Chapter1 Objective1
Alan Ivory
2025-12-02
pacman::p_load(dplyr, readxl, tidyverse, raster, vegan, tigris, sf, sp, plotly, ggrepel, kableExtra, brms, parameters, gt, katex, emmeans, bayestestR, cowplot, ggeffects, patchwork)
## Set seed
set.seed(97)
# Tree PCQ Data
tree_data <- read_excel("~/Library/CloudStorage/OneDrive-UniversityofFlorida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Tree_PCQ")
tree_data <- tree_data %>%
filter(Site %in% c("BRSF", "WSF", "Jay"))
# Soil Data
fuel_data <- read_excel("~/Library/CloudStorage/OneDrive-UniversityofFlorida/Desktop/School/PHD/01_Projects/05_SharedData/Field_Data_FL_AL_MS.xlsx",
sheet = "Fuel_Sampling")
fuel_data <- fuel_data %>%
filter(Site %in% c("BRSF", "WSF", "Jay"))
Seasonal_Fuel_Sampling <- read_excel("~/Library/CloudStorage/OneDrive-UniversityofFlorida/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(Site %in% c("BRSF", "WSF", "Jay"))
# Seasonal Sampling Locations
Seasonal_Sampling_Locations <- read_excel("~/Library/CloudStorage/OneDrive-UniversityofFlorida/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(Site %in% c("BRSF", "WSF", "Jay"))
# Bag Weights
bag_weights <- read_excel("~/Library/CloudStorage/OneDrive-UniversityofFlorida/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("~/Library/CloudStorage/OneDrive-UniversityofFlorida/Desktop/School/PHD/01_Projects/05_SharedData/CogonSites_FL_AL_MS.xlsx")
CogonSites <- CogonSites %>%
filter(Site %in% c("BRSF", "WSF", "Jay"))
# Only include Florida/Alabama Sites
CogonSites <- CogonSites[CogonSites$Site != "CNF" & CogonSites$Site != "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 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")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 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")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 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")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 |
Fuel Table
| 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>
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## </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>Objective 1- Cogongrass relationship with fuel dynamics
Data Cleaning
GLMM Live Fuel Biomass
Model Comparison
## Family: student
## Links: mu = identity
## Formula: avg_live_biomass ~ Status * Season + Status * Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 186)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 1.39 1.42 0.06 5.17 1.00 1992 4069
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -21.48 30.19 -101.84 13.22 1.00 2703
## StatusInvaded 18.91 3.68 11.83 26.17 1.00 6348
## SeasonSpring -0.11 0.18 -0.45 0.24 1.00 7815
## SeasonWinter -0.23 0.16 -0.53 0.08 1.00 7713
## Latitude 0.74 1.01 -0.42 3.42 1.00 2701
## StatusInvaded:SeasonSpring -0.70 0.30 -1.29 -0.09 1.00 8461
## StatusInvaded:SeasonWinter -0.99 0.28 -1.54 -0.43 1.00 9062
## StatusInvaded:Latitude -0.57 0.12 -0.81 -0.34 1.00 6366
## Tail_ESS
## Intercept 3239
## StatusInvaded 5499
## SeasonSpring 7975
## SeasonWinter 7903
## Latitude 3318
## StatusInvaded:SeasonSpring 8018
## StatusInvaded:SeasonWinter 8903
## StatusInvaded:Latitude 5544
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.59 0.08 0.45 0.75 1.00 7499 7593
## nu 2.40 0.67 1.45 4.00 1.00 6990 5116
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Using 10 posterior draws for ppc type 'dens_overlay' by default.
Estimated marginal means
## Season = Summer:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 1.96 1.626 2.30
##
## Season = Spring:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 1.25 0.766 1.78
##
## Season = Winter:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 0.96 0.523 1.43
##
## Point estimate displayed: median
## HPD interval probability: 0.95## Probability of Direction
##
## contrast | Season | pd
## -------------------------------------
## Invaded - Non_Invaded | Summer | 100%
## Invaded - Non_Invaded | Spring | 100%
## Invaded - Non_Invaded | Winter | 100%Live Biomass Summary Table
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## <td colspan="9" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Posterior Estimates for Live Biomass</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="a::stub"></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Estimate">Estimate</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Est.-Error">Est. Error</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Lower)">95% HPDI (Lower)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Upper)">95% HPDI (Upper)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Probability->-0">Probability > 0</th>
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## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Tail-ESS">Tail ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="R-hat">R-hat</th>
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## </thead>
## <tbody class="gt_table_body">
## <tr><th id="stub_1_1" scope="row" class="gt_row gt_left gt_stub">(Intercept)</th>
## <td headers="stub_1_1 Estimate" class="gt_row gt_right">−10.60</td>
## <td headers="stub_1_1 Est. Error" class="gt_row gt_right">18.56</td>
## <td headers="stub_1_1 95% HPDI (Lower)" class="gt_row gt_right">−90.50</td>
## <td headers="stub_1_1 95% HPDI (Upper)" class="gt_row gt_right">21.47</td>
## <td headers="stub_1_1 Probability > 0" class="gt_row gt_right">0.221</td>
## <td headers="stub_1_1 Bulk ESS" class="gt_row gt_right">2,676</td>
## <td headers="stub_1_1 Tail ESS" class="gt_row gt_right">3,232</td>
## <td headers="stub_1_1 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_2" scope="row" class="gt_row gt_left gt_stub">Latitude</th>
## <td headers="stub_1_2 Estimate" class="gt_row gt_right">0.37</td>
## <td headers="stub_1_2 Est. Error" class="gt_row gt_right">0.62</td>
## <td headers="stub_1_2 95% HPDI (Lower)" class="gt_row gt_right">−0.59</td>
## <td headers="stub_1_2 95% HPDI (Upper)" class="gt_row gt_right">3.14</td>
## <td headers="stub_1_2 Probability > 0" class="gt_row gt_right">0.802</td>
## <td headers="stub_1_2 Bulk ESS" class="gt_row gt_right">2,671</td>
## <td headers="stub_1_2 Tail ESS" class="gt_row gt_right">3,312</td>
## <td headers="stub_1_2 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_3" scope="row" class="gt_row gt_left gt_stub">Season-Spring</th>
## <td headers="stub_1_3 Estimate" class="gt_row gt_right">−0.11</td>
## <td headers="stub_1_3 Est. Error" class="gt_row gt_right">0.17</td>
## <td headers="stub_1_3 95% HPDI (Lower)" class="gt_row gt_right">−0.45</td>
## <td headers="stub_1_3 95% HPDI (Upper)" class="gt_row gt_right">0.25</td>
## <td headers="stub_1_3 Probability > 0" class="gt_row gt_right">0.269</td>
## <td headers="stub_1_3 Bulk ESS" class="gt_row gt_right">7,782</td>
## <td headers="stub_1_3 Tail ESS" class="gt_row gt_right">7,942</td>
## <td headers="stub_1_3 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_4" scope="row" class="gt_row gt_left gt_stub">Season-Winter</th>
## <td headers="stub_1_4 Estimate" class="gt_row gt_right">−0.23</td>
## <td headers="stub_1_4 Est. Error" class="gt_row gt_right">0.15</td>
## <td headers="stub_1_4 95% HPDI (Lower)" class="gt_row gt_right">−0.54</td>
## <td headers="stub_1_4 95% HPDI (Upper)" class="gt_row gt_right">0.07</td>
## <td headers="stub_1_4 Probability > 0" class="gt_row gt_right">0.070</td>
## <td headers="stub_1_4 Bulk ESS" class="gt_row gt_right">7,711</td>
## <td headers="stub_1_4 Tail ESS" class="gt_row gt_right">7,854</td>
## <td headers="stub_1_4 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_5" scope="row" class="gt_row gt_left gt_stub">Status-Invaded</th>
## <td headers="stub_1_5 Estimate" class="gt_row gt_right" style="font-weight: bold;">18.86</td>
## <td headers="stub_1_5 Est. Error" class="gt_row gt_right" style="font-weight: bold;">3.65</td>
## <td headers="stub_1_5 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">11.61</td>
## <td headers="stub_1_5 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">25.93</td>
## <td headers="stub_1_5 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">1.000</td>
## <td headers="stub_1_5 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">6,311</td>
## <td headers="stub_1_5 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">5,451</td>
## <td headers="stub_1_5 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.000</td></tr>
## <tr><th id="stub_1_6" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Latitude</th>
## <td headers="stub_1_6 Estimate" class="gt_row gt_right" style="font-weight: bold;">−0.57</td>
## <td headers="stub_1_6 Est. Error" class="gt_row gt_right" style="font-weight: bold;">0.12</td>
## <td headers="stub_1_6 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">−0.81</td>
## <td headers="stub_1_6 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">−0.34</td>
## <td headers="stub_1_6 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">0.000</td>
## <td headers="stub_1_6 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">6,324</td>
## <td headers="stub_1_6 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">5,494</td>
## <td headers="stub_1_6 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.000</td></tr>
## <tr><th id="stub_1_7" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Spring</th>
## <td headers="stub_1_7 Estimate" class="gt_row gt_right" style="font-weight: bold;">−0.70</td>
## <td headers="stub_1_7 Est. Error" class="gt_row gt_right" style="font-weight: bold;">0.31</td>
## <td headers="stub_1_7 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">−1.31</td>
## <td headers="stub_1_7 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">−0.12</td>
## <td headers="stub_1_7 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">0.012</td>
## <td headers="stub_1_7 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">8,379</td>
## <td headers="stub_1_7 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">7,997</td>
## <td headers="stub_1_7 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.000</td></tr>
## <tr><th id="stub_1_8" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Winter</th>
## <td headers="stub_1_8 Estimate" class="gt_row gt_right" style="font-weight: bold;">−1.00</td>
## <td headers="stub_1_8 Est. Error" class="gt_row gt_right" style="font-weight: bold;">0.28</td>
## <td headers="stub_1_8 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">−1.54</td>
## <td headers="stub_1_8 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">−0.43</td>
## <td headers="stub_1_8 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">0.001</td>
## <td headers="stub_1_8 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">9,046</td>
## <td headers="stub_1_8 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">8,890</td>
## <td headers="stub_1_8 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.000</td></tr>
## </tbody>
##
## </table>
## </div>Conditional Effects Live Biomass
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
Conditional Effects Live Biomass
GLMM Dead Fuel Biomass
Model Comparison
## Family: student
## Links: mu = identity
## Formula: avg_dead_biomass ~ Status * Season + Status * Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 186)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.79 0.98 0.02 3.49 1.00 2717 4115
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -1.69 19.34 -56.65 27.29 1.00 4677
## StatusInvaded 14.59 7.77 -0.10 29.97 1.00 6046
## SeasonSpring 0.45 0.28 -0.08 1.01 1.00 8672
## SeasonWinter 0.40 0.26 -0.07 0.94 1.00 8977
## Latitude 0.07 0.64 -0.90 1.89 1.00 4691
## StatusInvaded:SeasonSpring -0.26 0.45 -1.16 0.58 1.00 8156
## StatusInvaded:SeasonWinter 0.77 0.63 -0.47 2.00 1.00 8926
## StatusInvaded:Latitude -0.42 0.25 -0.92 0.07 1.00 6061
## Tail_ESS
## Intercept 2730
## StatusInvaded 7112
## SeasonSpring 8586
## SeasonWinter 8111
## Latitude 2814
## StatusInvaded:SeasonSpring 8460
## StatusInvaded:SeasonWinter 8771
## StatusInvaded:Latitude 7144
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.89 0.13 0.65 1.17 1.00 7359 6876
## nu 1.77 0.43 1.16 2.80 1.00 6924 5175
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Warning: There were 1 divergent transitions after warmup. Increasing
## adapt_delta above 0.95 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup## Family: student
## Links: mu = identity
## Formula: avg_dead_biomass ~ Status + Season + Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 186)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.68 0.76 0.01 2.80 1.01 1659 1183
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -0.19 14.31 -35.80 23.87 1.00 1286 489
## StatusInvaded 1.88 0.18 1.52 2.25 1.00 8429 6863
## SeasonSpring 0.33 0.21 -0.07 0.75 1.00 5586 2600
## SeasonWinter 0.44 0.23 0.01 0.92 1.00 5989 6885
## Latitude 0.02 0.48 -0.79 1.20 1.00 1233 387
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.83 0.13 0.60 1.10 1.00 4442 5641
## nu 1.60 0.36 1.08 2.46 1.00 4054 3151
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Using 10 posterior draws for ppc type 'dens_overlay' by default.
Estimated marginal means
## Season = Summer:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 2.13 1.53 2.81
##
## Season = Spring:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 1.89 1.13 2.61
##
## Season = Winter:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 2.95 1.75 4.11
##
## Point estimate displayed: median
## HPD interval probability: 0.95## Probability of Direction
##
## contrast | Season | pd
## -------------------------------------
## Invaded - Non_Invaded | Summer | 100%
## Invaded - Non_Invaded | Spring | 100%
## Invaded - Non_Invaded | Winter | 100%Dead Biomass Summary Table
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## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_heading">
## <td colspan="9" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Posterior Estimates for Dead Biomass</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="a::stub"></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Estimate">Estimate</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Est.-Error">Est. Error</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Lower)">95% HPDI (Lower)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Upper)">95% HPDI (Upper)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Probability->-0">Probability > 0</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Bulk-ESS">Bulk ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Tail-ESS">Tail ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="R-hat">R-hat</th>
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## <tbody class="gt_table_body">
## <tr><th id="stub_1_1" scope="row" class="gt_row gt_left gt_stub">(Intercept)</th>
## <td headers="stub_1_1 Estimate" class="gt_row gt_right">1.67</td>
## <td headers="stub_1_1 Est. Error" class="gt_row gt_right">7.89</td>
## <td headers="stub_1_1 95% HPDI (Lower)" class="gt_row gt_right">−49.68</td>
## <td headers="stub_1_1 95% HPDI (Upper)" class="gt_row gt_right">30.40</td>
## <td headers="stub_1_1 Probability > 0" class="gt_row gt_right">0.596</td>
## <td headers="stub_1_1 Bulk ESS" class="gt_row gt_right">4,664</td>
## <td headers="stub_1_1 Tail ESS" class="gt_row gt_right">2,722</td>
## <td headers="stub_1_1 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_2" scope="row" class="gt_row gt_left gt_stub">Latitude</th>
## <td headers="stub_1_2 Estimate" class="gt_row gt_right">−0.04</td>
## <td headers="stub_1_2 Est. Error" class="gt_row gt_right">0.26</td>
## <td headers="stub_1_2 95% HPDI (Lower)" class="gt_row gt_right">−1.01</td>
## <td headers="stub_1_2 95% HPDI (Upper)" class="gt_row gt_right">1.66</td>
## <td headers="stub_1_2 Probability > 0" class="gt_row gt_right">0.423</td>
## <td headers="stub_1_2 Bulk ESS" class="gt_row gt_right">4,677</td>
## <td headers="stub_1_2 Tail ESS" class="gt_row gt_right">2,805</td>
## <td headers="stub_1_2 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_3" scope="row" class="gt_row gt_left gt_stub">Season-Spring</th>
## <td headers="stub_1_3 Estimate" class="gt_row gt_right">0.45</td>
## <td headers="stub_1_3 Est. Error" class="gt_row gt_right">0.27</td>
## <td headers="stub_1_3 95% HPDI (Lower)" class="gt_row gt_right">−0.08</td>
## <td headers="stub_1_3 95% HPDI (Upper)" class="gt_row gt_right">1.01</td>
## <td headers="stub_1_3 Probability > 0" class="gt_row gt_right">0.953</td>
## <td headers="stub_1_3 Bulk ESS" class="gt_row gt_right">8,623</td>
## <td headers="stub_1_3 Tail ESS" class="gt_row gt_right">8,551</td>
## <td headers="stub_1_3 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_4" scope="row" class="gt_row gt_left gt_stub">Season-Winter</th>
## <td headers="stub_1_4 Estimate" class="gt_row gt_right">0.39</td>
## <td headers="stub_1_4 Est. Error" class="gt_row gt_right">0.25</td>
## <td headers="stub_1_4 95% HPDI (Lower)" class="gt_row gt_right">−0.09</td>
## <td headers="stub_1_4 95% HPDI (Upper)" class="gt_row gt_right">0.92</td>
## <td headers="stub_1_4 Probability > 0" class="gt_row gt_right">0.950</td>
## <td headers="stub_1_4 Bulk ESS" class="gt_row gt_right">8,922</td>
## <td headers="stub_1_4 Tail ESS" class="gt_row gt_right">8,079</td>
## <td headers="stub_1_4 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_5" scope="row" class="gt_row gt_left gt_stub">Status-Invaded</th>
## <td headers="stub_1_5 Estimate" class="gt_row gt_right" style="font-weight: bold;">14.44</td>
## <td headers="stub_1_5 Est. Error" class="gt_row gt_right" style="font-weight: bold;">7.82</td>
## <td headers="stub_1_5 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">0.21</td>
## <td headers="stub_1_5 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">30.25</td>
## <td headers="stub_1_5 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">0.974</td>
## <td headers="stub_1_5 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">6,031</td>
## <td headers="stub_1_5 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">7,090</td>
## <td headers="stub_1_5 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.001</td></tr>
## <tr><th id="stub_1_6" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Latitude</th>
## <td headers="stub_1_6 Estimate" class="gt_row gt_right">−0.41</td>
## <td headers="stub_1_6 Est. Error" class="gt_row gt_right">0.26</td>
## <td headers="stub_1_6 95% HPDI (Lower)" class="gt_row gt_right">−0.91</td>
## <td headers="stub_1_6 95% HPDI (Upper)" class="gt_row gt_right">0.07</td>
## <td headers="stub_1_6 Probability > 0" class="gt_row gt_right">0.047</td>
## <td headers="stub_1_6 Bulk ESS" class="gt_row gt_right">6,046</td>
## <td headers="stub_1_6 Tail ESS" class="gt_row gt_right">7,120</td>
## <td headers="stub_1_6 R-hat" class="gt_row gt_right">1.001</td></tr>
## <tr><th id="stub_1_7" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Spring</th>
## <td headers="stub_1_7 Estimate" class="gt_row gt_right">−0.26</td>
## <td headers="stub_1_7 Est. Error" class="gt_row gt_right">0.44</td>
## <td headers="stub_1_7 95% HPDI (Lower)" class="gt_row gt_right">−1.16</td>
## <td headers="stub_1_7 95% HPDI (Upper)" class="gt_row gt_right">0.58</td>
## <td headers="stub_1_7 Probability > 0" class="gt_row gt_right">0.277</td>
## <td headers="stub_1_7 Bulk ESS" class="gt_row gt_right">8,091</td>
## <td headers="stub_1_7 Tail ESS" class="gt_row gt_right">8,443</td>
## <td headers="stub_1_7 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_8" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Winter</th>
## <td headers="stub_1_8 Estimate" class="gt_row gt_right">0.78</td>
## <td headers="stub_1_8 Est. Error" class="gt_row gt_right">0.63</td>
## <td headers="stub_1_8 95% HPDI (Lower)" class="gt_row gt_right">−0.49</td>
## <td headers="stub_1_8 95% HPDI (Upper)" class="gt_row gt_right">1.98</td>
## <td headers="stub_1_8 Probability > 0" class="gt_row gt_right">0.884</td>
## <td headers="stub_1_8 Bulk ESS" class="gt_row gt_right">8,845</td>
## <td headers="stub_1_8 Tail ESS" class="gt_row gt_right">8,781</td>
## <td headers="stub_1_8 R-hat" class="gt_row gt_right">1.001</td></tr>
## </tbody>
##
## </table>
## </div>Conditional Effects Dead Biomass
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
GLMM Litter Fuel Biomass
Model Comparison
## Family: student
## Links: mu = identity
## Formula: avg_litter_biomass ~ Status * Season + Status * Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 186)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 3.00 2.65 0.33 10.17 1.00 2853 4003
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept 47.00 81.24 -47.14 274.66 1.00 3582
## StatusInvaded -7.14 14.47 -35.30 21.52 1.00 7845
## SeasonSpring 1.07 0.93 -0.76 2.96 1.00 9795
## SeasonWinter 0.89 0.82 -0.71 2.53 1.00 9814
## Latitude -1.38 2.72 -9.01 1.75 1.00 3578
## StatusInvaded:SeasonSpring 0.72 1.25 -1.76 3.19 1.00 10123
## StatusInvaded:SeasonWinter 0.55 1.20 -1.87 2.81 1.00 9329
## StatusInvaded:Latitude 0.20 0.48 -0.76 1.14 1.00 7907
## Tail_ESS
## Intercept 3300
## StatusInvaded 8159
## SeasonSpring 8334
## SeasonWinter 8820
## Latitude 3300
## StatusInvaded:SeasonSpring 8869
## StatusInvaded:SeasonWinter 8873
## StatusInvaded:Latitude 8076
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 2.82 0.31 2.25 3.44 1.00 7739 8274
## nu 5.41 3.11 2.45 13.02 1.00 8080 7044
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Warning: There were 26 divergent transitions after warmup. Increasing
## adapt_delta above 0.95 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup## Family: student
## Links: mu = identity
## Formula: avg_litter_biomass ~ Status + Season + Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 186)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 2.90 2.35 0.34 9.63 1.00 2179 1341
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 41.68 69.30 -47.17 236.31 1.00 2636 1137
## StatusInvaded -0.94 0.48 -1.88 0.01 1.00 10075 7979
## SeasonSpring 1.42 0.70 0.06 2.83 1.00 5539 2033
## SeasonWinter 1.15 0.58 -0.01 2.27 1.00 10048 8062
## Latitude -1.21 2.32 -7.68 1.73 1.00 2631 1134
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 2.83 0.29 2.28 3.43 1.00 4987 6185
## nu 5.62 3.20 2.56 13.61 1.00 6459 6905
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Using 10 posterior draws for ppc type 'dens_overlay' by default.
Estimated marginal means
## Season = Summer:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded -1.235 -2.57 0.0387
##
## Season = Spring:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded -0.513 -2.69 1.6490
##
## Season = Winter:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded -0.664 -2.62 1.2518
##
## Point estimate displayed: median
## HPD interval probability: 0.95## Probability of Direction
##
## contrast | Season | pd
## ---------------------------------------
## Invaded - Non_Invaded | Summer | 96.61%
## Invaded - Non_Invaded | Spring | 67.98%
## Invaded - Non_Invaded | Winter | 75.47%Litter Biomass Summary Table
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## <thead>
## <tr class="gt_heading">
## <td colspan="9" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Posterior Estimates for Litter Biomass</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="a::stub"></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Estimate">Estimate</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Est.-Error">Est. Error</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Lower)">95% HPDI (Lower)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Upper)">95% HPDI (Upper)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Probability->-0">Probability > 0</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Bulk-ESS">Bulk ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Tail-ESS">Tail ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="R-hat">R-hat</th>
## </tr>
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## <tbody class="gt_table_body">
## <tr><th id="stub_1_1" scope="row" class="gt_row gt_left gt_stub">(Intercept)</th>
## <td headers="stub_1_1 Estimate" class="gt_row gt_right">23.67</td>
## <td headers="stub_1_1 Est. Error" class="gt_row gt_right">44.75</td>
## <td headers="stub_1_1 95% HPDI (Lower)" class="gt_row gt_right">−62.10</td>
## <td headers="stub_1_1 95% HPDI (Upper)" class="gt_row gt_right">239.60</td>
## <td headers="stub_1_1 Probability > 0" class="gt_row gt_right">0.740</td>
## <td headers="stub_1_1 Bulk ESS" class="gt_row gt_right">3,546</td>
## <td headers="stub_1_1 Tail ESS" class="gt_row gt_right">3,279</td>
## <td headers="stub_1_1 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_2" scope="row" class="gt_row gt_left gt_stub">Latitude</th>
## <td headers="stub_1_2 Estimate" class="gt_row gt_right">−0.61</td>
## <td headers="stub_1_2 Est. Error" class="gt_row gt_right">1.50</td>
## <td headers="stub_1_2 95% HPDI (Lower)" class="gt_row gt_right">−7.58</td>
## <td headers="stub_1_2 95% HPDI (Upper)" class="gt_row gt_right">2.48</td>
## <td headers="stub_1_2 Probability > 0" class="gt_row gt_right">0.318</td>
## <td headers="stub_1_2 Bulk ESS" class="gt_row gt_right">3,543</td>
## <td headers="stub_1_2 Tail ESS" class="gt_row gt_right">3,279</td>
## <td headers="stub_1_2 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_3" scope="row" class="gt_row gt_left gt_stub">Season-Spring</th>
## <td headers="stub_1_3 Estimate" class="gt_row gt_right">1.06</td>
## <td headers="stub_1_3 Est. Error" class="gt_row gt_right">0.92</td>
## <td headers="stub_1_3 95% HPDI (Lower)" class="gt_row gt_right">−0.84</td>
## <td headers="stub_1_3 95% HPDI (Upper)" class="gt_row gt_right">2.86</td>
## <td headers="stub_1_3 Probability > 0" class="gt_row gt_right">0.876</td>
## <td headers="stub_1_3 Bulk ESS" class="gt_row gt_right">9,769</td>
## <td headers="stub_1_3 Tail ESS" class="gt_row gt_right">8,297</td>
## <td headers="stub_1_3 R-hat" class="gt_row gt_right">1.001</td></tr>
## <tr><th id="stub_1_4" scope="row" class="gt_row gt_left gt_stub">Season-Winter</th>
## <td headers="stub_1_4 Estimate" class="gt_row gt_right">0.90</td>
## <td headers="stub_1_4 Est. Error" class="gt_row gt_right">0.82</td>
## <td headers="stub_1_4 95% HPDI (Lower)" class="gt_row gt_right">−0.68</td>
## <td headers="stub_1_4 95% HPDI (Upper)" class="gt_row gt_right">2.55</td>
## <td headers="stub_1_4 Probability > 0" class="gt_row gt_right">0.862</td>
## <td headers="stub_1_4 Bulk ESS" class="gt_row gt_right">9,787</td>
## <td headers="stub_1_4 Tail ESS" class="gt_row gt_right">8,772</td>
## <td headers="stub_1_4 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_5" scope="row" class="gt_row gt_left gt_stub">Status-Invaded</th>
## <td headers="stub_1_5 Estimate" class="gt_row gt_right">−7.24</td>
## <td headers="stub_1_5 Est. Error" class="gt_row gt_right">14.36</td>
## <td headers="stub_1_5 95% HPDI (Lower)" class="gt_row gt_right">−35.14</td>
## <td headers="stub_1_5 95% HPDI (Upper)" class="gt_row gt_right">21.57</td>
## <td headers="stub_1_5 Probability > 0" class="gt_row gt_right">0.308</td>
## <td headers="stub_1_5 Bulk ESS" class="gt_row gt_right">7,865</td>
## <td headers="stub_1_5 Tail ESS" class="gt_row gt_right">8,140</td>
## <td headers="stub_1_5 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_6" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Latitude</th>
## <td headers="stub_1_6 Estimate" class="gt_row gt_right">0.20</td>
## <td headers="stub_1_6 Est. Error" class="gt_row gt_right">0.48</td>
## <td headers="stub_1_6 95% HPDI (Lower)" class="gt_row gt_right">−0.78</td>
## <td headers="stub_1_6 95% HPDI (Upper)" class="gt_row gt_right">1.11</td>
## <td headers="stub_1_6 Probability > 0" class="gt_row gt_right">0.663</td>
## <td headers="stub_1_6 Bulk ESS" class="gt_row gt_right">7,929</td>
## <td headers="stub_1_6 Tail ESS" class="gt_row gt_right">8,070</td>
## <td headers="stub_1_6 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_7" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Spring</th>
## <td headers="stub_1_7 Estimate" class="gt_row gt_right">0.72</td>
## <td headers="stub_1_7 Est. Error" class="gt_row gt_right">1.24</td>
## <td headers="stub_1_7 95% HPDI (Lower)" class="gt_row gt_right">−1.77</td>
## <td headers="stub_1_7 95% HPDI (Upper)" class="gt_row gt_right">3.17</td>
## <td headers="stub_1_7 Probability > 0" class="gt_row gt_right">0.718</td>
## <td headers="stub_1_7 Bulk ESS" class="gt_row gt_right">10,097</td>
## <td headers="stub_1_7 Tail ESS" class="gt_row gt_right">8,793</td>
## <td headers="stub_1_7 R-hat" class="gt_row gt_right">1.001</td></tr>
## <tr><th id="stub_1_8" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Winter</th>
## <td headers="stub_1_8 Estimate" class="gt_row gt_right">0.57</td>
## <td headers="stub_1_8 Est. Error" class="gt_row gt_right">1.20</td>
## <td headers="stub_1_8 95% HPDI (Lower)" class="gt_row gt_right">−1.86</td>
## <td headers="stub_1_8 95% HPDI (Upper)" class="gt_row gt_right">2.82</td>
## <td headers="stub_1_8 Probability > 0" class="gt_row gt_right">0.681</td>
## <td headers="stub_1_8 Bulk ESS" class="gt_row gt_right">9,309</td>
## <td headers="stub_1_8 Tail ESS" class="gt_row gt_right">8,834</td>
## <td headers="stub_1_8 R-hat" class="gt_row gt_right">1.000</td></tr>
## </tbody>
##
## </table>
## </div>Conditional Effects Litter Biomass
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
GLMM Live Moisture
Model Comparison
## Family: student
## Links: mu = identity
## Formula: avg_live_moisture_content ~ Status * Season + Status * Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 133)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 18.24 19.46 0.50 69.75 1.00 3487 5296
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept 439.26 496.60 -401.92 1588.10 1.00 4964
## StatusInvaded -203.99 271.70 -741.39 328.63 1.00 6478
## SeasonSpring -69.36 18.99 -106.03 -32.14 1.00 8543
## SeasonWinter -71.26 29.57 -129.69 -13.59 1.00 7459
## Latitude -9.15 16.56 -47.60 19.03 1.00 4985
## StatusInvaded:SeasonSpring 20.11 23.29 -25.55 65.56 1.00 8312
## StatusInvaded:SeasonWinter 41.02 32.42 -21.89 104.81 1.00 7376
## StatusInvaded:Latitude 6.38 9.07 -11.43 24.28 1.00 6523
## Tail_ESS
## Intercept 3796
## StatusInvaded 6828
## SeasonSpring 8421
## SeasonWinter 7532
## Latitude 3865
## StatusInvaded:SeasonSpring 8263
## StatusInvaded:SeasonWinter 7312
## StatusInvaded:Latitude 6814
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 44.32 4.35 35.30 52.48 1.00 7981 7263
## nu 15.28 11.24 3.55 45.65 1.00 7804 8346
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Warning: There were 41 divergent transitions after warmup. Increasing
## adapt_delta above 0.95 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup## Family: student
## Links: mu = identity
## Formula: avg_live_moisture_content ~ Status + Season + Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 133)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 18.03 18.09 0.56 68.10 1.00 2395 1650
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 286.58 409.09 -477.83 1298.49 1.00 2440 1269
## StatusInvaded -5.33 9.26 -23.58 12.64 1.00 9987 8198
## SeasonSpring -56.17 10.63 -77.08 -35.29 1.00 8863 8436
## SeasonWinter -38.05 11.86 -61.37 -15.05 1.00 9425 6963
## Latitude -4.21 13.61 -37.80 21.14 1.00 2465 1312
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 45.07 3.87 37.63 52.86 1.00 7038 6493
## nu 16.93 11.40 4.55 47.67 1.00 7404 8696
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Using 10 posterior draws for ppc type 'dens_overlay' by default.
## Warning: Removed 53 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 53 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 53 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Removed 53 rows containing missing values or values outside the scale range
## (`geom_point()`).
Estimated marginal means
## Season = Summer:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded -14.28 -37.1 7.23
##
## Season = Spring:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 5.71 -34.8 45.55
##
## Season = Winter:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 26.53 -32.9 84.28
##
## Point estimate displayed: median
## HPD interval probability: 0.95## Probability of Direction
##
## contrast | Season | pd
## ---------------------------------------
## Invaded - Non_Invaded | Summer | 89.58%
## Invaded - Non_Invaded | Spring | 61.14%
## Invaded - Non_Invaded | Winter | 81.66%Live Moisture Summary Table
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## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_heading">
## <td colspan="9" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Posterior Estimates for Live Moisture</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="a::stub"></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Estimate">Estimate</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Est.-Error">Est. Error</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Lower)">95% HPDI (Lower)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Upper)">95% HPDI (Upper)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Probability->-0">Probability > 0</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Bulk-ESS">Bulk ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Tail-ESS">Tail ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="R-hat">R-hat</th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><th id="stub_1_1" scope="row" class="gt_row gt_left gt_stub">(Intercept)</th>
## <td headers="stub_1_1 Estimate" class="gt_row gt_right">400.42</td>
## <td headers="stub_1_1 Est. Error" class="gt_row gt_right">339.69</td>
## <td headers="stub_1_1 95% HPDI (Lower)" class="gt_row gt_right">−508.11</td>
## <td headers="stub_1_1 95% HPDI (Upper)" class="gt_row gt_right">1,458.36</td>
## <td headers="stub_1_1 Probability > 0" class="gt_row gt_right">0.881</td>
## <td headers="stub_1_1 Bulk ESS" class="gt_row gt_right">4,957</td>
## <td headers="stub_1_1 Tail ESS" class="gt_row gt_right">3,729</td>
## <td headers="stub_1_1 R-hat" class="gt_row gt_right">1.001</td></tr>
## <tr><th id="stub_1_2" scope="row" class="gt_row gt_left gt_stub">Latitude</th>
## <td headers="stub_1_2 Estimate" class="gt_row gt_right">−7.82</td>
## <td headers="stub_1_2 Est. Error" class="gt_row gt_right">11.33</td>
## <td headers="stub_1_2 95% HPDI (Lower)" class="gt_row gt_right">−42.47</td>
## <td headers="stub_1_2 95% HPDI (Upper)" class="gt_row gt_right">23.08</td>
## <td headers="stub_1_2 Probability > 0" class="gt_row gt_right">0.234</td>
## <td headers="stub_1_2 Bulk ESS" class="gt_row gt_right">4,978</td>
## <td headers="stub_1_2 Tail ESS" class="gt_row gt_right">3,794</td>
## <td headers="stub_1_2 R-hat" class="gt_row gt_right">1.001</td></tr>
## <tr><th id="stub_1_3" scope="row" class="gt_row gt_left gt_stub">Season-Spring</th>
## <td headers="stub_1_3 Estimate" class="gt_row gt_right" style="font-weight: bold;">−69.17</td>
## <td headers="stub_1_3 Est. Error" class="gt_row gt_right" style="font-weight: bold;">19.18</td>
## <td headers="stub_1_3 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">−106.45</td>
## <td headers="stub_1_3 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">−32.68</td>
## <td headers="stub_1_3 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">0.000</td>
## <td headers="stub_1_3 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">8,529</td>
## <td headers="stub_1_3 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">8,406</td>
## <td headers="stub_1_3 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.000</td></tr>
## <tr><th id="stub_1_4" scope="row" class="gt_row gt_left gt_stub">Season-Winter</th>
## <td headers="stub_1_4 Estimate" class="gt_row gt_right" style="font-weight: bold;">−71.21</td>
## <td headers="stub_1_4 Est. Error" class="gt_row gt_right" style="font-weight: bold;">29.25</td>
## <td headers="stub_1_4 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">−126.79</td>
## <td headers="stub_1_4 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">−11.38</td>
## <td headers="stub_1_4 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">0.009</td>
## <td headers="stub_1_4 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">7,442</td>
## <td headers="stub_1_4 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">7,510</td>
## <td headers="stub_1_4 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.000</td></tr>
## <tr><th id="stub_1_5" scope="row" class="gt_row gt_left gt_stub">Status-Invaded</th>
## <td headers="stub_1_5 Estimate" class="gt_row gt_right">−204.73</td>
## <td headers="stub_1_5 Est. Error" class="gt_row gt_right">270.29</td>
## <td headers="stub_1_5 95% HPDI (Lower)" class="gt_row gt_right">−728.48</td>
## <td headers="stub_1_5 95% HPDI (Upper)" class="gt_row gt_right">335.58</td>
## <td headers="stub_1_5 Probability > 0" class="gt_row gt_right">0.223</td>
## <td headers="stub_1_5 Bulk ESS" class="gt_row gt_right">6,454</td>
## <td headers="stub_1_5 Tail ESS" class="gt_row gt_right">6,778</td>
## <td headers="stub_1_5 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_6" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Latitude</th>
## <td headers="stub_1_6 Estimate" class="gt_row gt_right">6.46</td>
## <td headers="stub_1_6 Est. Error" class="gt_row gt_right">9.06</td>
## <td headers="stub_1_6 95% HPDI (Lower)" class="gt_row gt_right">−11.53</td>
## <td headers="stub_1_6 95% HPDI (Upper)" class="gt_row gt_right">24.09</td>
## <td headers="stub_1_6 Probability > 0" class="gt_row gt_right">0.760</td>
## <td headers="stub_1_6 Bulk ESS" class="gt_row gt_right">6,500</td>
## <td headers="stub_1_6 Tail ESS" class="gt_row gt_right">6,760</td>
## <td headers="stub_1_6 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_7" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Spring</th>
## <td headers="stub_1_7 Estimate" class="gt_row gt_right">20.08</td>
## <td headers="stub_1_7 Est. Error" class="gt_row gt_right">23.19</td>
## <td headers="stub_1_7 95% HPDI (Lower)" class="gt_row gt_right">−25.56</td>
## <td headers="stub_1_7 95% HPDI (Upper)" class="gt_row gt_right">65.56</td>
## <td headers="stub_1_7 Probability > 0" class="gt_row gt_right">0.805</td>
## <td headers="stub_1_7 Bulk ESS" class="gt_row gt_right">8,193</td>
## <td headers="stub_1_7 Tail ESS" class="gt_row gt_right">8,225</td>
## <td headers="stub_1_7 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_8" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Winter</th>
## <td headers="stub_1_8 Estimate" class="gt_row gt_right">40.80</td>
## <td headers="stub_1_8 Est. Error" class="gt_row gt_right">31.77</td>
## <td headers="stub_1_8 95% HPDI (Lower)" class="gt_row gt_right">−22.71</td>
## <td headers="stub_1_8 95% HPDI (Upper)" class="gt_row gt_right">103.94</td>
## <td headers="stub_1_8 Probability > 0" class="gt_row gt_right">0.897</td>
## <td headers="stub_1_8 Bulk ESS" class="gt_row gt_right">7,353</td>
## <td headers="stub_1_8 Tail ESS" class="gt_row gt_right">7,287</td>
## <td headers="stub_1_8 R-hat" class="gt_row gt_right">1.000</td></tr>
## </tbody>
##
## </table>
## </div>Conditional Effects Live Moisture
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
GLMM Dead Moisture
Model Comparison
## Family: student
## Links: mu = identity
## Formula: avg_dead_moisture_content ~ Status * Season + Status * Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 135)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 5.75 4.67 0.65 17.94 1.00 4038 4387
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept 2.15 124.01 -249.74 257.94 1.00 5213
## StatusInvaded 16.64 44.31 -70.79 104.29 1.00 6577
## SeasonSpring 0.56 3.20 -5.97 6.64 1.00 7010
## SeasonWinter 1.64 3.17 -4.71 7.74 1.00 6853
## Latitude 0.36 4.15 -8.15 8.85 1.00 5178
## StatusInvaded:SeasonSpring -4.40 3.72 -11.40 3.14 1.00 7007
## StatusInvaded:SeasonWinter 0.32 3.99 -7.37 8.40 1.00 6737
## StatusInvaded:Latitude -0.45 1.50 -3.41 2.53 1.00 6509
## Tail_ESS
## Intercept 4714
## StatusInvaded 6915
## SeasonSpring 7082
## SeasonWinter 7551
## Latitude 4822
## StatusInvaded:SeasonSpring 7173
## StatusInvaded:SeasonWinter 7562
## StatusInvaded:Latitude 6766
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 5.93 0.88 4.41 7.85 1.00 7557 8243
## nu 1.23 0.18 1.01 1.67 1.00 6331 4334
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Family: student
## Links: mu = identity
## Formula: avg_dead_moisture_content ~ Status + Season + Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 135)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 5.61 4.16 0.69 16.41 1.00 3542 4374
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 13.38 109.52 -224.74 256.49 1.00 4301 4155
## StatusInvaded 1.58 1.52 -1.44 4.59 1.00 10019 7856
## SeasonSpring -2.34 1.73 -5.74 1.14 1.00 8520 7534
## SeasonWinter 1.36 1.97 -2.36 5.36 1.00 8008 7862
## Latitude 0.03 3.65 -8.13 7.99 1.00 4295 4090
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 5.97 0.87 4.46 7.85 1.00 8132 8018
## nu 1.24 0.18 1.01 1.69 1.00 6445 4213
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Using 10 posterior draws for ppc type 'dens_overlay' by default.
## Warning: Removed 51 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 51 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 51 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Removed 51 rows containing missing values or values outside the scale range
## (`geom_point()`).
Estimated marginal means
## Season = Summer:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 3.40 -2.40 8.94
##
## Season = Spring:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded -1.11 -5.58 3.69
##
## Season = Winter:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 3.61 -1.87 9.35
##
## Point estimate displayed: median
## HPD interval probability: 0.95## Probability of Direction
##
## contrast | Season | pd
## ---------------------------------------
## Invaded - Non_Invaded | Summer | 87.88%
## Invaded - Non_Invaded | Spring | 68.53%
## Invaded - Non_Invaded | Winter | 90.16%Dead Moisture Summary Table
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## <tr class="gt_heading">
## <td colspan="9" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Posterior Estimates for Dead Moisture</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="a::stub"></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Estimate">Estimate</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Est.-Error">Est. Error</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Lower)">95% HPDI (Lower)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Upper)">95% HPDI (Upper)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Probability->-0">Probability > 0</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Bulk-ESS">Bulk ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Tail-ESS">Tail ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="R-hat">R-hat</th>
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## <tbody class="gt_table_body">
## <tr><th id="stub_1_1" scope="row" class="gt_row gt_left gt_stub">(Intercept)</th>
## <td headers="stub_1_1 Estimate" class="gt_row gt_right">1.99</td>
## <td headers="stub_1_1 Est. Error" class="gt_row gt_right">85.03</td>
## <td headers="stub_1_1 95% HPDI (Lower)" class="gt_row gt_right">−236.14</td>
## <td headers="stub_1_1 95% HPDI (Upper)" class="gt_row gt_right">269.44</td>
## <td headers="stub_1_1 Probability > 0" class="gt_row gt_right">0.511</td>
## <td headers="stub_1_1 Bulk ESS" class="gt_row gt_right">5,238</td>
## <td headers="stub_1_1 Tail ESS" class="gt_row gt_right">4,703</td>
## <td headers="stub_1_1 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_2" scope="row" class="gt_row gt_left gt_stub">Latitude</th>
## <td headers="stub_1_2 Estimate" class="gt_row gt_right">0.37</td>
## <td headers="stub_1_2 Est. Error" class="gt_row gt_right">2.85</td>
## <td headers="stub_1_2 95% HPDI (Lower)" class="gt_row gt_right">−8.30</td>
## <td headers="stub_1_2 95% HPDI (Upper)" class="gt_row gt_right">8.58</td>
## <td headers="stub_1_2 Probability > 0" class="gt_row gt_right">0.554</td>
## <td headers="stub_1_2 Bulk ESS" class="gt_row gt_right">5,201</td>
## <td headers="stub_1_2 Tail ESS" class="gt_row gt_right">4,812</td>
## <td headers="stub_1_2 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_3" scope="row" class="gt_row gt_left gt_stub">Season-Spring</th>
## <td headers="stub_1_3 Estimate" class="gt_row gt_right">0.64</td>
## <td headers="stub_1_3 Est. Error" class="gt_row gt_right">3.10</td>
## <td headers="stub_1_3 95% HPDI (Lower)" class="gt_row gt_right">−5.60</td>
## <td headers="stub_1_3 95% HPDI (Upper)" class="gt_row gt_right">6.92</td>
## <td headers="stub_1_3 Probability > 0" class="gt_row gt_right">0.581</td>
## <td headers="stub_1_3 Bulk ESS" class="gt_row gt_right">6,992</td>
## <td headers="stub_1_3 Tail ESS" class="gt_row gt_right">7,063</td>
## <td headers="stub_1_3 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_4" scope="row" class="gt_row gt_left gt_stub">Season-Winter</th>
## <td headers="stub_1_4 Estimate" class="gt_row gt_right">1.68</td>
## <td headers="stub_1_4 Est. Error" class="gt_row gt_right">3.06</td>
## <td headers="stub_1_4 95% HPDI (Lower)" class="gt_row gt_right">−4.58</td>
## <td headers="stub_1_4 95% HPDI (Upper)" class="gt_row gt_right">7.82</td>
## <td headers="stub_1_4 Probability > 0" class="gt_row gt_right">0.707</td>
## <td headers="stub_1_4 Bulk ESS" class="gt_row gt_right">6,852</td>
## <td headers="stub_1_4 Tail ESS" class="gt_row gt_right">7,490</td>
## <td headers="stub_1_4 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_5" scope="row" class="gt_row gt_left gt_stub">Status-Invaded</th>
## <td headers="stub_1_5 Estimate" class="gt_row gt_right">16.94</td>
## <td headers="stub_1_5 Est. Error" class="gt_row gt_right">44.05</td>
## <td headers="stub_1_5 95% HPDI (Lower)" class="gt_row gt_right">−71.92</td>
## <td headers="stub_1_5 95% HPDI (Upper)" class="gt_row gt_right">102.87</td>
## <td headers="stub_1_5 Probability > 0" class="gt_row gt_right">0.646</td>
## <td headers="stub_1_5 Bulk ESS" class="gt_row gt_right">6,552</td>
## <td headers="stub_1_5 Tail ESS" class="gt_row gt_right">6,900</td>
## <td headers="stub_1_5 R-hat" class="gt_row gt_right">1.001</td></tr>
## <tr><th id="stub_1_6" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Latitude</th>
## <td headers="stub_1_6 Estimate" class="gt_row gt_right">−0.46</td>
## <td headers="stub_1_6 Est. Error" class="gt_row gt_right">1.48</td>
## <td headers="stub_1_6 95% HPDI (Lower)" class="gt_row gt_right">−3.29</td>
## <td headers="stub_1_6 95% HPDI (Upper)" class="gt_row gt_right">2.63</td>
## <td headers="stub_1_6 Probability > 0" class="gt_row gt_right">0.380</td>
## <td headers="stub_1_6 Bulk ESS" class="gt_row gt_right">6,485</td>
## <td headers="stub_1_6 Tail ESS" class="gt_row gt_right">6,745</td>
## <td headers="stub_1_6 R-hat" class="gt_row gt_right">1.001</td></tr>
## <tr><th id="stub_1_7" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Spring</th>
## <td headers="stub_1_7 Estimate" class="gt_row gt_right">−4.47</td>
## <td headers="stub_1_7 Est. Error" class="gt_row gt_right">3.62</td>
## <td headers="stub_1_7 95% HPDI (Lower)" class="gt_row gt_right">−11.69</td>
## <td headers="stub_1_7 95% HPDI (Upper)" class="gt_row gt_right">2.74</td>
## <td headers="stub_1_7 Probability > 0" class="gt_row gt_right">0.115</td>
## <td headers="stub_1_7 Bulk ESS" class="gt_row gt_right">6,975</td>
## <td headers="stub_1_7 Tail ESS" class="gt_row gt_right">7,159</td>
## <td headers="stub_1_7 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_8" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Winter</th>
## <td headers="stub_1_8 Estimate" class="gt_row gt_right">0.21</td>
## <td headers="stub_1_8 Est. Error" class="gt_row gt_right">3.96</td>
## <td headers="stub_1_8 95% HPDI (Lower)" class="gt_row gt_right">−7.56</td>
## <td headers="stub_1_8 95% HPDI (Upper)" class="gt_row gt_right">8.17</td>
## <td headers="stub_1_8 Probability > 0" class="gt_row gt_right">0.521</td>
## <td headers="stub_1_8 Bulk ESS" class="gt_row gt_right">6,658</td>
## <td headers="stub_1_8 Tail ESS" class="gt_row gt_right">7,549</td>
## <td headers="stub_1_8 R-hat" class="gt_row gt_right">1.000</td></tr>
## </tbody>
##
## </table>
## </div>Conditional Effects Dead Moisture
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
GLMM Litter Moisture
Model Comparison
## Family: student
## Links: mu = identity
## Formula: avg_litter_moisture_content ~ Status * Season + Status * Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 186)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 7.40 7.85 0.18 28.66 1.00 3682 5050
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -20.73 189.36 -338.89 453.60 1.00 4866
## StatusInvaded 42.20 81.22 -116.64 203.32 1.00 7529
## SeasonSpring -8.63 4.90 -18.81 0.45 1.00 6663
## SeasonWinter 15.88 6.35 3.62 28.39 1.00 6760
## Latitude 1.42 6.32 -14.42 12.11 1.00 4864
## StatusInvaded:SeasonSpring -0.37 6.11 -12.16 11.81 1.00 8316
## StatusInvaded:SeasonWinter -8.84 8.05 -24.24 7.01 1.00 6862
## StatusInvaded:Latitude -1.43 2.73 -6.84 3.87 1.00 7481
## Tail_ESS
## Intercept 3725
## StatusInvaded 7742
## SeasonSpring 6957
## SeasonWinter 7809
## Latitude 3767
## StatusInvaded:SeasonSpring 8530
## StatusInvaded:SeasonWinter 7895
## StatusInvaded:Latitude 7617
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 14.60 1.90 11.10 18.62 1.00 7257 7964
## nu 2.37 0.66 1.44 3.99 1.00 7227 6939
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Warning: There were 19 divergent transitions after warmup. Increasing
## adapt_delta above 0.95 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup## Family: student
## Links: mu = identity
## Formula: avg_litter_moisture_content ~ Status + Season + Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 186)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 7.60 7.56 0.23 27.56 1.00 1326 777
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -8.52 188.47 -396.74 457.32 1.00 653 225
## StatusInvaded -1.94 2.78 -7.53 3.43 1.00 7876 7832
## SeasonSpring -8.90 3.41 -15.99 -2.57 1.00 5587 6950
## SeasonWinter 10.33 3.98 2.61 18.28 1.00 9523 6889
## Latitude 1.03 6.28 -14.69 13.88 1.00 658 225
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 14.77 1.85 11.25 18.56 1.00 1134 272
## nu 2.43 0.66 1.45 4.00 1.00 1064 222
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Using 10 posterior draws for ppc type 'dens_overlay' by default.
Estimated marginal means
## Season = Summer:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded -0.305 -7.96 7.62
##
## Season = Spring:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded -0.665 -9.51 8.78
##
## Season = Winter:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded -9.259 -22.13 4.74
##
## Point estimate displayed: median
## HPD interval probability: 0.95## Probability of Direction
##
## contrast | Season | pd
## ---------------------------------------
## Invaded - Non_Invaded | Summer | 53.12%
## Invaded - Non_Invaded | Spring | 56.14%
## Invaded - Non_Invaded | Winter | 90.41%Litter Moisture Summary Table
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## <thead>
## <tr class="gt_heading">
## <td colspan="9" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Posterior Estimates for Litter Moisture</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="a::stub"></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Estimate">Estimate</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Est.-Error">Est. Error</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Lower)">95% HPDI (Lower)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Upper)">95% HPDI (Upper)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Probability->-0">Probability > 0</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Bulk-ESS">Bulk ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Tail-ESS">Tail ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="R-hat">R-hat</th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><th id="stub_1_1" scope="row" class="gt_row gt_left gt_stub">(Intercept)</th>
## <td headers="stub_1_1 Estimate" class="gt_row gt_right">−41.10</td>
## <td headers="stub_1_1 Est. Error" class="gt_row gt_right">112.79</td>
## <td headers="stub_1_1 95% HPDI (Lower)" class="gt_row gt_right">−374.51</td>
## <td headers="stub_1_1 95% HPDI (Upper)" class="gt_row gt_right">389.95</td>
## <td headers="stub_1_1 Probability > 0" class="gt_row gt_right">0.355</td>
## <td headers="stub_1_1 Bulk ESS" class="gt_row gt_right">4,804</td>
## <td headers="stub_1_1 Tail ESS" class="gt_row gt_right">3,691</td>
## <td headers="stub_1_1 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_2" scope="row" class="gt_row gt_left gt_stub">Latitude</th>
## <td headers="stub_1_2 Estimate" class="gt_row gt_right">2.09</td>
## <td headers="stub_1_2 Est. Error" class="gt_row gt_right">3.79</td>
## <td headers="stub_1_2 95% HPDI (Lower)" class="gt_row gt_right">−12.06</td>
## <td headers="stub_1_2 95% HPDI (Upper)" class="gt_row gt_right">13.51</td>
## <td headers="stub_1_2 Probability > 0" class="gt_row gt_right">0.705</td>
## <td headers="stub_1_2 Bulk ESS" class="gt_row gt_right">4,801</td>
## <td headers="stub_1_2 Tail ESS" class="gt_row gt_right">3,745</td>
## <td headers="stub_1_2 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_3" scope="row" class="gt_row gt_left gt_stub">Season-Spring</th>
## <td headers="stub_1_3 Estimate" class="gt_row gt_right">−8.51</td>
## <td headers="stub_1_3 Est. Error" class="gt_row gt_right">4.88</td>
## <td headers="stub_1_3 95% HPDI (Lower)" class="gt_row gt_right">−18.31</td>
## <td headers="stub_1_3 95% HPDI (Upper)" class="gt_row gt_right">0.84</td>
## <td headers="stub_1_3 Probability > 0" class="gt_row gt_right">0.033</td>
## <td headers="stub_1_3 Bulk ESS" class="gt_row gt_right">6,644</td>
## <td headers="stub_1_3 Tail ESS" class="gt_row gt_right">6,906</td>
## <td headers="stub_1_3 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_4" scope="row" class="gt_row gt_left gt_stub">Season-Winter</th>
## <td headers="stub_1_4 Estimate" class="gt_row gt_right" style="font-weight: bold;">15.89</td>
## <td headers="stub_1_4 Est. Error" class="gt_row gt_right" style="font-weight: bold;">6.46</td>
## <td headers="stub_1_4 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">4.17</td>
## <td headers="stub_1_4 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">28.87</td>
## <td headers="stub_1_4 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">0.994</td>
## <td headers="stub_1_4 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">6,744</td>
## <td headers="stub_1_4 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">7,743</td>
## <td headers="stub_1_4 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.000</td></tr>
## <tr><th id="stub_1_5" scope="row" class="gt_row gt_left gt_stub">Status-Invaded</th>
## <td headers="stub_1_5 Estimate" class="gt_row gt_right">40.41</td>
## <td headers="stub_1_5 Est. Error" class="gt_row gt_right">80.56</td>
## <td headers="stub_1_5 95% HPDI (Lower)" class="gt_row gt_right">−120.24</td>
## <td headers="stub_1_5 95% HPDI (Upper)" class="gt_row gt_right">198.99</td>
## <td headers="stub_1_5 Probability > 0" class="gt_row gt_right">0.700</td>
## <td headers="stub_1_5 Bulk ESS" class="gt_row gt_right">7,502</td>
## <td headers="stub_1_5 Tail ESS" class="gt_row gt_right">7,682</td>
## <td headers="stub_1_5 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_6" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Latitude</th>
## <td headers="stub_1_6 Estimate" class="gt_row gt_right">−1.37</td>
## <td headers="stub_1_6 Est. Error" class="gt_row gt_right">2.70</td>
## <td headers="stub_1_6 95% HPDI (Lower)" class="gt_row gt_right">−6.79</td>
## <td headers="stub_1_6 95% HPDI (Upper)" class="gt_row gt_right">3.90</td>
## <td headers="stub_1_6 Probability > 0" class="gt_row gt_right">0.300</td>
## <td headers="stub_1_6 Bulk ESS" class="gt_row gt_right">7,456</td>
## <td headers="stub_1_6 Tail ESS" class="gt_row gt_right">7,548</td>
## <td headers="stub_1_6 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_7" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Spring</th>
## <td headers="stub_1_7 Estimate" class="gt_row gt_right">−0.39</td>
## <td headers="stub_1_7 Est. Error" class="gt_row gt_right">5.97</td>
## <td headers="stub_1_7 95% HPDI (Lower)" class="gt_row gt_right">−12.21</td>
## <td headers="stub_1_7 95% HPDI (Upper)" class="gt_row gt_right">11.65</td>
## <td headers="stub_1_7 Probability > 0" class="gt_row gt_right">0.472</td>
## <td headers="stub_1_7 Bulk ESS" class="gt_row gt_right">8,286</td>
## <td headers="stub_1_7 Tail ESS" class="gt_row gt_right">8,517</td>
## <td headers="stub_1_7 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_8" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Winter</th>
## <td headers="stub_1_8 Estimate" class="gt_row gt_right">−8.91</td>
## <td headers="stub_1_8 Est. Error" class="gt_row gt_right">8.14</td>
## <td headers="stub_1_8 95% HPDI (Lower)" class="gt_row gt_right">−24.35</td>
## <td headers="stub_1_8 95% HPDI (Upper)" class="gt_row gt_right">6.84</td>
## <td headers="stub_1_8 Probability > 0" class="gt_row gt_right">0.141</td>
## <td headers="stub_1_8 Bulk ESS" class="gt_row gt_right">6,848</td>
## <td headers="stub_1_8 Tail ESS" class="gt_row gt_right">7,875</td>
## <td headers="stub_1_8 R-hat" class="gt_row gt_right">1.000</td></tr>
## </tbody>
##
## </table>
## </div>Conditional Effects Litter Moisture
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
GLMM Soil Moisture
Model Comparison
## Family: student
## Links: mu = identity
## Formula: avg_soil_moisture ~ Status * Season + Status * Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 186)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 2.85 2.98 0.11 10.88 1.00 2701 4147
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -47.03 72.34 -158.10 142.06 1.00 4287
## StatusInvaded 8.42 20.69 -31.06 49.08 1.00 7078
## SeasonSpring -0.68 1.17 -2.94 1.60 1.00 8839
## SeasonWinter 2.04 1.00 0.07 4.02 1.00 8436
## Latitude 1.80 2.41 -4.45 5.48 1.00 4302
## StatusInvaded:SeasonSpring -2.71 1.63 -5.89 0.48 1.00 8792
## StatusInvaded:SeasonWinter -1.82 1.56 -4.92 1.21 1.00 8508
## StatusInvaded:Latitude -0.21 0.69 -1.56 1.11 1.00 7113
## Tail_ESS
## Intercept 3086
## StatusInvaded 7490
## SeasonSpring 8829
## SeasonWinter 8673
## Latitude 3165
## StatusInvaded:SeasonSpring 8709
## StatusInvaded:SeasonWinter 8508
## StatusInvaded:Latitude 7362
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 3.23 0.33 2.61 3.93 1.00 8660 7810
## nu 2.02 0.37 1.40 2.87 1.00 9580 6613
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Warning: There were 16 divergent transitions after warmup. Increasing
## adapt_delta above 0.95 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup## Family: student
## Links: mu = identity
## Formula: avg_soil_moisture ~ Status + Season + Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 186)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 2.88 2.72 0.12 10.60 1.00 914 367
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept -45.48 67.71 -153.26 153.12 1.01 642 140
## StatusInvaded 1.02 0.67 -0.39 2.32 1.01 888 145
## SeasonSpring -2.01 0.87 -3.62 -0.27 1.00 2631 1742
## SeasonWinter 1.29 0.73 -0.13 2.73 1.00 6693 6156
## Latitude 1.77 2.25 -4.80 5.35 1.01 643 138
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 3.23 0.32 2.63 3.89 1.00 6637 7482
## nu 2.01 0.37 1.42 2.85 1.00 4751 5530
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Using 10 posterior draws for ppc type 'dens_overlay' by default.
Estimated marginal means
## Season = Summer:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 2.207 0.224 4.05
##
## Season = Spring:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded -0.498 -3.170 2.23
##
## Season = Winter:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 0.416 -2.107 2.70
##
## Point estimate displayed: median
## HPD interval probability: 0.95## Probability of Direction
##
## contrast | Season | pd
## ---------------------------------------
## Invaded - Non_Invaded | Summer | 99.05%
## Invaded - Non_Invaded | Spring | 63.97%
## Invaded - Non_Invaded | Winter | 63.02%Soil Moisture Summary Table
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## <thead>
## <tr class="gt_heading">
## <td colspan="9" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Posterior Estimates for Soil Moisture</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="a::stub"></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Estimate">Estimate</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Est.-Error">Est. Error</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Lower)">95% HPDI (Lower)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Upper)">95% HPDI (Upper)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Probability->-0">Probability > 0</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Bulk-ESS">Bulk ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Tail-ESS">Tail ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="R-hat">R-hat</th>
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## <tbody class="gt_table_body">
## <tr><th id="stub_1_1" scope="row" class="gt_row gt_left gt_stub">(Intercept)</th>
## <td headers="stub_1_1 Estimate" class="gt_row gt_right">−59.58</td>
## <td headers="stub_1_1 Est. Error" class="gt_row gt_right">35.63</td>
## <td headers="stub_1_1 95% HPDI (Lower)" class="gt_row gt_right">−169.65</td>
## <td headers="stub_1_1 95% HPDI (Upper)" class="gt_row gt_right">119.86</td>
## <td headers="stub_1_1 Probability > 0" class="gt_row gt_right">0.149</td>
## <td headers="stub_1_1 Bulk ESS" class="gt_row gt_right">4,267</td>
## <td headers="stub_1_1 Tail ESS" class="gt_row gt_right">3,066</td>
## <td headers="stub_1_1 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_2" scope="row" class="gt_row gt_left gt_stub">Latitude</th>
## <td headers="stub_1_2 Estimate" class="gt_row gt_right">2.23</td>
## <td headers="stub_1_2 Est. Error" class="gt_row gt_right">1.18</td>
## <td headers="stub_1_2 95% HPDI (Lower)" class="gt_row gt_right">−3.56</td>
## <td headers="stub_1_2 95% HPDI (Upper)" class="gt_row gt_right">6.10</td>
## <td headers="stub_1_2 Probability > 0" class="gt_row gt_right">0.867</td>
## <td headers="stub_1_2 Bulk ESS" class="gt_row gt_right">4,283</td>
## <td headers="stub_1_2 Tail ESS" class="gt_row gt_right">3,145</td>
## <td headers="stub_1_2 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_3" scope="row" class="gt_row gt_left gt_stub">Season-Spring</th>
## <td headers="stub_1_3 Estimate" class="gt_row gt_right">−0.66</td>
## <td headers="stub_1_3 Est. Error" class="gt_row gt_right">1.20</td>
## <td headers="stub_1_3 95% HPDI (Lower)" class="gt_row gt_right">−2.94</td>
## <td headers="stub_1_3 95% HPDI (Upper)" class="gt_row gt_right">1.59</td>
## <td headers="stub_1_3 Probability > 0" class="gt_row gt_right">0.284</td>
## <td headers="stub_1_3 Bulk ESS" class="gt_row gt_right">8,736</td>
## <td headers="stub_1_3 Tail ESS" class="gt_row gt_right">8,808</td>
## <td headers="stub_1_3 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_4" scope="row" class="gt_row gt_left gt_stub">Season-Winter</th>
## <td headers="stub_1_4 Estimate" class="gt_row gt_right" style="font-weight: bold;">2.05</td>
## <td headers="stub_1_4 Est. Error" class="gt_row gt_right" style="font-weight: bold;">0.99</td>
## <td headers="stub_1_4 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">0.06</td>
## <td headers="stub_1_4 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">3.99</td>
## <td headers="stub_1_4 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">0.978</td>
## <td headers="stub_1_4 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">8,403</td>
## <td headers="stub_1_4 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">8,653</td>
## <td headers="stub_1_4 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.000</td></tr>
## <tr><th id="stub_1_5" scope="row" class="gt_row gt_left gt_stub">Status-Invaded</th>
## <td headers="stub_1_5 Estimate" class="gt_row gt_right">8.02</td>
## <td headers="stub_1_5 Est. Error" class="gt_row gt_right">21.00</td>
## <td headers="stub_1_5 95% HPDI (Lower)" class="gt_row gt_right">−31.01</td>
## <td headers="stub_1_5 95% HPDI (Upper)" class="gt_row gt_right">49.12</td>
## <td headers="stub_1_5 Probability > 0" class="gt_row gt_right">0.649</td>
## <td headers="stub_1_5 Bulk ESS" class="gt_row gt_right">7,072</td>
## <td headers="stub_1_5 Tail ESS" class="gt_row gt_right">7,451</td>
## <td headers="stub_1_5 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_6" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Latitude</th>
## <td headers="stub_1_6 Estimate" class="gt_row gt_right">−0.20</td>
## <td headers="stub_1_6 Est. Error" class="gt_row gt_right">0.70</td>
## <td headers="stub_1_6 95% HPDI (Lower)" class="gt_row gt_right">−1.50</td>
## <td headers="stub_1_6 95% HPDI (Upper)" class="gt_row gt_right">1.16</td>
## <td headers="stub_1_6 Probability > 0" class="gt_row gt_right">0.385</td>
## <td headers="stub_1_6 Bulk ESS" class="gt_row gt_right">7,139</td>
## <td headers="stub_1_6 Tail ESS" class="gt_row gt_right">7,339</td>
## <td headers="stub_1_6 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_7" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Spring</th>
## <td headers="stub_1_7 Estimate" class="gt_row gt_right">−2.71</td>
## <td headers="stub_1_7 Est. Error" class="gt_row gt_right">1.64</td>
## <td headers="stub_1_7 95% HPDI (Lower)" class="gt_row gt_right">−5.90</td>
## <td headers="stub_1_7 95% HPDI (Upper)" class="gt_row gt_right">0.46</td>
## <td headers="stub_1_7 Probability > 0" class="gt_row gt_right">0.047</td>
## <td headers="stub_1_7 Bulk ESS" class="gt_row gt_right">8,781</td>
## <td headers="stub_1_7 Tail ESS" class="gt_row gt_right">8,655</td>
## <td headers="stub_1_7 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_8" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Winter</th>
## <td headers="stub_1_8 Estimate" class="gt_row gt_right">−1.80</td>
## <td headers="stub_1_8 Est. Error" class="gt_row gt_right">1.55</td>
## <td headers="stub_1_8 95% HPDI (Lower)" class="gt_row gt_right">−4.94</td>
## <td headers="stub_1_8 95% HPDI (Upper)" class="gt_row gt_right">1.19</td>
## <td headers="stub_1_8 Probability > 0" class="gt_row gt_right">0.122</td>
## <td headers="stub_1_8 Bulk ESS" class="gt_row gt_right">8,493</td>
## <td headers="stub_1_8 Tail ESS" class="gt_row gt_right">8,475</td>
## <td headers="stub_1_8 R-hat" class="gt_row gt_right">1.000</td></tr>
## </tbody>
##
## </table>
## </div>Conditional Effects Soil Moisture
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
GLMM Veg Height
Model Comparison
## Warning: There were 45 divergent transitions after warmup. Increasing
## adapt_delta above 0.95 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup## Family: student
## Links: mu = identity
## Formula: avg_height ~ Status * Season + Status * Latitude + (1 | Site)
## Data: merged_sites (Number of observations: 183)
## Draws: 4 chains, each with iter = 4000; warmup = 1000; thin = 1;
## total post-warmup draws = 12000
##
## Multilevel Hyperparameters:
## ~Site (Number of levels: 3)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.32 0.48 0.00 1.83 1.03 104 29
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept -0.18 4.49 -12.50 8.57 1.03 129
## StatusInvaded 3.76 0.68 2.40 5.09 1.00 6008
## SeasonSpring 0.09 0.04 0.01 0.18 1.01 3392
## SeasonWinter -0.02 0.04 -0.10 0.06 1.00 1997
## Latitude 0.01 0.15 -0.26 0.43 1.03 117
## StatusInvaded:SeasonSpring -0.16 0.06 -0.28 -0.04 1.01 320
## StatusInvaded:SeasonWinter 0.01 0.06 -0.11 0.12 1.01 540
## StatusInvaded:Latitude -0.11 0.02 -0.15 -0.06 1.00 5985
## Tail_ESS
## Intercept 274
## StatusInvaded 6271
## SeasonSpring 5552
## SeasonWinter 7570
## Latitude 263
## StatusInvaded:SeasonSpring 1289
## StatusInvaded:SeasonWinter 285
## StatusInvaded:Latitude 6538
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.14 0.01 0.11 0.16 1.00 6059 7344
## nu 5.05 2.30 2.55 10.52 1.00 5593 6245
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).## Using 10 posterior draws for ppc type 'dens_overlay' by default.
## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
Estimated marginal means
## Season = Summer:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 0.588 0.520 0.653
##
## Season = Spring:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 0.425 0.328 0.525
##
## Season = Winter:
## contrast estimate lower.HPD upper.HPD
## Invaded - Non_Invaded 0.595 0.498 0.695
##
## Point estimate displayed: median
## HPD interval probability: 0.95## Probability of Direction
##
## contrast | Season | pd
## -------------------------------------
## Invaded - Non_Invaded | Summer | 100%
## Invaded - Non_Invaded | Spring | 100%
## Invaded - Non_Invaded | Winter | 100%Veg Height Summary Table
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## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_heading">
## <td colspan="9" class="gt_heading gt_title gt_font_normal gt_bottom_border" style>Posterior Estimates for Vegetation Height</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="a::stub"></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Estimate">Estimate</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Est.-Error">Est. Error</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Lower)">95% HPDI (Lower)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="a95%-HPDI-(Upper)">95% HPDI (Upper)</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Probability->-0">Probability > 0</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Bulk-ESS">Bulk ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Tail-ESS">Tail ESS</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="R-hat">R-hat</th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><th id="stub_1_1" scope="row" class="gt_row gt_left gt_stub">(Intercept)</th>
## <td headers="stub_1_1 Estimate" class="gt_row gt_right">0.60</td>
## <td headers="stub_1_1 Est. Error" class="gt_row gt_right">1.89</td>
## <td headers="stub_1_1 95% HPDI (Lower)" class="gt_row gt_right">−10.09</td>
## <td headers="stub_1_1 95% HPDI (Upper)" class="gt_row gt_right">9.39</td>
## <td headers="stub_1_1 Probability > 0" class="gt_row gt_right">0.636</td>
## <td headers="stub_1_1 Bulk ESS" class="gt_row gt_right">192</td>
## <td headers="stub_1_1 Tail ESS" class="gt_row gt_right">285</td>
## <td headers="stub_1_1 R-hat" class="gt_row gt_right">1.012</td></tr>
## <tr><th id="stub_1_2" scope="row" class="gt_row gt_left gt_stub">Latitude</th>
## <td headers="stub_1_2 Estimate" class="gt_row gt_right">−0.01</td>
## <td headers="stub_1_2 Est. Error" class="gt_row gt_right">0.06</td>
## <td headers="stub_1_2 95% HPDI (Lower)" class="gt_row gt_right">−0.29</td>
## <td headers="stub_1_2 95% HPDI (Upper)" class="gt_row gt_right">0.35</td>
## <td headers="stub_1_2 Probability > 0" class="gt_row gt_right">0.414</td>
## <td headers="stub_1_2 Bulk ESS" class="gt_row gt_right">175</td>
## <td headers="stub_1_2 Tail ESS" class="gt_row gt_right">275</td>
## <td headers="stub_1_2 R-hat" class="gt_row gt_right">1.013</td></tr>
## <tr><th id="stub_1_3" scope="row" class="gt_row gt_left gt_stub">Season-Spring</th>
## <td headers="stub_1_3 Estimate" class="gt_row gt_right" style="font-weight: bold;">0.10</td>
## <td headers="stub_1_3 Est. Error" class="gt_row gt_right" style="font-weight: bold;">0.04</td>
## <td headers="stub_1_3 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">0.01</td>
## <td headers="stub_1_3 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">0.18</td>
## <td headers="stub_1_3 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">0.986</td>
## <td headers="stub_1_3 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">3,021</td>
## <td headers="stub_1_3 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">5,496</td>
## <td headers="stub_1_3 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.002</td></tr>
## <tr><th id="stub_1_4" scope="row" class="gt_row gt_left gt_stub">Season-Winter</th>
## <td headers="stub_1_4 Estimate" class="gt_row gt_right">−0.02</td>
## <td headers="stub_1_4 Est. Error" class="gt_row gt_right">0.04</td>
## <td headers="stub_1_4 95% HPDI (Lower)" class="gt_row gt_right">−0.10</td>
## <td headers="stub_1_4 95% HPDI (Upper)" class="gt_row gt_right">0.06</td>
## <td headers="stub_1_4 Probability > 0" class="gt_row gt_right">0.306</td>
## <td headers="stub_1_4 Bulk ESS" class="gt_row gt_right">1,902</td>
## <td headers="stub_1_4 Tail ESS" class="gt_row gt_right">7,526</td>
## <td headers="stub_1_4 R-hat" class="gt_row gt_right">1.000</td></tr>
## <tr><th id="stub_1_5" scope="row" class="gt_row gt_left gt_stub">Status-Invaded</th>
## <td headers="stub_1_5 Estimate" class="gt_row gt_right" style="font-weight: bold;">3.73</td>
## <td headers="stub_1_5 Est. Error" class="gt_row gt_right" style="font-weight: bold;">0.66</td>
## <td headers="stub_1_5 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">2.40</td>
## <td headers="stub_1_5 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">5.09</td>
## <td headers="stub_1_5 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">1.000</td>
## <td headers="stub_1_5 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">5,972</td>
## <td headers="stub_1_5 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">6,249</td>
## <td headers="stub_1_5 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.002</td></tr>
## <tr><th id="stub_1_6" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Latitude</th>
## <td headers="stub_1_6 Estimate" class="gt_row gt_right" style="font-weight: bold;">−0.11</td>
## <td headers="stub_1_6 Est. Error" class="gt_row gt_right" style="font-weight: bold;">0.02</td>
## <td headers="stub_1_6 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">−0.15</td>
## <td headers="stub_1_6 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">−0.06</td>
## <td headers="stub_1_6 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">0.000</td>
## <td headers="stub_1_6 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">5,969</td>
## <td headers="stub_1_6 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">6,501</td>
## <td headers="stub_1_6 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.002</td></tr>
## <tr><th id="stub_1_7" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Spring</th>
## <td headers="stub_1_7 Estimate" class="gt_row gt_right" style="font-weight: bold;">−0.16</td>
## <td headers="stub_1_7 Est. Error" class="gt_row gt_right" style="font-weight: bold;">0.06</td>
## <td headers="stub_1_7 95% HPDI (Lower)" class="gt_row gt_right" style="font-weight: bold;">−0.28</td>
## <td headers="stub_1_7 95% HPDI (Upper)" class="gt_row gt_right" style="font-weight: bold;">−0.05</td>
## <td headers="stub_1_7 Probability > 0" class="gt_row gt_right" style="font-weight: bold;">0.003</td>
## <td headers="stub_1_7 Bulk ESS" class="gt_row gt_right" style="font-weight: bold;">427</td>
## <td headers="stub_1_7 Tail ESS" class="gt_row gt_right" style="font-weight: bold;">1,278</td>
## <td headers="stub_1_7 R-hat" class="gt_row gt_right" style="font-weight: bold;">1.004</td></tr>
## <tr><th id="stub_1_8" scope="row" class="gt_row gt_left gt_stub">Status-Invaded x Season-Winter</th>
## <td headers="stub_1_8 Estimate" class="gt_row gt_right">0.01</td>
## <td headers="stub_1_8 Est. Error" class="gt_row gt_right">0.06</td>
## <td headers="stub_1_8 95% HPDI (Lower)" class="gt_row gt_right">−0.11</td>
## <td headers="stub_1_8 95% HPDI (Upper)" class="gt_row gt_right">0.12</td>
## <td headers="stub_1_8 Probability > 0" class="gt_row gt_right">0.545</td>
## <td headers="stub_1_8 Bulk ESS" class="gt_row gt_right">521</td>
## <td headers="stub_1_8 Tail ESS" class="gt_row gt_right">300</td>
## <td headers="stub_1_8 R-hat" class="gt_row gt_right">1.002</td></tr>
## </tbody>
##
## </table>
## </div>Conditional Effects Veg Height
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_ribbon()`).
## You are calculating adjusted predictions on the population-level (i.e.
## `type = "fixed"`) for a *generalized* linear mixed model.
## This may produce biased estimates due to Jensen's inequality. Consider
## setting `bias_correction = TRUE` to correct for this bias.
## See also the documentation of the `bias_correction` argument.
## Note: uncertainty of error terms are not taken into account. Consider
## setting `interval` to "prediction". This will call `posterior_predict()`
## instead of `posterior_epred()`.## Warning in check_dep_version(dep_pkg = "TMB"): package version mismatch:
## glmmTMB was built with TMB package version 1.9.17
## Current TMB package version is 1.9.18
## Please re-install glmmTMB from source or restore original 'TMB' package (see '?reinstalling' for more information)## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Ignoring unknown labels:
## • linetype : "Latitude"
## • shape : "Latitude"## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.