Final analysis for paper
Steven L. Peck and Andrew Heiss
Last run: September 12, 2020
knitr::opts_chunk$set(warning = FALSE)
library(tidyverse) # CRAN v1.3.0
library(broom) # [github::tidymodels/broom] v0.5.3.9000
library(randomForest) # CRAN v4.6-14
library(ggstance) # CRAN v0.3.3
library(ggtext) # [github::wilkelab/ggtext] v0.1.0
library(gghalves) # [github::erocoar/gghalves] v0.1.0
library(ggforce) # CRAN v0.3.1
library(patchwork) # CRAN v1.0.0
library(pander) # CRAN v0.6.3
library(scales) # CRAN v1.1.0
library(pluralize) # [github::hrbrmstr/pluralize] v0.1.0
library(glue) # CRAN v1.4.0
library(here) # CRAN v0.1
# Disable dplyr's automatic ungrouping message
options(dplyr.summarise.inform = FALSE)
SEED <- 1234
sim_hl <- read_rds(here("data", "derived_data", "sim_hl.rds"))
sim_ls <- read_rds(here("data", "derived_data", "sim_ls.rds"))
sim_high <- sim_hl %>% filter(repp == "High")
sim_low <- sim_hl %>% filter(repp == "Low")
constraint_combo_outcomes_hl <- read_rds(here("data", "derived_data",
"constraint_combo_outcomes_hl.rds")) %>%
mutate(constraint_combo = str_replace(constraint_combo, "selectfor_d", "species_drift"),
constraint_combo = str_replace(constraint_combo, "select", "selection"),
constraint_combo = str_replace(constraint_combo, "compete", "competition"),
constraint_combo = str_replace(constraint_combo, "disperse", "dispersal"))
constraint_combo_outcomes_ls <- read_rds(here("data", "derived_data",
"constraint_combo_outcomes_ls.rds")) %>%
mutate(constraint_combo = str_replace(constraint_combo, "selectfor_d", "species_drift"),
constraint_combo = str_replace(constraint_combo, "select", "selection"),
constraint_combo = str_replace(constraint_combo, "compete", "competition"),
constraint_combo = str_replace(constraint_combo, "disperse", "dispersal"))
diff_hl_outcomes_full <- sim_hl %>%
group_by(n_constraints_f) %>%
nest() %>%
mutate(fitness_test = map(data, ~t.test(landscape_fitness_linked ~ repp, data = .)),
evenness_test = map(data, ~t.test(avg_evenness_t ~ repp, data = .)),
richness_test = map(data, ~t.test(landscape_richness_mean ~ repp, data = .)),
turnover_test = map(data, ~t.test(turnover_diff ~ repp, data = .))) %>%
mutate_at(vars(ends_with("test")), list(tidy = ~map(., ~tidy(.)))) %>%
mutate_at(vars(ends_with("tidy")), list(diff = ~map_dbl(., "estimate"),
p.value = ~map_dbl(., "p.value"))) %>%
ungroup()
diff_hl_outcomes_plot <- diff_hl_outcomes_full %>%
select(-data, -ends_with("_test"), -ends_with("_tidy")) %>%
pivot_longer(cols = -n_constraints_f) %>%
mutate(name = str_replace(name, "_test_tidy_", "_")) %>%
separate(name, into = c("outcome", "statistic"), sep = "_") %>%
pivot_wider(names_from = "statistic") %>%
mutate(sig = ifelse(p.value < 0.05, "*", ""),
sig = ifelse(is.nan(p.value), "", sig)) %>%
mutate(nice_diff = glue("∆ = {round(diff, 3)}{sig}")) %>%
mutate(nice_diff = str_replace(nice_diff, "\\-", "−"))theme_constraint <- function(base_size = 8, base_family = "DejaVu Sans Condensed") {
ret <- theme_bw(base_size, base_family) +
theme(plot.title = element_text(size = rel(1.1), face = "bold",
family = "DejaVu Sans Condensed"),
plot.subtitle = element_text(size = rel(0.8), face = "plain",
family = "DejaVu Sans Condensed"),
plot.caption = element_text(size = rel(0.8), color = "grey50", face = "plain",
family = "DejaVu Sans Condensed",
margin = ggplot2::margin(t = 5)),
panel.border = element_rect(color = "grey50", fill = NA, size = 0.15),
panel.spacing = unit(1, "lines"),
panel.grid.minor = element_blank(),
strip.text = element_text(size = rel(0.9), hjust = 0,
family = "DejaVu Sans Condensed", face = "bold"),
strip.background = element_rect(fill = "#ffffff", colour = NA),
axis.ticks = element_blank(),
axis.title.x = element_text(margin = ggplot2::margin(t = 5)),
axis.text = element_text(family = "DejaVu Sans Condensed", face = "plain"),
legend.key = element_blank(),
legend.text = element_text(size = rel(1), family = "DejaVu Sans Condensed",
face = "plain"),
legend.spacing = unit(0.1, "lines"),
legend.box.margin = ggplot2::margin(t = -0.5, unit = "lines"),
legend.margin = ggplot2::margin(t = 0),
legend.position = "bottom")
ret
}
# Colors from https://medialab.github.io/iwanthue/
# 6 colors, default palette, soft (k-means)
clrs <- list(
select = "#6295cd", # blue
selection = "#6295cd", # blue
compete = "#b3943f", # gold
competition = "#b3943f", # gold
catastrophe = "#9466c9", # purple
create_network = "#62a85b", # green
dispersal = "#c85990", # pink
disperse = "#c85990", # pink
selectfor_d = "#ca5d46", # reddish
species_drift = "#ca5d46", # reddish
grey = "#7f7f7f" # grey50
)
color_constraints <- function(df, constraint_col) {
df %>%
mutate(constraint_colored = {{constraint_col}}) %>%
mutate(constraint_colored =
str_replace_all(constraint_colored, "\\bselect\\b",
glue("<span style='color:{clrs$select}'>select</span>")),
constraint_colored =
str_replace_all(constraint_colored, "\\bselection\\b",
glue("<span style='color:{clrs$selection}'>selection</span>")),
constraint_colored =
str_replace_all(constraint_colored, "\\bcompete\\b",
glue("<span style='color:{clrs$compete}'>compete</span>")),
constraint_colored =
str_replace_all(constraint_colored, "\\bcompetition\\b",
glue("<span style='color:{clrs$competition}'>competition</span>")),
constraint_colored =
str_replace_all(constraint_colored, "\\bcatastrophe\\b",
glue("<span style='color:{clrs$catastrophe}'>catastrophe</span>")),
constraint_colored =
str_replace_all(constraint_colored, "\\bcreate_network\\b",
glue("<span style='color:{clrs$create_network}'>create_network</span>")),
constraint_colored =
str_replace_all(constraint_colored, "\\bdisperse\\b",
glue("<span style='color:{clrs$disperse}'>disperse</span>")),
constraint_colored =
str_replace_all(constraint_colored, "\\bdispersal\\b",
glue("<span style='color:{clrs$dispersal}'>dispersal</span>")),
constraint_colored =
str_replace_all(constraint_colored, "\\bselectfor_d\\b",
glue("<span style='color:{clrs$selectfor_d}'>selectfor_d</span>")),
constraint_colored =
str_replace_all(constraint_colored, "\\bspecies_drift\\b",
glue("<span style='color:{clrs$species_drift}'>species_drift</span>")))
}
# Function for plotting outcomes across combinations of constraints
mega_means_plot <- function(df, var_name, xlab, title, add_to_right = 0, add_to_left = 0.03, xlim = NULL) {
constraint_combos <- df %>%
group_by(constraint_combo, n) %>%
summarize(avg = mean({{var_name}})) %>%
arrange(desc(n), desc(avg)) %>%
ungroup() %>%
mutate(constraint_combo = ifelse(n >= 4,
str_wrap(constraint_combo, width = 40, exdent = 8),
constraint_combo)) %>%
mutate(constraint_combo = str_replace_all(constraint_combo, " {8}", "<br>")) %>%
mutate(constraint_combo = recode(constraint_combo,
`select` = "selection",
`compete` = "competition",
`disperse` = "dispersal",
`selectfor_d` = "species_drift")) %>%
color_constraints(constraint_combo) %>%
mutate(constraint_colored = fct_inorder(constraint_colored),
n_title = map_chr(n, ~paste0(., pluralize(" constraint", .))))
plot_02 <- ggplot(filter(constraint_combos, n %in% c(0, 1, 2)),
aes(x = avg, y = constraint_colored)) +
geom_pointrangeh(aes(xmin = 0, xmax = avg)) +
scale_x_continuous(expand = expansion(add = c(add_to_right, add_to_left))) +
labs(x = xlab, y = NULL) +
facet_col(vars(n_title), scales = "free_y", space = "free") +
theme_constraint() +
theme(axis.text.y = element_markdown(),
panel.grid.major.y = element_blank(),
strip.background = element_rect(fill = "grey80"))
plot_3 <- ggplot(filter(constraint_combos, n == 3),
aes(x = avg, y = constraint_colored)) +
geom_pointrangeh(aes(xmin = 0, xmax = avg)) +
scale_x_continuous(expand = expansion(add = c(add_to_right, add_to_left))) +
labs(x = xlab, y = NULL) +
facet_col(vars(n_title), scales = "free_y", space = "free") +
theme_constraint() +
theme(axis.text.y = element_markdown(),
panel.grid.major.y = element_blank(),
strip.background = element_rect(fill = "grey80"))
plot_46 <- ggplot(filter(constraint_combos, n %in% c(4, 5, 6)),
aes(x = avg, y = constraint_colored)) +
geom_pointrangeh(aes(xmin = 0, xmax = avg)) +
scale_x_continuous(expand = expansion(add = c(add_to_right, add_to_left))) +
labs(x = xlab, y = NULL) +
facet_col(vars(n_title), scales = "free_y", space = "free") +
theme_constraint() +
theme(axis.text.y = element_markdown(),
panel.grid.major.y = element_blank(),
strip.background = element_rect(fill = "grey80"))
if (!is.null(xlim)) {
plot_02 <- plot_02 + coord_cartesian(xlim = xlim)
plot_3 <- plot_3 + coord_cartesian(xlim = xlim)
plot_46 <- plot_46 + coord_cartesian(xlim = xlim)
}
plot_all <- (plot_02 | plot_3 | plot_46) +
plot_annotation(title = title,
theme = theme(plot.title = element_text(size = rel(1.1), face = "bold",
family = "DejaVu Sans Condensed")))
plot_all
}Variable importance
To determine which constraints matter the most, we use random forests to determine variable importance. We don’t need to include all the interactions (e.g. create_network + select + create_network * select, etc.) because random forests inherently pick those up.
There are two ways to measure variable importance with random forests: (1) Gini-based node impurity, and (2) permutation-based increase in mean standard error. Gini-based node impurity is trickier to interpret. According to Liz Dinsdale:
The mean decrease in Gini coefficient is a measure of how each variable contributes to the homogeneity of the nodes and leaves in the resulting random forest. Each time a particular variable is used to split a node, the Gini coefficient for the child nodes are calculated and compared to that of the original node. The Gini coefficient is a measure of homogeneity from 0 (homogeneous) to 1 (heterogeneous). The changes in Gini are summed for each variable and normalized at the end of the calculation. Variables that result in nodes with higher purity have a higher decrease in Gini coefficient.
Or in other words, importance is the mean decrease (over all the trees) in the Gini index by splits of a predictor. Basically, the higher the decrease in impurity, the more important the variable in explaining the outcome.
However, node impurity tends to inflate or distort variable importance for continuous variables, and it uses the entire data set. A common alternative (that is also more interpretable!) is to look at the percent increase in mean standard error (see slide 15 here and this answer here). This measures how much the model’s MSE changes if you shuffle one of the variables (i.e. if you completely shuffle select, how wrong does the model become?). High values show that the variable is important for maintaining accuracy; low values show that the variable doesn’t matter all that much (since it can be random noise and result in similar accuracy). Like here, the Gini-based version finds that passenger ID is incredibly important for predicting survival on the Titanic, when really it’s just a random variable that doesn’t matter. The percent-change-in-MSE-based version of importance isn’t fooled by passenger ID, though.
Because of that, we use mean decrease in accuracy as our measure of variable importance.
library(titanic) # CRAN v0.1.0
titanic <- titanic::titanic_train %>%
drop_na() %>%
mutate(Sex = as.factor(Sex), Survived = as.factor(Survived), Pclass = as.factor(Pclass))
set.seed(SEED)
titanic_forest <- randomForest(Survived ~ Age + Sex + Pclass + PassengerId,
data = titanic, importance = TRUE)
varImpPlot(titanic_forest)
Figures
Figure 4: Landscape fitness
Fig 4. Effect of the number of constraints on landscape fitness (A) Boxplots showing landscape fitness across different numbers of constraints; (B) Percent increase in mean standard error in a random forest model when each constraint is removed; (C) Average landscape fitness across all possible combinations of constraints
plot_fitness_boxplot <- ggplot(sim_ls, aes(x = n_constraints_f, y = landscape_fitness_linked)) +
geom_half_point(size = 0.01, alpha = 0.01, side = "r", color = "grey50",
transformation_params = list(width = 0.25, height = 0, seed = 1234)) +
geom_half_boxplot(aes(fill = "Latin hypercube samples"),
size = 0.25, side = "l", outlier.shape = NA, errorbar.draw = TRUE, alpha = 0.5) +
stat_summary(geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = -0.19)) +
scale_y_reverse() +
scale_fill_manual(values = c("#0074D9"), name = NULL) +
guides(fill = guide_legend(direction = "horizontal", override.aes = list(alpha = 0.3, size = 0.25))) +
coord_cartesian(ylim = c(1, -0.02)) +
labs(x = "Number of constraints",
y = "Landscape fitness",
title = "A: Landscape fitness and constraints",
subtitle = "Values near zero indicate high fitness",
caption = glue("<span style='color:{clrs$grey}'>◆</span> = mean value")) +
theme_constraint() +
theme(panel.grid.major.x = element_blank(),
plot.caption = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_text(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.15, unit = "lines"),
legend.key.size = unit(0.75, "lines"))
plot_fitness_boxplot
ggsave(plot_fitness_boxplot, filename = here("analysis", "output", "fitness_A.pdf"),
width = params$fig_width, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(plot_fitness_boxplot, filename = here("analysis", "output", "fitness_A.png"),
width = params$fig_width, height = params$fig_height, units = "in", type = "cairo", dpi = 300)plot_fitness_hl_diffs <- ggplot(mapping = aes(x = n_constraints_f, y = landscape_fitness_linked, fill = repp)) +
geom_text(data = filter(diff_hl_outcomes_plot, outcome == "fitness"),
aes(y = 0, label = nice_diff, fill = NULL),
family = "DejaVu Sans Condensed", fontface = "plain", size = 1.5, vjust = -0.85) +
geom_half_boxplot(data = sim_high,
size = 0.25, side = "l", outlier.shape = NA, errorbar.draw = TRUE,
nudge = 0.05, alpha = 0.5) +
geom_half_boxplot(data = sim_low,
size = 0.25, side = "r", outlier.shape = NA, errorbar.draw = TRUE,
nudge = 0.05, alpha = 0.5) +
stat_summary(data = sim_high, geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = -0.22)) +
stat_summary(data = sim_low, geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = 0.22)) +
scale_y_reverse() +
scale_fill_manual(values = c("#2ECC40", "#FF851B"), name = NULL) +
guides(fill = guide_legend(direction = "horizontal", override.aes = list(alpha = 0.3, size = 0.25))) +
coord_cartesian(ylim = c(0.65, -0.02)) +
labs(x = "Number of constraints",
y = "Landscape fitness",
title = "B: Constraints across high/low",
subtitle = "∆ = high − low",
caption = glue("<span style='color:{clrs$grey}'>◆</span> = mean value; * = p < 0.05")) +
theme_constraint() +
theme(panel.grid.major.x = element_blank(),
plot.caption = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_text(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.15, unit = "lines"),
legend.key.size = unit(0.75, "lines"))
plot_fitness_hl_diffs
ggsave(combined_fitness, filename = here("analysis", "output", "fitness_boxplots.pdf"),
width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(combined_fitness, filename = here("analysis", "output", "fitness_boxplots.png"),
width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 300)
# ggsave(combined_fitness, filename = here("analysis", "output", "fitness_combined.eps"),
# width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_ps, fallback_resolution = 600)# This takes a few minutes to run
set.seed(SEED)
forest_model_fitness <-
randomForest(landscape_fitness_linked ~
create_network + select + disperse +
compete + selectfor_d + catastrophe,
importance = TRUE, data = sim_ls)
forest_model_fitness_low <-
randomForest(landscape_fitness_linked ~
create_network + select + disperse +
compete + selectfor_d + catastrophe,
importance = TRUE, data = filter(sim_hl, repp == "Low"))
forest_model_fitness_high <-
randomForest(landscape_fitness_linked ~
create_network + select + disperse +
compete + selectfor_d + catastrophe,
importance = TRUE, data = filter(sim_hl, repp == "High"))clrs <- list(
select = "#6295cd", # blue
selection = "#6295cd", # blue
compete = "#b3943f", # gold
competition = "#b3943f", # gold
catastrophe = "#9466c9", # purple
create_network = "#62a85b", # green
dispersal = "#c85990", # pink
disperse = "#c85990", # pink
selectfor_d = "#ca5d46", # reddish
species_drift = "#ca5d46", # reddish
grey = "#7f7f7f" # grey50
)
fitness_importance_ls <- importance(forest_model_fitness, type = 1, scale = TRUE) %>%
data.frame() %>%
rownames_to_column(var = "constraint") %>%
arrange(desc(X.IncMSE)) %>%
mutate(constraint = recode(constraint,
`select` = "selection",
`compete` = "competition",
`disperse` = "dispersal",
`selectfor_d` = "species_drift")) %>%
color_constraints(constraint) %>%
mutate_at(vars(starts_with("constraint")), fct_inorder) %>%
mutate(X.IncMSE = X.IncMSE / 100)
plot_fitness_importance_ls <- ggplot(fitness_importance_ls,
aes(x = X.IncMSE, y = fct_rev(constraint_colored),
color = fct_rev(constraint),
shape = "Latin hypercube samples")) +
geom_pointrangeh(aes(xmin = 0, xmax = X.IncMSE), size = 0.25) +
scale_x_continuous(labels = percent, expand = expansion(add = c(0, 0.02))) +
scale_color_manual(values = unlist(clrs), guide = FALSE) +
scale_shape_manual(values = c(19), name = NULL) +
guides(shape = guide_legend(direction = "horizontal",
override.aes = list(size = 0.25))) +
labs(x = "Percent increase in MSE when excluded", y = NULL,
title = "B: Constraint importance for landscape fitness",
subtitle = "Higher values indicate greater variable importance",
caption = "") +
theme_constraint() +
theme(panel.grid.major.y = element_blank(),
axis.text.y = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_markdown(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.75, unit = "lines"),
legend.key.width = unit(2.5, "lines"),
legend.key.size = unit(0.95, "lines"))
plot_fitness_importance_ls
ggsave(plot_fitness_importance_ls, filename = here("analysis", "output", "fitness_B.pdf"),
width = params$fig_width, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(plot_fitness_importance_ls, filename = here("analysis", "output", "fitness_B.png"),
width = params$fig_width, height = params$fig_height, units = "in", type = "cairo", dpi = 300)fitness_importance_low <- importance(forest_model_fitness_low, type = 1, scale = TRUE) %>%
data.frame() %>%
rownames_to_column(var = "constraint") %>%
mutate(repp = "Low")
fitness_importance_high <- importance(forest_model_fitness_high, type = 1, scale = TRUE) %>%
data.frame() %>%
rownames_to_column(var = "constraint") %>%
mutate(repp = "High")
fitness_importance_hl <- bind_rows(fitness_importance_low,
fitness_importance_high) %>%
mutate(repp = factor(repp, levels = c("Low", "High"), ordered = TRUE)) %>%
arrange(desc(repp), desc(X.IncMSE)) %>%
mutate(constraint = recode(constraint,
`select` = "selection",
`compete` = "competition",
`disperse` = "dispersal",
`selectfor_d` = "species_drift")) %>%
color_constraints(constraint) %>%
mutate_at(vars(starts_with("constraint")), fct_inorder) %>%
mutate(X.IncMSE = X.IncMSE / 100)
plot_fitness_importance_hl <- ggplot(fitness_importance_hl,
aes(x = X.IncMSE, y = fct_rev(constraint_colored), color = fct_rev(constraint),
linetype = repp, shape = repp)) +
geom_pointrangeh(aes(xmin = 0, xmax = X.IncMSE),
position = position_dodgev(height = 0.5), size = 0.25) +
scale_x_continuous(labels = percent, expand = expansion(add = c(0, 0.02))) +
scale_color_manual(values = unlist(clrs), guide = FALSE) +
scale_linetype_manual(values = c("dashed", "solid"), name = NULL) +
scale_shape_manual(values = c(17, 15), name = NULL) +
guides(linetype = guide_legend(direction = "horizontal", reverse = TRUE),
shape = guide_legend(direction = "horizontal", override.aes = list(size = 0.25),
reverse = TRUE)) +
labs(x = "Percent increase in MSE when excluded", y = NULL,
title = "D: Constraint importance across high/low",
subtitle = "",
caption = "") +
theme_constraint() +
theme(panel.grid.major.y = element_blank(),
axis.text.y = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_markdown(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.75, unit = "lines"),
legend.key.width = unit(2.5, "lines"),
legend.key.size = unit(0.95, "lines"))
plot_fitness_importance_hl
ggsave(importance_fitness, filename = here("analysis", "output", "fitness_importance.pdf"),
width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(importance_fitness, filename = here("analysis", "output", "fitness_importance.png"),
width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 300)
# ggsave(combined_fitness, filename = here("analysis", "output", "fitness_combined.tiff"),
# width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 600)
# ggsave(combined_fitness, filename = here("analysis", "output", "fitness_combined.eps"),
# width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_ps, fallback_resolution = 600) plot_fitness_all <- mega_means_plot(constraint_combo_outcomes_ls, landscape_fitness_linked,
xlab = "Average landscape fitness",
title = "Average landscape fitness across combinations of constraints")
plot_fitness_all
ggsave(plot_fitness_all, filename = here("analysis", "output", "fitness_means_all.pdf"),
width = params$fig_width_big, height = params$fig_height_big, units = "in", device = cairo_pdf)
ggsave(plot_fitness_all, filename = here("analysis", "output", "fitness_means_all.png"),
width = params$fig_width_big, height = params$fig_height_big, units = "in", type = "cairo", dpi = 300)Figure 5: Landscape fitness variance
Fig 5. Effect of the number of constraints on variance of landscape fitness Boxplots showing variance of landscape fitness across different numbers of constraints
plot_landscape_fitness_variance <- ggplot(sim_ls,
aes(x = n_constraints_f, y = landscape_fitness_var)) +
geom_half_point(size = 0.05, alpha = 0.1, side = "r", color = "grey50",
transformation_params = list(width = 0.25, height = 0, seed = 1234)) +
geom_half_boxplot(size = 0.25, side = "l", outlier.shape = NA, errorbar.draw = TRUE) +
stat_summary(geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = -0.19)) +
scale_y_reverse() +
coord_cartesian(ylim = c(0.1, 0)) +
labs(x = "Number of constraints",
y = "Landscape fitness, variance",
title = "Variance in landscape fitness and constraints",
subtitle = "Latin hypercube samples",
caption = glue("<span style='color:{clrs$grey}'>◆</span> = mean value")) +
theme_constraint() +
theme(panel.grid.major.x = element_blank(),
plot.caption = element_markdown())
plot_landscape_fitness_variance
ggsave(plot_landscape_fitness_variance, filename = here("analysis", "output", "fitness_variance.pdf"),
width = params$fig_width, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(plot_landscape_fitness_variance, filename = here("analysis", "output", "fitness_variance.png"),
width = params$fig_width, height = params$fig_height, units = "in", type = "cairo", dpi = 300)Figure 6: Ecological evenness
Fig 6. Effect of the number of constraints on ecological evenness (A) Boxplots showing ecological evenness across different numbers of constraints; (B) Percent increase in mean standard error in a random forest model when each constraint is removed; (C) Average ecological evenness across all possible combinations of constraints
plot_evenness_boxplot <- ggplot(sim_ls, aes(x = n_constraints_f, y = avg_evenness_t)) +
geom_half_point(size = 0.05, alpha = 0.1, side = "r", color = "grey50",
transformation_params = list(width = 0.25, height = 0, seed = 1234)) +
geom_half_boxplot(aes(fill = "Latin hypercube samples"),
size = 0.25, side = "l", outlier.shape = NA, errorbar.draw = TRUE, alpha = 0.5) +
stat_summary(geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = -0.19)) +
scale_y_reverse() +
scale_fill_manual(values = c("#0074D9"), name = NULL) +
guides(fill = guide_legend(direction = "horizontal", override.aes = list(alpha = 0.3, size = 0.25))) +
coord_cartesian(ylim = c(1, -0.02)) +
labs(x = "Number of constraints",
y = "Ecological evenness",
title = "A: Ecological evenness and constraints",
subtitle = "Values near zero indicate high evenness",
caption = glue("<span style='color:{clrs$grey}'>◆</span> = mean value")) +
theme_constraint() +
theme(panel.grid.major.x = element_blank(),
plot.caption = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_text(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.15, unit = "lines"),
legend.key.size = unit(0.75, "lines"))
plot_evenness_boxplot
ggsave(plot_evenness_boxplot, filename = here("analysis", "output", "evenness_A.pdf"),
width = params$fig_width, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(plot_evenness_boxplot, filename = here("analysis", "output", "evenness_A.png"),
width = params$fig_width, height = params$fig_height, units = "in", type = "cairo", dpi = 300)plot_evenness_hl_diffs <- ggplot(mapping = aes(x = n_constraints_f, y = avg_evenness_t, fill = repp)) +
geom_text(data = filter(diff_hl_outcomes_plot, outcome == "evenness"),
aes(y = 0, label = nice_diff, fill = NULL),
family = "DejaVu Sans Condensed", fontface = "plain", size = 1.5, vjust = -0.85) +
geom_half_boxplot(data = sim_high,
size = 0.25, side = "l", outlier.shape = NA, errorbar.draw = TRUE,
nudge = 0.05, alpha = 0.5) +
geom_half_boxplot(data = sim_low,
size = 0.25, side = "r", outlier.shape = NA, errorbar.draw = TRUE,
nudge = 0.05, alpha = 0.5) +
stat_summary(data = sim_high, geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = -0.22)) +
stat_summary(data = sim_low, geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = 0.22)) +
scale_y_reverse() +
scale_fill_manual(values = c("#2ECC40", "#FF851B"), name = NULL) +
guides(fill = guide_legend(direction = "horizontal", override.aes = list(alpha = 0.3, size = 0.25))) +
coord_cartesian(ylim = c(1, -0.02)) +
labs(x = "Number of constraints",
y = "Ecological evenness",
title = "B: Constraints across high/low",
subtitle = "∆ = high − low",
caption = glue("<span style='color:{clrs$grey}'>◆</span> = mean value; * = p < 0.05")) +
theme_constraint() +
theme(panel.grid.major.x = element_blank(),
plot.caption = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_text(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.15, unit = "lines"),
legend.key.size = unit(0.75, "lines"))
plot_evenness_hl_diffs
ggsave(combined_evenness, filename = here("analysis", "output", "evenness_boxplots.pdf"),
width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(combined_evenness, filename = here("analysis", "output", "evenness_boxplots.png"),
width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 300)
# ggsave(combined_evenness, filename = here("analysis", "output", "evenness_combined.tiff"),
# width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 600)
# ggsave(combined_evenness, filename = here("analysis", "output", "evenness_combined.eps"),
# width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_ps, fallback_resolution = 600) # This takes a few minutes to run
set.seed(SEED)
forest_model_evenness <-
randomForest(avg_evenness_t ~
create_network + select + disperse +
compete + selectfor_d + catastrophe,
importance = TRUE,
data = drop_na(sim_ls, avg_evenness_t))
forest_model_evenness_low <-
randomForest(avg_evenness_t ~
create_network + select + disperse +
compete + selectfor_d + catastrophe,
importance = TRUE,
data = filter(drop_na(sim_hl, avg_evenness_t), repp == "Low"))
forest_model_evenness_high <-
randomForest(avg_evenness_t ~
create_network + select + disperse +
compete + selectfor_d + catastrophe,
importance = TRUE,
data = filter(drop_na(sim_hl, avg_evenness_t), repp == "High"))evenness_importance_ls <- importance(forest_model_evenness, type = 1, scale = TRUE) %>%
data.frame() %>%
rownames_to_column(var = "constraint") %>%
arrange(desc(X.IncMSE)) %>%
mutate(constraint = recode(constraint,
`select` = "selection",
`compete` = "competition",
`disperse` = "dispersal",
`selectfor_d` = "species_drift")) %>%
color_constraints(constraint) %>%
mutate_at(vars(starts_with("constraint")), fct_inorder) %>%
mutate(X.IncMSE = X.IncMSE / 100)
plot_evenness_importance_ls <- ggplot(evenness_importance_ls,
aes(x = X.IncMSE, y = fct_rev(constraint_colored),
color = fct_rev(constraint), shape = "Latin hypercube samples")) +
geom_pointrangeh(aes(xmin = 0, xmax = X.IncMSE), size = 0.25) +
scale_x_continuous(labels = percent, expand = expansion(add = c(0, 0.02))) +
scale_color_manual(values = unlist(clrs), guide = FALSE) +
scale_shape_manual(values = c(19), name = NULL) +
guides(shape = guide_legend(direction = "horizontal",
override.aes = list(size = 0.25))) +
labs(x = "Percent increase in MSE when excluded", y = NULL,
title = "B: Constraint importance for ecological evenness",
subtitle = "Higher values indicate greater variable importance",
caption = "") +
theme_constraint() +
theme(panel.grid.major.y = element_blank(),
axis.text.y = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_markdown(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.75, unit = "lines"),
legend.key.width = unit(2.5, "lines"),
legend.key.size = unit(0.95, "lines"))
plot_evenness_importance_ls
ggsave(plot_evenness_importance_ls, filename = here("analysis", "output", "evenness_B.pdf"),
width = params$fig_width, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(plot_evenness_importance_ls, filename = here("analysis", "output", "evenness_B.png"),
width = params$fig_width, height = params$fig_height, units = "in", type = "cairo", dpi = 300)evenness_importance_low <- importance(forest_model_evenness_low, type = 1, scale = TRUE) %>%
data.frame() %>%
rownames_to_column(var = "constraint") %>%
mutate(repp = "Low")
evenness_importance_high <- importance(forest_model_evenness_high, type = 1, scale = TRUE) %>%
data.frame() %>%
rownames_to_column(var = "constraint") %>%
mutate(repp = "High")
evenness_importance_hl <- bind_rows(evenness_importance_low,
evenness_importance_high) %>%
mutate(repp = factor(repp, levels = c("Low", "High"), ordered = TRUE)) %>%
arrange(desc(repp), desc(X.IncMSE)) %>%
mutate(constraint = recode(constraint,
`select` = "selection",
`compete` = "competition",
`disperse` = "dispersal",
`selectfor_d` = "species_drift")) %>%
color_constraints(constraint) %>%
mutate_at(vars(starts_with("constraint")), fct_inorder) %>%
mutate(X.IncMSE = X.IncMSE / 100)
plot_evenness_importance_hl <- ggplot(evenness_importance_hl,
aes(x = X.IncMSE, y = fct_rev(constraint_colored),
color = fct_rev(constraint),
linetype = repp, shape = repp)) +
geom_pointrangeh(aes(xmin = 0, xmax = X.IncMSE),
position = position_dodgev(height = 0.5), size = 0.25) +
scale_x_continuous(labels = percent, expand = expansion(add = c(0, 0.02))) +
scale_color_manual(values = unlist(clrs), guide = FALSE) +
scale_linetype_manual(values = c("dashed", "solid"), name = NULL) +
scale_shape_manual(values = c(17, 15), name = NULL) +
guides(linetype = guide_legend(direction = "horizontal", reverse = TRUE),
shape = guide_legend(direction = "horizontal", override.aes = list(size = 0.25),
reverse = TRUE)) +
labs(x = "Percent increase in MSE when excluded", y = NULL,
title = "D: Constraint importance across high/low",
subtitle = "",
caption = "") +
theme_constraint() +
theme(panel.grid.major.y = element_blank(),
axis.text.y = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_markdown(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.75, unit = "lines"),
legend.key.width = unit(2.5, "lines"),
legend.key.size = unit(0.95, "lines"))
plot_evenness_importance_hl
importance_evenness <- plot_evenness_importance_ls + plot_evenness_importance_hl
importance_evenness
ggsave(importance_evenness, filename = here("analysis", "output", "evenness_importance.pdf"),
width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(importance_evenness, filename = here("analysis", "output", "evenness_importance.png"),
width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 300)
# ggsave(combined_fitness, filename = here("analysis", "output", "fitness_combined.eps"),
# width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_ps, fallback_resolution = 600) plot_evenness_all <- mega_means_plot(constraint_combo_outcomes_ls, avg_evenness_t,
xlab = "Average ecological evenness",
title = "Average ecological evenness across combinations of constraints",
add_to_left = 0.08)
plot_evenness_all
ggsave(plot_evenness_all, filename = here("analysis", "output", "evenness_means_all.pdf"),
width = params$fig_width_big, height = params$fig_height_big, units = "in", device = cairo_pdf)
ggsave(plot_evenness_all, filename = here("analysis", "output", "evenness_means_all.png"),
width = params$fig_width_big, height = params$fig_height_big, units = "in", type = "cairo", dpi = 300)Figure 7: Landscape richness
Fig 7. Effect of the number of constraints on species richness (A) Boxplots showing species richness across different numbers of constraints; (B) Percent increase in mean standard error in a random forest model when each constraint is removed; (C) Average species richness across all possible combinations of constraints
plot_richness_boxplot <- ggplot(sim_ls, aes(x = n_constraints_f, y = landscape_richness_mean)) +
geom_half_point(size = 0.01, alpha = 0.01, side = "r", color = "grey50",
transformation_params = list(width = 0.25, height = 0, seed = 1234)) +
geom_half_boxplot(aes(fill = "Latin hypercube samples"),
size = 0.25, side = "l", outlier.shape = NA, errorbar.draw = TRUE, alpha = 0.5) +
stat_summary(geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = -0.19)) +
scale_y_reverse() +
scale_fill_manual(values = c("#0074D9"), name = NULL) +
guides(fill = guide_legend(direction = "horizontal", override.aes = list(alpha = 0.3, size = 0.25))) +
coord_cartesian(ylim = c(35, -1)) +
labs(x = "Number of constraints",
y = "Landscape richness",
title = "A: Landscape richness and constraints",
subtitle = "Values near zero indicate low species richness",
caption = glue("<span style='color:{clrs$grey}'>◆</span> = mean value")) +
theme_constraint() +
theme(panel.grid.major.x = element_blank(),
plot.caption = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_text(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.15, unit = "lines"),
legend.key.size = unit(0.75, "lines"))
plot_richness_boxplot
ggsave(plot_richness_boxplot, filename = here("analysis", "output", "richness_A.pdf"),
width = params$fig_width, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(plot_richness_boxplot, filename = here("analysis", "output", "richness_A.png"),
width = params$fig_width, height = params$fig_height, units = "in", type = "cairo", dpi = 300)plot_richness_hl_diffs <- ggplot(mapping = aes(x = n_constraints_f, y = landscape_richness_mean, fill = repp)) +
geom_text(data = filter(diff_hl_outcomes_plot, outcome == "richness"),
aes(y = 0, label = nice_diff, fill = NULL),
family = "DejaVu Sans Condensed", fontface = "plain", size = 1.5, vjust = -0.85) +
geom_half_boxplot(data = sim_high,
size = 0.25, side = "l", outlier.shape = NA, errorbar.draw = TRUE,
nudge = 0.05, alpha = 0.5) +
geom_half_boxplot(data = sim_low,
size = 0.25, side = "r", outlier.shape = NA, errorbar.draw = TRUE,
nudge = 0.05, alpha = 0.5) +
stat_summary(data = sim_high, geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = -0.22)) +
stat_summary(data = sim_low, geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = 0.22)) +
scale_y_reverse() +
scale_fill_manual(values = c("#2ECC40", "#FF851B"), name = NULL) +
guides(fill = guide_legend(direction = "horizontal", override.aes = list(alpha = 0.3, size = 0.25))) +
coord_cartesian(ylim = c(35, -1)) +
labs(x = "Number of constraints",
y = "Landscape richness",
title = "B: Constraints across high/low",
subtitle = "∆ = high − low",
caption = glue("<span style='color:{clrs$grey}'>◆</span> = mean value; * = p < 0.05")) +
theme_constraint() +
theme(panel.grid.major.x = element_blank(),
plot.caption = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_text(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.15, unit = "lines"),
legend.key.size = unit(0.75, "lines"))
plot_richness_hl_diffs
ggsave(combined_richness, filename = here("analysis", "output", "richness_boxplots.pdf"),
width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(combined_richness, filename = here("analysis", "output", "richness_boxplots.png"),
width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 300)
# ggsave(combined_richness, filename = here("analysis", "output", "richness_combined.tiff"),
# width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 600)
# ggsave(combined_richness, filename = here("analysis", "output", "richness_combined.eps"),
# width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_ps, fallback_resolution = 600) # This takes a few minutes to run
set.seed(SEED)
forest_model_richness <-
randomForest(landscape_richness_mean ~
create_network + select + disperse +
compete + selectfor_d + catastrophe,
importance = TRUE,
data = drop_na(sim_ls, landscape_richness_mean))
forest_model_richness_low <-
randomForest(landscape_richness_mean ~
create_network + select + disperse +
compete + selectfor_d + catastrophe,
importance = TRUE,
data = filter(drop_na(sim_hl, landscape_richness_mean), repp == "Low"))
forest_model_richness_high <-
randomForest(landscape_richness_mean ~
create_network + select + disperse +
compete + selectfor_d + catastrophe,
importance = TRUE,
data = filter(drop_na(sim_hl, landscape_richness_mean), repp == "High"))richness_importance_ls <- importance(forest_model_richness, type = 1, scale = TRUE) %>%
data.frame() %>%
rownames_to_column(var = "constraint") %>%
arrange(desc(X.IncMSE)) %>%
mutate(constraint = recode(constraint,
`select` = "selection",
`compete` = "competition",
`disperse` = "dispersal",
`selectfor_d` = "species_drift")) %>%
color_constraints(constraint) %>%
mutate_at(vars(starts_with("constraint")), fct_inorder) %>%
mutate(X.IncMSE = X.IncMSE / 100)
plot_richness_importance_ls <- ggplot(richness_importance_ls,
aes(x = X.IncMSE, y = fct_rev(constraint_colored),
color = fct_rev(constraint), shape = "Latin hypercube samples")) +
geom_pointrangeh(aes(xmin = 0, xmax = X.IncMSE), size = 0.25) +
scale_x_continuous(labels = percent, expand = expansion(add = c(0, 0.02))) +
scale_color_manual(values = unlist(clrs), guide = FALSE) +
scale_shape_manual(values = c(19), name = NULL) +
guides(shape = guide_legend(direction = "horizontal",
override.aes = list(size = 0.25))) +
labs(x = "Percent increase in MSE when excluded", y = NULL,
title = "B: Constraint importance for landscape richness",
subtitle = "Higher values indicate greater variable importance",
caption = "") +
theme_constraint() +
theme(panel.grid.major.y = element_blank(),
axis.text.y = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_markdown(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.75, unit = "lines"),
legend.key.width = unit(2.5, "lines"),
legend.key.size = unit(0.95, "lines"))
plot_richness_importance_ls
ggsave(plot_richness_importance_ls, filename = here("analysis", "output", "richness_B.pdf"),
width = params$fig_width, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(plot_richness_importance_ls, filename = here("analysis", "output", "richness_B.png"),
width = params$fig_width, height = params$fig_height, units = "in", type = "cairo", dpi = 300)richness_importance_low <- importance(forest_model_richness_low, type = 1, scale = TRUE) %>%
data.frame() %>%
rownames_to_column(var = "constraint") %>%
mutate(repp = "Low")
richness_importance_high <- importance(forest_model_richness_high, type = 1, scale = TRUE) %>%
data.frame() %>%
rownames_to_column(var = "constraint") %>%
mutate(repp = "High")
richness_importance_hl <- bind_rows(richness_importance_low,
richness_importance_high) %>%
mutate(repp = factor(repp, levels = c("Low", "High"), ordered = TRUE)) %>%
arrange(desc(repp), desc(X.IncMSE)) %>%
mutate(constraint = recode(constraint,
`select` = "selection",
`compete` = "competition",
`disperse` = "dispersal",
`selectfor_d` = "species_drift")) %>%
color_constraints(constraint) %>%
mutate_at(vars(starts_with("constraint")), fct_inorder) %>%
mutate(X.IncMSE = X.IncMSE / 100)
plot_richness_importance_hl <- ggplot(richness_importance_hl,
aes(x = X.IncMSE, y = fct_rev(constraint_colored),
color = fct_rev(constraint),
linetype = repp, shape = repp)) +
geom_pointrangeh(aes(xmin = 0, xmax = X.IncMSE),
position = position_dodgev(height = 0.5), size = 0.25) +
scale_x_continuous(labels = percent, expand = expansion(add = c(0, 0.02))) +
scale_color_manual(values = unlist(clrs), guide = FALSE) +
scale_linetype_manual(values = c("dashed", "solid"), name = NULL) +
scale_shape_manual(values = c(17, 15), name = NULL) +
guides(linetype = guide_legend(direction = "horizontal", reverse = TRUE),
shape = guide_legend(direction = "horizontal", override.aes = list(size = 0.25),
reverse = TRUE)) +
labs(x = "Percent increase in MSE when excluded", y = NULL,
title = "D: Constraint importance across high/low",
subtitle = "",
caption = "") +
theme_constraint() +
theme(panel.grid.major.y = element_blank(),
axis.text.y = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_markdown(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.75, unit = "lines"),
legend.key.width = unit(2.5, "lines"),
legend.key.size = unit(0.95, "lines"))
plot_richness_importance_hl
importance_richness <- plot_richness_importance_ls + plot_richness_importance_hl
importance_richness
ggsave(importance_richness, filename = here("analysis", "output", "richness_importance.pdf"),
width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(importance_richness, filename = here("analysis", "output", "richness_importance.png"),
width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 300)
# ggsave(combined_fitness, filename = here("analysis", "output", "fitness_combined.tiff"),
# width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 600)
# ggsave(combined_fitness, filename = here("analysis", "output", "fitness_combined.eps"),
# width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_ps, fallback_resolution = 600) plot_richness_all <- mega_means_plot(constraint_combo_outcomes_ls, landscape_richness_mean,
xlab = "Average landscape richness",
title = "Average landscape richness across combinations of constraints",
add_to_left = 1)
plot_richness_all
ggsave(plot_richness_all, filename = here("analysis", "output", "richness_means_all.pdf"),
width = params$fig_width_big, height = params$fig_height_big, units = "in", device = cairo_pdf)
ggsave(plot_richness_all, filename = here("analysis", "output", "richness_means_all.png"),
width = params$fig_width_big, height = params$fig_height_big, units = "in", type = "cairo", dpi = 300)Figure 8: Difference in turnover
Fig 8. Effect of the number of constraints on the difference between turnover in linked and unlinked species (A) Boxplots showing difference between turnover in linked and unlinked species across different numbers of constraints; (B) Percent increase in mean standard error in a random forest model when each constraint is removed; (C) Average difference across all possible combinations of constraints
plot_turnover_boxplot <- ggplot(sim_ls, aes(x = n_constraints_f, y = turnover_diff)) +
geom_half_point(size = 0.01, alpha = 0.01, side = "r", color = "grey50",
transformation_params = list(width = 0.25, height = 0, seed = 1234)) +
geom_half_boxplot(aes(fill = "Latin hypercube samples"),
size = 0.25, side = "l", outlier.shape = NA, errorbar.draw = TRUE, alpha = 0.5) +
stat_summary(geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = -0.19)) +
scale_y_reverse() +
scale_fill_manual(values = c("#0074D9"), name = NULL) +
guides(fill = guide_legend(direction = "horizontal", override.aes = list(alpha = 0.3, size = 0.25))) +
coord_cartesian(ylim = c(1, -1)) +
labs(x = "Number of constraints",
y = "∆ turnover",
title = "A: ∆ turnover and constraints",
subtitle = "Turnover in linked species − turnover in unlinked species",
caption = glue("<span style='color:{clrs$grey}'>◆</span> = mean value")) +
theme_constraint() +
theme(panel.grid.major.x = element_blank(),
plot.caption = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_text(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.15, unit = "lines"),
legend.key.size = unit(0.75, "lines"))
plot_turnover_boxplot
ggsave(plot_turnover_boxplot, filename = here("analysis", "output", "turnover_A.pdf"),
width = params$fig_width, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(plot_turnover_boxplot, filename = here("analysis", "output", "turnover_A.png"),
width = params$fig_width, height = params$fig_height, units = "in", type = "cairo", dpi = 300)plot_turnover_hl_diffs <- ggplot(mapping = aes(x = n_constraints_f, y = turnover_diff, fill = repp)) +
geom_text(data = filter(diff_hl_outcomes_plot, outcome == "richness"),
aes(y = -0.35, label = nice_diff, fill = NULL),
family = "DejaVu Sans Condensed", fontface = "plain", size = 1.5, vjust = -0.85) +
geom_half_boxplot(data = sim_high,
size = 0.25, side = "l", outlier.shape = NA, errorbar.draw = TRUE,
nudge = 0.05, alpha = 0.5) +
geom_half_boxplot(data = sim_low,
size = 0.25, side = "r", outlier.shape = NA, errorbar.draw = TRUE,
nudge = 0.05, alpha = 0.5) +
stat_summary(data = sim_high, geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = -0.22)) +
stat_summary(data = sim_low, geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = 0.22)) +
scale_y_reverse() +
scale_fill_manual(values = c("#2ECC40", "#FF851B"), name = NULL) +
guides(fill = guide_legend(direction = "horizontal", override.aes = list(alpha = 0.3, size = 0.25))) +
coord_cartesian(ylim = c(1, -1)) +
labs(x = "Number of constraints",
y = "∆ turnover",
title = "B: Constraints across high/low",
subtitle = "∆ = high − low",
caption = glue("<span style='color:{clrs$grey}'>◆</span> = mean value; * = p < 0.05")) +
theme_constraint() +
theme(panel.grid.major.x = element_blank(),
plot.caption = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_text(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.15, unit = "lines"),
legend.key.size = unit(0.75, "lines"))
plot_turnover_hl_diffs
ggsave(combined_turnover, filename = here("analysis", "output", "turnover_boxplots.pdf"),
width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(combined_turnover, filename = here("analysis", "output", "turnover_boxplots.png"),
width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 300)
# ggsave(combined_turnover, filename = here("analysis", "output", "turnover_combined.tiff"),
# width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 600)
# ggsave(combined_turnover, filename = here("analysis", "output", "turnover_combined.eps"),
# width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_ps, fallback_resolution = 600) # This takes a few minutes to run
set.seed(SEED)
forest_model_turnover <-
randomForest(turnover_diff ~
create_network + select + disperse +
compete + selectfor_d + catastrophe,
importance = TRUE,
data = drop_na(sim_ls, turnover_diff))
forest_model_turnover_low <-
randomForest(turnover_diff ~
create_network + select + disperse +
compete + selectfor_d + catastrophe,
importance = TRUE,
data = filter(drop_na(sim_hl, turnover_diff), repp == "Low"))
forest_model_turnover_high <-
randomForest(turnover_diff ~
create_network + select + disperse +
compete + selectfor_d + catastrophe,
importance = TRUE,
data = filter(drop_na(sim_hl, turnover_diff), repp == "High"))turnover_importance_ls <- importance(forest_model_turnover, type = 1, scale = TRUE) %>%
data.frame() %>%
rownames_to_column(var = "constraint") %>%
arrange(desc(X.IncMSE)) %>%
mutate(constraint = recode(constraint,
`select` = "selection",
`compete` = "competition",
`disperse` = "dispersal",
`selectfor_d` = "species_drift")) %>%
color_constraints(constraint) %>%
mutate_at(vars(starts_with("constraint")), fct_inorder) %>%
mutate(X.IncMSE = X.IncMSE / 100)
plot_turnover_importance_ls <- ggplot(turnover_importance_ls,
aes(x = X.IncMSE, y = fct_rev(constraint_colored),
color = fct_rev(constraint),
shape = "Latin hypercube samples")) +
geom_pointrangeh(aes(xmin = 0, xmax = X.IncMSE), size = 0.25) +
scale_x_continuous(labels = percent, expand = expansion(add = c(0, 0.02))) +
scale_color_manual(values = unlist(clrs), guide = FALSE) +
scale_shape_manual(values = c(19), name = NULL) +
guides(shape = guide_legend(direction = "horizontal",
override.aes = list(size = 0.25))) +
labs(x = "Percent increase in MSE when excluded", y = NULL,
title = "B: Constraint importance for ∆ species turnover",
subtitle = "Higher values indicate greater variable importance",
caption = "") +
theme_constraint() +
theme(panel.grid.major.y = element_blank(),
axis.text.y = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_markdown(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.75, unit = "lines"),
legend.key.width = unit(2.5, "lines"),
legend.key.size = unit(0.95, "lines"))
plot_turnover_importance_ls
ggsave(plot_turnover_importance_ls, filename = here("analysis", "output", "turnover_B.pdf"),
width = params$fig_width, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(plot_turnover_importance_ls, filename = here("analysis", "output", "turnover_B.png"),
width = params$fig_width, height = params$fig_height, units = "in", type = "cairo", dpi = 300)turnover_importance_low <- importance(forest_model_turnover_low, type = 1, scale = TRUE) %>%
data.frame() %>%
rownames_to_column(var = "constraint") %>%
mutate(repp = "Low")
turnover_importance_high <- importance(forest_model_turnover_high, type = 1, scale = TRUE) %>%
data.frame() %>%
rownames_to_column(var = "constraint") %>%
mutate(repp = "High")
turnover_importance_hl <- bind_rows(turnover_importance_low,
turnover_importance_high) %>%
mutate(repp = factor(repp, levels = c("Low", "High"), ordered = TRUE)) %>%
arrange(desc(repp), desc(X.IncMSE)) %>%
mutate(constraint = recode(constraint,
`select` = "selection",
`compete` = "competition",
`disperse` = "dispersal",
`selectfor_d` = "species_drift")) %>%
color_constraints(constraint) %>%
mutate_at(vars(starts_with("constraint")), fct_inorder) %>%
mutate(X.IncMSE = X.IncMSE / 100)
plot_turnover_importance_hl <- ggplot(turnover_importance_hl,
aes(x = X.IncMSE, y = fct_rev(constraint_colored),
color = fct_rev(constraint),
linetype = repp, shape = repp)) +
geom_pointrangeh(aes(xmin = 0, xmax = X.IncMSE),
position = position_dodgev(height = 0.5), size = 0.25) +
scale_x_continuous(labels = percent, expand = expansion(add = c(0, 0.02))) +
scale_color_manual(values = unlist(clrs), guide = FALSE) +
scale_linetype_manual(values = c("dashed", "solid"), name = NULL) +
scale_shape_manual(values = c(17, 15), name = NULL) +
guides(linetype = guide_legend(direction = "horizontal", reverse = TRUE),
shape = guide_legend(direction = "horizontal", override.aes = list(size = 0.25),
reverse = TRUE)) +
labs(x = "Percent increase in MSE when excluded", y = NULL,
title = "D: Constraint importance across high/low",
subtitle = "",
caption = "") +
theme_constraint() +
theme(panel.grid.major.y = element_blank(),
axis.text.y = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_markdown(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.75, unit = "lines"),
legend.key.width = unit(2.5, "lines"),
legend.key.size = unit(0.95, "lines"))
plot_turnover_importance_hl
importance_turnover <- plot_turnover_importance_ls + plot_turnover_importance_hl
importance_turnover
ggsave(importance_turnover, filename = here("analysis", "output", "turnover_importance.pdf"),
width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(importance_turnover, filename = here("analysis", "output", "turnover_importance.png"),
width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 300)
# ggsave(combined_fitness, filename = here("analysis", "output", "fitness_combined.tiff"),
# width = params$fig_width2, height = params$fig_height, units = "in", type = "cairo", dpi = 600)
# ggsave(combined_fitness, filename = here("analysis", "output", "fitness_combined.eps"),
# width = params$fig_width2, height = params$fig_height, units = "in", device = cairo_ps, fallback_resolution = 600) plot_turnover_all <- mega_means_plot(constraint_combo_outcomes_ls, turnover_diff,
xlab = "Average ∆ turnover",
title = "Average ∆ in turnover (linked species − unlinked species) across combinations of constraints",
add_to_right = 0.02, add_to_left = 0.02, xlim = c(-0.1, 0.3))
plot_turnover_all
ggsave(plot_turnover_all, filename = here("analysis", "output", "turnover_means_all.pdf"),
width = params$fig_width_big, height = params$fig_height_big, units = "in", device = cairo_pdf)
ggsave(plot_turnover_all, filename = here("analysis", "output", "turnover_means_all.png"),
width = params$fig_width_big, height = params$fig_height_big, units = "in", type = "cairo", dpi = 300)Figure 9: Landscape variation
Fig 9. Overall landscape variation in species richness
plot_variation_boxplot <- ggplot(sim_ls, aes(x = n_constraints_f, y = landscape_species_number_var)) +
geom_half_point(size = 0.01, alpha = 0.01, side = "r", color = "grey50",
transformation_params = list(width = 0.25, height = 0, seed = 1234)) +
geom_half_boxplot(aes(fill = "Latin hypercube samples"),
size = 0.25, side = "l", outlier.shape = NA, errorbar.draw = TRUE, alpha = 0.5) +
stat_summary(geom = "point", fun = "mean",
size = 1, pch = 23, fill = clrs$grey, color = "white",
position = position_nudge(x = -0.19)) +
# scale_y_reverse() +
scale_fill_manual(values = c("#0074D9"), name = NULL) +
guides(fill = guide_legend(direction = "horizontal", override.aes = list(alpha = 0.3, size = 0.25))) +
coord_cartesian(ylim = c(0, 300)) +
labs(x = "Number of constraints",
y = "Landscape variation in species richness",
title = "A: Landscape variation in species richness",
# subtitle = "Values near zero indicate high variation",
caption = glue("<span style='color:{clrs$grey}'>◆</span> = mean value")) +
theme_constraint() +
theme(panel.grid.major.x = element_blank(),
plot.caption = element_markdown(),
plot.title.position = "plot",
legend.position = c(0, 0),
legend.justification = "left",
legend.text = element_text(size = rel(0.7)),
legend.box.margin = ggplot2::margin(l = -0.5, t = 4.15, unit = "lines"),
legend.key.size = unit(0.75, "lines"))
plot_variation_boxplot
ggsave(plot_variation_boxplot, filename = here("analysis", "output", "landscape_species.pdf"),
width = params$fig_width, height = params$fig_height, units = "in", device = cairo_pdf)
ggsave(plot_variation_boxplot, filename = here("analysis", "output", "landscape_species.png"),
width = params$fig_width, height = params$fig_height, units = "in", type = "cairo", dpi = 300)
Original computing environment
## # http://dirk.eddelbuettel.com/blog/2017/11/27/#011_faster_package_installation_one
## VER=
## CCACHE=ccache
## CC=$(CCACHE) gcc$(VER)
## CXX=$(CCACHE) g++$(VER)
## CXX11=$(CCACHE) g++$(VER)
## CXX14=$(CCACHE) g++$(VER)
## FC=$(CCACHE) gfortran$(VER)
## F77=$(CCACHE) gfortran$(VER)
##
## CXX14FLAGS=-O3 -march=native -mtune=native -fPIC## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.2 (2020-06-22)
## os macOS Catalina 10.15.6
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2020-09-12
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib source
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.0)
## backports 1.1.9 2020-08-24 [1] CRAN (R 4.0.2)
## base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.0.0)
## blob 1.2.1 2020-01-20 [1] CRAN (R 4.0.0)
## broom * 0.7.0 2020-07-09 [1] CRAN (R 4.0.2)
## callr 3.4.3 2020-03-28 [1] CRAN (R 4.0.0)
## cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.0)
## cli 2.0.2 2020-02-28 [1] CRAN (R 4.0.0)
## colorspace 1.4-1 2019-03-18 [1] CRAN (R 4.0.0)
## crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.0)
## curl 4.3 2019-12-02 [1] CRAN (R 4.0.0)
## DBI 1.1.0 2019-12-15 [1] CRAN (R 4.0.0)
## dbplyr 1.4.4 2020-05-27 [1] CRAN (R 4.0.2)
## desc 1.2.0 2018-05-01 [1] CRAN (R 4.0.0)
## devtools 2.3.1 2020-07-21 [1] CRAN (R 4.0.2)
## digest 0.6.25 2020-02-23 [1] CRAN (R 4.0.0)
## dplyr * 1.0.2 2020-08-18 [1] CRAN (R 4.0.2)
## ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.0)
## evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.0)
## fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.0)
## farver 2.0.3 2020-01-16 [1] CRAN (R 4.0.0)
## forcats * 0.5.0 2020-03-01 [1] CRAN (R 4.0.0)
## fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2)
## generics 0.0.2 2018-11-29 [1] CRAN (R 4.0.0)
## ggforce * 0.3.2 2020-06-23 [1] CRAN (R 4.0.2)
## gghalves * 0.1.0 2020-03-28 [1] CRAN (R 4.0.0)
## ggplot2 * 3.3.2 2020-06-19 [1] CRAN (R 4.0.2)
## ggstance * 0.3.4 2020-04-02 [1] CRAN (R 4.0.0)
## ggtext * 0.1.0 2020-05-20 [1] Github (wilkelab/ggtext@e978034)
## glue * 1.4.2 2020-08-27 [1] CRAN (R 4.0.2)
## gridtext 0.1.1 2020-02-24 [1] CRAN (R 4.0.0)
## gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.0)
## haven 2.3.1 2020-06-01 [1] CRAN (R 4.0.2)
## here * 0.1 2017-05-28 [1] CRAN (R 4.0.0)
## hms 0.5.3 2020-01-08 [1] CRAN (R 4.0.0)
## htmltools 0.5.0 2020-06-16 [1] CRAN (R 4.0.0)
## httr 1.4.2 2020-07-20 [1] CRAN (R 4.0.2)
## jsonlite 1.7.0 2020-06-25 [1] CRAN (R 4.0.2)
## knitr 1.29 2020-06-23 [1] CRAN (R 4.0.2)
## labeling 0.3 2014-08-23 [1] CRAN (R 4.0.0)
## lifecycle 0.2.0 2020-03-06 [1] CRAN (R 4.0.0)
## lubridate 1.7.9 2020-06-08 [1] CRAN (R 4.0.2)
## magrittr 1.5 2014-11-22 [1] CRAN (R 4.0.0)
## markdown 1.1 2019-08-07 [1] CRAN (R 4.0.0)
## MASS 7.3-52 2020-08-18 [1] CRAN (R 4.0.2)
## memoise 1.1.0 2017-04-21 [1] CRAN (R 4.0.0)
## modelr 0.1.8 2020-05-19 [1] CRAN (R 4.0.2)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.0)
## pander * 0.6.3 2018-11-06 [1] CRAN (R 4.0.0)
## patchwork * 1.0.1 2020-06-22 [1] CRAN (R 4.0.2)
## pillar 1.4.6 2020-07-10 [1] CRAN (R 4.0.2)
## pkgbuild 1.1.0 2020-07-13 [1] CRAN (R 4.0.2)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.0)
## pkgload 1.1.0 2020-05-29 [1] CRAN (R 4.0.2)
## pluralize * 0.2.0 2020-06-09 [1] CRAN (R 4.0.2)
## plyr 1.8.6 2020-03-03 [1] CRAN (R 4.0.0)
## polyclip 1.10-0 2019-03-14 [1] CRAN (R 4.0.0)
## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.0)
## processx 3.4.3 2020-07-05 [1] CRAN (R 4.0.0)
## ps 1.3.4 2020-08-11 [1] CRAN (R 4.0.2)
## purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.0.0)
## R6 2.4.1 2019-11-12 [1] CRAN (R 4.0.0)
## randomForest * 4.6-14 2018-03-25 [1] CRAN (R 4.0.2)
## Rcpp 1.0.5 2020-07-06 [1] CRAN (R 4.0.2)
## readr * 1.3.1 2018-12-21 [1] CRAN (R 4.0.0)
## readxl 1.3.1 2019-03-13 [1] CRAN (R 4.0.0)
## remotes 2.2.0 2020-07-21 [1] CRAN (R 4.0.2)
## reprex 0.3.0 2019-05-16 [1] CRAN (R 4.0.0)
## rlang 0.4.7 2020-07-09 [1] CRAN (R 4.0.2)
## rmarkdown 2.3 2020-06-18 [1] CRAN (R 4.0.2)
## rprojroot 1.3-2 2018-01-03 [1] CRAN (R 4.0.0)
## rstudioapi 0.11 2020-02-07 [1] CRAN (R 4.0.0)
## rvest 0.3.6 2020-07-25 [1] CRAN (R 4.0.2)
## scales * 1.1.1 2020-05-11 [1] CRAN (R 4.0.0)
## sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.0)
## stringi 1.4.6 2020-02-17 [1] CRAN (R 4.0.0)
## stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.0.0)
## testthat 2.3.2 2020-03-02 [1] CRAN (R 4.0.0)
## tibble * 3.0.3 2020-07-10 [1] CRAN (R 4.0.2)
## tidyr * 1.1.2 2020-08-27 [1] CRAN (R 4.0.2)
## tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.0)
## tidyverse * 1.3.0 2019-11-21 [1] CRAN (R 4.0.0)
## titanic * 0.1.0 2015-08-31 [1] CRAN (R 4.0.2)
## tweenr 1.0.1 2018-12-14 [1] CRAN (R 4.0.0)
## usethis 1.6.1 2020-04-29 [1] CRAN (R 4.0.0)
## V8 3.2.0 2020-06-19 [1] CRAN (R 4.0.2)
## vctrs 0.3.4 2020-08-29 [1] CRAN (R 4.0.2)
## withr 2.2.0 2020-04-20 [1] CRAN (R 4.0.0)
## xfun 0.16 2020-07-24 [1] CRAN (R 4.0.2)
## xml2 1.3.2 2020-04-23 [1] CRAN (R 4.0.0)
## yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.0)
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## [1] 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