Mammal data - Mediterranean Maquis - with subunit interactions
Ron Chen & Talia Gabay
2024-04-09
P <- P_byplot[grepl("Mediterranean Maquis",Unit),][order(Cycle_number,Site)][,Site:=factor(Site)]Mediterranean Maquis - Richness by camera
Monitoring started in 2014 (5 monitoring cycles), Each site has two types of plots - far or near settlements
There are 15 sites total, each site has 5 sampling points
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$Settlements))## [1] "Far" "Near"P.anal$Subunit <- as.factor(P.anal$Subunit)
print("Subunit has 3 levels")## [1] "Subunit has 3 levels"print(levels(P.anal$Subunit))## [1] "Carmel" "Galilee" "Judea"P.anal$Site <- as.factor(P.anal$Site)
print("Site has 15 levels")## [1] "Site has 15 levels"print(levels(P.anal$Site))## [1] "Aderet" "Beit Oren" "Ein Yaakov" "Givat Yearim"
## [5] "Givat Yeshayahu" "Goren" "Iftach" "Kerem Maharal"
## [9] "Kfar Shamai" "Kisalon" "Margaliot" "Nehusha"
## [13] "Nir Etzion" "Ofer" "Yagur"P.anal$Transect <- as.factor(P.anal$Transect)
print("Transect has 30 levels")## [1] "Transect has 30 levels"print(levels(P.anal$Transect))## [1] "Aderet Far" "Aderet Near" "Beit Oren Far"
## [4] "Beit Oren Near" "Ein Yaakov Far" "Ein Yaakov Near"
## [7] "Givat Yearim Far" "Givat Yearim Near" "Givat Yeshayahu Far"
## [10] "Givat Yeshayahu Near" "Goren Far" "Goren Near"
## [13] "Iftach Far" "Iftach Near" "Kerem Maharal Far"
## [16] "Kerem Maharal Near" "Kfar Shamai Far" "Kfar Shamai Near"
## [19] "Kisalon Far" "Kisalon Near" "Margaliot Far"
## [22] "Margaliot Near" "Nehusha Far" "Nehusha Near"
## [25] "Nir Etzion Far" "Nir Etzion Near" "Ofer Far"
## [28] "Ofer Near" "Yagur Far" "Yagur Near"P.anal$Transect_with_date <- as.factor(P.anal$Transect_with_date)
print("Transect with date has 150 levels")## [1] "Transect with date has 150 levels"print(levels(P.anal$Transect_with_date))## [1] "Aderet Far_03_10_2017" "Aderet Far_10_08_2021"
## [3] "Aderet Far_10_10_2015" "Aderet Far_17_07_2012"
## [5] "Aderet Far_29_09_2019" "Aderet Near_03_10_2017"
## [7] "Aderet Near_10_08_2021" "Aderet Near_17_07_2012"
## [9] "Aderet Near_22_10_2015" "Aderet Near_29_09_2019"
## [11] "Beit Oren Far_03_06_2015" "Beit Oren Far_05_11_2019"
## [13] "Beit Oren Far_07_07_2017" "Beit Oren Far_07_08_2021"
## [15] "Beit Oren Far_18_06_2012" "Beit Oren Near_05_11_2019"
## [17] "Beit Oren Near_07_07_2017" "Beit Oren Near_07_08_2021"
## [19] "Beit Oren Near_18_06_2012" "Beit Oren Near_25_06_2015"
## [21] "Ein Yaakov Far_03_06_2012" "Ein Yaakov Far_06_02_2020"
## [23] "Ein Yaakov Far_06_07_2021" "Ein Yaakov Far_11_11_2015"
## [25] "Ein Yaakov Far_20_10_2017" "Ein Yaakov Near_03_06_2012"
## [27] "Ein Yaakov Near_06_02_2020" "Ein Yaakov Near_06_07_2021"
## [29] "Ein Yaakov Near_10_11_2015" "Ein Yaakov Near_20_10_2017"
## [31] "Givat Yearim Far_03_07_2015" "Givat Yearim Far_11_07_2021"
## [33] "Givat Yearim Far_15_09_2019" "Givat Yearim Far_29_07_2017"
## [35] "Givat Yearim Far_31_07_2012" "Givat Yearim Near_11_07_2021"
## [37] "Givat Yearim Near_16_09_2019" "Givat Yearim Near_21_07_2015"
## [39] "Givat Yearim Near_29_07_2017" "Givat Yearim Near_31_07_2012"
## [41] "Givat Yeshayahu Far_03_09_2019" "Givat Yeshayahu Far_16_07_2012"
## [43] "Givat Yeshayahu Far_24_08_2017" "Givat Yeshayahu Far_28_06_2021"
## [45] "Givat Yeshayahu Far_31_08_2015" "Givat Yeshayahu Near_03_09_2019"
## [47] "Givat Yeshayahu Near_16_07_2012" "Givat Yeshayahu Near_24_08_2017"
## [49] "Givat Yeshayahu Near_28_06_2021" "Givat Yeshayahu Near_29_08_2015"
## [51] "Goren Far_03_09_2015" "Goren Far_14_01_2020"
## [53] "Goren Far_17_08_2021" "Goren Far_20_10_2017"
## [55] "Goren Far_22_05_2012" "Goren Near_03_09_2015"
## [57] "Goren Near_14_01_2020" "Goren Near_17_08_2021"
## [59] "Goren Near_20_10_2017" "Goren Near_22_05_2012"
## [61] "Iftach Far_04_11_2017" "Iftach Far_05_08_2015"
## [63] "Iftach Far_09_09_2021" "Iftach Far_16_04_2020"
## [65] "Iftach Far_21_05_2012" "Iftach Near_04_11_2017"
## [67] "Iftach Near_05_08_2015" "Iftach Near_09_09_2021"
## [69] "Iftach Near_16_04_2020" "Iftach Near_21_05_2012"
## [71] "Kerem Maharal Far_01_07_2012" "Kerem Maharal Far_07_12_2019"
## [73] "Kerem Maharal Far_11_08_2017" "Kerem Maharal Far_19_05_2015"
## [75] "Kerem Maharal Far_24_07_2021" "Kerem Maharal Near_01_07_2012"
## [77] "Kerem Maharal Near_07_12_2019" "Kerem Maharal Near_11_08_2017"
## [79] "Kerem Maharal Near_19_05_2015" "Kerem Maharal Near_23_07_2021"
## [81] "Kfar Shamai Far_09_11_2021" "Kfar Shamai Far_13_07_2015"
## [83] "Kfar Shamai Far_24_05_2020" "Kfar Shamai Far_28_09_2017"
## [85] "Kfar Shamai Far_29_04_2012" "Kfar Shamai Near_09_11_2021"
## [87] "Kfar Shamai Near_13_07_2015" "Kfar Shamai Near_24_05_2020"
## [89] "Kfar Shamai Near_28_09_2017" "Kfar Shamai Near_29_04_2012"
## [91] "Kisalon Far_19_07_2015" "Kisalon Far_19_11_2021"
## [93] "Kisalon Far_21_06_2017" "Kisalon Far_22_10_2019"
## [95] "Kisalon Far_30_07_2012" "Kisalon Near_01_07_2015"
## [97] "Kisalon Near_19_11_2021" "Kisalon Near_21_06_2017"
## [99] "Kisalon Near_22_10_2019" "Kisalon Near_30_07_2012"
## [101] "Margaliot Far_04_11_2017" "Margaliot Far_16_03_2020"
## [103] "Margaliot Far_18_01_2022" "Margaliot Far_20_09_2015"
## [105] "Margaliot Far_30_04_2012" "Margaliot Near_04_11_2017"
## [107] "Margaliot Near_16_03_2020" "Margaliot Near_18_01_2022"
## [109] "Margaliot Near_28_10_2015" "Margaliot Near_30_04_2012"
## [111] "Nehusha Far_02_10_2019" "Nehusha Far_05_09_2017"
## [113] "Nehusha Far_13_08_2012" "Nehusha Far_13_09_2015"
## [115] "Nehusha Far_23_08_2021" "Nehusha Near_02_10_2019"
## [117] "Nehusha Near_05_09_2017" "Nehusha Near_13_08_2012"
## [119] "Nehusha Near_23_08_2021" "Nehusha Near_26_09_2015"
## [121] "Nir Etzion Far_02_06_2015" "Nir Etzion Far_16_06_2017"
## [123] "Nir Etzion Far_17_06_2012" "Nir Etzion Far_25_11_2019"
## [125] "Nir Etzion Far_26_06_2021" "Nir Etzion Near_02_06_2015"
## [127] "Nir Etzion Near_16_06_2017" "Nir Etzion Near_17_06_2012"
## [129] "Nir Etzion Near_25_11_2019" "Nir Etzion Near_26_06_2021"
## [131] "Ofer Far_02_07_2012" "Ofer Far_04_01_2022"
## [133] "Ofer Far_13_05_2017" "Ofer Far_16_12_2019"
## [135] "Ofer Far_18_05_2015" "Ofer Near_02_07_2012"
## [137] "Ofer Near_04_01_2022" "Ofer Near_13_05_2017"
## [139] "Ofer Near_16_12_2019" "Ofer Near_18_05_2015"
## [141] "Yagur Far_04_06_2012" "Yagur Far_10_06_2021"
## [143] "Yagur Far_23_12_2019" "Yagur Far_28_06_2015"
## [145] "Yagur Far_28_08_2017" "Yagur Near_04_06_2012"
## [147] "Yagur Near_10_06_2021" "Yagur Near_23_12_2019"
## [149] "Yagur Near_28_08_2017" "Yagur Near_29_10_2015"print("RICHNESS WITH RARE SPECIES")## [1] "RICHNESS WITH RARE SPECIES"plot_alpha_diversity(P, x_val = "Subunit", y_val = "richness", ylab_val = "richness", xlab_val = "Subunit", fill_val = "Subunit")
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.Maquis <- all.mammal.abundances[grepl("Mediterranean Maquis",Unit),]
spp <- abu_by_spp.Maquis[,c(27:39)]
# filter out species with zero counts
print(colSums(spp))## Canis aureus Canis lupus Capra nubiana Equus hemionus Gazella dorcas
## 6971 44 0 0 0
## Gazella gazella Hyaena hyaena Hystrix indica Lepus capensis Meles meles
## 406 298 3019 10 408
## Oryx leucoryx Sus scrofa Vulpes vulpes
## 0 10124 1478spp <- spp[,.SD,.SDcols = colSums(spp)>0]
spp <- mvabund(spp)
plot(spp ~ P.anal$Subunit, 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", "red")
dotchart(P.anal$richness, ylab = "Subunit",
groups = P.anal$Subunit, gcolor = my_cols,
color = my_cols[P.anal$Subunit],
cex = 0.9, pch = 1, xlab = "richness")
kable(summary(P.anal[,.(richness, abundance, Subunit, rescaled_Time.Diff, cosinus_Monitoring.Time.Diff, sinus_Monitoring.Time.Diff, Site, Transect, Transect_with_date, Distance_rescaled)]))| richness | abundance | Subunit | rescaled_Time.Diff | cosinus_Monitoring.Time.Diff | sinus_Monitoring.Time.Diff | Site | Transect | Transect_with_date | Distance_rescaled | |
|---|---|---|---|---|---|---|---|---|---|---|
| Min. :0.000 | Min. : 0.00 | Carmel :376 | Min. :-1.8927 | Min. :-0.99996 | Min. :-0.999990 | Goren : 87 | Goren Near : 45 | Kerem Maharal Far_19_05_2015: 10 | Min. :-0.9136 | |
| 1st Qu.:2.000 | 1st Qu.: 4.00 | Galilee:417 | 1st Qu.:-0.9172 | 1st Qu.:-0.83907 | 1st Qu.:-0.602000 | Ein Yaakov : 85 | Ein Yaakov Near : 44 | Aderet Near_10_08_2021 : 9 | 1st Qu.:-0.8371 | |
| Median :2.000 | Median : 10.00 | Judea :324 | Median :-0.1836 | Median :-0.04866 | Median : 0.000000 | Kfar Shamai: 84 | Goren Far : 42 | Beit Oren Far_07_07_2017 : 9 | Median :-0.6362 | |
| Mean :2.569 | Mean : 20.37 | NA | Mean :-0.2623 | Mean :-0.08610 | Mean : 0.008294 | Margaliot : 82 | Kfar Shamai Far : 42 | Ein Yaakov Far_06_07_2021 : 9 | Mean :-0.5033 | |
| 3rd Qu.:3.000 | 3rd Qu.: 24.00 | NA | 3rd Qu.: 0.5940 | 3rd Qu.: 0.56975 | 3rd Qu.: 0.733190 | Iftach : 79 | Kfar Shamai Near: 42 | Ein Yaakov Far_11_11_2015 : 9 | 3rd Qu.:-0.3237 | |
| Max. :7.000 | Max. :341.00 | NA | Max. : 1.1755 | Max. : 1.00000 | Max. : 0.999990 | Nir Etzion : 77 | Margaliot Near : 42 | Ein Yaakov Near_03_06_2012 : 9 | Max. : 1.6119 | |
| NA | NA | NA | NA | NA | NA | (Other) :623 | (Other) :860 | (Other) :1062 | NA |
pairs(P.anal[,lapply(X = .SD,FUN = as.numeric),.SDcols=c("Subunit","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("Subunit","sinus_Monitoring.Time.Diff", "cosinus_Monitoring.Time.Diff", "rescaled_Time.Diff","Site","Transect", "Transect_with_date", "Distance_rescaled")]))| Subunit | sinus_Monitoring.Time.Diff | cosinus_Monitoring.Time.Diff | rescaled_Time.Diff | Site | Transect | Transect_with_date | Distance_rescaled | |
|---|---|---|---|---|---|---|---|---|
| Subunit | 1.0000000 | 0.0808235 | 0.1250776 | 0.0324596 | -0.3922386 | -0.3901787 | -0.3895513 | -0.1278084 |
| sinus_Monitoring.Time.Diff | 0.0808235 | 1.0000000 | 0.0926611 | 0.0071447 | 0.0360044 | 0.0345840 | 0.0366821 | -0.0592206 |
| cosinus_Monitoring.Time.Diff | 0.1250776 | 0.0926611 | 1.0000000 | 0.0589309 | -0.0782191 | -0.0774880 | -0.0767999 | -0.0119164 |
| rescaled_Time.Diff | 0.0324596 | 0.0071447 | 0.0589309 | 1.0000000 | -0.0128696 | -0.0125757 | -0.0165851 | -0.0556040 |
| Site | -0.3922386 | 0.0360044 | -0.0782191 | -0.0128696 | 1.0000000 | 0.9983334 | 0.9977772 | 0.0211274 |
| Transect | -0.3901787 | 0.0345840 | -0.0774880 | -0.0125757 | 0.9983334 | 1.0000000 | 0.9994624 | -0.0167655 |
| Transect_with_date | -0.3895513 | 0.0366821 | -0.0767999 | -0.0165851 | 0.9977772 | 0.9994624 | 1.0000000 | -0.0171086 |
| Distance_rescaled | -0.1278084 | -0.0592206 | -0.0119164 | -0.0556040 | 0.0211274 | -0.0167655 | -0.0171086 | 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")
Community analysis using package MVabund
Table of abundances per species by camera ID
abundance_and_cameras <- as.data.table(abu_by_spp.Maquis[,c(5,27:39)])
dim(abundance_and_cameras)## [1] 1117 14canisau <- abundance_and_cameras[,2] > 0
sum(canisau)## [1] 678canislup <- abundance_and_cameras[,3] > 0
sum(canislup)## [1] 20capra <- abundance_and_cameras[,4] > 0
sum(capra)## [1] 0Equus <- abundance_and_cameras[,5] > 0
sum(Equus)## [1] 0Gazellador <- abundance_and_cameras[,6] > 0
sum(Gazellador)## [1] 0Gazellagaz <- abundance_and_cameras[,7] > 0
sum(Gazellagaz)## [1] 141Hyaena <- abundance_and_cameras[,8] > 0
sum(Hyaena)## [1] 153Hystrix <- abundance_and_cameras[,9] > 0
sum(Hystrix)## [1] 599Lepus <- abundance_and_cameras[,10] > 0
sum(Lepus)## [1] 4Meles <- abundance_and_cameras[,11] > 0
sum(Meles)## [1] 178Oryx <- abundance_and_cameras[,12] > 0
sum(Oryx)## [1] 0Sus <- abundance_and_cameras[,13] > 0
sum(Sus)## [1] 703Vulpus <- abundance_and_cameras[,14] > 0
sum(Vulpus)## [1] 3941117 cameras. Species with a minimum of 20 cameras - Canis aureus, Canis lupus, Gazella gazella, Hyaena hyaena, Hystrix indica, Meles meles, Sus scorfa and Vulpus vulpus
spp_no_rare <- abu_by_spp.Maquis[,27:39]
print(colSums(spp_no_rare))## Canis aureus Canis lupus Capra nubiana Equus hemionus Gazella dorcas
## 6971 44 0 0 0
## Gazella gazella Hyaena hyaena Hystrix indica Lepus capensis Meles meles
## 406 298 3019 10 408
## Oryx leucoryx Sus scrofa Vulpes vulpes
## 0 10124 1478# 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 <- spp_no_rare[,c(-2,-6)]
spp_no_rare <- mvabund(spp_no_rare)
print(colSums(spp_no_rare))## Canis.aureus Canis.lupus Gazella.gazella Hyaena.hyaena Hystrix.indica
## 6971 44 406 298 3019
## Meles.meles Sus.scrofa Vulpes.vulpes
## 408 10124 1478#Sus scorfa abundance across subunit, sites and projectid
my_cols <- c("blue", "darkgreen", "red")
dotchart(P.anal$`Sus scrofa`, ylab = "Subunit",
groups = P.anal$Subunit, gcolor = my_cols,
color = my_cols[P.anal$Subunit],
cex = 0.9, pch = 1, xlab = "Abundance")
P.anal$Project.ID <- as.factor(P.anal$Project.ID)
my_cols <- c("blue", "darkgreen", "red", "purple", "darkorange")
dotchart(P.anal$`Sus scrofa`, ylab = "Project.ID",
groups = P.anal$Project.ID, gcolor = my_cols,
color = my_cols[P.anal$Project.ID],
cex = 0.9, pch = 1, xlab = "Abundance")
fun_color_range <- colorRampPalette(c("#1b98e0", "red"))
my_cols <- fun_color_range(15)
dotchart(P.anal$`Sus scrofa`, ylab = "Site",
groups = P.anal$Site, gcolor = my_cols,
color = my_cols[P.anal$Site],
cex = 0.9, pch = 1, xlab = "Abundance")
Species with 20 individuals or more - Canis aureus, Canis lupus, Gazella gazella, Hyaena hyaena, Hystrix indica, Meles meles, Sus scorfa and Vulpus vulpus
Left with 8 species
env_data <- abu_by_spp.Maquis[,..col_names]Many GLM with subunit interactions
mva_m0.nb <- manyglm(formula = spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + Subunit * Distance_rescaled + Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + sinus_Monitoring.Time.Diff, family = "negative.binomial", data = env_data)
drop1(mva_m0.nb)## Single term deletions
##
## Model:
## spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + Subunit *
## Distance_rescaled + Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff +
## sinus_Monitoring.Time.Diff
## Df AIC
## <none> 22814
## cosinus_Monitoring.Time.Diff 8 22853
## sinus_Monitoring.Time.Diff 8 22813
## Distance_rescaled:rescaled_Time.Diff 8 22814
## Distance_rescaled:Subunit 16 22900
## rescaled_Time.Diff:Subunit 16 22924mva_m1 <- manyglm(formula = spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + Subunit * Distance_rescaled + Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff, family = "negative.binomial", data = env_data)
drop1(mva_m1)## Single term deletions
##
## Model:
## spp_no_rare ~ Distance_rescaled * rescaled_Time.Diff + Subunit *
## Distance_rescaled + Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff
## Df AIC
## <none> 22813
## cosinus_Monitoring.Time.Diff 8 22851
## Distance_rescaled:rescaled_Time.Diff 8 22815
## Distance_rescaled:Subunit 16 22897
## rescaled_Time.Diff:Subunit 16 22920mva_m1 <- manyglm(formula = spp_no_rare ~ Subunit * Distance_rescaled + Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff, family = "negative.binomial", data = env_data)
drop1(mva_m1)## Single term deletions
##
## Model:
## spp_no_rare ~ Subunit * Distance_rescaled + Subunit * rescaled_Time.Diff +
## cosinus_Monitoring.Time.Diff
## Df AIC
## <none> 22815
## cosinus_Monitoring.Time.Diff 8 22850
## Subunit:Distance_rescaled 16 22912
## Subunit:rescaled_Time.Diff 16 22919Model includes cosin time, interaction between subunit and time and subunit and distance
plot(mva_m1, which=1:3)
# summ_m1 <- summary(mva_m1)
# saveRDS(summ_m1, "output/summ_maquis_model_with_subunit_interaction.RDS")
summ_m1 <- readRDS("output/summ_maquis_model_with_subunit_interaction.RDS")
print(summ_m1)##
## Test statistics:
## wald value Pr(>wald)
## (Intercept) 25.768 0.001 ***
## SubunitGalilee 7.995 0.001 ***
## SubunitJudea 14.892 0.001 ***
## Distance_rescaled 12.740 0.001 ***
## rescaled_Time.Diff 7.723 0.001 ***
## cosinus_Monitoring.Time.Diff 7.453 0.001 ***
## SubunitGalilee:Distance_rescaled 5.768 0.002 **
## SubunitJudea:Distance_rescaled 10.044 0.001 ***
## SubunitGalilee:rescaled_Time.Diff 6.877 0.001 ***
## SubunitJudea:rescaled_Time.Diff 7.906 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Test statistic: 34.44, 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_m1.uni <- anova(mva_m1, p.uni = "adjusted")
# saveRDS(anov_m1.uni, "output/anova_maquis_model_with_subunit_interaction.RDS")
anov_m1.uni <- readRDS("output/anova_maquis_model_with_subunit_interaction.RDS")
print(anov_m1.uni)## Analysis of Deviance Table
##
## Model: spp_no_rare ~ Subunit * Distance_rescaled + Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 1116
## Subunit 1114 2 863.6 0.001 ***
## Distance_rescaled 1113 1 199.4 0.001 ***
## rescaled_Time.Diff 1112 1 55.7 0.002 **
## cosinus_Monitoring.Time.Diff 1111 1 49.2 0.001 ***
## Subunit:Distance_rescaled 1109 2 138.5 0.001 ***
## Subunit:rescaled_Time.Diff 1107 2 135.8 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Univariate Tests:
## Canis.aureus Canis.lupus
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## Subunit 36.167 0.001 33.343 0.001
## Distance_rescaled 92.681 0.001 1.134 0.516
## rescaled_Time.Diff 6.789 0.077 1.291 0.756
## cosinus_Monitoring.Time.Diff 12.928 0.020 2.367 0.638
## Subunit:Distance_rescaled 19.085 0.005 0.7 0.245
## Subunit:rescaled_Time.Diff 43.537 0.001 4.213 0.462
## Gazella.gazella Hyaena.hyaena
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## Subunit 60.391 0.001 92.277 0.001
## Distance_rescaled 56.968 0.001 9.28 0.027
## rescaled_Time.Diff 10.739 0.015 0.496 0.893
## cosinus_Monitoring.Time.Diff 25.716 0.001 6.914 0.151
## Subunit:Distance_rescaled 4.176 0.183 12.171 0.011
## Subunit:rescaled_Time.Diff 0.989 0.699 2.5 0.699
## Hystrix.indica Meles.meles
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## Subunit 22.772 0.001 32.007 0.001
## Distance_rescaled 0.472 0.516 8.625 0.027
## rescaled_Time.Diff 17.991 0.007 0.042 0.946
## cosinus_Monitoring.Time.Diff 0.783 0.910 0.252 0.969
## Subunit:Distance_rescaled 41.145 0.001 19.296 0.005
## Subunit:rescaled_Time.Diff 1.771 0.699 3.351 0.616
## Sus.scrofa Vulpes.vulpes
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## Subunit 582.6 0.001 4.074 0.300
## Distance_rescaled 12.569 0.027 17.674 0.012
## rescaled_Time.Diff 18.183 0.007 0.123 0.946
## cosinus_Monitoring.Time.Diff 0.001 0.978 0.21 0.969
## Subunit:Distance_rescaled 23.227 0.005 18.657 0.006
## Subunit:rescaled_Time.Diff 74.121 0.001 5.291 0.428
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.coefp <- merge(data.table(t(coef(mva_m1)/log(2)),keep.rownames=TRUE), data.table(t(anov_m1.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","Galilee.coef","Judea.coef","Distance.coef","Time_diff.coef","cosin_time.coef","Galilee_distance.coef","Judea_distance.coef","Galilee_time.coef","Judea_time.coef","Intercept.p","Subunit.p","Distance.p","Time_diff.p","Cosin_time.p","Subunit_distance.p","Subunit_time.p","Species_abundance")
write.csv(coefp, "coefficients_Maquis_with_subunit_interactions.csv")Canis aureus
#####Canis aureus####
Canis.aureus <- spp_no_rare[,"Canis.aureus"]
glm.Canis.aureus <- glm.nb(formula = Canis.aureus ~ Subunit * Distance_rescaled + Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff, data = env_data)
coef(glm.Canis.aureus)## (Intercept) SubunitGalilee
## 0.7379764 0.2887068
## SubunitJudea Distance_rescaled
## -1.0561383 -1.0768661
## rescaled_Time.Diff cosinus_Monitoring.Time.Diff
## -0.4959726 -0.3461529
## SubunitGalilee:Distance_rescaled SubunitJudea:Distance_rescaled
## -0.4936521 -1.6603072
## SubunitGalilee:rescaled_Time.Diff SubunitJudea:rescaled_Time.Diff
## 0.7092476 0.5938064coef(mva_m1)## Canis.aureus Canis.lupus Gazella.gazella
## (Intercept) 0.7379710 -27.2250631 -2.4859914
## SubunitGalilee 0.2887180 24.2682149 1.4503002
## SubunitJudea -1.0561001 12.3780745 2.9386814
## Distance_rescaled -1.0768712 1.1350644 2.6217989
## rescaled_Time.Diff -0.4959796 21.0853272 0.5628646
## cosinus_Monitoring.Time.Diff -0.3461457 0.7561096 0.7631725
## SubunitGalilee:Distance_rescaled -0.4936408 -2.5477340 -1.4037854
## SubunitJudea:Distance_rescaled -1.6602494 -0.8556531 -0.9802417
## SubunitGalilee:rescaled_Time.Diff 0.7092584 -20.6510298 -0.3200343
## SubunitJudea:rescaled_Time.Diff 0.5938280 -21.2640594 -0.3190575
## Hyaena.hyaena Hystrix.indica Meles.meles
## (Intercept) -0.7137567 0.85541361 -0.76041046
## SubunitGalilee -3.2358897 -0.06300426 -0.50901932
## SubunitJudea -1.0144966 -0.69582359 -2.44603487
## Distance_rescaled 0.7266002 0.32577741 -0.67266833
## rescaled_Time.Diff -0.2257533 0.30194780 0.19389719
## cosinus_Monitoring.Time.Diff 0.3383593 -0.19146041 -0.02148409
## SubunitGalilee:Distance_rescaled -1.6743478 -0.46149675 1.52722200
## SubunitJudea:Distance_rescaled -1.6604428 -2.27715087 -2.15634188
## SubunitGalilee:rescaled_Time.Diff 0.3357001 -0.16599960 -0.39715356
## SubunitJudea:rescaled_Time.Diff 0.2708413 -0.08043878 -0.22230100
## Sus.scrofa Vulpes.vulpes
## (Intercept) 1.87496831 0.56493814
## SubunitGalilee 0.83817091 -0.70001232
## SubunitJudea -5.38303274 -0.63157362
## Distance_rescaled -0.26523514 0.93996114
## rescaled_Time.Diff 0.10444304 0.21421552
## cosinus_Monitoring.Time.Diff -0.17401890 0.02004382
## SubunitGalilee:Distance_rescaled 0.09848596 -1.26877799
## SubunitJudea:Distance_rescaled -1.87064515 -1.73195323
## SubunitGalilee:rescaled_Time.Diff -0.07139997 -0.32912464
## SubunitJudea:rescaled_Time.Diff 1.94738139 -0.21045200plot_model_interaction(P.anal = env_data, m = glm.Canis.aureus, eff2plot = "Distance_rescaled", modvar2plot = "Subunit", plot_points=FALSE, plot_residuals=FALSE, export_plot=TRUE, ylabel = NULL, fontname = "Almoni ML v5 AAA",fontsize=22, pdf_width=160, outpath = "output/maquis/", legend_position = "bottom")
plot_model_interaction(P.anal = env_data, m = glm.Canis.aureus, eff2plot = "rescaled_Time.Diff", modvar2plot = "Subunit", plot_points=FALSE, plot_residuals=FALSE, export_plot=TRUE, ylabel = NULL, fontname = "Almoni ML v5 AAA",fontsize=22, pdf_width=160, outpath = "output/maquis/", legend_position = "bottom")
#Test whether temporal trend is different from zero
print("Test whether temporal trend is different from zero")## [1] "Test whether temporal trend is different from zero"mm_rescaled_time <- emtrends(glm.Canis.aureus, specs = "Subunit", var = "rescaled_Time.Diff", type = "response")
print(mm_rescaled_time)## Subunit rescaled_Time.Diff.trend SE df asymp.LCL asymp.UCL
## Carmel -0.4960 0.0876 Inf -0.6676 -0.324
## Galilee 0.2133 0.0816 Inf 0.0533 0.373
## Judea 0.0978 0.0974 Inf -0.0931 0.289
##
## Confidence level used: 0.95test_results_mm_rescaled_time <- test(mm_rescaled_time, null = 0, adjust = "fdr")
print(test_results_mm_rescaled_time)## Subunit rescaled_Time.Diff.trend SE df z.ratio p.value
## Carmel -0.4960 0.0876 Inf -5.663 <.0001
## Galilee 0.2133 0.0816 Inf 2.613 0.0134
## Judea 0.0978 0.0974 Inf 1.004 0.3153
##
## P value adjustment: fdr method for 3 tests#Test whether spatial trend is different from zero
print("Test whether spatial trend is different from zero")## [1] "Test whether spatial trend is different from zero"mm_rescaled_distance <- emtrends(glm.Canis.aureus, specs = "Subunit", var = "Distance_rescaled", type = "response")
print(mm_rescaled_distance)## Subunit Distance_rescaled.trend SE df asymp.LCL asymp.UCL
## Carmel -1.08 0.159 Inf -1.39 -0.765
## Galilee -1.57 0.257 Inf -2.08 -1.066
## Judea -2.74 0.354 Inf -3.43 -2.043
##
## Confidence level used: 0.95test_results_mm_distance <- test(mm_rescaled_distance, null = 0, adjust = "fdr")
print(test_results_mm_distance)## Subunit Distance_rescaled.trend SE df z.ratio p.value
## Carmel -1.08 0.159 Inf -6.759 <.0001
## Galilee -1.57 0.257 Inf -6.100 <.0001
## Judea -2.74 0.354 Inf -7.725 <.0001
##
## P value adjustment: fdr method for 3 tests#Compare spatial trends among subunits
print("Compare spatial trends among subunits")## [1] "Compare spatial trends among subunits"pairwise_distance <- emmeans(object = glm.Canis.aureus, ~ Subunit*Distance_rescaled)
print(pairwise_distance)## Subunit Distance_rescaled emmean SE df asymp.LCL asymp.UCL
## Carmel -0.503 1.44 0.0919 Inf 1.26 1.62
## Galilee -0.503 1.79 0.0889 Inf 1.62 1.97
## Judea -0.503 1.06 0.1042 Inf 0.86 1.27
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95test_results_distance <- test(pairs(pairwise_distance, by="Distance_rescaled"), by=NULL, adjust="fdr")
print(test_results_distance)## contrast Distance_rescaled estimate SE df z.ratio p.value
## Carmel - Galilee -0.503 -0.351 0.128 Inf -2.743 0.0067
## Carmel - Judea -0.503 0.376 0.139 Inf 2.711 0.0067
## Galilee - Judea -0.503 0.727 0.140 Inf 5.204 <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: fdr method for 3 testsHyaena hyaena
#####Hyaena hyaena####
Hyaena.hyaena <- spp_no_rare[,"Hyaena.hyaena"]
glm.Hyaena.hyaena <- glm.nb(formula = Hyaena.hyaena ~ Subunit * Distance_rescaled + Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff, data = env_data)
coef(glm.Hyaena.hyaena)## (Intercept) SubunitGalilee
## -0.7137562 -3.2358901
## SubunitJudea Distance_rescaled
## -1.0144970 0.7266012
## rescaled_Time.Diff cosinus_Monitoring.Time.Diff
## -0.2257527 0.3383597
## SubunitGalilee:Distance_rescaled SubunitJudea:Distance_rescaled
## -1.6743486 -1.6604434
## SubunitGalilee:rescaled_Time.Diff SubunitJudea:rescaled_Time.Diff
## 0.3356996 0.2708408coef(mva_m1)## Canis.aureus Canis.lupus Gazella.gazella
## (Intercept) 0.7379710 -27.2250631 -2.4859914
## SubunitGalilee 0.2887180 24.2682149 1.4503002
## SubunitJudea -1.0561001 12.3780745 2.9386814
## Distance_rescaled -1.0768712 1.1350644 2.6217989
## rescaled_Time.Diff -0.4959796 21.0853272 0.5628646
## cosinus_Monitoring.Time.Diff -0.3461457 0.7561096 0.7631725
## SubunitGalilee:Distance_rescaled -0.4936408 -2.5477340 -1.4037854
## SubunitJudea:Distance_rescaled -1.6602494 -0.8556531 -0.9802417
## SubunitGalilee:rescaled_Time.Diff 0.7092584 -20.6510298 -0.3200343
## SubunitJudea:rescaled_Time.Diff 0.5938280 -21.2640594 -0.3190575
## Hyaena.hyaena Hystrix.indica Meles.meles
## (Intercept) -0.7137567 0.85541361 -0.76041046
## SubunitGalilee -3.2358897 -0.06300426 -0.50901932
## SubunitJudea -1.0144966 -0.69582359 -2.44603487
## Distance_rescaled 0.7266002 0.32577741 -0.67266833
## rescaled_Time.Diff -0.2257533 0.30194780 0.19389719
## cosinus_Monitoring.Time.Diff 0.3383593 -0.19146041 -0.02148409
## SubunitGalilee:Distance_rescaled -1.6743478 -0.46149675 1.52722200
## SubunitJudea:Distance_rescaled -1.6604428 -2.27715087 -2.15634188
## SubunitGalilee:rescaled_Time.Diff 0.3357001 -0.16599960 -0.39715356
## SubunitJudea:rescaled_Time.Diff 0.2708413 -0.08043878 -0.22230100
## Sus.scrofa Vulpes.vulpes
## (Intercept) 1.87496831 0.56493814
## SubunitGalilee 0.83817091 -0.70001232
## SubunitJudea -5.38303274 -0.63157362
## Distance_rescaled -0.26523514 0.93996114
## rescaled_Time.Diff 0.10444304 0.21421552
## cosinus_Monitoring.Time.Diff -0.17401890 0.02004382
## SubunitGalilee:Distance_rescaled 0.09848596 -1.26877799
## SubunitJudea:Distance_rescaled -1.87064515 -1.73195323
## SubunitGalilee:rescaled_Time.Diff -0.07139997 -0.32912464
## SubunitJudea:rescaled_Time.Diff 1.94738139 -0.21045200plot_model_interaction(P.anal = env_data, m = glm.Hyaena.hyaena, eff2plot = "Distance_rescaled", modvar2plot = "Subunit", plot_points=FALSE, plot_residuals=FALSE, export_plot=TRUE, ylabel = NULL, fontname = "Almoni ML v5 AAA", fontsize=22, pdf_width=160, outpath = "output/maquis/", legend_position = "bottom")
#Test whether spatial trend is different from zero
print("Test whether spatial trend is different from zero")## [1] "Test whether spatial trend is different from zero"mm_rescaled_distance <- emtrends(glm.Hyaena.hyaena, specs = "Subunit", var = "Distance_rescaled", type = "response")
print(mm_rescaled_distance)## Subunit Distance_rescaled.trend SE df asymp.LCL asymp.UCL
## Carmel 0.727 0.184 Inf 0.366 1.0876
## Galilee -0.948 1.096 Inf -3.096 1.2001
## Judea -0.934 0.519 Inf -1.951 0.0831
##
## Confidence level used: 0.95test_results_mm_distance <- test(mm_rescaled_distance, null = 0, adjust = "fdr")
print(test_results_mm_distance)## Subunit Distance_rescaled.trend SE df z.ratio p.value
## Carmel 0.727 0.184 Inf 3.944 0.0002
## Galilee -0.948 1.096 Inf -0.865 0.3871
## Judea -0.934 0.519 Inf -1.800 0.1078
##
## P value adjustment: fdr method for 3 tests#Compare spatial trends among subunits
print("Compare spatial trends among subunits")## [1] "Compare spatial trends among subunits"pairwise_distance <- emmeans(object = glm.Hyaena.hyaena, ~ Subunit*Distance_rescaled)
print(pairwise_distance)## Subunit Distance_rescaled emmean SE df asymp.LCL asymp.UCL
## Carmel -0.503 -1.05 0.138 Inf -1.32 -0.778
## Galilee -0.503 -3.53 0.342 Inf -4.20 -2.861
## Judea -0.503 -1.30 0.161 Inf -1.61 -0.985
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95test_results_distance <- test(pairs(pairwise_distance, by="Distance_rescaled"), by=NULL, adjust="fdr")
print(test_results_distance)## contrast Distance_rescaled estimate SE df z.ratio p.value
## Carmel - Galilee -0.503 2.48 0.369 Inf 6.719 <.0001
## Carmel - Judea -0.503 0.25 0.209 Inf 1.196 0.2316
## Galilee - Judea -0.503 -2.23 0.380 Inf -5.872 <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: fdr method for 3 testsHystrix indica
#####Hystrix indica####
Hystrix.indica <- spp_no_rare[,"Hystrix.indica"]
glm.Hystrix.indica <- glm.nb(formula = Hystrix.indica ~ Subunit * Distance_rescaled + Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff, data = env_data)
summary(glm.Hystrix.indica)##
## Call:
## glm.nb(formula = Hystrix.indica ~ Subunit * Distance_rescaled +
## Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff,
## data = env_data, init.theta = 0.4028111892, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5583 -1.2303 -0.5464 0.1358 3.1233
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.85541 0.10742 7.963 1.68e-15 ***
## SubunitGalilee -0.06300 0.20344 -0.310 0.75679
## SubunitJudea -0.69582 0.23328 -2.983 0.00286 **
## Distance_rescaled 0.32578 0.13876 2.348 0.01889 *
## rescaled_Time.Diff 0.30195 0.08675 3.480 0.00050 ***
## cosinus_Monitoring.Time.Diff -0.19146 0.08162 -2.346 0.01899 *
## SubunitGalilee:Distance_rescaled -0.46150 0.28653 -1.611 0.10726
## SubunitJudea:Distance_rescaled -2.27715 0.35563 -6.403 1.52e-10 ***
## SubunitGalilee:rescaled_Time.Diff -0.16600 0.12063 -1.376 0.16879
## SubunitJudea:rescaled_Time.Diff -0.08044 0.12467 -0.645 0.51880
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.4028) family taken to be 1)
##
## Null deviance: 1166.1 on 1116 degrees of freedom
## Residual deviance: 1075.9 on 1107 degrees of freedom
## AIC: 4433.8
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.4028
## Std. Err.: 0.0245
##
## 2 x log-likelihood: -4411.7900Interaction of subunit with time is not significant - will be dropped from H. indica model
glm.Hystrix.indica <- glm.nb(formula = Hystrix.indica ~ Subunit * Distance_rescaled + rescaled_Time.Diff + cosinus_Monitoring.Time.Diff, data = env_data)
summary(glm.Hystrix.indica)##
## Call:
## glm.nb(formula = Hystrix.indica ~ Subunit * Distance_rescaled +
## rescaled_Time.Diff + cosinus_Monitoring.Time.Diff, data = env_data,
## init.theta = 0.4018098414, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5446 -1.2182 -0.5649 0.1539 3.2922
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.82151 0.10519 7.810 5.73e-15 ***
## SubunitGalilee 0.00744 0.19850 0.037 0.97010
## SubunitJudea -0.65891 0.23035 -2.860 0.00423 **
## Distance_rescaled 0.29731 0.13844 2.148 0.03174 *
## rescaled_Time.Diff 0.21144 0.04902 4.314 1.61e-05 ***
## cosinus_Monitoring.Time.Diff -0.15609 0.07729 -2.020 0.04343 *
## SubunitGalilee:Distance_rescaled -0.42307 0.28597 -1.479 0.13903
## SubunitJudea:Distance_rescaled -2.22784 0.35518 -6.272 3.55e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.4018) family taken to be 1)
##
## Null deviance: 1164.2 on 1116 degrees of freedom
## Residual deviance: 1076.0 on 1109 degrees of freedom
## AIC: 4431.6
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.4018
## Std. Err.: 0.0244
##
## 2 x log-likelihood: -4413.5610coef(glm.Hystrix.indica)## (Intercept) SubunitGalilee
## 0.821514189 0.007440017
## SubunitJudea Distance_rescaled
## -0.658913418 0.297311123
## rescaled_Time.Diff cosinus_Monitoring.Time.Diff
## 0.211444812 -0.156093288
## SubunitGalilee:Distance_rescaled SubunitJudea:Distance_rescaled
## -0.423075330 -2.227842340coef(mva_m1)## Canis.aureus Canis.lupus Gazella.gazella
## (Intercept) 0.7379710 -27.2250631 -2.4859914
## SubunitGalilee 0.2887180 24.2682149 1.4503002
## SubunitJudea -1.0561001 12.3780745 2.9386814
## Distance_rescaled -1.0768712 1.1350644 2.6217989
## rescaled_Time.Diff -0.4959796 21.0853272 0.5628646
## cosinus_Monitoring.Time.Diff -0.3461457 0.7561096 0.7631725
## SubunitGalilee:Distance_rescaled -0.4936408 -2.5477340 -1.4037854
## SubunitJudea:Distance_rescaled -1.6602494 -0.8556531 -0.9802417
## SubunitGalilee:rescaled_Time.Diff 0.7092584 -20.6510298 -0.3200343
## SubunitJudea:rescaled_Time.Diff 0.5938280 -21.2640594 -0.3190575
## Hyaena.hyaena Hystrix.indica Meles.meles
## (Intercept) -0.7137567 0.85541361 -0.76041046
## SubunitGalilee -3.2358897 -0.06300426 -0.50901932
## SubunitJudea -1.0144966 -0.69582359 -2.44603487
## Distance_rescaled 0.7266002 0.32577741 -0.67266833
## rescaled_Time.Diff -0.2257533 0.30194780 0.19389719
## cosinus_Monitoring.Time.Diff 0.3383593 -0.19146041 -0.02148409
## SubunitGalilee:Distance_rescaled -1.6743478 -0.46149675 1.52722200
## SubunitJudea:Distance_rescaled -1.6604428 -2.27715087 -2.15634188
## SubunitGalilee:rescaled_Time.Diff 0.3357001 -0.16599960 -0.39715356
## SubunitJudea:rescaled_Time.Diff 0.2708413 -0.08043878 -0.22230100
## Sus.scrofa Vulpes.vulpes
## (Intercept) 1.87496831 0.56493814
## SubunitGalilee 0.83817091 -0.70001232
## SubunitJudea -5.38303274 -0.63157362
## Distance_rescaled -0.26523514 0.93996114
## rescaled_Time.Diff 0.10444304 0.21421552
## cosinus_Monitoring.Time.Diff -0.17401890 0.02004382
## SubunitGalilee:Distance_rescaled 0.09848596 -1.26877799
## SubunitJudea:Distance_rescaled -1.87064515 -1.73195323
## SubunitGalilee:rescaled_Time.Diff -0.07139997 -0.32912464
## SubunitJudea:rescaled_Time.Diff 1.94738139 -0.21045200plot_model_interaction(P.anal = env_data, m = glm.Hystrix.indica, eff2plot = "Distance_rescaled", modvar2plot = "Subunit", plot_points=FALSE, plot_residuals=FALSE, export_plot=TRUE, ylabel = NULL, fontname = "Almoni ML v5 AAA",fontsize=22, pdf_width=160, outpath = "output/maquis/", legend_position = "bottom")
#Test whether spatial trend is different from zero
print("Test whether spatial trend is different from zero")## [1] "Test whether spatial trend is different from zero"mm_rescaled_distance <- emtrends(glm.Hystrix.indica, specs = "Subunit", var = "Distance_rescaled", type = "response")
print(mm_rescaled_distance)## Subunit Distance_rescaled.trend SE df asymp.LCL asymp.UCL
## Carmel 0.297 0.138 Inf 0.026 0.569
## Galilee -0.126 0.250 Inf -0.615 0.364
## Judea -1.931 0.326 Inf -2.570 -1.291
##
## Confidence level used: 0.95test_results_distance <- test(mm_rescaled_distance, null = 0, adjust="fdr")
print(test_results_distance)## Subunit Distance_rescaled.trend SE df z.ratio p.value
## Carmel 0.297 0.138 Inf 2.148 0.0476
## Galilee -0.126 0.250 Inf -0.504 0.6146
## Judea -1.931 0.326 Inf -5.915 <.0001
##
## P value adjustment: fdr method for 3 testsplot_model_effect(P.anal = env_data, m = glm.Hystrix.indica, 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/maquis/")
Meles meles
#####Meles meles####
Meles.meles <- spp_no_rare[,"Meles.meles"]
glm.Meles.meles <- glm.nb(formula = Meles.meles ~ Subunit * Distance_rescaled + Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff, data = env_data)
coef(glm.Meles.meles)## (Intercept) SubunitGalilee
## -0.76042055 -0.50900866
## SubunitJudea Distance_rescaled
## -2.44602759 -0.67269550
## rescaled_Time.Diff cosinus_Monitoring.Time.Diff
## 0.19389911 -0.02148272
## SubunitGalilee:Distance_rescaled SubunitJudea:Distance_rescaled
## 1.52725051 -2.15632118
## SubunitGalilee:rescaled_Time.Diff SubunitJudea:rescaled_Time.Diff
## -0.39715549 -0.22230256coef(mva_m1)## Canis.aureus Canis.lupus Gazella.gazella
## (Intercept) 0.7379710 -27.2250631 -2.4859914
## SubunitGalilee 0.2887180 24.2682149 1.4503002
## SubunitJudea -1.0561001 12.3780745 2.9386814
## Distance_rescaled -1.0768712 1.1350644 2.6217989
## rescaled_Time.Diff -0.4959796 21.0853272 0.5628646
## cosinus_Monitoring.Time.Diff -0.3461457 0.7561096 0.7631725
## SubunitGalilee:Distance_rescaled -0.4936408 -2.5477340 -1.4037854
## SubunitJudea:Distance_rescaled -1.6602494 -0.8556531 -0.9802417
## SubunitGalilee:rescaled_Time.Diff 0.7092584 -20.6510298 -0.3200343
## SubunitJudea:rescaled_Time.Diff 0.5938280 -21.2640594 -0.3190575
## Hyaena.hyaena Hystrix.indica Meles.meles
## (Intercept) -0.7137567 0.85541361 -0.76041046
## SubunitGalilee -3.2358897 -0.06300426 -0.50901932
## SubunitJudea -1.0144966 -0.69582359 -2.44603487
## Distance_rescaled 0.7266002 0.32577741 -0.67266833
## rescaled_Time.Diff -0.2257533 0.30194780 0.19389719
## cosinus_Monitoring.Time.Diff 0.3383593 -0.19146041 -0.02148409
## SubunitGalilee:Distance_rescaled -1.6743478 -0.46149675 1.52722200
## SubunitJudea:Distance_rescaled -1.6604428 -2.27715087 -2.15634188
## SubunitGalilee:rescaled_Time.Diff 0.3357001 -0.16599960 -0.39715356
## SubunitJudea:rescaled_Time.Diff 0.2708413 -0.08043878 -0.22230100
## Sus.scrofa Vulpes.vulpes
## (Intercept) 1.87496831 0.56493814
## SubunitGalilee 0.83817091 -0.70001232
## SubunitJudea -5.38303274 -0.63157362
## Distance_rescaled -0.26523514 0.93996114
## rescaled_Time.Diff 0.10444304 0.21421552
## cosinus_Monitoring.Time.Diff -0.17401890 0.02004382
## SubunitGalilee:Distance_rescaled 0.09848596 -1.26877799
## SubunitJudea:Distance_rescaled -1.87064515 -1.73195323
## SubunitGalilee:rescaled_Time.Diff -0.07139997 -0.32912464
## SubunitJudea:rescaled_Time.Diff 1.94738139 -0.21045200plot_model_interaction(P.anal = env_data, m = glm.Meles.meles, eff2plot = "Distance_rescaled", modvar2plot = "Subunit", plot_points=FALSE, plot_residuals=FALSE, export_plot=TRUE, ylabel = NULL, fontname = "Almoni ML v5 AAA",fontsize=22, pdf_width=160, outpath = "output/maquis/", legend_position = "bottom")
#Test whether spatial trend is different from zero
print("Test whether spatial trend is different from zero")## [1] "Test whether spatial trend is different from zero"mm_distance<- emtrends(glm.Meles.meles, specs = "Subunit", var = "Distance_rescaled", type = "response")
print(mm_distance)## Subunit Distance_rescaled.trend SE df asymp.LCL asymp.UCL
## Carmel -0.673 0.261 Inf -1.1836 -0.162
## Galilee 0.855 0.459 Inf -0.0444 1.753
## Judea -2.829 0.773 Inf -4.3431 -1.315
##
## Confidence level used: 0.95test_results_distance <- test(mm_distance, null = 0, adjust = "fdr")
print(test_results_distance)## Subunit Distance_rescaled.trend SE df z.ratio p.value
## Carmel -0.673 0.261 Inf -2.581 0.0148
## Galilee 0.855 0.459 Inf 1.863 0.0624
## Judea -2.829 0.773 Inf -3.662 0.0008
##
## P value adjustment: fdr method for 3 testsSus scrofa
####Sus scrofa####
Sus.scrofa <- spp_no_rare[,"Sus.scrofa"]
glm.Sus.scrofa <- glm.nb(formula = Sus.scrofa ~ Subunit * Distance_rescaled + Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff, data = env_data)
coef(glm.Sus.scrofa)## (Intercept) SubunitGalilee
## 1.87496631 0.83817303
## SubunitJudea Distance_rescaled
## -5.38303071 -0.26524313
## rescaled_Time.Diff cosinus_Monitoring.Time.Diff
## 0.10444555 -0.17401554
## SubunitGalilee:Distance_rescaled SubunitJudea:Distance_rescaled
## 0.09849245 -1.87063640
## SubunitGalilee:rescaled_Time.Diff SubunitJudea:rescaled_Time.Diff
## -0.07140282 1.94737676coef(mva_m1)## Canis.aureus Canis.lupus Gazella.gazella
## (Intercept) 0.7379710 -27.2250631 -2.4859914
## SubunitGalilee 0.2887180 24.2682149 1.4503002
## SubunitJudea -1.0561001 12.3780745 2.9386814
## Distance_rescaled -1.0768712 1.1350644 2.6217989
## rescaled_Time.Diff -0.4959796 21.0853272 0.5628646
## cosinus_Monitoring.Time.Diff -0.3461457 0.7561096 0.7631725
## SubunitGalilee:Distance_rescaled -0.4936408 -2.5477340 -1.4037854
## SubunitJudea:Distance_rescaled -1.6602494 -0.8556531 -0.9802417
## SubunitGalilee:rescaled_Time.Diff 0.7092584 -20.6510298 -0.3200343
## SubunitJudea:rescaled_Time.Diff 0.5938280 -21.2640594 -0.3190575
## Hyaena.hyaena Hystrix.indica Meles.meles
## (Intercept) -0.7137567 0.85541361 -0.76041046
## SubunitGalilee -3.2358897 -0.06300426 -0.50901932
## SubunitJudea -1.0144966 -0.69582359 -2.44603487
## Distance_rescaled 0.7266002 0.32577741 -0.67266833
## rescaled_Time.Diff -0.2257533 0.30194780 0.19389719
## cosinus_Monitoring.Time.Diff 0.3383593 -0.19146041 -0.02148409
## SubunitGalilee:Distance_rescaled -1.6743478 -0.46149675 1.52722200
## SubunitJudea:Distance_rescaled -1.6604428 -2.27715087 -2.15634188
## SubunitGalilee:rescaled_Time.Diff 0.3357001 -0.16599960 -0.39715356
## SubunitJudea:rescaled_Time.Diff 0.2708413 -0.08043878 -0.22230100
## Sus.scrofa Vulpes.vulpes
## (Intercept) 1.87496831 0.56493814
## SubunitGalilee 0.83817091 -0.70001232
## SubunitJudea -5.38303274 -0.63157362
## Distance_rescaled -0.26523514 0.93996114
## rescaled_Time.Diff 0.10444304 0.21421552
## cosinus_Monitoring.Time.Diff -0.17401890 0.02004382
## SubunitGalilee:Distance_rescaled 0.09848596 -1.26877799
## SubunitJudea:Distance_rescaled -1.87064515 -1.73195323
## SubunitGalilee:rescaled_Time.Diff -0.07139997 -0.32912464
## SubunitJudea:rescaled_Time.Diff 1.94738139 -0.21045200plot_model_interaction(P.anal = env_data, m = glm.Sus.scrofa, eff2plot = "Distance_rescaled", modvar2plot = "Subunit", plot_points=FALSE, plot_residuals=FALSE, export_plot=TRUE, ylabel = NULL, fontname = "Almoni ML v5 AAA",fontsize=22, pdf_width=160, outpath = "output/maquis/", legend_position = "bottom")
sus_distance <- interact_plot(model = glm.Sus.scrofa, data=env_data, pred = Distance_rescaled, modx = Subunit, main.title = "", interval = T,plot.points = F, jitter = c(0.1,0), point.size = 3, cat.pred.point.size = 4, partial.residuals = F, colors = 'Dark2', modxvals = NULL, line.colors = 'black', point.alpha = 0.25)
sus_distance
# as.data.table(sus_distance$data)[Subunit=="Judea",Sus.scrofa]
# 1 - 0.0005676190/0.1249574967
plot_model_interaction(P.anal = env_data, m = glm.Sus.scrofa, eff2plot = "rescaled_Time.Diff", modvar2plot = "Subunit", plot_points=FALSE, plot_residuals=FALSE, export_plot=TRUE, ylabel = NULL, fontname = "Almoni ML v5 AAA",fontsize=22, pdf_width=160, outpath = "output/maquis/", legend_position = "bottom")
interact_plot(model = glm.Sus.scrofa, data=env_data, pred = rescaled_Time.Diff, modx = Subunit, main.title = "", interval = T,plot.points = F, jitter = c(0.1,0), point.size = 3, cat.pred.point.size = 4, partial.residuals = F, colors = 'Dark2', modxvals = NULL, line.colors = 'black', point.alpha = 0.25)
# generate trend of Judea on separate axes
sus_judea_plot <- plot_model_effect(P.anal = env_data, m = glm.Sus.scrofa, eff2plot = "rescaled_Time.Diff", plot_points = FALSE, plot_residuals = FALSE, export_plot = TRUE,
outpath = "output/maquis/", fontname = "Almoni ML v5 AAA", at_list = list(Subunit="Judea"))
file.remove("output/maquis/mammals__Sus.scrofa_rescaled_Time.Diff.pdf")## [1] TRUE# sus_judea_plot$layers[[2]]$aes_params$fill <- "#e3e2f0"
# sus_judea_plot$layers[[2]]$aes_params$alpha <- 0.8
sus_judea_plot$layers[[1]]$aes_params$linetype <- 2
sus_judea_plot$layers[[1]]$aes_params$colour <- "#7570b3"
sus_judea_plot$layers[[1]]$aes_params$linewidth <- 1.5
Cairo(file = paste0("output/maquis/","mammals__Sus.scrofa_rescaled_Time.Diff_Judea_only.pdf"), width = 160, height = 160*2/3, type = "PDF", units = "mm")
print(sus_judea_plot)
dev.off()## png
## 2#Test whether spatial trend is different from zero
print("Test whether spatial trend is different from zero")
## [1] "Test whether spatial trend is different from zero"mm_rescaled_distance <- emtrends(glm.Sus.scrofa, specs = "Subunit", var = "Distance_rescaled", type = "response")
print(mm_rescaled_distance)## Subunit Distance_rescaled.trend SE df asymp.LCL asymp.UCL
## Carmel -0.265 0.115 Inf -0.491 -0.0393
## Galilee -0.167 0.200 Inf -0.558 0.2249
## Judea -2.136 0.623 Inf -3.357 -0.9147
##
## Confidence level used: 0.95test_results_mm_distance <- test(mm_rescaled_distance, null = 0, adjust = "fdr")
print(test_results_mm_distance)## Subunit Distance_rescaled.trend SE df z.ratio p.value
## Carmel -0.265 0.115 Inf -2.301 0.0321
## Galilee -0.167 0.200 Inf -0.834 0.4041
## Judea -2.136 0.623 Inf -3.428 0.0018
##
## P value adjustment: fdr method for 3 tests#Test whether temporal trend is different from zero
print("Test whether temporal trend is different from zero")## [1] "Test whether temporal trend is different from zero"mm_rescaled_time <- emtrends(glm.Sus.scrofa, specs = "Subunit", var = "rescaled_Time.Diff", type = "response")
print(mm_rescaled_time)## Subunit rescaled_Time.Diff.trend SE df asymp.LCL asymp.UCL
## Carmel 0.104 0.0695 Inf -0.0317 0.241
## Galilee 0.033 0.0653 Inf -0.0949 0.161
## Judea 2.052 0.3233 Inf 1.4182 2.685
##
## Confidence level used: 0.95test_results_mm_rescaled_time <- test(mm_rescaled_time, null = 0, adjust = "fdr")
print(test_results_mm_rescaled_time)## Subunit rescaled_Time.Diff.trend SE df z.ratio p.value
## Carmel 0.104 0.0695 Inf 1.504 0.1990
## Galilee 0.033 0.0653 Inf 0.506 0.6127
## Judea 2.052 0.3233 Inf 6.347 <.0001
##
## P value adjustment: fdr method for 3 testsVulpes vulpes
#####Vulpes vulpes####
Vulpes.vulpes <- spp_no_rare[,"Vulpes.vulpes"]
glm.Vulpes.vulpes <- glm.nb(formula = Vulpes.vulpes ~ Subunit * Distance_rescaled + Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff, data = env_data)## Warning: glm.fit: algorithm did not convergecoef(glm.Vulpes.vulpes)## (Intercept) SubunitGalilee
## 0.56496673 -0.70024393
## SubunitJudea Distance_rescaled
## -0.63159581 0.94003503
## rescaled_Time.Diff cosinus_Monitoring.Time.Diff
## 0.21422379 0.01986557
## SubunitGalilee:Distance_rescaled SubunitJudea:Distance_rescaled
## -1.26906026 -1.73205012
## SubunitGalilee:rescaled_Time.Diff SubunitJudea:rescaled_Time.Diff
## -0.32932902 -0.21039373coef(mva_m1)## Canis.aureus Canis.lupus Gazella.gazella
## (Intercept) 0.7379710 -27.2250631 -2.4859914
## SubunitGalilee 0.2887180 24.2682149 1.4503002
## SubunitJudea -1.0561001 12.3780745 2.9386814
## Distance_rescaled -1.0768712 1.1350644 2.6217989
## rescaled_Time.Diff -0.4959796 21.0853272 0.5628646
## cosinus_Monitoring.Time.Diff -0.3461457 0.7561096 0.7631725
## SubunitGalilee:Distance_rescaled -0.4936408 -2.5477340 -1.4037854
## SubunitJudea:Distance_rescaled -1.6602494 -0.8556531 -0.9802417
## SubunitGalilee:rescaled_Time.Diff 0.7092584 -20.6510298 -0.3200343
## SubunitJudea:rescaled_Time.Diff 0.5938280 -21.2640594 -0.3190575
## Hyaena.hyaena Hystrix.indica Meles.meles
## (Intercept) -0.7137567 0.85541361 -0.76041046
## SubunitGalilee -3.2358897 -0.06300426 -0.50901932
## SubunitJudea -1.0144966 -0.69582359 -2.44603487
## Distance_rescaled 0.7266002 0.32577741 -0.67266833
## rescaled_Time.Diff -0.2257533 0.30194780 0.19389719
## cosinus_Monitoring.Time.Diff 0.3383593 -0.19146041 -0.02148409
## SubunitGalilee:Distance_rescaled -1.6743478 -0.46149675 1.52722200
## SubunitJudea:Distance_rescaled -1.6604428 -2.27715087 -2.15634188
## SubunitGalilee:rescaled_Time.Diff 0.3357001 -0.16599960 -0.39715356
## SubunitJudea:rescaled_Time.Diff 0.2708413 -0.08043878 -0.22230100
## Sus.scrofa Vulpes.vulpes
## (Intercept) 1.87496831 0.56493814
## SubunitGalilee 0.83817091 -0.70001232
## SubunitJudea -5.38303274 -0.63157362
## Distance_rescaled -0.26523514 0.93996114
## rescaled_Time.Diff 0.10444304 0.21421552
## cosinus_Monitoring.Time.Diff -0.17401890 0.02004382
## SubunitGalilee:Distance_rescaled 0.09848596 -1.26877799
## SubunitJudea:Distance_rescaled -1.87064515 -1.73195323
## SubunitGalilee:rescaled_Time.Diff -0.07139997 -0.32912464
## SubunitJudea:rescaled_Time.Diff 1.94738139 -0.21045200plot_model_interaction(P.anal = env_data, m = glm.Vulpes.vulpes, eff2plot = "Distance_rescaled", modvar2plot = "Subunit", plot_points=FALSE, plot_residuals=FALSE, export_plot=TRUE, ylabel = NULL, fontname = "Almoni ML v5 AAA",fontsize=22, pdf_width=160, outpath = "output/maquis/", legend_position = "bottom")
#Test whether spatial trend is different from zero
print("Test whether spatial trend is different from zero")## [1] "Test whether spatial trend is different from zero"mm_rescaled_distance <- emtrends(glm.Vulpes.vulpes, specs = "Subunit", var = "Distance_rescaled", type = "response")
print(mm_rescaled_distance)## Subunit Distance_rescaled.trend SE df asymp.LCL asymp.UCL
## Carmel 0.940 0.177 Inf 0.593 1.2874
## Galilee -0.329 0.337 Inf -0.990 0.3320
## Judea -0.792 0.419 Inf -1.614 0.0298
##
## Confidence level used: 0.95test_results_mm_distance <- test(mm_rescaled_distance, null = 0, adjust = "fdr")
print(test_results_mm_distance)## Subunit Distance_rescaled.trend SE df z.ratio p.value
## Carmel 0.940 0.177 Inf 5.304 <.0001
## Galilee -0.329 0.337 Inf -0.976 0.3293
## Judea -0.792 0.419 Inf -1.889 0.0883
##
## P value adjustment: fdr method for 3 testsn = 1117 (total number of cameras)
total abundance =
abundance of common species (8 species) = 22,748
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 2024-04-09
## 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)
## insight 0.19.9 2024-03-15 [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)
## performance * 0.10.3 2023-04-07 [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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