VarPart/Variation_Partitioning.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"
Variation Partitioning
Rodolfo Pelinson
07/04/2021
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
The used packages to run this analysis are:
vegan version 2.5-6
ade4 version 1.7-15
adespatial version 0.3-8
Some packages used to generate plots and tables
dplyr version 0.8.5
plotrix version 3.7-8
set.seed(5)
Reducing Multicolinearity
To remove multicolinear variables from our Environmental Matrix I built
a function that removes variables with the variance inflation factor
(VIF) higher than 3. The function VIF_selection() simply uses the
vif.cca() from the vegan package.
Broad_env_VIF <- VIF_selection(Broad_pa, Broad_env_st[,-7])
Broad_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth nvt
## 1.237565 1.488527 1.118756 1.340180 1.617198
## dist_to_forest
## 1.319102
SSF_env_VIF <- VIF_selection(SSF_pa, SSF_env_st)
SSF_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth nvt
## 1.287250 1.293051 1.287689 1.443912 1.492950
## dist_to_forest
## 1.136891
ST_env_VIF <- VIF_selection(ST_pa, ST_env_st)
ST_env_VIF$VIFs
## [[1]]
## hydroperiod area depth nvt dist_to_forest
## 27.479904 11.917983 5.386203 20.148713 1.807948
##
## [[2]]
## area depth nvt dist_to_forest
## 3.533032 2.360081 2.639484 1.668725
##
## [[3]]
## depth nvt dist_to_forest
## 1.444899 1.301229 1.367747
IC_env_VIF <- VIF_selection(IC_pa, IC_env_st)
IC_env_VIF$VIFs
## [[1]]
## hydroperiod area depth nvt dist_to_forest
## 5.342364 5.518649 3.465826 3.219143 3.331453
##
## [[2]]
## hydroperiod depth nvt dist_to_forest
## 2.895120 1.516899 1.606162 2.715728
NI_env_VIF <- VIF_selection(NI_pa, NI_env_st)
NI_env_VIF$VIFs
## [[1]]
## hydroperiod area depth nvt dist_to_forest
## 1.502173 4.868245 4.697667 1.834039 2.256836
##
## [[2]]
## hydroperiod depth nvt dist_to_forest
## 1.487856 1.157613 1.493676 1.441587
MD_env_VIF <- VIF_selection(MD_pa, MD_env_st)
MD_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth nvt
## 6.123821 36.928117 15.263901 3.075865 3.436439
## dist_to_forest
## 43.153516
##
## [[2]]
## hydroperiod canopy_cover area depth nvt
## 3.843659 1.637214 5.706331 2.499002 3.434896
##
## [[3]]
## hydroperiod canopy_cover depth nvt
## 1.770317 1.410612 1.879809 1.508743
JA_env_VIF <- VIF_selection(JA_pa, JA_env_st)
JA_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth nvt
## 14.962311 7.691347 3.081842 5.988699 3.453394
## dist_to_forest
## 12.622399
##
## [[2]]
## canopy_cover area depth nvt dist_to_forest
## 6.871158 1.961897 4.322304 3.356278 7.533870
##
## [[3]]
## canopy_cover area depth nvt
## 2.081341 1.450483 2.089961 2.864143
DRF_env_VIF <- VIF_selection(DRF_pa, DRF_env_st)
DRF_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth nvt
## 1.338500 1.272615 1.296349 1.356465 1.048673
## dist_to_forest
## 1.368314
UBA_env_VIF <- VIF_selection(UBA_pa, UBA_env_st)
UBA_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth dist_to_forest
## 1.436660 1.459906 7.001901 1.750901 6.699443
##
## [[2]]
## hydroperiod canopy_cover depth dist_to_forest
## 1.341421 1.415973 1.599294 1.643447
BER_env_VIF <- VIF_selection(BER_pa, BER_env_st)
BER_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth dist_to_forest
## 3.117432 1.663244 8.428997 2.304574 7.972957
##
## [[2]]
## hydroperiod canopy_cover depth dist_to_forest
## 2.192425 1.561949 2.253493 4.070420
##
## [[3]]
## hydroperiod canopy_cover depth
## 1.541888 1.384145 1.154773
ITA_env_VIF <- VIF_selection(ITA_pa, ITA_env_st)
ITA_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth nvt
## 2.342853 1.828709 1.671319 1.996469 1.458903
Same thing for climate variables
Broad_clim_VIF <- VIF_selection(Broad_pa, Broad_clim_st[,-6])
Broad_clim_VIF$VIFs
## [[1]]
## temp_Season range_temp total_prec prec_season
## 16.70111 11.16256 38.16025 37.55883
##
## [[2]]
## temp_Season range_temp prec_season
## 3.005540 2.148041 4.835358
##
## [[3]]
## temp_Season range_temp
## 1.168582 1.168582
SSF_clim_VIF <- VIF_selection(SSF_pa, SSF_clim_st)
SSF_clim_VIF$VIFs
## [[1]]
## temp_Season range_temp total_prec prec_season
## 44.759191 5.971934 3.197284 52.382832
##
## [[2]]
## temp_Season range_temp total_prec
## 2.026442 4.722364 3.061846
##
## [[3]]
## temp_Season total_prec
## 1.157234 1.157234
DRF_clim_VIF <- VIF_selection(DRF_pa, DRF_clim_st)
DRF_clim_VIF$VIFs
## [[1]]
## temp_Season range_temp total_prec prec_season
## 6.561095 3.339104 12.024627 6.439093
##
## [[2]]
## temp_Season range_temp prec_season
## 1.895339 2.812287 2.295711
Spatial Variables
Constructing matrices of spatial filters
set.seed(3, sample.kind = "default")
candidates_Broad <- listw.candidates(Broad_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup"), y_fdown = 5, y_fup = 0.5)
##
## PLEASE NOTE: The components "delsgs" and "summary" of the
## object returned by deldir() are now DATA FRAMES rather than
## matrices (as they were prior to release 0.0-18).
## See help("deldir").
##
## PLEASE NOTE: The process that deldir() uses for determining
## duplicated points has changed from that used in version
## 0.0-9 of this package (and previously). See help("deldir").
Broad_MEM <- listw.select(Broad_pa, candidates = candidates_Broad, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 5 is 0.286871 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 12 is 0.066193 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.395438 with 22 variables (> 0.393805)
## Procedure stopped (alpha criteria): pvalue for variable 16 is 0.072993 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.399941 with 31 variables (> 0.398728)
## Procedure stopped (alpha criteria): pvalue for variable 18 is 0.051195 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.371382 with 23 variables (> 0.369036)
Broad_MEM_FS <- Broad_MEM$best$MEM.select
adegraphics::s.label(Broad_coord, nb = candidates_Broad[[Broad_MEM$best.id]],
pnb.edge.col = "red", main = paste("Broad - ",names(Broad_MEM$best.id)), plot = TRUE, labels = NULL)
################################################################################
candidates_DRF <- listw.candidates(DRF_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup"), y_fdown = 5, y_fup = 0.25)
DRF_MEM <- listw.select(DRF_pa, candidates = candidates_DRF, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 8 is 0.083192 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 8 is 0.108089 (> 0.050000)
DRF_MEM_FS <- DRF_MEM$best$MEM.select
adegraphics::s.label(DRF_coord, nb = candidates_DRF[[DRF_MEM$best.id]],
pnb.edge.col = "red", main = paste("DRF - ",names(DRF_MEM$best.id)), plot = TRUE, labels = NULL)
################################################################################
candidates_SSF <- listw.candidates(SSF_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup"), y_fdown = 5, y_fup = 0.25)
SSF_MEM <- listw.select(SSF_pa, candidates = candidates_SSF, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.079183 with 2 variables (> 0.079183)
## Procedure stopped (alpha criteria): pvalue for variable 7 is 0.082992 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.304147 with 9 variables (> 0.302779)
## Procedure stopped (alpha criteria): pvalue for variable 7 is 0.071493 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 8 is 0.068893 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 9 is 0.071693 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 10 is 0.056094 (> 0.050000)
SSF_MEM_FS <- SSF_MEM$best$MEM.select
adegraphics::s.label(SSF_coord, nb = candidates_SSF[[SSF_MEM$best.id]],
pnb.edge.col = "red", main = paste("SSF - ",names(SSF_MEM$best.id)), plot = TRUE, labels = NULL)
################################################################################
#We restricted our options for optimization because of the low statistical power (low replication) that we had at our small spatial scale. We only used linear weights as we do not believe that a exponential decay would make sense at this scale (even relative large distances between sites are actually small). Also We restricted our graph conectivity matrix to only three that yields relatively different scenarios of conectance. Those are the Delauney triangulation, the Relative neighbour and the PCNM
################################################################################
candidates_ITA <- listw.candidates(ITA_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
ITA_MEM <- listw.select(ITA_pa, candidates = candidates_ITA, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
ITA_MEM_FS <- dbmem(ITA_coord, MEM.autocor = c("positive"), silent = TRUE)
################################################################################
candidates_BER <- listw.candidates(BER_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
BER_MEM <- listw.select(BER_pa, candidates = candidates_BER, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
BER_MEM_FS <- dbmem(BER_coord, MEM.autocor = c("positive"), silent = TRUE)
################################################################################
set.seed(2)
candidates_UBA <- listw.candidates(UBA_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
## Warning in nb2listw(nb.object, style = style, glist = lapply(nb.dist, f1, : zero
## sum general weights
UBA_MEM <- listw.select(UBA_pa, candidates = candidates_UBA, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 3 is 0.089791 (> 0.050000)
UBA_MEM_FS <- UBA_MEM$best$MEM.select
adegraphics::s.label(UBA_coord, nb = candidates_UBA[[UBA_MEM$best.id]],
pnb.edge.col = "red", main = paste("UBA - ",names(UBA_MEM$best.id)), plot = TRUE, labels = NULL)
################################################################################
candidates_ST <- listw.candidates(ST_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
## Warning in nb2listw(nb.object, style = style, glist = lapply(nb.dist, f1, : zero
## sum general weights
ST_MEM <- listw.select(ST_pa, candidates = candidates_ST, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
ST_MEM_FS <- dbmem(ST_coord, MEM.autocor = c("positive"), silent = TRUE)
################################################################################
candidates_IC <- listw.candidates(IC_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
IC_MEM <- listw.select(IC_pa, candidates = candidates_IC, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
IC_MEM_FS <- dbmem(IC_coord, MEM.autocor = c("positive"), silent = TRUE)
################################################################################
set.seed(5)
candidates_NI <- listw.candidates(NI_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
NI_MEM <- listw.select(NI_pa, candidates = candidates_NI, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
NI_MEM_FS <- NI_MEM$best$MEM.select
adegraphics::s.label(NI_coord, nb = candidates_NI[[NI_MEM$best.id]],
pnb.edge.col = "red", main = paste("NI - ",names(NI_MEM$best.id)), plot = TRUE, labels = NULL)
################################################################################
candidates_MD <- listw.candidates(MD_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
MD_MEM <- listw.select(MD_pa, candidates = candidates_MD, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
MD_MEM_FS <- dbmem(MD_coord, MEM.autocor = c("positive"), silent = TRUE)
################################################################################
candidates_JA <- listw.candidates(JA_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
JA_MEM <- listw.select(JA_pa, candidates = candidates_JA, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.084692 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 3 is 0.104390 (> 0.050000)
JA_MEM_FS <- JA_MEM$best$MEM.select
adegraphics::s.label(JA_coord, nb = candidates_JA[[JA_MEM$best.id]],
pnb.edge.col = "red", main = paste("JA - ",names(JA_MEM$best.id)), plot = TRUE, labels = NULL)
################################################################################
###PLOTING WEIGHTS ####
nb <- chooseCN(coordinates(UBA_coord), type = 1, plot.nb = FALSE)
#Delaunay triangulation (type 1)
#Gabriel graph (type 2)
#Relative neighbours (type 3)
#Minimum spanning tree (type 4)
#Neighbourhood by distance (type 5)
#K nearests neighbours (type 6)
#Inverse distances (type 7)
dist_UBA <- dist(UBA_coord)
distnb <- spdep::nbdists(nb, as.matrix(UBA_coord))
a_fdown <- 5
a_f_up <- 0.5
fdist <- lapply(distnb, function(x) 1 - x/max(dist_UBA)) #linear
fdist <- lapply(distnb, function(x) 1 - (x/max(dist_UBA))^a_fdown) #fdown
fdist <- lapply(distnb, function(x) 1 / x^a_f_up) #fup
lw <- nb2listw(nb, style = 'B', zero.policy = TRUE, glist = fdist)
m1 <- as.matrix(dist_UBA)
m2 <- spdep::listw2mat(lw)
plot(m1[!diag(ncol(m1))], m2[!diag(ncol(m2))], pch = 20, xlab = "distance", ylab = "spatial weights")
################
Forward Selection
Similarly to removing variables with VIF higher than 3, I built a
function to select the most important variables, when they could
significantly explain species occurrences. This function mostly rely on
the function fs() from package adespatial. Forward selection is only
performed when the whole predictor matrix can significantly explain (p
\< 0.05) the variation in species occurrences.
Broad Spatial Extent
set.seed(5)
Broad_env_Forward <- forward_selection(Broad_pa, Broad_env_VIF$variables)
## Testing variable 1
## Testing variable 2
## Testing variable 3
## Testing variable 4
## Testing variable 5
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.176872 with 5 variables (> 0.175018)
Broad_env_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 nvt 5 0.12280383 0.1228038 0.1134720 13.159611 0.0001
## 2 dist_to_forest 6 0.03553797 0.1583418 0.1402416 3.926809 0.0001
## 3 canopy_cover 2 0.02254986 0.1808917 0.1541816 2.532738 0.0018
## 4 hydroperiod 1 0.02048938 0.2013810 0.1662769 2.334697 0.0066
Broad_env_FS <- Broad_env_Forward$selected_variables
Broad_clim_Forward <- forward_selection(Broad_pa, Broad_clim_VIF$variables)
## Testing variable 1
## Testing variable 2
Broad_clim_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 range_temp 2 0.18651094 0.1865109 0.1778568 21.551646 1e-04
## 2 temp_Season 1 0.04168414 0.2281951 0.2115971 5.022804 1e-04
Broad_clim_FS <- Broad_clim_Forward$selected_variables
#_______________________________________________________________
SSF_env_Forward <- forward_selection(SSF_pa, SSF_env_VIF$variables)
## Testing variable 1
## Testing variable 2
## Testing variable 3
## Testing variable 4
## Testing variable 5
## Procedure stopped (alpha criteria): pvalue for variable 5 is 0.120800 (> 0.050000)
SSF_env_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 dist_to_forest 6 0.06279225 0.06279225 0.04149207 2.947968 0.0013
## 2 hydroperiod 1 0.05740783 0.12020008 0.07927915 2.805793 0.0017
## 3 depth 4 0.05098863 0.17118871 0.11198790 2.583848 0.0030
## 4 canopy_cover 2 0.04138347 0.21257218 0.13574995 2.154765 0.0128
SSF_env_FS <- SSF_env_Forward$selected_variables
SSF_clim_Forward <- forward_selection(SSF_pa, SSF_clim_VIF$variables)
## Testing variable 1
## Testing variable 2
SSF_clim_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 total_prec 2 0.1030335 0.1030335 0.08264788 5.054228 1e-04
## 2 temp_Season 1 0.1050119 0.2080454 0.17121029 5.701730 1e-04
SSF_clim_FS <- SSF_clim_Forward$selected_variables
#_______________________________________________________________
ST_env_Forward <- forward_selection(ST_pa, ST_env_VIF$variables)
## Forward selection NOT performed. p > 0.05
ST_env_Forward$forward_results
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 9999
##
## Model: rda(X = New_Y, Y = New_X)
## Df Variance F Pr(>F)
## Model 3 1.5233 1.632 0.0992 .
## Residual 4 1.2446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ST_env_FS <- ST_env_Forward$selected_variables
IC_env_Forward <- forward_selection(IC_pa, IC_env_VIF$variables)
## Testing variable 1
## Testing variable 2
## Testing variable 3
## Procedure stopped (alpha criteria): pvalue for variable 3 is 0.419400 (> 0.050000)
IC_env_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 dist_to_forest 4 0.2762401 0.2762401 0.2038641 3.816737 0.0013
## 2 hydroperiod 1 0.1448824 0.4211225 0.2924830 2.252534 0.0107
IC_env_FS <- IC_env_Forward$selected_variables
NI_env_Forward <- forward_selection(NI_pa, NI_env_VIF$variables)
## Forward selection NOT performed. p > 0.05
NI_env_Forward$forward_results
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 9999
##
## Model: rda(X = New_Y, Y = New_X)
## Df Variance F Pr(>F)
## Model 4 2.2203 1.3581 0.1531
## Residual 3 1.2261
NI_env_FS <- NI_env_Forward$selected_variables
MD_env_Forward <- forward_selection(MD_pa, MD_env_VIF$variables)
## Forward selection NOT performed. p > 0.05
MD_env_Forward$forward_results
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 9999
##
## Model: rda(X = New_Y, Y = New_X)
## Df Variance F Pr(>F)
## Model 4 1.9377 1.2819 0.2587
## Residual 3 1.1337
MD_env_FS <- MD_env_Forward$selected_variables
JA_env_Forward <- forward_selection(JA_pa, JA_env_VIF$variables)
## Testing variable 1
## Testing variable 2
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.058700 (> 0.050000)
JA_env_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 depth 3 0.2978698 0.2978698 0.2101035 3.393898 0.0028
JA_env_FS <- JA_env_Forward$selected_variables
#______________________________________________________________
DRF_env_Forward <- forward_selection(DRF_pa, DRF_env_VIF$variables)
## Testing variable 1
## Testing variable 2
## Testing variable 3
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.082422 with 3 variables (> 0.081064)
DRF_env_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 canopy_cover 2 0.06947213 0.06947213 0.05008614 3.583624 0.0005
## 2 depth 4 0.04152619 0.11099833 0.07316847 2.195419 0.0220
DRF_env_FS <- DRF_env_Forward$selected_variables
DRF_clim_Forward <- forward_selection(DRF_pa, DRF_clim_VIF$variables)
## Testing variable 1
## Testing variable 2
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.059316 with 2 variables (> 0.055078)
DRF_clim_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 range_temp 2 0.06823593 0.06823593 0.04882418 3.515187 2e-04
DRF_clim_FS <- DRF_clim_Forward$selected_variables
#______________________________________________________________
UBA_env_Forward <- forward_selection(UBA_pa, UBA_env_VIF$variables)
## Testing variable 1
## Testing variable 2
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.263700 (> 0.050000)
UBA_env_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 canopy_cover 2 0.1360444 0.1360444 0.09490361 3.306803 0.0012
UBA_env_FS <- UBA_env_Forward$selected_variables
BER_env_Forward <- forward_selection(BER_pa, BER_env_VIF$variables)
## Forward selection NOT performed. p > 0.05
BER_env_Forward$forward_results
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 9999
##
## Model: rda(X = New_Y, Y = New_X)
## Df Variance F Pr(>F)
## Model 3 0.61185 0.8438 0.6915
## Residual 8 1.93360
BER_env_FS <- BER_env_Forward$selected_variables
ITA_env_Forward <- forward_selection(ITA_pa, ITA_env_VIF$variables)
## Forward selection NOT performed. p > 0.05
ITA_env_Forward$forward_results
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 9999
##
## Model: rda(X = New_Y, Y = New_X)
## Df Variance F Pr(>F)
## Model 5 1.0943 1.1421 0.3235
## Residual 9 1.7247
ITA_env_FS <- ITA_env_Forward$selected_variables
We can plot the important spatial variables to better understand what
spatial patterns they describe.
We are only going to plot the four most important ones
par(mfrow = c(2,2))
ylim <- c(min(Broad_coord[,"lat"])*1.005, max(Broad_coord[,"lat"])*0.995)
xlim <- c(min(Broad_coord[,"long"])*1.005, max(Broad_coord[,"long"])*0.995)
sr_value(Broad_coord, data.frame(Broad_MEM$best$MEM.select)[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("LARGE SCALE", colnames(data.frame(Broad_MEM$best$MEM.select))[1]), line = 3, outer = F, adj = 1)
sr_value(Broad_coord, data.frame(Broad_MEM$best$MEM.select)[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("LARGE SCALE", colnames(data.frame(Broad_MEM$best$MEM.select))[2]), line = 3, outer = F, adj = 1)
sr_value(Broad_coord, data.frame(Broad_MEM$best$MEM.select)[,3], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("LARGE SCALE", colnames(data.frame(Broad_MEM$best$MEM.select))[3]), line = 3, outer = F, adj = 1)
sr_value(Broad_coord, data.frame(Broad_MEM$best$MEM.select)[,4], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("LARGE SCALE", colnames(data.frame(Broad_MEM$best$MEM.select))[4]), line = 3, outer = F, adj = 1)
Intermediate Scale SSF.
par(mfrow = c(2,2))
ylim <- c(min(SSF_coord[,"lat"])*1.005, max(SSF_coord[,"lat"])*0.995)
xlim <- c(min(SSF_coord[,"long"])*1.005, max(SSF_coord[,"long"])*0.995)
sr_value(SSF_coord, data.frame(SSF_MEM$best$MEM.select)[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("SSF", colnames(data.frame(SSF_MEM$best$MEM.select))[1]), line = 3, outer = F, adj = 1)
sr_value(SSF_coord, data.frame(SSF_MEM$best$MEM.select)[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("SSF", colnames(data.frame(SSF_MEM$best$MEM.select))[2]), line = 3, outer = F, adj = 1)
sr_value(SSF_coord, data.frame(SSF_MEM$best$MEM.select)[,3], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("SSF", colnames(data.frame(SSF_MEM$best$MEM.select))[3]), line = 3, outer = F, adj = 1)
sr_value(SSF_coord, data.frame(SSF_MEM$best$MEM.select)[,4], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("SSF", colnames(data.frame(SSF_MEM$best$MEM.select))[4]), line = 3, outer = F, adj = 1)
Intermediate Scale DRF.
par(mfrow = c(2,2))
ylim <- c(min(DRF_coord[,"lat"])*1.005, max(DRF_coord[,"lat"])*0.995)
xlim <- c(min(DRF_coord[,"long"])*1.005, max(DRF_coord[,"long"])*0.995)
sr_value(DRF_coord, data.frame(DRF_MEM$best$MEM.select)[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("DRF", colnames(data.frame(DRF_MEM$best$MEM.select))[1]), line = 3, outer = F, adj = 1)
sr_value(DRF_coord, data.frame(DRF_MEM$best$MEM.select)[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("DRF", colnames(data.frame(DRF_MEM$best$MEM.select))[2]), line = 3, outer = F, adj = 1)
sr_value(DRF_coord, data.frame(DRF_MEM$best$MEM.select)[,3], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("DRF", colnames(data.frame(DRF_MEM$best$MEM.select))[3]), line = 3, outer = F, adj = 1)
sr_value(DRF_coord, data.frame(DRF_MEM$best$MEM.select)[,4], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("DRF", colnames(data.frame(DRF_MEM$best$MEM.select))[4]), line = 3, outer = F, adj = 1)
Small Extent SSF - Santa Fé do Sul
par(mfrow = c(1,2))
ylim <- c(min(ST_coord[,"lat"])*1.0001, max(ST_coord[,"lat"])*0.9999)
xlim <- c(min(ST_coord[,"long"])*1.0001, max(ST_coord[,"long"])*0.9999)
sr_value(ST_coord, ST_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Santa Fé do Sul -", colnames(ST_MEM_FS)[1]), line = 3, outer = F, adj = 1)
sr_value(ST_coord, ST_MEM_FS[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Santa Fé do Sul -", colnames(ST_MEM_FS)[2]), line = 3, outer = F, adj = 1)
Small Extent SSF - Icém
par(mfrow = c(1,2))
ylim <- c(min(IC_coord[,"lat"])*1.0001, max(IC_coord[,"lat"])*0.9999)
xlim <- c(min(IC_coord[,"long"])*1.0001, max(IC_coord[,"long"])*0.9999)
sr_value(IC_coord, IC_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Icém -", colnames(IC_MEM_FS)[1]), line = 3, outer = F, adj = 1)
sr_value(IC_coord, IC_MEM_FS[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Icém -", colnames(IC_MEM_FS)[2]), line = 3, outer = F, adj = 1)
Small Extent SSF - Nova Itapirema.
par(mfrow = c(1,1))
ylim <- c(min(NI_coord[,"lat"])*1.0001, max(NI_coord[,"lat"])*0.9999)
xlim <- c(min(NI_coord[,"long"])*1.0001, max(NI_coord[,"long"])*0.9999)
sr_value(NI_coord, NI_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Nova Itapirema -", colnames(NI_MEM_FS)[1]), line = 3, outer = F, adj = 1)
Small Extent SSF - Morro do Diabo.
par(mfrow = c(1,1))
ylim <- c(min(MD_coord[,"lat"])*1.0025, max(MD_coord[,"lat"])*0.9975)
xlim <- c(min(MD_coord[,"long"])*1.0025, max(MD_coord[,"long"])*0.9975)
sr_value(MD_coord, MD_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 0.8, xax = 2, yax = 1, method = "bubble")
title(main = paste("Morro do Diabo -", colnames(MD_MEM_FS)[1]), line = 3, outer = F, adj = 1)
Small Extent SSF - Jataí.
par(mfrow = c(1,2))
ylim <- c(min(JA_coord[,"lat"])*1.0001, max(JA_coord[,"lat"])*0.9999)
xlim <- c(min(JA_coord[,"long"])*1.0001, max(JA_coord[,"long"])*0.9999)
sr_value(JA_coord, JA_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Jataí -", colnames(JA_MEM_FS)[1]), line = 3, outer = F, adj = 1)
sr_value(JA_coord, JA_MEM_FS[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Jataí -", colnames(JA_MEM_FS)[2]), line = 3, outer = F, adj = 1)
Small Extent DRF - Ubatuba
par(mfrow = c(1,2))
ylim <- c(min(UBA_coord[,"lat"])*1.00015, max(UBA_coord[,"lat"])*0.99985)
xlim <- c(min(UBA_coord[,"long"])*1.00015, max(UBA_coord[,"long"])*0.99985)
sr_value(UBA_coord, data.frame(UBA_MEM$best$MEM.select)[,1], ylim = c(-23.37694,-23.33123), xlim = c(-44.95004, -44.80492), grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("UBA", colnames(data.frame(UBA_MEM$best$MEM.select))[1]), line = 3, outer = F, adj = 1)
sr_value(UBA_coord, data.frame(UBA_MEM$best$MEM.select)[,2], ylim = c(-23.37694,-23.33123), xlim = c(-44.95004, -44.80492), grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("UBA", colnames(data.frame(UBA_MEM$best$MEM.select))[2]), line = 3, outer = F, adj = 1)
Small Extent SSF - Bertioga.
par(mfrow = c(1,1))
ylim <- c(min(BER_coord[,"lat"])*1.0002, max(BER_coord[,"lat"])*0.9998)
xlim <- c(min(BER_coord[,"long"])*1.0002, max(BER_coord[,"long"])*0.9998)
sr_value(BER_coord, BER_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Bertioga -", colnames(UBA_MEM_FS)[1]), line = 3, outer = F, adj = 1)
Small Extent DRF - Itanhaém
par(mfrow = c(1,2))
ylim <- c(min(ITA_coord[,"lat"])*1.003, max(ITA_coord[,"lat"])*0.997)
xlim <- c(min(ITA_coord[,"long"])*1.003, max(ITA_coord[,"long"])*0.997)
sr_value(ITA_coord, ITA_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Itanhaém -", colnames(ITA_MEM_FS)[1]), line = 3, outer = F, adj = 1)
sr_value(ITA_coord, ITA_MEM_FS[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Itanhaém -", colnames(ITA_MEM_FS)[2]), line = 3, outer = F, adj = 1)
Variation Partitioning
Large Extent
set.seed(10)
#Variation partitioning Broad
Broad_varpart <- var_partitioning(Y = Broad_pa,
env = Broad_env_FS,
clim = Broad_clim_FS,
spa = Broad_MEM_FS, percent_r2 = F)
#Testing significande of spatially structured environment
candidates_Broad_env <- listw.candidates(Broad_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup","fdown"), y_fdown = 5, y_fup = 0.5)
##
## PLEASE NOTE: The components "delsgs" and "summary" of the
## object returned by deldir() are now DATA FRAMES rather than
## matrices (as they were prior to release 0.0-18).
## See help("deldir").
##
## PLEASE NOTE: The process that deldir() uses for determining
## duplicated points has changed from that used in version
## 0.0-9 of this package (and previously). See help("deldir").
Broad_MEM_env <- listw.select(Broad_env_FS, candidates = candidates_Broad_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.253549 with 3 variables (> 0.251115)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.306761 with 6 variables (> 0.306454)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.296619 with 6 variables (> 0.295208)
## Procedure stopped (alpha criteria): pvalue for variable 10 is 0.063094 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.404339 with 14 variables (> 0.396839)
## Procedure stopped (alpha criteria): pvalue for variable 12 is 0.051595 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 10 is 0.050095 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.368117 with 13 variables (> 0.364769)
## Procedure stopped (alpha criteria): pvalue for variable 12 is 0.069793 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 13 is 0.050895 (> 0.050000)
Broad_env_spa_sig <- envspace.test(spe = Broad_pa, env = Broad_env_FS, coord = Broad_coord,
MEM.spe = Broad_MEM_FS,
listw.env = candidates_Broad_env[[Broad_MEM_env$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
#Testing significande of spatially structured climate
Broad_MEM_clim <- listw.select(Broad_clim_FS, candidates = candidates_Broad_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.876851 with 4 variables (> 0.876357)
## Procedure stopped (alpha criteria): pvalue for variable 20 is 0.089291 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 23 is 0.083192 (> 0.050000)
## Procedure stopped (R2more criteria): variable 35 explains only 0.000994 of the variance.
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.962677 with 30 variables (> 0.962304)
## Procedure stopped (alpha criteria): pvalue for variable 30 is 0.055894 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 36 is 0.051695 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.942164 with 26 variables (> 0.941798)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.938844 with 26 variables (> 0.938675)
## Procedure stopped (alpha criteria): pvalue for variable 30 is 0.063194 (> 0.050000)
Broad_clim_spa_sig <- envspace.test(spe = Broad_pa, env = Broad_clim_FS, coord = Broad_coord,
MEM.spe = data.frame(Broad_MEM$best$MEM.select),
listw.env = candidates_Broad_env[[Broad_MEM_clim$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
Broad_varpart
## Adj_R2 Df F p
## All 0.415 36 2.874901 0.0001
## Env 0.166 4 5.736676 0.0001
## Clim 0.212 2 13.748385 0.0001
## Spa 0.395 30 3.063807 0.0001
## Pure_Env 0.011 4 1.283095 0.0420
## Pure_Clim 0.009 2 1.466479 0.0293
## Pure_Spa 0.136 30 1.691412 0.0001
## Env_Spa 0.057 NA NA NA
## Env_Clim 0.001 NA NA NA
## Spa_Clim 0.104 NA NA NA
## Spa_Clim_Env 0.097 NA NA NA
## Resid 0.585 NA NA NA
Broad_env_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.1544202
##
## Based on 10000 replicates
## Simulated p-value: 9.999e-05
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 6.1970326756 0.0208156944 0.0004648091
Broad_clim_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.2013072
##
## Based on 10000 replicates
## Simulated p-value: 9.999e-05
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 5.5124965211 0.0450111357 0.0008038948
Intermediate Extent
DRF_varpart <- var_partitioning(Y = DRF_pa,
env = DRF_env_FS,
clim = DRF_clim_FS,
spa = DRF_MEM_FS, percent_r2 = F)
#Testing significande of spatially structured environment
candidates_DRF_env <- listw.candidates(DRF_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup","fdown"), y_fdown = 5, y_fup = 0.5)
DRF_MEM_env <- listw.select(DRF_env_FS, candidates = candidates_DRF_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.075892 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 3 is 0.058894 (> 0.050000)
DRF_env_spa_sig <- envspace.test(spe = DRF_pa, env = DRF_env_FS, coord = DRF_coord,
MEM.spe = data.frame(DRF_MEM$best$MEM.select),
listw.env = candidates_DRF_env[[DRF_MEM_env$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
#Testing significande of spatially structured climate
DRF_MEM_clim <- listw.select(DRF_clim_FS, candidates = candidates_DRF_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.853267 with 3 variables (> 0.853267)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.873833 with 5 variables (> 0.859533)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.846575 with 6 variables (> 0.839674)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.953992 with 15 variables (> 0.953021)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.955943 with 16 variables (> 0.952317)
## Procedure stopped (alpha criteria): pvalue for variable 9 is 0.091391 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 15 is 0.064794 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 10 is 0.050195 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.890885 with 8 variables (> 0.888316)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.880739 with 14 variables (> 0.871375)
DRF_clim_spa_sig <- envspace.test(spe = DRF_pa, env = DRF_clim_FS, coord = DRF_coord,
MEM.spe = data.frame(DRF_MEM$best$MEM.select),
listw.env = candidates_DRF_env[[DRF_MEM_clim$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
###########################################################################################
SSF_varpart <- var_partitioning(Y = SSF_pa,
env = SSF_env_FS,
clim = SSF_clim_FS,
spa = SSF_MEM_FS, percent_r2 = F)
#Testing significande of spatially structured environment
candidates_SSF_env <- listw.candidates(SSF_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup","fdown"), y_fdown = 5, y_fup = 0.5)
SSF_MEM_env <- listw.select(SSF_env_FS, candidates = candidates_SSF_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.119788 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 6 is 0.096290 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.358144 with 8 variables (> 0.340049)
## Procedure stopped (alpha criteria): pvalue for variable 10 is 0.051395 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 10 is 0.082992 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 7 is 0.057394 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 7 is 0.052595 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 8 is 0.051895 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 8 is 0.087591 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 3 is 0.052895 (> 0.050000)
SSF_env_spa_sig <- envspace.test(spe = SSF_pa, env = SSF_env_FS, coord = SSF_coord,
MEM.spe = data.frame(SSF_MEM$best$MEM.select),
listw.env = candidates_SSF_env[[SSF_MEM_env$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
#Testing significande of spatially structured climate
SSF_MEM_clim <- listw.select(SSF_clim_FS, candidates = candidates_SSF_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 6 is 0.057994 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 7 is 0.056694 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.964587 with 12 variables (> 0.963727)
## Procedure stopped (alpha criteria): pvalue for variable 12 is 0.071993 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 15 is 0.069693 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 14 is 0.070293 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.938182 with 8 variables (> 0.934646)
## Procedure stopped (alpha criteria): pvalue for variable 9 is 0.062394 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 9 is 0.057594 (> 0.050000)
SSF_clim_spa_sig <- envspace.test(spe = SSF_pa, env = SSF_clim_FS, coord = SSF_coord,
MEM.spe = data.frame(SSF_MEM$best$MEM.select),
listw.env = candidates_SSF_env[[SSF_MEM_clim$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
DRF_varpart
## Adj_R2 Df F p
## All 0.191 10 2.154740 0.0001
## Env 0.073 2 2.934146 0.0002
## Clim 0.049 1 3.515187 0.0003
## Spa 0.165 7 2.378271 0.0001
## Pure_Env 0.016 2 1.401642 0.0845
## Pure_Clim 0.006 1 1.292474 0.1843
## Pure_Spa 0.092 7 1.743533 0.0006
## Env_Spa 0.034 NA NA NA
## Env_Clim 0.004 NA NA NA
## Spa_Clim 0.020 NA NA NA
## Spa_Clim_Env 0.018 NA NA NA
## Resid 0.809 NA NA NA
DRF_env_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.05287454
##
## Based on 10000 replicates
## Simulated p-value: 0.01249875
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 2.4273813307 0.0119539831 0.0002841888
DRF_clim_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.03846861
##
## Based on 10000 replicates
## Simulated p-value: 0.03269673
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 1.5784918115 0.0130585778 0.0002591348
SSF_varpart
## Adj_R2 Df F p
## All 0.346 14 2.702616 0.0001
## Env 0.136 4 2.767066 0.0001
## Clim 0.171 2 5.648020 0.0001
## Spa 0.294 8 3.344447 0.0001
## Pure_Env 0.034 4 1.449166 0.0192
## Pure_Clim 0.009 2 1.223076 0.1821
## Pure_Spa 0.078 8 1.579172 0.0005
## Env_Spa 0.064 NA NA NA
## Env_Clim 0.010 NA NA NA
## Spa_Clim 0.124 NA NA NA
## Spa_Clim_Env 0.029 NA NA NA
## Resid 0.654 NA NA NA
SSF_env_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.09248927
##
## Based on 10000 replicates
## Simulated p-value: 0.00909909
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 2.525610622 0.033365911 0.000548006
SSF_clim_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.1526691
##
## Based on 10000 replicates
## Simulated p-value: 0.00039996
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 4.2205352914 0.0458711769 0.0006403098
Small Extent
Small Extent allowing negative fractions
ST_varpart <- var_partitioning(ST_pa, ST_env_FS, spa = ST_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
IC_varpart <- var_partitioning(IC_pa, IC_env_FS, spa = IC_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
NI_varpart <- var_partitioning(NI_pa, NI_env_FS, spa = NI_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
#Testing significande of spatially structured environment
candidates_NI_env <- listw.candidates(NI_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup","fdown"), y_fdown = 5, y_fup = 0.5)
NI_MEM_env <- listw.select(NI_env_FS, candidates = candidates_NI_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.1, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.175515 with 1 variables (> 0.175515)
# We are not adjusting p-values here because we assume that if there was a significant spatial and environmental strucutre in species distributions, we want to find spatial patterns in the environment
#There was no significant spatial structure in the environmental variables
NI_env_spa_sig <- envspace.test(spe = NI_pa, env = NI_env_FS, coord = NI_coord,
MEM.spe = NI_MEM_FS,
listw.env = candidates_NI_env$DBEM_PCNM, MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
## No significant relation between 'spe' and 'env'; the test was not performed
MD_varpart <- var_partitioning(MD_pa, MD_env_FS, spa = MD_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
JA_varpart <- var_partitioning(JA_pa, JA_env_FS, spa = JA_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
#Testing significande of spatially structured environment
candidates_JA_env <- listw.candidates(JA_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup","fdown"), y_fdown = 5, y_fup = 0.5)
JA_MEM_env <- listw.select(JA_env_FS, candidates = candidates_JA_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.1, p.adjust = FALSE, verbose = FALSE) # We are not adjusting p-values here because we assume that if there was a significant spatial and environmental strucutre in species distributions, we want to find spatial patterns in the environment
## Procedure stopped (alpha criteria): pvalue for variable 1 is 0.058694 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 1 is 0.057094 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.892776 with 2 variables (> 0.874947)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.841344 with 2 variables (> 0.808686)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.852574 with 3 variables (> 0.825464)
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.061594 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.138886 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.855931 with 2 variables (> 0.843916)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.859910 with 3 variables (> 0.837980)
#There was no significant spatial structure in the environmental variables
JA_env_spa_sig <- envspace.test(spe = JA_pa, env = JA_env_FS, coord = JA_coord,
MEM.spe = data.frame(JA_MEM$best$MEM.select),
listw.env = candidates_JA_env[[JA_MEM_env$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
UBA_varpart <- var_partitioning(UBA_pa, UBA_env_FS, spa = UBA_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
#Testing significande of spatially structured environment
candidates_UBA_env <- listw.candidates(UBA_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup","fdown"), y_fdown = 5, y_fup = 0.5)
## Warning in nb2listw(nb.object, style = style, glist = lapply(nb.dist, f1, : zero
## sum general weights
## Warning in nb2listw(nb.object, style = style, glist =
## lapply(nbdists(nb.object, : zero sum general weights
## Warning in nb2listw(nb.object, style = style, glist = lapply(nb.dist, f1, : zero
## sum general weights
## Warning in nb2listw(nb.object, style = style, glist =
## lapply(nbdists(nb.object, : zero sum general weights
UBA_MEM_env <- listw.select(UBA_env_FS, candidates = candidates_UBA_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.610050 with 4 variables (> 0.570723)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.532181 with 3 variables (> 0.504917)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.531064 with 2 variables (> 0.526818)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.584362 with 3 variables (> 0.552325)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.544172 with 3 variables (> 0.543362)
#There was no significant spatial structure in the environmental variables, thus we used the one selected for the species
UBA_env_spa_sig <- envspace.test(spe = UBA_pa, env = UBA_env_FS, coord = UBA_coord,
MEM.spe = data.frame(UBA_MEM$best$MEM.select),
listw.env = candidates_UBA_env[[UBA_MEM$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
BER_varpart <- var_partitioning(BER_pa, BER_env_FS, spa = BER_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
ITA_varpart <- var_partitioning(ITA_pa, ITA_env_FS, spa = ITA_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
ST_varpart
## Adj_R2 Df F p
## All 0.197 5 1.3440574 0.2575
## Env 0.213 3 1.6319731 0.1074
## Clim NA NA NA NA
## Spa -0.085 2 0.7256147 0.8144
## Pure_Env 0.282 3 1.5862081 0.2626
## Pure_Clim NA NA NA NA
## Pure_Spa -0.016 2 0.9605140 0.5192
## Env_Spa -0.069 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.803 NA NA NA
IC_varpart
## Adj_R2 Df F p
## All 0.291 4 2.1287607 0.0034
## Env 0.292 2 3.2736653 0.0002
## Clim NA NA NA NA
## Spa -0.041 2 0.7833254 0.7166
## Pure_Env 0.332 2 3.1073625 0.0018
## Pure_Clim NA NA NA NA
## Pure_Spa -0.001 2 0.9906546 0.5015
## Env_Spa -0.040 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.709 NA NA NA
NI_varpart
## Adj_R2 Df F p
## All 0.140 5 1.2271911 0.2850
## Env 0.170 4 1.3581374 0.1484
## Clim NA NA NA NA
## Spa 0.159 1 2.3197422 0.0158
## Pure_Env -0.019 4 0.9668644 0.5430
## Pure_Clim NA NA NA NA
## Pure_Spa -0.030 1 0.8944824 0.5182
## Env_Spa 0.189 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.860 NA NA NA
NI_env_spa_sig
## NULL
MD_varpart
## Adj_R2 Df F p
## All 0.282 5 1.5500378 0.1634
## Env 0.139 4 1.2819349 0.2614
## Clim NA NA NA NA
## Spa -0.005 1 0.9665789 0.3781
## Pure_Env 0.287 4 1.5993494 0.1713
## Pure_Clim NA NA NA NA
## Pure_Spa 0.143 1 1.5988564 0.2709
## Env_Spa -0.148 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.718 NA NA NA
JA_varpart
## Adj_R2 Df F p
## All 0.425 3 3.220404 0.0017
## Env 0.210 1 3.393898 0.0027
## Clim NA NA NA NA
## Spa 0.314 2 3.060817 0.0053
## Pure_Env 0.111 1 2.354789 0.0577
## Pure_Clim NA NA NA NA
## Pure_Spa 0.215 2 2.498105 0.0415
## Env_Spa 0.099 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.575 NA NA NA
JA_env_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.09888151
##
## Based on 10000 replicates
## Simulated p-value: 0.4475552
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 0.155249466 0.091925975 0.002007249
UBA_varpart
## Adj_R2 Df F p
## All 0.151 3 2.308338 0.0009
## Env 0.095 1 3.306803 0.0009
## Clim NA NA NA NA
## Spa 0.117 2 2.450992 0.0029
## Pure_Env 0.035 1 1.821646 0.0481
## Pure_Clim NA NA NA NA
## Pure_Spa 0.056 2 1.699031 0.0244
## Env_Spa 0.060 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.849 NA NA NA
UBA_env_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.06004112
##
## Based on 10000 replicates
## Simulated p-value: 0.02989701
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 1.7891770392 0.0072893001 0.0008692971
BER_varpart
## Adj_R2 Df F p
## All -0.085 4 0.7852969 0.7952
## Env -0.044 3 0.8438125 0.6912
## Clim NA NA NA NA
## Spa -0.012 1 0.8660103 0.5760
## Pure_Env -0.072 3 0.7776483 0.7527
## Pure_Clim NA NA NA NA
## Pure_Spa -0.040 1 0.7035544 0.6921
## Env_Spa 0.028 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 1.085 NA NA NA
ITA_varpart
## Adj_R2 Df F p
## All 0.065 7 1.1390639 0.3530
## Env 0.048 5 1.1420928 0.3329
## Clim NA NA NA NA
## Spa -0.008 2 0.9443449 0.4926
## Pure_Env 0.073 5 1.1874488 0.3179
## Pure_Clim NA NA NA NA
## Pure_Spa 0.017 2 1.0804479 0.3988
## Env_Spa -0.025 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.935 NA NA NA
Not allowing negative fractions
Broad_varpart2 <- var_partitioning(Y = Broad_pa,
env = Broad_env_FS,
clim = Broad_clim_FS,
spa = Broad_MEM_FS, percent_r2 = F, allow_negative_r2 = F)
DRF_varpart2 <- var_partitioning(Y = DRF_pa,
env = DRF_env_FS,
clim = DRF_clim_FS,
spa = DRF_MEM_FS, percent_r2 = F, allow_negative_r2 = F)
SSF_varpart2 <- var_partitioning(Y = SSF_pa,
env = SSF_env_FS,
clim = SSF_clim_FS,
spa = SSF_MEM_FS, percent_r2 = F, allow_negative_r2 = F)
ST_varpart2 <- var_partitioning(ST_pa, ST_env_FS, spa = ST_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
IC_varpart2 <- var_partitioning(IC_pa, IC_env_FS, spa = IC_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
NI_varpart2 <- var_partitioning(NI_pa, NI_env_FS, spa = NI_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
MD_varpart2 <- var_partitioning(MD_pa, MD_env_FS, spa = MD_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
JA_varpart2 <- var_partitioning(JA_pa, JA_env_FS, spa = JA_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
UBA_varpart2 <- var_partitioning(UBA_pa, UBA_env_FS, spa = UBA_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
BER_varpart2 <- var_partitioning(BER_pa, BER_env_FS, spa = BER_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
ITA_varpart2 <- var_partitioning(ITA_pa, ITA_env_FS, spa = ITA_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
Broad_varpart2
## Adj_R2 Df F p
## All 0.415 36 2.874901 0.0001
## Env 0.166 4 5.736676 0.0001
## Clim 0.212 2 13.748385 0.0001
## Spa 0.395 30 3.063807 0.0001
## Pure_Env 0.011 4 1.283095 0.0436
## Pure_Clim 0.009 2 1.466479 0.0282
## Pure_Spa 0.136 30 1.691412 0.0001
## Env_Spa 0.057 NA NA NA
## Env_Clim 0.001 NA NA NA
## Spa_Clim 0.104 NA NA NA
## Spa_Clim_Env 0.097 NA NA NA
## Resid 0.585 NA NA NA
SSF_varpart2
## Adj_R2 Df F p
## All 0.346 14 2.702616 0.0001
## Env 0.136 4 2.767066 0.0001
## Clim 0.171 2 5.648020 0.0001
## Spa 0.294 8 3.344447 0.0001
## Pure_Env 0.034 4 1.449166 0.0191
## Pure_Clim 0.009 2 1.223076 0.1799
## Pure_Spa 0.078 8 1.579172 0.0012
## Env_Spa 0.064 NA NA NA
## Env_Clim 0.010 NA NA NA
## Spa_Clim 0.124 NA NA NA
## Spa_Clim_Env 0.029 NA NA NA
## Resid 0.654 NA NA NA
DRF_varpart2
## Adj_R2 Df F p
## All 0.191 10 2.154740 0.0001
## Env 0.073 2 2.934146 0.0003
## Clim 0.049 1 3.515187 0.0004
## Spa 0.165 7 2.378271 0.0001
## Pure_Env 0.016 2 1.401642 0.0897
## Pure_Clim 0.006 1 1.292474 0.1951
## Pure_Spa 0.092 7 1.743533 0.0006
## Env_Spa 0.034 NA NA NA
## Env_Clim 0.004 NA NA NA
## Spa_Clim 0.020 NA NA NA
## Spa_Clim_Env 0.018 NA NA NA
## Resid 0.809 NA NA NA
ST_varpart2
## Adj_R2 Df F p
## All 0.213 3 1.631973 0.1045
## Env 0.213 3 1.631973 0.1045
## Clim NA NA NA NA
## Spa 0.000 NA NA NA
## Pure_Env 0.213 3 1.631973 0.1045
## Pure_Clim NA NA NA NA
## Pure_Spa 0.000 NA NA NA
## Env_Spa 0.000 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.787 NA NA NA
IC_varpart2
## Adj_R2 Df F p
## All 0.292 2 3.273665 2e-04
## Env 0.292 2 3.273665 2e-04
## Clim NA NA NA NA
## Spa 0.000 NA NA NA
## Pure_Env 0.292 2 3.273665 2e-04
## Pure_Clim NA NA NA NA
## Pure_Spa 0.000 NA NA NA
## Env_Spa 0.000 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.708 NA NA NA
NI_varpart2
## Adj_R2 Df F p
## All 0.170 4 1.358137 0.1483
## Env 0.170 4 1.358137 0.1483
## Clim NA NA NA NA
## Spa 0.159 1 2.319742 0.0153
## Pure_Env 0.011 NA NA NA
## Pure_Clim NA NA NA NA
## Pure_Spa 0.000 NA NA NA
## Env_Spa 0.159 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.830 NA NA NA
MD_varpart2
## Adj_R2 Df F p
## All 0.139 4 1.281935 0.2653
## Env 0.139 4 1.281935 0.2653
## Clim NA NA NA NA
## Spa 0.000 NA NA NA
## Pure_Env 0.139 4 1.281935 0.2653
## Pure_Clim NA NA NA NA
## Pure_Spa 0.000 NA NA NA
## Env_Spa 0.000 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.861 NA NA NA
JA_varpart2
## Adj_R2 Df F p
## All 0.425 3 3.220404 0.0029
## Env 0.210 1 3.393898 0.0027
## Clim NA NA NA NA
## Spa 0.314 2 3.060817 0.0060
## Pure_Env 0.111 1 2.354789 0.0568
## Pure_Clim NA NA NA NA
## Pure_Spa 0.215 2 2.498105 0.0398
## Env_Spa 0.099 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.575 NA NA NA
UBA_varpart2
## Adj_R2 Df F p
## All 0.151 3 2.308338 0.0004
## Env 0.095 1 3.306803 0.0013
## Clim NA NA NA NA
## Spa 0.117 2 2.450992 0.0035
## Pure_Env 0.035 1 1.821646 0.0493
## Pure_Clim NA NA NA NA
## Pure_Spa 0.056 2 1.699031 0.0239
## Env_Spa 0.060 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.849 NA NA NA
BER_varpart2
## Adj_R2 Df F p
## All 0 NA NA NA
## Env 0 NA NA NA
## Clim NA NA NA NA
## Spa 0 NA NA NA
## Pure_Env 0 NA NA NA
## Pure_Clim NA NA NA NA
## Pure_Spa 0 NA NA NA
## Env_Spa 0 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 1 NA NA NA
ITA_varpart2
## Adj_R2 Df F p
## All 0.048 5 1.142093 0.3284
## Env 0.048 5 1.142093 0.3284
## Clim NA NA NA NA
## Spa 0.000 NA NA NA
## Pure_Env 0.048 5 1.142093 0.3284
## Pure_Clim NA NA NA NA
## Pure_Spa 0.000 NA NA NA
## Env_Spa 0.000 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.952 NA NA NA
Constructing a matrix with all R2 values (with negative values)
Varpart_plot_neg <- data.frame(Broad_varpart[,1],
SSF_varpart[,1],
DRF_varpart[,1],
ST_varpart[,1],
IC_varpart[,1],
NI_varpart[,1],
MD_varpart[,1],
JA_varpart[,1],
UBA_varpart[,1],
BER_varpart[,1],
ITA_varpart[,1])
colnames(Varpart_plot_neg) <- c("Broad", "SSF", "DRF", "Santa Fé do Sul", "Icém", "Nova Itapirema", "Morro do Diabo", "Jataí", "Ubatuba", "Bertioga", "Itanhaém")
for(i in 1:dim(Varpart_plot_neg)[1]){
for(j in 1:dim(Varpart_plot_neg)[2]){
if(is.na(Varpart_plot_neg[i,j])){Varpart_plot_neg[i,j] <- 0}
}
}
Varpart_plot_neg
## Broad SSF DRF Santa Fé do Sul Icém Nova Itapirema
## All 0.415 0.346 0.191 0.197 0.291 0.140
## Env 0.166 0.136 0.073 0.213 0.292 0.170
## Clim 0.212 0.171 0.049 0.000 0.000 0.000
## Spa 0.395 0.294 0.165 -0.085 -0.041 0.159
## Pure_Env 0.011 0.034 0.016 0.282 0.332 -0.019
## Pure_Clim 0.009 0.009 0.006 0.000 0.000 0.000
## Pure_Spa 0.136 0.078 0.092 -0.016 -0.001 -0.030
## Env_Spa 0.057 0.064 0.034 -0.069 -0.040 0.189
## Env_Clim 0.001 0.010 0.004 0.000 0.000 0.000
## Spa_Clim 0.104 0.124 0.020 0.000 0.000 0.000
## Spa_Clim_Env 0.097 0.029 0.018 0.000 0.000 0.000
## Resid 0.585 0.654 0.809 0.803 0.709 0.860
## Morro do Diabo Jataí Ubatuba Bertioga Itanhaém
## All 0.282 0.425 0.151 -0.085 0.065
## Env 0.139 0.210 0.095 -0.044 0.048
## Clim 0.000 0.000 0.000 0.000 0.000
## Spa -0.005 0.314 0.117 -0.012 -0.008
## Pure_Env 0.287 0.111 0.035 -0.072 0.073
## Pure_Clim 0.000 0.000 0.000 0.000 0.000
## Pure_Spa 0.143 0.215 0.056 -0.040 0.017
## Env_Spa -0.148 0.099 0.060 0.028 -0.025
## Env_Clim 0.000 0.000 0.000 0.000 0.000
## Spa_Clim 0.000 0.000 0.000 0.000 0.000
## Spa_Clim_Env 0.000 0.000 0.000 0.000 0.000
## Resid 0.718 0.575 0.849 1.085 0.935
Constructing a matrix with all p values (with negative R2 values)
Varpart_plot_p_neg <- data.frame(Broad_varpart[,4],
SSF_varpart[,4],
DRF_varpart[,4],
ST_varpart[,4],
IC_varpart[,4],
NI_varpart[,4],
MD_varpart[,4],
JA_varpart[,4],
UBA_varpart[,4],
BER_varpart[,4],
ITA_varpart[,4])
colnames(Varpart_plot_p_neg) <- c("Broad", "SSF", "DRF", "Santa Fé do Sul", "Icém", "Nova Itapirema", "Morro do Diabo", "Jataí", "Ubatuba", "Bertioga", "Itanhaém")
Varpart_plot_p_neg
## Broad SSF DRF Santa Fé do Sul Icém Nova Itapirema
## All 0.0001 0.0001 0.0001 0.2575 0.0034 0.2850
## Env 0.0001 0.0001 0.0002 0.1074 0.0002 0.1484
## Clim 0.0001 0.0001 0.0003 NA NA NA
## Spa 0.0001 0.0001 0.0001 0.8144 0.7166 0.0158
## Pure_Env 0.0420 0.0192 0.0845 0.2626 0.0018 0.5430
## Pure_Clim 0.0293 0.1821 0.1843 NA NA NA
## Pure_Spa 0.0001 0.0005 0.0006 0.5192 0.5015 0.5182
## Env_Spa NA NA NA NA NA NA
## Env_Clim NA NA NA NA NA NA
## Spa_Clim NA NA NA NA NA NA
## Spa_Clim_Env NA NA NA NA NA NA
## Resid NA NA NA NA NA NA
## Morro do Diabo Jataí Ubatuba Bertioga Itanhaém
## All 0.1634 0.0017 0.0009 0.7952 0.3530
## Env 0.2614 0.0027 0.0009 0.6912 0.3329
## Clim NA NA NA NA NA
## Spa 0.3781 0.0053 0.0029 0.5760 0.4926
## Pure_Env 0.1713 0.0577 0.0481 0.7527 0.3179
## Pure_Clim NA NA NA NA NA
## Pure_Spa 0.2709 0.0415 0.0244 0.6921 0.3988
## Env_Spa NA NA NA NA NA
## Env_Clim NA NA NA NA NA
## Spa_Clim NA NA NA NA NA
## Spa_Clim_Env NA NA NA NA NA
## Resid NA NA NA NA NA
Constructing a matrix with all R2 values (without negative values)
Varpart_plot <- data.frame(Broad_varpart2[,1],
SSF_varpart2[,1],
DRF_varpart2[,1],
ST_varpart2[,1],
IC_varpart2[,1],
NI_varpart2[,1],
MD_varpart2[,1],
JA_varpart2[,1],
UBA_varpart2[,1],
BER_varpart2[,1],
ITA_varpart2[,1])
colnames(Varpart_plot) <- c("Broad", "SSF", "DRF", "Santa Fé do Sul", "Icém", "Nova Itapirema", "Morro do Diabo", "Jataí", "Ubatuba", "Bertioga", "Itanhaém")
for(i in 1:dim(Varpart_plot)[1]){
for(j in 1:dim(Varpart_plot)[2]){
if(is.na(Varpart_plot[i,j])){Varpart_plot[i,j] <- 0}
}
}
Varpart_plot
## Broad SSF DRF Santa Fé do Sul Icém Nova Itapirema
## All 0.415 0.346 0.191 0.213 0.292 0.170
## Env 0.166 0.136 0.073 0.213 0.292 0.170
## Clim 0.212 0.171 0.049 0.000 0.000 0.000
## Spa 0.395 0.294 0.165 0.000 0.000 0.159
## Pure_Env 0.011 0.034 0.016 0.213 0.292 0.011
## Pure_Clim 0.009 0.009 0.006 0.000 0.000 0.000
## Pure_Spa 0.136 0.078 0.092 0.000 0.000 0.000
## Env_Spa 0.057 0.064 0.034 0.000 0.000 0.159
## Env_Clim 0.001 0.010 0.004 0.000 0.000 0.000
## Spa_Clim 0.104 0.124 0.020 0.000 0.000 0.000
## Spa_Clim_Env 0.097 0.029 0.018 0.000 0.000 0.000
## Resid 0.585 0.654 0.809 0.787 0.708 0.830
## Morro do Diabo Jataí Ubatuba Bertioga Itanhaém
## All 0.139 0.425 0.151 0 0.048
## Env 0.139 0.210 0.095 0 0.048
## Clim 0.000 0.000 0.000 0 0.000
## Spa 0.000 0.314 0.117 0 0.000
## Pure_Env 0.139 0.111 0.035 0 0.048
## Pure_Clim 0.000 0.000 0.000 0 0.000
## Pure_Spa 0.000 0.215 0.056 0 0.000
## Env_Spa 0.000 0.099 0.060 0 0.000
## Env_Clim 0.000 0.000 0.000 0 0.000
## Spa_Clim 0.000 0.000 0.000 0 0.000
## Spa_Clim_Env 0.000 0.000 0.000 0 0.000
## Resid 0.861 0.575 0.849 1 0.952
Constructing a matrix with all p values (without negative R2 values)
Varpart_plot_p <- data.frame(Broad_varpart2[,4],
SSF_varpart2[,4],
DRF_varpart2[,4],
ST_varpart2[,4],
IC_varpart2[,4],
NI_varpart2[,4],
MD_varpart2[,4],
JA_varpart2[,4],
UBA_varpart2[,4],
BER_varpart2[,4],
ITA_varpart2[,4])
colnames(Varpart_plot_p) <- c("Broad", "SSF", "DRF", "Santa Fé do Sul", "Icém", "Nova Itapirema", "Morro do Diabo", "Jataí", "Ubatuba", "Bertioga", "Itanhaém")
Varpart_plot_p
## Broad SSF DRF Santa Fé do Sul Icém Nova Itapirema
## All 0.0001 0.0001 0.0001 0.1045 2e-04 0.1483
## Env 0.0001 0.0001 0.0003 0.1045 2e-04 0.1483
## Clim 0.0001 0.0001 0.0004 NA NA NA
## Spa 0.0001 0.0001 0.0001 NA NA 0.0153
## Pure_Env 0.0436 0.0191 0.0897 0.1045 2e-04 NA
## Pure_Clim 0.0282 0.1799 0.1951 NA NA NA
## Pure_Spa 0.0001 0.0012 0.0006 NA NA NA
## Env_Spa NA NA NA NA NA NA
## Env_Clim NA NA NA NA NA NA
## Spa_Clim NA NA NA NA NA NA
## Spa_Clim_Env NA NA NA NA NA NA
## Resid NA NA NA NA NA NA
## Morro do Diabo Jataí Ubatuba Bertioga Itanhaém
## All 0.2653 0.0029 0.0004 NA 0.3284
## Env 0.2653 0.0027 0.0013 NA 0.3284
## Clim NA NA NA NA NA
## Spa NA 0.0060 0.0035 NA NA
## Pure_Env 0.2653 0.0568 0.0493 NA 0.3284
## Pure_Clim NA NA NA NA NA
## Pure_Spa NA 0.0398 0.0239 NA NA
## Env_Spa NA NA NA NA NA
## Env_Clim NA NA NA NA NA
## Spa_Clim NA NA NA NA NA
## Spa_Clim_Env NA NA NA NA NA
## Resid NA NA NA NA NA
Plotting the Variation Partioning as barplots.
Varpart_barplot <- Varpart_plot[-c(1:4),]
Varpart_barplot_break <- Varpart_barplot; Varpart_barplot_break[8,] <- Varpart_barplot_break[8,]-0.2
Varpart_barplot_break <- Varpart_barplot_break[c(1,4,3,6,2,5,7,8),]
par(mfrow = c(1,1))
barplot(as.matrix(Varpart_barplot_break), axes = F, col = c(Pure_Env = "forestgreen",
Env_Spa = mix_color(alpha = 0.4,"forestgreen","cornflowerblue"),
Pure_Spa = "cornflowerblue",
Spa_Clim = mix_color(alpha = 0.4,"brown1","cornflowerblue"),
Pure_Clim = "brown1",
Env_Clim = mix_color(alpha = 0.4,"brown1","forestgreen"),
Spa_Clim_Env = mix_color(alpha = 0.7,"brown1","grey"),
Resid = "grey80"), space = c(0,2,1,2,1,1,1,1,2,1,1), border = "white",
legend.text = c("Environment","Environment-Space","Space","Climate-Space","Climate","Climate-Environment","All three","Residual"), ylim = c(0,0.8),
args.legend = list(x = -0.5,y = 0.9, yjust = 1, xjust = 0, horiz = F, ncol = 4, box.col = "transparent",text.width = 5, border = "white"), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25)
axis(2, at = c(0,0.1,0.2,0.3,0.4,0.5,0.6, 0.7, 0.8), labels = c("0 %","10 %","20 %","30 %","40 %","50 %","60 %","70%","100%"))
axis.break(2, 0.75, style = "slash")
axis(1,at = c(0.5,4.5,16),line = 2, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
axis(1,at = c(12.5,21.5),line = 0.5, labels =c("SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.1)
text(c(0.5, 3.5,5.5 ,8.5,10.5,12.5,14.5,16.5, 19.5,21.5,23.5),rep(0.79,11), labels =c("All","SSF","DRF","Santa Fé do Sul","Icém","Nova Itapirema","Morro do Diabo","Jataí","Ubatuba","Bertioga","Itanhaém"), srt = 90, adj = 1, col = "grey45")
#text(c(10.5,12.5,16.5,16.5,19.5),c(0.1,0.08,0.17,0.59,0.03), labels =c("*","*","*","*","*","*"), adj = 0.5, col = "white", cex = 2)
#text(c(0.5,0.5,0.5),c(0.01,0.12,0.299), labels =c("*","*","*"), adj = 0.5, col = "white", cex = 2)
#text(c(3.5,3.5),c(0.02,0.1), labels =c("*","*","*"), adj = 0.5, col = "white", cex = 2)
#text(c(5.5,5.5),c(0.01,0.065), labels =c("*","*","*"), adj = 0.5, col = "white", cex = 2)
#text(c(0.5,0.3,0.7,0.5),c(0.24,0.35,0.35,0.035), labels =c("*","*","*","*"), adj = 0.5, col = c("brown1","brown1","forestgreen","forestgreen"), cex = 2)
#text(c(3.5,3.3,3.7),c(0.22,0.3075,0.3075), labels =c("*","*","*"), adj = 0.5, col = c("brown1","brown1","forestgreen"), cex = 2)
#text(c(5.5,5.3, 5.7),c(0.115,0.145,0.145), labels =c("*","*","*"), adj = 0.5, col = c("brown1","brown1","forestgreen"), cex = 2)
###Mudar para simbolos
#cada simbolo representa env, clim e spa
#os shared serão os simbolos de spa com cores dos outros
The asterisks represent significant fractions.
Creating some new important fractions
Env_Spatially_Structured <- Varpart_plot["Env_Spa",] + Varpart_plot["Spa_Clim_Env",]
Env_Non_Spatially_Structured <- Varpart_plot["Pure_Env",] + Varpart_plot["Env_Clim",]
Clim_Spatially_Structured <- Varpart_plot["Spa_Clim",] + Varpart_plot["Spa_Clim_Env",]
Clim_Non_Spatially_Structured <- Varpart_plot["Pure_Clim",] + Varpart_plot["Env_Clim",]
Non_Spatially_Climate_Environment <- Varpart_plot["Pure_Env",] + Varpart_plot["Pure_Clim",] + Varpart_plot["Env_Clim",]
Spatially_Climate_Environment <- Varpart_plot["Spa_Clim",] + Varpart_plot["Env_Spa",] + Varpart_plot["Spa_Clim_Env",]
Total_Climate_Environment <- Spatially_Climate_Environment + Non_Spatially_Climate_Environment
#Total_Climate_Environment["Nova Itapirema"] <- 0
Varpart_plot <- rbind(Varpart_plot,
Env_Spatially_Structured = Env_Spatially_Structured,
Env_Non_Spatially_Structured = Env_Non_Spatially_Structured,
Clim_Spatially_Structured = Clim_Spatially_Structured,
Clim_Non_Spatially_Structured = Clim_Non_Spatially_Structured,
Non_Spatially_Climate_Environment = Non_Spatially_Climate_Environment,
Spatially_Climate_Environment = Spatially_Climate_Environment,
Total_Climate_Environment = Total_Climate_Environment)
Means of fractions in all extents
se <- function(x){
sd(x)/sqrt(length(x))
}
Means_spatial_extent <- data.frame(Broad_Extent = Varpart_plot[,1],
Intermediate_Extent = apply(Varpart_plot[,2:3],1,mean),
Small_Extent = apply(Varpart_plot[,4:11],1,mean))
se_spatial_extent <- data.frame(Intermediate_Extent = apply(Varpart_plot[,2:3],1,se),
Small_Extent = apply(Varpart_plot[,4:11],1,se))
se_spatial_extent_upper <- Means_spatial_extent[,2:3] + se_spatial_extent
se_spatial_extent_lower <- Means_spatial_extent[,2:3] - se_spatial_extent
Means_spatial_extent
## Broad_Extent Intermediate_Extent Small_Extent
## All 0.415 0.2685 0.179750
## Env 0.166 0.1045 0.145875
## Clim 0.212 0.1100 0.000000
## Spa 0.395 0.2295 0.073750
## Pure_Env 0.011 0.0250 0.106125
## Pure_Clim 0.009 0.0075 0.000000
## Pure_Spa 0.136 0.0850 0.033875
## Env_Spa 0.057 0.0490 0.039750
## Env_Clim 0.001 0.0070 0.000000
## Spa_Clim 0.104 0.0720 0.000000
## Spa_Clim_Env 0.097 0.0235 0.000000
## Resid 0.585 0.7315 0.820250
## Env_Spatially_Structured 0.154 0.0725 0.039750
## Env_Non_Spatially_Structured 0.012 0.0320 0.106125
## Clim_Spatially_Structured 0.201 0.0955 0.000000
## Clim_Non_Spatially_Structured 0.010 0.0145 0.000000
## Non_Spatially_Climate_Environment 0.021 0.0395 0.106125
## Spatially_Climate_Environment 0.258 0.1445 0.039750
## Total_Climate_Environment 0.279 0.1840 0.145875
Means of fractions in small extent
Means_small_extent <- data.frame(SSF = apply(Varpart_plot[,4:8],1,mean),
DRF = apply(Varpart_plot[,9:11],1,mean))
se_small_extent <- data.frame(SSF = apply(Varpart_plot[,4:8],1,se),
DRF = apply(Varpart_plot[,9:11],1,se))
se_small_extent_upper <- Means_small_extent + se_small_extent
se_small_extent_lower <- Means_small_extent - se_small_extent
Means_small_extent
## SSF DRF
## All 0.2478 0.06633333
## Env 0.2048 0.04766667
## Clim 0.0000 0.00000000
## Spa 0.0946 0.03900000
## Pure_Env 0.1532 0.02766667
## Pure_Clim 0.0000 0.00000000
## Pure_Spa 0.0430 0.01866667
## Env_Spa 0.0516 0.02000000
## Env_Clim 0.0000 0.00000000
## Spa_Clim 0.0000 0.00000000
## Spa_Clim_Env 0.0000 0.00000000
## Resid 0.7522 0.93366667
## Env_Spatially_Structured 0.0516 0.02000000
## Env_Non_Spatially_Structured 0.1532 0.02766667
## Clim_Spatially_Structured 0.0000 0.00000000
## Clim_Non_Spatially_Structured 0.0000 0.00000000
## Non_Spatially_Climate_Environment 0.1532 0.02766667
## Spatially_Climate_Environment 0.0516 0.02000000
## Total_Climate_Environment 0.2048 0.04766667
par(mfrow = c(1,3))
barplot(as.matrix(Means_spatial_extent[c(16,15),]), axes = F, col = c("brown1",mix_color(alpha = 0.5,"brown1","grey25")), space = c(0,1,1),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Climate")
arrows(y0 = c(as.matrix(se_spatial_extent_lower[3,])),
y1 = c(as.matrix(se_spatial_extent_upper[3,])),
x1 = c(2.5,4.5),
x0 = c(2.5,4.5), code = 3, angle = 90, length = 0.1, lwd = 1)
## Warning in arrows(y0 = c(as.matrix(se_spatial_extent_lower[3, ])), y1 =
## c(as.matrix(se_spatial_extent_upper[3, : zero-length arrow is of indeterminate
## angle and so skipped
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(0.5,2.5,4.5),line = 0, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
barplot(as.matrix(Means_spatial_extent[c(14,13),]), axes = F, col = c("forestgreen",mix_color(alpha = 0.5,"forestgreen","grey25")), space = c(0,1,1),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Local Environment")
arrows(y0 = c(as.matrix(se_spatial_extent_lower[2,])),
y1 = c(as.matrix(se_spatial_extent_upper[2,])),
x1 = c(2.5,4.5),
x0 = c(2.5,4.5), code = 3, angle = 90, length = 0.1, lwd = 1)
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(0.5,2.5,4.5),line = 0, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
barplot(as.matrix(Means_spatial_extent[c(7,18),]), axes = F, col = c("cornflowerblue",mix_color(alpha = 0.5,"cornflowerblue","grey25")), space = c(0,1,1),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Space")
arrows(y0 = c(as.matrix(se_spatial_extent_lower[4,])),
y1 = c(as.matrix(se_spatial_extent_upper[4,])),
x1 = c(2.5,4.5),
x0 = c(2.5,4.5), code = 3, angle = 90, length = 0.1, lwd = 1)
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(0.5,2.5,4.5),line = 0, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
par(mfrow = c(1,3))
barplot(as.matrix(Varpart_plot[c("Clim_Non_Spatially_Structured", "Clim_Spatially_Structured"),c("SSF","DRF")]),
axes = F, col = c("brown1",mix_color(alpha = 0.5,"brown1","grey25")), space = c(0,1,2,1),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Climate")
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(1.5,6.5),line = 1, labels =c("Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
axis(1,at = c(0.5,2.5,5.5,7.5),line = -0.5, labels =c("SSF","DRF", "SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
barplot(as.matrix(data.frame(Varpart_plot[c("Env_Non_Spatially_Structured", "Env_Spatially_Structured"),c("SSF","DRF")],
Means_small_extent[c("Env_Non_Spatially_Structured","Env_Spatially_Structured"),])),
axes = F, col = c("forestgreen",mix_color(alpha = 0.5,"forestgreen","grey25")), space = c(0,1,2,1),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Local Environment")
arrows(y0 = c(as.matrix(se_small_extent_lower["Env",])),
y1 = c(as.matrix(se_small_extent_upper["Env",])),
x1 = c(5.5,7.5),
x0 = c(5.5,7.5), code = 3, angle = 90, length = 0.1, lwd = 1)
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(1.5,6.5),line = 1, labels =c("Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
axis(1,at = c(0.5,2.5,5.5,7.5),line = -0.5, labels =c("SSF","DRF", "SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
barplot(as.matrix(data.frame(Varpart_plot[c("Pure_Spa", "Spatially_Climate_Environment"),c("SSF","DRF")],
Means_small_extent[c("Pure_Spa","Spatially_Climate_Environment"),])),
axes = F, col = c("cornflowerblue",mix_color(alpha = 0.5,"cornflowerblue","grey25")), space = c(0,1,2,1),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Space")
arrows(y0 = c(as.matrix(se_small_extent_lower["Spa",])),
y1 = c(as.matrix(se_small_extent_upper["Spa",])),
x1 = c(5.5,7.5),
x0 = c(5.5,7.5), code = 3, angle = 90, length = 0.1, lwd = 1)
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(1.5,6.5),line = 1, labels =c("Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
axis(1,at = c(0.5,2.5,5.5,7.5),line = -0.5, labels =c("SSF","DRF", "SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
par(mfrow = c(1,3))
barplot(as.matrix(Varpart_plot[c("Clim_Non_Spatially_Structured", "Clim_Spatially_Structured"),c("Broad","SSF","DRF")]),
axes = F, col = c("brown1",mix_color(alpha = 0.5,"brown1","grey25")), space = c(0.75,1.5,0.5,1.5,0.5),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Climate", xlim = c(0,9.75) )
## Warning in space + width: comprimento do objeto maior não é múltiplo do
## comprimento do objeto menor
## Warning in w.r - delta: comprimento do objeto maior não é múltiplo do
## comprimento do objeto menor
## Warning in w.m - delta: comprimento do objeto maior não é múltiplo do
## comprimento do objeto menor
## Warning in space + width: comprimento do objeto maior não é múltiplo do
## comprimento do objeto menor
## Warning in w.r - delta: comprimento do objeto maior não é múltiplo do
## comprimento do objeto menor
## Warning in w.m - delta: comprimento do objeto maior não é múltiplo do
## comprimento do objeto menor
arrows(y0 = c(as.matrix(se_small_extent_lower["Clim",])),
y1 = c(as.matrix(se_small_extent_upper["Clim",])),
x1 = c(7.75,9.25),
x0 = c(7.75,9.25), code = 3, angle = 90, length = 0.1, lwd = 1)
## Warning in arrows(y0 = c(as.matrix(se_small_extent_lower["Clim", ])),
## y1 = c(as.matrix(se_small_extent_upper["Clim", : zero-length arrow is of
## indeterminate angle and so skipped
## Warning in arrows(y0 = c(as.matrix(se_small_extent_lower["Clim", ])),
## y1 = c(as.matrix(se_small_extent_upper["Clim", : zero-length arrow is of
## indeterminate angle and so skipped
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(1.25,4.5,8.5),line = 1, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3, gap.axis = -1)
axis(1,at = c(3.75,5.25,7.75,9.25),line = -0.5, labels =c("SSF","DRF", "SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3, gap.axis = -1)
barplot(as.matrix(data.frame(Varpart_plot[c("Env_Non_Spatially_Structured", "Env_Spatially_Structured"),c("Broad","SSF","DRF")],
Means_small_extent[c("Env_Non_Spatially_Structured","Env_Spatially_Structured"),])),
axes = F, col = c("forestgreen",mix_color(alpha = 0.5,"forestgreen","grey25")), space = c(0.75,1.5,0.5,1.5,0.5),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "local Environment", xlim = c(0,9.75) )
arrows(y0 = c(as.matrix(se_small_extent_lower["Env",])),
y1 = c(as.matrix(se_small_extent_upper["Env",])),
x1 = c(7.75,9.25),
x0 = c(7.75,9.25), code = 3, angle = 90, length = 0.1, lwd = 1)
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(1.25,4.5,8.5),line = 1, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3, gap.axis = -1)
axis(1,at = c(3.75,5.25,7.75,9.25),line = -0.5, labels =c("SSF","DRF", "SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3, gap.axis = -1)
barplot(as.matrix(data.frame(Varpart_plot[c("Pure_Spa", "Spatially_Climate_Environment"),c("Broad","SSF","DRF")],
Means_small_extent[c("Pure_Spa","Spatially_Climate_Environment"),])),
axes = F, col = c("cornflowerblue",mix_color(alpha = 0.5,"cornflowerblue","grey25")), space = c(0.75,1.5,0.5,1.5,0.5),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Space", xlim = c(0,9.75) )
arrows(y0 = c(as.matrix(se_small_extent_lower["Spa",])),
y1 = c(as.matrix(se_small_extent_upper["Spa",])),
x1 = c(7.75,9.25),
x0 = c(7.75,9.25), code = 3, angle = 90, length = 0.1, lwd = 1)
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(1.25,4.5,8.5),line = 1, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3, gap.axis = -1)
axis(1,at = c(3.75,5.25,7.75,9.25),line = -0.5, labels =c("SSF","DRF", "SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3, gap.axis = -1)
RodolfoPelinson/AtlanticForestMetacommunity documentation built on Aug. 5, 2023, 3:53 p.m.
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Variation Partitioning
Rodolfo Pelinson 07/04/2021
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
The used packages to run this analysis are:
vegan version 2.5-6
ade4 version 1.7-15
adespatial version 0.3-8
Some packages used to generate plots and tables
dplyr version 0.8.5
plotrix version 3.7-8
set.seed(5)
Reducing Multicolinearity
To remove multicolinear variables from our Environmental Matrix I built
a function that removes variables with the variance inflation factor
(VIF) higher than 3. The function VIF_selection() simply uses the
vif.cca() from the vegan package.
Broad_env_VIF <- VIF_selection(Broad_pa, Broad_env_st[,-7])
Broad_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth nvt
## 1.237565 1.488527 1.118756 1.340180 1.617198
## dist_to_forest
## 1.319102
SSF_env_VIF <- VIF_selection(SSF_pa, SSF_env_st)
SSF_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth nvt
## 1.287250 1.293051 1.287689 1.443912 1.492950
## dist_to_forest
## 1.136891
ST_env_VIF <- VIF_selection(ST_pa, ST_env_st)
ST_env_VIF$VIFs
## [[1]]
## hydroperiod area depth nvt dist_to_forest
## 27.479904 11.917983 5.386203 20.148713 1.807948
##
## [[2]]
## area depth nvt dist_to_forest
## 3.533032 2.360081 2.639484 1.668725
##
## [[3]]
## depth nvt dist_to_forest
## 1.444899 1.301229 1.367747
IC_env_VIF <- VIF_selection(IC_pa, IC_env_st)
IC_env_VIF$VIFs
## [[1]]
## hydroperiod area depth nvt dist_to_forest
## 5.342364 5.518649 3.465826 3.219143 3.331453
##
## [[2]]
## hydroperiod depth nvt dist_to_forest
## 2.895120 1.516899 1.606162 2.715728
NI_env_VIF <- VIF_selection(NI_pa, NI_env_st)
NI_env_VIF$VIFs
## [[1]]
## hydroperiod area depth nvt dist_to_forest
## 1.502173 4.868245 4.697667 1.834039 2.256836
##
## [[2]]
## hydroperiod depth nvt dist_to_forest
## 1.487856 1.157613 1.493676 1.441587
MD_env_VIF <- VIF_selection(MD_pa, MD_env_st)
MD_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth nvt
## 6.123821 36.928117 15.263901 3.075865 3.436439
## dist_to_forest
## 43.153516
##
## [[2]]
## hydroperiod canopy_cover area depth nvt
## 3.843659 1.637214 5.706331 2.499002 3.434896
##
## [[3]]
## hydroperiod canopy_cover depth nvt
## 1.770317 1.410612 1.879809 1.508743
JA_env_VIF <- VIF_selection(JA_pa, JA_env_st)
JA_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth nvt
## 14.962311 7.691347 3.081842 5.988699 3.453394
## dist_to_forest
## 12.622399
##
## [[2]]
## canopy_cover area depth nvt dist_to_forest
## 6.871158 1.961897 4.322304 3.356278 7.533870
##
## [[3]]
## canopy_cover area depth nvt
## 2.081341 1.450483 2.089961 2.864143
DRF_env_VIF <- VIF_selection(DRF_pa, DRF_env_st)
DRF_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth nvt
## 1.338500 1.272615 1.296349 1.356465 1.048673
## dist_to_forest
## 1.368314
UBA_env_VIF <- VIF_selection(UBA_pa, UBA_env_st)
UBA_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth dist_to_forest
## 1.436660 1.459906 7.001901 1.750901 6.699443
##
## [[2]]
## hydroperiod canopy_cover depth dist_to_forest
## 1.341421 1.415973 1.599294 1.643447
BER_env_VIF <- VIF_selection(BER_pa, BER_env_st)
BER_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth dist_to_forest
## 3.117432 1.663244 8.428997 2.304574 7.972957
##
## [[2]]
## hydroperiod canopy_cover depth dist_to_forest
## 2.192425 1.561949 2.253493 4.070420
##
## [[3]]
## hydroperiod canopy_cover depth
## 1.541888 1.384145 1.154773
ITA_env_VIF <- VIF_selection(ITA_pa, ITA_env_st)
ITA_env_VIF$VIFs
## [[1]]
## hydroperiod canopy_cover area depth nvt
## 2.342853 1.828709 1.671319 1.996469 1.458903
Same thing for climate variables
Broad_clim_VIF <- VIF_selection(Broad_pa, Broad_clim_st[,-6])
Broad_clim_VIF$VIFs
## [[1]]
## temp_Season range_temp total_prec prec_season
## 16.70111 11.16256 38.16025 37.55883
##
## [[2]]
## temp_Season range_temp prec_season
## 3.005540 2.148041 4.835358
##
## [[3]]
## temp_Season range_temp
## 1.168582 1.168582
SSF_clim_VIF <- VIF_selection(SSF_pa, SSF_clim_st)
SSF_clim_VIF$VIFs
## [[1]]
## temp_Season range_temp total_prec prec_season
## 44.759191 5.971934 3.197284 52.382832
##
## [[2]]
## temp_Season range_temp total_prec
## 2.026442 4.722364 3.061846
##
## [[3]]
## temp_Season total_prec
## 1.157234 1.157234
DRF_clim_VIF <- VIF_selection(DRF_pa, DRF_clim_st)
DRF_clim_VIF$VIFs
## [[1]]
## temp_Season range_temp total_prec prec_season
## 6.561095 3.339104 12.024627 6.439093
##
## [[2]]
## temp_Season range_temp prec_season
## 1.895339 2.812287 2.295711
Spatial Variables
Constructing matrices of spatial filters
set.seed(3, sample.kind = "default")
candidates_Broad <- listw.candidates(Broad_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup"), y_fdown = 5, y_fup = 0.5)
##
## PLEASE NOTE: The components "delsgs" and "summary" of the
## object returned by deldir() are now DATA FRAMES rather than
## matrices (as they were prior to release 0.0-18).
## See help("deldir").
##
## PLEASE NOTE: The process that deldir() uses for determining
## duplicated points has changed from that used in version
## 0.0-9 of this package (and previously). See help("deldir").
Broad_MEM <- listw.select(Broad_pa, candidates = candidates_Broad, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 5 is 0.286871 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 12 is 0.066193 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.395438 with 22 variables (> 0.393805)
## Procedure stopped (alpha criteria): pvalue for variable 16 is 0.072993 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.399941 with 31 variables (> 0.398728)
## Procedure stopped (alpha criteria): pvalue for variable 18 is 0.051195 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.371382 with 23 variables (> 0.369036)
Broad_MEM_FS <- Broad_MEM$best$MEM.select
adegraphics::s.label(Broad_coord, nb = candidates_Broad[[Broad_MEM$best.id]],
pnb.edge.col = "red", main = paste("Broad - ",names(Broad_MEM$best.id)), plot = TRUE, labels = NULL)
################################################################################
candidates_DRF <- listw.candidates(DRF_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup"), y_fdown = 5, y_fup = 0.25)
DRF_MEM <- listw.select(DRF_pa, candidates = candidates_DRF, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 8 is 0.083192 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 8 is 0.108089 (> 0.050000)
DRF_MEM_FS <- DRF_MEM$best$MEM.select
adegraphics::s.label(DRF_coord, nb = candidates_DRF[[DRF_MEM$best.id]],
pnb.edge.col = "red", main = paste("DRF - ",names(DRF_MEM$best.id)), plot = TRUE, labels = NULL)
################################################################################
candidates_SSF <- listw.candidates(SSF_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup"), y_fdown = 5, y_fup = 0.25)
SSF_MEM <- listw.select(SSF_pa, candidates = candidates_SSF, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.079183 with 2 variables (> 0.079183)
## Procedure stopped (alpha criteria): pvalue for variable 7 is 0.082992 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.304147 with 9 variables (> 0.302779)
## Procedure stopped (alpha criteria): pvalue for variable 7 is 0.071493 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 8 is 0.068893 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 9 is 0.071693 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 10 is 0.056094 (> 0.050000)
SSF_MEM_FS <- SSF_MEM$best$MEM.select
adegraphics::s.label(SSF_coord, nb = candidates_SSF[[SSF_MEM$best.id]],
pnb.edge.col = "red", main = paste("SSF - ",names(SSF_MEM$best.id)), plot = TRUE, labels = NULL)
################################################################################
#We restricted our options for optimization because of the low statistical power (low replication) that we had at our small spatial scale. We only used linear weights as we do not believe that a exponential decay would make sense at this scale (even relative large distances between sites are actually small). Also We restricted our graph conectivity matrix to only three that yields relatively different scenarios of conectance. Those are the Delauney triangulation, the Relative neighbour and the PCNM
################################################################################
candidates_ITA <- listw.candidates(ITA_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
ITA_MEM <- listw.select(ITA_pa, candidates = candidates_ITA, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
ITA_MEM_FS <- dbmem(ITA_coord, MEM.autocor = c("positive"), silent = TRUE)
################################################################################
candidates_BER <- listw.candidates(BER_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
BER_MEM <- listw.select(BER_pa, candidates = candidates_BER, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
BER_MEM_FS <- dbmem(BER_coord, MEM.autocor = c("positive"), silent = TRUE)
################################################################################
set.seed(2)
candidates_UBA <- listw.candidates(UBA_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
## Warning in nb2listw(nb.object, style = style, glist = lapply(nb.dist, f1, : zero
## sum general weights
UBA_MEM <- listw.select(UBA_pa, candidates = candidates_UBA, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 3 is 0.089791 (> 0.050000)
UBA_MEM_FS <- UBA_MEM$best$MEM.select
adegraphics::s.label(UBA_coord, nb = candidates_UBA[[UBA_MEM$best.id]],
pnb.edge.col = "red", main = paste("UBA - ",names(UBA_MEM$best.id)), plot = TRUE, labels = NULL)
################################################################################
candidates_ST <- listw.candidates(ST_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
## Warning in nb2listw(nb.object, style = style, glist = lapply(nb.dist, f1, : zero
## sum general weights
ST_MEM <- listw.select(ST_pa, candidates = candidates_ST, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
ST_MEM_FS <- dbmem(ST_coord, MEM.autocor = c("positive"), silent = TRUE)
################################################################################
candidates_IC <- listw.candidates(IC_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
IC_MEM <- listw.select(IC_pa, candidates = candidates_IC, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
IC_MEM_FS <- dbmem(IC_coord, MEM.autocor = c("positive"), silent = TRUE)
################################################################################
set.seed(5)
candidates_NI <- listw.candidates(NI_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
NI_MEM <- listw.select(NI_pa, candidates = candidates_NI, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
NI_MEM_FS <- NI_MEM$best$MEM.select
adegraphics::s.label(NI_coord, nb = candidates_NI[[NI_MEM$best.id]],
pnb.edge.col = "red", main = paste("NI - ",names(NI_MEM$best.id)), plot = TRUE, labels = NULL)
################################################################################
candidates_MD <- listw.candidates(MD_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
MD_MEM <- listw.select(MD_pa, candidates = candidates_MD, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
MD_MEM_FS <- dbmem(MD_coord, MEM.autocor = c("positive"), silent = TRUE)
################################################################################
candidates_JA <- listw.candidates(JA_coord, style = "B", nb = c("del", "rel", "pcnm"),
weights = c("flin"))
JA_MEM <- listw.select(JA_pa, candidates = candidates_JA, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = TRUE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.084692 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 3 is 0.104390 (> 0.050000)
JA_MEM_FS <- JA_MEM$best$MEM.select
adegraphics::s.label(JA_coord, nb = candidates_JA[[JA_MEM$best.id]],
pnb.edge.col = "red", main = paste("JA - ",names(JA_MEM$best.id)), plot = TRUE, labels = NULL)
################################################################################
###PLOTING WEIGHTS ####
nb <- chooseCN(coordinates(UBA_coord), type = 1, plot.nb = FALSE)
#Delaunay triangulation (type 1)
#Gabriel graph (type 2)
#Relative neighbours (type 3)
#Minimum spanning tree (type 4)
#Neighbourhood by distance (type 5)
#K nearests neighbours (type 6)
#Inverse distances (type 7)
dist_UBA <- dist(UBA_coord)
distnb <- spdep::nbdists(nb, as.matrix(UBA_coord))
a_fdown <- 5
a_f_up <- 0.5
fdist <- lapply(distnb, function(x) 1 - x/max(dist_UBA)) #linear
fdist <- lapply(distnb, function(x) 1 - (x/max(dist_UBA))^a_fdown) #fdown
fdist <- lapply(distnb, function(x) 1 / x^a_f_up) #fup
lw <- nb2listw(nb, style = 'B', zero.policy = TRUE, glist = fdist)
m1 <- as.matrix(dist_UBA)
m2 <- spdep::listw2mat(lw)
plot(m1[!diag(ncol(m1))], m2[!diag(ncol(m2))], pch = 20, xlab = "distance", ylab = "spatial weights")
################
Forward Selection
Similarly to removing variables with VIF higher than 3, I built a
function to select the most important variables, when they could
significantly explain species occurrences. This function mostly rely on
the function fs() from package adespatial. Forward selection is only
performed when the whole predictor matrix can significantly explain (p
\< 0.05) the variation in species occurrences.
Broad Spatial Extent
set.seed(5)
Broad_env_Forward <- forward_selection(Broad_pa, Broad_env_VIF$variables)
## Testing variable 1
## Testing variable 2
## Testing variable 3
## Testing variable 4
## Testing variable 5
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.176872 with 5 variables (> 0.175018)
Broad_env_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 nvt 5 0.12280383 0.1228038 0.1134720 13.159611 0.0001
## 2 dist_to_forest 6 0.03553797 0.1583418 0.1402416 3.926809 0.0001
## 3 canopy_cover 2 0.02254986 0.1808917 0.1541816 2.532738 0.0018
## 4 hydroperiod 1 0.02048938 0.2013810 0.1662769 2.334697 0.0066
Broad_env_FS <- Broad_env_Forward$selected_variables
Broad_clim_Forward <- forward_selection(Broad_pa, Broad_clim_VIF$variables)
## Testing variable 1
## Testing variable 2
Broad_clim_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 range_temp 2 0.18651094 0.1865109 0.1778568 21.551646 1e-04
## 2 temp_Season 1 0.04168414 0.2281951 0.2115971 5.022804 1e-04
Broad_clim_FS <- Broad_clim_Forward$selected_variables
#_______________________________________________________________
SSF_env_Forward <- forward_selection(SSF_pa, SSF_env_VIF$variables)
## Testing variable 1
## Testing variable 2
## Testing variable 3
## Testing variable 4
## Testing variable 5
## Procedure stopped (alpha criteria): pvalue for variable 5 is 0.120800 (> 0.050000)
SSF_env_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 dist_to_forest 6 0.06279225 0.06279225 0.04149207 2.947968 0.0013
## 2 hydroperiod 1 0.05740783 0.12020008 0.07927915 2.805793 0.0017
## 3 depth 4 0.05098863 0.17118871 0.11198790 2.583848 0.0030
## 4 canopy_cover 2 0.04138347 0.21257218 0.13574995 2.154765 0.0128
SSF_env_FS <- SSF_env_Forward$selected_variables
SSF_clim_Forward <- forward_selection(SSF_pa, SSF_clim_VIF$variables)
## Testing variable 1
## Testing variable 2
SSF_clim_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 total_prec 2 0.1030335 0.1030335 0.08264788 5.054228 1e-04
## 2 temp_Season 1 0.1050119 0.2080454 0.17121029 5.701730 1e-04
SSF_clim_FS <- SSF_clim_Forward$selected_variables
#_______________________________________________________________
ST_env_Forward <- forward_selection(ST_pa, ST_env_VIF$variables)
## Forward selection NOT performed. p > 0.05
ST_env_Forward$forward_results
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 9999
##
## Model: rda(X = New_Y, Y = New_X)
## Df Variance F Pr(>F)
## Model 3 1.5233 1.632 0.0992 .
## Residual 4 1.2446
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ST_env_FS <- ST_env_Forward$selected_variables
IC_env_Forward <- forward_selection(IC_pa, IC_env_VIF$variables)
## Testing variable 1
## Testing variable 2
## Testing variable 3
## Procedure stopped (alpha criteria): pvalue for variable 3 is 0.419400 (> 0.050000)
IC_env_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 dist_to_forest 4 0.2762401 0.2762401 0.2038641 3.816737 0.0013
## 2 hydroperiod 1 0.1448824 0.4211225 0.2924830 2.252534 0.0107
IC_env_FS <- IC_env_Forward$selected_variables
NI_env_Forward <- forward_selection(NI_pa, NI_env_VIF$variables)
## Forward selection NOT performed. p > 0.05
NI_env_Forward$forward_results
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 9999
##
## Model: rda(X = New_Y, Y = New_X)
## Df Variance F Pr(>F)
## Model 4 2.2203 1.3581 0.1531
## Residual 3 1.2261
NI_env_FS <- NI_env_Forward$selected_variables
MD_env_Forward <- forward_selection(MD_pa, MD_env_VIF$variables)
## Forward selection NOT performed. p > 0.05
MD_env_Forward$forward_results
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 9999
##
## Model: rda(X = New_Y, Y = New_X)
## Df Variance F Pr(>F)
## Model 4 1.9377 1.2819 0.2587
## Residual 3 1.1337
MD_env_FS <- MD_env_Forward$selected_variables
JA_env_Forward <- forward_selection(JA_pa, JA_env_VIF$variables)
## Testing variable 1
## Testing variable 2
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.058700 (> 0.050000)
JA_env_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 depth 3 0.2978698 0.2978698 0.2101035 3.393898 0.0028
JA_env_FS <- JA_env_Forward$selected_variables
#______________________________________________________________
DRF_env_Forward <- forward_selection(DRF_pa, DRF_env_VIF$variables)
## Testing variable 1
## Testing variable 2
## Testing variable 3
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.082422 with 3 variables (> 0.081064)
DRF_env_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 canopy_cover 2 0.06947213 0.06947213 0.05008614 3.583624 0.0005
## 2 depth 4 0.04152619 0.11099833 0.07316847 2.195419 0.0220
DRF_env_FS <- DRF_env_Forward$selected_variables
DRF_clim_Forward <- forward_selection(DRF_pa, DRF_clim_VIF$variables)
## Testing variable 1
## Testing variable 2
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.059316 with 2 variables (> 0.055078)
DRF_clim_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 range_temp 2 0.06823593 0.06823593 0.04882418 3.515187 2e-04
DRF_clim_FS <- DRF_clim_Forward$selected_variables
#______________________________________________________________
UBA_env_Forward <- forward_selection(UBA_pa, UBA_env_VIF$variables)
## Testing variable 1
## Testing variable 2
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.263700 (> 0.050000)
UBA_env_Forward$forward_results
## variables order R2 R2Cum AdjR2Cum F pvalue
## 1 canopy_cover 2 0.1360444 0.1360444 0.09490361 3.306803 0.0012
UBA_env_FS <- UBA_env_Forward$selected_variables
BER_env_Forward <- forward_selection(BER_pa, BER_env_VIF$variables)
## Forward selection NOT performed. p > 0.05
BER_env_Forward$forward_results
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 9999
##
## Model: rda(X = New_Y, Y = New_X)
## Df Variance F Pr(>F)
## Model 3 0.61185 0.8438 0.6915
## Residual 8 1.93360
BER_env_FS <- BER_env_Forward$selected_variables
ITA_env_Forward <- forward_selection(ITA_pa, ITA_env_VIF$variables)
## Forward selection NOT performed. p > 0.05
ITA_env_Forward$forward_results
## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 9999
##
## Model: rda(X = New_Y, Y = New_X)
## Df Variance F Pr(>F)
## Model 5 1.0943 1.1421 0.3235
## Residual 9 1.7247
ITA_env_FS <- ITA_env_Forward$selected_variables
We can plot the important spatial variables to better understand what spatial patterns they describe.
We are only going to plot the four most important ones
par(mfrow = c(2,2))
ylim <- c(min(Broad_coord[,"lat"])*1.005, max(Broad_coord[,"lat"])*0.995)
xlim <- c(min(Broad_coord[,"long"])*1.005, max(Broad_coord[,"long"])*0.995)
sr_value(Broad_coord, data.frame(Broad_MEM$best$MEM.select)[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("LARGE SCALE", colnames(data.frame(Broad_MEM$best$MEM.select))[1]), line = 3, outer = F, adj = 1)
sr_value(Broad_coord, data.frame(Broad_MEM$best$MEM.select)[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("LARGE SCALE", colnames(data.frame(Broad_MEM$best$MEM.select))[2]), line = 3, outer = F, adj = 1)
sr_value(Broad_coord, data.frame(Broad_MEM$best$MEM.select)[,3], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("LARGE SCALE", colnames(data.frame(Broad_MEM$best$MEM.select))[3]), line = 3, outer = F, adj = 1)
sr_value(Broad_coord, data.frame(Broad_MEM$best$MEM.select)[,4], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("LARGE SCALE", colnames(data.frame(Broad_MEM$best$MEM.select))[4]), line = 3, outer = F, adj = 1)
Intermediate Scale SSF.
par(mfrow = c(2,2))
ylim <- c(min(SSF_coord[,"lat"])*1.005, max(SSF_coord[,"lat"])*0.995)
xlim <- c(min(SSF_coord[,"long"])*1.005, max(SSF_coord[,"long"])*0.995)
sr_value(SSF_coord, data.frame(SSF_MEM$best$MEM.select)[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("SSF", colnames(data.frame(SSF_MEM$best$MEM.select))[1]), line = 3, outer = F, adj = 1)
sr_value(SSF_coord, data.frame(SSF_MEM$best$MEM.select)[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("SSF", colnames(data.frame(SSF_MEM$best$MEM.select))[2]), line = 3, outer = F, adj = 1)
sr_value(SSF_coord, data.frame(SSF_MEM$best$MEM.select)[,3], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("SSF", colnames(data.frame(SSF_MEM$best$MEM.select))[3]), line = 3, outer = F, adj = 1)
sr_value(SSF_coord, data.frame(SSF_MEM$best$MEM.select)[,4], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("SSF", colnames(data.frame(SSF_MEM$best$MEM.select))[4]), line = 3, outer = F, adj = 1)
Intermediate Scale DRF.
par(mfrow = c(2,2))
ylim <- c(min(DRF_coord[,"lat"])*1.005, max(DRF_coord[,"lat"])*0.995)
xlim <- c(min(DRF_coord[,"long"])*1.005, max(DRF_coord[,"long"])*0.995)
sr_value(DRF_coord, data.frame(DRF_MEM$best$MEM.select)[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("DRF", colnames(data.frame(DRF_MEM$best$MEM.select))[1]), line = 3, outer = F, adj = 1)
sr_value(DRF_coord, data.frame(DRF_MEM$best$MEM.select)[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("DRF", colnames(data.frame(DRF_MEM$best$MEM.select))[2]), line = 3, outer = F, adj = 1)
sr_value(DRF_coord, data.frame(DRF_MEM$best$MEM.select)[,3], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("DRF", colnames(data.frame(DRF_MEM$best$MEM.select))[3]), line = 3, outer = F, adj = 1)
sr_value(DRF_coord, data.frame(DRF_MEM$best$MEM.select)[,4], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("DRF", colnames(data.frame(DRF_MEM$best$MEM.select))[4]), line = 3, outer = F, adj = 1)
Small Extent SSF - Santa Fé do Sul
par(mfrow = c(1,2))
ylim <- c(min(ST_coord[,"lat"])*1.0001, max(ST_coord[,"lat"])*0.9999)
xlim <- c(min(ST_coord[,"long"])*1.0001, max(ST_coord[,"long"])*0.9999)
sr_value(ST_coord, ST_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Santa Fé do Sul -", colnames(ST_MEM_FS)[1]), line = 3, outer = F, adj = 1)
sr_value(ST_coord, ST_MEM_FS[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Santa Fé do Sul -", colnames(ST_MEM_FS)[2]), line = 3, outer = F, adj = 1)
Small Extent SSF - Icém
par(mfrow = c(1,2))
ylim <- c(min(IC_coord[,"lat"])*1.0001, max(IC_coord[,"lat"])*0.9999)
xlim <- c(min(IC_coord[,"long"])*1.0001, max(IC_coord[,"long"])*0.9999)
sr_value(IC_coord, IC_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Icém -", colnames(IC_MEM_FS)[1]), line = 3, outer = F, adj = 1)
sr_value(IC_coord, IC_MEM_FS[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Icém -", colnames(IC_MEM_FS)[2]), line = 3, outer = F, adj = 1)
Small Extent SSF - Nova Itapirema.
par(mfrow = c(1,1))
ylim <- c(min(NI_coord[,"lat"])*1.0001, max(NI_coord[,"lat"])*0.9999)
xlim <- c(min(NI_coord[,"long"])*1.0001, max(NI_coord[,"long"])*0.9999)
sr_value(NI_coord, NI_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Nova Itapirema -", colnames(NI_MEM_FS)[1]), line = 3, outer = F, adj = 1)
Small Extent SSF - Morro do Diabo.
par(mfrow = c(1,1))
ylim <- c(min(MD_coord[,"lat"])*1.0025, max(MD_coord[,"lat"])*0.9975)
xlim <- c(min(MD_coord[,"long"])*1.0025, max(MD_coord[,"long"])*0.9975)
sr_value(MD_coord, MD_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 0.8, xax = 2, yax = 1, method = "bubble")
title(main = paste("Morro do Diabo -", colnames(MD_MEM_FS)[1]), line = 3, outer = F, adj = 1)
Small Extent SSF - Jataí.
par(mfrow = c(1,2))
ylim <- c(min(JA_coord[,"lat"])*1.0001, max(JA_coord[,"lat"])*0.9999)
xlim <- c(min(JA_coord[,"long"])*1.0001, max(JA_coord[,"long"])*0.9999)
sr_value(JA_coord, JA_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Jataí -", colnames(JA_MEM_FS)[1]), line = 3, outer = F, adj = 1)
sr_value(JA_coord, JA_MEM_FS[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Jataí -", colnames(JA_MEM_FS)[2]), line = 3, outer = F, adj = 1)
Small Extent DRF - Ubatuba
par(mfrow = c(1,2))
ylim <- c(min(UBA_coord[,"lat"])*1.00015, max(UBA_coord[,"lat"])*0.99985)
xlim <- c(min(UBA_coord[,"long"])*1.00015, max(UBA_coord[,"long"])*0.99985)
sr_value(UBA_coord, data.frame(UBA_MEM$best$MEM.select)[,1], ylim = c(-23.37694,-23.33123), xlim = c(-44.95004, -44.80492), grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("UBA", colnames(data.frame(UBA_MEM$best$MEM.select))[1]), line = 3, outer = F, adj = 1)
sr_value(UBA_coord, data.frame(UBA_MEM$best$MEM.select)[,2], ylim = c(-23.37694,-23.33123), xlim = c(-44.95004, -44.80492), grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("UBA", colnames(data.frame(UBA_MEM$best$MEM.select))[2]), line = 3, outer = F, adj = 1)
Small Extent SSF - Bertioga.
par(mfrow = c(1,1))
ylim <- c(min(BER_coord[,"lat"])*1.0002, max(BER_coord[,"lat"])*0.9998)
xlim <- c(min(BER_coord[,"long"])*1.0002, max(BER_coord[,"long"])*0.9998)
sr_value(BER_coord, BER_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Bertioga -", colnames(UBA_MEM_FS)[1]), line = 3, outer = F, adj = 1)
Small Extent DRF - Itanhaém
par(mfrow = c(1,2))
ylim <- c(min(ITA_coord[,"lat"])*1.003, max(ITA_coord[,"lat"])*0.997)
xlim <- c(min(ITA_coord[,"long"])*1.003, max(ITA_coord[,"long"])*0.997)
sr_value(ITA_coord, ITA_MEM_FS[,1], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Itanhaém -", colnames(ITA_MEM_FS)[1]), line = 3, outer = F, adj = 1)
sr_value(ITA_coord, ITA_MEM_FS[,2], ylim = ylim, xlim = xlim, grid=F, csize = 0.8, clegend = 1, xax = 2, yax = 1, method = "bubble")
title(main = paste("Itanhaém -", colnames(ITA_MEM_FS)[2]), line = 3, outer = F, adj = 1)
Variation Partitioning
Large Extent
set.seed(10)
#Variation partitioning Broad
Broad_varpart <- var_partitioning(Y = Broad_pa,
env = Broad_env_FS,
clim = Broad_clim_FS,
spa = Broad_MEM_FS, percent_r2 = F)
#Testing significande of spatially structured environment
candidates_Broad_env <- listw.candidates(Broad_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup","fdown"), y_fdown = 5, y_fup = 0.5)
##
## PLEASE NOTE: The components "delsgs" and "summary" of the
## object returned by deldir() are now DATA FRAMES rather than
## matrices (as they were prior to release 0.0-18).
## See help("deldir").
##
## PLEASE NOTE: The process that deldir() uses for determining
## duplicated points has changed from that used in version
## 0.0-9 of this package (and previously). See help("deldir").
Broad_MEM_env <- listw.select(Broad_env_FS, candidates = candidates_Broad_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.253549 with 3 variables (> 0.251115)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.306761 with 6 variables (> 0.306454)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.296619 with 6 variables (> 0.295208)
## Procedure stopped (alpha criteria): pvalue for variable 10 is 0.063094 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.404339 with 14 variables (> 0.396839)
## Procedure stopped (alpha criteria): pvalue for variable 12 is 0.051595 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 10 is 0.050095 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.368117 with 13 variables (> 0.364769)
## Procedure stopped (alpha criteria): pvalue for variable 12 is 0.069793 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 13 is 0.050895 (> 0.050000)
Broad_env_spa_sig <- envspace.test(spe = Broad_pa, env = Broad_env_FS, coord = Broad_coord,
MEM.spe = Broad_MEM_FS,
listw.env = candidates_Broad_env[[Broad_MEM_env$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
#Testing significande of spatially structured climate
Broad_MEM_clim <- listw.select(Broad_clim_FS, candidates = candidates_Broad_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.876851 with 4 variables (> 0.876357)
## Procedure stopped (alpha criteria): pvalue for variable 20 is 0.089291 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 23 is 0.083192 (> 0.050000)
## Procedure stopped (R2more criteria): variable 35 explains only 0.000994 of the variance.
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.962677 with 30 variables (> 0.962304)
## Procedure stopped (alpha criteria): pvalue for variable 30 is 0.055894 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 36 is 0.051695 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.942164 with 26 variables (> 0.941798)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.938844 with 26 variables (> 0.938675)
## Procedure stopped (alpha criteria): pvalue for variable 30 is 0.063194 (> 0.050000)
Broad_clim_spa_sig <- envspace.test(spe = Broad_pa, env = Broad_clim_FS, coord = Broad_coord,
MEM.spe = data.frame(Broad_MEM$best$MEM.select),
listw.env = candidates_Broad_env[[Broad_MEM_clim$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
Broad_varpart
## Adj_R2 Df F p
## All 0.415 36 2.874901 0.0001
## Env 0.166 4 5.736676 0.0001
## Clim 0.212 2 13.748385 0.0001
## Spa 0.395 30 3.063807 0.0001
## Pure_Env 0.011 4 1.283095 0.0420
## Pure_Clim 0.009 2 1.466479 0.0293
## Pure_Spa 0.136 30 1.691412 0.0001
## Env_Spa 0.057 NA NA NA
## Env_Clim 0.001 NA NA NA
## Spa_Clim 0.104 NA NA NA
## Spa_Clim_Env 0.097 NA NA NA
## Resid 0.585 NA NA NA
Broad_env_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.1544202
##
## Based on 10000 replicates
## Simulated p-value: 9.999e-05
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 6.1970326756 0.0208156944 0.0004648091
Broad_clim_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.2013072
##
## Based on 10000 replicates
## Simulated p-value: 9.999e-05
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 5.5124965211 0.0450111357 0.0008038948
Intermediate Extent
DRF_varpart <- var_partitioning(Y = DRF_pa,
env = DRF_env_FS,
clim = DRF_clim_FS,
spa = DRF_MEM_FS, percent_r2 = F)
#Testing significande of spatially structured environment
candidates_DRF_env <- listw.candidates(DRF_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup","fdown"), y_fdown = 5, y_fup = 0.5)
DRF_MEM_env <- listw.select(DRF_env_FS, candidates = candidates_DRF_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.075892 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 3 is 0.058894 (> 0.050000)
DRF_env_spa_sig <- envspace.test(spe = DRF_pa, env = DRF_env_FS, coord = DRF_coord,
MEM.spe = data.frame(DRF_MEM$best$MEM.select),
listw.env = candidates_DRF_env[[DRF_MEM_env$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
#Testing significande of spatially structured climate
DRF_MEM_clim <- listw.select(DRF_clim_FS, candidates = candidates_DRF_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.853267 with 3 variables (> 0.853267)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.873833 with 5 variables (> 0.859533)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.846575 with 6 variables (> 0.839674)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.953992 with 15 variables (> 0.953021)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.955943 with 16 variables (> 0.952317)
## Procedure stopped (alpha criteria): pvalue for variable 9 is 0.091391 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 15 is 0.064794 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 10 is 0.050195 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.890885 with 8 variables (> 0.888316)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.880739 with 14 variables (> 0.871375)
DRF_clim_spa_sig <- envspace.test(spe = DRF_pa, env = DRF_clim_FS, coord = DRF_coord,
MEM.spe = data.frame(DRF_MEM$best$MEM.select),
listw.env = candidates_DRF_env[[DRF_MEM_clim$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
###########################################################################################
SSF_varpart <- var_partitioning(Y = SSF_pa,
env = SSF_env_FS,
clim = SSF_clim_FS,
spa = SSF_MEM_FS, percent_r2 = F)
#Testing significande of spatially structured environment
candidates_SSF_env <- listw.candidates(SSF_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup","fdown"), y_fdown = 5, y_fup = 0.5)
SSF_MEM_env <- listw.select(SSF_env_FS, candidates = candidates_SSF_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.119788 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 6 is 0.096290 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.358144 with 8 variables (> 0.340049)
## Procedure stopped (alpha criteria): pvalue for variable 10 is 0.051395 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 10 is 0.082992 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 7 is 0.057394 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 7 is 0.052595 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 8 is 0.051895 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 8 is 0.087591 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 3 is 0.052895 (> 0.050000)
SSF_env_spa_sig <- envspace.test(spe = SSF_pa, env = SSF_env_FS, coord = SSF_coord,
MEM.spe = data.frame(SSF_MEM$best$MEM.select),
listw.env = candidates_SSF_env[[SSF_MEM_env$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
#Testing significande of spatially structured climate
SSF_MEM_clim <- listw.select(SSF_clim_FS, candidates = candidates_SSF_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (alpha criteria): pvalue for variable 6 is 0.057994 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 7 is 0.056694 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.964587 with 12 variables (> 0.963727)
## Procedure stopped (alpha criteria): pvalue for variable 12 is 0.071993 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 15 is 0.069693 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 14 is 0.070293 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.938182 with 8 variables (> 0.934646)
## Procedure stopped (alpha criteria): pvalue for variable 9 is 0.062394 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 9 is 0.057594 (> 0.050000)
SSF_clim_spa_sig <- envspace.test(spe = SSF_pa, env = SSF_clim_FS, coord = SSF_coord,
MEM.spe = data.frame(SSF_MEM$best$MEM.select),
listw.env = candidates_SSF_env[[SSF_MEM_clim$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
DRF_varpart
## Adj_R2 Df F p
## All 0.191 10 2.154740 0.0001
## Env 0.073 2 2.934146 0.0002
## Clim 0.049 1 3.515187 0.0003
## Spa 0.165 7 2.378271 0.0001
## Pure_Env 0.016 2 1.401642 0.0845
## Pure_Clim 0.006 1 1.292474 0.1843
## Pure_Spa 0.092 7 1.743533 0.0006
## Env_Spa 0.034 NA NA NA
## Env_Clim 0.004 NA NA NA
## Spa_Clim 0.020 NA NA NA
## Spa_Clim_Env 0.018 NA NA NA
## Resid 0.809 NA NA NA
DRF_env_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.05287454
##
## Based on 10000 replicates
## Simulated p-value: 0.01249875
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 2.4273813307 0.0119539831 0.0002841888
DRF_clim_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.03846861
##
## Based on 10000 replicates
## Simulated p-value: 0.03269673
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 1.5784918115 0.0130585778 0.0002591348
SSF_varpart
## Adj_R2 Df F p
## All 0.346 14 2.702616 0.0001
## Env 0.136 4 2.767066 0.0001
## Clim 0.171 2 5.648020 0.0001
## Spa 0.294 8 3.344447 0.0001
## Pure_Env 0.034 4 1.449166 0.0192
## Pure_Clim 0.009 2 1.223076 0.1821
## Pure_Spa 0.078 8 1.579172 0.0005
## Env_Spa 0.064 NA NA NA
## Env_Clim 0.010 NA NA NA
## Spa_Clim 0.124 NA NA NA
## Spa_Clim_Env 0.029 NA NA NA
## Resid 0.654 NA NA NA
SSF_env_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.09248927
##
## Based on 10000 replicates
## Simulated p-value: 0.00909909
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 2.525610622 0.033365911 0.000548006
SSF_clim_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.1526691
##
## Based on 10000 replicates
## Simulated p-value: 0.00039996
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 4.2205352914 0.0458711769 0.0006403098
Small Extent
Small Extent allowing negative fractions
ST_varpart <- var_partitioning(ST_pa, ST_env_FS, spa = ST_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
IC_varpart <- var_partitioning(IC_pa, IC_env_FS, spa = IC_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
NI_varpart <- var_partitioning(NI_pa, NI_env_FS, spa = NI_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
#Testing significande of spatially structured environment
candidates_NI_env <- listw.candidates(NI_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup","fdown"), y_fdown = 5, y_fup = 0.5)
NI_MEM_env <- listw.select(NI_env_FS, candidates = candidates_NI_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.1, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.175515 with 1 variables (> 0.175515)
# We are not adjusting p-values here because we assume that if there was a significant spatial and environmental strucutre in species distributions, we want to find spatial patterns in the environment
#There was no significant spatial structure in the environmental variables
NI_env_spa_sig <- envspace.test(spe = NI_pa, env = NI_env_FS, coord = NI_coord,
MEM.spe = NI_MEM_FS,
listw.env = candidates_NI_env$DBEM_PCNM, MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
## No significant relation between 'spe' and 'env'; the test was not performed
MD_varpart <- var_partitioning(MD_pa, MD_env_FS, spa = MD_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
JA_varpart <- var_partitioning(JA_pa, JA_env_FS, spa = JA_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
#Testing significande of spatially structured environment
candidates_JA_env <- listw.candidates(JA_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup","fdown"), y_fdown = 5, y_fup = 0.5)
JA_MEM_env <- listw.select(JA_env_FS, candidates = candidates_JA_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.1, p.adjust = FALSE, verbose = FALSE) # We are not adjusting p-values here because we assume that if there was a significant spatial and environmental strucutre in species distributions, we want to find spatial patterns in the environment
## Procedure stopped (alpha criteria): pvalue for variable 1 is 0.058694 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 1 is 0.057094 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.892776 with 2 variables (> 0.874947)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.841344 with 2 variables (> 0.808686)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.852574 with 3 variables (> 0.825464)
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.061594 (> 0.050000)
## Procedure stopped (alpha criteria): pvalue for variable 2 is 0.138886 (> 0.050000)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.855931 with 2 variables (> 0.843916)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.859910 with 3 variables (> 0.837980)
#There was no significant spatial structure in the environmental variables
JA_env_spa_sig <- envspace.test(spe = JA_pa, env = JA_env_FS, coord = JA_coord,
MEM.spe = data.frame(JA_MEM$best$MEM.select),
listw.env = candidates_JA_env[[JA_MEM_env$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
UBA_varpart <- var_partitioning(UBA_pa, UBA_env_FS, spa = UBA_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
#Testing significande of spatially structured environment
candidates_UBA_env <- listw.candidates(UBA_coord, style = "B", nb = c("del", "gab", "rel", "pcnm"),
weights = c("flin", "fup","fdown"), y_fdown = 5, y_fup = 0.5)
## Warning in nb2listw(nb.object, style = style, glist = lapply(nb.dist, f1, : zero
## sum general weights
## Warning in nb2listw(nb.object, style = style, glist =
## lapply(nbdists(nb.object, : zero sum general weights
## Warning in nb2listw(nb.object, style = style, glist = lapply(nb.dist, f1, : zero
## sum general weights
## Warning in nb2listw(nb.object, style = style, glist =
## lapply(nbdists(nb.object, : zero sum general weights
UBA_MEM_env <- listw.select(UBA_env_FS, candidates = candidates_UBA_env, MEM.autocor = c("positive"), method = c("FWD"),
MEM.all = FALSE, nperm = 10000, nperm.global = 10000, alpha = 0.05, p.adjust = FALSE, verbose = FALSE)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.610050 with 4 variables (> 0.570723)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.532181 with 3 variables (> 0.504917)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.531064 with 2 variables (> 0.526818)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.584362 with 3 variables (> 0.552325)
## Procedure stopped (adjR2thresh criteria) adjR2cum = 0.544172 with 3 variables (> 0.543362)
#There was no significant spatial structure in the environmental variables, thus we used the one selected for the species
UBA_env_spa_sig <- envspace.test(spe = UBA_pa, env = UBA_env_FS, coord = UBA_coord,
MEM.spe = data.frame(UBA_MEM$best$MEM.select),
listw.env = candidates_UBA_env[[UBA_MEM$best.id]], MEM.autocor = c("positive"),
regular = FALSE, nperm = 10000, MSR.method = "singleton", alpha = 0.05)
BER_varpart <- var_partitioning(BER_pa, BER_env_FS, spa = BER_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
ITA_varpart <- var_partitioning(ITA_pa, ITA_env_FS, spa = ITA_MEM_FS, allow_negative_r2 = T, percent_r2 = F)
ST_varpart
## Adj_R2 Df F p
## All 0.197 5 1.3440574 0.2575
## Env 0.213 3 1.6319731 0.1074
## Clim NA NA NA NA
## Spa -0.085 2 0.7256147 0.8144
## Pure_Env 0.282 3 1.5862081 0.2626
## Pure_Clim NA NA NA NA
## Pure_Spa -0.016 2 0.9605140 0.5192
## Env_Spa -0.069 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.803 NA NA NA
IC_varpart
## Adj_R2 Df F p
## All 0.291 4 2.1287607 0.0034
## Env 0.292 2 3.2736653 0.0002
## Clim NA NA NA NA
## Spa -0.041 2 0.7833254 0.7166
## Pure_Env 0.332 2 3.1073625 0.0018
## Pure_Clim NA NA NA NA
## Pure_Spa -0.001 2 0.9906546 0.5015
## Env_Spa -0.040 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.709 NA NA NA
NI_varpart
## Adj_R2 Df F p
## All 0.140 5 1.2271911 0.2850
## Env 0.170 4 1.3581374 0.1484
## Clim NA NA NA NA
## Spa 0.159 1 2.3197422 0.0158
## Pure_Env -0.019 4 0.9668644 0.5430
## Pure_Clim NA NA NA NA
## Pure_Spa -0.030 1 0.8944824 0.5182
## Env_Spa 0.189 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.860 NA NA NA
NI_env_spa_sig
## NULL
MD_varpart
## Adj_R2 Df F p
## All 0.282 5 1.5500378 0.1634
## Env 0.139 4 1.2819349 0.2614
## Clim NA NA NA NA
## Spa -0.005 1 0.9665789 0.3781
## Pure_Env 0.287 4 1.5993494 0.1713
## Pure_Clim NA NA NA NA
## Pure_Spa 0.143 1 1.5988564 0.2709
## Env_Spa -0.148 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.718 NA NA NA
JA_varpart
## Adj_R2 Df F p
## All 0.425 3 3.220404 0.0017
## Env 0.210 1 3.393898 0.0027
## Clim NA NA NA NA
## Spa 0.314 2 3.060817 0.0053
## Pure_Env 0.111 1 2.354789 0.0577
## Pure_Clim NA NA NA NA
## Pure_Spa 0.215 2 2.498105 0.0415
## Env_Spa 0.099 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.575 NA NA NA
JA_env_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.09888151
##
## Based on 10000 replicates
## Simulated p-value: 0.4475552
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 0.155249466 0.091925975 0.002007249
UBA_varpart
## Adj_R2 Df F p
## All 0.151 3 2.308338 0.0009
## Env 0.095 1 3.306803 0.0009
## Clim NA NA NA NA
## Spa 0.117 2 2.450992 0.0029
## Pure_Env 0.035 1 1.821646 0.0481
## Pure_Clim NA NA NA NA
## Pure_Spa 0.056 2 1.699031 0.0244
## Env_Spa 0.060 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.849 NA NA NA
UBA_env_spa_sig
## Monte-Carlo test
## Call: as.randtest(sim = E.b, obs = R2.b, alter = alternative)
##
## Observation: 0.06004112
##
## Based on 10000 replicates
## Simulated p-value: 0.02989701
## Alternative hypothesis: greater
##
## Std.Obs Expectation Variance
## 1.7891770392 0.0072893001 0.0008692971
BER_varpart
## Adj_R2 Df F p
## All -0.085 4 0.7852969 0.7952
## Env -0.044 3 0.8438125 0.6912
## Clim NA NA NA NA
## Spa -0.012 1 0.8660103 0.5760
## Pure_Env -0.072 3 0.7776483 0.7527
## Pure_Clim NA NA NA NA
## Pure_Spa -0.040 1 0.7035544 0.6921
## Env_Spa 0.028 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 1.085 NA NA NA
ITA_varpart
## Adj_R2 Df F p
## All 0.065 7 1.1390639 0.3530
## Env 0.048 5 1.1420928 0.3329
## Clim NA NA NA NA
## Spa -0.008 2 0.9443449 0.4926
## Pure_Env 0.073 5 1.1874488 0.3179
## Pure_Clim NA NA NA NA
## Pure_Spa 0.017 2 1.0804479 0.3988
## Env_Spa -0.025 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.935 NA NA NA
Not allowing negative fractions
Broad_varpart2 <- var_partitioning(Y = Broad_pa,
env = Broad_env_FS,
clim = Broad_clim_FS,
spa = Broad_MEM_FS, percent_r2 = F, allow_negative_r2 = F)
DRF_varpart2 <- var_partitioning(Y = DRF_pa,
env = DRF_env_FS,
clim = DRF_clim_FS,
spa = DRF_MEM_FS, percent_r2 = F, allow_negative_r2 = F)
SSF_varpart2 <- var_partitioning(Y = SSF_pa,
env = SSF_env_FS,
clim = SSF_clim_FS,
spa = SSF_MEM_FS, percent_r2 = F, allow_negative_r2 = F)
ST_varpart2 <- var_partitioning(ST_pa, ST_env_FS, spa = ST_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
IC_varpart2 <- var_partitioning(IC_pa, IC_env_FS, spa = IC_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
NI_varpart2 <- var_partitioning(NI_pa, NI_env_FS, spa = NI_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
MD_varpart2 <- var_partitioning(MD_pa, MD_env_FS, spa = MD_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
JA_varpart2 <- var_partitioning(JA_pa, JA_env_FS, spa = JA_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
UBA_varpart2 <- var_partitioning(UBA_pa, UBA_env_FS, spa = UBA_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
BER_varpart2 <- var_partitioning(BER_pa, BER_env_FS, spa = BER_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
ITA_varpart2 <- var_partitioning(ITA_pa, ITA_env_FS, spa = ITA_MEM_FS, allow_negative_r2 = F, percent_r2 = F)
Broad_varpart2
## Adj_R2 Df F p
## All 0.415 36 2.874901 0.0001
## Env 0.166 4 5.736676 0.0001
## Clim 0.212 2 13.748385 0.0001
## Spa 0.395 30 3.063807 0.0001
## Pure_Env 0.011 4 1.283095 0.0436
## Pure_Clim 0.009 2 1.466479 0.0282
## Pure_Spa 0.136 30 1.691412 0.0001
## Env_Spa 0.057 NA NA NA
## Env_Clim 0.001 NA NA NA
## Spa_Clim 0.104 NA NA NA
## Spa_Clim_Env 0.097 NA NA NA
## Resid 0.585 NA NA NA
SSF_varpart2
## Adj_R2 Df F p
## All 0.346 14 2.702616 0.0001
## Env 0.136 4 2.767066 0.0001
## Clim 0.171 2 5.648020 0.0001
## Spa 0.294 8 3.344447 0.0001
## Pure_Env 0.034 4 1.449166 0.0191
## Pure_Clim 0.009 2 1.223076 0.1799
## Pure_Spa 0.078 8 1.579172 0.0012
## Env_Spa 0.064 NA NA NA
## Env_Clim 0.010 NA NA NA
## Spa_Clim 0.124 NA NA NA
## Spa_Clim_Env 0.029 NA NA NA
## Resid 0.654 NA NA NA
DRF_varpart2
## Adj_R2 Df F p
## All 0.191 10 2.154740 0.0001
## Env 0.073 2 2.934146 0.0003
## Clim 0.049 1 3.515187 0.0004
## Spa 0.165 7 2.378271 0.0001
## Pure_Env 0.016 2 1.401642 0.0897
## Pure_Clim 0.006 1 1.292474 0.1951
## Pure_Spa 0.092 7 1.743533 0.0006
## Env_Spa 0.034 NA NA NA
## Env_Clim 0.004 NA NA NA
## Spa_Clim 0.020 NA NA NA
## Spa_Clim_Env 0.018 NA NA NA
## Resid 0.809 NA NA NA
ST_varpart2
## Adj_R2 Df F p
## All 0.213 3 1.631973 0.1045
## Env 0.213 3 1.631973 0.1045
## Clim NA NA NA NA
## Spa 0.000 NA NA NA
## Pure_Env 0.213 3 1.631973 0.1045
## Pure_Clim NA NA NA NA
## Pure_Spa 0.000 NA NA NA
## Env_Spa 0.000 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.787 NA NA NA
IC_varpart2
## Adj_R2 Df F p
## All 0.292 2 3.273665 2e-04
## Env 0.292 2 3.273665 2e-04
## Clim NA NA NA NA
## Spa 0.000 NA NA NA
## Pure_Env 0.292 2 3.273665 2e-04
## Pure_Clim NA NA NA NA
## Pure_Spa 0.000 NA NA NA
## Env_Spa 0.000 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.708 NA NA NA
NI_varpart2
## Adj_R2 Df F p
## All 0.170 4 1.358137 0.1483
## Env 0.170 4 1.358137 0.1483
## Clim NA NA NA NA
## Spa 0.159 1 2.319742 0.0153
## Pure_Env 0.011 NA NA NA
## Pure_Clim NA NA NA NA
## Pure_Spa 0.000 NA NA NA
## Env_Spa 0.159 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.830 NA NA NA
MD_varpart2
## Adj_R2 Df F p
## All 0.139 4 1.281935 0.2653
## Env 0.139 4 1.281935 0.2653
## Clim NA NA NA NA
## Spa 0.000 NA NA NA
## Pure_Env 0.139 4 1.281935 0.2653
## Pure_Clim NA NA NA NA
## Pure_Spa 0.000 NA NA NA
## Env_Spa 0.000 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.861 NA NA NA
JA_varpart2
## Adj_R2 Df F p
## All 0.425 3 3.220404 0.0029
## Env 0.210 1 3.393898 0.0027
## Clim NA NA NA NA
## Spa 0.314 2 3.060817 0.0060
## Pure_Env 0.111 1 2.354789 0.0568
## Pure_Clim NA NA NA NA
## Pure_Spa 0.215 2 2.498105 0.0398
## Env_Spa 0.099 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.575 NA NA NA
UBA_varpart2
## Adj_R2 Df F p
## All 0.151 3 2.308338 0.0004
## Env 0.095 1 3.306803 0.0013
## Clim NA NA NA NA
## Spa 0.117 2 2.450992 0.0035
## Pure_Env 0.035 1 1.821646 0.0493
## Pure_Clim NA NA NA NA
## Pure_Spa 0.056 2 1.699031 0.0239
## Env_Spa 0.060 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.849 NA NA NA
BER_varpart2
## Adj_R2 Df F p
## All 0 NA NA NA
## Env 0 NA NA NA
## Clim NA NA NA NA
## Spa 0 NA NA NA
## Pure_Env 0 NA NA NA
## Pure_Clim NA NA NA NA
## Pure_Spa 0 NA NA NA
## Env_Spa 0 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 1 NA NA NA
ITA_varpart2
## Adj_R2 Df F p
## All 0.048 5 1.142093 0.3284
## Env 0.048 5 1.142093 0.3284
## Clim NA NA NA NA
## Spa 0.000 NA NA NA
## Pure_Env 0.048 5 1.142093 0.3284
## Pure_Clim NA NA NA NA
## Pure_Spa 0.000 NA NA NA
## Env_Spa 0.000 NA NA NA
## Env_Clim NA NA NA NA
## Spa_Clim NA NA NA NA
## Spa_Clim_Env NA NA NA NA
## Resid 0.952 NA NA NA
Constructing a matrix with all R2 values (with negative values)
Varpart_plot_neg <- data.frame(Broad_varpart[,1],
SSF_varpart[,1],
DRF_varpart[,1],
ST_varpart[,1],
IC_varpart[,1],
NI_varpart[,1],
MD_varpart[,1],
JA_varpart[,1],
UBA_varpart[,1],
BER_varpart[,1],
ITA_varpart[,1])
colnames(Varpart_plot_neg) <- c("Broad", "SSF", "DRF", "Santa Fé do Sul", "Icém", "Nova Itapirema", "Morro do Diabo", "Jataí", "Ubatuba", "Bertioga", "Itanhaém")
for(i in 1:dim(Varpart_plot_neg)[1]){
for(j in 1:dim(Varpart_plot_neg)[2]){
if(is.na(Varpart_plot_neg[i,j])){Varpart_plot_neg[i,j] <- 0}
}
}
Varpart_plot_neg
## Broad SSF DRF Santa Fé do Sul Icém Nova Itapirema
## All 0.415 0.346 0.191 0.197 0.291 0.140
## Env 0.166 0.136 0.073 0.213 0.292 0.170
## Clim 0.212 0.171 0.049 0.000 0.000 0.000
## Spa 0.395 0.294 0.165 -0.085 -0.041 0.159
## Pure_Env 0.011 0.034 0.016 0.282 0.332 -0.019
## Pure_Clim 0.009 0.009 0.006 0.000 0.000 0.000
## Pure_Spa 0.136 0.078 0.092 -0.016 -0.001 -0.030
## Env_Spa 0.057 0.064 0.034 -0.069 -0.040 0.189
## Env_Clim 0.001 0.010 0.004 0.000 0.000 0.000
## Spa_Clim 0.104 0.124 0.020 0.000 0.000 0.000
## Spa_Clim_Env 0.097 0.029 0.018 0.000 0.000 0.000
## Resid 0.585 0.654 0.809 0.803 0.709 0.860
## Morro do Diabo Jataí Ubatuba Bertioga Itanhaém
## All 0.282 0.425 0.151 -0.085 0.065
## Env 0.139 0.210 0.095 -0.044 0.048
## Clim 0.000 0.000 0.000 0.000 0.000
## Spa -0.005 0.314 0.117 -0.012 -0.008
## Pure_Env 0.287 0.111 0.035 -0.072 0.073
## Pure_Clim 0.000 0.000 0.000 0.000 0.000
## Pure_Spa 0.143 0.215 0.056 -0.040 0.017
## Env_Spa -0.148 0.099 0.060 0.028 -0.025
## Env_Clim 0.000 0.000 0.000 0.000 0.000
## Spa_Clim 0.000 0.000 0.000 0.000 0.000
## Spa_Clim_Env 0.000 0.000 0.000 0.000 0.000
## Resid 0.718 0.575 0.849 1.085 0.935
Constructing a matrix with all p values (with negative R2 values)
Varpart_plot_p_neg <- data.frame(Broad_varpart[,4],
SSF_varpart[,4],
DRF_varpart[,4],
ST_varpart[,4],
IC_varpart[,4],
NI_varpart[,4],
MD_varpart[,4],
JA_varpart[,4],
UBA_varpart[,4],
BER_varpart[,4],
ITA_varpart[,4])
colnames(Varpart_plot_p_neg) <- c("Broad", "SSF", "DRF", "Santa Fé do Sul", "Icém", "Nova Itapirema", "Morro do Diabo", "Jataí", "Ubatuba", "Bertioga", "Itanhaém")
Varpart_plot_p_neg
## Broad SSF DRF Santa Fé do Sul Icém Nova Itapirema
## All 0.0001 0.0001 0.0001 0.2575 0.0034 0.2850
## Env 0.0001 0.0001 0.0002 0.1074 0.0002 0.1484
## Clim 0.0001 0.0001 0.0003 NA NA NA
## Spa 0.0001 0.0001 0.0001 0.8144 0.7166 0.0158
## Pure_Env 0.0420 0.0192 0.0845 0.2626 0.0018 0.5430
## Pure_Clim 0.0293 0.1821 0.1843 NA NA NA
## Pure_Spa 0.0001 0.0005 0.0006 0.5192 0.5015 0.5182
## Env_Spa NA NA NA NA NA NA
## Env_Clim NA NA NA NA NA NA
## Spa_Clim NA NA NA NA NA NA
## Spa_Clim_Env NA NA NA NA NA NA
## Resid NA NA NA NA NA NA
## Morro do Diabo Jataí Ubatuba Bertioga Itanhaém
## All 0.1634 0.0017 0.0009 0.7952 0.3530
## Env 0.2614 0.0027 0.0009 0.6912 0.3329
## Clim NA NA NA NA NA
## Spa 0.3781 0.0053 0.0029 0.5760 0.4926
## Pure_Env 0.1713 0.0577 0.0481 0.7527 0.3179
## Pure_Clim NA NA NA NA NA
## Pure_Spa 0.2709 0.0415 0.0244 0.6921 0.3988
## Env_Spa NA NA NA NA NA
## Env_Clim NA NA NA NA NA
## Spa_Clim NA NA NA NA NA
## Spa_Clim_Env NA NA NA NA NA
## Resid NA NA NA NA NA
Constructing a matrix with all R2 values (without negative values)
Varpart_plot <- data.frame(Broad_varpart2[,1],
SSF_varpart2[,1],
DRF_varpart2[,1],
ST_varpart2[,1],
IC_varpart2[,1],
NI_varpart2[,1],
MD_varpart2[,1],
JA_varpart2[,1],
UBA_varpart2[,1],
BER_varpart2[,1],
ITA_varpart2[,1])
colnames(Varpart_plot) <- c("Broad", "SSF", "DRF", "Santa Fé do Sul", "Icém", "Nova Itapirema", "Morro do Diabo", "Jataí", "Ubatuba", "Bertioga", "Itanhaém")
for(i in 1:dim(Varpart_plot)[1]){
for(j in 1:dim(Varpart_plot)[2]){
if(is.na(Varpart_plot[i,j])){Varpart_plot[i,j] <- 0}
}
}
Varpart_plot
## Broad SSF DRF Santa Fé do Sul Icém Nova Itapirema
## All 0.415 0.346 0.191 0.213 0.292 0.170
## Env 0.166 0.136 0.073 0.213 0.292 0.170
## Clim 0.212 0.171 0.049 0.000 0.000 0.000
## Spa 0.395 0.294 0.165 0.000 0.000 0.159
## Pure_Env 0.011 0.034 0.016 0.213 0.292 0.011
## Pure_Clim 0.009 0.009 0.006 0.000 0.000 0.000
## Pure_Spa 0.136 0.078 0.092 0.000 0.000 0.000
## Env_Spa 0.057 0.064 0.034 0.000 0.000 0.159
## Env_Clim 0.001 0.010 0.004 0.000 0.000 0.000
## Spa_Clim 0.104 0.124 0.020 0.000 0.000 0.000
## Spa_Clim_Env 0.097 0.029 0.018 0.000 0.000 0.000
## Resid 0.585 0.654 0.809 0.787 0.708 0.830
## Morro do Diabo Jataí Ubatuba Bertioga Itanhaém
## All 0.139 0.425 0.151 0 0.048
## Env 0.139 0.210 0.095 0 0.048
## Clim 0.000 0.000 0.000 0 0.000
## Spa 0.000 0.314 0.117 0 0.000
## Pure_Env 0.139 0.111 0.035 0 0.048
## Pure_Clim 0.000 0.000 0.000 0 0.000
## Pure_Spa 0.000 0.215 0.056 0 0.000
## Env_Spa 0.000 0.099 0.060 0 0.000
## Env_Clim 0.000 0.000 0.000 0 0.000
## Spa_Clim 0.000 0.000 0.000 0 0.000
## Spa_Clim_Env 0.000 0.000 0.000 0 0.000
## Resid 0.861 0.575 0.849 1 0.952
Constructing a matrix with all p values (without negative R2 values)
Varpart_plot_p <- data.frame(Broad_varpart2[,4],
SSF_varpart2[,4],
DRF_varpart2[,4],
ST_varpart2[,4],
IC_varpart2[,4],
NI_varpart2[,4],
MD_varpart2[,4],
JA_varpart2[,4],
UBA_varpart2[,4],
BER_varpart2[,4],
ITA_varpart2[,4])
colnames(Varpart_plot_p) <- c("Broad", "SSF", "DRF", "Santa Fé do Sul", "Icém", "Nova Itapirema", "Morro do Diabo", "Jataí", "Ubatuba", "Bertioga", "Itanhaém")
Varpart_plot_p
## Broad SSF DRF Santa Fé do Sul Icém Nova Itapirema
## All 0.0001 0.0001 0.0001 0.1045 2e-04 0.1483
## Env 0.0001 0.0001 0.0003 0.1045 2e-04 0.1483
## Clim 0.0001 0.0001 0.0004 NA NA NA
## Spa 0.0001 0.0001 0.0001 NA NA 0.0153
## Pure_Env 0.0436 0.0191 0.0897 0.1045 2e-04 NA
## Pure_Clim 0.0282 0.1799 0.1951 NA NA NA
## Pure_Spa 0.0001 0.0012 0.0006 NA NA NA
## Env_Spa NA NA NA NA NA NA
## Env_Clim NA NA NA NA NA NA
## Spa_Clim NA NA NA NA NA NA
## Spa_Clim_Env NA NA NA NA NA NA
## Resid NA NA NA NA NA NA
## Morro do Diabo Jataí Ubatuba Bertioga Itanhaém
## All 0.2653 0.0029 0.0004 NA 0.3284
## Env 0.2653 0.0027 0.0013 NA 0.3284
## Clim NA NA NA NA NA
## Spa NA 0.0060 0.0035 NA NA
## Pure_Env 0.2653 0.0568 0.0493 NA 0.3284
## Pure_Clim NA NA NA NA NA
## Pure_Spa NA 0.0398 0.0239 NA NA
## Env_Spa NA NA NA NA NA
## Env_Clim NA NA NA NA NA
## Spa_Clim NA NA NA NA NA
## Spa_Clim_Env NA NA NA NA NA
## Resid NA NA NA NA NA
Plotting the Variation Partioning as barplots.
Varpart_barplot <- Varpart_plot[-c(1:4),]
Varpart_barplot_break <- Varpart_barplot; Varpart_barplot_break[8,] <- Varpart_barplot_break[8,]-0.2
Varpart_barplot_break <- Varpart_barplot_break[c(1,4,3,6,2,5,7,8),]
par(mfrow = c(1,1))
barplot(as.matrix(Varpart_barplot_break), axes = F, col = c(Pure_Env = "forestgreen",
Env_Spa = mix_color(alpha = 0.4,"forestgreen","cornflowerblue"),
Pure_Spa = "cornflowerblue",
Spa_Clim = mix_color(alpha = 0.4,"brown1","cornflowerblue"),
Pure_Clim = "brown1",
Env_Clim = mix_color(alpha = 0.4,"brown1","forestgreen"),
Spa_Clim_Env = mix_color(alpha = 0.7,"brown1","grey"),
Resid = "grey80"), space = c(0,2,1,2,1,1,1,1,2,1,1), border = "white",
legend.text = c("Environment","Environment-Space","Space","Climate-Space","Climate","Climate-Environment","All three","Residual"), ylim = c(0,0.8),
args.legend = list(x = -0.5,y = 0.9, yjust = 1, xjust = 0, horiz = F, ncol = 4, box.col = "transparent",text.width = 5, border = "white"), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25)
axis(2, at = c(0,0.1,0.2,0.3,0.4,0.5,0.6, 0.7, 0.8), labels = c("0 %","10 %","20 %","30 %","40 %","50 %","60 %","70%","100%"))
axis.break(2, 0.75, style = "slash")
axis(1,at = c(0.5,4.5,16),line = 2, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
axis(1,at = c(12.5,21.5),line = 0.5, labels =c("SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.1)
text(c(0.5, 3.5,5.5 ,8.5,10.5,12.5,14.5,16.5, 19.5,21.5,23.5),rep(0.79,11), labels =c("All","SSF","DRF","Santa Fé do Sul","Icém","Nova Itapirema","Morro do Diabo","Jataí","Ubatuba","Bertioga","Itanhaém"), srt = 90, adj = 1, col = "grey45")
#text(c(10.5,12.5,16.5,16.5,19.5),c(0.1,0.08,0.17,0.59,0.03), labels =c("*","*","*","*","*","*"), adj = 0.5, col = "white", cex = 2)
#text(c(0.5,0.5,0.5),c(0.01,0.12,0.299), labels =c("*","*","*"), adj = 0.5, col = "white", cex = 2)
#text(c(3.5,3.5),c(0.02,0.1), labels =c("*","*","*"), adj = 0.5, col = "white", cex = 2)
#text(c(5.5,5.5),c(0.01,0.065), labels =c("*","*","*"), adj = 0.5, col = "white", cex = 2)
#text(c(0.5,0.3,0.7,0.5),c(0.24,0.35,0.35,0.035), labels =c("*","*","*","*"), adj = 0.5, col = c("brown1","brown1","forestgreen","forestgreen"), cex = 2)
#text(c(3.5,3.3,3.7),c(0.22,0.3075,0.3075), labels =c("*","*","*"), adj = 0.5, col = c("brown1","brown1","forestgreen"), cex = 2)
#text(c(5.5,5.3, 5.7),c(0.115,0.145,0.145), labels =c("*","*","*"), adj = 0.5, col = c("brown1","brown1","forestgreen"), cex = 2)
###Mudar para simbolos
#cada simbolo representa env, clim e spa
#os shared serão os simbolos de spa com cores dos outros
The asterisks represent significant fractions.
Creating some new important fractions
Env_Spatially_Structured <- Varpart_plot["Env_Spa",] + Varpart_plot["Spa_Clim_Env",]
Env_Non_Spatially_Structured <- Varpart_plot["Pure_Env",] + Varpart_plot["Env_Clim",]
Clim_Spatially_Structured <- Varpart_plot["Spa_Clim",] + Varpart_plot["Spa_Clim_Env",]
Clim_Non_Spatially_Structured <- Varpart_plot["Pure_Clim",] + Varpart_plot["Env_Clim",]
Non_Spatially_Climate_Environment <- Varpart_plot["Pure_Env",] + Varpart_plot["Pure_Clim",] + Varpart_plot["Env_Clim",]
Spatially_Climate_Environment <- Varpart_plot["Spa_Clim",] + Varpart_plot["Env_Spa",] + Varpart_plot["Spa_Clim_Env",]
Total_Climate_Environment <- Spatially_Climate_Environment + Non_Spatially_Climate_Environment
#Total_Climate_Environment["Nova Itapirema"] <- 0
Varpart_plot <- rbind(Varpart_plot,
Env_Spatially_Structured = Env_Spatially_Structured,
Env_Non_Spatially_Structured = Env_Non_Spatially_Structured,
Clim_Spatially_Structured = Clim_Spatially_Structured,
Clim_Non_Spatially_Structured = Clim_Non_Spatially_Structured,
Non_Spatially_Climate_Environment = Non_Spatially_Climate_Environment,
Spatially_Climate_Environment = Spatially_Climate_Environment,
Total_Climate_Environment = Total_Climate_Environment)
Means of fractions in all extents
se <- function(x){
sd(x)/sqrt(length(x))
}
Means_spatial_extent <- data.frame(Broad_Extent = Varpart_plot[,1],
Intermediate_Extent = apply(Varpart_plot[,2:3],1,mean),
Small_Extent = apply(Varpart_plot[,4:11],1,mean))
se_spatial_extent <- data.frame(Intermediate_Extent = apply(Varpart_plot[,2:3],1,se),
Small_Extent = apply(Varpart_plot[,4:11],1,se))
se_spatial_extent_upper <- Means_spatial_extent[,2:3] + se_spatial_extent
se_spatial_extent_lower <- Means_spatial_extent[,2:3] - se_spatial_extent
Means_spatial_extent
## Broad_Extent Intermediate_Extent Small_Extent
## All 0.415 0.2685 0.179750
## Env 0.166 0.1045 0.145875
## Clim 0.212 0.1100 0.000000
## Spa 0.395 0.2295 0.073750
## Pure_Env 0.011 0.0250 0.106125
## Pure_Clim 0.009 0.0075 0.000000
## Pure_Spa 0.136 0.0850 0.033875
## Env_Spa 0.057 0.0490 0.039750
## Env_Clim 0.001 0.0070 0.000000
## Spa_Clim 0.104 0.0720 0.000000
## Spa_Clim_Env 0.097 0.0235 0.000000
## Resid 0.585 0.7315 0.820250
## Env_Spatially_Structured 0.154 0.0725 0.039750
## Env_Non_Spatially_Structured 0.012 0.0320 0.106125
## Clim_Spatially_Structured 0.201 0.0955 0.000000
## Clim_Non_Spatially_Structured 0.010 0.0145 0.000000
## Non_Spatially_Climate_Environment 0.021 0.0395 0.106125
## Spatially_Climate_Environment 0.258 0.1445 0.039750
## Total_Climate_Environment 0.279 0.1840 0.145875
Means of fractions in small extent
Means_small_extent <- data.frame(SSF = apply(Varpart_plot[,4:8],1,mean),
DRF = apply(Varpart_plot[,9:11],1,mean))
se_small_extent <- data.frame(SSF = apply(Varpart_plot[,4:8],1,se),
DRF = apply(Varpart_plot[,9:11],1,se))
se_small_extent_upper <- Means_small_extent + se_small_extent
se_small_extent_lower <- Means_small_extent - se_small_extent
Means_small_extent
## SSF DRF
## All 0.2478 0.06633333
## Env 0.2048 0.04766667
## Clim 0.0000 0.00000000
## Spa 0.0946 0.03900000
## Pure_Env 0.1532 0.02766667
## Pure_Clim 0.0000 0.00000000
## Pure_Spa 0.0430 0.01866667
## Env_Spa 0.0516 0.02000000
## Env_Clim 0.0000 0.00000000
## Spa_Clim 0.0000 0.00000000
## Spa_Clim_Env 0.0000 0.00000000
## Resid 0.7522 0.93366667
## Env_Spatially_Structured 0.0516 0.02000000
## Env_Non_Spatially_Structured 0.1532 0.02766667
## Clim_Spatially_Structured 0.0000 0.00000000
## Clim_Non_Spatially_Structured 0.0000 0.00000000
## Non_Spatially_Climate_Environment 0.1532 0.02766667
## Spatially_Climate_Environment 0.0516 0.02000000
## Total_Climate_Environment 0.2048 0.04766667
par(mfrow = c(1,3))
barplot(as.matrix(Means_spatial_extent[c(16,15),]), axes = F, col = c("brown1",mix_color(alpha = 0.5,"brown1","grey25")), space = c(0,1,1),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Climate")
arrows(y0 = c(as.matrix(se_spatial_extent_lower[3,])),
y1 = c(as.matrix(se_spatial_extent_upper[3,])),
x1 = c(2.5,4.5),
x0 = c(2.5,4.5), code = 3, angle = 90, length = 0.1, lwd = 1)
## Warning in arrows(y0 = c(as.matrix(se_spatial_extent_lower[3, ])), y1 =
## c(as.matrix(se_spatial_extent_upper[3, : zero-length arrow is of indeterminate
## angle and so skipped
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(0.5,2.5,4.5),line = 0, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
barplot(as.matrix(Means_spatial_extent[c(14,13),]), axes = F, col = c("forestgreen",mix_color(alpha = 0.5,"forestgreen","grey25")), space = c(0,1,1),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Local Environment")
arrows(y0 = c(as.matrix(se_spatial_extent_lower[2,])),
y1 = c(as.matrix(se_spatial_extent_upper[2,])),
x1 = c(2.5,4.5),
x0 = c(2.5,4.5), code = 3, angle = 90, length = 0.1, lwd = 1)
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(0.5,2.5,4.5),line = 0, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
barplot(as.matrix(Means_spatial_extent[c(7,18),]), axes = F, col = c("cornflowerblue",mix_color(alpha = 0.5,"cornflowerblue","grey25")), space = c(0,1,1),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Space")
arrows(y0 = c(as.matrix(se_spatial_extent_lower[4,])),
y1 = c(as.matrix(se_spatial_extent_upper[4,])),
x1 = c(2.5,4.5),
x0 = c(2.5,4.5), code = 3, angle = 90, length = 0.1, lwd = 1)
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(0.5,2.5,4.5),line = 0, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
par(mfrow = c(1,3))
barplot(as.matrix(Varpart_plot[c("Clim_Non_Spatially_Structured", "Clim_Spatially_Structured"),c("SSF","DRF")]),
axes = F, col = c("brown1",mix_color(alpha = 0.5,"brown1","grey25")), space = c(0,1,2,1),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Climate")
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(1.5,6.5),line = 1, labels =c("Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
axis(1,at = c(0.5,2.5,5.5,7.5),line = -0.5, labels =c("SSF","DRF", "SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
barplot(as.matrix(data.frame(Varpart_plot[c("Env_Non_Spatially_Structured", "Env_Spatially_Structured"),c("SSF","DRF")],
Means_small_extent[c("Env_Non_Spatially_Structured","Env_Spatially_Structured"),])),
axes = F, col = c("forestgreen",mix_color(alpha = 0.5,"forestgreen","grey25")), space = c(0,1,2,1),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Local Environment")
arrows(y0 = c(as.matrix(se_small_extent_lower["Env",])),
y1 = c(as.matrix(se_small_extent_upper["Env",])),
x1 = c(5.5,7.5),
x0 = c(5.5,7.5), code = 3, angle = 90, length = 0.1, lwd = 1)
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(1.5,6.5),line = 1, labels =c("Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
axis(1,at = c(0.5,2.5,5.5,7.5),line = -0.5, labels =c("SSF","DRF", "SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
barplot(as.matrix(data.frame(Varpart_plot[c("Pure_Spa", "Spatially_Climate_Environment"),c("SSF","DRF")],
Means_small_extent[c("Pure_Spa","Spatially_Climate_Environment"),])),
axes = F, col = c("cornflowerblue",mix_color(alpha = 0.5,"cornflowerblue","grey25")), space = c(0,1,2,1),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Space")
arrows(y0 = c(as.matrix(se_small_extent_lower["Spa",])),
y1 = c(as.matrix(se_small_extent_upper["Spa",])),
x1 = c(5.5,7.5),
x0 = c(5.5,7.5), code = 3, angle = 90, length = 0.1, lwd = 1)
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(1.5,6.5),line = 1, labels =c("Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
axis(1,at = c(0.5,2.5,5.5,7.5),line = -0.5, labels =c("SSF","DRF", "SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3)
par(mfrow = c(1,3))
barplot(as.matrix(Varpart_plot[c("Clim_Non_Spatially_Structured", "Clim_Spatially_Structured"),c("Broad","SSF","DRF")]),
axes = F, col = c("brown1",mix_color(alpha = 0.5,"brown1","grey25")), space = c(0.75,1.5,0.5,1.5,0.5),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Climate", xlim = c(0,9.75) )
## Warning in space + width: comprimento do objeto maior não é múltiplo do
## comprimento do objeto menor
## Warning in w.r - delta: comprimento do objeto maior não é múltiplo do
## comprimento do objeto menor
## Warning in w.m - delta: comprimento do objeto maior não é múltiplo do
## comprimento do objeto menor
## Warning in space + width: comprimento do objeto maior não é múltiplo do
## comprimento do objeto menor
## Warning in w.r - delta: comprimento do objeto maior não é múltiplo do
## comprimento do objeto menor
## Warning in w.m - delta: comprimento do objeto maior não é múltiplo do
## comprimento do objeto menor
arrows(y0 = c(as.matrix(se_small_extent_lower["Clim",])),
y1 = c(as.matrix(se_small_extent_upper["Clim",])),
x1 = c(7.75,9.25),
x0 = c(7.75,9.25), code = 3, angle = 90, length = 0.1, lwd = 1)
## Warning in arrows(y0 = c(as.matrix(se_small_extent_lower["Clim", ])),
## y1 = c(as.matrix(se_small_extent_upper["Clim", : zero-length arrow is of
## indeterminate angle and so skipped
## Warning in arrows(y0 = c(as.matrix(se_small_extent_lower["Clim", ])),
## y1 = c(as.matrix(se_small_extent_upper["Clim", : zero-length arrow is of
## indeterminate angle and so skipped
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(1.25,4.5,8.5),line = 1, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3, gap.axis = -1)
axis(1,at = c(3.75,5.25,7.75,9.25),line = -0.5, labels =c("SSF","DRF", "SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3, gap.axis = -1)
barplot(as.matrix(data.frame(Varpart_plot[c("Env_Non_Spatially_Structured", "Env_Spatially_Structured"),c("Broad","SSF","DRF")],
Means_small_extent[c("Env_Non_Spatially_Structured","Env_Spatially_Structured"),])),
axes = F, col = c("forestgreen",mix_color(alpha = 0.5,"forestgreen","grey25")), space = c(0.75,1.5,0.5,1.5,0.5),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "local Environment", xlim = c(0,9.75) )
arrows(y0 = c(as.matrix(se_small_extent_lower["Env",])),
y1 = c(as.matrix(se_small_extent_upper["Env",])),
x1 = c(7.75,9.25),
x0 = c(7.75,9.25), code = 3, angle = 90, length = 0.1, lwd = 1)
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(1.25,4.5,8.5),line = 1, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3, gap.axis = -1)
axis(1,at = c(3.75,5.25,7.75,9.25),line = -0.5, labels =c("SSF","DRF", "SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3, gap.axis = -1)
barplot(as.matrix(data.frame(Varpart_plot[c("Pure_Spa", "Spatially_Climate_Environment"),c("Broad","SSF","DRF")],
Means_small_extent[c("Pure_Spa","Spatially_Climate_Environment"),])),
axes = F, col = c("cornflowerblue",mix_color(alpha = 0.5,"cornflowerblue","grey25")), space = c(0.75,1.5,0.5,1.5,0.5),
border = "white", ylim = c(0,0.4), axisnames= F, ylab = "Adjusted R²", cex.lab = 1.25, main = "Space", xlim = c(0,9.75) )
arrows(y0 = c(as.matrix(se_small_extent_lower["Spa",])),
y1 = c(as.matrix(se_small_extent_upper["Spa",])),
x1 = c(7.75,9.25),
x0 = c(7.75,9.25), code = 3, angle = 90, length = 0.1, lwd = 1)
axis(2, at = c(0,0.05,0.1,0.15,0.2, 0.25,0.3,0.35,0.4), labels = c("0 %","5 %","10 %","15 %","20 %", "25 %", "30 %", "35 %", "40 %"))
axis(1,at = c(1.25,4.5,8.5),line = 1, labels =c("Large","Intermediate","Small"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3, gap.axis = -1)
axis(1,at = c(3.75,5.25,7.75,9.25),line = -0.5, labels =c("SSF","DRF", "SSF","DRF"), tick = F,las = 1, hadj = 0.5, cex.axis = 1.3, gap.axis = -1)
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