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

# Tree PCQ Data
tree_data <- read_excel("C:/Users/alanivory34428/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/alanivory34428/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/alanivory34428/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/alanivory34428/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/alanivory34428/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/alanivory34428/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/alanivory34428/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/alanivory34428/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

Tree quantiles

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              2.98             2.95                5.15
## 3 Invaded     Winter              1.74             4.44                6.05
## 4 Non_Invaded Green_Up            0.685            1.13                7.29
## 5 Non_Invaded Summer              0.745            0.597               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	2.9807190	2.9508017	5.150015	151.4404	23.72638	28.40688	10.227848	0.8572616
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	0.7451523	0.5974198	5.940153	145.1575	20.34470	25.25510	7.483537	0.2696502
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	2.161600	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	1.9560000	2.609067	3.7072000	1.3474667	2.4234667	4.0458667	2.652533	4.274133	6.774667	133.52828	148.91918	167.7775	13.037626	17.99824	27.12177	12.411588	19.05224	32.37791	5.833333	10.366667	13.016667	0.6850000	0.8400000	1.0300000
Invaded	Winter	0.9458667	1.497600	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	0.508800	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	0.3402667	0.636800	1.0261333	0.2090667	0.4392000	0.8254667	3.882600	6.036267	7.348533	104.15527	140.30346	176.0684	7.881773	16.09442	24.74012	10.345724	17.65342	30.36058	3.991667	6.333333	9.108333	0.1533333	0.2366667	0.3366667
Non_Invaded	Winter	0.2112000	0.272800	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	2.9807190	0.1720883	2.9508017	0.2451984	5.150015	0.3859746	151.4404	3.121855	23.72638	2.582500	28.40688	2.946816
Summer	Non_Invaded	0.7451523	0.0600392	0.5974198	0.0676550	5.940153	0.3165720	145.1575	7.851655	20.34470	3.492849	25.25510	2.492416
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

Authority/ Season Fuel Grouping

# Join Authority info into avg_fuel_values using merged_sites
avg_fuel_values <- avg_fuel_values %>%
  left_join(merged_sites %>% select(Plot, Authority), by = "Plot")

## Limit included Authorities to WSF, BCNWR, and Jay
avg_fuel_values <- avg_fuel_values %>%
  filter(Authority %in% c("WSF", "BCNWR", "Jay"))

Fuel_model_quantiles <- avg_fuel_values %>%
  group_by(Status, Season, Authority) %>%
  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(Fuel_model_quantiles)

Status	Season	Authority	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	Jay	0.6501333	0.8229333	1.3648000	2.0757333	2.2330667	2.3221333	8.898133	11.343467	12.225067	48.91230	99.81592	147.2470	8.965548	13.15133	14.63821	7.257746	13.82589	14.47516	4.833333	5.533333	5.766667	0.5133333	0.5400000	0.5533333
Invaded	Green_Up	WSF	2.1700000	2.8021333	3.7316000	1.6244000	3.2938667	4.1924000	2.544533	4.494400	8.770533	115.48878	127.96643	134.1173	10.143128	15.18726	21.07649	11.693896	14.13321	19.84493	3.716667	5.866667	8.000000	0.8966667	0.9533333	1.0075000
Invaded	Summer	BCNWR	2.2928000	2.6112000	3.5613333	2.2278667	2.5856000	3.3176000	2.939200	4.560800	5.829600	124.88201	150.68603	155.9803	16.526722	21.19631	31.31935	14.851499	17.40024	21.37483	9.425000	12.133333	15.950000	0.7075000	0.8366667	0.9591667
Invaded	Summer	Jay	1.4024000	2.0778667	2.5557333	1.3885333	2.1373333	2.8680667	3.213200	5.290400	6.662200	134.98735	152.05245	180.4044	15.882278	18.63651	27.12177	12.653322	21.36175	34.04749	9.991667	11.416667	12.975000	0.6300000	0.7150000	0.8091667
Invaded	Summer	WSF	2.2320000	3.0906667	4.3061333	2.0952000	4.3029333	6.4848000	2.156800	3.228267	8.358400	128.31559	139.81814	160.6238	7.017115	15.29927	19.91720	6.818544	16.49354	32.65476	2.266667	5.166667	12.500000	0.8100000	0.9433333	1.0300000
Invaded	Winter	Jay	0.4152000	0.6282667	1.3842667	2.4860000	5.3805333	8.2733333	4.861067	7.533867	8.101067	104.26003	122.59266	139.2627	13.546890	25.47941	36.73561	26.963906	28.75557	34.81777	10.400000	12.750000	15.183333	0.6366667	0.6933333	0.8016667
Invaded	Winter	WSF	1.4976000	2.4528000	2.9674667	1.2464000	4.5269333	5.9984000	3.105067	5.272533	6.480000	76.53177	116.79568	147.9002	14.523860	23.08161	49.71313	13.437933	18.69010	57.54190	3.500000	6.166667	11.000000	0.8500000	0.9333333	1.0566667
Non_Invaded	Green_Up	Jay	0.4330667	0.7029333	0.9269333	0.4730667	1.3616000	1.4208000	7.514667	7.722133	12.513600	46.80387	53.18221	121.8970	14.183561	16.96538	19.18748	3.984839	17.27920	18.34091	4.566667	12.100000	14.500000	0.2800000	0.3500000	0.3800000
Non_Invaded	Green_Up	WSF	0.2461333	0.4634667	0.7173333	0.1685333	0.6178667	1.9002667	2.537067	5.141333	8.560933	80.33146	111.76471	178.3178	12.884978	15.81039	16.79261	12.496842	13.40984	21.47287	3.066667	3.583333	6.541667	0.2258333	0.3316667	0.4466667
Non_Invaded	Summer	BCNWR	0.7610667	0.9928000	1.1841333	0.5150667	0.6960000	0.8938667	3.684533	6.465867	7.610267	81.40065	101.07464	124.6043	12.356979	16.35686	19.00990	14.016309	17.90944	20.82878	6.266667	6.800000	8.183333	0.1925000	0.3000000	0.3808333
Non_Invaded	Summer	Jay	0.3898667	0.5568000	0.8997333	0.1936000	0.3290667	0.5092000	4.286667	7.035733	7.270400	124.91000	145.69841	169.7640	15.008337	22.84473	29.56521	10.459233	25.90467	41.94239	4.858333	9.050000	12.595833	0.1566667	0.2133333	0.3233333
Non_Invaded	Summer	WSF	0.1765333	0.4080000	0.8608000	0.0621333	0.2549333	0.9941333	4.268533	6.035200	7.835467	139.80952	166.38736	196.9048	6.441751	11.61039	23.73844	7.915593	13.90976	31.22375	2.433333	3.733333	5.083333	0.1800000	0.2466667	0.3383333
Non_Invaded	Winter	Jay	0.1500000	0.2218667	0.2446000	0.3136000	0.3541333	1.5200000	5.508267	6.700267	9.480800	199.83098	199.83098	199.8310	15.881540	28.06942	42.46625	22.012001	42.14637	47.47021	10.933333	12.133333	13.000000	0.0700000	0.1050000	0.1350000
Non_Invaded	Winter	WSF	0.2272000	0.4496000	1.0314667	0.4741333	0.9034667	1.9034667	4.305600	5.164267	7.777067	30.15808	32.12374	138.2468	11.290522	15.58828	52.94137	11.770516	32.11633	56.60572	3.333333	6.533333	8.933333	0.2366667	0.2800000	0.4100000

Regional Fuel Comparison

Alan Ivory

2025-06-05

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

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

Tree quantiles

Shrub Cover Conversion

Herbacous Cover Conversion

Merging Herb cover with Shrub

Merge Cogongrass Cover with Cogon Site Data

Net Weight Method

Live

Dead

Litter

Average Bag Weight Method

Live

Dead

Litter

Fill in gaps within Net method with Avg method.

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

25th, 50th and 75 quantiles of fuel values

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

Authority/ Season Fuel Grouping