Chapter 1- Fuel Dynamics

pacman::p_load(dplyr, readxl, tidyr, raster, vegan, tigris, sf, sp, plotly, ggrepel, kableExtra)

# Tree PCQ Data
tree_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
                        sheet = "Tree_PCQ")

# Soil Data
fuel_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
                        sheet = "Fuel_Sampling")

Seasonal_Fuel_Sampling <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/01_FuelDynamics/02_Data/02_Fuel_Data/Seasonal_Fuel_Sampling.xlsx",
                                     sheet = "Fuel_Data")

# Seasonal Sampling Locations
Seasonal_Sampling_Locations <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/01_FuelDynamics/02_Data/02_Fuel_Data/Seasonal_Fuel_Sampling.xlsx",
                                          sheet = "Sites")

# Bag Weights
bag_weights <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/01_FuelDynamics/02_Data/02_Fuel_Data/Seasonal_Fuel_Sampling.xlsx",
                                          sheet = "Bag_Avg")

## New names:
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`

# Veg Data
Veg_Cover <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
                        sheet = "Veg_Cover")

# Shrub Cover Data
shrub_data <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
                         sheet = "Shrub_Cover")

# Site Data
CogonSites <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/CogonSites_FL_AL_MS.xlsx")

# Only include Florida/Alabama Sites
CogonSites <- CogonSites[CogonSites$Authority != "CNF" & CogonSites$Authority != "DSNF", ]

Filter All data to only include specified species (Per PLANTS database)

Filter all data to only include species found at 3% of all sites

Shrub Cover Conversion

Herbacous Cover Conversion

Merging Herb cover with Shrub

This species matrix includes herbaceous and shrub species

#Summarize Cogongrass Cover

Merge Cogongrass Cover with Cogon Site Data

#Fuel Dynamics ## Combine seasonal fuel data

Net Weight Method

Live

Comb_Live_Data_Net <- Combined_Data_Net %>%
  mutate(
    Live_Bag = as.numeric(Live_Bag),
    Live_Weight_Post = as.numeric(Live_Weight_Post),
    Live_Weight_Initial = as.numeric(Live_Weight_Initial),
    Live_Height = as.numeric(Height),
    Net_Live = as.numeric(Net_Live),
    Status = as.character(Status)  # Status as a character
  )

## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Live_Height = as.numeric(Height)`.
## Caused by warning:
## ! NAs introduced by coercion

Comb_Live_Data_Net <- Comb_Live_Data_Net %>%
  mutate(biomass = Net_Live)

Comb_Live_Data_Net <- Comb_Live_Data_Net %>%
  filter(biomass >= 0)

Comb_Live_Data_Net <- Comb_Live_Data_Net %>%
  mutate(relative_moisture_content = ifelse(biomass > bioT, ((Live_Weight_Initial - Live_Bag) - Net_Live) / Net_Live * 100, NA))

avg_live_values_Net <- Comb_Live_Data_Net %>%
  group_by(Plot, Season, Status) %>%
  summarize(avg_live_biomass = mean(biomass, na.rm = TRUE),
            avg_live_moisture_content = mean(relative_moisture_content, na.rm = TRUE),
            avg_soil_moisture = mean(Soil_Moisture, na.rm = TRUE),
            avg_height = mean(Live_Height, na.rm = TRUE) / 100,
            .groups = "drop")

Dead

Comb_Dead_Data_Net <- Combined_Data_Net %>%
  mutate(
    Dead_Bag = as.numeric(Dead_Bag),
    Dead_Weight_Post = as.numeric(Dead_Weight_Post),
    Dead_Weight_Initial = as.numeric(Dead_Weight_Initial),
    Net_Dead = as.numeric(Net_Dead),
    Status = as.character(Status)  # Status as a character
  )

Comb_Dead_Data_Net <- Comb_Dead_Data_Net %>%
  mutate(biomass = Net_Dead)

Comb_Dead_Data_Net <- Comb_Dead_Data_Net %>%
  filter(biomass >= 0)

Comb_Dead_Data_Net <- Comb_Dead_Data_Net %>%
  mutate(relative_moisture_content = ifelse(biomass > bioT, ((Dead_Weight_Initial - Dead_Bag) - Net_Dead) / Net_Dead * 100, NA))

avg_dead_values_Net <- Comb_Dead_Data_Net %>%
  group_by(Plot, Season, Status) %>%
  summarize(avg_dead_biomass = mean(biomass, na.rm = TRUE),
            avg_dead_moisture_content = mean(relative_moisture_content, na.rm = TRUE),
            avg_soil_moisture = mean(Soil_Moisture, na.rm = TRUE),
            .groups = "drop")

Litter

Comb_Litter_Data_Net <- Combined_Data_Net %>%
  mutate(
    Litter_Bag = as.numeric(Litter_Bag),
    Litter_Weight_Post = as.numeric(Litter_Weight_Post),
    Litter_Weight_Initial = as.numeric(Litter_Weight_Initial),
    Net_Litter = as.numeric(Net_Litter),
    Status = as.character(Status)  # Status as a character
  )

Comb_Litter_Data_Net <- Comb_Litter_Data_Net %>%
  mutate(biomass = Net_Litter)

Comb_Litter_Data_Net <- Comb_Litter_Data_Net %>%
  filter(biomass >= 0)

Comb_Litter_Data_Net <- Comb_Litter_Data_Net %>%
  mutate(relative_moisture_content = ifelse(biomass > bioT, ((Litter_Weight_Initial - Litter_Bag) - Net_Litter) / Net_Litter * 100, NA))

avg_litter_values_Net <- Comb_Litter_Data_Net %>%
  group_by(Plot, Season, Status) %>%
  summarize(avg_litter_biomass = mean(biomass, na.rm = TRUE),
            avg_litter_moisture_content = mean(relative_moisture_content, na.rm = TRUE),
            avg_soil_moisture = mean(Soil_Moisture, na.rm = TRUE),
            .groups = "drop")

Average Bag Weight Method

Live

Comb_Live_Data_Avg <- Combined_Data_Avg %>%
  mutate(
    Live_Bag = as.numeric(Live_Bag),
    Live_Weight_Post = as.numeric(Live_Weight_Post),
    Live_Weight_Initial = as.numeric(Live_Weight_Initial),
    Live_Height = as.numeric(Height),
    Dry_LiveBag = as.numeric(Dry_LiveBag),
    Status = as.character(Status)  # Status as a character
  )

## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `Live_Height = as.numeric(Height)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

Comb_Live_Data_Avg <- Comb_Live_Data_Avg %>%
  mutate(biomass = Live_Weight_Post - Dry_LiveBag)

Comb_Live_Data_Avg <- Comb_Live_Data_Avg %>%
  filter(biomass >= 0)

Comb_Live_Data_Avg <- Comb_Live_Data_Avg %>%
  mutate(relative_moisture_content = ifelse(biomass > bioT, (Live_Weight_Initial - Live_Weight_Post) / biomass * 100, NA))

avg_live_values_Avg <- Comb_Live_Data_Avg %>%
  group_by(Plot, Season, Status) %>%
  summarize(avg_live_biomass = mean(biomass, na.rm = TRUE),
            avg_live_moisture_content = mean(relative_moisture_content, na.rm = TRUE),
            avg_soil_moisture = mean(Soil_Moisture, na.rm = TRUE),
            avg_height = mean(Live_Height, na.rm = TRUE) / 100,
            .groups = "drop")

Dead

Comb_Dead_Data_Avg <- Combined_Data_Avg %>%
  mutate(
    Dead_Bag = as.numeric(Dead_Bag),
    Dead_Weight_Post = as.numeric(Dead_Weight_Post),
    Dead_Weight_Initial = as.numeric(Dead_Weight_Initial),
    Dry_DeadBag = as.numeric(Dry_DeadBag),
    Status = as.character(Status)  # Status as a character
  )

## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Dry_DeadBag = as.numeric(Dry_DeadBag)`.
## Caused by warning:
## ! NAs introduced by coercion

Comb_Dead_Data_Avg <- Comb_Dead_Data_Avg %>%
  mutate(biomass = Dead_Weight_Post - Dry_DeadBag)

Comb_Dead_Data_Avg <- Comb_Dead_Data_Avg %>%
  filter(biomass >= 0)

Comb_Dead_Data_Avg <- Comb_Dead_Data_Avg %>%
  mutate(relative_moisture_content = ifelse(biomass > bioT, (Dead_Weight_Initial - Dead_Weight_Post) / biomass * 100, NA))

avg_dead_values_Avg <- Comb_Dead_Data_Avg %>%
  group_by(Plot, Season, Status) %>%
  summarize(avg_dead_biomass = mean(biomass, na.rm = TRUE),
            avg_dead_moisture_content = mean(relative_moisture_content, na.rm = TRUE),
            avg_soil_moisture = mean(Soil_Moisture, na.rm = TRUE),
            .groups = "drop")

Litter

Comb_Litter_Data_Avg <- Combined_Data_Avg %>%
  mutate(
    Litter_Bag = as.numeric(Litter_Bag),
    Litter_Weight_Post = as.numeric(Litter_Weight_Post),
    Litter_Weight_Initial = as.numeric(Litter_Weight_Initial),
    Dry_LitterBag = as.numeric(Dry_LitterBag),
    Status = as.character(Status)  # Status as a character
  )

## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Dry_LitterBag = as.numeric(Dry_LitterBag)`.
## Caused by warning:
## ! NAs introduced by coercion

Comb_Litter_Data_Avg <- Comb_Litter_Data_Avg %>%
  mutate(biomass = Litter_Weight_Post - Dry_LitterBag)

Comb_Litter_Data_Avg <- Comb_Litter_Data_Avg %>%
  filter(biomass >= 0)

Comb_Litter_Data_Avg <- Comb_Litter_Data_Avg %>%
  mutate(relative_moisture_content = ifelse(biomass > bioT, (Litter_Weight_Initial - Litter_Weight_Post) / biomass * 100, NA))

avg_litter_values_Avg <- Comb_Litter_Data_Avg %>%
  group_by(Plot, Season, Status) %>%
  summarize(avg_litter_biomass = mean(biomass, na.rm = TRUE),
            avg_litter_moisture_content = mean(relative_moisture_content, na.rm = TRUE),
            avg_soil_moisture = mean(Soil_Moisture, na.rm = TRUE),
            .groups = "drop")

Fill in gaps within Net method with Avg method.

# Live
avg_live_values_Combined <- avg_live_values_Net %>%
  full_join(avg_live_values_Avg, by = "Plot", suffix = c("_Net", "_Avg")) %>%
  mutate(
    avg_live_biomass = coalesce(avg_live_biomass_Net, avg_live_biomass_Avg),
    avg_live_moisture_content = coalesce(avg_live_moisture_content_Net, avg_live_moisture_content_Avg),
    avg_soil_moisture = coalesce(avg_soil_moisture_Net, avg_soil_moisture_Avg),
    avg_height = coalesce(avg_height_Net, avg_height_Avg),
    Season = coalesce(Season_Net, Season_Avg),
    Status = coalesce(Status_Net, Status_Avg)
  ) %>%
  select(Plot, Season, Status, avg_live_biomass, avg_live_moisture_content, avg_soil_moisture, avg_height)

# Dead
avg_dead_values_Combined <- avg_dead_values_Net %>%
  full_join(avg_dead_values_Avg, by = "Plot", suffix = c("_Net", "_Avg")) %>%
  mutate(
    avg_dead_biomass = coalesce(avg_dead_biomass_Net, avg_dead_biomass_Avg),
    avg_dead_moisture_content = coalesce(avg_dead_moisture_content_Net, avg_dead_moisture_content_Avg),
    avg_soil_moisture = coalesce(avg_soil_moisture_Net, avg_soil_moisture_Avg),
    Season = coalesce(Season_Net, Season_Avg),
    Status = coalesce(Status_Net, Status_Avg)
  ) %>%
  select(Plot, Season, Status, avg_dead_biomass, avg_dead_moisture_content, avg_soil_moisture)

# Litter
avg_litter_values_Combined <- avg_litter_values_Net %>%
  full_join(avg_litter_values_Avg, by = "Plot", suffix = c("_Net", "_Avg")) %>%
  mutate(
    avg_litter_biomass = coalesce(avg_litter_biomass_Net, avg_litter_biomass_Avg),
    avg_litter_moisture_content = coalesce(avg_litter_moisture_content_Net, avg_litter_moisture_content_Avg),
    avg_soil_moisture = coalesce(avg_soil_moisture_Net, avg_soil_moisture_Avg),
    Season = coalesce(Season_Net, Season_Avg),
    Status = coalesce(Status_Net, Status_Avg)
  ) %>%
  select(Plot, Season, Status, avg_litter_biomass, avg_litter_moisture_content, avg_soil_moisture)

Combine summer sampling (CogonSites) locations with winter sampling locations

Merge avg_live_values, avg_dead_values, and avg_litter_values

Average Fuel Values by Invasion Status and Season

There are 10,000 square meters in a hectare. Biomass is from 25 cm by 25 cm quadrats, so we have 0.0625 square meters. Therefore, 10,000/0.0625 = 160,000. So biomass gets multiplied by 160,000 and divided by 1,000,000 to convert from grams to tonnes.

Fuel_model_values <- avg_fuel_values %>%
  group_by(Status, Season) %>%
  summarize(avg_live_biomass = mean(avg_live_biomass, na.rm = TRUE) * 0.16,
            avg_dead_biomass = mean(avg_dead_biomass, na.rm = TRUE) * 0.16,
            avg_litter_biomass = mean(avg_litter_biomass, na.rm = TRUE) * 0.16,
            avg_live_moisture_content = mean(avg_live_moisture_content, na.rm = TRUE),
            avg_dead_moisture_content = mean(avg_dead_moisture_content, na.rm = TRUE),
            avg_litter_moisture_content = mean(avg_litter_moisture_content, na.rm = TRUE),
            avg_soil_moisture = mean(avg_soil_moisture, na.rm = TRUE),
            avg_height = mean(avg_height, na.rm = TRUE),
            .groups = "drop")

Fuel_model_values

## # A tibble: 6 × 10
##   Status      Season   avg_live_biomass avg_dead_biomass avg_litter_biomass
##   <chr>       <chr>               <dbl>            <dbl>              <dbl>
## 1 Invaded     Green_Up            2.31             2.91                6.48
## 2 Invaded     Summer              2.99             2.99                5.27
## 3 Invaded     Winter              1.74             4.44                6.05
## 4 Non_Invaded Green_Up            0.685            1.13                7.29
## 5 Non_Invaded Summer              0.683            0.570               5.94
## 6 Non_Invaded Winter              0.484            1.38                6.90
## # ℹ 5 more variables: avg_live_moisture_content <dbl>,
## #   avg_dead_moisture_content <dbl>, avg_litter_moisture_content <dbl>,
## #   avg_soil_moisture <dbl>, avg_height <dbl>

kable(Fuel_model_values)

Status	Season	avg_live_biomass	avg_dead_biomass	avg_litter_biomass	avg_live_moisture_content	avg_dead_moisture_content	avg_litter_moisture_content	avg_soil_moisture	avg_height
Invaded	Green_Up	2.3124825	2.9124571	6.483302	100.4510	13.79576	12.97215	6.617460	0.7676190
Invaded	Summer	2.9927111	2.9925037	5.268986	153.2355	23.59798	30.91334	9.757672	0.8610635
Invaded	Winter	1.7428174	4.4411333	6.053600	122.8786	29.78985	40.40572	13.658333	0.8368116
Non_Invaded	Green_Up	0.6850032	1.1300063	7.290438	105.0924	14.80502	12.95389	6.136508	0.3406349
Non_Invaded	Summer	0.6833641	0.5695724	5.935867	159.2312	22.93934	26.95630	7.465909	0.2604872
Non_Invaded	Winter	0.4841623	1.3757222	6.896544	105.2473	35.23478	43.74353	11.298611	0.2688406

25th, 50th and 75 quantiles of fuel values

Fuel_model_quantiles <- avg_fuel_values %>%
  group_by(Status, Season) %>%
  summarize(avg_live_biomass_25 = quantile(avg_live_biomass, 0.25, na.rm = TRUE) * 0.16,
            avg_live_biomass_50 = quantile(avg_live_biomass, 0.50, na.rm = TRUE) * 0.16,
            avg_live_biomass_75 = quantile(avg_live_biomass, 0.75, na.rm = TRUE) * 0.16,
            avg_dead_biomass_25 = quantile(avg_dead_biomass, 0.25, na.rm = TRUE) * 0.16,
            avg_dead_biomass_50 = quantile(avg_dead_biomass, 0.50, na.rm = TRUE) * 0.16,
            avg_dead_biomass_75 = quantile(avg_dead_biomass, 0.75, na.rm = TRUE) * 0.16,
            avg_litter_biomass_25 = quantile(avg_litter_biomass, 0.25, na.rm = TRUE) * 0.16,
            avg_litter_biomass_50 = quantile(avg_litter_biomass, 0.50, na.rm = TRUE) * 0.16,
            avg_litter_biomass_75 = quantile(avg_litter_biomass, 0.75, na.rm = TRUE) * 0.16,
            avg_live_moisture_content_25 = quantile(avg_live_moisture_content, 0.25, na.rm = TRUE),
            avg_live_moisture_content_50 = quantile(avg_live_moisture_content, 0.50, na.rm = TRUE),
            avg_live_moisture_content_75 = quantile(avg_live_moisture_content, 0.75, na.rm = TRUE),
            avg_dead_moisture_content_25 = quantile(avg_dead_moisture_content, 0.25, na.rm = TRUE),
            avg_dead_moisture_content_50 = quantile(avg_dead_moisture_content, 0.50, na.rm = TRUE),
            avg_dead_moisture_content_75 = quantile(avg_dead_moisture_content, 0.75, na.rm = TRUE),
            avg_litter_moisture_content_25 = quantile(avg_litter_moisture_content, 0.25, na.rm = TRUE),
            avg_litter_moisture_content_50 = quantile(avg_litter_moisture_content, 0.50, na.rm = TRUE),
            avg_litter_moisture_content_75 = quantile(avg_litter_moisture_content, 0.75, na.rm = TRUE),
            avg_soil_moisture_25 = quantile(avg_soil_moisture, 0.25, na.rm = TRUE),
            avg_soil_moisture_50 = quantile(avg_soil_moisture, 0.50, na.rm = TRUE),
            avg_soil_moisture_75 = quantile(avg_soil_moisture, 0.75, na.rm = TRUE),
            avg_height_25 = quantile(avg_height, 0.25, na.rm = TRUE),
            avg_height_50 = quantile(avg_height, 0.50, na.rm = TRUE),
            avg_height_75 = quantile(avg_height, 0.75, na.rm = TRUE),
            .groups = "drop")

# Kable table of quantiles
kable(Fuel_model_quantiles)

Status	Season	avg_live_biomass_25	avg_live_biomass_50	avg_live_biomass_75	avg_dead_biomass_25	avg_dead_biomass_50	avg_dead_biomass_75	avg_litter_biomass_25	avg_litter_biomass_50	avg_litter_biomass_75	avg_live_moisture_content_25	avg_live_moisture_content_50	avg_live_moisture_content_75	avg_dead_moisture_content_25	avg_dead_moisture_content_50	avg_dead_moisture_content_75	avg_litter_moisture_content_25	avg_litter_moisture_content_50	avg_litter_moisture_content_75	avg_soil_moisture_25	avg_soil_moisture_50	avg_soil_moisture_75	avg_height_25	avg_height_50	avg_height_75
Invaded	Green_Up	1.1562667	2.1616000	3.4720000	1.8922667	2.4192000	3.1082667	3.486400	4.974400	9.215467	54.66557	117.06158	133.0470	9.195465	10.39843	16.57708	9.487179	11.44906	14.92138	4.833333	5.766667	7.100000	0.5600000	0.7533333	0.9500000
Invaded	Summer	1.8281333	2.6090667	3.7072000	1.1582667	2.3248000	4.1821333	2.652533	4.274133	6.908533	134.21460	148.22515	169.7768	11.796183	16.95230	24.29539	11.461619	20.08565	37.21042	4.000000	9.866667	12.500000	0.6800000	0.8533333	1.0316667
Invaded	Winter	0.9458667	1.4976000	2.4898667	1.9476000	3.4101333	6.1982667	3.023867	5.620267	7.916800	95.76815	119.49396	151.1411	13.788625	19.72430	36.73561	15.979345	27.86209	47.96388	5.933333	10.916667	13.891667	0.7116667	0.8500000	0.9733333
Non_Invaded	Green_Up	0.3685333	0.5088000	0.7546667	0.3370667	0.7482667	1.3616000	3.924267	6.187200	8.342933	51.67528	80.33146	146.1973	9.750584	15.46700	17.59368	7.290356	12.30107	17.27920	3.066667	4.033333	9.300000	0.2333333	0.3333333	0.4366667
Non_Invaded	Summer	0.2538667	0.5589333	1.0144000	0.1840000	0.3442667	0.6352667	3.882600	6.017333	7.348400	121.64076	162.55358	186.2774	6.772142	13.95802	29.64863	9.698856	16.49039	37.45831	3.383333	5.666667	9.891667	0.1533333	0.2250000	0.3266667
Non_Invaded	Winter	0.2112000	0.2728000	0.5125333	0.3168000	0.7242667	1.8066667	4.612133	6.603733	9.257733	30.64949	85.18529	184.4349	11.679660	16.25337	49.32975	15.910721	42.65335	56.80946	6.233333	9.566667	12.375000	0.1116667	0.2366667	0.3583333

Summary statistics of fuel values by season and fuel type (mean and SE)

Fuel_model_means <- avg_fuel_values %>%
  group_by(Season, Status) %>%
  summarize(
    mean_live_biomass = mean(avg_live_biomass * 0.16, na.rm = TRUE),
    se_live_biomass = sd(avg_live_biomass * 0.16, na.rm = TRUE) / sqrt(sum(!is.na(avg_live_biomass))),
    
    mean_dead_biomass = mean(avg_dead_biomass * 0.16, na.rm = TRUE),
    se_dead_biomass = sd(avg_dead_biomass * 0.16, na.rm = TRUE) / sqrt(sum(!is.na(avg_dead_biomass))),
    
    mean_litter_biomass = mean(avg_litter_biomass * 0.16, na.rm = TRUE),
    se_litter_biomass = sd(avg_litter_biomass * 0.16, na.rm = TRUE) / sqrt(sum(!is.na(avg_litter_biomass))),
    
    mean_live_moisture = mean(avg_live_moisture_content, na.rm = TRUE),
    se_live_moisture = sd(avg_live_moisture_content, na.rm = TRUE) / sqrt(sum(!is.na(avg_live_moisture_content))),
    
    mean_dead_moisture = mean(avg_dead_moisture_content, na.rm = TRUE),
    se_dead_moisture = sd(avg_dead_moisture_content, na.rm = TRUE) / sqrt(sum(!is.na(avg_dead_moisture_content))),
    
    mean_litter_moisture = mean(avg_litter_moisture_content, na.rm = TRUE),
    se_litter_moisture = sd(avg_litter_moisture_content, na.rm = TRUE) / sqrt(sum(!is.na(avg_litter_moisture_content))),
    
    .groups = "drop"
  )

# Display in a kable table
kable(Fuel_model_means)

Season	Status	mean_live_biomass	se_live_biomass	mean_dead_biomass	se_dead_biomass	mean_litter_biomass	se_litter_biomass	mean_live_moisture	se_live_moisture	mean_dead_moisture	se_dead_moisture	mean_litter_moisture	se_litter_moisture
Green_Up	Invaded	2.3124825	0.3517363	2.9124571	0.4475738	6.483302	0.9265862	100.4510	10.320975	13.79576	1.586765	12.97215	1.479776
Green_Up	Non_Invaded	0.6850032	0.1313605	1.1300063	0.2848109	7.290438	1.0891618	105.0924	23.082865	14.80502	1.746026	12.95389	1.727758
Summer	Invaded	2.9927111	0.2060268	2.9925037	0.3017310	5.268986	0.4530606	153.2355	3.407950	23.59798	3.217104	30.91334	3.628753
Summer	Non_Invaded	0.6833641	0.0704620	0.5695724	0.0841108	5.935867	0.3607011	159.2312	9.399586	22.93934	5.209189	26.95630	3.046985
Winter	Invaded	1.7428174	0.2376417	4.4411333	0.6450260	6.053600	0.7982883	122.8786	10.287898	29.78985	4.777131	40.40572	7.043990
Winter	Non_Invaded	0.4841623	0.0948255	1.3757222	0.3233364	6.896544	0.6680547	105.2473	35.049744	35.23478	9.740564	43.74353	6.637136

Chapter 1

Fuel Dynamics Alterations Resulting from Plant Invasion ## Objective 1 Determine the influence of seasonal growth stage and cover on fuel characteristics

Plotting linear regressions