Mammal data - Mediterranean Desert Transition Zone
Talia Gabay & Ron Chen
2024-04-08
P <- P_byplot[grepl("Mediterranean Desert Transition Zone",Unit),][order(Cycle_number,Site)][,Site:=factor(Site)]Mediterranean Desert Transition Zone - Richness by camera
Monitoring started in 2014 (5 monitoring cycles), Each site as two types of plots - near or far from Settlements. There are 5 sites total - Beit Yatir, Har Amasa, Lahav, Lehavim, Mirsham Each has 5 sampling points except for Mirsham which is missing samples from the first year (2014)
Let’s first validate the data:
#include rare species in analysis
P.anal <- copy(P) # set a fixed variable name for analysis, if want to switch between data WITH rare #species and data WITHOUT rare species then only change once here
#Validate factors and levels
P.anal$Settlements <- as.factor(P.anal$Settlements)
print("Settlements has 2 levels")## [1] "Settlements has 2 levels"print(levels(P.anal$Settlements))## [1] "Far" "Near"P.anal$Site <- as.factor(P.anal$Site)
print("Site has 5 levels")## [1] "Site has 5 levels"print(levels(P.anal$Site))## [1] "Beit Yatir" "Har Amasa" "Lahav" "Lehavim" "Mirsham"P.anal$Transect <- as.factor(P.anal$Transect)
print("Transect has 10 levels")## [1] "Transect has 10 levels"print(levels(P.anal$Transect))## [1] "Beit Yatir Far" "Beit Yatir Near" "Har Amasa Far" "Har Amasa Near"
## [5] "Lahav Far" "Lahav Near" "Lehavim Far" "Lehavim Near"
## [9] "Mirsham Far" "Mirsham Near"P.anal$Transect_with_date <- as.factor(P.anal$Transect_with_date)
print("Transect with date has 49 levels - Missing data for Mirsham near in 2014")## [1] "Transect with date has 49 levels - Missing data for Mirsham near in 2014"print(levels(P.anal$Transect_with_date))## [1] "Beit Yatir Far_03_01_2017" "Beit Yatir Far_13_07_2014"
## [3] "Beit Yatir Far_16_11_2020" "Beit Yatir Far_20_11_2018"
## [5] "Beit Yatir Far_26_02_2023" "Beit Yatir Near_03_01_2017"
## [7] "Beit Yatir Near_13_07_2014" "Beit Yatir Near_16_11_2020"
## [9] "Beit Yatir Near_20_11_2018" "Beit Yatir Near_26_02_2023"
## [11] "Har Amasa Far_16_11_2020" "Har Amasa Far_17_07_2014"
## [13] "Har Amasa Far_22_12_2016" "Har Amasa Far_24_11_2018"
## [15] "Har Amasa Far_24_11_2022" "Har Amasa Near_16_11_2020"
## [17] "Har Amasa Near_17_07_2014" "Har Amasa Near_22_12_2016"
## [19] "Har Amasa Near_24_11_2018" "Har Amasa Near_24_11_2022"
## [21] "Lahav Far_13_11_2022" "Lahav Far_20_11_2016"
## [23] "Lahav Far_25_08_2020" "Lahav Far_30_07_2014"
## [25] "Lahav Far_30_10_2018" "Lahav Near_13_11_2022"
## [27] "Lahav Near_20_11_2016" "Lahav Near_25_08_2020"
## [29] "Lahav Near_30_07_2014" "Lahav Near_30_10_2018"
## [31] "Lehavim Far_05_09_2020" "Lehavim Far_05_11_2016"
## [33] "Lehavim Far_08_12_2018" "Lehavim Far_27_11_2022"
## [35] "Lehavim Far_31_07_2014" "Lehavim Near_05_09_2020"
## [37] "Lehavim Near_05_11_2016" "Lehavim Near_08_12_2018"
## [39] "Lehavim Near_27_11_2022" "Lehavim Near_31_07_2014"
## [41] "Mirsham Far_03_12_2016" "Mirsham Far_04_08_2014"
## [43] "Mirsham Far_13_08_2020" "Mirsham Far_24_02_2023"
## [45] "Mirsham Far_29_10_2018" "Mirsham Near_03_12_2016"
## [47] "Mirsham Near_13_08_2020" "Mirsham Near_24_02_2023"
## [49] "Mirsham Near_29_10_2018"#distance.rescaled <- scale(all.mammal.abundances$Distance)
#write.csv(distance.rescaled, file = "Distance_rescaled.csv")print("RICHNESS WITH RARE SPECIES")## [1] "RICHNESS WITH RARE SPECIES"plot_alpha_diversity(P, x_val = "Settlements", y_val = "richness", ylab_val = "richness", xlab_val = "Settlements", fill_val = "Settlements")
col_names <- c("Cycle_number", "Deployment.id_new", "Unit", "Subunit", "Site", "Settlements", "Distance_rescaled", "Lon", "Lat", "year", "rescaled_Time.Diff", "sinus_Monitoring.Time.Diff", "cosinus_Monitoring.Time.Diff", "Transect", "Transect_with_date")
abu_by_spp.MDtransitionalzone <- all.mammal.abundances[grepl("Mediterranean Desert Transition Zone",Unit),]
write.csv(abu_by_spp.MDtransitionalzone, "data_table_MD_tranzitional_zone.csv")
spp <- abu_by_spp.MDtransitionalzone[,27:40]
#spp <- abu_by_spp.MDtransitionalzone[,25:38]
# filter out species with zero counts
print(colSums(spp))## Canis aureus Canis lupus Capra nubiana Dama mesopotamica
## 467 0 0 0
## Equus hemionus Gazella dorcas Gazella gazella Hyaena hyaena
## 0 0 940 22
## Hystrix indica Lepus capensis Meles meles Oryx leucoryx
## 97 181 39 0
## Sus scrofa Vulpes vulpes
## 0 708spp <- spp[,.SD,.SDcols = colSums(spp)>0]
spp <- mvabund(spp)
plot(spp ~ P.anal$Settlements, overall.main="raw abundances", transformation = "no")## Overlapping points were shifted along the y-axis to make them visible.##
## PIPING TO 2nd MVFACTOR
my_cols <- c("blue", "darkgreen")
dotchart(P.anal$richness, ylab = "Settlements",
groups = P.anal$Settlements, gcolor = my_cols,
color = my_cols[P.anal$Settlements],
cex = 0.9, pch = 1, xlab = "richness")
my_cols <- c("blue", "darkgreen", "red", "orange", "purple")
dotchart(P.anal$richness, ylab = "Site",
groups = P.anal$Site, gcolor = my_cols,
color = my_cols[P.anal$Site],
cex = 0.9, pch = 1, xlab = "richness")
kable(summary(P.anal[,.(richness, abundance, rescaled_Time.Diff, cosinus_Monitoring.Time.Diff, sinus_Monitoring.Time.Diff, Site, Transect, Transect_with_date, Distance_rescaled)]))| richness | abundance | rescaled_Time.Diff | cosinus_Monitoring.Time.Diff | sinus_Monitoring.Time.Diff | Site | Transect | Transect_with_date | Distance_rescaled | |
|---|---|---|---|---|---|---|---|---|---|
| Min. :1.000 | Min. : 1.000 | Min. :-1.1972 | Min. :-0.99996 | Min. :-0.9454 | Beit Yatir:64 | Beit Yatir Far :34 | Mirsham Near_03_12_2016 : 10 | Min. :-0.9136 | |
| 1st Qu.:1.000 | 1st Qu.: 2.000 | 1st Qu.:-0.4256 | 1st Qu.:-0.36729 | 1st Qu.:-0.8818 | Har Amasa :60 | Har Amasa Near :31 | Beit Yatir Far_26_02_2023: 9 | 1st Qu.:-0.6122 | |
| Median :2.000 | Median : 5.000 | Median : 0.1810 | Median :-0.06634 | Median : 0.3959 | Lahav :50 | Mirsham Near :31 | Har Amasa Near_24_11_2022: 9 | Median :-0.4099 | |
| Mean :2.114 | Mean : 8.733 | Mean : 0.3403 | Mean : 0.02591 | Mean : 0.1136 | Lehavim :49 | Beit Yatir Near:30 | Mirsham Near_24_02_2023 : 9 | Mean :-0.2132 | |
| 3rd Qu.:3.000 | 3rd Qu.:10.000 | 3rd Qu.: 1.4339 | 3rd Qu.: 0.58421 | 3rd Qu.: 0.9333 | Mirsham :58 | Har Amasa Far :29 | Mirsham Near_29_10_2018 : 9 | 3rd Qu.: 0.1560 | |
| Max. :5.000 | Max. :89.000 | Max. : 1.5246 | Max. : 0.99984 | Max. : 0.9978 | NA | Lahav Near :27 | Beit Yatir Far_03_01_2017: 8 | Max. : 1.2141 | |
| NA | NA | NA | NA | NA | NA | (Other) :99 | (Other) :227 | NA |
pairs(P.anal[,lapply(X = .SD,FUN = as.numeric),.SDcols=c("sinus_Monitoring.Time.Diff", "cosinus_Monitoring.Time.Diff","rescaled_Time.Diff","Site","Transect", "Transect_with_date", "Distance_rescaled")])
kable(cor(P.anal[,lapply(X = .SD,FUN = as.numeric),.SDcols=c("sinus_Monitoring.Time.Diff", "cosinus_Monitoring.Time.Diff","rescaled_Time.Diff","Site","Transect", "Transect_with_date", "Distance_rescaled")]))| sinus_Monitoring.Time.Diff | cosinus_Monitoring.Time.Diff | rescaled_Time.Diff | Site | Transect | Transect_with_date | Distance_rescaled | |
|---|---|---|---|---|---|---|---|
| sinus_Monitoring.Time.Diff | 1.0000000 | 0.0471620 | -0.2553691 | 0.2151478 | 0.2149245 | 0.1688694 | -0.0721399 |
| cosinus_Monitoring.Time.Diff | 0.0471620 | 1.0000000 | -0.1553489 | -0.0579959 | -0.0577145 | -0.0685072 | -0.1504014 |
| rescaled_Time.Diff | -0.2553691 | -0.1553489 | 1.0000000 | -0.0123308 | -0.0043925 | 0.0254338 | -0.0474549 |
| Site | 0.2151478 | -0.0579959 | -0.0123308 | 1.0000000 | 0.9857080 | 0.9813614 | 0.0812471 |
| Transect | 0.2149245 | -0.0577145 | -0.0043925 | 0.9857080 | 1.0000000 | 0.9949286 | -0.0511837 |
| Transect_with_date | 0.1688694 | -0.0685072 | 0.0254338 | 0.9813614 | 0.9949286 | 1.0000000 | -0.0450595 |
| Distance_rescaled | -0.0721399 | -0.1504014 | -0.0474549 | 0.0812471 | -0.0511837 | -0.0450595 | 1.0000000 |
meanvar.plot(spp, xlab = "mean abundance of a given species across sites", ylab = "variance of the abundance of a given species across sites")
mdl_r.poiss.int <- glm(data = P.anal, formula = richness ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + sinus_Monitoring.Time.Diff + Site + Transect_with_date, family = poisson)
od <- mdl_r.poiss.int$deviance/mdl_r.poiss.int$df.residual
print("Estimating overdispersion parameter phi: (Res. Dev.)/(n-p) where n=number of observations; p=number of parameters in the model.")## [1] "Estimating overdispersion parameter phi: (Res. Dev.)/(n-p) where n=number of observations; p=number of parameters in the model."print(paste0('od = ',od))## [1] "od = 0.401906034280057"Overdispersion parameter is <1 (underdispersion) and therefore will use Poisson
m0 <- glm(data = P.anal, formula = richness ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + sinus_Monitoring.Time.Diff, family = poisson)
me0transectwithdate <- glmer(data = P.anal, formula = richness ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + sinus_Monitoring.Time.Diff + (1|Transect_with_date), family = poisson, verbose = 1)## start par. = 1 fn = 931.4645
## At return
## eval: 15 fn: 850.23888 par: 0.00000
## (NM) 20: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 40: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 60: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 80: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 100: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 120: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 140: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 160: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 180: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 200: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 220: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273## boundary (singular) fit: see help('isSingular')me0transect <- glmer(data = P.anal, formula = richness ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + sinus_Monitoring.Time.Diff + (1|Transect), family = poisson, verbose = 1)## start par. = 1 fn = 880.8692
## At return
## eval: 14 fn: 850.23888 par: 0.00000
## (NM) 20: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 40: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 60: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 80: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 100: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 120: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 140: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 160: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 180: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 200: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 220: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273## boundary (singular) fit: see help('isSingular')me0site <- glmer(data = P.anal, formula = richness ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + sinus_Monitoring.Time.Diff + (1|Site), family = poisson, verbose = 1)## start par. = 1 fn = 867.4803
## At return
## eval: 14 fn: 850.23888 par: 0.00000
## (NM) 20: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 40: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 60: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 80: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 100: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 120: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 140: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 160: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 180: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273
## (NM) 200: f = 850.239 at 0 0.675095 0.073017 0.208805 0.0149138 0.015864 0.00544273## boundary (singular) fit: see help('isSingular')All GLMERs result in singular fit, will use regular GLM:
m1 <- step(m0)## Start: AIC=862.24
## richness ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff +
## sinus_Monitoring.Time.Diff
##
## Df Deviance AIC
## - Distance_rescaled:rescaled_Time.Diff 1 132.90 860.24
## - cosinus_Monitoring.Time.Diff 1 132.95 860.29
## - sinus_Monitoring.Time.Diff 1 132.98 860.32
## <none> 132.90 862.24
##
## Step: AIC=860.24
## richness ~ Distance_rescaled + rescaled_Time.Diff + cosinus_Monitoring.Time.Diff +
## sinus_Monitoring.Time.Diff
##
## Df Deviance AIC
## - cosinus_Monitoring.Time.Diff 1 132.95 858.29
## - sinus_Monitoring.Time.Diff 1 132.98 858.32
## - Distance_rescaled 1 133.75 859.09
## <none> 132.90 860.24
## - rescaled_Time.Diff 1 150.64 875.98
##
## Step: AIC=858.29
## richness ~ Distance_rescaled + rescaled_Time.Diff + sinus_Monitoring.Time.Diff
##
## Df Deviance AIC
## - sinus_Monitoring.Time.Diff 1 133.03 856.37
## - Distance_rescaled 1 133.75 857.09
## <none> 132.95 858.29
## - rescaled_Time.Diff 1 150.93 874.27
##
## Step: AIC=856.37
## richness ~ Distance_rescaled + rescaled_Time.Diff
##
## Df Deviance AIC
## - Distance_rescaled 1 133.79 855.13
## <none> 133.03 856.37
## - rescaled_Time.Diff 1 151.56 872.90
##
## Step: AIC=855.13
## richness ~ rescaled_Time.Diff
##
## Df Deviance AIC
## <none> 133.79 855.13
## - rescaled_Time.Diff 1 151.99 871.32# m1 <- glmer(data = P.anal, formula = richness ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + (1|Transect_with_date), family = poisson)
# drop1(m1)# m2 <- glmer(data = P.anal, formula = richness ~ Distance_rescaled * rescaled_Time.Diff + (1|Transect_with_date), family = poisson)
# drop1(m2)Final model includes time difference
summ(m1, exp = TRUE, digits=3)| Observations | 281 |
| Dependent variable | richness |
| Type | Generalized linear model |
| Family | poisson |
| Link | log |
| χ²(1) | 18.194 |
| Pseudo-R² (Cragg-Uhler) | 0.066 |
| Pseudo-R² (McFadden) | 0.021 |
| AIC | 855.130 |
| BIC | 862.407 |
| exp(Est.) | 2.5% | 97.5% | z val. | p | |
|---|---|---|---|---|---|
| (Intercept) | 1.944 | 1.772 | 2.133 | 14.084 | 0.000 |
| rescaled_Time.Diff | 1.222 | 1.113 | 1.340 | 4.235 | 0.000 |
| Standard errors: MLE |
effect_plot(model = m1, data=P.anal, pred = rescaled_Time.Diff, interval = T, plot.points = F, jitter = c(0.1,0), point.size = 3, cat.pred.point.size = 4, partial.residuals = F, colors = "Qual1", main.title = "Temporal effect (points are partial residuals)")
#m_coef <- coef(m2)
#P.anal$Cycle_number <- as.numeric(P.anal$Cycle_number)
#plot(P.anal$Cycle_number,
# exp(m_coef["rescaled_Time.Diff"]*P.anal$rescaled_Time.Diff),
#xlab = "Cycle number", ylab = "contribution to richness")Time difference significantly affects species richness
Community analysis using package MVabund
Table of abundances per species by camera ID
abundance_and_cameras <- as.data.table(abu_by_spp.MDtransitionalzone[,c(5,27:40)])
dim(abundance_and_cameras)## [1] 281 15canisau <- abundance_and_cameras[,2] > 0
sum(canisau)## [1] 125canislup <- abundance_and_cameras[,3] > 0
sum(canislup)## [1] 0capra <- abundance_and_cameras[,4] > 0
sum(capra)## [1] 0Doma <- abundance_and_cameras[,5] > 0
sum(Doma)## [1] 0Equus <- abundance_and_cameras[,6] > 0
sum(Equus)## [1] 0Gazellador <- abundance_and_cameras[,7] > 0
sum(Gazellador)## [1] 0Gazellagaz <- abundance_and_cameras[,8] > 0
sum(Gazellagaz)## [1] 112Hyaena <- abundance_and_cameras[,9] > 0
sum(Hyaena)## [1] 20Hystrix <- abundance_and_cameras[,10] > 0
sum(Hystrix)## [1] 42Lepus <- abundance_and_cameras[,11] > 0
sum(Lepus)## [1] 55Meles <- abundance_and_cameras[,12] > 0
sum(Meles)## [1] 35Oryx <- abundance_and_cameras[,13] > 0
sum(Oryx)## [1] 0Sus <- abundance_and_cameras[,14] > 0
sum(Sus)## [1] 0Vulpus <- abundance_and_cameras[,15] > 0
sum(Vulpus)## [1] 205281 cameras More than 20 cameras - Vulpus vulpus, Meles meles, Lepus capensis, Hystrix indica, Hyaena hyaena, Gazella gazella and Canis aureus
spp_no_rare <- abu_by_spp.MDtransitionalzone[,27:40]
#spp_no_rare <- abu_by_spp.MDtransitionalzone[,25:38]
print(colSums(spp_no_rare))## Canis aureus Canis lupus Capra nubiana Dama mesopotamica
## 467 0 0 0
## Equus hemionus Gazella dorcas Gazella gazella Hyaena hyaena
## 0 0 940 22
## Hystrix indica Lepus capensis Meles meles Oryx leucoryx
## 97 181 39 0
## Sus scrofa Vulpes vulpes
## 0 708# filter out species with less than 20 individuals (rare species)
spp_no_rare <- spp_no_rare[,.SD,.SDcols = colSums(spp_no_rare)>=20]
spp_no_rare <- mvabund(spp_no_rare)
print(colSums(spp_no_rare))## Canis.aureus Gazella.gazella Hyaena.hyaena Hystrix.indica Lepus.capensis
## 467 940 22 97 181
## Meles.meles Vulpes.vulpes
## 39 708Left with 7 species
env_data <- abu_by_spp.MDtransitionalzone[,..col_names]
mva_m0.po <- manyglm(formula =spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + sinus_Monitoring.Time.Diff + Site, family = "poisson", data = env_data)
mva_m0.nb <- manyglm(formula = spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + sinus_Monitoring.Time.Diff + Site, family = "negative.binomial", data = env_data)
aic_dt <- data.table(nb = mva_m0.nb$aic, po=mva_m0.po$aic)
colMeans(aic_dt)## nb po
## 590.5388 837.5866print("POISSON")## [1] "POISSON"plot(mva_m0.po, which=1:3)## Warning in default.plot.manyglm(x, res.type = res.type, which = which, caption
## = caption, : Only the first 7 colors will be used for plotting.
print("NEGATIVE BINOMIAL")## [1] "NEGATIVE BINOMIAL"plot(mva_m0.nb, which=1:3)## Warning in default.plot.manyglm(x, res.type = res.type, which = which, caption
## = caption, : Only the first 7 colors will be used for plotting.
prefer negative binomial because standardized residuals are smaller.
# mva_m0 <- mva_m0.po
# mva_m1 <- manyglm(formula = spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + dunes*rescaled_Time.Diff + Distance_rescaled:dunes + cos_td_rad + sin_td_rad + site, family = "poisson", data = env_data)
# aic_dt$po1 <- mva_m1$aic
mva_m0 <- mva_m0.nb
mva_m1 <- manyglm(formula = spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + sinus_Monitoring.Time.Diff + Transect_with_date, data = env_data)
aic_dt$nb1 <- mva_m1$aic
colMeans(aic_dt)## nb po nb1
## 590.5388 837.5866 596.8722mva_m2 <- manyglm(formula = spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + sinus_Monitoring.Time.Diff + Transect, data = env_data)
aic_dt$nb2 <- mva_m2$aic
colMeans(aic_dt)## nb po nb1 nb2
## 590.5388 837.5866 596.8722 586.2624mva_m3 <- manyglm(formula = spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + sinus_Monitoring.Time.Diff + Site, data = env_data)
aic_dt$nb3 <- mva_m2$aic
colMeans(aic_dt)## nb po nb1 nb2 nb3
## 590.5388 837.5866 596.8722 586.2624 586.2624Transect and Site improve the model
##Many GLM with site:
mva_m0.nb <- manyglm(formula = spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + sinus_Monitoring.Time.Diff + Site, family = "negative.binomial", data = env_data)
drop1(mva_m0.nb)## Single term deletions
##
## Model:
## spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff +
## sinus_Monitoring.Time.Diff + Site
## Df AIC
## <none> 4133.8
## cosinus_Monitoring.Time.Diff 7 4162.7
## sinus_Monitoring.Time.Diff 7 4129.7
## Site 28 4293.6
## Distance_rescaled:rescaled_Time.Diff 7 4141.1Dropped Sin monitoring time
# mva_m2 <- manyglm(formula = spp_no_rare ~ Distance_rescaled + dunes*rescaled_Time.Diff + Distance_rescaled:dunes + cos_td_rad + sin_td_rad, family = "poisson", data = env_data)
mva_m2 <- manyglm(formula = spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + Site, family = "negative.binomial", data = env_data)
drop1(mva_m2)## Single term deletions
##
## Model:
## spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff +
## Site
## Df AIC
## <none> 4129.7
## cosinus_Monitoring.Time.Diff 7 4156.8
## Site 28 4292.4
## Distance_rescaled:rescaled_Time.Diff 7 4135.7Final model includes cosin time, site and the interaction between distance from settlement and time difference.
# mva_m2 <- manyglm(formula = spp_no_rare ~ Distance_rescaled + dunes + rescaled_Time.Diff + Distance_rescaled:dunes + cos_td_rad + sin_td_rad, family = "poisson", data = env_data)
# mva_m2 <- manyglm(spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + Site, family = "negative.binomial", data = env_data)
# drop1(mva_m2)# mva_m2 <- manyglm(formula = spp_no_rare ~ Distance_rescaled + dunes + rescaled_Time.Diff + Distance_rescaled:dunes + cos_td_rad + sin_td_rad, family = "poisson", data = env_data)
# mva_m2 <- manyglm(formula = spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + Site, family = "negative.binomial", data = env_data)
# drop1(mva_m2)plot(mva_m2, which=1:3)## Warning in default.plot.manyglm(x, res.type = res.type, which = which, caption
## = caption, : Only the first 7 colors will be used for plotting.
# summ_m2 <- summary(mva_m2)
# saveRDS(summ_m2, file = "output/summ_md_zone_999iter.rds")
summ_m2 <- readRDS(file = "output/summ_md_zone_999iter.rds")
print(summ_m2)##
## Test statistics:
## wald value Pr(>wald)
## (Intercept) 11.708 0.001 ***
## Distance_rescaled 2.844 0.366
## rescaled_Time.Diff 10.298 0.001 ***
## cosinus_Monitoring.Time.Diff 6.435 0.001 ***
## SiteHar Amasa 4.799 0.003 **
## SiteLahav 7.012 0.001 ***
## SiteLehavim 8.936 0.001 ***
## SiteMirsham 6.585 0.001 ***
## Distance_rescaled:rescaled_Time.Diff 4.488 0.014 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Test statistic: 19.12, p-value: 0.001
## Arguments:
## Test statistics calculated assuming response assumed to be uncorrelated
## P-value calculated using 999 resampling iterations via pit.trap resampling (to account for correlation in testing).# anov_m2.uni <- anova(mva_m2, p.uni = "adjusted")
# saveRDS(anov_m2.uni, file = "output/anova_md_zone_999iter.rds")
anov_m2.uni <- readRDS(file = "output/anova_md_zone_999iter.rds")
print(anov_m2.uni)## Analysis of Deviance Table
##
## Model: spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + Site
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 280
## Distance_rescaled 279 1 8.26 0.431
## rescaled_Time.Diff 278 1 97.93 0.001 ***
## cosinus_Monitoring.Time.Diff 277 1 41.87 0.001 ***
## Site 273 4 217.63 0.001 ***
## Distance_rescaled:rescaled_Time.Diff 272 1 20.02 0.010 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Univariate Tests:
## Canis.aureus Gazella.gazella
## Dev Pr(>Dev) Dev
## (Intercept)
## Distance_rescaled 7.309 0.098 0.143
## rescaled_Time.Diff 0.62 0.753 52.311
## cosinus_Monitoring.Time.Diff 0.104 0.941 28.269
## Site 37.665 0.001 80.17
## Distance_rescaled:rescaled_Time.Diff 16.132 0.002 1.916
## Hyaena.hyaena
## Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## Distance_rescaled 0.992 0 0.994
## rescaled_Time.Diff 0.001 0.583 0.753
## cosinus_Monitoring.Time.Diff 0.001 0.1 0.941
## Site 0.001 2.213 0.750
## Distance_rescaled:rescaled_Time.Diff 0.669 0.034 0.928
## Hystrix.indica Lepus.capensis
## Dev Pr(>Dev) Dev
## (Intercept)
## Distance_rescaled 0.278 0.992 0.482
## rescaled_Time.Diff 2.348 0.413 5.071
## cosinus_Monitoring.Time.Diff 2.923 0.465 1.416
## Site 45.406 0.001 44.074
## Distance_rescaled:rescaled_Time.Diff 0.751 0.910 0.334
## Meles.meles
## Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## Distance_rescaled 0.992 0.012 0.994
## rescaled_Time.Diff 0.144 3.238 0.314
## cosinus_Monitoring.Time.Diff 0.739 0.436 0.899
## Site 0.001 4.192 0.750
## Distance_rescaled:rescaled_Time.Diff 0.923 0.699 0.910
## Vulpes.vulpes
## Dev Pr(>Dev)
## (Intercept)
## Distance_rescaled 0.037 0.993
## rescaled_Time.Diff 33.757 0.001
## cosinus_Monitoring.Time.Diff 8.616 0.036
## Site 3.909 0.750
## Distance_rescaled:rescaled_Time.Diff 0.155 0.928
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.Multivariate test shows that cosin monitoring time, Site, time difference and interaction between distance from settlement and time difference are statistically significant
coefp <- merge(data.table(t(coef(mva_m2)/log(2)),keep.rownames=TRUE), data.table(t(anov_m2.uni$uni.p), keep.rownames = TRUE), by = "rn") # coefficients are exponentiated because the link function used for poisson is log -> above 1 is positive and below 1 is negative
# add total species abundance
coefp <- merge(coefp,as.data.table(colSums(spp_no_rare),keep.rownames = TRUE), by.x = "rn", by.y = "V1")
colnames(coefp) <- c("SciName","Intercept.coef","Distance.coef","Time_diff.coef","Cosin_time.coef","Site_Har_Amasa.coef.coef","Site_Lahav.coef","Site_Lehavim.coef","Site_Mirsham.coef","Distance_time.coef", "Intercept.p","Distance.p","Time_diff.p","Cosin_time.p","Site.p","Distance_time.p","species_abundance")
write.csv(coefp, "coefficients_MD_transitional_zone.csv")
print(coefp)## SciName Intercept.coef Distance.coef Time_diff.coef Cosin_time.coef
## 1: Canis.aureus 1.0426105 -0.49842766 -0.2547011 -0.7703213
## 2: Gazella.gazella 1.0806655 -0.03783560 1.4691821 -0.8122920
## 3: Hyaena.hyaena -4.1444314 -0.11357307 0.3509670 0.2731952
## 4: Hystrix.indica -7.5445021 -0.81949108 -0.2388161 3.4396769
## 5: Lepus.capensis -0.9643288 0.55167154 0.8515688 0.4104079
## 6: Meles.meles -4.2475336 -0.02100113 0.6439979 0.3446737
## 7: Vulpes.vulpes 1.1132860 0.32760272 0.7923119 0.5083757
## Site_Har_Amasa.coef.coef Site_Lahav.coef Site_Lehavim.coef Site_Mirsham.coef
## 1: -1.6322308 0.3809060 -2.7546359 -1.2060801
## 2: -0.9964718 -6.4625798 -3.8012093 0.0947497
## 3: 0.8451841 0.2244599 -0.7139241 0.5949948
## 4: 2.7516229 4.3285701 3.8180456 8.5896454
## 5: -0.1058152 -4.8690602 1.7640321 -3.1111254
## 6: 1.1492627 1.0279397 1.4993780 1.5967657
## 7: 0.1112995 -0.2973630 -0.2838546 -0.3863128
## Distance_time.coef Intercept.p Distance.p Time_diff.p Cosin_time.p Site.p
## 1: -1.45272648 NA 0.098 0.753 0.941 0.001
## 2: -0.53265390 NA 0.992 0.001 0.001 0.001
## 3: 0.12838062 NA 0.994 0.753 0.941 0.750
## 4: 0.60211957 NA 0.992 0.413 0.465 0.001
## 5: 0.36984801 NA 0.992 0.144 0.739 0.001
## 6: 0.44010429 NA 0.994 0.314 0.899 0.750
## 7: -0.09700433 NA 0.993 0.001 0.036 0.750
## Distance_time.p species_abundance
## 1: 0.002 467
## 2: 0.669 940
## 3: 0.928 22
## 4: 0.910 97
## 5: 0.923 181
## 6: 0.910 39
## 7: 0.928 708#### Canis aureus####
Canis.aureus <- spp_no_rare[,"Canis.aureus"]
glm.Canis.aureus <- glm.nb(formula = Canis.aureus ~ Distance_rescaled * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + Site, data = env_data)
coef(glm.Canis.aureus)## (Intercept) Distance_rescaled
## 0.7226852 -0.3454709
## rescaled_Time.Diff cosinus_Monitoring.Time.Diff
## -0.1765497 -0.5339577
## SiteHar Amasa SiteLahav
## -1.1313627 0.2640154
## SiteLehavim SiteMirsham
## -1.9093470 -0.8359891
## Distance_rescaled:rescaled_Time.Diff
## -1.0069294coef(mva_m2)## Canis.aureus Gazella.gazella Hyaena.hyaena
## (Intercept) 0.7226825 0.74906022 -2.87270092
## Distance_rescaled -0.3454837 -0.02622564 -0.07872285
## rescaled_Time.Diff -0.1765454 1.01835942 0.24327181
## cosinus_Monitoring.Time.Diff -0.5339460 -0.56303790 0.18936451
## SiteHar Amasa -1.1313762 -0.69070165 0.58583696
## SiteLahav 0.2640240 -4.47951898 0.15558375
## SiteLehavim -1.9093681 -2.63479750 -0.49485445
## SiteMirsham -0.8359910 0.06567549 0.41241896
## Distance_rescaled:rescaled_Time.Diff -1.0069533 -0.36920755 0.08898667
## Hystrix.indica Lepus.capensis Meles.meles
## (Intercept) -5.2294503 -0.66842180 -2.94416591
## Distance_rescaled -0.5680279 0.38238957 -0.01455687
## rescaled_Time.Diff -0.1655347 0.59026253 0.44638532
## cosinus_Monitoring.Time.Diff 2.3842023 0.28447305 0.23890958
## SiteHar Amasa 1.9072796 -0.07334553 0.79660820
## SiteLahav 3.0003362 -3.37497537 0.71251350
## SiteLehavim 2.6464676 1.22273387 1.03928967
## SiteMirsham 5.9538885 -2.15646780 1.10679362
## Distance_rescaled:rescaled_Time.Diff 0.4173575 0.25635910 0.30505705
## Vulpes.vulpes
## (Intercept) 0.77167103
## Distance_rescaled 0.22707690
## rescaled_Time.Diff 0.54918876
## cosinus_Monitoring.Time.Diff 0.35237916
## SiteHar Amasa 0.07714693
## SiteLahav -0.20611633
## SiteLehavim -0.19675301
## SiteMirsham -0.26777162
## Distance_rescaled:rescaled_Time.Diff -0.06723827print(sum(Canis.aureus))## [1] 467plot_model_interaction(P.anal = env_data, m = glm.Canis.aureus, eff2plot = "rescaled_Time.Diff", modvar2plot = "Distance_rescaled", plot_points=FALSE, plot_residuals=FALSE, export_plot=TRUE, ylabel = NULL, fontname = "Almoni ML v5 AAA", fontsize=22, pdf_width=160, outpath = "output/md_zone/", legend_position = "bottom")## Loading required package: extrafont## Registering fonts with R
# interact_plot(model = glm.Canis.aureus, data=env_data, pred = rescaled_Time.Diff, modx = Distance_rescaled, modx.values = "plus-minus", partial.residuals = F, jitter = c(0.2), point.size = 3, rug = F, point.shape = F, interval = T, colors = "Dark2") + scale_x_continuous(breaks=c(-1.233466888,-0.601840099,0.028922631,0.66054942,1.291312151), labels=c("June 2014", "June 2016", "June 2018", "June 2020", "June 2022"), name = "Time")
#Testing whether trends and means are different from zero
#Calculate mean and sd of distance
sd_distance <- sd(env_data$Distance_rescaled)
print(sd_distance)## [1] 0.5278765mean_distance <- mean(env_data$Distance_rescaled)
print(mean_distance)## [1] -0.2131719#calculate +/1 1 SD
small_distance <- mean_distance - sd_distance
large_distance <- mean_distance + sd_distance
#Test whether temporal trend is different from zero
mm_rescaled_time <- emtrends(glm.Canis.aureus, specs = "Distance_rescaled", var = "rescaled_Time.Diff", type = "response", at = list(Distance_rescaled = c(small_distance,large_distance)))
print(mm_rescaled_time)## Distance_rescaled rescaled_Time.Diff.trend SE df asymp.LCL asymp.UCL
## -0.741 0.570 0.171 Inf 0.235 0.904
## 0.315 -0.493 0.192 Inf -0.869 -0.118
##
## Results are averaged over the levels of: Site
## Confidence level used: 0.95test_results_mm_rescaled_time <- test(mm_rescaled_time, null = 0, adjust = "fdr")
print(test_results_mm_rescaled_time)## Distance_rescaled rescaled_Time.Diff.trend SE df z.ratio p.value
## -0.741 0.570 0.171 Inf 3.335 0.0017
## 0.315 -0.493 0.192 Inf -2.576 0.0100
##
## Results are averaged over the levels of: Site
## P value adjustment: fdr method for 2 tests#Testing whether abundance is higher near or far from settlements
pairwise_distance <- emmeans(object = glm.Canis.aureus, ~Distance_rescaled*rescaled_Time.Diff, at = list(Distance_rescaled = c(small_distance,large_distance)))
test_results_distance <- test(pairs(pairwise_distance, by="rescaled_Time.Diff"), by=NULL, adjust="fdr")
print(test_results_distance)## contrast
## (Distance_rescaled-0.741048431885884) - Distance_rescaled0.31470454181471
## rescaled_Time.Diff estimate SE df z.ratio p.value
## 0.34 0.727 0.246 Inf 2.952 0.0032
##
## Results are averaged over the levels of: Site
## Results are given on the log (not the response) scale.Temporal trends are significantly different from zero for both large (p = 0.001) and small (p = 0.01) distances from settlements
On average over time, Jackal abundance is significantly higher near settlements (p = 0.003)
#### Gazella gazella####
Gazella.gazella <- spp_no_rare[,"Gazella.gazella"]
glm.Gazella.gazella <- glm.nb(formula = Gazella.gazella ~ Distance_rescaled + rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + Site, data = env_data)
coef(glm.Gazella.gazella)## (Intercept) Distance_rescaled
## 0.7496189 -0.1220151
## rescaled_Time.Diff cosinus_Monitoring.Time.Diff
## 1.0639549 -0.5311252
## SiteHar Amasa SiteLahav
## -0.6811244 -4.4244071
## SiteLehavim SiteMirsham
## -2.5844055 0.1011102coef(mva_m2)## Canis.aureus Gazella.gazella Hyaena.hyaena
## (Intercept) 0.7226825 0.74906022 -2.87270092
## Distance_rescaled -0.3454837 -0.02622564 -0.07872285
## rescaled_Time.Diff -0.1765454 1.01835942 0.24327181
## cosinus_Monitoring.Time.Diff -0.5339460 -0.56303790 0.18936451
## SiteHar Amasa -1.1313762 -0.69070165 0.58583696
## SiteLahav 0.2640240 -4.47951898 0.15558375
## SiteLehavim -1.9093681 -2.63479750 -0.49485445
## SiteMirsham -0.8359910 0.06567549 0.41241896
## Distance_rescaled:rescaled_Time.Diff -1.0069533 -0.36920755 0.08898667
## Hystrix.indica Lepus.capensis Meles.meles
## (Intercept) -5.2294503 -0.66842180 -2.94416591
## Distance_rescaled -0.5680279 0.38238957 -0.01455687
## rescaled_Time.Diff -0.1655347 0.59026253 0.44638532
## cosinus_Monitoring.Time.Diff 2.3842023 0.28447305 0.23890958
## SiteHar Amasa 1.9072796 -0.07334553 0.79660820
## SiteLahav 3.0003362 -3.37497537 0.71251350
## SiteLehavim 2.6464676 1.22273387 1.03928967
## SiteMirsham 5.9538885 -2.15646780 1.10679362
## Distance_rescaled:rescaled_Time.Diff 0.4173575 0.25635910 0.30505705
## Vulpes.vulpes
## (Intercept) 0.77167103
## Distance_rescaled 0.22707690
## rescaled_Time.Diff 0.54918876
## cosinus_Monitoring.Time.Diff 0.35237916
## SiteHar Amasa 0.07714693
## SiteLahav -0.20611633
## SiteLehavim -0.19675301
## SiteMirsham -0.26777162
## Distance_rescaled:rescaled_Time.Diff -0.06723827print(sum(Gazella.gazella))## [1] 940source("functions/plot_model_effect_mammals.R")
plot_model_effect(P.anal = env_data, m = glm.Gazella.gazella, eff2plot = "rescaled_Time.Diff", plot_points=FALSE, plot_residuals=FALSE, export_plot=TRUE, ylabel = NULL, fontname = "Almoni ML v5 AAA", fontsize=22, pdf_width=160, outpath = "output/md_zone/")
# effect_plot(model = glm.Gazella.gazella, data=env_data, pred = rescaled_Time.Diff, interval = T, plot.points = F, jitter = c(0.1,0), partial.residuals = F, colors = "Qual1") + ylim(0,15) + scale_x_continuous(breaks=c(-1.233466888,-0.601840099,0.028922631,0.66054942,1.291312151), labels=c("June 2014", "June 2016", "June 2018", "June 2020", "June 2022"), name = "Time")
#### Vulpes vulpes####
Vulpes.vulpes <- spp_no_rare[,"Vulpes.vulpes"]
glm.Vulpes.vulpes <- glm.nb(formula = Vulpes.vulpes ~ Distance_rescaled + rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + Site, data = env_data)
coef(glm.Vulpes.vulpes)## (Intercept) Distance_rescaled
## 0.76602777 0.21065333
## rescaled_Time.Diff cosinus_Monitoring.Time.Diff
## 0.56631374 0.36083191
## SiteHar Amasa SiteLahav
## 0.07968601 -0.20764925
## SiteLehavim SiteMirsham
## -0.19653653 -0.25479826coef(mva_m2)## Canis.aureus Gazella.gazella Hyaena.hyaena
## (Intercept) 0.7226825 0.74906022 -2.87270092
## Distance_rescaled -0.3454837 -0.02622564 -0.07872285
## rescaled_Time.Diff -0.1765454 1.01835942 0.24327181
## cosinus_Monitoring.Time.Diff -0.5339460 -0.56303790 0.18936451
## SiteHar Amasa -1.1313762 -0.69070165 0.58583696
## SiteLahav 0.2640240 -4.47951898 0.15558375
## SiteLehavim -1.9093681 -2.63479750 -0.49485445
## SiteMirsham -0.8359910 0.06567549 0.41241896
## Distance_rescaled:rescaled_Time.Diff -1.0069533 -0.36920755 0.08898667
## Hystrix.indica Lepus.capensis Meles.meles
## (Intercept) -5.2294503 -0.66842180 -2.94416591
## Distance_rescaled -0.5680279 0.38238957 -0.01455687
## rescaled_Time.Diff -0.1655347 0.59026253 0.44638532
## cosinus_Monitoring.Time.Diff 2.3842023 0.28447305 0.23890958
## SiteHar Amasa 1.9072796 -0.07334553 0.79660820
## SiteLahav 3.0003362 -3.37497537 0.71251350
## SiteLehavim 2.6464676 1.22273387 1.03928967
## SiteMirsham 5.9538885 -2.15646780 1.10679362
## Distance_rescaled:rescaled_Time.Diff 0.4173575 0.25635910 0.30505705
## Vulpes.vulpes
## (Intercept) 0.77167103
## Distance_rescaled 0.22707690
## rescaled_Time.Diff 0.54918876
## cosinus_Monitoring.Time.Diff 0.35237916
## SiteHar Amasa 0.07714693
## SiteLahav -0.20611633
## SiteLehavim -0.19675301
## SiteMirsham -0.26777162
## Distance_rescaled:rescaled_Time.Diff -0.06723827print(sum(Vulpes.vulpes))## [1] 708plot_model_effect(P.anal = env_data, m = glm.Vulpes.vulpes, eff2plot = "rescaled_Time.Diff", plot_points=FALSE, plot_residuals=FALSE, export_plot=TRUE, ylabel = NULL, fontname = "Almoni ML v5 AAA", fontsize=22, pdf_width=160, outpath = "output/md_zone/")
# effect_plot(model = glm.Vulpes.vulpes, data=env_data, pred = rescaled_Time.Diff, interval = T, plot.points = F, jitter = c(0.1,0), partial.residuals = F, colors = "Qual1") + ylim(0,10) + scale_x_continuous(breaks=c(-1.233466888,-0.601840099,0.028922631,0.66054942,1.291312151), labels=c("June 2014", "June 2016", "June 2018", "June 2020", "June 2022"), name = "Time")n = 281 (total number of cameras)
total abundance = 2454 (7 species), total abundance without rare species is the same (all rare species had zero observations)
Session information
session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.2.3 (2023-03-15 ucrt)
## os Windows 10 x64 (build 22631)
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate Hebrew_Israel.utf8
## ctype Hebrew_Israel.utf8
## tz Asia/Jerusalem
## date 2024-04-08
## pandoc 3.1.1 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## abind 1.4-7 2017-09-03 [1] R-Forge (R 4.2.3)
## betareg 3.2-0 2021-02-09 [1] R-Forge (R 4.2.3)
## boot 1.3-28.1 2022-11-22 [1] CRAN (R 4.2.3)
## bslib 0.4.2 2022-12-16 [1] CRAN (R 4.2.3)
## cachem 1.0.7 2023-02-24 [1] CRAN (R 4.2.3)
## Cairo * 1.6-0 2022-07-05 [1] CRAN (R 4.2.2)
## callr 3.7.3 2022-11-02 [1] CRAN (R 4.2.3)
## car * 3.1-2 2023-03-30 [1] CRAN (R 4.2.3)
## carData * 3.0-5 2022-01-06 [1] CRAN (R 4.2.3)
## cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.2.3)
## chron * 2.3-61 2023-05-02 [1] CRAN (R 4.2.3)
## cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.3)
## cluster 2.1.4 2022-08-22 [1] CRAN (R 4.2.3)
## coda 0.19-4 2020-09-30 [1] CRAN (R 4.2.3)
## codetools 0.2-19 2023-02-01 [1] CRAN (R 4.2.2)
## colorspace 2.1-1 2023-03-08 [1] R-Forge (R 4.2.2)
## crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.3)
## curl 5.0.0 2023-01-12 [1] CRAN (R 4.2.3)
## data.table * 1.14.8 2023-02-17 [1] CRAN (R 4.2.3)
## devtools * 2.4.5 2022-10-11 [1] CRAN (R 4.2.3)
## digest 0.6.31 2022-12-11 [1] CRAN (R 4.2.3)
## doParallel 1.0.17 2022-02-07 [1] CRAN (R 4.2.3)
## dplyr * 1.1.1 2023-03-22 [1] CRAN (R 4.2.3)
## ecoCopula * 1.0.2 2022-03-02 [1] CRAN (R 4.2.3)
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.3)
## emmeans * 1.8.6 2023-05-11 [1] CRAN (R 4.2.3)
## estimability 1.4.1 2022-08-05 [1] CRAN (R 4.2.1)
## evaluate 0.20 2023-01-17 [1] CRAN (R 4.2.3)
## extrafont * 0.19 2023-01-18 [1] CRAN (R 4.2.2)
## extrafontdb 1.0 2012-06-11 [1] CRAN (R 4.2.0)
## fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.3)
## farver 2.1.1 2022-07-06 [1] CRAN (R 4.2.3)
## fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.3)
## flexmix 2.3-19 2023-03-16 [1] CRAN (R 4.2.3)
## foreach 1.5.2 2022-02-02 [1] CRAN (R 4.2.3)
## Formula 1.2-6 2023-02-25 [1] R-Forge (R 4.2.2)
## fs 1.6.1 2023-02-06 [1] CRAN (R 4.2.3)
## generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.3)
## ggplot2 * 3.5.0 2024-02-23 [1] CRAN (R 4.2.3)
## glm2 1.2.1 2018-08-11 [1] CRAN (R 4.2.0)
## glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.3)
## gtable 0.3.3 2023-03-21 [1] CRAN (R 4.2.3)
## highr 0.10 2022-12-22 [1] CRAN (R 4.2.3)
## htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.2.3)
## htmlwidgets 1.6.2 2023-03-17 [1] CRAN (R 4.2.3)
## httpuv 1.6.9 2023-02-14 [1] CRAN (R 4.2.3)
## httr 1.4.5 2023-02-24 [1] CRAN (R 4.2.3)
## interactions * 1.1.5 2021-07-02 [1] CRAN (R 4.2.3)
## iterators 1.0.14 2022-02-05 [1] CRAN (R 4.2.3)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.2.3)
## jsonlite 1.8.4 2022-12-06 [1] CRAN (R 4.2.3)
## jtools * 2.2.1 2022-12-02 [1] CRAN (R 4.2.3)
## kableExtra * 1.4.0 2024-01-24 [1] CRAN (R 4.2.3)
## knitr 1.42 2023-01-25 [1] CRAN (R 4.2.3)
## labeling 0.4.2 2020-10-20 [1] CRAN (R 4.2.0)
## later 1.3.0 2021-08-18 [1] CRAN (R 4.2.3)
## lattice * 0.21-8 2023-04-05 [1] CRAN (R 4.2.3)
## lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.3)
## lme4 * 1.1-32 2023-03-14 [1] CRAN (R 4.2.3)
## lmtest 0.9-40 2022-03-21 [1] CRAN (R 4.2.3)
## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.3)
## MASS * 7.3-58.3 2023-03-07 [1] CRAN (R 4.2.3)
## Matrix * 1.5-5 2023-04-05 [1] R-Forge (R 4.2.3)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.3)
## mgcv 1.8-42 2023-03-02 [1] CRAN (R 4.2.3)
## mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
## miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.3)
## minqa 1.2.5 2022-10-19 [1] CRAN (R 4.2.3)
## modeltools 0.2-23 2020-03-05 [1] CRAN (R 4.2.0)
## multcomp 1.4-23 2023-03-09 [1] CRAN (R 4.2.3)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.3)
## mvabund * 4.2.1 2022-02-16 [1] CRAN (R 4.2.3)
## mvtnorm 1.2-0 2023-04-05 [1] R-Forge (R 4.2.3)
## nlme 3.1-162 2023-01-31 [1] CRAN (R 4.2.3)
## nloptr 2.0.3 2022-05-26 [1] CRAN (R 4.2.3)
## nnet 7.3-18 2022-09-28 [1] CRAN (R 4.2.3)
## numDeriv 2022.9-1 2022-09-27 [1] R-Forge (R 4.2.1)
## ordinal 2022.11-16 2022-11-16 [1] CRAN (R 4.2.3)
## pander 0.6.5 2022-03-18 [1] CRAN (R 4.2.3)
## permute * 0.9-7 2022-01-27 [1] CRAN (R 4.2.3)
## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.3)
## pkgbuild 1.4.2.9000 2023-07-11 [1] Github (r-lib/pkgbuild@7048654)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.3)
## pkgload 1.3.2 2022-11-16 [1] CRAN (R 4.2.3)
## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.3)
## processx 3.8.0 2022-10-26 [1] CRAN (R 4.2.3)
## profvis 0.3.7 2020-11-02 [1] CRAN (R 4.2.3)
## promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.2.3)
## ps 1.7.4 2023-04-02 [1] CRAN (R 4.2.3)
## purrr 1.0.1 2023-01-10 [1] CRAN (R 4.2.3)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.3)
## RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.2.0)
## Rcpp 1.0.10 2023-01-22 [1] CRAN (R 4.2.3)
## readxl * 1.4.2 2023-02-09 [1] CRAN (R 4.2.3)
## remotes 2.4.2 2021-11-30 [1] CRAN (R 4.2.3)
## rlang * 1.1.0 2023-03-14 [1] CRAN (R 4.2.3)
## rmarkdown 2.21 2023-03-26 [1] CRAN (R 4.2.3)
## rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.3)
## Rttf2pt1 1.3.12 2023-01-22 [1] CRAN (R 4.2.2)
## sandwich 3.1-0 2023-04-04 [1] R-Forge (R 4.2.3)
## sass 0.4.5 2023-01-24 [1] CRAN (R 4.2.3)
## scales 1.3.0 2023-11-28 [1] CRAN (R 4.2.3)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.3)
## shiny 1.7.4 2022-12-15 [1] CRAN (R 4.2.3)
## statmod 1.5.0 2023-01-06 [1] CRAN (R 4.2.3)
## stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.2)
## stringr 1.5.0 2022-12-02 [1] CRAN (R 4.2.3)
## survival 3.5-5 2023-03-12 [1] CRAN (R 4.2.3)
## svglite 2.1.1 2023-01-10 [1] CRAN (R 4.2.3)
## systemfonts 1.0.4 2022-02-11 [1] CRAN (R 4.2.3)
## TH.data 1.1-2 2022-11-07 [1] R-Forge (R 4.2.3)
## tibble 3.2.1 2023-03-20 [1] CRAN (R 4.2.3)
## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.3)
## tweedie 2.3.5 2022-08-17 [1] CRAN (R 4.2.3)
## ucminf 1.1-4.1 2022-09-29 [1] CRAN (R 4.2.1)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.3)
## usethis * 2.1.6 2022-05-25 [1] CRAN (R 4.2.3)
## utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.3)
## vctrs 0.6.1 2023-03-22 [1] CRAN (R 4.2.3)
## vegan * 2.6-4 2022-10-11 [1] CRAN (R 4.2.3)
## viridisLite 0.4.1 2022-08-22 [1] CRAN (R 4.2.3)
## withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.3)
## xfun 0.38 2023-03-24 [1] CRAN (R 4.2.3)
## xml2 1.3.3 2021-11-30 [1] CRAN (R 4.2.3)
## xtable 1.8-6 2020-06-19 [1] R-Forge (R 4.2.3)
## yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.3)
## zoo 1.8-11 2022-09-17 [1] CRAN (R 4.2.3)
##
## [1] C:/Users/Ron Chen/AppData/Local/R/win-library/4.2.3
## [2] C:/Program Files/R/R-4.2.3/library
##
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