Chapter1 Objective1

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

# 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")
tree_data <- tree_data %>%
  filter(Authority %in% c("BRSF", "WSF", "Jay"))

# 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")
fuel_data <- fuel_data %>%
  filter(Authority %in% c("BRSF", "WSF", "Jay"))

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_Fuel_Sampling <- Seasonal_Fuel_Sampling %>%
  filter(Authority %in% c("BRSF", "WSF", "Jay"))

# 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")
Seasonal_Sampling_Locations <- Seasonal_Sampling_Locations %>%
  filter(Authority %in% c("BRSF", "WSF", "Jay"))

# 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`

# Site Data
CogonSites <- read_excel("C:/Users/DrewIvory/OneDrive - University of Florida/Desktop/School/PHD/01_Projects/05_SharedData/CogonSites_FL_AL_MS.xlsx")
CogonSites <- CogonSites %>%
  filter(Authority %in% c("BRSF", "WSF", "Jay"))

# 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

#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.

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	3.108267	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	7.10000	0.5600000	0.7533333	0.9500000
Invaded	Summer	1.7720000	2.4472000	3.7221333	1.2645333	2.3952000	4.480933	2.325600	4.085333	6.754400	133.21727	147.42139	168.2493	13.454489	18.01118	27.29767	11.681481	21.20772	35.50451	6.316667	10.900000	12.95833	0.6633333	0.8050000	1.0100000
Invaded	Winter	0.9458667	1.4976000	2.4898667	1.9476000	3.4101333	6.198267	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	13.89167	0.7116667	0.8500000	0.9733333
Non_Invaded	Green_Up	0.3685333	0.5088000	0.7546667	0.3370667	0.7482667	1.361600	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	9.30000	0.2333333	0.3333333	0.4366667
Non_Invaded	Summer	0.2821333	0.5946667	1.0218667	0.1384000	0.3152000	0.896000	3.992200	6.060000	7.308933	133.27391	163.91720	191.7656	8.801051	15.42178	29.73250	10.007541	21.21443	47.66848	3.741667	5.500000	11.65833	0.1533333	0.2366667	0.3366667
Non_Invaded	Winter	0.2112000	0.2728000	0.5125333	0.3168000	0.7242667	1.806667	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	12.37500	0.1116667	0.2366667	0.3583333

Objective 1- Cogongrass relationship with fuel dynamics

Data Cleaning

GLMM Live Fuel Biomass

Model Comparison

Conditional Effects Live Biomass

GLMM Dead Fuel Biomass

Model Comparison

GLMM Litter Fuel Biomass

Model Comparison

GLMM Live Moisture

Model Comparison

GLMM Dead Moisture

Model Comparison

GLMM Litter Moisture

Model Comparison

GLMM Soil Moisture

Model Comparison

GLMM Veg Height

Model Comparison