EMS/EMS.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"
Elements of Metacommunity Structure
Rodolfo Pelinson
12/04/2021
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")
The packages used to run this analyses are:
vegan version 2.5-6
metacom version 1.5.3
Running the EMS analysis. The IdentifyStructure function is a function
created to run the same analyses for multiple datasets and automatically
identify the idealized metacommunity structure.
Metacommunities <- IdentifyStructure(list(Broad_pa,
DRF_pa,#(Dense Rain Forest)
UBA_pa,#(Ubatuba)
BER_pa,#(Bertioga)
ITA_pa,#(Itanhaém)
SSF_pa,#(Seasonal Semideciduous Forest)
ST_pa,#(Santa Fé do Sul)
IC_pa,#(Icém)
NI_pa,#(Nova Itapirema)
MD_pa,#(Morro do Diabo)
JA_pa),#(Estação Ecológica do Jataí)
names = c("Broad",
"DRF",
"Ubatuba",
"Bertioga",
"Itanhaém",
"SSF",
"Santa Fé do Sul",
"Icém",
"Nova Itapirema",
"Morro do Diabo",
"Jataí"),
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 2, sims = 10000, round = 3)
FALSE | | | 0% | |====== | 9% | |============= | 18% | |=================== | 27% | |========================= | 36% | |================================ | 45% | |====================================== | 55% | |============================================= | 64% | |=================================================== | 73% | |========================================================= | 82% | |================================================================ | 91% | |======================================================================| 100%
Metacommunities
FALSE Embeded_Absences Simulated_Embeded_Absences
FALSE Broad 1107 2808.635
FALSE DRF 490 547.387
FALSE Ubatuba 105 152.446
FALSE Bertioga 45 41.470
FALSE Itanhaém 33 60.155
FALSE SSF 596 675.566
FALSE Santa Fé do Sul 9 15.751
FALSE Icém 54 61.013
FALSE Nova Itapirema 16 21.450
FALSE Morro do Diabo 16 24.564
FALSE Jataí 11 25.636
FALSE percent_difference_EmbAbs z_Coherence p_Coherence Turnover
FALSE Broad 0.606 -10.951 0.000 566973
FALSE DRF 0.105 -1.600 0.110 16547
FALSE Ubatuba 0.311 -3.043 0.002 2976
FALSE Bertioga -0.085 0.480 0.631 301
FALSE Itanhaém 0.451 -2.373 0.018 682
FALSE SSF 0.118 -1.818 0.069 23810
FALSE Santa Fé do Sul 0.429 -1.926 0.054 147
FALSE Icém 0.115 -1.064 0.287 683
FALSE Nova Itapirema 0.254 -1.763 0.078 151
FALSE Morro do Diabo 0.349 -2.203 0.028 184
FALSE Jataí 0.571 -3.290 0.001 342
FALSE Simulated_Turnover percent_difference_Turn z_Turnover
FALSE Broad 463271.838 -0.224 3.064
FALSE DRF 13342.278 -0.240 1.001
FALSE Ubatuba 3479.922 0.145 -0.835
FALSE Bertioga 220.016 -0.368 1.325
FALSE Itanhaém 752.835 0.094 -0.463
FALSE SSF 18926.324 -0.258 1.293
FALSE Santa Fé do Sul 171.268 0.142 -0.537
FALSE Icém 590.874 -0.156 0.603
FALSE Nova Itapirema 98.680 -0.530 1.430
FALSE Morro do Diabo 173.677 -0.059 0.273
FALSE Jataí 280.460 -0.219 0.815
FALSE p_Turnover I_Index p_I_Index N_sites N_species
FALSE Broad 0.002 2.505 0.000 96 52
FALSE DRF 0.317 3.450 0.000 50 24
FALSE Ubatuba 0.404 1.467 0.014 23 20
FALSE Bertioga 0.185 1.795 0.029 12 14
FALSE Itanhaém 0.643 1.529 0.050 15 15
FALSE SSF 0.196 2.038 0.000 46 32
FALSE Santa Fé do Sul 0.591 1.015 0.388 8 11
FALSE Icém 0.546 1.385 0.029 12 18
FALSE Nova Itapirema 0.153 1.050 0.331 8 18
FALSE Morro do Diabo 0.785 1.158 0.165 8 14
FALSE Jataí 0.415 1.516 0.019 10 12
FALSE Structure
FALSE Broad Clementsian
FALSE DRF Random
FALSE Ubatuba Quasi-Nested with clumped species loss
FALSE Bertioga Random
FALSE Itanhaém Quasi-Nested with random species loss
FALSE SSF Random
FALSE Santa Fé do Sul Random
FALSE Icém Random
FALSE Nova Itapirema Random
FALSE Morro do Diabo Quasi-Gleasonian
FALSE Jataí Quasi-Clementsian
Correlation with Environmental Gradients
We only analyzed the correlation between the underlying gradient with
true environmental gradients for the metacommunities that exhibited a
coherent metacommunity structures.
Broad Spatial Extent
Spearman rank Correlations or Kruskal-Wallis analysis.
pca <- rda(Broad_clim_st)
relative_eigenvalues <- pca$CA$eig/sum(pca$CA$eig)
PC1 <- pca$CA$u[,1]
Broad_env$ecoregion <- as.factor(Broad_env$ecoregion)
Broad_spearman <- My_spearman(Broad_pa, data.frame(Broad_env, PC1))
round(Broad_spearman,3)
## rho chi-squared p adj_p
## hydroperiod -0.053 NA 0.608 0.608
## canopy_cover 0.545 NA 0.000 0.000
## area -0.078 NA 0.451 0.516
## depth 0.209 NA 0.041 0.055
## nvt 0.655 NA 0.000 0.000
## dist_to_forest -0.687 NA 0.000 0.000
## ecoregion NA 71.143 0.000 0.000
## PC1 0.760 NA 0.000 0.000
Plotting it.
My_Imagine(comm = Broad_pa, col = c("white", "black", "grey50"),
order = T, scores = 1, fill = T, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Clementsian Structure", xlab_line = 0.25, cex.xlab = 1.5,
Env1 = PC1, Env.col_1 = c("white","red"), Env.label_1 = "Climate",
Env2 = Broad_env_st$dist_to_forest, Env.col_2 = c("forestgreen","lemonchiffon"), Env.label_2 = "Dist. Forest",
Env3 = as.numeric(Broad_env$nvt), Env.col_3 = c("lemonchiffon","forestgreen"), Env.label_3 = "Vegetation",
Env4 = as.numeric(Broad_env$canopy_cover), Env.col_4= c("lemonchiffon","forestgreen"), Env.label_4 = "Canopy Cover",
Env5 = as.numeric(Broad_env$ecoregion), Env.col_5= c("forestgreen","darkolivegreen2"), Env.label_5 = "Ecoregion")
Intermediate Spatial Extent
DRF - Dense Rain Forest
My_Imagine(comm = DRF_pa, col = c("white", "black"),
order = T, scores = 1, fill = F, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Random Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Dense Rain Forest", main_line = 3.5, cex.main = 1.25)
SSF - Seasonal Semideciduous Forest
My_Imagine(comm = SSF_pa, col = c("white", "black"),
order = T, scores = 1, fill = F, cex.site = 0.6, cex.species = 0.8,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Random Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Seasonal Semideciduous Forest", main_line = 3.5, cex.main = 1.25)
Intermediate Spatial Extent
DRF - Dense Rain Forest
Ubatuba
UBA_spearman <- My_spearman(UBA_pa, UBA_env)
round(UBA_spearman,3)
## rho chi-squared p adj_p
## hydroperiod -0.172 NA 0.432 0.721
## canopy_cover 0.241 NA 0.269 0.682
## area 0.083 NA 0.706 0.772
## depth -0.064 NA 0.772 0.772
## dist_to_forest -0.239 NA 0.273 0.682
My_Imagine(comm = UBA_pa, col = c("white", "black", "grey50"),
order = T, scores = 1, fill = T, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Quasi-Nested Structure: Clumped Species Loss", xlab_line = 0.25, cex.xlab = 1,
main = "Ubatuba", main_line = 3.5, cex.main = 1.25)
Bertioga
My_Imagine(comm = BER_pa, col = c("white", "black"),
order = T, scores = 1, fill = F, cex.site = 0.6, cex.species = 0.8,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Random Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Bertioga", main_line = 3.5, cex.main = 1.25)
Itanhaém
ITA_spearman <- My_spearman(ITA_pa, ITA_env)
round(ITA_spearman,3)
## rho chi-squared p adj_p
## hydroperiod 0.477 NA 0.072 0.306
## canopy_cover -0.009 NA 0.975 0.975
## area 0.065 NA 0.819 0.975
## depth 0.417 NA 0.122 0.306
## nvt -0.329 NA 0.231 0.384
My_Imagine(comm = ITA_pa, col = c("white", "black", "grey50"),
order = T, scores = 1, fill = T, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Quasi-Nested Structure: Random Species Loss", xlab_line = 0.25, cex.xlab = 1,
main = "Itanhaém", main_line = 3.5, cex.main = 1.25)
SSF - Seasonal Semideciduous Forest
Santa Fé do Sul
ST_spearman <- My_spearman(ST_pa, ST_env)
round(ST_spearman,3)
## rho chi-squared p adj_p
## hydroperiod 0.845 NA 0.008 0.041
## area 0.333 NA 0.428 0.428
## depth 0.719 NA 0.045 0.112
## nvt 0.620 NA 0.101 0.169
## dist_to_forest 0.405 NA 0.327 0.409
My_Imagine(comm = ST_pa, col = c("white", "black"),
order = T, scores = 1, fill = F, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Random Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Santa Fé do Sul", main_line = 3.5, cex.main = 1.25)
Icém
IC_spearman <- My_spearman(IC_pa, IC_env)
round(IC_spearman,3)
## rho chi-squared p adj_p
## hydroperiod -0.809 NA 0.001 0.007
## area -0.695 NA 0.012 0.028
## depth 0.060 NA 0.854 0.854
## nvt -0.519 NA 0.084 0.105
## dist_to_forest 0.673 NA 0.017 0.028
My_Imagine(comm = IC_pa, col = c("white", "black"),
order = T, scores = 1, fill = F, cex.site = 0.6, cex.species = 0.8,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Random Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Icém", main_line = 3.5, cex.main = 1.25)
Nova Itapirema
NI_spearman <- My_spearman(NI_pa, NI_env)
round(NI_spearman,3)
## rho chi-squared p adj_p
## hydroperiod 0.282 NA 0.499 0.774
## area -0.214 NA 0.619 0.774
## depth -0.331 NA 0.423 0.774
## nvt 0.082 NA 0.846 0.846
## dist_to_forest -0.762 NA 0.037 0.184
My_Imagine(comm = NI_pa, col = c("white", "black"),
order = T, scores = 1, fill = F, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Random Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Nova Itapirema", main_line = 3.5, cex.main = 1.25)
Morro do Diabo
MD_spearman <- My_spearman(MD_pa, MD_env)
round(MD_spearman,3)
## rho chi-squared p adj_p
## hydroperiod -0.620 NA 0.101 0.304
## canopy_cover 0.756 NA 0.030 0.180
## area -0.371 NA 0.365 0.498
## depth -0.361 NA 0.379 0.498
## nvt 0.326 NA 0.431 0.498
## dist_to_forest -0.282 NA 0.498 0.498
My_Imagine(comm = MD_pa, col = c("white", "black", "grey50"),
order = T, scores = 1, fill = T, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Quasi-Gleasonian Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Morro do Diabo", main_line = 3.5, cex.main = 1.25,
Env1 = as.numeric(MD_env$canopy_cover), Env.col_1= c("lemonchiffon","forestgreen"), Env.label_1 = "Canopy Cover")
Jataí
JA_spearman <- My_spearman(JA_pa, JA_env)
round(JA_spearman,3)
## rho chi-squared p adj_p
## hydroperiod -0.853 NA 0.002 0.010
## canopy_cover -0.700 NA 0.024 0.048
## area -0.322 NA 0.364 0.364
## depth -0.448 NA 0.194 0.233
## nvt -0.803 NA 0.005 0.016
## dist_to_forest 0.546 NA 0.102 0.154
My_Imagine(comm = JA_pa, col = c("white", "black", "grey50"),
order = T, scores = 1, fill = T, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Quasi-Clementsian Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Jataí", main_line = 3.5, cex.main = 1.25,
Env1 = as.numeric(JA_env$hydroperiod), Env.col_1 = c("lightskyblue", "royalblue"), Env.label_1 = "Hydroperiod",
Env2 = as.numeric(JA_env$nvt), Env.col_2 = c("lemonchiffon","forestgreen"), Env.label_2 = "Vegetation",
Env3 = as.numeric(JA_env$canopy_cover), Env.col_3 = c("lemonchiffon","forestgreen"), Env.label_3 = "Canopy Cover")
Exploring multiple sample sizes
We clearly have very different sample sizes among scales. 96 on the
Broad scale, 46 and 50 on the intermediate one, and from 8 to 23 on the
smallest ones. The number of communities in each scale clearly affects
our ability to detect true co-occurrence patterns. Therefore, we
repeated our analysis for the intermediate and large scale varying the
number of observed communities to have a better idea of the validity of
our results for the intermediate and small scales.
Large scale with N = 50
We got Random structures at the intermediate scales with an N \~ 50, and
a Clementsian structure at the large scale with an N \~ 100. Lets see if
we would get a similar result at large scale with an N = 50.
To do so we sampled 50 communities from a total of 96 for 1000 times and
calculated the metacommunity structure. To keep the a good
representation of spatial scale we restricted our sampling procedure
following:
- 5 communities from each of the 5 areas of the SSF for a total of 25
communities from the SSF ecoregion.
- 8 communities from each of the 3 areas of the DRF plus 1 community
from any of those areas for a total of 25 communities from the DRF
ecoregion.
set.seed(3)
Broad_matrices_pa_50 <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
DRF_locality_levels <-levels(as.factor(DRF_locality))
id_SSF <- list()
id_DRF <- list()
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id_SSF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == SSF_locality_levels[i],]),5))
}
ids_SSF <- unlist(id_SSF)
for(i in 1:length(DRF_locality_levels)){
id_DRF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == DRF_locality_levels[i],]),8))
}
ids_DRF <- unlist(id_DRF)
ids_DRF <- c(ids_DRF, sample(rownames(DRF_pa)[rownames(DRF_pa) %in% id_DRF == FALSE],1))
ids <- c(ids_SSF,ids_DRF)
oc_matrix <- Broad_pa[match(ids, rownames(Broad_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
Broad_matrices_pa_50[[j]] <- oc_matrix
}
Metacommunities_Broad_50 <- IdentifyStructure2(Broad_matrices_pa_50,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our
subsamples:
length(Metacommunities_Broad_50$p_Coherence[Metacommunities_Broad_50$p_Coherence < 0.05]) / length(Metacommunities_Broad_50$p_Coherence)
## [1] 0.959
These are the percentages of different structures identified. The
asterisk indicate the Structure identified using all of the 96
communities.
names_structures <- c("Clementsian",
"Quasi-Clementsian",
"Gleasonian",
"Quasi-Gleasonian",
"Evenly-Spaced",
"Quasi-Evenly-Spaced",
"Nested with clumped species loss",
"Quasi-Nested with clumped species loss",
"Nested with random species loss",
"Quasi-Nested with random species loss",
"Nested with hyperdispersed species loss",
"Quasi-Nested with hyperdispersed species loss",
"Random")
Structures_50 <- c(rep(NA, 13))
names(Structures_50) <- names_structures
prop_Structures <- table(Metacommunities_Broad_50$Structure) / sum(table(Metacommunities_Broad_50$Structure))
Structures_50[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_50[is.na(Structures_50)] <- 0
Structures_50 <- Structures_50*100
Structures_50 <- as.matrix(t(data.frame(Structures = Structures_50[c(1,3,5,7,9,11,13)], Quasi = c(Structures_50[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_50,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "N = 50", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_50[,1],5), y = 0.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
It seems that a sample size of 50 would be more than enough to identify
the true co-occurrence pattern.
Large and intermediate scales with N = 20
Now lets see if we would get a similar results for the large and
intermediate scales with an N = 20.
Intermediate Scale
SSF
We sampled 20 communities from a total of 46 for 1000 times and
calculated the Metacommunity Structure (i.e. co-occurrence pattern). To
keep a good representation of spatial scale we restricted our sampling
procedure following:
- 4 communities from each of the 5 areas of the SSF for a total of 20
communities.
SSF_matrices_pa_20 <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
id <- list()
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id[[i]] <- c(sample(rownames(SSF_pa[SSF_locality == SSF_locality_levels[i],]),4))
}
ids <- unlist(id)
oc_matrix <- SSF_pa[match(ids, rownames(SSF_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
SSF_matrices_pa_20[[j]] <- oc_matrix
}
Metacommunities_SSF_20 <- IdentifyStructure2(SSF_matrices_pa_20,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our
subsamples:
length(Metacommunities_SSF_20$p_Coherence[Metacommunities_SSF_20$p_Coherence < 0.05]) / length(Metacommunities_SSF_20$p_Coherence)
## [1] 0.232
These are the percentages of different structures identified. The
asterisk indicate the Structure identified using all of the 46
communities.
Structures_SSF_20 <- c(rep(NA, 13))
names(Structures_SSF_20) <- names_structures
prop_Structures <- table(Metacommunities_SSF_20$Structure) / sum(table(Metacommunities_SSF_20$Structure))
Structures_SSF_20[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_SSF_20[is.na(Structures_SSF_20)] <- 0
Structures_SSF_20 <- Structures_SSF_20*100
Structures_SSF_20 <- as.matrix(t(data.frame(Structures = Structures_SSF_20[c(1,3,5,7,9,11,13)], Quasi = c(Structures_SSF_20[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_SSF_20,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "SSF, N = 20", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_SSF_20[,7],5), y = 9.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
DRF
We sampled 20 communities from a total of 50 for 1000 times and
calculated the Metacommunity Structure (i.e. co-occurrence pattern). To
keep a good representation of spatial scale we restricted our sampling
procedure following:
- 7 communities from each of the 3 areas of the DRF, minus one of any
of the selected communities for a total of 20 communities.
DRF_matrices_pa_20 <- list()
DRF_locality_levels <-levels(as.factor(DRF_locality))
id <- list()
for(j in 1:1000){
for(i in 1:length(DRF_locality_levels)){
id[[i]] <- c(sample(rownames(DRF_pa[DRF_locality == DRF_locality_levels[i],]),7))
}
ids <- unlist(id)
ids <- ids[-sample(ids,1)]
oc_matrix <- DRF_pa[match(ids, rownames(DRF_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
DRF_matrices_pa_20[[j]] <- oc_matrix
}
Metacommunities_DRF_20 <- IdentifyStructure2(DRF_matrices_pa_20,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our
subsamples:
length(Metacommunities_DRF_20$p_Coherence[Metacommunities_DRF_20$p_Coherence < 0.05]) / length(Metacommunities_DRF_20$p_Coherence)
## [1] 0.446
These are the percentages of different structures identified. The
asterisk indicate the Structure identified using all of the 46
communities.
Structures_DRF_20 <- c(rep(NA, 13))
names(Structures_DRF_20) <- names_structures
prop_Structures <- table(Metacommunities_DRF_20$Structure) / sum(table(Metacommunities_DRF_20$Structure))
Structures_DRF_20[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_DRF_20[is.na(Structures_DRF_20)] <- 0
Structures_DRF_20 <- Structures_DRF_20*100
Structures_DRF_20 <- as.matrix(t(data.frame(Structures = Structures_DRF_20[c(1,3,5,7,9,11,13)], Quasi = c(Structures_DRF_20[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_DRF_20,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "DRF, N = 20", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_DRF_20[,7],5), y = 9.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
Large scale with N = 20
We sampled 20 communities from a total of 96 for 1000 times and
calculated the Metacommunity Structure (i.e. co-occurrence pattern). To
keep a good representation of spatial scale we restricted our sampling
procedure following:
- 2 communities from each of the 5 areas of the SSF for a total of 10
communities from the SSF ecoregion.
- 3 communities from each of the 3 areas of the DRF plus 1 community
from any of those areas for a total of 10 communities from the DRF
ecoregion.
set.seed(3)
Broad_matrices_pa_20 <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
DRF_locality_levels <-levels(as.factor(DRF_locality))
id_SSF <- list()
id_DRF <- list()
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id_SSF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == SSF_locality_levels[i],]),2))
}
ids_SSF <- unlist(id_SSF)
for(i in 1:length(DRF_locality_levels)){
id_DRF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == DRF_locality_levels[i],]),3))
}
ids_DRF <- unlist(id_DRF)
ids_DRF <- c(ids_DRF, sample(rownames(DRF_pa)[rownames(DRF_pa) %in% id_DRF == FALSE],1))
ids <- c(ids_SSF,ids_DRF)
oc_matrix <- Broad_pa[match(ids, rownames(Broad_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
Broad_matrices_pa_20[[j]] <- oc_matrix
}
Metacommunities_Broad_20 <- IdentifyStructure2(Broad_matrices_pa_20,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our
subsamples:
length(Metacommunities_Broad_20$p_Coherence[Metacommunities_Broad_20$p_Coherence < 0.05]) / length(Metacommunities_Broad_20$p_Coherence)
## [1] 0.951
These are the percentages of different structures identified. The
asterisk indicate the Structure identified using all of the 96
communities.
Structures_Broad_20 <- c(rep(NA, 13))
names(Structures_Broad_20) <- names_structures
prop_Structures <- table(Metacommunities_Broad_20$Structure) / sum(table(Metacommunities_Broad_20$Structure))
Structures_Broad_20[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_Broad_20[is.na(Structures_Broad_20)] <- 0
Structures_Broad_20 <- Structures_Broad_20*100
Structures_Broad_20 <- as.matrix(t(data.frame(Structures = Structures_Broad_20[c(1,3,5,7,9,11,13)], Quasi = c(Structures_Broad_20[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_Broad_20,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "Large, N = 20", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_Broad_20[,1],5), y = 0.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
It seems that a sample size of 20 gives a good idea of the true
co-occurrence pattern.
Large and intermediate scales with N = 15
Now lets see if we would get a similar results for the large and
intermediate scales with an N = 20.
Intermediate Scale
SSF
We sampled 15 communities from a total of 46 for 1000 times and
calculated the Metacommunity Structure (i.e. co-occurrence pattern). To
keep a good representation of spatial scale we restricted our sampling
procedure following:
- 3 communities from each of the 5 areas of the SSF for a total of 15
communities.
set.seed(3)
SSF_matrices_pa_15 <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
id <- list()
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id[[i]] <- c(sample(rownames(SSF_pa[SSF_locality == SSF_locality_levels[i],]),3))
}
ids <- unlist(id)
oc_matrix <- SSF_pa[match(ids, rownames(SSF_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
SSF_matrices_pa_15[[j]] <- oc_matrix
}
Metacommunities_SSF_15 <- IdentifyStructure2(SSF_matrices_pa_15,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our
subsamples:
length(Metacommunities_SSF_15$p_Coherence[Metacommunities_SSF_15$p_Coherence < 0.05 & Metacommunities_SSF_15$z_Coherence < 0]) / length(Metacommunities_SSF_15$p_Coherence)
## [1] 0.203
These are the percentages of different structures identified. The
asterisk indicate the Structure identified using all of the 46
communities.
Structures_SSF_15 <- c(rep(NA, 13))
names(Structures_SSF_15) <- names_structures
prop_Structures <- table(Metacommunities_SSF_15$Structure) / sum(table(Metacommunities_SSF_15$Structure))
Structures_SSF_15[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_SSF_15[is.na(Structures_SSF_15)] <- 0
Structures_SSF_15 <- Structures_SSF_15*100
Structures_SSF_15 <- as.matrix(t(data.frame(Structures = Structures_SSF_15[c(1,3,5,7,9,11,13)], Quasi = c(Structures_SSF_15[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_SSF_15,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "SSF, N = 15", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_SSF_15[,7],5), y = 9.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
DRF
We sampled 15 communities from a total of 50 for 1500 times and
calculated the Metacommunity Structure (i.e. co-occurrence pattern). To
keep a good representation of spatial scale we restricted our sampling
procedure following:
- 5 communities from each of the 3 areas of the DRF for a total of 15
communities.
set.seed(3)
DRF_matrices_pa_15 <- list()
DRF_locality_levels <-levels(as.factor(DRF_locality))
id <- list()
for(j in 1:1000){
for(i in 1:length(DRF_locality_levels)){
id[[i]] <- c(sample(rownames(DRF_pa[DRF_locality == DRF_locality_levels[i],]),5))
}
ids <- unlist(id)
oc_matrix <- DRF_pa[match(ids, rownames(DRF_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
DRF_matrices_pa_15[[j]] <- oc_matrix
}
nrow(DRF_matrices_pa_15[[10]])
Metacommunities_DRF_15 <- IdentifyStructure2(DRF_matrices_pa_15,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our
subsamples:
length(Metacommunities_DRF_15$p_Coherence[Metacommunities_DRF_15$p_Coherence < 0.05 & Metacommunities_DRF_15$z_Coherence < 0]) / length(Metacommunities_DRF_15$p_Coherence)
## [1] 0.269
These are the percentages of different structures identified. The
asterisk indicate the Structure identified using all of the 46
communities.
Structures_DRF_15 <- c(rep(NA, 13))
names(Structures_DRF_15) <- names_structures
prop_Structures <- table(Metacommunities_DRF_15$Structure) / sum(table(Metacommunities_DRF_15$Structure))
Structures_DRF_15[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_DRF_15[is.na(Structures_DRF_15)] <- 0
Structures_DRF_15 <- Structures_DRF_15*100
Structures_DRF_15 <- as.matrix(t(data.frame(Structures = Structures_DRF_15[c(1,3,5,7,9,11,13)], Quasi = c(Structures_DRF_15[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_DRF_15,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "DRF, N = 15", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_DRF_15[,7],5), y = 9.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
Large scale with N = 15
We sampled 15 communities from a total of 96 for 1000 times and
calculated the Metacommunity Structure (i.e. co-occurrence pattern). To
keep a good representation of spatial scale we restricted our sampling
procedure following:
- 2 community from each of the 5 areas of the SSF minus 2 or 3 of
those communities for a total of 7 or 8 communities from the SSF
ecoregion.
- 2 communities from each of the 3 areas of the DRF, plus 1 or 2
communities from any of those areas for a total of 7 or 8
communities from the DRF ecoregion.
set.seed(3)
Broad_matrices_pa_15 <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
DRF_locality_levels <-levels(as.factor(DRF_locality))
id_SSF <- list()
id_DRF <- list()
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id_SSF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == SSF_locality_levels[i],]),2))
}
ids_SSF <- unlist(id_SSF)
minus <- sample(c(2,3),1)
ids_SSF<- ids_SSF[-sample(1:length(ids_SSF),minus)]
for(i in 1:length(DRF_locality_levels)){
id_DRF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == DRF_locality_levels[i],]),2))
}
ids_DRF <- unlist(id_DRF)
ids_DRF <- c(ids_DRF, sample(rownames(DRF_pa)[rownames(DRF_pa) %in% id_DRF == FALSE],(minus-1) ) )
ids <- c(ids_SSF,ids_DRF)
oc_matrix <- Broad_pa[match(ids, rownames(Broad_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
Broad_matrices_pa_15[[j]] <- oc_matrix
}
Metacommunities_Broad_15 <- IdentifyStructure2(Broad_matrices_pa_15,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our
subsamples:
length(Metacommunities_Broad_15$p_Coherence[Metacommunities_Broad_15$p_Coherence < 0.05 & Metacommunities_Broad_15$z_Coherence < 0]) / length(Metacommunities_Broad_15$p_Coherence)
## [1] 0.948
These are the percentages of different structures identified. The
asterisk indicate the Structure identified using all of the 96
communities.
Structures_Broad_15 <- c(rep(NA, 13))
names(Structures_Broad_15) <- names_structures
prop_Structures <- table(Metacommunities_Broad_15$Structure) / sum(table(Metacommunities_Broad_15$Structure))
Structures_Broad_15[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_Broad_15[is.na(Structures_Broad_15)] <- 0
Structures_Broad_15 <- Structures_Broad_15*100
Structures_Broad_15 <- as.matrix(t(data.frame(Structures = Structures_Broad_15[c(1,3,5,7,9,11,13)], Quasi = c(Structures_Broad_15[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_Broad_15,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "Large, N = 15", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_Broad_15[,1],5), y = 0.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
It seems that a sample size of 15 would only capture co-occurrence
patterns that are too strong.
Large and intermediate scales with N = 10
Now lets see if we would get a similar results for the large and
intermediate scales with an N = 20.
Intermediate Scale
SSF
We sampled 10 communities from a total of 46 for 1000 times and
calculated the Metacommunity Structure (i.e. co-occurrence pattern). To
keep a good representation of spatial scale we restricted our sampling
procedure following:
- 2 communities from each of the 5 areas of the SSF for a total of 10
communities.
SSF_matrices_pa <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
id <- list()
set.seed(3)
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id[[i]] <- c(sample(rownames(SSF_pa[SSF_locality == SSF_locality_levels[i],]),2))
}
ids <- unlist(id)
oc_matrix <- SSF_pa[match(ids, rownames(SSF_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
SSF_matrices_pa[[j]] <- oc_matrix
}
Metacommunities_SSF <- IdentifyStructure2(SSF_matrices_pa,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our
subsamples:
length(Metacommunities_SSF$p_Coherence[Metacommunities_SSF$p_Coherence < 0.05 & Metacommunities_SSF$z_Coherence < 0]) / length(Metacommunities_SSF$p_Coherence)
## [1] 0.129
These are the percentages of different structures identified. The
asterisk indicate the Structure identified using all of the 46
communities.
Structures_SSF <- c(rep(NA, 13))
names(Structures_SSF) <- names_structures
prop_Structures <- table(Metacommunities_SSF$Structure) / sum(table(Metacommunities_SSF$Structure))
Structures_SSF[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_SSF[is.na(Structures_SSF)] <- 0
Structures_SSF <- Structures_SSF*100
Structures_SSF <- as.matrix(t(data.frame(Structures = Structures_SSF[c(1,3,5,7,9,11,13)], Quasi = c(Structures_SSF[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_SSF,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "SSF, N = 10", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_SSF[,7],5), y = 9.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
DRF
We sampled 10 communities from a total of 50 for 1000 times and
calculated the Metacommunity Structure (i.e. co-occurrence pattern). To
keep a good representation of spatial scale we restricted our sampling
procedure following:
- 3 communities from each of the 3 areas of the DRF, plus one from any
of those areas for a total of 10 communities.
DRF_matrices_pa <- list()
DRF_locality_levels <-levels(as.factor(DRF_locality))
id <- list()
set.seed(3)
for(j in 1:1000){
for(i in 1:length(DRF_locality_levels)){
id[[i]] <- c(sample(rownames(DRF_pa[DRF_locality == DRF_locality_levels[i],]),3))
}
ids <- unlist(id)
ids <- c(ids, sample(rownames(DRF_pa)[rownames(DRF_pa) %in% ids == FALSE],1))
oc_matrix <- DRF_pa[match(ids, rownames(DRF_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
DRF_matrices_pa[[j]] <- oc_matrix
}
Metacommunities_DRF <- IdentifyStructure2(DRF_matrices_pa,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our
subsamples:
length(Metacommunities_DRF$p_Coherence[Metacommunities_DRF$p_Coherence < 0.05 & Metacommunities_DRF$z_Coherence < 0]) / length(Metacommunities_DRF$p_Coherence)
## [1] 0.154
These are the percentages of different structures identified. The
asterisk indicate the Structure identified using all of the 46
communities.
Structures_DRF <- c(rep(NA, 13))
names(Structures_DRF) <- names_structures
prop_Structures <- table(Metacommunities_DRF$Structure) / sum(table(Metacommunities_DRF$Structure))
Structures_DRF[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_DRF[is.na(Structures_DRF)] <- 0
Structures_DRF <- Structures_DRF*100
Structures_DRF <- as.matrix(t(data.frame(Structures = Structures_DRF[c(1,3,5,7,9,11,13)], Quasi = c(Structures_DRF[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_DRF,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "DRF, N = 10", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_DRF[,7],5), y = 9.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
Large scale with N = 10
We sampled 10 communities from a total of 96 for 1000 times and
calculated the Metacommunity Structure (i.e. co-occurrence pattern). To
keep a good representation of spatial scale we restricted our sampling
procedure following:
- 1 community from each of the 5 areas of the SSF for a total of 5
communities from the SSF ecoregion.
- 2 communities from each of the 3 areas of the DRF, minus 1 community
from any of those areas for a total of 5 communities from the DRF
ecoregion.
set.seed(3)
Broad_matrices_pa <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
DRF_locality_levels <-levels(as.factor(DRF_locality))
id_SSF <- list()
id_DRF <- list()
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id_SSF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == SSF_locality_levels[i],]),1))
}
ids_SSF <- unlist(id_SSF)
for(i in 1:length(DRF_locality_levels)){
id_DRF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == DRF_locality_levels[i],]),2))
}
ids_DRF <- unlist(id_DRF)
ids_DRF <- ids_DRF[-sample(1:length(ids_DRF),1)]
ids <- c(ids_SSF,ids_DRF)
oc_matrix <- Broad_pa[match(ids, rownames(Broad_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
Broad_matrices_pa[[j]] <- oc_matrix
}
Metacommunities_Broad <- IdentifyStructure2(Broad_matrices_pa,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our
subsamples:
length(Metacommunities_Broad$p_Coherence[Metacommunities_Broad$p_Coherence < 0.05]) / length(Metacommunities_Broad$p_Coherence)
## [1] 0.756
These are the percentages of different structures identified. The
asterisk indicate the Structure identified using all of the 96
communities.
Structures_Broad <- c(rep(NA, 13))
names(Structures_Broad) <- names_structures
prop_Structures <- table(Metacommunities_Broad$Structure) / sum(table(Metacommunities_Broad$Structure))
Structures_Broad[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_Broad[is.na(Structures_Broad)] <- 0
Structures_Broad <- Structures_Broad*100
Structures_Broad <- as.matrix(t(data.frame(Structures = Structures_Broad[c(1,3,5,7,9,11,13)], Quasi = c(Structures_Broad[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_Broad,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "Large, N = 10", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_Broad[,1],5), y = 0.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
It seems that a sample size of 10 is not enough to capture the true
structure. It seems to be able to capture coherence when it exists
though.
RodolfoPelinson/AtlanticForestMetacommunity documentation built on Aug. 5, 2023, 3:53 p.m.
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Elements of Metacommunity Structure
Rodolfo Pelinson 12/04/2021
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")
The packages used to run this analyses are:
vegan version 2.5-6
metacom version 1.5.3
Running the EMS analysis. The IdentifyStructure function is a function
created to run the same analyses for multiple datasets and automatically
identify the idealized metacommunity structure.
Metacommunities <- IdentifyStructure(list(Broad_pa,
DRF_pa,#(Dense Rain Forest)
UBA_pa,#(Ubatuba)
BER_pa,#(Bertioga)
ITA_pa,#(Itanhaém)
SSF_pa,#(Seasonal Semideciduous Forest)
ST_pa,#(Santa Fé do Sul)
IC_pa,#(Icém)
NI_pa,#(Nova Itapirema)
MD_pa,#(Morro do Diabo)
JA_pa),#(Estação Ecológica do Jataí)
names = c("Broad",
"DRF",
"Ubatuba",
"Bertioga",
"Itanhaém",
"SSF",
"Santa Fé do Sul",
"Icém",
"Nova Itapirema",
"Morro do Diabo",
"Jataí"),
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 2, sims = 10000, round = 3)
FALSE | | | 0% | |====== | 9% | |============= | 18% | |=================== | 27% | |========================= | 36% | |================================ | 45% | |====================================== | 55% | |============================================= | 64% | |=================================================== | 73% | |========================================================= | 82% | |================================================================ | 91% | |======================================================================| 100%
Metacommunities
FALSE Embeded_Absences Simulated_Embeded_Absences
FALSE Broad 1107 2808.635
FALSE DRF 490 547.387
FALSE Ubatuba 105 152.446
FALSE Bertioga 45 41.470
FALSE Itanhaém 33 60.155
FALSE SSF 596 675.566
FALSE Santa Fé do Sul 9 15.751
FALSE Icém 54 61.013
FALSE Nova Itapirema 16 21.450
FALSE Morro do Diabo 16 24.564
FALSE Jataí 11 25.636
FALSE percent_difference_EmbAbs z_Coherence p_Coherence Turnover
FALSE Broad 0.606 -10.951 0.000 566973
FALSE DRF 0.105 -1.600 0.110 16547
FALSE Ubatuba 0.311 -3.043 0.002 2976
FALSE Bertioga -0.085 0.480 0.631 301
FALSE Itanhaém 0.451 -2.373 0.018 682
FALSE SSF 0.118 -1.818 0.069 23810
FALSE Santa Fé do Sul 0.429 -1.926 0.054 147
FALSE Icém 0.115 -1.064 0.287 683
FALSE Nova Itapirema 0.254 -1.763 0.078 151
FALSE Morro do Diabo 0.349 -2.203 0.028 184
FALSE Jataí 0.571 -3.290 0.001 342
FALSE Simulated_Turnover percent_difference_Turn z_Turnover
FALSE Broad 463271.838 -0.224 3.064
FALSE DRF 13342.278 -0.240 1.001
FALSE Ubatuba 3479.922 0.145 -0.835
FALSE Bertioga 220.016 -0.368 1.325
FALSE Itanhaém 752.835 0.094 -0.463
FALSE SSF 18926.324 -0.258 1.293
FALSE Santa Fé do Sul 171.268 0.142 -0.537
FALSE Icém 590.874 -0.156 0.603
FALSE Nova Itapirema 98.680 -0.530 1.430
FALSE Morro do Diabo 173.677 -0.059 0.273
FALSE Jataí 280.460 -0.219 0.815
FALSE p_Turnover I_Index p_I_Index N_sites N_species
FALSE Broad 0.002 2.505 0.000 96 52
FALSE DRF 0.317 3.450 0.000 50 24
FALSE Ubatuba 0.404 1.467 0.014 23 20
FALSE Bertioga 0.185 1.795 0.029 12 14
FALSE Itanhaém 0.643 1.529 0.050 15 15
FALSE SSF 0.196 2.038 0.000 46 32
FALSE Santa Fé do Sul 0.591 1.015 0.388 8 11
FALSE Icém 0.546 1.385 0.029 12 18
FALSE Nova Itapirema 0.153 1.050 0.331 8 18
FALSE Morro do Diabo 0.785 1.158 0.165 8 14
FALSE Jataí 0.415 1.516 0.019 10 12
FALSE Structure
FALSE Broad Clementsian
FALSE DRF Random
FALSE Ubatuba Quasi-Nested with clumped species loss
FALSE Bertioga Random
FALSE Itanhaém Quasi-Nested with random species loss
FALSE SSF Random
FALSE Santa Fé do Sul Random
FALSE Icém Random
FALSE Nova Itapirema Random
FALSE Morro do Diabo Quasi-Gleasonian
FALSE Jataí Quasi-Clementsian
Correlation with Environmental Gradients
We only analyzed the correlation between the underlying gradient with true environmental gradients for the metacommunities that exhibited a coherent metacommunity structures.
Broad Spatial Extent
Spearman rank Correlations or Kruskal-Wallis analysis.
pca <- rda(Broad_clim_st)
relative_eigenvalues <- pca$CA$eig/sum(pca$CA$eig)
PC1 <- pca$CA$u[,1]
Broad_env$ecoregion <- as.factor(Broad_env$ecoregion)
Broad_spearman <- My_spearman(Broad_pa, data.frame(Broad_env, PC1))
round(Broad_spearman,3)
## rho chi-squared p adj_p
## hydroperiod -0.053 NA 0.608 0.608
## canopy_cover 0.545 NA 0.000 0.000
## area -0.078 NA 0.451 0.516
## depth 0.209 NA 0.041 0.055
## nvt 0.655 NA 0.000 0.000
## dist_to_forest -0.687 NA 0.000 0.000
## ecoregion NA 71.143 0.000 0.000
## PC1 0.760 NA 0.000 0.000
Plotting it.
My_Imagine(comm = Broad_pa, col = c("white", "black", "grey50"),
order = T, scores = 1, fill = T, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Clementsian Structure", xlab_line = 0.25, cex.xlab = 1.5,
Env1 = PC1, Env.col_1 = c("white","red"), Env.label_1 = "Climate",
Env2 = Broad_env_st$dist_to_forest, Env.col_2 = c("forestgreen","lemonchiffon"), Env.label_2 = "Dist. Forest",
Env3 = as.numeric(Broad_env$nvt), Env.col_3 = c("lemonchiffon","forestgreen"), Env.label_3 = "Vegetation",
Env4 = as.numeric(Broad_env$canopy_cover), Env.col_4= c("lemonchiffon","forestgreen"), Env.label_4 = "Canopy Cover",
Env5 = as.numeric(Broad_env$ecoregion), Env.col_5= c("forestgreen","darkolivegreen2"), Env.label_5 = "Ecoregion")
Intermediate Spatial Extent
DRF - Dense Rain Forest
My_Imagine(comm = DRF_pa, col = c("white", "black"),
order = T, scores = 1, fill = F, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Random Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Dense Rain Forest", main_line = 3.5, cex.main = 1.25)
SSF - Seasonal Semideciduous Forest
My_Imagine(comm = SSF_pa, col = c("white", "black"),
order = T, scores = 1, fill = F, cex.site = 0.6, cex.species = 0.8,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Random Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Seasonal Semideciduous Forest", main_line = 3.5, cex.main = 1.25)
Intermediate Spatial Extent
DRF - Dense Rain Forest
Ubatuba
UBA_spearman <- My_spearman(UBA_pa, UBA_env)
round(UBA_spearman,3)
## rho chi-squared p adj_p
## hydroperiod -0.172 NA 0.432 0.721
## canopy_cover 0.241 NA 0.269 0.682
## area 0.083 NA 0.706 0.772
## depth -0.064 NA 0.772 0.772
## dist_to_forest -0.239 NA 0.273 0.682
My_Imagine(comm = UBA_pa, col = c("white", "black", "grey50"),
order = T, scores = 1, fill = T, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Quasi-Nested Structure: Clumped Species Loss", xlab_line = 0.25, cex.xlab = 1,
main = "Ubatuba", main_line = 3.5, cex.main = 1.25)
Bertioga
My_Imagine(comm = BER_pa, col = c("white", "black"),
order = T, scores = 1, fill = F, cex.site = 0.6, cex.species = 0.8,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Random Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Bertioga", main_line = 3.5, cex.main = 1.25)
Itanhaém
ITA_spearman <- My_spearman(ITA_pa, ITA_env)
round(ITA_spearman,3)
## rho chi-squared p adj_p
## hydroperiod 0.477 NA 0.072 0.306
## canopy_cover -0.009 NA 0.975 0.975
## area 0.065 NA 0.819 0.975
## depth 0.417 NA 0.122 0.306
## nvt -0.329 NA 0.231 0.384
My_Imagine(comm = ITA_pa, col = c("white", "black", "grey50"),
order = T, scores = 1, fill = T, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Quasi-Nested Structure: Random Species Loss", xlab_line = 0.25, cex.xlab = 1,
main = "Itanhaém", main_line = 3.5, cex.main = 1.25)
SSF - Seasonal Semideciduous Forest
Santa Fé do Sul
ST_spearman <- My_spearman(ST_pa, ST_env)
round(ST_spearman,3)
## rho chi-squared p adj_p
## hydroperiod 0.845 NA 0.008 0.041
## area 0.333 NA 0.428 0.428
## depth 0.719 NA 0.045 0.112
## nvt 0.620 NA 0.101 0.169
## dist_to_forest 0.405 NA 0.327 0.409
My_Imagine(comm = ST_pa, col = c("white", "black"),
order = T, scores = 1, fill = F, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Random Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Santa Fé do Sul", main_line = 3.5, cex.main = 1.25)
Icém
IC_spearman <- My_spearman(IC_pa, IC_env)
round(IC_spearman,3)
## rho chi-squared p adj_p
## hydroperiod -0.809 NA 0.001 0.007
## area -0.695 NA 0.012 0.028
## depth 0.060 NA 0.854 0.854
## nvt -0.519 NA 0.084 0.105
## dist_to_forest 0.673 NA 0.017 0.028
My_Imagine(comm = IC_pa, col = c("white", "black"),
order = T, scores = 1, fill = F, cex.site = 0.6, cex.species = 0.8,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Random Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Icém", main_line = 3.5, cex.main = 1.25)
Nova Itapirema
NI_spearman <- My_spearman(NI_pa, NI_env)
round(NI_spearman,3)
## rho chi-squared p adj_p
## hydroperiod 0.282 NA 0.499 0.774
## area -0.214 NA 0.619 0.774
## depth -0.331 NA 0.423 0.774
## nvt 0.082 NA 0.846 0.846
## dist_to_forest -0.762 NA 0.037 0.184
My_Imagine(comm = NI_pa, col = c("white", "black"),
order = T, scores = 1, fill = F, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Random Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Nova Itapirema", main_line = 3.5, cex.main = 1.25)
Morro do Diabo
MD_spearman <- My_spearman(MD_pa, MD_env)
round(MD_spearman,3)
## rho chi-squared p adj_p
## hydroperiod -0.620 NA 0.101 0.304
## canopy_cover 0.756 NA 0.030 0.180
## area -0.371 NA 0.365 0.498
## depth -0.361 NA 0.379 0.498
## nvt 0.326 NA 0.431 0.498
## dist_to_forest -0.282 NA 0.498 0.498
My_Imagine(comm = MD_pa, col = c("white", "black", "grey50"),
order = T, scores = 1, fill = T, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Quasi-Gleasonian Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Morro do Diabo", main_line = 3.5, cex.main = 1.25,
Env1 = as.numeric(MD_env$canopy_cover), Env.col_1= c("lemonchiffon","forestgreen"), Env.label_1 = "Canopy Cover")
Jataí
JA_spearman <- My_spearman(JA_pa, JA_env)
round(JA_spearman,3)
## rho chi-squared p adj_p
## hydroperiod -0.853 NA 0.002 0.010
## canopy_cover -0.700 NA 0.024 0.048
## area -0.322 NA 0.364 0.364
## depth -0.448 NA 0.194 0.233
## nvt -0.803 NA 0.005 0.016
## dist_to_forest 0.546 NA 0.102 0.154
My_Imagine(comm = JA_pa, col = c("white", "black", "grey50"),
order = T, scores = 1, fill = T, cex.site = 0.6, cex.species = 0.8, cex.envlab = 0.9,
top_margin = 5, left_margin = 1.25, sitenames = FALSE, bottom_margin = 1.25, right_margin = 0.25, xline = -0.75,
ylab = "Sites", ylab_line = 0, cex.ylab = 1.5, xlab = "Quasi-Clementsian Structure", xlab_line = 0.25, cex.xlab = 1,
main = "Jataí", main_line = 3.5, cex.main = 1.25,
Env1 = as.numeric(JA_env$hydroperiod), Env.col_1 = c("lightskyblue", "royalblue"), Env.label_1 = "Hydroperiod",
Env2 = as.numeric(JA_env$nvt), Env.col_2 = c("lemonchiffon","forestgreen"), Env.label_2 = "Vegetation",
Env3 = as.numeric(JA_env$canopy_cover), Env.col_3 = c("lemonchiffon","forestgreen"), Env.label_3 = "Canopy Cover")
Exploring multiple sample sizes
We clearly have very different sample sizes among scales. 96 on the Broad scale, 46 and 50 on the intermediate one, and from 8 to 23 on the smallest ones. The number of communities in each scale clearly affects our ability to detect true co-occurrence patterns. Therefore, we repeated our analysis for the intermediate and large scale varying the number of observed communities to have a better idea of the validity of our results for the intermediate and small scales.
Large scale with N = 50
We got Random structures at the intermediate scales with an N \~ 50, and a Clementsian structure at the large scale with an N \~ 100. Lets see if we would get a similar result at large scale with an N = 50.
To do so we sampled 50 communities from a total of 96 for 1000 times and calculated the metacommunity structure. To keep the a good representation of spatial scale we restricted our sampling procedure following:
- 5 communities from each of the 5 areas of the SSF for a total of 25 communities from the SSF ecoregion.
- 8 communities from each of the 3 areas of the DRF plus 1 community from any of those areas for a total of 25 communities from the DRF ecoregion.
set.seed(3)
Broad_matrices_pa_50 <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
DRF_locality_levels <-levels(as.factor(DRF_locality))
id_SSF <- list()
id_DRF <- list()
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id_SSF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == SSF_locality_levels[i],]),5))
}
ids_SSF <- unlist(id_SSF)
for(i in 1:length(DRF_locality_levels)){
id_DRF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == DRF_locality_levels[i],]),8))
}
ids_DRF <- unlist(id_DRF)
ids_DRF <- c(ids_DRF, sample(rownames(DRF_pa)[rownames(DRF_pa) %in% id_DRF == FALSE],1))
ids <- c(ids_SSF,ids_DRF)
oc_matrix <- Broad_pa[match(ids, rownames(Broad_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
Broad_matrices_pa_50[[j]] <- oc_matrix
}
Metacommunities_Broad_50 <- IdentifyStructure2(Broad_matrices_pa_50,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our subsamples:
length(Metacommunities_Broad_50$p_Coherence[Metacommunities_Broad_50$p_Coherence < 0.05]) / length(Metacommunities_Broad_50$p_Coherence)
## [1] 0.959
These are the percentages of different structures identified. The asterisk indicate the Structure identified using all of the 96 communities.
names_structures <- c("Clementsian",
"Quasi-Clementsian",
"Gleasonian",
"Quasi-Gleasonian",
"Evenly-Spaced",
"Quasi-Evenly-Spaced",
"Nested with clumped species loss",
"Quasi-Nested with clumped species loss",
"Nested with random species loss",
"Quasi-Nested with random species loss",
"Nested with hyperdispersed species loss",
"Quasi-Nested with hyperdispersed species loss",
"Random")
Structures_50 <- c(rep(NA, 13))
names(Structures_50) <- names_structures
prop_Structures <- table(Metacommunities_Broad_50$Structure) / sum(table(Metacommunities_Broad_50$Structure))
Structures_50[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_50[is.na(Structures_50)] <- 0
Structures_50 <- Structures_50*100
Structures_50 <- as.matrix(t(data.frame(Structures = Structures_50[c(1,3,5,7,9,11,13)], Quasi = c(Structures_50[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_50,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "N = 50", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_50[,1],5), y = 0.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
It seems that a sample size of 50 would be more than enough to identify the true co-occurrence pattern.
Large and intermediate scales with N = 20
Now lets see if we would get a similar results for the large and intermediate scales with an N = 20.
Intermediate Scale
SSF
We sampled 20 communities from a total of 46 for 1000 times and calculated the Metacommunity Structure (i.e. co-occurrence pattern). To keep a good representation of spatial scale we restricted our sampling procedure following:
- 4 communities from each of the 5 areas of the SSF for a total of 20 communities.
SSF_matrices_pa_20 <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
id <- list()
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id[[i]] <- c(sample(rownames(SSF_pa[SSF_locality == SSF_locality_levels[i],]),4))
}
ids <- unlist(id)
oc_matrix <- SSF_pa[match(ids, rownames(SSF_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
SSF_matrices_pa_20[[j]] <- oc_matrix
}
Metacommunities_SSF_20 <- IdentifyStructure2(SSF_matrices_pa_20,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our subsamples:
length(Metacommunities_SSF_20$p_Coherence[Metacommunities_SSF_20$p_Coherence < 0.05]) / length(Metacommunities_SSF_20$p_Coherence)
## [1] 0.232
These are the percentages of different structures identified. The asterisk indicate the Structure identified using all of the 46 communities.
Structures_SSF_20 <- c(rep(NA, 13))
names(Structures_SSF_20) <- names_structures
prop_Structures <- table(Metacommunities_SSF_20$Structure) / sum(table(Metacommunities_SSF_20$Structure))
Structures_SSF_20[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_SSF_20[is.na(Structures_SSF_20)] <- 0
Structures_SSF_20 <- Structures_SSF_20*100
Structures_SSF_20 <- as.matrix(t(data.frame(Structures = Structures_SSF_20[c(1,3,5,7,9,11,13)], Quasi = c(Structures_SSF_20[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_SSF_20,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "SSF, N = 20", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_SSF_20[,7],5), y = 9.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
DRF
We sampled 20 communities from a total of 50 for 1000 times and calculated the Metacommunity Structure (i.e. co-occurrence pattern). To keep a good representation of spatial scale we restricted our sampling procedure following:
- 7 communities from each of the 3 areas of the DRF, minus one of any of the selected communities for a total of 20 communities.
DRF_matrices_pa_20 <- list()
DRF_locality_levels <-levels(as.factor(DRF_locality))
id <- list()
for(j in 1:1000){
for(i in 1:length(DRF_locality_levels)){
id[[i]] <- c(sample(rownames(DRF_pa[DRF_locality == DRF_locality_levels[i],]),7))
}
ids <- unlist(id)
ids <- ids[-sample(ids,1)]
oc_matrix <- DRF_pa[match(ids, rownames(DRF_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
DRF_matrices_pa_20[[j]] <- oc_matrix
}
Metacommunities_DRF_20 <- IdentifyStructure2(DRF_matrices_pa_20,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our subsamples:
length(Metacommunities_DRF_20$p_Coherence[Metacommunities_DRF_20$p_Coherence < 0.05]) / length(Metacommunities_DRF_20$p_Coherence)
## [1] 0.446
These are the percentages of different structures identified. The asterisk indicate the Structure identified using all of the 46 communities.
Structures_DRF_20 <- c(rep(NA, 13))
names(Structures_DRF_20) <- names_structures
prop_Structures <- table(Metacommunities_DRF_20$Structure) / sum(table(Metacommunities_DRF_20$Structure))
Structures_DRF_20[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_DRF_20[is.na(Structures_DRF_20)] <- 0
Structures_DRF_20 <- Structures_DRF_20*100
Structures_DRF_20 <- as.matrix(t(data.frame(Structures = Structures_DRF_20[c(1,3,5,7,9,11,13)], Quasi = c(Structures_DRF_20[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_DRF_20,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "DRF, N = 20", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_DRF_20[,7],5), y = 9.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
Large scale with N = 20
We sampled 20 communities from a total of 96 for 1000 times and calculated the Metacommunity Structure (i.e. co-occurrence pattern). To keep a good representation of spatial scale we restricted our sampling procedure following:
- 2 communities from each of the 5 areas of the SSF for a total of 10 communities from the SSF ecoregion.
- 3 communities from each of the 3 areas of the DRF plus 1 community from any of those areas for a total of 10 communities from the DRF ecoregion.
set.seed(3)
Broad_matrices_pa_20 <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
DRF_locality_levels <-levels(as.factor(DRF_locality))
id_SSF <- list()
id_DRF <- list()
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id_SSF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == SSF_locality_levels[i],]),2))
}
ids_SSF <- unlist(id_SSF)
for(i in 1:length(DRF_locality_levels)){
id_DRF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == DRF_locality_levels[i],]),3))
}
ids_DRF <- unlist(id_DRF)
ids_DRF <- c(ids_DRF, sample(rownames(DRF_pa)[rownames(DRF_pa) %in% id_DRF == FALSE],1))
ids <- c(ids_SSF,ids_DRF)
oc_matrix <- Broad_pa[match(ids, rownames(Broad_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
Broad_matrices_pa_20[[j]] <- oc_matrix
}
Metacommunities_Broad_20 <- IdentifyStructure2(Broad_matrices_pa_20,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our subsamples:
length(Metacommunities_Broad_20$p_Coherence[Metacommunities_Broad_20$p_Coherence < 0.05]) / length(Metacommunities_Broad_20$p_Coherence)
## [1] 0.951
These are the percentages of different structures identified. The asterisk indicate the Structure identified using all of the 96 communities.
Structures_Broad_20 <- c(rep(NA, 13))
names(Structures_Broad_20) <- names_structures
prop_Structures <- table(Metacommunities_Broad_20$Structure) / sum(table(Metacommunities_Broad_20$Structure))
Structures_Broad_20[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_Broad_20[is.na(Structures_Broad_20)] <- 0
Structures_Broad_20 <- Structures_Broad_20*100
Structures_Broad_20 <- as.matrix(t(data.frame(Structures = Structures_Broad_20[c(1,3,5,7,9,11,13)], Quasi = c(Structures_Broad_20[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_Broad_20,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "Large, N = 20", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_Broad_20[,1],5), y = 0.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
It seems that a sample size of 20 gives a good idea of the true co-occurrence pattern.
Large and intermediate scales with N = 15
Now lets see if we would get a similar results for the large and intermediate scales with an N = 20.
Intermediate Scale
SSF
We sampled 15 communities from a total of 46 for 1000 times and calculated the Metacommunity Structure (i.e. co-occurrence pattern). To keep a good representation of spatial scale we restricted our sampling procedure following:
- 3 communities from each of the 5 areas of the SSF for a total of 15 communities.
set.seed(3)
SSF_matrices_pa_15 <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
id <- list()
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id[[i]] <- c(sample(rownames(SSF_pa[SSF_locality == SSF_locality_levels[i],]),3))
}
ids <- unlist(id)
oc_matrix <- SSF_pa[match(ids, rownames(SSF_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
SSF_matrices_pa_15[[j]] <- oc_matrix
}
Metacommunities_SSF_15 <- IdentifyStructure2(SSF_matrices_pa_15,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our subsamples:
length(Metacommunities_SSF_15$p_Coherence[Metacommunities_SSF_15$p_Coherence < 0.05 & Metacommunities_SSF_15$z_Coherence < 0]) / length(Metacommunities_SSF_15$p_Coherence)
## [1] 0.203
These are the percentages of different structures identified. The asterisk indicate the Structure identified using all of the 46 communities.
Structures_SSF_15 <- c(rep(NA, 13))
names(Structures_SSF_15) <- names_structures
prop_Structures <- table(Metacommunities_SSF_15$Structure) / sum(table(Metacommunities_SSF_15$Structure))
Structures_SSF_15[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_SSF_15[is.na(Structures_SSF_15)] <- 0
Structures_SSF_15 <- Structures_SSF_15*100
Structures_SSF_15 <- as.matrix(t(data.frame(Structures = Structures_SSF_15[c(1,3,5,7,9,11,13)], Quasi = c(Structures_SSF_15[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_SSF_15,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "SSF, N = 15", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_SSF_15[,7],5), y = 9.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
DRF
We sampled 15 communities from a total of 50 for 1500 times and calculated the Metacommunity Structure (i.e. co-occurrence pattern). To keep a good representation of spatial scale we restricted our sampling procedure following:
- 5 communities from each of the 3 areas of the DRF for a total of 15 communities.
set.seed(3)
DRF_matrices_pa_15 <- list()
DRF_locality_levels <-levels(as.factor(DRF_locality))
id <- list()
for(j in 1:1000){
for(i in 1:length(DRF_locality_levels)){
id[[i]] <- c(sample(rownames(DRF_pa[DRF_locality == DRF_locality_levels[i],]),5))
}
ids <- unlist(id)
oc_matrix <- DRF_pa[match(ids, rownames(DRF_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
DRF_matrices_pa_15[[j]] <- oc_matrix
}
nrow(DRF_matrices_pa_15[[10]])
Metacommunities_DRF_15 <- IdentifyStructure2(DRF_matrices_pa_15,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our subsamples:
length(Metacommunities_DRF_15$p_Coherence[Metacommunities_DRF_15$p_Coherence < 0.05 & Metacommunities_DRF_15$z_Coherence < 0]) / length(Metacommunities_DRF_15$p_Coherence)
## [1] 0.269
These are the percentages of different structures identified. The asterisk indicate the Structure identified using all of the 46 communities.
Structures_DRF_15 <- c(rep(NA, 13))
names(Structures_DRF_15) <- names_structures
prop_Structures <- table(Metacommunities_DRF_15$Structure) / sum(table(Metacommunities_DRF_15$Structure))
Structures_DRF_15[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_DRF_15[is.na(Structures_DRF_15)] <- 0
Structures_DRF_15 <- Structures_DRF_15*100
Structures_DRF_15 <- as.matrix(t(data.frame(Structures = Structures_DRF_15[c(1,3,5,7,9,11,13)], Quasi = c(Structures_DRF_15[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_DRF_15,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "DRF, N = 15", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_DRF_15[,7],5), y = 9.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
Large scale with N = 15
We sampled 15 communities from a total of 96 for 1000 times and calculated the Metacommunity Structure (i.e. co-occurrence pattern). To keep a good representation of spatial scale we restricted our sampling procedure following:
- 2 community from each of the 5 areas of the SSF minus 2 or 3 of those communities for a total of 7 or 8 communities from the SSF ecoregion.
- 2 communities from each of the 3 areas of the DRF, plus 1 or 2 communities from any of those areas for a total of 7 or 8 communities from the DRF ecoregion.
set.seed(3)
Broad_matrices_pa_15 <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
DRF_locality_levels <-levels(as.factor(DRF_locality))
id_SSF <- list()
id_DRF <- list()
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id_SSF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == SSF_locality_levels[i],]),2))
}
ids_SSF <- unlist(id_SSF)
minus <- sample(c(2,3),1)
ids_SSF<- ids_SSF[-sample(1:length(ids_SSF),minus)]
for(i in 1:length(DRF_locality_levels)){
id_DRF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == DRF_locality_levels[i],]),2))
}
ids_DRF <- unlist(id_DRF)
ids_DRF <- c(ids_DRF, sample(rownames(DRF_pa)[rownames(DRF_pa) %in% id_DRF == FALSE],(minus-1) ) )
ids <- c(ids_SSF,ids_DRF)
oc_matrix <- Broad_pa[match(ids, rownames(Broad_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
Broad_matrices_pa_15[[j]] <- oc_matrix
}
Metacommunities_Broad_15 <- IdentifyStructure2(Broad_matrices_pa_15,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our subsamples:
length(Metacommunities_Broad_15$p_Coherence[Metacommunities_Broad_15$p_Coherence < 0.05 & Metacommunities_Broad_15$z_Coherence < 0]) / length(Metacommunities_Broad_15$p_Coherence)
## [1] 0.948
These are the percentages of different structures identified. The asterisk indicate the Structure identified using all of the 96 communities.
Structures_Broad_15 <- c(rep(NA, 13))
names(Structures_Broad_15) <- names_structures
prop_Structures <- table(Metacommunities_Broad_15$Structure) / sum(table(Metacommunities_Broad_15$Structure))
Structures_Broad_15[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_Broad_15[is.na(Structures_Broad_15)] <- 0
Structures_Broad_15 <- Structures_Broad_15*100
Structures_Broad_15 <- as.matrix(t(data.frame(Structures = Structures_Broad_15[c(1,3,5,7,9,11,13)], Quasi = c(Structures_Broad_15[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_Broad_15,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "Large, N = 15", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_Broad_15[,1],5), y = 0.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
It seems that a sample size of 15 would only capture co-occurrence patterns that are too strong.
Large and intermediate scales with N = 10
Now lets see if we would get a similar results for the large and intermediate scales with an N = 20.
Intermediate Scale
SSF
We sampled 10 communities from a total of 46 for 1000 times and calculated the Metacommunity Structure (i.e. co-occurrence pattern). To keep a good representation of spatial scale we restricted our sampling procedure following:
- 2 communities from each of the 5 areas of the SSF for a total of 10 communities.
SSF_matrices_pa <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
id <- list()
set.seed(3)
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id[[i]] <- c(sample(rownames(SSF_pa[SSF_locality == SSF_locality_levels[i],]),2))
}
ids <- unlist(id)
oc_matrix <- SSF_pa[match(ids, rownames(SSF_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
SSF_matrices_pa[[j]] <- oc_matrix
}
Metacommunities_SSF <- IdentifyStructure2(SSF_matrices_pa,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our subsamples:
length(Metacommunities_SSF$p_Coherence[Metacommunities_SSF$p_Coherence < 0.05 & Metacommunities_SSF$z_Coherence < 0]) / length(Metacommunities_SSF$p_Coherence)
## [1] 0.129
These are the percentages of different structures identified. The asterisk indicate the Structure identified using all of the 46 communities.
Structures_SSF <- c(rep(NA, 13))
names(Structures_SSF) <- names_structures
prop_Structures <- table(Metacommunities_SSF$Structure) / sum(table(Metacommunities_SSF$Structure))
Structures_SSF[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_SSF[is.na(Structures_SSF)] <- 0
Structures_SSF <- Structures_SSF*100
Structures_SSF <- as.matrix(t(data.frame(Structures = Structures_SSF[c(1,3,5,7,9,11,13)], Quasi = c(Structures_SSF[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_SSF,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "SSF, N = 10", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_SSF[,7],5), y = 9.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
DRF
We sampled 10 communities from a total of 50 for 1000 times and calculated the Metacommunity Structure (i.e. co-occurrence pattern). To keep a good representation of spatial scale we restricted our sampling procedure following:
- 3 communities from each of the 3 areas of the DRF, plus one from any of those areas for a total of 10 communities.
DRF_matrices_pa <- list()
DRF_locality_levels <-levels(as.factor(DRF_locality))
id <- list()
set.seed(3)
for(j in 1:1000){
for(i in 1:length(DRF_locality_levels)){
id[[i]] <- c(sample(rownames(DRF_pa[DRF_locality == DRF_locality_levels[i],]),3))
}
ids <- unlist(id)
ids <- c(ids, sample(rownames(DRF_pa)[rownames(DRF_pa) %in% ids == FALSE],1))
oc_matrix <- DRF_pa[match(ids, rownames(DRF_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
DRF_matrices_pa[[j]] <- oc_matrix
}
Metacommunities_DRF <- IdentifyStructure2(DRF_matrices_pa,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our subsamples:
length(Metacommunities_DRF$p_Coherence[Metacommunities_DRF$p_Coherence < 0.05 & Metacommunities_DRF$z_Coherence < 0]) / length(Metacommunities_DRF$p_Coherence)
## [1] 0.154
These are the percentages of different structures identified. The asterisk indicate the Structure identified using all of the 46 communities.
Structures_DRF <- c(rep(NA, 13))
names(Structures_DRF) <- names_structures
prop_Structures <- table(Metacommunities_DRF$Structure) / sum(table(Metacommunities_DRF$Structure))
Structures_DRF[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_DRF[is.na(Structures_DRF)] <- 0
Structures_DRF <- Structures_DRF*100
Structures_DRF <- as.matrix(t(data.frame(Structures = Structures_DRF[c(1,3,5,7,9,11,13)], Quasi = c(Structures_DRF[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_DRF,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "DRF, N = 10", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_DRF[,7],5), y = 9.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
Large scale with N = 10
We sampled 10 communities from a total of 96 for 1000 times and calculated the Metacommunity Structure (i.e. co-occurrence pattern). To keep a good representation of spatial scale we restricted our sampling procedure following:
- 1 community from each of the 5 areas of the SSF for a total of 5 communities from the SSF ecoregion.
- 2 communities from each of the 3 areas of the DRF, minus 1 community from any of those areas for a total of 5 communities from the DRF ecoregion.
set.seed(3)
Broad_matrices_pa <- list()
SSF_locality_levels <-levels(as.factor(SSF_locality))
DRF_locality_levels <-levels(as.factor(DRF_locality))
id_SSF <- list()
id_DRF <- list()
for(j in 1:1000){
for(i in 1:length(SSF_locality_levels)){
id_SSF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == SSF_locality_levels[i],]),1))
}
ids_SSF <- unlist(id_SSF)
for(i in 1:length(DRF_locality_levels)){
id_DRF[[i]] <- c(sample(rownames(Broad_pa[Broad_locality == DRF_locality_levels[i],]),2))
}
ids_DRF <- unlist(id_DRF)
ids_DRF <- ids_DRF[-sample(1:length(ids_DRF),1)]
ids <- c(ids_SSF,ids_DRF)
oc_matrix <- Broad_pa[match(ids, rownames(Broad_pa)),]
oc_matrix <- oc_matrix[,colSums(oc_matrix) > 0]
Broad_matrices_pa[[j]] <- oc_matrix
}
Metacommunities_Broad <- IdentifyStructure2(Broad_matrices_pa,
CoherenceMethod = "curveball",
turnoverMethod = "EMS",
orderNulls = T, seed = 3, sims = 1000, round = 3, multicore = TRUE, n_cores = 10)
This is the percentage of coherent metacommunities found in our subsamples:
length(Metacommunities_Broad$p_Coherence[Metacommunities_Broad$p_Coherence < 0.05]) / length(Metacommunities_Broad$p_Coherence)
## [1] 0.756
These are the percentages of different structures identified. The asterisk indicate the Structure identified using all of the 96 communities.
Structures_Broad <- c(rep(NA, 13))
names(Structures_Broad) <- names_structures
prop_Structures <- table(Metacommunities_Broad$Structure) / sum(table(Metacommunities_Broad$Structure))
Structures_Broad[match(names(prop_Structures), names_structures)] <- prop_Structures
Structures_Broad[is.na(Structures_Broad)] <- 0
Structures_Broad <- Structures_Broad*100
Structures_Broad <- as.matrix(t(data.frame(Structures = Structures_Broad[c(1,3,5,7,9,11,13)], Quasi = c(Structures_Broad[c(2,4,5,8,10,12)],0))))
par(mar = c(3.5,16,2,0.75), bty = "l")
barplot(Structures_Broad,
axes = F, col = c("grey50", "grey80"), space = c(0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5),
border = "black", xlim = c(0,100), axisnames= F, xlab = "", cex.lab = 1.25, main = "Large, N = 10", horiz = TRUE, ylim = c(0,10))
title(xlab = "%", line = 2.5, cex.lab = 1.5)
axis(1)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = -0.25, labels =names_structures[c(1,3,5,7,9,11,13)], tick = F,las = 1, hadj = 1, cex.axis = 1, gap.axis = -1, padj = 0.4)
axis(2,at = c(0.5, 2, 3.5, 5, 6.5, 8, 9.5),line = 0, labels =FALSE, tick = T,las = 1, hadj = 1, cex.axis = 0.75, gap.axis = -1, padj = 0.4)
text(x = sum(Structures_Broad[,1],5), y = 0.5,labels = "*", cex = 2)
box()
Light grey bars are Quasi structures.
It seems that a sample size of 10 is not enough to capture the true structure. It seems to be able to capture coherence when it exists though.
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