Reconsidering Archaeological Garden Hunting: A View from the Northern U.S. Southwest

code
stable isotopes
subsistence strategies
agroecology
niche modeling
Authors
Affiliations

Jonathan Dombrosky

Crow Canyon Archaeological Center

Department of Anthropology, University of New Mexico

Caitlin S. Ainsworth

Department of Anthropology, University of New Mexico

Abigail A. Judkins

Department of Anthropology, University of New Mexico

Jana Valesca Meyer

Department of Anthropology, University of New Mexico

Michael A. Adler

Department of Anthropology, Southern Methodist University

Emily Lena Jones

Department of Anthropology, University of New Mexico

Published

October 25, 2023

The following is the supplemental material from this Journal of Archaeological Science: Reports article.

1 Libraries

library(SIBER)
library(tidyverse)
library(cowplot)
library(broom)
library(gt)
library(ggtext)

# For the map
library(tmap)
library(sf)
library(spData)
library(spDataLarge) #https://github.com/Nowosad/spDataLarge
library(raster)
library(terra)
library(grid)

2 Figure 1

2.1 Load in DEM

Link to USGS DEM. This file is required to make Figure 1, but it is not included in the GitHub repo because it is too large. Go to the link, download the file, and save it in your working directory

west_dem <- rast("dem90_hf.tif")

2.2 Set Region Bounding Box

four_corners <- us_states %>%
  filter(NAME %in% c("Colorado", "New Mexico", "Arizona", "Utah"))

four_corners <-  st_transform(four_corners, crs(west_dem))

region <- st_bbox(c(xmin = -1200000, xmax = -750000, ymin = 1200000, 
                    ymax = 1800000), crs = st_crs(four_corners)) %>% 
  st_as_sfc()

2.3 Crop and Mask DEM with Region Bounding Box

dem_crop <- crop(west_dem, vect(region))
dem_mask <- mask(dem_crop, vect(region))

2.4 Map DEM

rast <- tm_shape(dem_mask, bbox = region) +
  tm_raster(style = "cont", palette = "Greys", legend.show = TRUE, 
            title = "elevation (m)") +
  tm_scale_bar(position = c("left", "bottom"), width = .2) +
  tm_compass(type = "arrow", position = c("left", 0.09)) +
  tm_layout(legend.frame = TRUE,
            legend.outside.position = "right",
            legend.position = c("center", "bottom"),
            legend.title.size = 0.6,
            legend.text.size = 0.5,
            legend.title.fontface = "bold",
            inner.margins = c(0, 0, 0, 0))

2.5 Create Sites and Cities Dataframes with Lon Lat and Transform

# this where to create an object called df that contains site location information it includes the following fields: Site, Longitude, and Latitude

sites <- st_as_sf(df, coords = c("Longitude", "Latitude"), crs = 4326)
sites <- st_transform(sites, crs(west_dem))

df2 <- data.frame(
  City = c("Gallup", "Farmington", "Santa Fe", "Albuquerque", "Cortez"),
  Longitude = c(-108.742584, -108.173378, -105.937798, -106.650421, -108.585922),
  Latitude = c(35.528076, 36.748150, 35.686974, 35.084385, 37.348885),
  xmod = c(0, 0, 0, -0.2, 0)
)

cities <- st_as_sf(df2, coords = c("Longitude", "Latitude"), crs = 4326)
cities <- st_transform(cities, crs(west_dem))

2.6 Map Sites and Cities Dataframes

site_map <- tm_shape(sites) +
  tm_symbols(shape = 21, col = "#619cff", size = .4, alpha = 0.5, 
             border.alpha = 1, border.col = "#265dab") +
  tm_text("Site", size = .6, ymod = -0.5, fontface = "bold", just = "right",
          col = "#265dab") +
  tm_layout(frame = TRUE)

city_map <- tm_shape(cities) +
  tm_text("City", size = .4, just = "center", xmod = "xmod")

2.7 Combine Maps

full_map <- rast +
  site_map +
  city_map

2.8 Create North America Insert

world1 <- world %>%
  filter(continent == "North America") %>%
  filter(!name_long == "United States") %>%
  dplyr::select(name_long, geom) %>%
  rename(NAME = name_long,
         geometry = geom)

us_states1 <- us_states %>%
  dplyr::select(NAME, geometry)

alaska1 <- alaska %>%
  dplyr::select(NAME, geometry)

us_states1 <- st_transform(us_states1, crs(world1))
alaska1 <- st_transform(alaska1, crs(world1))

north_am <- world1 %>%
  rbind(us_states1, alaska1)

north_am_map <- tm_shape(north_am, projection = 2163) + tm_polygons(lwd = .75) +
  tm_shape(region) + tm_borders(lwd = 1.5, col = "#265dab") +
  tm_layout(frame = TRUE)

2.9 Figure 1

vp <- viewport(0.8, 0.154, width = 0.34, height = 0.24)

full_map
print(north_am_map, vp = vp)

tmap_save(full_map, "Figure 1.jpg", dpi = 300, insets_tm = north_am_map, 
          insets_vp = vp, height = 4.6, width = 3.5, units = "in")

3 Figure 2

Plant genera reported by Scribner and Krysl (1982) were assigned a photosynthetic pathway from the following sources: Basinger and Robertson (1997), Bruhl and Wilson (2007), Danneberger (1999), Giussani et al. (2001), Kocacinar and Sage (2003), Nelson (2012), Osborne et al. (2014), and Syvertsen et al. (1976).

Table_2 <- read.csv("Scribner and Krysl_Table 2/Table 2.csv", header = TRUE)

Pathway <- Table_2 %>%
  dplyr::select(Environmental.Context, Photosynthetic.pathway, DF....) %>%
  group_by(Environmental.Context, Photosynthetic.pathway) %>%
  summarize(sum_DF = sum(DF....), .groups = "keep")

labels <- c("Agricultural Playa Basins" = "Agricultural Context",
            "Playa Basins" = "Non-Agricultural Context")

Pathway %>%
ggplot(aes(x = reorder(Photosynthetic.pathway, -sum_DF), y = sum_DF)) +
  geom_bar(stat = "identity", linewidth = 0.75, alpha = 0.5, color = "#4d4d4d") +
  facet_wrap(~ Environmental.Context, ncol = 2, 
             labeller = labeller(Environmental.Context = labels)) +
  scale_x_discrete(labels = parse(text = c("C[4]", "C[3]", "C[3]/C[4]"))) +
  theme_classic() +
  theme(legend.position="none",
        strip.background = element_blank(),
        strip.text.x = element_text(color = "#4d4d4d", size = 12, 
                                    face = "bold"),
        axis.line = element_line(color = "#4d4d4d", linewidth = 0.75),
        axis.text.x = element_text(color = "#4d4d4d", size = 10),
        axis.text.y = element_text(color = "#4d4d4d", size = 10),
        axis.title.x = element_text(color = "#4d4d4d", size = 12, 
                                    face = "bold"),
        axis.title.y = element_text(color = "#4d4d4d", size = 12, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 0.75),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 0.75)) +
        labs(x = "Photosynthetic Pathway", y = "% Diet")

ggsave("Figure 2.jpg", dpi = 300, height = 4, width = 8)

4 Isotope Data

We demineralized bone from the archaeo dataset in 0.5 N hydrochloric acid, removed lipids using 2:1 chloroform:methanol, and freeze-dried the resulting collagen pseudomorph overnight. We weighed out between 0.5 and 0.6 mg of collagen for the analysis of δ13C and δ15N. All seeds from the seeds dataset were purchased from Native seeds/SEARCH. We used between 5.0 and 6.0 mg of ground corn and 2.0 and 2.5 mg of ground bean/squash for the analysis of δ13C and δ15N. δ13C and δ15N were measured at the University of New Mexico Center for Stable Isotopes (UNM CSI, Albuquerque, NM) on a Thermo Scientific Delta V isotope ratio mass spectrometer (IRMS) with a dual inlet and Conflo IV interface coupled to a Costech 4010 elemental analyzer (EA). Stable isotope values are reported as parts per mil (‰).

The humans and turkeys isotope values come from the following sources: Chisholm and Matson (1994), Coltrain, Janetski, and Carlyle (2007), Conrad et al. (2016), Jones et al. (2016), Kellner et al. (2010), Kennett et al. (2017), Martin (1999), McCaffery et al. (2014), and Rawlings and Driver (2010).

archaeo <- read.csv("archaeological.csv", header = TRUE)
seeds <- read.csv("modern seeds.csv", header = TRUE)
turkeys <- read.csv("turkeys.csv", header = TRUE) 
humans <- read.csv("humans.csv", header = TRUE)

5 Assessing Collagen Purity

Boxplots of C:Natomic values of archaeological leporid collagen per site. The blue box represents the acceptable range of collagen purity (2.9-3.6) reported by Ambrose (1990).

archaeo %>%
  mutate(CNatomic = CN * (14/12)) %>%
  ggplot(mapping = aes(y = CNatomic, x = Site.Name, group = Site.Name)) +
  geom_boxplot(color = "#4d4d4d", linewidth = 0.75) +
  labs(y = expression("C:N"[atomic]), x = "Archaeological Site") +
  theme_classic() +
  theme(legend.position="none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_text(color = "#4d4d4d", size = 14),
        axis.title.y = element_text(color = "#4d4d4d", size = 14),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) +
    scale_y_continuous(limits=c(2.9, 3.6)) +
  annotate(geom = "rect", xmin = -Inf, xmax = Inf, ymin = 2.9, ymax = 3.6, 
           color = "#5da5d8", fill =  "#5da5d8", alpha = 0.5, linewidth = 1)

6 Data Wrangling

A 13C Suess correction of 2.0‰ was applied to the modern seed data (Dombrosky 2020).

seeds <- seeds %>%
  mutate(d13Csuess = d13C + 2)

archaeo_SIBER <- archaeo %>%
  unite(group, Site.Name, Genus, sep = " ") %>%
  dplyr::select(group, d13C, d15N)

seeds_SIBER <- seeds %>%
  dplyr::select(Comparative.Group, d13Csuess, d15N) %>%
  rename(group = Comparative.Group,
         d13C = d13Csuess)

turkeys_SIBER <- turkeys %>%
  mutate(animal = "Turkey") %>%
  unite(group, Diet.Type, animal, sep = " ") %>%
  dplyr::select(group, d13C, d15N)
  

humans_SIBER <-  humans %>%
  mutate(group = "Humans") %>%
  dplyr::select(group, d13C, d15N)

SIBER_data <- rbind(archaeo_SIBER, seeds_SIBER, turkeys_SIBER, humans_SIBER)

7 Figure 3

7.1 Sand Canyon Pueblo Figures

sand_label_df <- data.frame(
  group = c("Sand Canyon Pueblo Lepus", "Sand Canyon Pueblo Sylvilagus",
            "Free-ranging Turkey", "Humans", "Maize-fed Turkey", "Bean",
            "Squash", "Corn"),
  label = c("Sand Canyon Pueblo\nJackrabbbits",
            "Sand Canyon Pueblo\nCottontails",
            "Free-Ranging\nTurkeys", "Humans", "Maize-Fed Turkeys", "Beans",
            "Squash", "Maize"),
  d13C = c(-17, -20, -13.25, -5.8, -8, -25.25, -24, -9.75),
  d15N = c(-0.75, 11, 2.5, 12.2, 4.1, -2, 13.25, -1),
  hjust = c(0.5, 0.5, 0.5, 0, 0, 1, 1, 1),
  vjust = c(1, 0, 0, 1, 1, 1, 0, 1))

sand_label_df$group <- factor(sand_label_df$group, 
                          levels = c("Sand Canyon Pueblo Lepus",
                                     "Sand Canyon Pueblo Sylvilagus",
                                     "Free-ranging Turkey", "Humans", 
                                     "Maize-fed Turkey", "Bean", "Squash",
                                     "Corn"))

sand_plot <- SIBER_data %>%
  filter(group %in% c("Sand Canyon Pueblo Lepus", 
                      "Sand Canyon Pueblo Sylvilagus",
                      "Bean", "Corn", "Squash", "Free-ranging Turkey",
                      "Maize-fed Turkey", "Humans"))
  
sand_plot$group <- factor(sand_plot$group, 
                          levels = c("Sand Canyon Pueblo Lepus",
                                     "Sand Canyon Pueblo Sylvilagus",
                                     "Free-ranging Turkey", "Humans", 
                                     "Maize-fed Turkey", "Bean", "Squash",
                                     "Corn")) 
  
sand_p1 <- ggplot(sand_plot, aes(x = d13C, y = d15N)) +
  geom_point(aes(fill = group, color = group), stroke = 1, size = 4, 
             alpha = 0.5, shape = 21) +
  geom_point(aes(color = group), fill = NA, stroke = 1, size = 4, 
             shape = 21) +
  labs(x = "&delta;<sup>13</sup>C (&permil;)", 
       y = "&delta;<sup>15</sup>N (&permil;)") +
  theme_classic() +
  theme(legend.position = "none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.title.y = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) + 
  scale_color_viridis_d() +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.25, linewidth = 0.75, linetype = 3, level = 0.95, 
               type = "t", geom = "polygon") + 
  geom_text(data = sand_label_df,aes(x = d13C, y = d15N, 
                                label = label, color = group, hjust = hjust,
                                vjust = vjust),
            size = 10/.pt, fontface = "bold") +
  scale_fill_viridis_d() +
  scale_x_continuous(limits=c(-27.5, -4), 
                     breaks = c(-25, -20, -15, -10, -5)) +
  scale_y_continuous(limits=c(-3, 15))

sand_p1

sand_p2 <- ggplot(sand_plot, aes(x = d13C, y = d15N)) +
  labs(x = "&delta;<sup>13</sup>C (&permil;)", 
       y = "&delta;<sup>15</sup>N (&permil;)") +
  theme_classic() +
  theme(legend.position = "none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.title.y = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) + 
  scale_color_viridis_d() +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.5, linewidth = 1.1, linetype = 1, level = 0.40, 
               type = "t", geom = "polygon") +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.25, linewidth = 0.75, linetype = 3, level = 0.95, 
               type = "t", geom = "polygon") + 
  geom_text(data = sand_label_df,aes(x = d13C, y = d15N, 
                                label = label, color = group, hjust = hjust,
                                vjust = vjust),
            size = 10/.pt, fontface = "bold") +
  scale_fill_viridis_d() +
  scale_x_continuous(limits=c(-27.5, -4), 
                     breaks = c(-25, -20, -15, -10, -5)) +
  scale_y_continuous(limits=c(-3, 15))

sand_p2

7.2 Hummingbird Pueblo Figures

hum_label_df <- data.frame(
  group = c("Hummingbird Pueblo Lepus", "Hummingbird Pueblo Sylvilagus",
            "Free-ranging Turkey", "Humans", "Maize-fed Turkey", "Bean",
            "Squash", "Corn"),
  label = c("Hummingbird Pueblo\nJackrabbbits",
            "Hummingbird Pueblo\nCottontails",
            "Free-Ranging\nTurkeys", "Humans", "Maize-Fed Turkeys", "Beans",
            "Squash", "Maize"),
  d13C = c(-20, -14.25, -20, -5.8, -8, -25.25, -24, -9.75),
  d15N = c(1.5, -1.25, 10, 12.2, 4.1, -2, 13.25, -1),
  hjust = c(0.5, 0.5, 0.5, 0, 0, 1, 1, 1),
  vjust = c(1, 0, 0, 1, 1, 1, 0, 1))

hum_label_df$group <- factor(hum_label_df$group, 
                          levels = c("Hummingbird Pueblo Lepus",
                                     "Hummingbird Pueblo Sylvilagus",
                                     "Free-ranging Turkey", "Humans", 
                                     "Maize-fed Turkey", "Bean", "Squash",
                                     "Corn"))

hum_plot <- SIBER_data %>%
  filter(group %in% c("Hummingbird Pueblo Lepus", 
                      "Hummingbird Pueblo Sylvilagus",
                      "Bean", "Corn", "Squash", "Free-ranging Turkey",
                      "Maize-fed Turkey", "Humans"))
  
hum_plot$group <- factor(hum_plot$group, 
                          levels = c("Hummingbird Pueblo Lepus",
                                     "Hummingbird Pueblo Sylvilagus",
                                     "Free-ranging Turkey", "Humans", 
                                     "Maize-fed Turkey", "Bean", "Squash",
                                     "Corn")) 
  
hum_p1 <- ggplot(hum_plot, aes(x = d13C, y = d15N)) +
  geom_point(aes(fill = group, color = group), stroke = 1, size = 4, 
             alpha = 0.5, shape = 21) +
  geom_point(aes(color = group), fill = NA, stroke = 1, size = 4, 
             shape = 21) +
  labs(x = "&delta;<sup>13</sup>C (&permil;)", 
       y = "&delta;<sup>15</sup>N (&permil;)") + 
  theme_classic() +
  theme(legend.position = "none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.title.y = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) + 
  scale_color_viridis_d() +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.25, linewidth = 0.75, linetype = 3, level = 0.95, 
               type = "t", geom = "polygon") + 
  geom_text(data = hum_label_df,aes(x = d13C, y = d15N, 
                                label = label, color = group, hjust = hjust,
                                vjust = vjust),
            size = 10/.pt, fontface = "bold") +
  scale_fill_viridis_d() +
  scale_x_continuous(limits=c(-27.5, -4), 
                     breaks = c(-25, -20, -15, -10, -5)) +
  scale_y_continuous(limits=c(-3, 15))

hum_p1

hum_p2 <- ggplot(hum_plot, aes(x = d13C, y = d15N)) +
  labs(x = "&delta;<sup>13</sup>C (&permil;)", 
       y = "&delta;<sup>15</sup>N (&permil;)") + 
  theme_classic() +
  theme(legend.position = "none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.title.y = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) + 
  scale_color_viridis_d() +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.5, linewidth = 1.1, linetype = 1, level = 0.40, type = "t", 
               geom = "polygon") +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.25, linewidth = 0.75, linetype = 3, level = 0.95, 
               type = "t", geom = "polygon") + 
  geom_text(data = hum_label_df,aes(x = d13C, y = d15N, 
                                label = label, color = group, hjust = hjust,
                                vjust = vjust),
            size = 10/.pt, fontface = "bold") +
  scale_fill_viridis_d() +
  scale_x_continuous(limits=c(-27.5, -4), 
                     breaks = c(-25, -20, -15, -10, -5)) +
  scale_y_continuous(limits=c(-3, 15))

hum_p2

7.3 Tijeras Pueblo Figures

tij_label_df <- data.frame(
  group = c("Tijeras Pueblo Lepus", "Tijeras Pueblo Sylvilagus",
            "Free-ranging Turkey", "Humans", "Maize-fed Turkey", "Bean",
            "Squash", "Corn"),
  label = c("Tijeras Pueblo\nJackrabbbits",
            "Tijeras Pueblo\nCottontails",
            "Free-Ranging\nTurkeys", "Humans", "Maize-Fed Turkeys", "Beans",
            "Squash", "Maize"),
  d13C = c(-21, -17.5, -17.25, -5.8, -8, -25.25, -24, -9.75),
  d15N = c(1.5, -1.75, 9.5, 12.2, 4.1, -2, 13.25, -1),
  hjust = c(0.5, 0.5, 0.5, 0, 0, 1, 1, 1),
  vjust = c(1, 0, 0, 1, 1, 1, 0, 1))

tij_label_df$group <- factor(tij_label_df$group, 
                          levels = c("Tijeras Pueblo Lepus",
                                     "Tijeras Pueblo Sylvilagus",
                                     "Free-ranging Turkey", "Humans", 
                                     "Maize-fed Turkey", "Bean", "Squash",
                                     "Corn"))

tij_plot <- SIBER_data %>%
  filter(group %in% c("Tijeras Pueblo Lepus", 
                      "Tijeras Pueblo Sylvilagus",
                      "Bean", "Corn", "Squash", "Free-ranging Turkey",
                      "Maize-fed Turkey", "Humans"))
  
tij_plot$group <- factor(tij_plot$group, 
                          levels = c("Tijeras Pueblo Lepus",
                                     "Tijeras Pueblo Sylvilagus",
                                     "Free-ranging Turkey", "Humans", 
                                     "Maize-fed Turkey", "Bean", "Squash",
                                     "Corn")) 
  
tij_p1 <- ggplot(tij_plot, aes(x = d13C, y = d15N)) +
  geom_point(aes(fill = group, color = group), stroke = 1, size = 4, 
             alpha = 0.5, shape = 21) +
  geom_point(aes(color = group), fill = NA, stroke = 1, size = 4, 
             shape = 21) +
  labs(x = "&delta;<sup>13</sup>C (&permil;)", 
       y = "&delta;<sup>15</sup>N (&permil;)") +
  theme_classic() +
  theme(legend.position = "none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.title.y = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) + 
  scale_color_viridis_d() +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.25, linewidth = 0.75, linetype = 3, level = 0.95, 
               type = "t", geom = "polygon") + 
  geom_text(data = tij_label_df,aes(x = d13C, y = d15N, 
                                label = label, color = group, hjust = hjust,
                                vjust = vjust),
            size = 10/.pt, fontface = "bold") +
  scale_fill_viridis_d() +
  scale_x_continuous(limits=c(-27.5, -4), 
                     breaks = c(-25, -20, -15, -10, -5)) +
  scale_y_continuous(limits=c(-3, 15))

tij_p1

tij_p2 <- ggplot(tij_plot, aes(x = d13C, y = d15N)) +
  labs(x = "&delta;<sup>13</sup>C (&permil;)", 
       y = "&delta;<sup>15</sup>N (&permil;)") + 
  theme_classic() +
  theme(legend.position = "none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.title.y = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) + 
  scale_color_viridis_d() +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.5, linewidth = 1.1, linetype = 1, level = 0.40, 
               type = "t", geom = "polygon") +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.25, linewidth = 0.75, linetype = 3, level = 0.95, 
               type = "t", geom = "polygon") + 
  geom_text(data = tij_label_df,aes(x = d13C, y = d15N, 
                                label = label, color = group, hjust = hjust,
                                vjust = vjust),
            size = 10/.pt, fontface = "bold") +
  scale_fill_viridis_d() +
  scale_x_continuous(limits=c(-27.5, -4), 
                     breaks = c(-25, -20, -15, -10, -5)) +
  scale_y_continuous(limits=c(-3, 15))

tij_p2

7.4 Combine Figures

all_plots <- plot_grid(sand_p1, sand_p2, hum_p1, hum_p2, tij_p1, tij_p2,
                       labels = "AUTO", label_colour = "#4d4d4d", 
                       label_size = 20, ncol = 2, nrow = 3)

all_plots

ggsave("Figure 3.jpg", dpi = 300, height = 15, width = 14)

8 Ellipse Area Calculations

8.1 Functions

This function is to summarize p-values in tables. Scientific notation is too long.

p_val_format <- function(x){
  z <- scales::pvalue_format()(x)
  z[!is.finite(x)] <- ""
  z
}

8.2 Test for Normality

Some isotope values per group are non-normal. Thus, ellipses in Figure 2 are visualized based on the t-distribution, which is also good for small sample sizes.

SIBER_data %>%
  group_by(group) %>%
  do(tidy(shapiro.test(.$d13C))) %>%
  dplyr::select(group, statistic, p.value) %>%
  ungroup() %>%
  gt() %>%
  fmt(columns = p.value,
      fns = p_val_format) %>%
  fmt_number(columns = statistic, decimals = 2) %>%
  cols_label(group = md("**Group**"), 
             statistic = md("***W* Statistic**"), 
             p.value = md("**P-Value**")) %>%
  tab_header(title = html("<b>δ<sup>13</sup>C Normality</b>"))
δ13C Normality
Group W Statistic P-Value
Bean 0.88 0.139
Corn 0.98 0.928
Free-ranging Turkey 0.96 0.462
Humans 0.91 <0.001
Hummingbird Pueblo Lepus 0.96 0.565
Hummingbird Pueblo Sylvilagus 0.96 0.502
Maize-fed Turkey 0.94 <0.001
Sand Canyon Pueblo Lepus 0.86 0.033
Sand Canyon Pueblo Sylvilagus 0.88 0.041
Squash 0.96 0.788
Tijeras Pueblo Lepus 0.90 0.251
Tijeras Pueblo Sylvilagus 0.86 0.088
 
SIBER_data %>%
  group_by(group) %>%
  do(tidy(shapiro.test(.$d15N))) %>%
  dplyr::select(group, statistic, p.value) %>%
  ungroup() %>%
  gt() %>%
  fmt(columns = p.value,
      fns = p_val_format) %>%
  fmt_number(columns = statistic, decimals = 2) %>%
  cols_label(group = md("**Group**"), 
             statistic = md("***W* Statistic**"), 
             p.value = md("**P-Value**")) %>%
  tab_header(title = html("<b>δ<sup>15</sup>N Normality</b>"))
δ15N Normality
Group W Statistic P-Value
Bean 0.96 0.766
Corn 0.82 0.002
Free-ranging Turkey 0.96 0.462
Humans 0.94 0.002
Hummingbird Pueblo Lepus 0.95 0.390
Hummingbird Pueblo Sylvilagus 0.90 0.050
Maize-fed Turkey 0.99 0.079
Sand Canyon Pueblo Lepus 0.88 0.066
Sand Canyon Pueblo Sylvilagus 0.94 0.376
Squash 0.90 0.314
Tijeras Pueblo Lepus 0.82 0.035
Tijeras Pueblo Sylvilagus 0.92 0.371

8.3 SIBER Area Calculations

TA = Total Area, SEA = Standard Ellipse Area, and SEAc = Small Sample Size Corrected Standard Ellipse Area (Jackson et al. 2011).

siber.example <- SIBER_data %>%
  dplyr::select(d13C, d15N, group) %>%
  mutate(community = 1) %>%
  rename(iso1 = d13C,
         iso2 = d15N)
  
siber.example <- createSiberObject(siber.example)

group.ML1 <- data.frame(groupMetricsML(siber.example)) %>%
  rename("Hummingbird Jackrabbits" = X1.Hummingbird.Pueblo.Lepus,
         "Hummingbird Cottontails" = X1.Hummingbird.Pueblo.Sylvilagus,
         "Sand Canyon Jackrabbit" = X1.Sand.Canyon.Pueblo.Lepus,
         "Sand Canyon Cottontails" = X1.Sand.Canyon.Pueblo.Sylvilagus,
         "Tijeras Pueblo Jackrabbits" = X1.Tijeras.Pueblo.Lepus,
         "Tijeras Pueblo Cottontails" = X1.Tijeras.Pueblo.Sylvilagus,
         "Beans" = X1.Bean,
         "Maize" = X1.Corn,
         "Squash" = X1.Squash,
         "Free-ranging Turkeys" = X1.Free.ranging.Turkey,
         "Maize-fed Turkeys" = X1.Maize.fed.Turkey,
         "Humans" = X1.Humans) %>%
  t() %>%
  round(digits = 2)

group.ML1 %>%
  data.frame() %>%
  rownames_to_column() %>%
  rename(group = rowname) %>%
  gt() %>%
  cols_label(group = md("**Group**"),
             TA = md("**TA**"), 
             SEA = md("**SEA**"), 
             SEAc = html("<b>SEA<sub>c</sub></b>")) %>%
  tab_header(title = md("**SIBER Area Calculations**")) %>%
  tab_footnote(
    footnote = html("All values are ‰<sup>2</sup>"),
    locations = cells_title(groups = "title"))
SIBER Area Calculations1
Group TA SEA SEAc
Hummingbird Jackrabbits 28.91 13.02 13.88
Hummingbird Cottontails 44.81 15.47 16.38
Sand Canyon Jackrabbit 29.74 13.82 14.97
Sand Canyon Cottontails 18.75 6.43 6.89
Tijeras Pueblo Cottontails 33.91 17.58 20.09
Tijeras Pueblo Jackrabbits 11.71 7.01 8.01
Beans 8.41 4.53 5.10
Maize 8.21 2.40 2.53
Squash 10.09 5.47 6.38
Free-ranging Turkeys 19.40 6.16 6.46
Maize-fed Turkeys 28.55 5.19 5.22
Humans 46.75 8.81 8.93
1 All values are ‰2

8.4 Table 1

SIBER Maximum Likelihood Overlap with Humans Calculations

samp_size <- data.frame(siber.example[["sample.sizes"]]) %>% 
  rename("Hummingbird Pueblo Jackrabbits" = Hummingbird.Pueblo.Lepus,
         "Hummingbird Pueblo Cottontails" = Hummingbird.Pueblo.Sylvilagus,
         "Sand Canyon Pueblo Jackrabbits" = Sand.Canyon.Pueblo.Lepus,
         "Sand Canyon Pueblo Cottontails" = Sand.Canyon.Pueblo.Sylvilagus,
         "Tijeras Pueblo Jackrabbits" = Tijeras.Pueblo.Lepus,
         "Tijeras Pueblo Cottontails" = Tijeras.Pueblo.Sylvilagus,
         "Beans" = Bean,
         "Maize" = Corn,
         "Squash" = Squash,
         "Free-ranging Turkeys" = Free.ranging.Turkey,
         "Maize-fed Turkeys" = Maize.fed.Turkey,
         "Humans" = Humans) %>% 
  pivot_longer(cols = 1:12, names_to = "group", values_to = "n")
results <- data.frame()
taxa <- c("1.Sand Canyon Pueblo Lepus", "1.Sand Canyon Pueblo Sylvilagus",
          "1.Hummingbird Pueblo Lepus", "1.Hummingbird Pueblo Sylvilagus", 
          "1.Tijeras Pueblo Lepus", 
          "1.Tijeras Pueblo Sylvilagus",
          "1.Maize-fed Turkey")

for (i in seq_along(taxa)) {
  
  sea.overlap <- maxLikOverlap(taxa[[i]], "1.Humans", siber.example, 
                             p.interval = 0.95, n = 100)
  
  results[i, 1] <- taxa[[i]]
  results[i, 2] <- round(sea.overlap[[3]], digits = 2)
  results[i, 3] <- round(sea.overlap[[3]]/sea.overlap[[2]]*100, digits = 2)
  results[i, 4] <- round(sea.overlap[[3]]/sea.overlap[[1]]*100, digits = 2)
  results[i, 5] <- round(sea.overlap[[3]]/(sea.overlap[[2]] + 
                                             sea.overlap[[1]] - 
                                             sea.overlap[[3]])*100, digits = 2)
}

colnames(results) <- c("group", "overlap ‰", "% human niche", "% group niche", 
                       "% overlap")

results$group <- gsub("1.","", as.character(results$group))
results$group <- gsub("Lepus","Jackrabbits", as.character(results$group))
results$group <- gsub("Sylvilagus","Cottontails", as.character(results$group))
results$group <- gsub("Turkey","Turkeys", as.character(results$group))

results %>%
  left_join(., samp_size, by = "group") %>% 
  dplyr::select(group, n, `overlap ‰`, `% human niche`, `% group niche`, 
                `% overlap`) %>% 
  gt() %>%
  cols_label(group = md("**Group**"),
             n = md("**n**"),
             "overlap ‰" = html("<b>Overlap ‰<sup>2</sup></b>"), 
             "% human niche" = md("**% Human Niche**"), 
             "% group niche" = md("**% Group's Niche**"),
             "% overlap" = md("**% Overlap**")) %>%
  tab_header(title = md("**Human Isotopic Niche Overlap Calculations**")) %>%
  tab_footnote(
    footnote = "Proportion of overlap relative to non-overlapping area.",
    locations = cells_column_labels(columns = "% overlap")
  )
Human Isotopic Niche Overlap Calculations
Group n Overlap ‰2 % Human Niche % Group’s Niche % Overlap1
Sand Canyon Pueblo Jackrabbits 14 0.00 0.00 0.00 0.00
Sand Canyon Pueblo Cottontails 16 0.00 0.00 0.00 0.00
Hummingbird Pueblo Jackrabbits 17 4.55 8.50 5.47 3.44
Hummingbird Pueblo Cottontails 19 4.61 8.62 4.70 3.14
Tijeras Pueblo Jackrabbits 9 0.00 0.00 0.00 0.00
Tijeras Pueblo Cottontails 9 2.38 4.45 1.98 1.39
Maize-fed Turkeys 180 31.23 58.39 100.00 58.39
1 Proportion of overlap relative to non-overlapping area. 
round(mean(results$`% human niche`[1:6]), digits = 2)
[1] 3.59
round(mean(results$`% group niche`[1:6]), digits = 2)
[1] 2.02

9 Figure 4

This example uses functional programming instead of the above for loop.

set.seed(4611)

# options for running jags
parms <- list()
parms$n.iter <- 2 * 10^4
parms$n.burnin <- 1 * 10^3
parms$n.thin <- 10
parms$n.chains <- 2

# define the priors
priors <- list()
priors$R <- 1 * diag(2)
priors$k <- 2
priors$tau.mu <- 1.0E-3

ellipses.posterior <- siberMVN(siber.example, parms, priors)
bayes95.overlap <- function(x) bayesianOverlap(x, "1.Humans", 
                                               ellipses.posterior, draws = 1000, 
                                               p.interval = 0.95, n = 100)
set.seed(6809)

overlap_res <- taxa %>% 
  map_df(bayes95.overlap, .progress = TRUE) %>% 
  mutate(group = rep(c("Hummingbird Pueblo Jackrabbits", 
                       "Hummingbird Pueblo Cottontails", 
                       "Sand Canyon Pueblo Jackrabbits", 
                       "Sand Canyon Pueblo Cottontails",
                       "Tijeras Pueblo Jackrabbits", 
                       "Tijeras Pueblo Cottontails",
                       "Maize-fed Turkey"),
                     each = 1000))
overlap_res$group <- factor(overlap_res$group, 
                            levels = c("Sand Canyon Pueblo Jackrabbits", 
                            "Sand Canyon Pueblo Cottontails",
                            "Hummingbird Pueblo Jackrabbits", 
                            "Hummingbird Pueblo Cottontails", 
                            "Tijeras Pueblo Jackrabbits", 
                            "Tijeras Pueblo Cottontails",
                            "Maize-fed Turkey"))

overlap_res %>% 
  mutate(`% Overlap` = overlap / ((area1 + area2) - overlap) * 100) %>% 
  ggplot(aes(x = `% Overlap`, color = group, fill = group)) +
  geom_histogram(binwidth = 2.5, alpha = 0.5) +
  scale_fill_viridis_d() +
  scale_color_viridis_d() +
  scale_y_continuous(expand = c(0, 0)) +
  scale_x_continuous(breaks = seq(0, 89, by = 10)) +
  facet_wrap(~ group, ncol = 2) +
  theme_classic() +
  labs(x = "% Overlap", y = "Frequency") +
  theme(legend.position = "none",
        strip.background = element_blank())

ggsave("Figure 4.jpg", dpi = 300, height = 8, width = 5)

10 Trophic Discrimination Factors (TDFs)

We did not use TDFs in our analysis for a few different reasons. To our knowledge, there are no known feeding experiments using the amount of C4 resources that Ancestral Pueblo people relied on. Thus, all of the models developed to estimate TDFs based off diet quality do not capture this amount of variation. When we apply TDF models developed by Caut, Angulo, and Courchamp (2009) (see below) humans are projected into extremely unlikely carbon isotope space. Commonly relied on TDF estimation models appear to introduce more error than they help correct. Additionally, we see that these TDF-corrected values do not impact human-leporid overlap to any substantial degree. We argue that our reliance on 95% data ellipse overlap calculations (above) better incorporates error caused by trophic discrimination.

mammal <- c("Lepus", "Sylvilagus", "Human")

SIBER_data_tdf <- SIBER_data %>% 
  mutate(d13C = case_when(str_detect(group, paste(mammal, collapse = "|")) ~ 
                           d13C - (-0.417 * d13C - 7.878),
                         str_detect(group, "Turkey") ~ 
                           d13C - (1.76 * 0.64), .default = d13C),
         new = case_when(str_detect(group, paste(mammal, collapse = "|")) ~ 
                           d15N - (-0.141 * d15N + 3.975),
                         str_detect(group, "Turkey") ~ 
                           d15N - (2.37 * 0.47), .default = d15N))

10.1 Sand Canyon TDF

sand_label_df <- data.frame(
  group = c("Sand Canyon Pueblo Lepus", "Sand Canyon Pueblo Sylvilagus",
            "Free-ranging Turkey", "Humans", "Maize-fed Turkey", "Bean",
            "Squash", "Corn"),
  label = c("Sand Canyon Pueblo\nJackrabbbits",
            "Sand Canyon Pueblo\nCottontails",
            "Free-Ranging\nTurkeys", "Humans", "Maize-Fed Turkeys", "Beans",
            "Squash", "Maize"),
  d13C = c(-17, -20, -13.25, -5.8, -8, -25.25, -24, -9.75),
  d15N = c(-0.75, 11, 2.5, 12.2, 4.1, -2, 13.25, -1),
  hjust = c(0.5, 0.5, 0.5, 0, 0, 1, 1, 1),
  vjust = c(1, 0, 0, 1, 1, 1, 0, 1))

sand_label_df$group <- factor(sand_label_df$group, 
                          levels = c("Sand Canyon Pueblo Lepus",
                                     "Sand Canyon Pueblo Sylvilagus",
                                     "Free-ranging Turkey", "Humans", 
                                     "Maize-fed Turkey", "Bean", "Squash",
                                     "Corn"))

sand_plot <- SIBER_data_tdf %>%
  filter(group %in% c("Sand Canyon Pueblo Lepus", 
                      "Sand Canyon Pueblo Sylvilagus",
                      "Bean", "Corn", "Squash", "Free-ranging Turkey",
                      "Maize-fed Turkey", "Humans"))
  
sand_plot$group <- factor(sand_plot$group, 
                          levels = c("Sand Canyon Pueblo Lepus",
                                     "Sand Canyon Pueblo Sylvilagus",
                                     "Free-ranging Turkey", "Humans", 
                                     "Maize-fed Turkey", "Bean", "Squash",
                                     "Corn")) 
  
sand_p1 <- ggplot(sand_plot, aes(x = d13C, y = d15N)) +
  geom_point(aes(fill = group, color = group), stroke = 1, size = 4, 
             alpha = 0.5, shape = 21) +
  geom_point(aes(color = group), fill = NA, stroke = 1, size = 4, 
             shape = 21) +
  labs(x = "&delta;<sup>13</sup>C (&permil;)", 
       y = "&delta;<sup>15</sup>N (&permil;)") +
  theme_classic() +
  theme(legend.position = "none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.title.y = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) + 
  scale_color_viridis_d() +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.25, linewidth = 0.75, linetype = 3, level = 0.95, 
               type = "t", geom = "polygon") + 
  geom_text(data = sand_label_df,aes(x = d13C, y = d15N, 
                                label = label, color = group, hjust = hjust,
                                vjust = vjust),
            size = 10/.pt, fontface = "bold") +
  scale_fill_viridis_d() +
  scale_x_continuous(limits=c(-30, 5), 
                     breaks = c(-30, -25, -20, -15, -10, -5, 0, 5)) +
  scale_y_continuous(limits=c(-3, 15))

sand_p1

sand_p2 <- ggplot(sand_plot, aes(x = d13C, y = d15N)) +
  labs(x = "&delta;<sup>13</sup>C (&permil;)", 
       y = "&delta;<sup>15</sup>N (&permil;)") +
  theme_classic() +
  theme(legend.position = "none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.title.y = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) + 
  scale_color_viridis_d() +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.5, linewidth = 1.1, linetype = 1, level = 0.40, 
               type = "t", geom = "polygon") +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.25, linewidth = 0.75, linetype = 3, level = 0.95, 
               type = "t", geom = "polygon") + 
  geom_text(data = sand_label_df,aes(x = d13C, y = d15N, 
                                label = label, color = group, hjust = hjust,
                                vjust = vjust),
            size = 10/.pt, fontface = "bold") +
  scale_fill_viridis_d() +
  scale_x_continuous(limits=c(-30, 5), 
                     breaks = c(-30, -25, -20, -15, -10, -5, 0, 5)) +
  scale_y_continuous(limits=c(-3, 15))

sand_p2

10.2 Hummingbird TDF

hum_label_df <- data.frame(
  group = c("Hummingbird Pueblo Lepus", "Hummingbird Pueblo Sylvilagus",
            "Free-ranging Turkey", "Humans", "Maize-fed Turkey", "Bean",
            "Squash", "Corn"),
  label = c("Hummingbird Pueblo\nJackrabbbits",
            "Hummingbird Pueblo\nCottontails",
            "Free-Ranging\nTurkeys", "Humans", "Maize-Fed Turkeys", "Beans",
            "Squash", "Maize"),
  d13C = c(-20, -14.25, -20, -5.8, -8, -25.25, -24, -9.75),
  d15N = c(1.5, -1.25, 10, 12.2, 4.1, -2, 13.25, -1),
  hjust = c(0.5, 0.5, 0.5, 0, 0, 1, 1, 1),
  vjust = c(1, 0, 0, 1, 1, 1, 0, 1))

hum_label_df$group <- factor(hum_label_df$group, 
                          levels = c("Hummingbird Pueblo Lepus",
                                     "Hummingbird Pueblo Sylvilagus",
                                     "Free-ranging Turkey", "Humans", 
                                     "Maize-fed Turkey", "Bean", "Squash",
                                     "Corn"))

hum_plot <- SIBER_data_tdf %>%
  filter(group %in% c("Hummingbird Pueblo Lepus", 
                      "Hummingbird Pueblo Sylvilagus",
                      "Bean", "Corn", "Squash", "Free-ranging Turkey",
                      "Maize-fed Turkey", "Humans"))
  
hum_plot$group <- factor(hum_plot$group, 
                          levels = c("Hummingbird Pueblo Lepus",
                                     "Hummingbird Pueblo Sylvilagus",
                                     "Free-ranging Turkey", "Humans", 
                                     "Maize-fed Turkey", "Bean", "Squash",
                                     "Corn")) 
  
hum_p1 <- ggplot(hum_plot, aes(x = d13C, y = d15N)) +
  geom_point(aes(fill = group, color = group), stroke = 1, size = 4, 
             alpha = 0.5, shape = 21) +
  geom_point(aes(color = group), fill = NA, stroke = 1, size = 4, 
             shape = 21) +
  labs(x = "&delta;<sup>13</sup>C (&permil;)", 
       y = "&delta;<sup>15</sup>N (&permil;)") + 
  theme_classic() +
  theme(legend.position = "none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.title.y = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) + 
  scale_color_viridis_d() +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.25, linewidth = 0.75, linetype = 3, level = 0.95, 
               type = "t", geom = "polygon") + 
  geom_text(data = hum_label_df,aes(x = d13C, y = d15N, 
                                label = label, color = group, hjust = hjust,
                                vjust = vjust),
            size = 10/.pt, fontface = "bold") +
  scale_fill_viridis_d() +
  scale_x_continuous(limits=c(-30, 5), 
                     breaks = c(-30, -25, -20, -15, -10, -5, 0, 5)) +
  scale_y_continuous(limits=c(-3, 15))

hum_p1

hum_p2 <- ggplot(hum_plot, aes(x = d13C, y = d15N)) +
  labs(x = "&delta;<sup>13</sup>C (&permil;)", 
       y = "&delta;<sup>15</sup>N (&permil;)") + 
  theme_classic() +
  theme(legend.position = "none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.title.y = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) + 
  scale_color_viridis_d() +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.5, linewidth = 1.1, linetype = 1, level = 0.40, type = "t", 
               geom = "polygon") +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.25, linewidth = 0.75, linetype = 3, level = 0.95, 
               type = "t", geom = "polygon") + 
  geom_text(data = hum_label_df,aes(x = d13C, y = d15N, 
                                label = label, color = group, hjust = hjust,
                                vjust = vjust),
            size = 10/.pt, fontface = "bold") +
  scale_fill_viridis_d() +
  scale_x_continuous(limits=c(-30, 5), 
                     breaks = c(-30, -25, -20, -15, -10, -5, 0, 5)) +
  scale_y_continuous(limits=c(-3, 15))

hum_p2

10.3 Tijeras TDF

tij_label_df <- data.frame(
  group = c("Tijeras Pueblo Lepus", "Tijeras Pueblo Sylvilagus",
            "Free-ranging Turkey", "Humans", "Maize-fed Turkey", "Bean",
            "Squash", "Corn"),
  label = c("Tijeras Pueblo\nJackrabbbits",
            "Tijeras Pueblo\nCottontails",
            "Free-Ranging\nTurkeys", "Humans", "Maize-Fed Turkeys", "Beans",
            "Squash", "Maize"),
  d13C = c(-21, -17.5, -17.25, -5.8, -8, -25.25, -24, -9.75),
  d15N = c(1.5, -1.75, 9.5, 12.2, 4.1, -2, 13.25, -1),
  hjust = c(0.5, 0.5, 0.5, 0, 0, 1, 1, 1),
  vjust = c(1, 0, 0, 1, 1, 1, 0, 1))

tij_label_df$group <- factor(tij_label_df$group, 
                          levels = c("Tijeras Pueblo Lepus",
                                     "Tijeras Pueblo Sylvilagus",
                                     "Free-ranging Turkey", "Humans", 
                                     "Maize-fed Turkey", "Bean", "Squash",
                                     "Corn"))

tij_plot <- SIBER_data_tdf %>%
  filter(group %in% c("Tijeras Pueblo Lepus", 
                      "Tijeras Pueblo Sylvilagus",
                      "Bean", "Corn", "Squash", "Free-ranging Turkey",
                      "Maize-fed Turkey", "Humans"))
  
tij_plot$group <- factor(tij_plot$group, 
                          levels = c("Tijeras Pueblo Lepus",
                                     "Tijeras Pueblo Sylvilagus",
                                     "Free-ranging Turkey", "Humans", 
                                     "Maize-fed Turkey", "Bean", "Squash",
                                     "Corn")) 
  
tij_p1 <- ggplot(tij_plot, aes(x = d13C, y = d15N)) +
  geom_point(aes(fill = group, color = group), stroke = 1, size = 4, 
             alpha = 0.5, shape = 21) +
  geom_point(aes(color = group), fill = NA, stroke = 1, size = 4, 
             shape = 21) +
  labs(x = "&delta;<sup>13</sup>C (&permil;)", 
       y = "&delta;<sup>15</sup>N (&permil;)") +
  theme_classic() +
  theme(legend.position = "none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.title.y = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) + 
  scale_color_viridis_d() +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.25, linewidth = 0.75, linetype = 3, level = 0.95, 
               type = "t", geom = "polygon") + 
  geom_text(data = tij_label_df,aes(x = d13C, y = d15N, 
                                label = label, color = group, hjust = hjust,
                                vjust = vjust),
            size = 10/.pt, fontface = "bold") +
  scale_fill_viridis_d() +
  scale_x_continuous(limits=c(-30, 5), 
                     breaks = c(-30, -25, -20, -15, -10, -5, 0, 5)) +
  scale_y_continuous(limits=c(-3, 15))

tij_p1

tij_p2 <- ggplot(tij_plot, aes(x = d13C, y = d15N)) +
  labs(x = "&delta;<sup>13</sup>C (&permil;)", 
       y = "&delta;<sup>15</sup>N (&permil;)") + 
  theme_classic() +
  theme(legend.position = "none",
        axis.line = element_line(color = "#4d4d4d", linewidth = 1),
        axis.text.x = element_text(color = "#4d4d4d", size = 12),
        axis.text.y = element_text(color = "#4d4d4d", size = 12),
        axis.title.x = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.title.y = element_markdown(color = "#4d4d4d", size = 14, 
                                    face = "bold"),
        axis.ticks.x = element_line(color = "#4d4d4d", linewidth = 1),
        axis.ticks.y = element_line(color = "#4d4d4d", linewidth = 1)) + 
  scale_color_viridis_d() +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.5, linewidth = 1.1, linetype = 1, level = 0.40, 
               type = "t", geom = "polygon") +
  stat_ellipse(aes(group = interaction(group), color = group, fill = group), 
               alpha = 0.25, linewidth = 0.75, linetype = 3, level = 0.95, 
               type = "t", geom = "polygon") + 
  geom_text(data = tij_label_df,aes(x = d13C, y = d15N, 
                                label = label, color = group, hjust = hjust,
                                vjust = vjust),
            size = 10/.pt, fontface = "bold") +
  scale_fill_viridis_d() +
  scale_x_continuous(limits=c(-30, 5), 
                     breaks = c(-30, -25, -20, -15, -10, -5, 0, 5)) +
  scale_y_continuous(limits=c(-3, 15))

tij_p2

10.4 All TDF Plots

all_plots <- plot_grid(sand_p1, sand_p2, hum_p1, hum_p2, tij_p1, tij_p2,
                       labels = "AUTO", label_colour = "#4d4d4d", 
                       label_size = 20, ncol = 2, nrow = 3)

all_plots

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