Fit GLMMs

Load libraries

library("readxl")
library("tidyverse")
library("dplyr")
library("ggeffects")
library("here")
library("glmmTMB")
library("lubridate")
library("ggpubr")
library("kableExtra")
library("DHARMa")
library("SDMTools")
library("ggthemes")
library("patchwork")
library("epiboxwoodblight")

Import disease data

disease_dat <- read_excel(system.file(
  "extdata",
  "NC_disease_data.xlsx",
  package = "epiboxwoodblight",
  mustWork = TRUE
)) %>%
  dplyr::mutate(year = as.factor(year)) %>%
  dplyr::mutate(location = as.factor(location)) %>%
  dplyr::mutate(spread_event = as.factor(spread_event)) %>%
  dplyr::mutate(replicate = as.factor(replicate)) %>%
  dplyr::mutate(treatment = as.factor(treatment)) %>%
  dplyr::mutate(total_count = as.integer(total_count)) %>%
  dplyr::mutate(month = as.factor(months(date_in))) %>%
  dplyr::mutate(spev_duration = as.integer(difftime(date_out, date_in))) %>%
  relocate(month, .after = date_out) %>%
  na.omit() %>%
  group_by(
    year,
    location,
    spread_event,
    month,
    treatment,
    date_in,
    date_out,
    cultivar,
    spev_duration
  ) %>%
  summarise(total_count = sum(total_count))

#openxlsx::write.xlsx(disease_dat, "Table S1.xlsx", rowNames=FALSE)

Import weather data

# Filter rainy periods to calculate average wind speed, wind direction & temperature wet period
weather_dat_rain <- read_excel(system.file(
  "extdata",
  "NC_weather_data.xlsx",
  package = "epiboxwoodblight",
  mustWork = TRUE
)) %>%
  select(year,
         wind_speed,
         wind_direction,
         temperature,
         precipitation,
         location,
         spread_event) %>%
  dplyr::mutate(year = as.factor(year)) %>%
  dplyr::mutate(location = as.factor(location)) %>%
  dplyr::mutate(spread_event = as.factor(spread_event)) %>%
  dplyr::mutate(rain_duration = as.integer(precipitation > 0)) %>%
  filter(precipitation > 0) %>%
  group_by(year, location, spread_event) %>%
  summarise(
    total_rain = round(sum(precipitation), 5),
    mean_ws = round(mean(wind_speed), 2),
    rain_duration = round(sum(rain_duration * 15 / 60), 2),
    mean_wd = round(circular.averaging(wind_direction), 2),
    mean_temp = round(mean(temperature), 2)
  )

# Filter rainless periods to calculate mean RH
weather_dat_no_rain <-
  read_excel(system.file(
    "extdata",
    "NC_weather_data.xlsx",
    package = "epiboxwoodblight",
    mustWork = TRUE
  )) %>%
  select(
    year,
    relative_humidity,
    leaf_wetness_duration,
    precipitation,
    location,
    spread_event,
    date
  ) %>%
  dplyr::mutate(year = as.factor(year)) %>%
  dplyr::mutate(location = as.factor(location)) %>%
  dplyr::mutate(spread_event = as.factor(spread_event)) %>%
  filter(precipitation == 0) %>%
  group_by(year, location, spread_event) %>%
  summarise(mean_rh = round(mean(relative_humidity * 100), 2))

# Combine data
weather_dat_comb <-
  left_join(weather_dat_rain,
            weather_dat_no_rain,
            by = c("year", "location", "spread_event"))

# Leaf wetness duration both inside and outside rainy periods
weather_wet <- read_excel(system.file(
  "extdata",
  "NC_weather_data.xlsx",
  package = "epiboxwoodblight",
  mustWork = TRUE
)) %>%
  dplyr::mutate(year = as.factor(year)) %>%
  dplyr::mutate(location = as.factor(location)) %>%
  dplyr::mutate(spread_event = as.factor(spread_event)) %>%
  group_by(year, location, spread_event) %>%
  summarise(lwd_duration = round(sum(leaf_wetness_duration / 60), 2))

weather_dat <-
  left_join(weather_dat_comb,
            weather_wet,
            by = c("year", "location", "spread_event"))

# Divide week 1 of 2014 rain/rain duration/wetness duration by 4 & that of week 2 & 3 by 3 to convert to per week data because the duration of spread event was 4 and 3 weeks, respectively.

weather_dat <- weather_dat %>%
  mutate(
    total_rain = ifelse(
      year == "2017" & spread_event == "1",
      total_rain / 4,
      ifelse(
        year == "2017" &
          spread_event %in% c("2", "3"),
        total_rain / 3,
        total_rain
      )
    ),
    rain_duration = ifelse(
      year == "2017" & spread_event == "1",
      rain_duration / 4,
      ifelse(
        year == "2017" &
          spread_event %in% c("2", "3"),
        rain_duration / 3,
        rain_duration
      )
    ),
    lwd_duration = ifelse(
      year == "2017" & spread_event == "1",
      lwd_duration / 4,
      ifelse(
        year == "2017" &
          spread_event %in% c("2", "3"),
        lwd_duration / 3,
        lwd_duration
      )
    )
  )

Cobmine weather & disease data

Combine weather and disease data

dat_NC <-
  left_join(disease_dat,
            weather_dat,
            by = c("year", "location", "spread_event")) %>%
  # Replace NA with zero because NA are introduced due to data munging. Original values were zero
  dplyr::mutate(total_rain = replace_na(total_rain, 0)) %>%
  dplyr::mutate(rain_duration = replace_na(rain_duration, 0))

# Since we filtered data separately for precipitation and then without precipitation, NAs are introduced. In this step, data (in which values were added manually) is imported

dat_missing <- read_excel(system.file(
  "extdata",
  "NC_missing_data.xlsx",
  package = "epiboxwoodblight",
  mustWork = TRUE
)) %>%
  dplyr::mutate(year = as.factor(year)) %>%
  dplyr::mutate(location = as.factor(location)) %>%
  dplyr::mutate(spread_event = as.factor(spread_event))

# Combine data to replace NA values with distinct data
dat_nc <-
  left_join(dat_NC, dat_missing, by = c("year", "location", "spread_event")) %>%
  mutate(mean_ws = coalesce(mean_ws.x, mean_ws.y)) %>%
  select(-mean_ws.x, -mean_ws.y) %>%
  mutate(mean_temp = coalesce(mean_temp.x, mean_temp.y)) %>%
  select(-mean_temp.x, -mean_temp.y) %>%
  mutate(mean_rh = coalesce(mean_rh.x, mean_rh.y)) %>%
  select(-mean_rh.x, -mean_rh.y) %>%
  mutate(mean_wd = coalesce(mean_wd.x, mean_wd.y)) %>%
  select(-mean_wd.x, -mean_wd.y) %>%
  mutate(lwd_duration = coalesce(lwd_duration.x, lwd_duration.y)) %>%
  select(-lwd_duration.x, -lwd_duration.y) %>%
  distinct()

## Warning in left_join(dat_NC, dat_missing, by = c("year", "location", "spread_event")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 50 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

dat_nc <- dat_nc %>%
  mutate(daily_rain = round(total_rain/spev_duration, 2),
         daily_lwd  = round(lwd_duration/spev_duration, 2))



# Filter out mulch treatment. Use non-mulch and CP only.
dat_nc_ncb <- dat_nc %>%
  filter(treatment != "mulch", treatment != "between_row") # filter non-mulch, CP and between row treatments data

# Data considering only CP treatment
dat_cp <- dat_nc %>%
  filter(treatment == "CP")

# Data considering only leaf debris treatment
dat_ld <- dat_nc %>%
  filter(treatment == "non_mulch")

# Data considering only between row treatment
dat_br <- dat_nc %>%
  filter(treatment == "between_row")

# Data for Lambsburg site only
dat_lambsburg <- dat_nc %>%
  filter(location == "Lambsburg") %>%
  filter(treatment != "mulch", treatment != "between_row")

Check data

kable(dat_nc_ncb,
      format = "html",
      table.attr = "class='table table-hover'")

year	location	spread_event	month	treatment	date_in	date_out	cultivar	spev_duration	total_count	total_rain	rain_duration	mean_ws	mean_temp	mean_rh	mean_wd	lwd_duration	daily_rain	daily_lwd
2014	Lambsburg	1	May	CP	2014-05-26	2014-06-02	Suffruticosa	7	0	6.20000	3.25000	0.26	20.59	80.14	110.16	42.98000	0.89	6.14
2014	Lambsburg	2	June	CP	2014-06-09	2014-06-16	Suffruticosa	7	0	6.60000	3.25000	0.28	20.78	79.89	233.53	57.58000	0.94	8.23
2014	Lambsburg	2	June	non_mulch	2014-06-09	2014-06-16	Suffruticosa	7	0	6.60000	3.25000	0.28	20.78	79.89	233.53	57.58000	0.94	8.23
2014	Lambsburg	3	June	CP	2014-06-16	2014-06-23	Suffruticosa	7	0	6.00000	1.50000	0.40	22.23	77.92	118.47	33.83000	0.86	4.83
2014	Lambsburg	3	June	non_mulch	2014-06-16	2014-06-23	Suffruticosa	7	4	6.00000	1.50000	0.40	22.23	77.92	118.47	33.83000	0.86	4.83
2014	Lambsburg	4	June	CP	2014-06-23	2014-06-30	Suffruticosa	7	3	54.00000	7.75000	0.26	19.71	91.19	104.58	69.38000	7.71	9.91
2014	Lambsburg	4	June	non_mulch	2014-06-23	2014-06-30	Suffruticosa	7	13	54.00000	7.75000	0.26	19.71	91.19	104.58	69.38000	7.71	9.91
2014	Lambsburg	5	June	CP	2014-06-30	2014-07-07	Suffruticosa	7	0	0.60000	0.25000	0.40	26.20	77.35	85.00	1.87000	0.09	0.27
2014	Lambsburg	5	June	non_mulch	2014-06-30	2014-07-07	Suffruticosa	7	0	0.60000	0.25000	0.40	26.20	77.35	85.00	1.87000	0.09	0.27
2014	Lambsburg	6	July	CP	2014-07-07	2014-07-14	Suffruticosa	7	16	27.00000	3.75000	0.19	20.49	86.95	211.68	93.13000	3.86	13.30
2014	Lambsburg	6	July	non_mulch	2014-07-07	2014-07-14	Suffruticosa	7	17	27.00000	3.75000	0.19	20.49	86.95	211.68	93.13000	3.86	13.30
2014	Lambsburg	7	July	CP	2014-07-14	2014-07-21	Suffruticosa	7	0	3.00000	2.75000	0.11	18.87	80.31	302.73	67.03000	0.43	9.58
2014	Lambsburg	7	July	non_mulch	2014-07-14	2014-07-21	Suffruticosa	7	0	3.00000	2.75000	0.11	18.87	80.31	302.73	67.03000	0.43	9.58
2014	Lambsburg	8	July	CP	2014-07-21	2014-07-28	Suffruticosa	7	7	29.80000	5.00000	0.31	21.85	89.26	65.58	109.03000	4.26	15.58
2014	Lambsburg	8	July	non_mulch	2014-07-21	2014-07-28	Suffruticosa	7	31	29.80000	5.00000	0.31	21.85	89.26	65.58	109.03000	4.26	15.58
2014	Lambsburg	9	July	CP	2014-07-28	2014-08-04	Suffruticosa	7	3	38.20000	14.00000	0.11	17.11	82.11	257.13	67.52000	5.46	9.65
2014	Lambsburg	9	July	non_mulch	2014-07-28	2014-08-04	Suffruticosa	7	30	38.20000	14.00000	0.11	17.11	82.11	257.13	67.52000	5.46	9.65
2014	Lambsburg	10	August	CP	2014-08-04	2014-08-11	Suffruticosa	7	83	114.60000	35.75000	0.23	18.82	89.30	24.30	95.98000	16.37	13.71
2014	Lambsburg	10	August	non_mulch	2014-08-04	2014-08-11	Suffruticosa	7	215	114.60000	35.75000	0.23	18.82	89.30	24.30	95.98000	16.37	13.71
2014	Lambsburg	11	August	CP	2014-08-11	2014-08-18	Suffruticosa	7	31	51.20000	17.00000	0.09	19.76	88.44	44.67	54.92000	7.31	7.85
2014	Lambsburg	11	August	non_mulch	2014-08-11	2014-08-18	Suffruticosa	7	50	51.20000	17.00000	0.09	19.76	88.44	44.67	54.92000	7.31	7.85
2014	Lambsburg	12	September	CP	2014-09-08	2014-09-15	Suffruticosa	7	276	46.40000	10.75000	0.14	18.88	94.59	263.90	160.38000	6.63	22.91
2014	Lambsburg	12	September	non_mulch	2014-09-08	2014-09-15	Suffruticosa	7	120	46.40000	10.75000	0.14	18.88	94.59	263.90	160.38000	6.63	22.91
2014	Lambsburg	13	September	CP	2014-09-15	2014-09-22	Suffruticosa	7	0	0.60000	0.50000	0.20	18.40	89.87	309.50	127.93000	0.09	18.28
2014	Lambsburg	13	September	non_mulch	2014-09-15	2014-09-22	Suffruticosa	7	0	0.60000	0.50000	0.20	18.40	89.87	309.50	127.93000	0.09	18.28
2014	Lambsburg	14	September	CP	2014-09-22	2014-09-29	Suffruticosa	7	0	2.80000	3.00000	0.07	16.12	87.44	266.64	139.80000	0.40	19.97
2014	Lambsburg	14	September	non_mulch	2014-09-22	2014-09-29	Suffruticosa	7	0	2.80000	3.00000	0.07	16.12	87.44	266.64	139.80000	0.40	19.97
2014	Lambsburg	15	September	CP	2014-09-29	2014-10-06	Suffruticosa	7	0	14.00000	5.25000	0.14	18.26	85.67	226.82	137.05000	2.00	19.58
2014	Lambsburg	15	September	non_mulch	2014-09-29	2014-10-06	Suffruticosa	7	7	14.00000	5.25000	0.14	18.26	85.67	226.82	137.05000	2.00	19.58
2014	Lambsburg	16	October	CP	2014-10-06	2014-10-13	Suffruticosa	7	48	49.20000	25.00000	0.16	14.14	95.62	62.53	154.52000	7.03	22.07
2014	Lambsburg	16	October	non_mulch	2014-10-06	2014-10-13	Suffruticosa	7	44	49.20000	25.00000	0.16	14.14	95.62	62.53	154.52000	7.03	22.07
2014	Lambsburg	17	October	CP	2014-10-13	2014-10-20	Suffruticosa	7	247	46.60000	9.50000	0.52	16.82	87.97	235.50	148.83000	6.66	21.26
2014	Lambsburg	17	October	non_mulch	2014-10-13	2014-10-20	Suffruticosa	7	134	46.60000	9.50000	0.52	16.82	87.97	235.50	148.83000	6.66	21.26
2014	Lambsburg	18	October	CP	2014-10-20	2014-10-27	Suffruticosa	7	0	0.00000	0.00000	0.82	12.73	65.00	69.17	13.00000	0.00	1.86
2014	Lambsburg	18	October	non_mulch	2014-10-20	2014-10-27	Suffruticosa	7	0	0.00000	0.00000	0.82	12.73	65.00	69.17	13.00000	0.00	1.86
2014	Lambsburg	19	October	CP	2014-10-27	2014-11-03	Suffruticosa	7	0	12.20000	10.00000	0.94	4.72	67.92	91.25	85.38000	1.74	12.20
2014	Lambsburg	19	October	non_mulch	2014-10-27	2014-11-03	Suffruticosa	7	0	12.20000	10.00000	0.94	4.72	67.92	91.25	85.38000	1.74	12.20
2014	Lambsburg	20	November	CP	2014-11-03	2014-11-10	Suffruticosa	7	0	1.80000	1.25000	0.50	11.66	64.90	299.99	55.88000	0.26	7.98
2014	Lambsburg	20	November	non_mulch	2014-11-03	2014-11-10	Suffruticosa	7	0	1.80000	1.25000	0.50	11.66	64.90	299.99	55.88000	0.26	7.98
2015	Lowgap	1	May	CP	2015-05-19	2015-05-26	JustinBrouwers	7	2	1.00000	1.25000	0.02	16.30	66.07	0.02	13.02000	0.14	1.86
2015	Lowgap	1	May	non_mulch	2015-05-19	2015-05-26	JustinBrouwers	7	2	1.00000	1.25000	0.02	16.30	66.07	0.02	13.02000	0.14	1.86
2015	Lowgap	2	May	CP	2015-05-26	2015-06-02	JustinBrouwers	7	18	27.00000	6.25000	0.18	20.16	85.33	326.02	80.72000	3.86	11.53
2015	Lowgap	2	May	non_mulch	2015-05-26	2015-06-02	JustinBrouwers	7	362	27.00000	6.25000	0.18	20.16	85.33	326.02	80.72000	3.86	11.53
2015	Lowgap	3	June	CP	2015-06-02	2015-06-09	JustinBrouwers	7	135	43.40000	11.75000	0.09	19.85	91.85	20.26	66.92000	6.20	9.56
2015	Lowgap	3	June	non_mulch	2015-06-02	2015-06-09	JustinBrouwers	7	684	43.40000	11.75000	0.09	19.85	91.85	20.26	66.92000	6.20	9.56
2015	Lowgap	4	June	CP	2015-06-09	2015-06-16	JustinBrouwers	7	34	0.00000	0.00000	0.25	25.45	76.61	0.47	98.08000	0.00	14.01
2015	Lowgap	4	June	non_mulch	2015-06-09	2015-06-16	JustinBrouwers	7	123	0.00000	0.00000	0.25	25.45	76.61	0.47	98.08000	0.00	14.01
2015	Lowgap	5	June	CP	2015-06-16	2015-06-23	JustinBrouwers	7	21	8.20000	3.50000	0.46	24.32	82.28	306.14	74.22000	1.17	10.60
2015	Lowgap	5	June	non_mulch	2015-06-16	2015-06-23	JustinBrouwers	7	6	8.20000	3.50000	0.46	24.32	82.28	306.14	74.22000	1.17	10.60
2015	Lowgap	6	June	CP	2015-06-23	2015-06-30	JustinBrouwers	7	1	2.00000	1.75000	0.07	22.21	76.82	342.84	9.68000	0.29	1.38
2015	Lowgap	6	June	non_mulch	2015-06-23	2015-06-30	JustinBrouwers	7	3	2.00000	1.75000	0.07	22.21	76.82	342.84	9.68000	0.29	1.38
2015	Lowgap	7	June	CP	2015-06-30	2015-07-07	JustinBrouwers	7	156	10.00000	6.00000	0.09	21.83	89.15	320.23	90.62000	1.43	12.95
2015	Lowgap	7	June	non_mulch	2015-06-30	2015-07-07	JustinBrouwers	7	52	10.00000	6.00000	0.09	21.83	89.15	320.23	90.62000	1.43	12.95
2015	Lowgap	8	July	CP	2015-07-07	2015-07-14	JustinBrouwers	7	1	6.40000	5.00000	0.72	19.99	78.89	259.55	27.95000	0.91	3.99
2015	Lowgap	8	July	non_mulch	2015-07-07	2015-07-14	JustinBrouwers	7	0	6.40000	5.00000	0.72	19.99	78.89	259.55	27.95000	0.91	3.99
2015	Lowgap	9	July	CP	2015-07-14	2015-07-21	JustinBrouwers	7	0	0.80000	0.75000	0.64	26.07	77.02	285.79	12.73000	0.11	1.82
2015	Lowgap	9	July	non_mulch	2015-07-14	2015-07-21	JustinBrouwers	7	0	0.80000	0.75000	0.64	26.07	77.02	285.79	12.73000	0.11	1.82
2015	Lowgap	10	July	CP	2015-07-21	2015-07-28	JustinBrouwers	7	0	18.60000	2.25000	0.37	23.21	83.30	258.63	43.67000	2.66	6.24
2015	Lowgap	10	July	non_mulch	2015-07-21	2015-07-28	JustinBrouwers	7	0	18.60000	2.25000	0.37	23.21	83.30	258.63	43.67000	2.66	6.24
2015	Lowgap	11	July	CP	2015-07-28	2015-08-04	JustinBrouwers	7	0	0.40000	0.50000	0.10	27.30	72.98	324.50	28.67000	0.06	4.10
2015	Lowgap	11	July	non_mulch	2015-07-28	2015-08-04	JustinBrouwers	7	0	0.40000	0.50000	0.10	27.30	72.98	324.50	28.67000	0.06	4.10
2015	Lowgap	12	August	CP	2015-08-04	2015-08-11	JustinBrouwers	7	0	11.80000	3.50000	0.14	22.34	84.90	314.66	57.37000	1.69	8.20
2015	Lowgap	12	August	non_mulch	2015-08-04	2015-08-11	JustinBrouwers	7	0	11.80000	3.50000	0.14	22.34	84.90	314.66	57.37000	1.69	8.20
2015	Lowgap	13	August	CP	2015-08-11	2015-08-17	JustinBrouwers	6	2	1.40000	1.25000	0.08	20.88	80.67	339.84	42.82000	0.23	7.14
2015	Lowgap	13	August	non_mulch	2015-08-11	2015-08-17	JustinBrouwers	6	5	1.40000	1.25000	0.08	20.88	80.67	339.84	42.82000	0.23	7.14
2015	Lowgap	14	August	CP	2015-08-17	2015-08-25	JustinBrouwers	8	25	52.20000	9.50000	0.07	22.08	83.08	330.33	61.87000	6.53	7.73
2015	Lowgap	14	August	non_mulch	2015-08-17	2015-08-25	JustinBrouwers	8	0	52.20000	9.50000	0.07	22.08	83.08	330.33	61.87000	6.53	7.73
2015	Lowgap	15	August	CP	2015-08-25	2015-09-01	JustinBrouwers	7	2	0.00000	0.00000	0.17	20.98	75.15	328.89	16.45000	0.00	2.35
2015	Lowgap	15	August	non_mulch	2015-08-25	2015-09-01	JustinBrouwers	7	1	0.00000	0.00000	0.17	20.98	75.15	328.89	16.45000	0.00	2.35
2015	Lowgap	16	September	CP	2015-09-01	2015-09-08	JustinBrouwers	7	33	13.80000	2.25000	0.27	20.50	81.08	324.62	49.92000	1.97	7.13
2015	Lowgap	16	September	non_mulch	2015-09-01	2015-09-08	JustinBrouwers	7	8	13.80000	2.25000	0.27	20.50	81.08	324.62	49.92000	1.97	7.13
2015	Lowgap	17	September	CP	2015-09-08	2015-09-15	JustinBrouwers	7	12	5.80000	3.50000	0.05	19.61	79.25	332.97	50.47000	0.83	7.21
2015	Lowgap	17	September	non_mulch	2015-09-08	2015-09-15	JustinBrouwers	7	10	5.80000	3.50000	0.05	19.61	79.25	332.97	50.47000	0.83	7.21
2015	Lowgap	18	September	CP	2015-09-15	2015-09-23	JustinBrouwers	8	7	18.20000	7.25000	0.09	16.40	80.07	134.73	77.83000	2.28	9.73
2015	Lowgap	18	September	non_mulch	2015-09-15	2015-09-23	JustinBrouwers	8	5	18.20000	7.25000	0.09	16.40	80.07	134.73	77.83000	2.28	9.73
2015	Lowgap	19	September	CP	2015-09-23	2015-09-30	JustinBrouwers	7	650	94.40000	31.75000	0.05	17.37	91.74	7.15	141.15000	13.49	20.16
2015	Lowgap	19	September	non_mulch	2015-09-23	2015-09-30	JustinBrouwers	7	321	94.40000	31.75000	0.05	17.37	91.74	7.15	141.15000	13.49	20.16
2015	Lowgap	20	September	CP	2015-09-30	2015-10-07	JustinBrouwers	7	67	48.40000	13.75000	0.07	12.43	89.34	271.70	99.43000	6.91	14.20
2015	Lowgap	20	September	non_mulch	2015-09-30	2015-10-07	JustinBrouwers	7	35	48.40000	13.75000	0.07	12.43	89.34	271.70	99.43000	6.91	14.20
2015	Lowgap	21	October	CP	2015-10-07	2015-10-14	JustinBrouwers	7	11	0.40000	0.25000	0.30	14.50	85.89	57.00	84.80000	0.06	12.11
2015	Lowgap	21	October	non_mulch	2015-10-07	2015-10-14	JustinBrouwers	7	2	0.40000	0.25000	0.30	14.50	85.89	57.00	84.80000	0.06	12.11
2015	Lowgap	22	October	CP	2015-10-14	2015-10-21	JustinBrouwers	7	11	0.00000	0.00000	0.37	8.55	69.15	228.79	19.40000	0.00	2.77
2015	Lowgap	22	October	non_mulch	2015-10-14	2015-10-21	JustinBrouwers	7	7	0.00000	0.00000	0.37	8.55	69.15	228.79	19.40000	0.00	2.77
2015	Lowgap	23	October	CP	2015-10-21	2015-10-29	JustinBrouwers	8	40	135.40000	31.75000	0.11	11.10	80.30	30.65	80.72000	16.92	10.09
2015	Lowgap	23	October	non_mulch	2015-10-21	2015-10-29	JustinBrouwers	8	77	135.40000	31.75000	0.11	11.10	80.30	30.65	80.72000	16.92	10.09
2015	Lowgap	24	October	CP	2015-10-29	2015-11-04	JustinBrouwers	6	15	37.60000	34.50000	0.01	13.70	88.36	334.43	93.87000	6.27	15.65
2015	Lowgap	24	October	non_mulch	2015-10-29	2015-11-04	JustinBrouwers	6	3	37.60000	34.50000	0.01	13.70	88.36	334.43	93.87000	6.27	15.65
2015	Lowgap	25	November	CP	2015-11-04	2015-11-11	JustinBrouwers	7	126	61.80000	24.50000	0.11	10.21	82.13	289.80	59.50000	8.83	8.50
2015	Lowgap	25	November	non_mulch	2015-11-04	2015-11-11	JustinBrouwers	7	6	61.80000	24.50000	0.11	10.21	82.13	289.80	59.50000	8.83	8.50
2015	Lowgap	26	November	CP	2015-11-11	2015-11-17	JustinBrouwers	6	18	0.00000	0.00000	0.42	8.95	56.14	237.77	2.16000	0.00	0.36
2015	Lowgap	26	November	non_mulch	2015-11-11	2015-11-17	JustinBrouwers	6	27	0.00000	0.00000	0.42	8.95	56.14	237.77	2.16000	0.00	0.36
2016	Lowgap	1	June	CP	2016-06-29	2016-07-06	JustinBrouwers	7	4	55.20000	8.00000	0.13	20.92	84.26	107.07	78.68000	7.89	11.24
2016	Lowgap	1	June	non_mulch	2016-06-29	2016-07-06	JustinBrouwers	7	2	55.20000	8.00000	0.13	20.92	84.26	107.07	78.68000	7.89	11.24
2016	Lowgap	2	July	CP	2016-07-06	2016-07-13	JustinBrouwers	7	6	12.60000	3.50000	0.95	21.89	78.64	90.96	71.95000	1.80	10.28
2016	Lowgap	2	July	non_mulch	2016-07-06	2016-07-13	JustinBrouwers	7	6	12.60000	3.50000	0.95	21.89	78.64	90.96	71.95000	1.80	10.28
2016	Lowgap	3	July	CP	2016-07-13	2016-07-21	JustinBrouwers	8	11	52.20000	5.00000	0.28	21.48	79.95	67.29	96.35000	6.53	12.04
2016	Lowgap	3	July	non_mulch	2016-07-13	2016-07-21	JustinBrouwers	8	1	52.20000	5.00000	0.28	21.48	79.95	67.29	96.35000	6.53	12.04
2016	Lowgap	4	July	CP	2016-07-21	2016-07-28	JustinBrouwers	7	12	4.60000	1.00000	0.91	25.82	77.77	107.52	72.63000	0.66	10.38
2016	Lowgap	4	July	non_mulch	2016-07-21	2016-07-28	JustinBrouwers	7	0	4.60000	1.00000	0.91	25.82	77.77	107.52	72.63000	0.66	10.38
2016	Lowgap	5	July	CP	2016-07-28	2016-08-04	JustinBrouwers	7	1	105.60000	24.50000	0.11	21.06	80.12	298.63	83.70000	15.09	11.96
2016	Lowgap	5	July	non_mulch	2016-07-28	2016-08-04	JustinBrouwers	7	2	105.60000	24.50000	0.11	21.06	80.12	298.63	83.70000	15.09	11.96
2016	Lowgap	6	August	CP	2016-08-04	2016-08-11	JustinBrouwers	7	5	36.80000	10.25000	0.05	21.12	89.19	358.08	95.92000	5.26	13.70
2016	Lowgap	6	August	non_mulch	2016-08-04	2016-08-11	JustinBrouwers	7	7	36.80000	10.25000	0.05	21.12	89.19	358.08	95.92000	5.26	13.70
2016	Lowgap	7	August	CP	2016-08-11	2016-08-16	JustinBrouwers	5	5	5.00000	1.25000	0.04	25.34	84.69	71.87	69.53000	1.00	13.91
2016	Lowgap	7	August	non_mulch	2016-08-11	2016-08-16	JustinBrouwers	5	0	5.00000	1.25000	0.04	25.34	84.69	71.87	69.53000	1.00	13.91
2016	Lowgap	8	August	CP	2016-08-16	2016-08-24	JustinBrouwers	8	15	17.80000	6.75000	0.32	23.76	83.89	54.73	115.45000	2.22	14.43
2016	Lowgap	8	August	non_mulch	2016-08-16	2016-08-24	JustinBrouwers	8	4	17.80000	6.75000	0.32	23.76	83.89	54.73	115.45000	2.22	14.43
2016	Lowgap	9	August	CP	2016-08-24	2016-08-31	JustinBrouwers	7	10	5.20000	1.50000	0.08	22.23	82.86	26.20	90.45000	0.74	12.92
2016	Lowgap	9	August	non_mulch	2016-08-24	2016-08-31	JustinBrouwers	7	3	5.20000	1.50000	0.08	22.23	82.86	26.20	90.45000	0.74	12.92
2016	Lowgap	10	August	CP	2016-08-31	2016-09-07	JustinBrouwers	7	140	28.80000	1.75000	0.39	20.56	77.86	33.93	51.08000	4.11	7.30
2016	Lowgap	10	August	non_mulch	2016-08-31	2016-09-07	JustinBrouwers	7	1	28.80000	1.75000	0.39	20.56	77.86	33.93	51.08000	4.11	7.30
2016	Lowgap	11	September	CP	2016-09-07	2016-09-14	JustinBrouwers	7	1	0.00000	0.00000	0.21	22.88	78.37	30.26	81.56000	0.00	11.65
2016	Lowgap	11	September	non_mulch	2016-09-07	2016-09-14	JustinBrouwers	7	0	0.00000	0.00000	0.21	22.88	78.37	30.26	81.56000	0.00	11.65
2016	Lowgap	12	September	CP	2016-09-14	2016-09-21	JustinBrouwers	7	9	5.00000	3.50000	0.01	19.09	82.45	307.08	88.03000	0.71	12.58
2016	Lowgap	12	September	non_mulch	2016-09-14	2016-09-21	JustinBrouwers	7	7	5.00000	3.50000	0.01	19.09	82.45	307.08	88.03000	0.71	12.58
2016	Lowgap	13	September	CP	2016-09-21	2016-09-28	JustinBrouwers	7	166	14.60000	8.00000	0.00	19.25	85.90	353.03	107.27000	2.09	15.32
2016	Lowgap	13	September	non_mulch	2016-09-21	2016-09-28	JustinBrouwers	7	35	14.60000	8.00000	0.00	19.25	85.90	353.03	107.27000	2.09	15.32
2016	Lowgap	14	September	CP	2016-09-28	2016-10-05	JustinBrouwers	7	567	44.40000	4.00000	0.12	18.12	86.75	84.36	108.83000	6.34	15.55
2016	Lowgap	14	September	non_mulch	2016-09-28	2016-10-05	JustinBrouwers	7	91	44.40000	4.00000	0.12	18.12	86.75	84.36	108.83000	6.34	15.55
2016	Lowgap	15	October	CP	2016-10-05	2016-10-12	JustinBrouwers	7	158	49.40000	21.25000	0.06	17.55	78.70	25.21	89.83000	7.06	12.83
2016	Lowgap	15	October	non_mulch	2016-10-05	2016-10-12	JustinBrouwers	7	80	49.40000	21.25000	0.06	17.55	78.70	25.21	89.83000	7.06	12.83
2016	Lowgap	16	October	CP	2016-10-12	2016-10-19	JustinBrouwers	7	0	0.80000	1.00000	0.28	15.45	84.64	48.75	89.62000	0.11	12.80
2016	Lowgap	16	October	non_mulch	2016-10-12	2016-10-19	JustinBrouwers	7	0	0.80000	1.00000	0.28	15.45	84.64	48.75	89.62000	0.11	12.80
2016	Lowgap	17	October	CP	2016-10-19	2016-10-26	JustinBrouwers	7	0	0.20000	0.25000	0.00	11.90	69.48	0.10	50.07000	0.03	7.15
2016	Lowgap	17	October	non_mulch	2016-10-19	2016-10-26	JustinBrouwers	7	2	0.20000	0.25000	0.00	11.90	69.48	0.10	50.07000	0.03	7.15
2016	Lowgap	18	October	CP	2016-10-26	2016-11-02	JustinBrouwers	7	0	1.00000	1.00000	0.05	13.18	81.89	19.29	82.12000	0.14	11.73
2016	Lowgap	18	October	non_mulch	2016-10-26	2016-11-02	JustinBrouwers	7	0	1.00000	1.00000	0.05	13.18	81.89	19.29	82.12000	0.14	11.73
2016	Lowgap	19	November	CP	2016-11-02	2016-11-09	JustinBrouwers	7	0	1.00000	1.00000	0.83	11.52	70.57	106.47	59.77000	0.14	8.54
2016	Lowgap	19	November	non_mulch	2016-11-02	2016-11-09	JustinBrouwers	7	7	1.00000	1.00000	0.83	11.52	70.57	106.47	59.77000	0.14	8.54
2016	Lowgap	20	November	CP	2016-11-09	2016-11-17	JustinBrouwers	8	0	0.20000	0.25000	0.00	5.00	64.59	0.10	33.65000	0.03	4.21
2016	Lowgap	20	November	non_mulch	2016-11-09	2016-11-17	JustinBrouwers	8	1	0.20000	0.25000	0.00	5.00	64.59	0.10	33.65000	0.03	4.21
2016	Lowgap	21	November	CP	2016-11-17	2016-11-23	JustinBrouwers	6	1	0.00000	0.00000	0.93	5.42	57.68	73.84	25.18000	0.00	4.20
2016	Lowgap	21	November	non_mulch	2016-11-17	2016-11-23	JustinBrouwers	6	0	0.00000	0.00000	0.93	5.42	57.68	73.84	25.18000	0.00	4.20
2016	Lowgap	22	November	CP	2016-11-23	2016-12-01	JustinBrouwers	8	2	72.60000	15.25000	0.21	12.99	73.61	109.76	57.33000	9.07	7.17
2016	Lowgap	22	November	non_mulch	2016-11-23	2016-12-01	JustinBrouwers	8	2	72.60000	15.25000	0.21	12.99	73.61	109.76	57.33000	9.07	7.17
2016	Lowgap	23	December	CP	2016-12-01	2016-12-07	JustinBrouwers	6	46	37.20000	20.00000	0.04	4.66	74.37	352.08	53.38000	6.20	8.90
2016	Lowgap	23	December	non_mulch	2016-12-01	2016-12-07	JustinBrouwers	6	9	37.20000	20.00000	0.04	4.66	74.37	352.08	53.38000	6.20	8.90
2016	Lowgap	24	December	CP	2016-12-07	2016-12-14	JustinBrouwers	7	4	3.60000	3.00000	0.03	4.50	69.70	4.80	38.35000	0.51	5.48
2016	Lowgap	24	December	non_mulch	2016-12-07	2016-12-14	JustinBrouwers	7	0	3.60000	3.00000	0.03	4.50	69.70	4.80	38.35000	0.51	5.48
2017	Lowgap	1	March	CP	2017-03-08	2017-04-05	JustinBrouwers	28	0	30.20000	9.56250	0.20	10.31	63.02	35.04	38.61250	1.08	1.38
2017	Lowgap	1	March	non_mulch	2017-03-08	2017-04-05	JustinBrouwers	28	0	30.20000	9.56250	0.20	10.31	63.02	35.04	38.61250	1.08	1.38
2017	Lowgap	2	April	CP	2017-04-05	2017-04-26	JustinBrouwers	21	0	66.06667	15.91667	0.07	11.35	72.24	70.51	64.46667	3.15	3.07
2017	Lowgap	2	April	non_mulch	2017-04-05	2017-04-26	JustinBrouwers	21	0	66.06667	15.91667	0.07	11.35	72.24	70.51	64.46667	3.15	3.07
2017	Lowgap	3	April	CP	2017-04-26	2017-05-17	JustinBrouwers	21	0	33.46667	11.25000	0.18	14.05	74.07	11.84	72.58333	1.59	3.46
2017	Lowgap	3	April	non_mulch	2017-04-26	2017-05-17	JustinBrouwers	21	0	33.46667	11.25000	0.18	14.05	74.07	11.84	72.58333	1.59	3.46
2017	Lowgap	4	May	CP	2017-05-17	2017-05-24	JustinBrouwers	7	0	133.60000	32.50000	0.05	15.96	82.27	351.71	99.63000	19.09	14.23
2017	Lowgap	4	May	non_mulch	2017-05-17	2017-05-24	JustinBrouwers	7	11	133.60000	32.50000	0.05	15.96	82.27	351.71	99.63000	19.09	14.23
2017	Lowgap	5	May	CP	2017-05-24	2017-05-31	JustinBrouwers	7	0	33.20000	10.75000	0.08	16.73	75.23	160.40	80.83000	4.74	11.55
2017	Lowgap	5	May	non_mulch	2017-05-24	2017-05-31	JustinBrouwers	7	0	33.20000	10.75000	0.08	16.73	75.23	160.40	80.83000	4.74	11.55
2017	Lowgap	6	May	CP	2017-05-31	2017-06-07	JustinBrouwers	7	0	8.20000	4.00000	0.19	20.25	72.42	27.15	69.63000	1.17	9.95
2017	Lowgap	6	May	non_mulch	2017-05-31	2017-06-07	JustinBrouwers	7	0	8.20000	4.00000	0.19	20.25	72.42	27.15	69.63000	1.17	9.95
2017	Lowgap	7	June	CP	2017-06-07	2017-06-14	JustinBrouwers	7	0	4.60000	1.75000	0.33	19.46	75.49	71.59	79.95000	0.66	11.42
2017	Lowgap	7	June	non_mulch	2017-06-07	2017-06-14	JustinBrouwers	7	0	4.60000	1.75000	0.33	19.46	75.49	71.59	79.95000	0.66	11.42
2017	Lowgap	8	June	CP	2017-06-14	2017-06-21	JustinBrouwers	7	0	52.60000	6.50000	0.22	21.98	84.01	71.68	86.60000	7.51	12.37
2017	Lowgap	8	June	non_mulch	2017-06-14	2017-06-21	JustinBrouwers	7	0	52.60000	6.50000	0.22	21.98	84.01	71.68	86.60000	7.51	12.37
2017	Lowgap	9	June	CP	2017-06-21	2017-06-28	JustinBrouwers	7	0	10.00000	5.00000	0.21	21.72	75.81	24.06	66.35000	1.43	9.48
2017	Lowgap	9	June	non_mulch	2017-06-21	2017-06-28	JustinBrouwers	7	0	10.00000	5.00000	0.21	21.72	75.81	24.06	66.35000	1.43	9.48
2017	Lowgap	10	June	CP	2017-06-28	2017-07-05	JustinBrouwers	7	0	35.80000	7.25000	0.03	21.60	78.94	46.53	79.17000	5.11	11.31
2017	Lowgap	10	June	non_mulch	2017-06-28	2017-07-05	JustinBrouwers	7	0	35.80000	7.25000	0.03	21.60	78.94	46.53	79.17000	5.11	11.31
2017	Lowgap	11	July	CP	2017-07-05	2017-07-12	JustinBrouwers	7	0	3.80000	2.00000	0.04	20.76	75.39	102.92	83.13000	0.54	11.88
2017	Lowgap	11	July	non_mulch	2017-07-05	2017-07-12	JustinBrouwers	7	0	3.80000	2.00000	0.04	20.76	75.39	102.92	83.13000	0.54	11.88
2017	Lowgap	12	July	CP	2017-07-12	2017-07-19	JustinBrouwers	7	87	19.60000	4.00000	0.29	24.22	81.99	61.99	97.00000	2.80	13.86
2017	Lowgap	12	July	non_mulch	2017-07-12	2017-07-19	JustinBrouwers	7	0	19.60000	4.00000	0.29	24.22	81.99	61.99	97.00000	2.80	13.86
2017	Lowgap	13	July	CP	2017-07-19	2017-07-26	JustinBrouwers	7	20	28.00000	3.25000	0.17	22.08	75.43	72.67	62.38000	4.00	8.91
2017	Lowgap	13	July	non_mulch	2017-07-19	2017-07-26	JustinBrouwers	7	0	28.00000	3.25000	0.17	22.08	75.43	72.67	62.38000	4.00	8.91
2017	Lowgap	14	July	CP	2017-07-26	2017-08-02	JustinBrouwers	7	17	18.00000	3.50000	0.04	24.22	80.84	3.86	86.40000	2.57	12.34
2017	Lowgap	14	July	non_mulch	2017-07-26	2017-08-02	JustinBrouwers	7	0	18.00000	3.50000	0.04	24.22	80.84	3.86	86.40000	2.57	12.34
2017	Lowgap	15	August	CP	2017-08-02	2017-08-09	JustinBrouwers	7	0	23.00000	10.50000	0.02	20.73	77.74	40.84	85.93000	3.29	12.28
2017	Lowgap	15	August	non_mulch	2017-08-02	2017-08-09	JustinBrouwers	7	0	23.00000	10.50000	0.02	20.73	77.74	40.84	85.93000	3.29	12.28
2017	Lowgap	16	August	CP	2017-08-09	2017-08-16	JustinBrouwers	7	160	41.00000	14.25000	0.02	21.96	86.88	24.03	99.27000	5.86	14.18
2017	Lowgap	16	August	non_mulch	2017-08-09	2017-08-16	JustinBrouwers	7	0	41.00000	14.25000	0.02	21.96	86.88	24.03	99.27000	5.86	14.18

Use set.seed() for reproducibility purposes.

set.seed(42)

Fit univariate/bivariate glmms

Mod_1 (Total rain)

mod_1 <-
  glmmTMB(total_count ~ total_rain +  (1 | spread_event),
          family = nbinom2,
          data = dat_nc_ncb)

summary(mod_1)

##  Family: nbinom2  ( log )
## Formula:          total_count ~ total_rain + (1 | spread_event)
## Data: dat_nc_ncb
## 
##      AIC      BIC   logLik deviance df.resid 
##   1163.1   1175.7   -577.6   1155.1      167 
## 
## Random effects:
## 
## Conditional model:
##  Groups       Name        Variance  Std.Dev. 
##  spread_event (Intercept) 6.689e-08 0.0002586
## Number of obs: 171, groups:  spread_event, 26
## 
## Dispersion parameter for nbinom2 family (): 0.198 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) 2.148594   0.277974   7.729 1.08e-14 ***
## total_rain  0.039518   0.008408   4.700 2.60e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Mod_2 (Wind speed)

mod_2 <-
  glmmTMB(total_count ~ mean_wd * mean_ws + (1 | spread_event),
          family = nbinom2,
          data = dat_nc_ncb)

summary(mod_2)

##  Family: nbinom2  ( log )
## Formula:          total_count ~ mean_wd * mean_ws + (1 | spread_event)
## Data: dat_nc_ncb
## 
##      AIC      BIC   logLik deviance df.resid 
##   1183.4   1202.2   -585.7   1171.4      165 
## 
## Random effects:
## 
## Conditional model:
##  Groups       Name        Variance Std.Dev.
##  spread_event (Intercept) 0.2581   0.508   
## Number of obs: 171, groups:  spread_event, 26
## 
## Dispersion parameter for nbinom2 family (): 0.185 
## 
## Conditional model:
##                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)      4.475529   0.521003   8.590  < 2e-16 ***
## mean_wd         -0.003402   0.002498  -1.362  0.17330    
## mean_ws         -4.807255   1.666290  -2.885  0.00391 ** 
## mean_wd:mean_ws  0.013986   0.011680   1.197  0.23116    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Mod_3 (Wind speed)

mod_3 <-
  glmmTMB(total_count ~ mean_wd + (1 | spread_event),
          family = nbinom2,
          data = dat_nc_ncb)

summary(mod_3)

##  Family: nbinom2  ( log )
## Formula:          total_count ~ mean_wd + (1 | spread_event)
## Data: dat_nc_ncb
## 
##      AIC      BIC   logLik deviance df.resid 
##   1187.9   1200.4   -589.9   1179.9      167 
## 
## Random effects:
## 
## Conditional model:
##  Groups       Name        Variance  Std.Dev. 
##  spread_event (Intercept) 1.126e-07 0.0003356
## Number of obs: 171, groups:  spread_event, 26
## 
## Dispersion parameter for nbinom2 family (): 0.168 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  3.845204   0.284833  13.500   <2e-16 ***
## mean_wd     -0.001804   0.001380  -1.307    0.191    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Mod_4 (Leaf wetness duration)

mod_4 <-
  glmmTMB(total_count ~  lwd_duration +  (1 | spread_event),
          family = nbinom2,
          data = dat_nc_ncb)

summary(mod_4)

##  Family: nbinom2  ( log )
## Formula:          total_count ~ lwd_duration + (1 | spread_event)
## Data: dat_nc_ncb
## 
##      AIC      BIC   logLik deviance df.resid 
##   1162.3   1174.9   -577.2   1154.3      167 
## 
## Random effects:
## 
## Conditional model:
##  Groups       Name        Variance Std.Dev.
##  spread_event (Intercept) 0.4358   0.6601  
## Number of obs: 171, groups:  spread_event, 26
## 
## Dispersion parameter for nbinom2 family (): 0.214 
## 
## Conditional model:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.77909    0.51350   1.517    0.129    
## lwd_duration  0.03086    0.00605   5.101 3.38e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Mod_5 (Relative humidity)

mod_5 <-
  glmmTMB(total_count ~  mean_rh  + (1 | spread_event),
          family = nbinom2,
          data = dat_nc_ncb)

summary(mod_5)

##  Family: nbinom2  ( log )
## Formula:          total_count ~ mean_rh + (1 | spread_event)
## Data: dat_nc_ncb
## 
##      AIC      BIC   logLik deviance df.resid 
##   1152.6   1165.1   -572.3   1144.6      167 
## 
## Random effects:
## 
## Conditional model:
##  Groups       Name        Variance Std.Dev.
##  spread_event (Intercept) 0.9533   0.9764  
## Number of obs: 171, groups:  spread_event, 26
## 
## Dispersion parameter for nbinom2 family (): 0.246 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept) -10.2932     2.3607  -4.360  1.3e-05 ***
## mean_rh       0.1645     0.0290   5.673  1.4e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mod_6 (Temperature)

mod_6 <-
  glmmTMB(total_count ~  mean_temp + (1 | spread_event),
          family = nbinom2,
          data = dat_nc_ncb)

summary(mod_6)

##  Family: nbinom2  ( log )
## Formula:          total_count ~ mean_temp + (1 | spread_event)
## Data: dat_nc_ncb
## 
##      AIC      BIC   logLik deviance df.resid 
##   1188.3   1200.8   -590.1   1180.3      167 
## 
## Random effects:
## 
## Conditional model:
##  Groups       Name        Variance Std.Dev.
##  spread_event (Intercept) 0.3913   0.6255  
## Number of obs: 171, groups:  spread_event, 26
## 
## Dispersion parameter for nbinom2 family (): 0.177 
## 
## Conditional model:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  2.82529    1.02370   2.760  0.00578 **
## mean_temp    0.03357    0.05452   0.616  0.53810   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Fit multivariate glmms

mod_7 <-
  glmmTMB(
    total_count ~ mean_rh + total_rain + mean_wd * mean_ws + mean_temp * lwd_duration + location + (1 | spread_event), family = nbinom2,
    data = dat_nc_ncb
  )

summary(mod_7)

##  Family: nbinom2  ( log )
## Formula:          
## total_count ~ mean_rh + total_rain + mean_wd * mean_ws + mean_temp *  
##     lwd_duration + location + (1 | spread_event)
## Data: dat_nc_ncb
## 
##      AIC      BIC   logLik deviance df.resid 
##   1128.9   1166.6   -552.4   1104.9      159 
## 
## Random effects:
## 
## Conditional model:
##  Groups       Name        Variance  Std.Dev. 
##  spread_event (Intercept) 3.206e-08 0.0001791
## Number of obs: 171, groups:  spread_event, 26
## 
## Dispersion parameter for nbinom2 family (): 0.277 
## 
## Conditional model:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)            -6.7456826  2.4922534  -2.707  0.00680 ** 
## mean_rh                 0.1750779  0.0437951   3.998 6.40e-05 ***
## total_rain              0.0287701  0.0102161   2.816  0.00486 ** 
## mean_wd                -0.0001317  0.0019612  -0.067  0.94647    
## mean_ws                 0.5106963  1.4771096   0.346  0.72954    
## mean_temp              -0.3977193  0.0852342  -4.666 3.07e-06 ***
## lwd_duration           -0.1057611  0.0239992  -4.407 1.05e-05 ***
## locationLowgap          1.3460468  0.4700772   2.863  0.00419 ** 
## mean_wd:mean_ws         0.0050870  0.0085342   0.596  0.55113    
## mean_temp:lwd_duration  0.0060753  0.0011822   5.139 2.76e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Model Diagnostics

Simulate model residuals

Check if model met data assumptions, and if the model predictions can be trusted.

simulateResiduals(mod_7, plot = T, quantreg = T)

## Object of Class DHARMa with simulated residuals based on 250 simulations with refit = FALSE . See ?DHARMa::simulateResiduals for help. 
##  
## Scaled residual values: 0.06916953 0.3061759 0.1201552 0.4451339 0.9662278 0.3547005 0.4547746 0.1893003 0.9241854 0.688 0.652516 0.1541348 0.2992751 0.4713096 0.6199157 0.5902045 0.944 0.444 0.708 0.7273926 ...

Plot model

Plot main effects of weather variables using ggplot2

# Relative humidity graph
p1 <- ggpredict(mod_7, "mean_rh[56:96]",
                type = "random") %>%
  as.data.frame() %>%
  rename(mean_rh = x, total_count = predicted) %>%
  mutate(mean_rh = as.numeric(as.character(mean_rh))) %>%
  ggplot(aes(mean_rh, total_count)) +
  geom_line() +
  geom_ribbon(colour = NA,
              alpha = 0.1,
              aes(ymin = conf.low, ymax = conf.high)) +
  geom_point(data = dat_nc_ncb,
             size = 1) +
  annotate("text",
           x = 80,
           y = 500,
           label = "p=0.0001") +
  # scale_color_distiller(palette = "Spectral") +
  # scale_fill_distiller(palette = "Spectral", guide = "none") +
  #coord_cartesian(ylim = range(dat_nc_ncb$total_count), xlim = range(dat_nc_ncb$mean_rh)) +
  coord_cartesian(ylim = c(0, 690)) +
  #scale_y_continuous(trans = "log1p") +
  theme_few(base_size = 11) +
  labs(x = "Mean RH (%)", y = "Number of infected leaves")

p1

# Rainfall graph
p2 <- ggpredict(mod_7, "total_rain[0:135]",
                type = "random") %>%
  as.data.frame() %>%
  rename(total_rain = x, total_count = predicted) %>%
  #mutate(total_rain= as.numeric(as.character(total_rain))) %>%
  ggplot(aes(total_rain, total_count)) +
  geom_line() +
  geom_point(data = dat_nc_ncb,
             size = 1) +
  geom_ribbon(
    colour = NA,
    fill = "black",
    alpha = 0.1,
    aes(ymin = conf.low, ymax = conf.high)
  ) +
  annotate("text",
           x = 80,
           y = 500,
           label = "p=0.0049") +
  # scale_color_distiller(palette = "Spectral") +
  # scale_fill_distiller(palette = "Spectral", guide = "none") +
  #coord_cartesian(ylim = range(dat_nc_ncb$total_count), xlim = range(dat_nc_ncb$mean_rh)) +
  coord_cartesian(ylim = c(0, 690), xlim = c(0, 135)) +
  theme_few(base_size = 11) +
  labs(x = "Total rain (mm)", y = "")

p2

fig_3 <- p1 + p2 + plot_layout(tag_level = 'new') +
  plot_annotation(tag_levels = list(c('(a)', '(b)'))) &
    theme(plot.tag = element_text(face = 'bold', size = 11))

Plot interaction effect using `ggplot2`

fig_4 <- ggpredict(mod_7, terms = c("lwd_duration[1:160]", "mean_temp[10:27]"), type = "random") %>%
    as.data.frame() %>%
    rename(lwd_duration = x, mean_temp = group, total_count = predicted) %>%
    mutate(mean_temp = as.numeric(as.character(mean_temp)))
ggplot() +
    geom_line(data = fig_4, aes(lwd_duration, total_count, color = mean_temp, group = mean_temp), alpha = 0.8) +
    geom_point(data = dat_nc_ncb, aes(x = lwd_duration, y = total_count, fill = mean_temp), shape = 21, color = "black", size = 2.5) +
    scale_color_distiller(palette = "Spectral") +
    scale_fill_distiller(palette = "Spectral", guide = "none") +
   coord_cartesian(ylim=c(0, 690),xlim = c(1,160)) +
    annotate("text", x=80, y=500, label= "p=0.0001") +
    theme_few(base_size = 11) +
    labs(x = "Leaf Wetness Duration", y = "Number of Infected Leaves", color = "Mean Temperature (°C)")

Ihsan Khaliq

2024-07-15

Import disease data

Import disease data

Import weather data

Cobmine weather & disease data

Check data

Fit univariate/bivariate glmms

Mod_1 (Total rain)

Mod_2 (Wind speed)

Mod_3 (Wind speed)

Mod_4 (Leaf wetness duration)

Mod_5 (Relative humidity)

mod_6 (Temperature)

Fit multivariate glmms

Model Diagnostics

Simulate model residuals

Plot model

Plot interaction effect using `ggplot2`

Fit GLMMs

Ihsan Khaliq

2024-07-15

Import disease data

Import disease data

Import weather data

Cobmine weather & disease data

Check data

Fit univariate/bivariate glmms

Mod_1 (Total rain)

Mod_2 (Wind speed)

Mod_3 (Wind speed)

Mod_4 (Leaf wetness duration)

Mod_5 (Relative humidity)

mod_6 (Temperature)

Fit multivariate glmms

Model Diagnostics

Simulate model residuals

Plot model

Plot interaction effect using ggplot2

Plot interaction effect using `ggplot2`