Mammal data - Planted Conifer Forests
Talia Gabay & Ron Chen
2024-04-09
Planted Conifer Forests - Richness by camera
Monitoring started in 2014 (5 monitoring cycles), Each site has only one plot that is far from Settlements.
There are 15 sites total, each site has 5 sampling points
Let’s first validate the data:
## [1] "Settlements has 1 level"## [1] "Far"## [1] "Subunit has 3 levels"## [1] "Carmel" "Galilee" "Judea"## [1] "Site has 15 levels"## [1] "Aderet" "Amatzia" "Bat Shlomo" "Eitanim"
## [5] "Elyakim" "Eshtaol" "Givat Yeshayahu" "Kabri"
## [9] "Kerem Maharal" "Manara" "Meron" "Ofer"
## [13] "Ramat Hashofet" "Ramot Naftali" "Zuriel"## [1] "Transect has 15 levels"## [1] "Aderet Far" "Amatzia Far" "Bat Shlomo Far"
## [4] "Eitanim Far" "Elyakim Far" "Eshtaol Far"
## [7] "Givat Yeshayahu Far" "Kabri Far" "Kerem Maharal Far"
## [10] "Manara Far" "Meron Far" "Ofer Far"
## [13] "Ramat Hashofet Far" "Ramot Naftali Far" "Zuriel Far"## [1] "Transect with date has 75 levels"## [1] "Aderet Far_02_06_2013" "Aderet Far_16_10_2021"
## [3] "Aderet Far_21_08_2017" "Aderet Far_24_05_2015"
## [5] "Aderet Far_28_10_2019" "Amatzia Far_02_06_2013"
## [7] "Amatzia Far_04_12_2021" "Amatzia Far_12_02_2018"
## [9] "Amatzia Far_17_12_2019" "Amatzia Far_24_05_2015"
## [11] "Bat Shlomo Far_18_11_2017" "Bat Shlomo Far_21_08_2021"
## [13] "Bat Shlomo Far_28_06_2015" "Bat Shlomo Far_30_06_2013"
## [15] "Bat Shlomo Far_30_11_2019" "Eitanim Far_01_10_2017"
## [17] "Eitanim Far_08_10_2021" "Eitanim Far_09_06_2015"
## [19] "Eitanim Far_09_11_2019" "Eitanim Far_17_06_2013"
## [21] "Elyakim Far_04_09_2021" "Elyakim Far_06_09_2015"
## [23] "Elyakim Far_07_11_2017" "Elyakim Far_16_02_2020"
## [25] "Elyakim Far_30_06_2013" "Eshtaol Far_01_12_2019"
## [27] "Eshtaol Far_07_06_2015" "Eshtaol Far_17_06_2013"
## [29] "Eshtaol Far_22_10_2017" "Eshtaol Far_22_10_2021"
## [31] "Givat Yeshayahu Far_03_06_2013" "Givat Yeshayahu Far_16_10_2021"
## [33] "Givat Yeshayahu Far_23_05_2015" "Givat Yeshayahu Far_23_08_2017"
## [35] "Givat Yeshayahu Far_28_10_2019" "Kabri Far_09_08_2015"
## [37] "Kabri Far_14_12_2019" "Kabri Far_15_10_2021"
## [39] "Kabri Far_22_12_2017" "Kabri Far_28_07_2013"
## [41] "Kerem Maharal Far_04_09_2021" "Kerem Maharal Far_06_09_2015"
## [43] "Kerem Maharal Far_07_11_2017" "Kerem Maharal Far_16_06_2013"
## [45] "Kerem Maharal Far_16_11_2019" "Manara Far_03_10_2021"
## [47] "Manara Far_06_08_2015" "Manara Far_08_12_2017"
## [49] "Manara Far_11_01_2020" "Manara Far_15_07_2013"
## [51] "Meron Far_08_12_2017" "Meron Far_10_01_2020"
## [53] "Meron Far_15_07_2013" "Meron Far_15_07_2015"
## [55] "Meron Far_31_10_2021" "Ofer Far_03_06_2015"
## [57] "Ofer Far_07_11_2017" "Ofer Far_10_07_2021"
## [59] "Ofer Far_16_06_2013" "Ofer Far_16_11_2019"
## [61] "Ramat Hashofet Far_01_07_2013" "Ramat Hashofet Far_18_11_2017"
## [63] "Ramat Hashofet Far_26_01_2020" "Ramat Hashofet Far_26_06_2015"
## [65] "Ramat Hashofet Far_30_11_2021" "Ramot Naftali Far_08_12_2017"
## [67] "Ramot Naftali Far_11_01_2020" "Ramot Naftali Far_14_07_2013"
## [69] "Ramot Naftali Far_14_07_2015" "Ramot Naftali Far_17_10_2021"
## [71] "Zuriel Far_06_08_2015" "Zuriel Far_15_10_2021"
## [73] "Zuriel Far_22_12_2017" "Zuriel Far_28_07_2013"
## [75] "Zuriel Far_28_12_2019"## [1] "RICHNESS WITH RARE SPECIES"
Explore abundances:
## Canis aureus Canis lupus Capra nubiana Equus hemionus Gazella dorcas
## 1350 85 0 0 0
## Gazella gazella Hyaena hyaena Hystrix indica Lepus capensis Meles meles
## 671 43 655 27 74
## Oryx leucoryx Sus scrofa Vulpes vulpes
## 0 2025 683## Overlapping points were shifted along the y-axis to make them visible.##
## PIPING TO 2nd MVFACTOR
Sus scrofa only:
Fix outlier observation of 186 wild boar, by setting the count to be equal to the highest count observed.
## Overlapping points were shifted along the y-axis to make them visible.##
## PIPING TO 2nd MVFACTOR
Fix outlier observations of canis lupus, gazella gazella and hystrix indica, by setting the count to be equal to the highest count observed for the species.
## Overlapping points were shifted along the y-axis to make them visible.##
## PIPING TO 2nd MVFACTOR
| 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 :185 | Min. :-1.5480 | Min. :-0.99750 | Min. :-0.99061 | Bat Shlomo : 42 | Bat Shlomo Far : 42 | Bat Shlomo Far_28_06_2015 : 10 | Min. :-0.86297 | |
| 1st Qu.:1.000 | 1st Qu.: 3.00 | Galilee:185 | 1st Qu.:-0.9164 | 1st Qu.:-0.83907 | 1st Qu.:-0.64354 | Zuriel : 42 | Zuriel Far : 42 | Bat Shlomo Far_30_06_2013 : 10 | 1st Qu.:-0.23014 | |
| Median :2.000 | Median : 6.00 | Judea :181 | Median :-0.1491 | Median : 0.10159 | Median : 0.07072 | Ramat Hashofet: 41 | Ramat Hashofet Far: 41 | Kabri Far_09_08_2015 : 10 | Median : 0.04593 | |
| Mean :2.334 | Mean : 10.19 | NA | Mean :-0.2012 | Mean :-0.01326 | Mean : 0.01837 | Ramot Naftali : 41 | Ramot Naftali Far : 41 | Ofer Far_16_06_2013 : 10 | Mean : 0.12773 | |
| 3rd Qu.:3.000 | 3rd Qu.: 13.00 | NA | 3rd Qu.: 0.5379 | 3rd Qu.: 0.71796 | 3rd Qu.: 0.69608 | Aderet : 39 | Aderet Far : 39 | Ramat Hashofet Far_01_07_2013: 10 | 3rd Qu.: 0.31041 | |
| Max. :7.000 | Max. :207.00 | NA | Max. : 1.1366 | Max. : 0.99859 | Max. : 0.99952 | Eitanim : 39 | Eitanim Far : 39 | Aderet Far_16_10_2021 : 9 | Max. : 1.86789 | |
| NA | NA | NA | NA | NA | NA | (Other) :307 | (Other) :307 | (Other) :492 | NA |
| Subunit | sinus_Monitoring.Time.Diff | cosinus_Monitoring.Time.Diff | rescaled_Time.Diff | Site | Transect | Transect_with_date | Distance_rescaled | |
|---|---|---|---|---|---|---|---|---|
| Subunit | 1.0000000 | 0.0269857 | -0.2929366 | 0.0722343 | -0.3990533 | -0.3990533 | -0.3976850 | -0.0426536 |
| sinus_Monitoring.Time.Diff | 0.0269857 | 1.0000000 | 0.0004384 | -0.1194839 | 0.0164255 | 0.0164255 | 0.0072200 | 0.0290773 |
| cosinus_Monitoring.Time.Diff | -0.2929366 | 0.0004384 | 1.0000000 | -0.1073206 | 0.1899325 | 0.1899325 | 0.1723042 | 0.0083534 |
| rescaled_Time.Diff | 0.0722343 | -0.1194839 | -0.1073206 | 1.0000000 | -0.0131054 | -0.0131054 | -0.0115445 | -0.0524663 |
| Site | -0.3990533 | 0.0164255 | 0.1899325 | -0.0131054 | 1.0000000 | 1.0000000 | 0.9979562 | -0.2302542 |
| Transect | -0.3990533 | 0.0164255 | 0.1899325 | -0.0131054 | 1.0000000 | 1.0000000 | 0.9979562 | -0.2302542 |
| Transect_with_date | -0.3976850 | 0.0072200 | 0.1723042 | -0.0115445 | 0.9979562 | 0.9979562 | 1.0000000 | -0.2313637 |
| Distance_rescaled | -0.0426536 | 0.0290773 | 0.0083534 | -0.0524663 | -0.2302542 | -0.2302542 | -0.2313637 | 1.0000000 |
Community analysis using package MVabund
Table of abundances per species by camera ID
## [1] 551 14## [1] 279## [1] 13## [1] 0## [1] 0## [1] 0## [1] 145## [1] 35## [1] 201## [1] 10## [1] 49## [1] 0## [1] 301## [1] 253298 cameras. Species with a minimum of 10 cameras - Canis aureus, Canis lupus, Gazella gazella, Hyaena hyaena, Hystrix indica, Lepus capensis, Meles meles, Sus scorfa and Vulpus vulpus
## Canis aureus Canis lupus Capra nubiana Equus hemionus Gazella dorcas
## 1350 53 0 0 0
## Gazella gazella Hyaena hyaena Hystrix indica Lepus capensis Meles meles
## 658 43 635 27 74
## Oryx leucoryx Sus scrofa Vulpes vulpes
## 0 1872 683## Canis.aureus Canis.lupus Gazella.gazella Hyaena.hyaena Hystrix.indica
## 1350 53 658 43 635
## Lepus.capensis Meles.meles Sus.scrofa Vulpes.vulpes
## 27 74 1872 683Species with 20 individuals or more - Canis aureus, Canis lupus, Gazella gazella, Hyaena hyaena, Hystrix indica, Lepus capensis, Meles meles, Sus scorfa and Vulpus vulpus
Left with 9 species
## nb po
## 1073.793 1711.534## [1] "POISSON"
## [1] "NEGATIVE BINOMIAL"
prefer negative binomial because standardized residuals are smaller.
## nb po nb1
## 1073.793 1711.534 1113.120## nb po nb1 nb2
## 1073.793 1711.534 1113.120 1030.756## nb po nb1 nb2 nb3
## 1073.793 1711.534 1113.120 1030.756 1030.756## nb po nb1 nb2 nb3 nb4
## 1073.793 1711.534 1113.120 1030.756 1030.756 1030.756## nb po nb1 nb2 nb3 nb4 nb5
## 1073.793 1711.534 1113.120 1030.756 1030.756 1030.756 1030.756Transect, Site and Subunit improve the model. Will proceed with Subunit:
Many GLM with Sub-unit - no interactions
## Single term deletions
##
## Model:
## spp_no_rare ~ rescaled_Time.Diff + cosinus_Monitoring.Time.Diff +
## sinus_Monitoring.Time.Diff + Subunit
## Df AIC
## <none> 9664.1
## rescaled_Time.Diff 9 9670.2
## cosinus_Monitoring.Time.Diff 9 9685.0
## sinus_Monitoring.Time.Diff 9 9664.6
## Subunit 18 10018.1Drop sine
## Single term deletions
##
## Model:
## spp_no_rare ~ rescaled_Time.Diff + cosinus_Monitoring.Time.Diff +
## Subunit
## Df AIC
## <none> 9664.6
## rescaled_Time.Diff 9 9674.7
## cosinus_Monitoring.Time.Diff 9 9687.5
## Subunit 18 10019.4Final model includes Sub-unit, cosin and time
##
## Test statistics:
## wald value Pr(>wald)
## (Intercept) 21.318 0.001 ***
## rescaled_Time.Diff 5.034 0.015 *
## cosinus_Monitoring.Time.Diff 6.370 0.001 ***
## SubunitGalilee 6.610 0.001 ***
## SubunitJudea 16.740 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Test statistic: 20.82, 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).## Analysis of Deviance Table
##
## Model: spp_no_rare ~ rescaled_Time.Diff + cosinus_Monitoring.Time.Diff + Subunit
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 550
## rescaled_Time.Diff 549 1 19.3 0.029 *
## cosinus_Monitoring.Time.Diff 548 1 57.3 0.001 ***
## Subunit 546 2 390.8 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)
## rescaled_Time.Diff 3.308 0.441 1.662 0.663
## cosinus_Monitoring.Time.Diff 0.659 0.803 10.822 0.015
## Subunit 7.977 0.075 22.196 0.001
## Gazella.gazella Hyaena.hyaena
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## rescaled_Time.Diff 0.504 0.727 5.571 0.156
## cosinus_Monitoring.Time.Diff 0.112 0.932 0.007 0.943
## Subunit 48.317 0.001 5.972 0.133
## Hystrix.indica Lepus.capensis
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## rescaled_Time.Diff 1.121 0.727 0.886 0.727
## cosinus_Monitoring.Time.Diff 8.583 0.036 6.387 0.105
## Subunit 31.449 0.001 10.968 0.031
## Meles.meles Sus.scrofa
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## rescaled_Time.Diff 0.824 0.727 3.436 0.441
## cosinus_Monitoring.Time.Diff 2.586 0.424 25.244 0.001
## Subunit 8.647 0.061 254.523 0.001
## Vulpes.vulpes
## Dev Pr(>Dev)
## (Intercept)
## rescaled_Time.Diff 1.989 0.643
## cosinus_Monitoring.Time.Diff 2.854 0.424
## Subunit 0.779 0.734
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.Multivariate test shows that Sub-unit, cosin time of sampling and time affect species composition However time variable is not significant for any of the species. Therefore re-run without time variable.
## Single term deletions
##
## Model:
## spp_no_rare ~ cosinus_Monitoring.Time.Diff + Subunit
## Df AIC
## <none> 9674.7
## cosinus_Monitoring.Time.Diff 9 9698.2
## Subunit 18 10019.8
##
## Test statistics:
## wald value Pr(>wald)
## (Intercept) 21.216 0.001 ***
## cosinus_Monitoring.Time.Diff 6.525 0.001 ***
## SubunitGalilee 6.556 0.001 ***
## SubunitJudea 16.535 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Test statistic: 20.32, 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).## Analysis of Deviance Table
##
## Model: spp_no_rare ~ cosinus_Monitoring.Time.Diff + Subunit
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 550
## cosinus_Monitoring.Time.Diff 549 1 58.2 0.001 ***
## Subunit 547 2 381.0 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)
## cosinus_Monitoring.Time.Diff 1.135 0.623 8.267 0.044
## Subunit 9.674 0.056 21.146 0.001
## Gazella.gazella Hyaena.hyaena
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## cosinus_Monitoring.Time.Diff 0.042 0.895 0.179 0.895
## Subunit 48.026 0.001 4.871 0.221
## Hystrix.indica Lepus.capensis
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## cosinus_Monitoring.Time.Diff 9.523 0.032 6.075 0.102
## Subunit 30.398 0.001 10.936 0.035
## Meles.meles Sus.scrofa
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## cosinus_Monitoring.Time.Diff 2.822 0.332 26.363 0.001
## Subunit 8.211 0.082 246.983 0.001
## Vulpes.vulpes
## Dev Pr(>Dev)
## (Intercept)
## cosinus_Monitoring.Time.Diff 3.793 0.255
## Subunit 0.787 0.726
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.Final model includes Sub-unit and cosin time of sampling
Canis lupus
remove Carmel observations because they are all zeros.
##
## Call:
## glm.nb(formula = Canis.lupus ~ cosinus_Monitoring.Time.Diff +
## Subunit, data = env_data[Subunit != "Carmel"], init.theta = 0.03403984248,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.49674 -0.35621 -0.17970 -0.03864 1.45013
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.1294 0.5320 -4.003 6.25e-05 ***
## cosinus_Monitoring.Time.Diff 2.3499 0.7578 3.101 0.00193 **
## SubunitJudea -3.3018 1.1958 -2.761 0.00576 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.034) family taken to be 1)
##
## Null deviance: 58.999 on 365 degrees of freedom
## Residual deviance: 34.979 on 363 degrees of freedom
## AIC: 159.01
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.0340
## Std. Err.: 0.0123
##
## 2 x log-likelihood: -151.0100## Loading required package: Cairo## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
Compute predicted average difference in C. lupus abundance between Judea and Galilee based on model:
## [1] "C. lupus abundance in Galilee is higher than Judea by 2616.11505848149 %"Gazella gazella
## (Intercept) cosinus_Monitoring.Time.Diff
## -1.4415372 0.2833693
## SubunitGalilee SubunitJudea
## 1.5479080 2.3092798## Canis.aureus Canis.lupus Gazella.gazella
## (Intercept) 1.15639383 -15.265119 -1.4415371
## cosinus_Monitoring.Time.Diff -0.01277888 2.349795 0.2833692
## SubunitGalilee -0.23563268 13.135720 1.5479081
## SubunitJudea -0.65695209 9.833992 2.3092794
## Hyaena.hyaena Hystrix.indica Lepus.capensis
## (Intercept) -2.38932951 -0.1085528 -2.789939
## cosinus_Monitoring.Time.Diff 0.01226337 -0.2274492 1.545720
## SubunitGalilee -0.88707807 -0.4336437 -12.261799
## SubunitJudea 0.13990694 0.7355202 -1.202820
## Meles.meles Sus.scrofa Vulpes.vulpes
## (Intercept) -1.5382238 1.4022934 0.13707508
## cosinus_Monitoring.Time.Diff -0.6454851 0.3089406 0.21357692
## SubunitGalilee -0.6794879 0.2706287 0.04402336
## SubunitJudea -1.2169837 -3.1504033 0.16147197## [1] 658
## Subunit emmean SE df asymp.LCL asymp.UCL
## Carmel -1.445 0.233 Inf -1.902 -0.989
## Galilee 0.103 0.191 Inf -0.272 0.477
## Judea 0.864 0.193 Inf 0.485 1.243
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95## contrast estimate SE df z.ratio p.value
## Carmel - Galilee -1.548 0.297 Inf -5.206 <.0001
## Carmel - Judea -2.309 0.310 Inf -7.458 <.0001
## Galilee - Judea -0.761 0.278 Inf -2.742 0.0061
##
## Results are given on the log (not the response) scale.
## P value adjustment: fdr method for 3 testsCompute predicted average difference in G. gazella abundance among all subunits based on model:
## [1] "G. gazella abundance in Judea is higher than Carmel by 906.717136260404 %"## [1] "G. gazella abundance in Judea is higher than Galilee by 114.121147912162 %"## [1] "G. gazella abundance in Galilee is higher than Carmel by 370.16240388987 %"Hystrix indica
## (Intercept) cosinus_Monitoring.Time.Diff
## -0.1085528 -0.2274502
## SubunitGalilee SubunitJudea
## -0.4336430 0.7355195## Canis.aureus Canis.lupus Gazella.gazella
## (Intercept) 1.15639383 -15.265119 -1.4415371
## cosinus_Monitoring.Time.Diff -0.01277888 2.349795 0.2833692
## SubunitGalilee -0.23563268 13.135720 1.5479081
## SubunitJudea -0.65695209 9.833992 2.3092794
## Hyaena.hyaena Hystrix.indica Lepus.capensis
## (Intercept) -2.38932951 -0.1085528 -2.789939
## cosinus_Monitoring.Time.Diff 0.01226337 -0.2274492 1.545720
## SubunitGalilee -0.88707807 -0.4336437 -12.261799
## SubunitJudea 0.13990694 0.7355202 -1.202820
## Meles.meles Sus.scrofa Vulpes.vulpes
## (Intercept) -1.5382238 1.4022934 0.13707508
## cosinus_Monitoring.Time.Diff -0.6454851 0.3089406 0.21357692
## SubunitGalilee -0.6794879 0.2706287 0.04402336
## SubunitJudea -1.2169837 -3.1504033 0.16147197
## Subunit emmean SE df asymp.LCL asymp.UCL
## Carmel -0.106 0.150 Inf -0.400 0.189
## Galilee -0.539 0.161 Inf -0.854 -0.224
## Judea 0.630 0.145 Inf 0.345 0.915
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95## contrast estimate SE df z.ratio p.value
## Carmel - Galilee 0.434 0.219 Inf 1.981 0.0476
## Carmel - Judea -0.736 0.213 Inf -3.450 0.0008
## Galilee - Judea -1.169 0.220 Inf -5.314 <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: fdr method for 3 testsCompute predicted average difference in H. indica abundance among all subunits based on model:
## [1] "H. indica abundance in Judea is higher than Carmel by 108.656566090015 %"## [1] "H. indica abundance in Judea is higher than Galilee by 221.929523047568 %"## [1] "H. indica abundance in Carmel is higher than Galilee by 54.2867924456719 %"Lepus cepensis
remove Galilee observations because they are all zeros.
##
## Call:
## glm.nb(formula = Lepus.capensis ~ cosinus_Monitoring.Time.Diff +
## Subunit, data = env_data[Subunit != "Galilee"], init.theta = 0.03079447589,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.3782 -0.2946 -0.1809 -0.1036 2.0200
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.7900 0.5638 -4.948 7.49e-07 ***
## cosinus_Monitoring.Time.Diff 1.5458 0.6681 2.314 0.0207 *
## SubunitJudea -1.2028 0.9361 -1.285 0.1988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.0308) family taken to be 1)
##
## Null deviance: 43.181 on 365 degrees of freedom
## Residual deviance: 32.236 on 363 degrees of freedom
## AIC: 127.01
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.0308
## Std. Err.: 0.0145
##
## 2 x log-likelihood: -119.0130After removing Galilee, subunit is not a significant factor on Lepus capensis abundance.
Sus scrofa
although significant, exclude model because appears again later in model with subunit*time interaction.
Many GLM with interaction between subunit and time
## Single term deletions
##
## Model:
## spp_no_rare ~ Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff +
## sinus_Monitoring.Time.Diff
## Df AIC
## <none> 9649.3
## cosinus_Monitoring.Time.Diff 9 9675.6
## sinus_Monitoring.Time.Diff 9 9651.3
## Subunit:rescaled_Time.Diff 18 9664.1drop sine
## Single term deletions
##
## Model:
## spp_no_rare ~ Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff
## Df AIC
## <none> 9651.3
## cosinus_Monitoring.Time.Diff 9 9681.0
## Subunit:rescaled_Time.Diff 18 9664.6Final model includes cosin time, and the interaction between subunit and time
##
## Test statistics:
## wald value Pr(>wald)
## (Intercept) 19.897 0.001 ***
## SubunitGalilee 6.900 0.001 ***
## SubunitJudea 13.712 0.001 ***
## rescaled_Time.Diff 4.659 0.018 *
## cosinus_Monitoring.Time.Diff 7.041 0.001 ***
## SubunitGalilee:rescaled_Time.Diff 3.729 0.095 .
## SubunitJudea:rescaled_Time.Diff 5.697 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Test statistic: 19.43, 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).## Analysis of Deviance Table
##
## Model: spp_no_rare ~ Subunit * rescaled_Time.Diff + cosinus_Monitoring.Time.Diff
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 550
## Subunit 548 2 397.8 0.001 ***
## rescaled_Time.Diff 547 1 28.7 0.005 **
## cosinus_Monitoring.Time.Diff 546 1 40.9 0.001 ***
## Subunit:rescaled_Time.Diff 544 2 49.3 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 10.799 0.041 20.911 0.002
## rescaled_Time.Diff 1.08 0.720 3.385 0.403
## cosinus_Monitoring.Time.Diff 0.066 0.921 10.384 0.029
## Subunit:rescaled_Time.Diff 10.124 0.047 0.828 0.750
## Gazella.gazella Hyaena.hyaena
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## Subunit 45.182 0.001 5.049 0.312
## rescaled_Time.Diff 0.168 0.720 6.355 0.118
## cosinus_Monitoring.Time.Diff 3.583 0.280 0.147 0.921
## Subunit:rescaled_Time.Diff 5.377 0.347 2.992 0.714
## Hystrix.indica Lepus.capensis
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## Subunit 35.944 0.001 12.792 0.020
## rescaled_Time.Diff 2.088 0.653 0.995 0.720
## cosinus_Monitoring.Time.Diff 3.121 0.280 4.454 0.201
## Subunit:rescaled_Time.Diff 2.935 0.714 0.658 0.750
## Meles.meles Sus.scrofa
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## Subunit 4.537 0.312 262.411 0.001
## rescaled_Time.Diff 1.242 0.720 11.33 0.023
## cosinus_Monitoring.Time.Diff 6.278 0.103 9.464 0.040
## Subunit:rescaled_Time.Diff 1.194 0.750 23.219 0.002
## Vulpes.vulpes
## Dev Pr(>Dev)
## (Intercept)
## Subunit 0.172 0.927
## rescaled_Time.Diff 2.073 0.653
## cosinus_Monitoring.Time.Diff 3.377 0.280
## Subunit:rescaled_Time.Diff 1.961 0.750
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.Multivariate test shows that subunit x time and cosin time affect species composition
Sus scrofa
## (Intercept) SubunitGalilee
## 1.41055124 0.26258260
## SubunitJudea rescaled_Time.Diff
## -3.73253998 0.03934223
## cosinus_Monitoring.Time.Diff SubunitGalilee:rescaled_Time.Diff
## 0.37166673 0.15529898
## SubunitJudea:rescaled_Time.Diff
## 1.60040468## Canis.aureus Canis.lupus Gazella.gazella
## (Intercept) 0.92543101 -15.4308536 -1.8332971
## SubunitGalilee -0.03074436 12.6432995 1.8857368
## SubunitJudea -0.36640001 -2.2782729 2.7685771
## rescaled_Time.Diff -0.42047111 -0.5434586 -0.7183219
## cosinus_Monitoring.Time.Diff 0.09945115 2.4821167 0.4126787
## SubunitGalilee:rescaled_Time.Diff 0.43345435 -0.3680742 0.5778458
## SubunitJudea:rescaled_Time.Diff 0.61366644 -7.8694219 0.8264836
## Hyaena.hyaena Hystrix.indica Lepus.capensis
## (Intercept) -2.3908425 -0.06936227 -3.4165292
## SubunitGalilee -1.3611449 -0.47199689 -11.7373210
## SubunitJudea 0.1675971 0.68651809 -0.4949739
## rescaled_Time.Diff 0.1818044 0.15367707 -0.7543741
## cosinus_Monitoring.Time.Diff 0.1975235 -0.22235591 2.0190428
## SubunitGalilee:rescaled_Time.Diff 1.0969510 0.13600423 0.9141765
## SubunitJudea:rescaled_Time.Diff 0.4022983 -0.22366184 0.9937627
## Meles.meles Sus.scrofa Vulpes.vulpes
## (Intercept) -1.46354722 1.4105503 0.06199016
## SubunitGalilee -0.77235943 0.2625836 0.12571886
## SubunitJudea -1.34887689 -3.7325367 0.22039441
## rescaled_Time.Diff 0.32320482 0.0393417 -0.19055860
## cosinus_Monitoring.Time.Diff -0.70482555 0.3716676 0.22892562
## SubunitGalilee:rescaled_Time.Diff -0.42232025 0.1552999 0.25459349
## SubunitJudea:rescaled_Time.Diff -0.08611442 1.6004043 0.09657084
Test whether temporal trend is different from zero
## Subunit rescaled_Time.Diff.trend SE df asymp.LCL asymp.UCL
## Carmel 0.0393 0.1031 Inf -0.16264 0.241
## Galilee 0.1946 0.0986 Inf 0.00135 0.388
## Judea 1.6397 0.3869 Inf 0.88150 2.398
##
## Confidence level used: 0.95## Subunit rescaled_Time.Diff.trend SE df z.ratio p.value
## Carmel 0.0393 0.1031 Inf 0.382 0.7026
## Galilee 0.1946 0.0986 Inf 1.974 0.0726
## Judea 1.6397 0.3869 Inf 4.238 0.0001
##
## P value adjustment: fdr method for 3 testsCompute predicted decrease in S. scrofa across monitoring time span based on model:
## [1] "start date: 02/06/2013"## [1] "end date: 04/12/2021"## [1] "Increase from 0.0102188269796157 to 0.834094255411367 which is 8062.32877878448 %"Test whether spatial differences in subunit are different from zero
## contrast rescaled_Time.Diff estimate SE df z.ratio p.value
## Carmel - Galilee -0.201 -0.231 0.133 Inf -1.734 0.0829
## Carmel - Judea -0.201 4.055 0.420 Inf 9.653 <.0001
## Galilee - Judea -0.201 4.286 0.419 Inf 10.221 <.0001
##
## Results are given on the log (not the response) scale.
## P value adjustment: fdr method for 3 testsCompute predicted average difference in S. scrofa abundance between Judea-Carmel and Judea-Galilee based on model:
## [1] "S. scrofa abundance in Carmel is higher than Judea by 5707.25986834471 %"## [1] "S. scrofa abundance in Galilee is higher than Judea by 7213.71399264238 %"Canis aureus
## (Intercept) SubunitGalilee
## 0.92543131 -0.03074468
## SubunitJudea rescaled_Time.Diff
## -0.36640052 -0.42047027
## cosinus_Monitoring.Time.Diff SubunitGalilee:rescaled_Time.Diff
## 0.09945076 0.43345366
## SubunitJudea:rescaled_Time.Diff
## 0.61366445## Canis.aureus Canis.lupus Gazella.gazella
## (Intercept) 0.92543101 -15.4308536 -1.8332971
## SubunitGalilee -0.03074436 12.6432995 1.8857368
## SubunitJudea -0.36640001 -2.2782729 2.7685771
## rescaled_Time.Diff -0.42047111 -0.5434586 -0.7183219
## cosinus_Monitoring.Time.Diff 0.09945115 2.4821167 0.4126787
## SubunitGalilee:rescaled_Time.Diff 0.43345435 -0.3680742 0.5778458
## SubunitJudea:rescaled_Time.Diff 0.61366644 -7.8694219 0.8264836
## Hyaena.hyaena Hystrix.indica Lepus.capensis
## (Intercept) -2.3908425 -0.06936227 -3.4165292
## SubunitGalilee -1.3611449 -0.47199689 -11.7373210
## SubunitJudea 0.1675971 0.68651809 -0.4949739
## rescaled_Time.Diff 0.1818044 0.15367707 -0.7543741
## cosinus_Monitoring.Time.Diff 0.1975235 -0.22235591 2.0190428
## SubunitGalilee:rescaled_Time.Diff 1.0969510 0.13600423 0.9141765
## SubunitJudea:rescaled_Time.Diff 0.4022983 -0.22366184 0.9937627
## Meles.meles Sus.scrofa Vulpes.vulpes
## (Intercept) -1.46354722 1.4105503 0.06199016
## SubunitGalilee -0.77235943 0.2625836 0.12571886
## SubunitJudea -1.34887689 -3.7325367 0.22039441
## rescaled_Time.Diff 0.32320482 0.0393417 -0.19055860
## cosinus_Monitoring.Time.Diff -0.70482555 0.3716676 0.22892562
## SubunitGalilee:rescaled_Time.Diff -0.42232025 0.1552999 0.25459349
## SubunitJudea:rescaled_Time.Diff -0.08611442 1.6004043 0.09657084
Test whether temporal trend is different from zero
## [1] "Canis aureus"## Subunit rescaled_Time.Diff.trend SE df asymp.LCL asymp.UCL
## Carmel -0.420 0.144 Inf -0.704 -0.137
## Galilee 0.013 0.141 Inf -0.264 0.290
## Judea 0.193 0.151 Inf -0.103 0.490
##
## Confidence level used: 0.95## Subunit rescaled_Time.Diff.trend SE df z.ratio p.value
## Carmel -0.420 0.144 Inf -2.911 0.0108
## Galilee 0.013 0.141 Inf 0.092 0.9267
## Judea 0.193 0.151 Inf 1.278 0.3021
##
## P value adjustment: fdr method for 3 testsCompute predicted decrease in C. aureus across monitoring time span based on model:
## [1] "start date: 02/06/2013"## [1] "end date: 04/12/2021"## [1] "Decrease from 5.20890920835611 to 1.68465894204887 which is -67.6581242893166 %"Test whether spatial differences in subunit are different from zero
## contrast rescaled_Time.Diff estimate SE df z.ratio p.value
## Carmel - Galilee -0.201 0.118 0.190 Inf 0.621 0.5349
## Carmel - Judea -0.201 0.490 0.205 Inf 2.395 0.0499
## Galilee - Judea -0.201 0.372 0.203 Inf 1.834 0.1000
##
## Results are given on the log (not the response) scale.
## P value adjustment: fdr method for 3 testsCompute predicted average difference in C. aureus abundance between Carmel and Judea based on model:
## [1] "C. aureus abundance in Carmel is higher than Judea by 63.6586044456967 %"n = 551 (total number of cameras)
total abundance = 5613 (9 species), total abundance without rare species is the same
Session information
## ─ 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)
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## bslib 0.4.2 2022-12-16 [1] CRAN (R 4.2.3)
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## Cairo * 1.6-0 2022-07-05 [1] CRAN (R 4.2.2)
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## coda 0.19-4 2020-09-30 [1] CRAN (R 4.2.3)
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## colorspace 2.1-1 2023-03-08 [1] R-Forge (R 4.2.2)
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## Matrix * 1.5-5 2023-04-05 [1] R-Forge (R 4.2.3)
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## 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)
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## 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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