pacman::p_load(dplyr, readxl, tidyverse, raster, vegan, tigris, sf, sp, plotly, ggrepel, kableExtra, glmmTMB, parameters, gt, performance, car)
## Set seed
set.seed(97)
# 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", ]#Fuel Dynamics ## Combine seasonal fuel data
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 coercionComb_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")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")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")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")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 coercionComb_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")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 coercionComb_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")# 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)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_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 |
| Fuel and Moisture Quantiles by Invasion Status and Season | ||||||
| Variable | Invaded - Spring | Invaded - Summer | Invaded - Winter | Non Invaded - Spring | Non Invaded - Summer | Non Invaded - Winter |
|---|---|---|---|---|---|---|
| Live Biomass (25%) | 1.16 | 1.77 | 0.95 | 0.37 | 0.28 | 0.21 |
| Live Biomass (50%) | 2.16 | 2.45 | 1.50 | 0.51 | 0.59 | 0.27 |
| Live Biomass (75%) | 3.47 | 3.72 | 2.49 | 0.75 | 1.02 | 0.51 |
| Dead Biomass (25%) | 1.89 | 1.26 | 1.95 | 0.34 | 0.14 | 0.32 |
| Dead Biomass (50%) | 2.42 | 2.40 | 3.41 | 0.75 | 0.32 | 0.72 |
| Dead Biomass (75%) | 3.11 | 4.48 | 6.20 | 1.36 | 0.90 | 1.81 |
| Litter Biomass (25%) | 3.49 | 2.33 | 3.02 | 3.92 | 3.99 | 4.61 |
| Litter Biomass (50%) | 4.97 | 4.09 | 5.62 | 6.19 | 6.06 | 6.60 |
| Litter Biomass (75%) | 9.22 | 6.75 | 7.92 | 8.34 | 7.31 | 9.26 |
| Live Moisture Content (25%) | 54.67 | 133.22 | 95.77 | 51.68 | 133.27 | 30.65 |
| Live Moisture Content (50%) | 117.06 | 147.42 | 119.49 | 80.33 | 163.92 | 85.19 |
| Live Moisture Content (75%) | 133.05 | 168.25 | 151.14 | 146.20 | 191.77 | 184.43 |
| Dead Moisture Content (25%) | 9.20 | 13.45 | 13.79 | 9.75 | 8.80 | 11.68 |
| Dead Moisture Content (50%) | 10.40 | 18.01 | 19.72 | 15.47 | 15.42 | 16.25 |
| Dead Moisture Content (75%) | 16.58 | 27.30 | 36.74 | 17.59 | 29.73 | 49.33 |
| Litter Moisture Content (25%) | 9.49 | 11.68 | 15.98 | 7.29 | 10.01 | 15.91 |
| Litter Moisture Content (50%) | 11.45 | 21.21 | 27.86 | 12.30 | 21.21 | 42.65 |
| Litter Moisture Content (75%) | 14.92 | 35.50 | 47.96 | 17.28 | 47.67 | 56.81 |
| Soil Moisture (25%) | 4.83 | 6.32 | 5.93 | 3.07 | 3.74 | 6.23 |
| Soil Moisture (50%) | 5.77 | 10.90 | 10.92 | 4.03 | 5.50 | 9.57 |
| Soil Moisture (75%) | 7.10 | 12.96 | 13.89 | 9.30 | 11.66 | 12.38 |
| Height (25%) | 0.56 | 0.66 | 0.71 | 0.23 | 0.15 | 0.11 |
| Height (50%) | 0.75 | 0.80 | 0.85 | 0.33 | 0.24 | 0.24 |
| Height (75%) | 0.95 | 1.01 | 0.97 | 0.44 | 0.34 | 0.36 |
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## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_heading">
## <td colspan="7" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Fuel and Moisture Quantiles by Invasion Status and Season</td>
## </tr>
##
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Variable">Variable</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Invaded---Spring">Invaded - Spring</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Invaded---Summer">Invaded - Summer</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Invaded---Winter">Invaded - Winter</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Non_Invaded---Spring">Non Invaded - Spring</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Non_Invaded---Summer">Non Invaded - Summer</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Non_Invaded---Winter">Non Invaded - Winter</th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="Variable" class="gt_row gt_left">Live Biomass (25%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">1.16</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">1.77</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">0.95</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">0.37</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">0.28</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">0.21</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Live Biomass (50%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">2.16</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">2.45</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">1.50</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">0.51</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">0.59</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">0.27</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Live Biomass (75%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">3.47</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">3.72</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">2.49</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">0.75</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">1.02</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">0.51</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Dead Biomass (25%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">1.89</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">1.26</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">1.95</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">0.34</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">0.14</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">0.32</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Dead Biomass (50%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">2.42</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">2.40</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">3.41</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">0.75</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">0.32</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">0.72</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Dead Biomass (75%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">3.11</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">4.48</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">6.20</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">1.36</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">0.90</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">1.81</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Litter Biomass (25%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">3.49</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">2.33</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">3.02</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">3.92</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">3.99</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">4.61</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Litter Biomass (50%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">4.97</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">4.09</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">5.62</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">6.19</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">6.06</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">6.60</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Litter Biomass (75%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">9.22</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">6.75</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">7.92</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">8.34</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">7.31</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">9.26</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Live Moisture Content (25%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">54.67</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">133.22</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">95.77</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">51.68</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">133.27</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">30.65</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Live Moisture Content (50%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">117.06</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">147.42</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">119.49</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">80.33</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">163.92</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">85.19</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Live Moisture Content (75%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">133.05</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">168.25</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">151.14</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">146.20</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">191.77</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">184.43</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Dead Moisture Content (25%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">9.20</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">13.45</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">13.79</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">9.75</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">8.80</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">11.68</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Dead Moisture Content (50%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">10.40</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">18.01</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">19.72</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">15.47</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">15.42</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">16.25</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Dead Moisture Content (75%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">16.58</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">27.30</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">36.74</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">17.59</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">29.73</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">49.33</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Litter Moisture Content (25%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">9.49</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">11.68</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">15.98</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">7.29</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">10.01</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">15.91</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Litter Moisture Content (50%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">11.45</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">21.21</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">27.86</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">12.30</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">21.21</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">42.65</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Litter Moisture Content (75%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">14.92</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">35.50</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">47.96</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">17.28</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">47.67</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">56.81</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Soil Moisture (25%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">4.83</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">6.32</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">5.93</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">3.07</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">3.74</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">6.23</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Soil Moisture (50%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">5.77</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">10.90</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">10.92</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">4.03</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">5.50</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">9.57</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Soil Moisture (75%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">7.10</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">12.96</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">13.89</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">9.30</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">11.66</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">12.38</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Height (25%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">0.56</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">0.66</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">0.71</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">0.23</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">0.15</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">0.11</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Height (50%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">0.75</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">0.80</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">0.85</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">0.33</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">0.24</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">0.24</td></tr>
## <tr><td headers="Variable" class="gt_row gt_left">Height (75%)</td>
## <td headers="Invaded - Spring" class="gt_row gt_right">0.95</td>
## <td headers="Invaded - Summer" class="gt_row gt_right">1.01</td>
## <td headers="Invaded - Winter" class="gt_row gt_right">0.97</td>
## <td headers="Non_Invaded - Spring" class="gt_row gt_right">0.44</td>
## <td headers="Non_Invaded - Summer" class="gt_row gt_right">0.34</td>
## <td headers="Non_Invaded - Winter" class="gt_row gt_right">0.36</td></tr>
## </tbody>
##
##
## </table>
## </div>## Some of the nested models seem to be identical and probably only vary in
## their random effects.## Random effect variances not available. Returned R2 does not account for random effects.## Following indices with missing values are not used for ranking:
## R2_conditional, R2_marginal, AIC_wt, AICc_wt, BIC_wt## # Comparison of Model Performance Indices
##
## Name | Model | RMSE | Sigma | Performance-Score
## -------------------------------------------------------------
## LiveB_gaussian | glmmTMB | 0.550 | 0.777 | 65.26%
## LiveB_student | glmmTMB | 1.154 | 0.535 | 50.00%
## LiveB_gamma | glmmTMB | 0.950 | 0.678 | 46.46%
## LiveB_lognormal | glmmTMB | 0.684 | 0.884 | 38.91%## Family: t ( identity )
## Formula:
## avg_live_biomass ~ Status * Season + Status * Region + (1 | Plot)
## Data: merged_sites
##
## AIC BIC logLik -2*log(L) df.resid
## 515.3 550.6 -246.7 493.3 172
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Plot (Intercept) 2.619e-09 5.118e-05
## Number of obs: 183, groups: Plot, 183
##
## Dispersion estimate for t family (sigma^2): 0.287
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.61105 0.10288 5.939 2.86e-09 ***
## StatusInvaded 1.35149 0.18024 7.498 6.47e-14 ***
## SeasonSpring -0.04727 0.14939 -0.316 0.75169
## SeasonWinter -0.21173 0.14245 -1.486 0.13720
## RegionCF -0.04895 0.11984 -0.408 0.68295
## StatusInvaded:SeasonSpring -0.72440 0.27367 -2.647 0.00812 **
## StatusInvaded:SeasonWinter -0.91711 0.28116 -3.262 0.00111 **
## StatusInvaded:RegionCF 1.17842 0.24705 4.770 1.84e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: avg_live_biomass
## Chisq Df Pr(>Chisq)
## (Intercept) 35.2748 1 2.863e-09 ***
## Status 56.2232 1 6.469e-14 ***
## Season 2.2561 2 0.323661
## Region 0.1668 1 0.682953
## Status:Season 13.3384 2 0.001269 **
## Status:Region 22.7524 1 1.843e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Some of the nested models seem to be identical and probably only vary in
## their random effects.## Random effect variances not available. Returned R2 does not account for random effects.## Following indices with missing values are not used for ranking:
## R2_conditional, R2_marginal, AIC_wt, AICc_wt, BIC_wt## # Comparison of Model Performance Indices
##
## Name | Model | RMSE | Sigma | Performance-Score
## ------------------------------------------------------------
## DeadB_gaussian | glmmTMB | 0.980 | 1.386 | 50.00%
## DeadB_student | glmmTMB | 2.034 | 0.801 | 50.00%## Family: t ( identity )
## Formula:
## avg_dead_biomass ~ Status * Season + Status * Region + (1 | Plot)
## Data: merged_sites
##
## AIC BIC logLik -2*log(L) df.resid
## 724.0 759.2 -351.0 702.0 170
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Plot (Intercept) 2.553e-09 5.053e-05
## Number of obs: 181, groups: Plot, 181
##
## Dispersion estimate for t family (sigma^2): 0.642
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.35940 0.13813 2.602 0.00927 **
## StatusInvaded 1.61304 0.26476 6.092 1.11e-09 ***
## SeasonSpring 0.36698 0.25425 1.443 0.14891
## SeasonWinter 0.30786 0.22576 1.364 0.17268
## RegionCF 0.10972 0.18659 0.588 0.55651
## StatusInvaded:SeasonSpring -0.08331 0.41599 -0.200 0.84127
## StatusInvaded:SeasonWinter 0.97185 0.58093 1.673 0.09435 .
## StatusInvaded:RegionCF 0.87437 0.54328 1.609 0.10753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: avg_dead_biomass
## Chisq Df Pr(>Chisq)
## (Intercept) 6.7700 1 0.00927 **
## Status 37.1185 1 1.112e-09 ***
## Season 3.1907 2 0.20284
## Region 0.3458 1 0.55651
## Status:Season 3.2263 2 0.19926
## Status:Region 2.5902 1 0.10753
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Some of the nested models seem to be identical and probably only vary in
## their random effects.## Following indices with missing values are not used for ranking: AIC_wt,
## AICc_wt, BIC_wt## # Comparison of Model Performance Indices
##
## Name | Model | RMSE | Sigma | Performance-Score
## ---------------------------------------------------------------------
## LitterB_model_gamma | glmmTMB | 3.301 | 0.573 | 59.51%
## LitterB_model_gaussian | glmmTMB | 1.845 | 2.609 | 57.12%
## LitterB_model_lognormal | glmmTMB | 2.725 | 2.947 | 25.52%
## LitterB_model_student | glmmTMB | 3.643 | 2.646 | 6.34%## Family: t ( identity )
## Formula:
## avg_litter_biomass ~ Status * Season + Status * Region + (1 | Plot)
## Data: merged_sites
##
## AIC BIC logLik -2*log(L) df.resid
## 1019.3 1054.8 -498.6 997.3 175
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Plot (Intercept) 0.3995 0.632
## Number of obs: 186, groups: Plot, 186
##
## Dispersion estimate for t family (sigma^2): 7
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.1447 0.5115 12.014 <2e-16 ***
## StatusInvaded -1.1551 0.7575 -1.525 0.127
## SeasonSpring 0.4144 0.9105 0.455 0.649
## SeasonWinter 0.9210 0.7901 1.166 0.244
## RegionCF -0.7632 0.6629 -1.151 0.250
## StatusInvaded:SeasonSpring 0.7403 1.2823 0.577 0.564
## StatusInvaded:SeasonWinter 0.5278 1.1671 0.452 0.651
## StatusInvaded:RegionCF -0.3086 0.9702 -0.318 0.750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: avg_litter_biomass
## Chisq Df Pr(>Chisq)
## (Intercept) 144.3416 1 <2e-16 ***
## Status 2.3251 1 0.1273
## Season 1.3819 2 0.5011
## Region 1.3253 1 0.2497
## Status:Season 0.4254 2 0.8084
## Status:Region 0.1012 1 0.7505
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Some of the nested models seem to be identical and probably only vary in
## their random effects.## Random effect variances not available. Returned R2 does not account for random effects.## Following indices with missing values are not used for ranking:
## R2_conditional, R2_marginal## # Comparison of Model Performance Indices
##
## Name | Model | RMSE | Sigma | AIC weights
## ------------------------------------------------------------------
## LiveM_model_lognormal | glmmTMB | 44.539 | 45.939 | 0.817
## LiveM_model_gaussian | glmmTMB | 9.561e-04 | 0.214 | 0.134
## LiveM_model_gamma | glmmTMB | 48.093 | 0.424 | 2.37e-08
## LiveM_model_student | glmmTMB | 47.893 | 47.917 | 0.049
##
## Name | AICc weights | BIC weights | Performance-Score
## ----------------------------------------------------------------------
## LiveM_model_lognormal | 0.824 | 0.849 | 62.31%
## LiveM_model_gaussian | 0.135 | 0.139 | 49.83%
## LiveM_model_gamma | 2.39e-08 | 2.46e-08 | 19.91%
## LiveM_model_student | 0.041 | 0.012 | 2.57%## Family: t ( identity )
## Formula:
## avg_live_moisture_content ~ Status * Season + Status * Region + (1 | Plot)
## Data: merged_sites
##
## AIC BIC logLik -2*log(L) df.resid
## 1428.8 1460.6 -703.4 1406.8 122
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Plot (Intercept) 2.275 1.508
## Number of obs: 133, groups: Plot, 133
##
## Dispersion estimate for t family (sigma^2): 2.3e+03
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 160.338 10.775 14.880 < 2e-16 ***
## StatusInvaded -7.665 13.680 -0.560 0.575275
## SeasonSpring -63.248 16.901 -3.742 0.000182 ***
## SeasonWinter -69.764 22.211 -3.141 0.001684 **
## RegionCF 17.606 14.503 1.214 0.224762
## StatusInvaded:SeasonSpring 10.705 21.208 0.505 0.613708
## StatusInvaded:SeasonWinter 38.959 25.873 1.506 0.132132
## StatusInvaded:RegionCF -16.966 17.923 -0.947 0.343853
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: avg_live_moisture_content
## Chisq Df Pr(>Chisq)
## (Intercept) 221.4238 1 < 2.2e-16 ***
## Status 0.3139 1 0.5753
## Season 19.8255 2 4.954e-05 ***
## Region 1.4737 1 0.2248
## Status:Season 2.2916 2 0.3180
## Status:Region 0.8960 1 0.3439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Some of the nested models seem to be identical and probably only vary in
## their random effects.## Random effect variances not available. Returned R2 does not account for random effects.## Following indices with missing values are not used for ranking:
## R2_conditional, R2_marginal, AIC_wt, AICc_wt, BIC_wt## # Comparison of Model Performance Indices
##
## Name | Model | RMSE | Sigma | Performance-Score
## ---------------------------------------------------------------------------
## DeadM_model_gamma | glmmTMB | 2.180e-05 | 4.970e-04 | 100.00%
## DeadM_model_gaussian | glmmTMB | 0.102 | 1.547 | 95.98%
## DeadM_model_student | glmmTMB | 26.048 | 5.123 | 37.35%
## DeadM_model_lognormal | glmmTMB | 24.170 | 20.245 | 3.61%## Family: t ( identity )
## Formula:
## avg_dead_moisture_content ~ Status * Season + Status * Region + (1 | Plot)
## Data: merged_sites
##
## AIC BIC logLik -2*log(L) df.resid
## 1136.2 1168.2 -557.1 1114.2 124
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Plot (Intercept) 6.496e-05 0.00806
## Number of obs: 135, groups: Plot, 135
##
## Dispersion estimate for t family (sigma^2): 26.2
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 13.6175 2.5782 5.282 1.28e-07 ***
## StatusInvaded 3.0004 2.8424 1.056 0.291
## SeasonSpring 0.1124 2.8765 0.039 0.969
## SeasonWinter 0.8695 2.6067 0.334 0.739
## RegionCF -1.1129 2.3638 -0.471 0.638
## StatusInvaded:SeasonSpring -5.5254 3.3495 -1.650 0.099 .
## StatusInvaded:SeasonWinter -0.8392 3.4316 -0.245 0.807
## StatusInvaded:RegionCF 1.4954 2.8695 0.521 0.602
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: avg_dead_moisture_content
## Chisq Df Pr(>Chisq)
## (Intercept) 27.8980 1 1.279e-07 ***
## Status 1.1143 1 0.2912
## Season 0.1393 2 0.9327
## Region 0.2217 1 0.6378
## Status:Season 3.1124 2 0.2109
## Status:Region 0.2716 1 0.6023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Some of the nested models seem to be identical and probably only vary in
## their random effects.## Random effect variances not available. Returned R2 does not account for random effects.## Following indices with missing values are not used for ranking:
## R2_conditional, R2_marginal, AIC_wt, AICc_wt, BIC_wt## # Comparison of Model Performance Indices
##
## Name | Model | RMSE | Sigma | Performance-Score
## -----------------------------------------------------------------------
## LitterM_model_gamma | glmmTMB | 16.938 | 0.620 | 100.00%
## LitterM_model_student | glmmTMB | 28.107 | 12.681 | 30.29%
## LitterM_model_gaussian | glmmTMB | 25.549 | 25.851 | 20.22%
## LitterM_model_lognormal | glmmTMB | 26.674 | 31.214 | 6.41%## Family: t ( identity )
## Formula:
## avg_litter_moisture_content ~ Status * Season + Status * Region +
## (1 | Plot)
## Data: merged_sites
##
## AIC BIC logLik -2*log(L) df.resid
## 1717.5 1753.0 -847.8 1695.5 175
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Plot (Intercept) 0.0001714 0.01309
## Number of obs: 186, groups: Plot, 186
##
## Dispersion estimate for t family (sigma^2): 161
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 21.3313 4.1776 5.106 3.29e-07 ***
## StatusInvaded -0.2478 4.6825 -0.053 0.95779
## SeasonSpring -6.4317 4.3504 -1.478 0.13929
## SeasonWinter 19.0172 6.8618 2.771 0.00558 **
## RegionCF -4.4238 3.9831 -1.111 0.26673
## StatusInvaded:SeasonSpring -1.0300 5.4397 -0.189 0.84983
## StatusInvaded:SeasonWinter -12.6049 8.2149 -1.534 0.12493
## StatusInvaded:RegionCF 1.4974 5.1639 0.290 0.77183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: avg_litter_moisture_content
## Chisq Df Pr(>Chisq)
## (Intercept) 26.0729 1 3.288e-07 ***
## Status 0.0028 1 0.9577921
## Season 16.5156 2 0.0002592 ***
## Region 1.2335 1 0.2667269
## Status:Season 2.5237 2 0.2831252
## Status:Region 0.0841 1 0.7718343
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Some of the nested models seem to be identical and probably only vary in
## their random effects.## Random effect variances not available. Returned R2 does not account for random effects.## Following indices with missing values are not used for ranking:
## R2_conditional, R2_marginal## # Comparison of Model Performance Indices
##
## Name | Model | RMSE | Sigma | AIC weights | AICc weights
## ----------------------------------------------------------------------------
## SoilM_model_lognormal | glmmTMB | 8.306 | 5.957 | 1.000 | 1.000
## SoilM_model_gamma | glmmTMB | 4.566 | 0.506 | 5.22e-11 | 5.22e-11
## SoilM_model_gaussian | glmmTMB | 3.773 | 5.433 | 7.34e-43 | 7.34e-43
## SoilM_model_student | glmmTMB | 8.280 | 2.819 | 1.81e-14 | 1.59e-14
##
## Name | BIC weights | Performance-Score
## -------------------------------------------------------
## SoilM_model_lognormal | 1.000 | 60.00%
## SoilM_model_gamma | 5.22e-11 | 36.50%
## SoilM_model_gaussian | 7.34e-43 | 21.92%
## SoilM_model_student | 3.60e-15 | 11.63%## Family: t ( identity )
## Formula: avg_soil_moisture ~ Status * Season + Status * Region + (1 |
## Plot)
## Data: merged_sites
##
## AIC BIC logLik -2*log(L) df.resid
## 1182.3 1217.8 -580.2 1160.3 175
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Plot (Intercept) 1.172 1.083
## Number of obs: 186, groups: Plot, 186
##
## Dispersion estimate for t family (sigma^2): 7.95
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 9.0705 0.7966 11.386 <2e-16 ***
## StatusInvaded 1.9826 1.0220 1.940 0.0524 .
## SeasonSpring -0.8295 1.1751 -0.706 0.4802
## SeasonWinter 2.0181 0.9587 2.105 0.0353 *
## RegionCF -5.2176 0.8796 -5.932 3e-09 ***
## StatusInvaded:SeasonSpring -2.6436 1.6192 -1.633 0.1025
## StatusInvaded:SeasonWinter -1.8378 1.4837 -1.239 0.2155
## StatusInvaded:RegionCF 0.4283 1.3785 0.311 0.7560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: avg_soil_moisture
## Chisq Df Pr(>Chisq)
## (Intercept) 129.6486 1 < 2e-16 ***
## Status 3.7636 1 0.05238 .
## Season 6.0657 2 0.04818 *
## Region 35.1837 1 3e-09 ***
## Status:Season 3.2218 2 0.19971
## Status:Region 0.0965 1 0.75604
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Some of the nested models seem to be identical and probably only vary in
## their random effects.## # Comparison of Model Performance Indices
##
## Name | Model | RMSE | Sigma | AIC weights | AICc weights
## ---------------------------------------------------------------------------
## VegH_model_lognormal | glmmTMB | 0.138 | 0.157 | 0.932 | 0.939
## VegH_model_gaussian | glmmTMB | 0.091 | 0.128 | 2.72e-07 | 2.74e-07
## VegH_model_student | glmmTMB | 0.182 | 0.123 | 0.068 | 0.061
## VegH_model_gamma | glmmTMB | 0.167 | 0.427 | 4.10e-14 | 4.13e-14
##
## Name | BIC weights | Performance-Score
## ------------------------------------------------------
## VegH_model_lognormal | 0.985 | 87.37%
## VegH_model_gaussian | 2.87e-07 | 39.65%
## VegH_model_student | 0.015 | 23.05%
## VegH_model_gamma | 4.33e-14 | 3.19%## Family: t ( identity )
## Formula: avg_height ~ Status * Season + Status * Region + (1 | Plot)
## Data: merged_sites
##
## AIC BIC logLik -2*log(L) df.resid
## -110.2 -74.9 66.1 -132.2 172
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Plot (Intercept) 0.0003042 0.01744
## Number of obs: 183, groups: Plot, 183
##
## Dispersion estimate for t family (sigma^2): 0.0151
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.224913 0.024634 9.130 < 2e-16 ***
## StatusInvaded 0.479359 0.035934 13.340 < 2e-16 ***
## SeasonSpring 0.098124 0.039048 2.513 0.01197 *
## SeasonWinter -0.023158 0.038322 -0.604 0.54565
## RegionCF 0.043416 0.031111 1.396 0.16285
## StatusInvaded:SeasonSpring -0.163873 0.055447 -2.955 0.00312 **
## StatusInvaded:SeasonWinter 0.008513 0.055334 0.154 0.87773
## StatusInvaded:RegionCF 0.227708 0.045494 5.005 5.58e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: avg_height
## Chisq Df Pr(>Chisq)
## (Intercept) 83.3619 1 < 2.2e-16 ***
## Status 177.9569 1 < 2.2e-16 ***
## Season 8.1046 2 0.017382 *
## Region 1.9476 1 0.162851
## Status:Season 10.1211 2 0.006342 **
## Status:Region 25.0525 1 5.579e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1