🐎 min-max trophic networks

code/03_network_pfim.jl

@@ -16,7 +16,6 @@ matrix_names = readdir("../data/clean/trait_maximal")
 matrix_names = replace.(matrix_names, ".csv" => "")
 
 # maximal feeding rules
-
 feeding_rules = DataFrame(CSV.File("../data/clean/feeding_rules/feeding_rules_maximal.csv"))
 
 for i in eachindex(matrix_names)
@@ -182,34 +181,68 @@ save_object(
     topology[:, ["id", "model", "network"]],
 )
 
-# trophic species
+# trophic species - minimum rules
 
 # get the name of all communities
-matrix_names = readdir("../data/clean/trophic")
+matrix_names = readdir("../data/clean/trophic_minimum")
 matrix_names = replace.(matrix_names, ".csv" => "")
 
 topology = topo_df();
 
 for i in eachindex(matrix_names)
 
     file_name = matrix_names[i]
-    df = DataFrame(CSV.File.(joinpath("../data/clean/trophic", "$file_name.csv"),))
+    df = DataFrame(CSV.File.(joinpath("../data/clean/trophic_minimum", "$file_name.csv"),))
+
+    d = model_summary(df, file_name, "pfim"; feeding_rules = feeding_rules, downsample = false)
+
+    d[:model] = "pfim_trophic_minimum"
+    push!(topology, d)
+
+end
+
+# write summaries as .csv
+CSV.write(
+    "../data/processed/topology/topology_pfim_trophic_minimum.csv",
+    topology[:, setdiff(names(topology), ["network"])],
+)
+# write networks as object
+save_object(
+    "../data/processed/networks/pfim_networks_trophic_minimum.jlds",
+    topology[:, ["id", "model", "network"]],
+)
+
+# trophic species - maximal rules
+
+# get the name of all communities
+matrix_names = readdir("../data/clean/trophic_maximal")
+matrix_names = replace.(matrix_names, ".csv" => "")
+
+# maximal feeding rules
+feeding_rules = DataFrame(CSV.File("../data/clean/feeding_rules/feeding_rules_maximal.csv"))
+
+topology = topo_df();
+
+for i in eachindex(matrix_names)
+
+    file_name = matrix_names[i]
+    df = DataFrame(CSV.File.(joinpath("../data/clean/trophic_maximal", "$file_name.csv"),))
 
     d = model_summary(df, file_name, "pfim"; feeding_rules = feeding_rules, downsample = false)
 
-    d[:model] = "pfim_trophic"
+    d[:model] = "pfim_trophic_maximal"
     push!(topology, d)
 
 end
 
 # write summaries as .csv
 CSV.write(
-    "../data/processed/topology/topology_pfim_trophic.csv",
+    "../data/processed/topology/topology_pfim_trophic_maximal.csv",
     topology[:, setdiff(names(topology), ["network"])],
 )
 # write networks as object
 save_object(
-    "../data/processed/networks/pfim_networks_trophic.jlds",
+    "../data/processed/networks/pfim_networks_trophic_maximal.jlds",
     topology[:, ["id", "model", "network"]],
 )
 

data/processed/topology/topology_pfim_trophic_maximal.csv

@@ -0,0 +1,4 @@
+id,model,richness,connectance,deficiency,complexity,distance,basal,top,generality,vulnerability,S1,S2,S4,S5
+during_traits,pfim_trophic_maximal,13,0.05325443786982249,0.8461538461538461,0.8037424105974534,2.0,11.0,5.0,2.213015114597254,0.6304251719561151,0.0,0.0,0.0,0.0
+post_traits,pfim_trophic_maximal,13,0.04142011834319527,0.8461538461538461,0.834630053001768,2.0,11.0,7.0,1.6641005886756874,0.6602252917735248,0.0,0.0,0.0,0.0
+pre_traits,pfim_trophic_maximal,37,0.042366691015339665,0.8648648648648649,0.8666874205374346,2.0,32.0,14.0,4.494073508154567,1.4821257357919053,0.0,0.0,0.0,31.0

data/processed/topology/topology_pfim_trophic_minimum.csv

@@ -0,0 +1,4 @@
+id,model,richness,connectance,deficiency,complexity,distance,basal,top,generality,vulnerability,S1,S2,S4,S5
+during_traits,pfim_trophic_minimum,13,0.011834319526627219,0.9230769230769231,NaN,2.0,12.0,11.0,0.5547001962252291,0.3755338080994054,0.0,0.0,0.0,0.0
+post_traits,pfim_trophic_minimum,13,0.011834319526627219,0.9230769230769231,NaN,2.0,12.0,11.0,0.5547001962252291,0.3755338080994054,0.0,0.0,0.0,0.0
+pre_traits,pfim_trophic_minimum,30,0.0044444444444444444,0.9333333333333333,0.9475713327707453,2.0,28.0,27.0,0.5074162634049248,0.43417248545530474,0.0,0.0,0.0,0.0

notebooks/code/figures_pfim.R

@@ -64,7 +64,7 @@ for (i in seq_along(plot_list)) {
     xlab("time") +
     ylab("value") +
     coord_cartesian(clip = "off") +
-    scale_colour_brewer(palette = "Dark2") +
+    #scale_colour_brewer(palette = "Dark2") +
     labs(title = levs[i]) +
     theme(panel.border = element_rect(colour = 'black',
                                       fill = "#ffffff00"),
@@ -99,7 +99,7 @@ pca_res <- prcomp(df_pca[3:8], scale. = TRUE)
 
 autoplot(pca_res, data = df_pca, colour = 'model', shape = 'id', size = 4, alpha = 0.7) +
   theme_classic() +
-  scale_colour_brewer(palette = "Dark2") +
+  #scale_colour_brewer(palette = "Dark2") +
   theme(panel.border = element_rect(colour = 'black',
                                     fill = "#ffffff00"),
         axis.ticks.x = element_blank())