Big sagebrush (A. tridentata) response to prescribed fire

Background

This project occurred during the summer of 2023 at the USDA Agricultural Research Services Fort Keogh Livestock and Range Research Laboratory, located in Miles City, MT. The purpose of this study is to determine the effects of prescribed fire on big sagebrush (Artemisia tridentata) in the US Great Northern Plains. Plots 4 and 8 were omitted from the study due to low shrub density.

Data Visualization

Data Table

SagebrushData <-
  read_xlsx("NoahTridentataData.xlsx")

df <-
  SagebrushData %>%
  select(- date) %>%
  dplyr::group_by(plot) %>%
  dplyr::summarize(frequency = n(),
                   'μ height' = round(mean(height)))

df %>%
  kbl() %>%
  kable_styling(full_width = F) %>%
  column_spec(1, bold = T, border_right = T)

plot	frequency	μ height
1	334	65
2	499	61
3	242	75
5	100	69
6	259	59
7	70	67
9	184	69
10	211	77
11	211	69
12	292	65
13	401	66
14	175	64
15	335	65
16	179	70
17	130	66
18	126	67
19	412	61
20	98	66
21	370	56
22	270	65
23	293	62
24	240	61
25	297	60
26	366	62
27	366	69

Graphs

Descriptive Statistics

# Descriptive Statistics

stat.desc(SagebrushData$height)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 6.460000e+03 0.000000e+00 0.000000e+00 6.000000e+00 1.510000e+02 1.450000e+02 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 4.172963e+05 6.548908e+01 6.459695e+01 2.563626e-01 5.025557e-01 4.245628e+02 
##      std.dev     coef.var 
## 2.060492e+01 3.189767e-01

# Ratio between mean and median

knitr::kable(tibble(Mean = mean(SagebrushData$height),
                    Median = median(SagebrushData$height),
                    Ratio = Mean/Median))

Mean	Median	Ratio
64.59695	65.48908	0.9863775

# Histograms

meanvline1 <-
  SagebrushData %>%
  dplyr::summarise(count = n(),
            mean = mean(height))%>%
  mutate(lab = paste("μ = ", round(mean), "\nn = ", count))

histall <-
  ggplot(data = SagebrushData,
         aes(x = height)) +
  geom_histogram(data = SagebrushData,
                 aes(y = after_stat(.data[["density"]])),
                 binwidth = 8,
                 colour = "black",
                 fill = "lightyellow") +
  geom_density(alpha = .5, fill = "lightblue") +
  stat_function(fun = dnorm,
                args = list(mean = 64.59695,
                            sd = 20.60492),
                colour = "red",
                linewidth = 1.1) +
  geom_vline(data = meanvline1,
             mapping = aes(xintercept = mean),
             linewidth = 1,
             color = "black") +
  labs(y = "density")

histall + geom_text(data = meanvline1,
              aes(label = lab),
              x = Inf, y = Inf,
              hjust = 1,
              vjust = 1.2,
              size =15/.pt)

meanvline2 <-
  SagebrushData %>%
  group_by(plot) %>%
  dplyr::summarise(count = n(),
            mean = mean(height))%>%
  mutate(lab = paste("μ = ", round(mean), "\nn = ", count))

p <-
  ggplot(data = SagebrushData,
         aes(x = height)) +
  geom_histogram(data = SagebrushData,
                 aes(y = after_stat(.data[["density"]])),
                 fill = "white",
                 colour = "black",
                 binwidth = 8) +
  geom_density(data = SagebrushData,
               col = "red",
               fill = "red",
               alpha = 0.1) +
  geom_vline(data = meanvline2,
             mapping = aes(xintercept = mean),
             linewidth = .4,
             color = "red") +
  facet_wrap(~ plot)+
  labs(y = "density")

p + geom_text(data = meanvline2,
              aes(label = lab),
              x = Inf, y = Inf,
              hjust = 1,
              vjust = 1.2,
              size = 7/.pt)