pa_orig)

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SSF

ncol(SSF_pa_orig)

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Small Extent

SSF - Santa Fé do Sul

ncol(ST_pa_orig)

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SSF - Icém

ncol(IC_pa_orig)

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SSF - Nova Itapirema

ncol(NI_pa_orig)

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SSF - Morro do Diabo

ncol(MD_pa_orig)

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SSF - Jataí

ncol(JA_pa_orig)

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DRF - Ubatuba

ncol(UBA_pa_orig)

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DRF - Bertioga

ncol(BER_pa_orig)

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DRF - Itanhaém

ncol(ITA_pa_orig)

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Species Shared

Intermediate Extent

sp_comm <- na.omit(match(colnames(DRF_pa_orig), colnames(SSF_pa_orig)))
length(sp_comm)
colnames(SSF_pa_orig)[sp_comm]

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Small Extent

Santa Fé do Sul - Icém

sp_comm <- na.omit(match(colnames(ST_pa_orig), colnames(IC_pa_orig)))
length(sp_comm)
colnames(IC_pa_orig)[sp_comm]

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Santa Fé do Sul - Nova Itapirema

sp_comm <- na.omit(match(colnames(ST_pa_orig), colnames(NI_pa_orig)))
length(sp_comm)
colnames(NI_pa_orig)[sp_comm]

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Santa Fé do Sul - Morro do Diabo

sp_comm <- na.omit(match(colnames(ST_pa_orig), colnames(MD_pa_orig)))
length(sp_comm)
colnames(MD_pa_orig)[sp_comm]

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Santa Fé do Sul - Jataí

sp_comm <- na.omit(match(colnames(ST_pa_orig), colnames(JA_pa_orig)))
length(sp_comm)
colnames(JA_pa_orig)[sp_comm]

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Icém - Nova Itapirema

sp_comm <- na.omit(match(colnames(IC_pa_orig), colnames(NI_pa_orig)))
length(sp_comm)
colnames(NI_pa_orig)[sp_comm]

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Icém - Morro do Diabo

sp_comm <- na.omit(match(colnames(IC_pa_orig), colnames(MD_pa_orig)))
length(sp_comm)
colnames(MD_pa_orig)[sp_comm]

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Icém - Jataí

sp_comm <- na.omit(match(colnames(IC_pa_orig), colnames(JA_pa_orig)))
length(sp_comm)
colnames(JA_pa_orig)[sp_comm]

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Nova Itapirema - Morro do Diabo

sp_comm <- na.omit(match(colnames(NI_pa_orig), colnames(MD_pa_orig)))
length(sp_comm)
colnames(MD_pa_orig)[sp_comm]

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Nova Itapirema - Jataí

sp_comm <- na.omit(match(colnames(NI_pa_orig), colnames(JA_pa_orig)))
length(sp_comm)
colnames(JA_pa_orig)[sp_comm]

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Morro do Diabo - Jataí

sp_comm <- na.omit(match(colnames(MD_pa_orig), colnames(JA_pa_orig)))
length(sp_comm)
colnames(JA_pa_orig)[sp_comm]

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Ubatuba - Bertioga

sp_comm <- na.omit(match(colnames(UBA_pa_orig), colnames(BER_pa_orig)))
length(sp_comm)
colnames(BER_pa_orig)[sp_comm]

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Ubatuba - Itanhaém

sp_comm <- na.omit(match(colnames(UBA_pa_orig), colnames(ITA_pa_orig)))
length(sp_comm)
colnames(ITA_pa_orig)[sp_comm]

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Itanhaém - Bertioga

sp_comm <- na.omit(match(colnames(ITA_pa_orig), colnames(BER_pa_orig)))
length(sp_comm)
colnames(BER_pa_orig)[sp_comm]

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Means, maximum and minimum of predictor variables

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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))
data.frame(DRF_min = apply(DRF_env, 2, min), DRF_mean = apply(DRF_env, 2, mean), DRF_max = apply(DRF_env, 2, max))

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SSF

data.frame(SSF_min = apply(SSF_clim, 2, min), SSF_mean = apply(SSF_clim, 2, mean), SSF_max = apply(SSF_clim, 2, max))
data.frame(SSF_min = apply(SSF_env, 2, min), SSF_mean = apply(SSF_env, 2, mean), SSF_max = apply(SSF_env, 2, max))

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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))

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Icém

data.frame(IC_min = apply(IC_env, 2, min),
  IC_mean = apply(IC_env, 2, mean),
  IC_max = apply(IC_env, 2, max))

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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))

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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))

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Jataí

data.frame(JA_min = apply(JA_env, 2, min),
  JA_mean = apply(JA_env, 2, mean),
  JA_max = apply(JA_env, 2, max))

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Ubatuba

data.frame(UBA_min = apply(UBA_env, 2, min),
  UBA_mean = apply(UBA_env, 2, mean),
  UBA_max = apply(UBA_env, 2, max))

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Bertioga

data.frame(BER_min = apply(BER_env, 2, min),
  BER_mean = apply(BER_env, 2, mean),
  BER_max = apply(BER_env, 2, max))

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Itanhaém

data.frame(ITA_min = apply(ITA_env, 2, min),
  ITA_mean = apply(ITA_env, 2, mean),
  ITA_max = apply(ITA_env, 2, max))

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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
Int_Env_cv

apply(Int_Clim_cv, 2, mean)
apply(Int_Env_cv, 2, mean)

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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
apply(na.omit(Sm_Env_cv), 2, mean)

RodolfoPelinson/AtlanticForestMetacommunity documentation built on Aug. 5, 2023, 3:53 p.m.