Birds_Med_Des_Trans_Zone_analysis_by_plot
Ron Chen
2024-04-10
## Loading required package: readxl## Loading required package: lubridate##
## Attaching package: 'lubridate'## The following objects are masked from 'package:data.table':
##
## hour, isoweek, mday, minute, month, quarter, second, wday, week,
## yday, year## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union## Loading required package: solartime## - Winter data excluded.
## - The counts of all passing / wintering species (not breeding in the sampled unit) were set to zero. Abreed_and_non_breed contains the non-breeders as well.
## - Species less likely to be interacting with sampling location were EXCLUDED
## - observations with counts greater than or equal to 10 standard deviations AND greater than or equal to 60 individuals were set to 1,
## under the assumption that these are migrating or local wandering phenomena.
## No outliers found in unit Arid South.
## 2 outliers set equal to 1 in Herbaceous and Dwarf-Shrub Vegetation unit.
## unit point_name year
## 1: Herbaceous and Dwarf-Shrub Vegetation Gamla Near 3 2014
## 2: Herbaceous and Dwarf-Shrub Vegetation Yavneel Far 2 2018
## SciName count_under_250 Z_score
## 1: Passer domesticus 62 11.90316
## 2: Passer hispaniolensis 60 11.35733
## No outliers found in unit Inland Sands.
## No outliers found in unit Loess Covered Areas in the Northern Negev.
## No outliers found in unit Mediterranean-Desert Transition Zone.
## 5 outliers set equal to 1 in Mediterranean Maquis unit.
## unit point_name year SciName
## 1: Mediterranean Maquis Beit Oren Near 1 2019 Curruca curruca
## 2: Mediterranean Maquis Beit Oren Near 3 2019 Curruca curruca
## 3: Mediterranean Maquis Kerem Maharal Near 3 2021 Chloris chloris
## 4: Mediterranean Maquis Kerem Maharal Near 3 2021 Garrulus glandarius
## 5: Mediterranean Maquis Nir Etzion Far 11 2021 Columba livia
## count_under_250 Z_score
## 1: 99 11.94535
## 2: 142 17.16927
## 3: 80 19.19266
## 4: 150 20.50138
## 5: 100 14.85118
## 2 outliers set equal to 1 in Negev Highlands unit.
## unit point_name year SciName
## 1: Negev Highlands Yeruham Far Slope 5 2018 Carpospiza brachydactyla
## 2: Negev Highlands Bislach Near Slope 6 2020 Passer hispaniolensis
## count_under_250 Z_score
## 1: 70 14.34802
## 2: 100 10.61343
## No outliers found in unit Planted Conifer Forest.Med-Desert Transition Zone
Factors are proximity to settlements and time. Total 5 campaigns. 5 sites with 6 plots per site. Raw data Total abundance: 6574 Number of observations: 1841 Total richness: 102
Filtered data Total abundance: 3339 Number of observations: 1029 Total richness: 51
Model gma, abundance and richness
Richness done with rare species. Abundance and mean abundance done without rare species. Full models include cosine and sine of the time difference from June 21st (in radians).
richness
Explore data and plot mean-variance plot. There is a strong relationship, indicating that employing GLMs is the proper way to analyze, rather than OLS (assumption of homogeneity is violated).
## [1] "RICHNESS WITH RARE SPECIES"
| richness | year_ct | site | settlements | td_sc | cos_td_rad | sin_td_rad | h_from_sunrise | cos_hsun | sin_hsun | monitors_name | wind | precipitation | temperature | clouds | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 1.00 | Min. :0.000 | Beit Yatir:24 | Far :75 | Min. :-2.06451 | Min. :-0.2009 | Min. :-0.99677 | Length:150 | Min. :0.4726 | Min. :-0.2411 | Eyal Shochat: 25 | 0 : 0 | 0 : 0 | 0 : 0 | 0 : 0 | |
| 1st Qu.: 4.00 | 1st Qu.:2.000 | Dvir : 6 | Near:75 | 1st Qu.:-0.41912 | 1st Qu.: 0.5142 | 1st Qu.:-0.85766 | Class :difftime | 1st Qu.:0.8607 | 1st Qu.: 0.1797 | Other : 5 | 1 : 0 | 3 : 0 | 1 : 0 | 1 : 0 | |
| Median : 6.00 | Median :4.000 | Har Amasa :29 | NA | Median : 0.07688 | Median : 0.6917 | Median :-0.72216 | Mode :numeric | Median :0.9336 | Median : 0.3584 | Adi Domer : 0 | 2 : 0 | NA’s:150 | 2 : 0 | 2 : 0 | |
| Mean : 6.86 | Mean :3.987 | Lahav :31 | NA | Mean :-0.03383 | Mean : 0.6007 | Mean :-0.69390 | NA | Mean :0.8946 | Mean : 0.3483 | Asaf Mayrose: 0 | 3 : 0 | NA | 3 : 0 | 3 : 0 | |
| 3rd Qu.: 9.00 | 3rd Qu.:6.000 | Lehavim :30 | NA | 3rd Qu.: 0.22473 | 3rd Qu.: 0.7383 | 3rd Qu.:-0.67444 | NA | 3rd Qu.:0.9828 | 3rd Qu.: 0.5090 | Eliraz Dvir : 0 | NA’s:150 | NA | NA’s:150 | NA’s:150 | |
| Max. :18.00 | Max. :8.000 | Meitar : 6 | NA | Max. : 1.90350 | Max. : 1.0000 | Max. : 0.01721 | NA | Max. :1.0000 | Max. : 0.8813 | (Other) : 0 | NA | NA | NA | NA | |
| NA | NA | Mirsham :24 | NA | NA | NA | NA | NA | NA’s :61 | NA’s :61 | NA’s :120 | NA | NA | NA | NA |
no observation for all 4 weather variables. many NAs (61) for sampling time of day variables. exclude from model.
## Warning in cor(P.anal[, lapply(X = .SD, FUN = as.numeric), .SDcols =
## IVs[1:11]], : the standard deviation is zero| richness | year_ct | site | settlements | td_sc | cos_td_rad | sin_td_rad | h_from_sunrise | cos_hsun | sin_hsun | monitors_name | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| richness | 1.0000000 | 0.0024429 | 0.1326347 | 0.6790665 | 0.0423691 | 0.0857132 | -0.0086319 | -0.0973908 | 0.1999944 | -0.0757602 | 0.0440223 |
| year_ct | 0.0024429 | 1.0000000 | 0.0000166 | -0.0047063 | -0.4784485 | -0.4613745 | -0.4697934 | -0.4334083 | 0.3814165 | -0.4327722 | NA |
| site | 0.1326347 | 0.0000166 | 1.0000000 | 0.0035169 | -0.2288120 | -0.1363567 | -0.3069781 | -0.1032496 | 0.1542517 | -0.0901054 | 0.0447214 |
| settlements | 0.6790665 | -0.0047063 | 0.0035169 | 1.0000000 | 0.0121481 | 0.0062718 | 0.0170858 | 0.0349726 | 0.0873478 | 0.0587772 | 0.2683282 |
| td_sc | 0.0423691 | -0.4784485 | -0.2288120 | 0.0121481 | 1.0000000 | 0.9576971 | 0.9330875 | -0.0786776 | 0.0634242 | -0.0849265 | -0.1529258 |
| cos_td_rad | 0.0857132 | -0.4613745 | -0.1363567 | 0.0062718 | 0.9576971 | 1.0000000 | 0.7938914 | -0.1011147 | 0.0827235 | -0.1075303 | -0.1580054 |
| sin_td_rad | -0.0086319 | -0.4697934 | -0.3069781 | 0.0170858 | 0.9330875 | 0.7938914 | 1.0000000 | 0.0563677 | -0.0500522 | 0.0519939 | -0.0915087 |
| h_from_sunrise | -0.0973908 | -0.4334083 | -0.1032496 | 0.0349726 | -0.0786776 | -0.1011147 | 0.0563677 | 1.0000000 | -0.9216766 | 0.9965659 | 0.2604084 |
| cos_hsun | 0.1999944 | 0.3814165 | 0.1542517 | 0.0873478 | 0.0634242 | 0.0827235 | -0.0500522 | -0.9216766 | 1.0000000 | -0.8880762 | -0.2261490 |
| sin_hsun | -0.0757602 | -0.4327722 | -0.0901054 | 0.0587772 | -0.0849265 | -0.1075303 | 0.0519939 | 0.9965659 | -0.8880762 | 1.0000000 | 0.2583855 |
| monitors_name | 0.0440223 | NA | 0.0447214 | 0.2683282 | -0.1529258 | -0.1580054 | -0.0915087 | 0.2604084 | -0.2261490 | 0.2583855 | 1.0000000 |
Fit Poisson glm, check for existence of overdispersion
## [1] "Estimating overdispersion parameter phi: (Res. Dev.)/(n-p) where n=number of observations; p=number of parameters in the model."## [1] "od = 0.894821514091859"Overdispersion parameter is <1 (underdispersion) and therefore Poisson is preferable to negative binomial.
| Observations | 89 (61 missing obs. deleted) |
| Dependent variable | richness |
| Type | Generalized linear model |
| Family | poisson |
| Link | log |
| χ²(7) | 119.84 |
| Pseudo-R² (Cragg-Uhler) | 0.74 |
| Pseudo-R² (McFadden) | 0.23 |
| AIC | 407.38 |
| BIC | 427.29 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 2.12 | 1.45 | 1.46 | 0.14 |
| settlementsNear | 0.35 | 0.35 | 0.99 | 0.32 |
| year_ct | -0.01 | 0.05 | -0.26 | 0.79 |
| cos_td_rad | -0.35 | 0.40 | -0.88 | 0.38 |
| sin_td_rad | 1.62 | 0.96 | 1.69 | 0.09 |
| cos_hsun | 0.94 | 0.98 | 0.96 | 0.34 |
| sin_hsun | -0.02 | 0.45 | -0.04 | 0.97 |
| settlementsNear:year_ct | 0.08 | 0.06 | 1.47 | 0.14 |
| Standard errors: MLE |
cosine and sine of hours from sunrise are not significant. remove them because of many NAs.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: richness ~ settlements * year_ct + cos_td_rad + sin_td_rad +
## (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 690.8 711.9 -338.4 676.8 143
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.12699 -0.74859 -0.03467 0.52894 2.53237
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0006558 0.02561
## Number of obs: 150, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.95430 0.27969 3.412 0.000645 ***
## settlementsNear 0.59575 0.11320 5.263 1.42e-07 ***
## year_ct -0.02016 0.02033 -0.992 0.321354
## cos_td_rad 0.49938 0.20321 2.457 0.013993 *
## sin_td_rad -0.44147 0.22517 -1.961 0.049924 *
## settlementsNear:year_ct 0.03750 0.02348 1.597 0.110238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct cs_td_ sn_td_
## settlmntsNr -0.292
## year_ct -0.254 0.613
## cos_td_rad -0.903 0.021 0.090
## sin_td_rad 0.871 -0.032 0.113 -0.771
## sttlmntsN:_ 0.233 -0.807 -0.781 -0.013 0.021center time of year (cosine and sine) variables - highly correlated with intercept
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: richness ~ settlements * year_ct + cos_td_rad_c + sin_td_rad_c +
## (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 690.8 711.9 -338.4 676.8 143
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.12698 -0.74858 -0.03468 0.52895 2.53237
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0006556 0.0256
## Number of obs: 150, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.56061 0.09581 16.289 < 2e-16 ***
## settlementsNear 0.59576 0.11320 5.263 1.42e-07 ***
## year_ct -0.02016 0.02033 -0.992 0.3214
## cos_td_rad_c 0.49937 0.20321 2.457 0.0140 *
## sin_td_rad_c -0.44148 0.22517 -1.961 0.0499 *
## settlementsNear:year_ct 0.03750 0.02348 1.597 0.1102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct cs_t__ sn_t__
## settlmntsNr -0.772
## year_ct -0.811 0.613
## cos_td_rd_c -0.105 0.021 0.090
## sin_td_rd_c -0.072 -0.032 0.113 -0.771
## sttlmntsN:_ 0.630 -0.807 -0.781 -0.013 0.021perform stepwise model selection of poisson mixed model.
## Single term deletions
##
## Model:
## richness ~ settlements * year_ct + cos_td_rad_c + sin_td_rad_c +
## (1 | site)
## npar AIC
## <none> 690.79
## cos_td_rad_c 1 694.73
## sin_td_rad_c 1 692.67
## settlements:year_ct 1 691.35drop settlements * year.
## Single term deletions
##
## Model:
## richness ~ settlements + year_ct + cos_td_rad_c + sin_td_rad_c +
## (1 | site)
## npar AIC
## <none> 691.35
## settlements 1 822.30
## year_ct 1 689.51
## cos_td_rad_c 1 695.37
## sin_td_rad_c 1 693.35drop year.
## Single term deletions
##
## Model:
## richness ~ settlements + cos_td_rad_c + sin_td_rad_c + (1 | site)
## npar AIC
## <none> 689.51
## settlements 1 820.50
## cos_td_rad_c 1 693.37
## sin_td_rad_c 1 692.00settlements and time of year remain. Final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: richness ~ settlements + cos_td_rad_c + sin_td_rad_c + (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 689.5 704.6 -339.8 679.5 145
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.18315 -0.75018 -0.04449 0.60796 2.82290
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0004421 0.02103
## Number of obs: 150, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.48240 0.05569 26.620 <2e-16 ***
## settlementsNear 0.74384 0.06671 11.150 <2e-16 ***
## cos_td_rad_c 0.49152 0.20254 2.427 0.0152 *
## sin_td_rad_c -0.46595 0.22093 -2.109 0.0349 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN cs_t__
## settlmntsNr -0.811
## cos_td_rd_c -0.046 0.019
## sin_td_rd_c 0.040 -0.025 -0.824## $site
## (Intercept)
## Beit Yatir -0.0079097816
## Dvir 0.0007649121
## Har Amasa -0.0030138047
## Lahav 0.0078284551
## Lehavim -0.0036991931
## Meitar -0.0001437228
## Mirsham 0.0062593182
##
## with conditional variances for "site"## $site
## Registered S3 methods overwritten by 'broom':
## method from
## tidy.glht jtools
## tidy.summary.glht jtools| Observations | 150 |
| Dependent variable | richness |
| Type | Mixed effects generalized linear model |
| Family | poisson |
| Link | log |
| AIC | 689.513 |
| BIC | 704.566 |
| Pseudo-R² (fixed effects) | 0.517 |
| Pseudo-R² (total) | 0.518 |
| exp(Est.) | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 4.403 | 0.056 | 26.620 | 0.000 |
| settlementsNear | 2.104 | 0.067 | 11.150 | 0.000 |
| cos_td_rad_c | 1.635 | 0.203 | 2.427 | 0.015 |
| sin_td_rad_c | 0.628 | 0.221 | -2.109 | 0.035 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| site | (Intercept) | 0.021 |
| Group | # groups | ICC |
|---|---|---|
| site | 7 | 0.000 |
## Loading required package: extrafont## Registering fonts with R## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.
## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.## 1 2
## 4.403495 9.264964## [1] 110.4002
There is a statistically significant effect for proximity to settlements and time of year.
Near plots have on average 4.8614687 more species than far plots, which is 110.4002235 percent higher.
No statistically significant effect for the other factors tested.
geometric mean of abundance
Explore data. Exclude time of day because of high number of NAs.
## [1] "GEOMETRIC MEAN ABUNDANCE WITHOUT RARE SPECIES"
| gma | year_ct | site | settlements | td_sc | cos_td_rad | sin_td_rad | |
|---|---|---|---|---|---|---|---|
| Min. : 1.000 | Min. :0.000 | Lahav :31 | Far :75 | Min. :-2.06451 | Min. :-0.2009 | Min. :-0.99677 | |
| 1st Qu.: 1.646 | 1st Qu.:2.000 | Lehavim :30 | Near:75 | 1st Qu.:-0.41912 | 1st Qu.: 0.5142 | 1st Qu.:-0.85766 | |
| Median : 2.285 | Median :4.000 | Har Amasa :29 | NA | Median : 0.07688 | Median : 0.6917 | Median :-0.72216 | |
| Mean : 2.432 | Mean :3.987 | Beit Yatir:24 | NA | Mean :-0.03383 | Mean : 0.6007 | Mean :-0.69390 | |
| 3rd Qu.: 2.860 | 3rd Qu.:6.000 | Mirsham :24 | NA | 3rd Qu.: 0.22473 | 3rd Qu.: 0.7383 | 3rd Qu.:-0.67444 | |
| Max. :10.247 | Max. :8.000 | Dvir : 6 | NA | Max. : 1.90350 | Max. : 1.0000 | Max. : 0.01721 | |
| NA | NA | (Other) : 6 | NA | NA | NA | NA |
Fit glm, compare gamma, gaussian (poisson inappropriate because response is not discrete)
Gamma seems better than gaussian. Remove rows 80 and 19 (1 species with abundance of 5 and 2 species with abundance of 38, respectively). Fit fixed and mixed models.
Mixed model converged:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( inverse )
## Formula: gma ~ settlements * year_ct + cos_td_rad + sin_td_rad + (1 |
## site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 316.0 339.9 -150.0 300.0 140
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5813 -0.7148 -0.1712 0.6185 3.5987
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 2.729e-07 0.0005224
## Residual 9.584e-02 0.3095743
## Number of obs: 148, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 0.507804 0.093741 5.417 6.06e-08 ***
## settlementsNear -0.047911 0.029549 -1.621 0.1049
## year_ct 0.036045 0.006260 5.758 8.52e-09 ***
## cos_td_rad -0.155633 0.074803 -2.081 0.0375 *
## sin_td_rad 0.087974 0.069988 1.257 0.2088
## settlementsNear:year_ct -0.011281 0.007612 -1.482 0.1384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct cs_td_ sn_td_
## settlmntsNr -0.213
## year_ct -0.269 0.523
## cos_td_rad -0.939 0.025 0.179
## sin_td_rad 0.877 -0.027 0.033 -0.770
## sttlmntsN:_ 0.139 -0.717 -0.738 -0.004 0.003center time of year (cosine and sine) variables - highly correlated with intercept
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( inverse )
## Formula: gma ~ settlements * year_ct + cos_td_rad_c + sin_td_rad_c + (1 |
## site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 316.0 339.9 -150.0 300.0 140
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5813 -0.7148 -0.1712 0.6185 3.5987
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 2.679e-07 0.0005175
## Residual 9.584e-02 0.3095743
## Number of obs: 148, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 0.352631 0.025026 14.090 < 2e-16 ***
## settlementsNear -0.047912 0.029549 -1.621 0.1049
## year_ct 0.036045 0.006260 5.758 8.53e-09 ***
## cos_td_rad_c -0.155633 0.074815 -2.080 0.0375 *
## sin_td_rad_c 0.087974 0.070029 1.256 0.2090
## settlementsNear:year_ct -0.011281 0.007612 -1.482 0.1384
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct cs_t__ sn_t__
## settlmntsNr -0.702
## year_ct -0.746 0.523
## cos_td_rd_c -0.216 0.025 0.179
## sin_td_rd_c -0.045 -0.028 0.033 -0.770
## sttlmntsN:_ 0.508 -0.717 -0.738 -0.004 0.003perform stepwise model selection of Gaussian model.
## boundary (singular) fit: see help('isSingular')## Single term deletions
##
## Model:
## gma ~ settlements * year_ct + cos_td_rad_c + sin_td_rad_c + (1 |
## site)
## npar AIC
## <none> 315.97
## cos_td_rad_c 1 318.34
## sin_td_rad_c 1 315.55
## settlements:year_ct 1 316.15drop settlements * year.
## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## Single term deletions
##
## Model:
## gma ~ settlements + year_ct + cos_td_rad_c + sin_td_rad_c + (1 |
## site)
## npar AIC
## <none> 316.15
## settlements 1 328.41
## year_ct 1 349.53
## cos_td_rad_c 1 318.46
## sin_td_rad_c 1 315.72drop sine of time of year
## Single term deletions
##
## Model:
## gma ~ settlements + year_ct + cos_td_rad_c + (1 | site)
## npar AIC
## <none> 315.72
## settlements 1 327.61
## year_ct 1 347.79
## cos_td_rad_c 1 316.81drop cosine because \(\Delta AIC<2\).
## Single term deletions
##
## Model:
## gma ~ settlements + year_ct + (1 | site)
## npar AIC
## <none> 316.81
## settlements 1 328.23
## year_ct 1 373.72Settlements and year remain. This is the final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( inverse )
## Formula: gma ~ settlements + year_ct + (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 316.8 331.8 -153.4 306.8 143
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4865 -0.7138 -0.1938 0.6896 3.8425
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0001207 0.01099
## Residual 0.1000007 0.31623
## Number of obs: 148, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 0.356323 0.020849 17.090 < 2e-16 ***
## settlementsNear -0.078152 0.021059 -3.711 0.000206 ***
## year_ct 0.032875 0.003891 8.449 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN
## settlmntsNr -0.640
## year_ct -0.536 0.054## $site
## (Intercept)
## Beit Yatir 0.0015694547
## Dvir 0.0016137637
## Har Amasa 0.0028023650
## Lahav -0.0060807603
## Lehavim 0.0021981925
## Meitar 0.0001680174
## Mirsham -0.0028066602
##
## with conditional variances for "site"## $site
| Observations | 148 |
| Dependent variable | gma |
| Type | Mixed effects generalized linear model |
| Family | Gamma |
| Link | inverse |
| AIC | 316.807 |
| BIC | 331.793 |
| Pseudo-R² (fixed effects) | NA |
| Pseudo-R² (total) | NA |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 0.356 | 0.021 | 17.090 | 0.000 |
| settlementsNear | -0.078 | 0.021 | -3.711 | 0.000 |
| year_ct | 0.033 | 0.004 | 8.449 | 0.000 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| site | (Intercept) | 0.011 |
| Residual | 0.316 |
| Group | # groups | ICC |
|---|---|---|
| site | 7 | 0.001 |
## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.
## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.## 1 2
## 2.048067 2.438350## [1] 19.0562## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.
## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.## 1 2 3 4 5 6 7 8
## 2.806445 2.785677 2.765214 2.745049 2.725176 2.705589 2.686281 2.667247
## 9 10 11 12 13 14 15 16
## 2.648481 2.629977 2.611730 2.593734 2.575985 2.558477 2.541205 2.524165
## 17 18 19 20 21 22 23 24
## 2.507352 2.490761 2.474389 2.458230 2.442281 2.426537 2.410996 2.395652
## 25 26 27 28 29 30 31 32
## 2.380502 2.365543 2.350770 2.336181 2.321772 2.307539 2.293480 2.279591
## 33 34 35 36 37 38 39 40
## 2.265869 2.252312 2.238916 2.225678 2.212596 2.199666 2.186887 2.174256
## 41 42 43 44 45 46 47 48
## 2.161769 2.149426 2.137222 2.125156 2.113226 2.101429 2.089763 2.078225
## 49 50 51 52 53 54 55 56
## 2.066815 2.055529 2.044365 2.033323 2.022398 2.011591 2.000898 1.990319
## 57 58 59 60 61 62 63 64
## 1.979851 1.969492 1.959241 1.949097 1.939057 1.929119 1.919283 1.909547
## 65 66 67 68 69 70 71 72
## 1.899910 1.890369 1.880923 1.871571 1.862312 1.853144 1.844066 1.835076
## 73 74 75 76 77 78 79 80
## 1.826173 1.817357 1.808625 1.799977 1.791411 1.782926 1.774521 1.766195
## 81 82 83 84 85 86 87 88
## 1.757947 1.749775 1.741679 1.733658 1.725710 1.717835 1.710031 1.702298
## 89 90 91 92 93 94 95 96
## 1.694635 1.687040 1.679513 1.672053 1.664658 1.657329 1.650065 1.642863
## 97 98 99 100
## 1.635724 1.628647 1.621631 1.614675## [1] 42.46546## [1] 0.4246547## [1] 1.738087Very significant temporal decrease in gma as well as lower gma far from settlements.
Near plots have on average 0.3902837 more individuals per species than far plots, which is 19.0561994 percent higher. GMA is decreasing on average in 0.1489713 individuals per species per year.
abundance
Explore data
## [1] "ABUNDANCE WITHOUT RARE SPECIES"
| abundance | year_ct | site | settlements | td_sc | cos_td_rad | sin_td_rad | |
|---|---|---|---|---|---|---|---|
| Min. : 2.00 | Min. :0.000 | Lahav :31 | Far :75 | Min. :-2.06451 | Min. :-0.2009 | Min. :-0.99677 | |
| 1st Qu.: 7.25 | 1st Qu.:2.000 | Lehavim :30 | Near:75 | 1st Qu.:-0.41912 | 1st Qu.: 0.5142 | 1st Qu.:-0.85766 | |
| Median :16.00 | Median :4.000 | Har Amasa :29 | NA | Median : 0.07688 | Median : 0.6917 | Median :-0.72216 | |
| Mean :21.25 | Mean :3.987 | Beit Yatir:24 | NA | Mean :-0.03383 | Mean : 0.6007 | Mean :-0.69390 | |
| 3rd Qu.:30.00 | 3rd Qu.:6.000 | Mirsham :24 | NA | 3rd Qu.: 0.22473 | 3rd Qu.: 0.7383 | 3rd Qu.:-0.67444 | |
| Max. :93.00 | Max. :8.000 | Dvir : 6 | NA | Max. : 1.90350 | Max. : 1.0000 | Max. : 0.01721 | |
| NA | NA | (Other) : 6 | NA | NA | NA | NA |
Outlier with total abundance >80. Examine:
## unit subunit site year year_ct
## 1: Mediterranean-Desert Transition Zone <NA> Lehavim 2012 0
## settlements agriculture habitat dunes land_use point_name date
## 1: Near <NA> <NA> <NA> <NA> Lehavim Near 4 2012-04-25
## datetime td_sc td_rad cos_td_rad sin_td_rad timediff_Jun21
## 1: <NA> -0.3094269 -0.9812098 0.5560174 -0.8311706 -57 days
## monitors_name wind precipitation temperature clouds h_from_sunrise cos_hsun
## 1: <NA> <NA> <NA> <NA> <NA> NA hours NA
## sin_hsun pilot richness abundance gma
## 1: NA TRUE 10 93 4.8029Decided to keep point with abundance of 93 (Lehavim Near 4 from 2012). Nearest total abundances are 72 and 69.
PHI>1, hence choose negative binomial. Fit fixed and mixed models. Choose mixed model if possible, otherwise choose a model with fixed-effects only.
Mixed model converged:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(4.8393) ( log )
## Formula: abundance ~ settlements * year_ct + cos_td_rad + sin_td_rad +
## (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 1077.3 1101.4 -530.7 1061.3 142
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6391 -0.7596 -0.1664 0.7179 3.4342
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 4.075e-05 0.006383
## Number of obs: 150, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.67793 0.35924 4.671 3.00e-06 ***
## settlementsNear 0.84785 0.14199 5.971 2.36e-09 ***
## year_ct -0.12846 0.02475 -5.190 2.10e-07 ***
## cos_td_rad 0.93204 0.26342 3.538 0.000403 ***
## sin_td_rad -0.77776 0.30449 -2.554 0.010641 *
## settlementsNear:year_ct 0.08170 0.02994 2.729 0.006352 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct cs_td_ sn_td_
## settlmntsNr -0.165
## year_ct -0.229 0.585
## cos_td_rad -0.917 -0.037 0.098
## sin_td_rad 0.879 0.089 0.120 -0.774
## sttlmntsN:_ 0.153 -0.789 -0.745 0.024 -0.039center time of year (cosine and sine) variables - highly correlated with intercept
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(4.8393) ( log )
## Formula: abundance ~ settlements * year_ct + cos_td_rad_c + sin_td_rad_c +
## (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 1077.3 1101.4 -530.7 1061.3 142
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6391 -0.7596 -0.1664 0.7179 3.4342
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 4.081e-05 0.006388
## Number of obs: 150, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.77750 0.11360 24.449 < 2e-16 ***
## settlementsNear 0.84785 0.14199 5.971 2.36e-09 ***
## year_ct -0.12846 0.02475 -5.190 2.10e-07 ***
## cos_td_rad_c 0.93204 0.26341 3.538 0.000403 ***
## sin_td_rad_c -0.77776 0.30447 -2.554 0.010636 *
## settlementsNear:year_ct 0.08170 0.02994 2.729 0.006352 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct cs_t__ sn_t__
## settlmntsNr -0.737
## year_ct -0.810 0.585
## cos_td_rd_c -0.066 -0.037 0.098
## sin_td_rd_c -0.160 0.089 0.120 -0.774
## sttlmntsN:_ 0.591 -0.789 -0.745 0.024 -0.039Perform stepwise model selection of mixed model.
## boundary (singular) fit: see help('isSingular')## Single term deletions
##
## Model:
## abundance ~ settlements * year_ct + cos_td_rad_c + sin_td_rad_c +
## (1 | site)
## npar AIC
## <none> 1077.3
## cos_td_rad_c 1 1086.4
## sin_td_rad_c 1 1081.6
## settlements:year_ct 1 1082.8all terms remain. The final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(4.8393) ( log )
## Formula: abundance ~ settlements * year_ct + cos_td_rad_c + sin_td_rad_c +
## (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 1077.3 1101.4 -530.7 1061.3 142
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6391 -0.7596 -0.1664 0.7179 3.4342
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 4.081e-05 0.006388
## Number of obs: 150, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.77750 0.11360 24.449 < 2e-16 ***
## settlementsNear 0.84785 0.14199 5.971 2.36e-09 ***
## year_ct -0.12846 0.02475 -5.190 2.10e-07 ***
## cos_td_rad_c 0.93204 0.26341 3.538 0.000403 ***
## sin_td_rad_c -0.77776 0.30447 -2.554 0.010636 *
## settlementsNear:year_ct 0.08170 0.02994 2.729 0.006352 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct cs_t__ sn_t__
## settlmntsNr -0.737
## year_ct -0.810 0.585
## cos_td_rd_c -0.066 -0.037 0.098
## sin_td_rd_c -0.160 0.089 0.120 -0.774
## sttlmntsN:_ 0.591 -0.789 -0.745 0.024 -0.039## $site
## (Intercept)
## Beit Yatir -3.549638e-04
## Dvir 1.980054e-05
## Har Amasa -4.699015e-04
## Lahav -4.879645e-05
## Lehavim 2.869112e-04
## Meitar -1.190116e-04
## Mirsham 6.860578e-04
##
## with conditional variances for "site"## $site
Interpretation of abundance model:
| Observations | 150 |
| Dependent variable | abundance |
| Type | Mixed effects generalized linear model |
| Family | Negative Binomial(4.8393) |
| Link | log |
| AIC | 1077.349 |
| BIC | 1101.434 |
| Pseudo-R² (fixed effects) | 0.910 |
| Pseudo-R² (total) | 0.910 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 2.777 | 0.114 | 24.449 | 0.000 |
| settlementsNear | 0.848 | 0.142 | 5.971 | 0.000 |
| year_ct | -0.128 | 0.025 | -5.190 | 0.000 |
| cos_td_rad_c | 0.932 | 0.263 | 3.538 | 0.000 |
| sin_td_rad_c | -0.778 | 0.304 | -2.554 | 0.011 |
| settlementsNear:year_ct | 0.082 | 0.030 | 2.729 | 0.006 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| site | (Intercept) | 0.006 |
| Group | # groups | ICC |
|---|---|---|
| site | 7 | 0.000 |
## [1] 31.2092## [1] 0.3120919## [1] 64.21687## [1] 0.6421687
## settlements year_ct.trend SE df asymp.LCL asymp.UCL
## Far -0.1285 0.0248 Inf -0.1770 -0.07995
## Near -0.0468 0.0201 Inf -0.0862 -0.00733
##
## Confidence level used: 0.95## settlements year_ct.trend SE df z.ratio p.value
## Far -0.1285 0.0248 Inf -5.190 <.0001
## Near -0.0468 0.0201 Inf -2.324 0.0201
##
## P value adjustment: fdr method for 2 tests## settlements year_ct emmean SE df asymp.LCL asymp.UCL
## Far 3.99 2.27 0.0669 Inf 2.13 2.40
## Near 3.99 3.44 0.0567 Inf 3.33 3.55
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95## contrast year_ct estimate SE df z.ratio p.value
## Far - Near 3.99 -1.17 0.0875 Inf -13.405 <.0001
##
## Results are given on the log (not the response) scale.Significant higher abundance near settlements, and stronger relative decrease in total abundance far from settlements:
On average, a near plot has 20.763981 more individuals than a far plot, which is 67.4427346 percent higher.
The average rate of decrease in abundance far from settlements is NEARLY TRIPLE the rate near settlements, 12.0552769 vs. 4.5686008, respectively.
community analysis using package MVabund
## Overlapping points were shifted along the y-axis to make them visible.##
## PIPING TO 2nd MVFACTOR## Only the variables Passer.domesticus, Galerida.cristata, Columba.livia, Streptopelia.decaocto, Prinia.gracilis, Chloris.chloris, Pycnonotus.xanthopygos, Spilopelia.senegalensis, Corvus.monedula, Curruca.conspicillata, Cecropis.daurica, Alectoris.chukar were included in the plot
## (the variables with highest total abundance).
start model specification:
## nb po
## 246.6567 302.9650## [1] "POISSON"
## [1] "NEGATIVE BINOMIAL"
negative binomial model is better than poisson according to residuals and AIC comparison.
## nb po nb1
## 246.6567 302.9650 239.5285The addition of the explanatory variable ‘site’ is improving the AIC of the model. stepwise selection of model:
## Single term deletions
##
## Model:
## spp_no_rare ~ settlements * year_ct + cos_td_rad + sin_td_rad +
## site
## Df AIC
## <none> 6706.8
## cos_td_rad 28 6687.6
## sin_td_rad 28 6701.8
## site 168 6906.4
## settlements:year_ct 28 6699.4drop cosine.
## Single term deletions
##
## Model:
## spp_no_rare ~ settlements * year_ct + sin_td_rad + site
## Df AIC
## <none> 6687.6
## sin_td_rad 28 6692.8
## site 168 6912.9
## settlements:year_ct 28 6681.4drop interaction of settlements with year.
## Single term deletions
##
## Model:
## spp_no_rare ~ settlements + year_ct + sin_td_rad + site
## Df AIC
## <none> 6681.4
## settlements 28 7535.2
## year_ct 28 6720.6
## sin_td_rad 28 6688.4
## site 168 6895.1final model includes settlements, year, sampling time of year and site.
##
## Test statistics:
## wald value Pr(>wald)
## (Intercept) 12.267 0.001 ***
## settlementsNear 23.289 0.001 ***
## year_ct 10.018 0.001 ***
## sin_td_rad 8.176 0.004 **
## siteDvir 7.240 0.001 ***
## siteHar Amasa 7.041 0.004 **
## siteLahav 8.997 0.001 ***
## siteLehavim 8.065 0.001 ***
## siteMeitar 6.484 0.001 ***
## siteMirsham 9.693 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Test statistic: 32.95, 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 ~ settlements + year_ct + sin_td_rad + site
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 149
## settlements 148 1 757.8 0.01 **
## year_ct 147 1 106.8 0.01 **
## sin_td_rad 146 1 52.4 0.04 *
## site 140 6 549.8 0.01 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Univariate Tests:
## Acridotheres.tristis Alectoris.chukar
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 47.832 0.01 2.828 0.39
## year_ct 19.477 0.01 1.321 0.97
## sin_td_rad 2.853 0.92 3.177 0.87
## site 18.048 0.15 8.687 0.91
## Anthus.similis Burhinus.oedicnemus
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 22.266 0.01 0 0.94
## year_ct 0.023 1.00 0.071 1.00
## sin_td_rad 0.014 1.00 1.984 0.98
## site 8.018 0.91 7.761 0.91
## Cecropis.daurica Chloris.chloris Cinnyris.osea
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 32.021 0.01 46.076 0.01 41.406
## year_ct 0.056 1.00 1.089 0.98 8.852
## sin_td_rad 0.415 1.00 1.543 1.00 6.317
## site 27.913 0.01 16.681 0.16 18.063
## Columba.livia Corvus.cornix
## Pr(>Dev) Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 0.01 53 0.01 50.914 0.01
## year_ct 0.10 0.485 1.00 1.249 0.98
## sin_td_rad 0.37 0.265 1.00 0.961 1.00
## site 0.15 15.727 0.25 35.085 0.01
## Corvus.monedula Curruca.conspicillata
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 9.184 0.04 57.418 0.01
## year_ct 1.44 0.97 4.035 0.63
## sin_td_rad 0.27 1.00 0.912 1.00
## site 44.159 0.01 3.5 0.99
## Curruca.melanocephala Emberiza.calandra
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 5.2 0.15 0.961 0.69
## year_ct 3.848 0.66 0.711 1.00
## sin_td_rad 0.612 1.00 0.924 1.00
## site 54.683 0.01 24.943 0.03
## Falco.tinnunculus Galerida.cristata
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 1.443 0.65 37.532 0.01
## year_ct 0.071 1.00 16.713 0.01
## sin_td_rad 0.544 1.00 1.284 1.00
## site 4.054 0.99 35.053 0.01
## Garrulus.glandarius Lanius.senator
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 27.928 0.01 7.661 0.06
## year_ct 6.706 0.23 2.843 0.84
## sin_td_rad 0.47 1.00 3.753 0.80
## site 9.637 0.91 3.202 0.99
## Oenanthe.melanoleuca Parus.major
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 2.775 0.39 22.544 0.01
## year_ct 0.302 1.00 0.071 1.00
## sin_td_rad 0.954 1.00 0.624 1.00
## site 29.726 0.01 24.605 0.03
## Passer.domesticus Prinia.gracilis
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 34.617 0.01 3.78 0.23
## year_ct 12.018 0.03 1.108 0.98
## sin_td_rad 4.031 0.80 1.665 1.00
## site 23.604 0.06 22.037 0.06
## Pycnonotus.xanthopygos Spilopelia.senegalensis
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 24.463 0.01 68.189 0.01
## year_ct 4.364 0.63 10.358 0.03
## sin_td_rad 0.165 1.00 7.402 0.26
## site 22.544 0.06 11.889 0.67
## Streptopelia.decaocto Streptopelia.turtur
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 81.258 0.01 17.901 0.01
## year_ct 0.33 1.00 5.018 0.46
## sin_td_rad 0.105 1.00 2.344 0.96
## site 9.569 0.91 26.877 0.02
## Turdus.merula Upupa.epops Vanellus.spinosus
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 41.91 0.01 0.355 0.76 16.373
## year_ct 3.432 0.76 0.506 1.00 0.33
## sin_td_rad 6.131 0.39 2.566 0.93 0.163
## site 15.523 0.25 5.663 0.95 22.515
##
## Pr(>Dev)
## (Intercept)
## settlements 0.01
## year_ct 1.00
## sin_td_rad 1.00
## site 0.06
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 99 iterations via PIT-trap resampling.All factors (settlements, year, time of year and site) have a statistically significant effect on community composition.
## Loading required package: ggnewscale## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## [1] "black arrow is median of synanthrope/invasive\ngreen arrow is median of batha specialists\nred arrow is median of endangered\n\n"
## [1] "black arrow is median of synanthrope/invasive\ngreen arrow is median of batha specialists\nred arrow is median of endangered\n\n"
Significant effect of settlement proximity: synanthrope / invasive species near settlements, endangered and / or batha specialists far from settlements.
Synanthrope local species decreasing while synanthrope invasive species increasing
Significant negative temporal effect on crested lark, house sparrow and laughing dove, and positive effect on common myna.
## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -3.19814420 2.89705871 -0.01238059 0.01275034 -0.86596715
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 1.04169767 1.11930105 1.41581895 0.86058066 -0.79193622## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -3.19813999 2.89705635 -0.01238155 0.01274407 -0.86597053
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 1.04169668 1.11929858 1.41581039 0.86057512 -0.79193710## [1] 0.03852899 0.69817330## [1] 1712.073
## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## 1.25496613 -1.19594013 -0.06850155 0.45136555 1.52709323
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 0.31063784 0.26250633 0.89269549 0.72997866 0.80682753## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## 1.25496619 -1.19593983 -0.06850164 0.45136495 1.52709237
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 0.31063778 0.26250653 0.89269494 0.72997832 0.80682702## [1] 2.564497 2.550341 2.536263 2.522262 2.508338 2.494492 2.480722 2.467028
## [9] 2.453409 2.439866 2.426398 2.413003 2.399683 2.386436 2.373263 2.360162
## [17] 2.347133 2.334177 2.321292 2.308478 2.295734 2.283062 2.270459 2.257925
## [25] 2.245461 2.233066 2.220739 2.208480 2.196289 2.184165 2.172108 2.160117
## [33] 2.148193 2.136334 2.124541 2.112814 2.101150 2.089552 2.078017 2.066546
## [41] 2.055138 2.043793 2.032511 2.021291 2.010133 1.999037 1.988002 1.977028
## [49] 1.966114 1.955261 1.944468 1.933734 1.923059 1.912444 1.901886 1.891388
## [57] 1.880947 1.870564 1.860238 1.849969 1.839757 1.829601 1.819501 1.809457
## [65] 1.799469 1.789535 1.779657 1.769833 1.760063 1.750347 1.740685 1.731076
## [73] 1.721520 1.712017 1.702566 1.693168 1.683821 1.674526 1.665282 1.656090
## [81] 1.646948 1.637856 1.628815 1.619823 1.610882 1.601989 1.593146 1.584352
## [89] 1.575606 1.566908 1.558258 1.549656 1.541102 1.532595 1.524135 1.515721
## [97] 1.507354 1.499033 1.490758 1.482529## [1] 42.19027## [1] 0.4219026## [1] 1.9516386 0.5902138## [1] 230.6664## [1] 2.306664## [1] 3.306664
## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -14.4769942 14.5363087 -0.2637631 -1.9187965 -11.9872840
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 0.2471728 -0.5600265 -0.5835720 0.1596259 -0.1256885## (Intercept) year_ct sin_td_rad siteDvir siteHar Amasa
## 0.05931718 -0.26376383 -1.91879680 -37.62480300 0.24717515
## siteLahav siteLehavim siteMeitar siteMirsham
## -0.56002776 -0.58357131 0.15962450 -0.12569286## [1] 3.9831056 3.8991070 3.8168799 3.7363868 3.6575913 3.5804574 3.5049502
## [8] 3.4310353 3.3586792 3.2878490 3.2185126 3.1506383 3.0841954 3.0191537
## [15] 2.9554837 2.8931564 2.8321434 2.7724172 2.7139505 2.6567168 2.6006901
## [22] 2.5458449 2.4921563 2.4396000 2.3881520 2.3377889 2.2884880 2.2402268
## [29] 2.1929833 2.1467361 2.1014642 2.0571470 2.0137645 1.9712968 1.9297247
## [36] 1.8890293 1.8491921 1.8101951 1.7720204 1.7346508 1.6980692 1.6622591
## [43] 1.6272043 1.5928886 1.5592967 1.5264131 1.4942230 1.4627118 1.4318651
## [50] 1.4016689 1.3721095 1.3431735 1.3148477 1.2871193 1.2599756 1.2334044
## [57] 1.2073935 1.1819311 1.1570057 1.1326060 1.1087208 1.0853393 1.0624509
## [64] 1.0400452 1.0181120 0.9966413 0.9756234 0.9550488 0.9349080 0.9151920
## [71] 0.8958918 0.8769986 0.8585039 0.8403991 0.8226762 0.8053270 0.7883437
## [78] 0.7717186 0.7554440 0.7395127 0.7239173 0.7086508 0.6937063 0.6790769
## [85] 0.6647561 0.6507372 0.6370140 0.6235802 0.6104297 0.5975566 0.5849549
## [92] 0.5726189 0.5605431 0.5487220 0.5371502 0.5258224 0.5147335 0.5038784
## [99] 0.4932523 0.4828502## [1] 87.87754## [1] 0.9482312## [1] 19.31665
## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -1.1106547 3.3180220 -0.3217530 -2.3706172 0.1593572
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -0.8070834 -1.1022411 -0.1485289 -1.7699350 1.1851742## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -1.1105960 3.3179648 -0.3217529 -2.3706068 0.1593137
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -0.8070914 -1.1022516 -0.1485468 -1.7699354 1.1851270## [1] 1.7063833 1.6625887 1.6199181 1.5783427 1.5378343 1.4983656 1.4599098
## [8] 1.4224411 1.3859339 1.3503637 1.3157065 1.2819387 1.2490375 1.2169808
## [15] 1.1857468 1.1553145 1.1256632 1.0967729 1.0686240 1.0411977 1.0144752
## [22] 0.9884385 0.9630701 0.9383528 0.9142698 0.8908050 0.8679423 0.8456665
## [29] 0.8239623 0.8028152 0.7822109 0.7621353 0.7425750 0.7235167 0.7049475
## [36] 0.6868550 0.6692267 0.6520509 0.6353160 0.6190105 0.6031235 0.5876443
## [43] 0.5725623 0.5578674 0.5435497 0.5295994 0.5160071 0.5027637 0.4898602
## [50] 0.4772879 0.4650382 0.4531030 0.4414740 0.4301435 0.4191038 0.4083475
## [57] 0.3978672 0.3876559 0.3777066 0.3680127 0.3585676 0.3493649 0.3403984
## [64] 0.3316621 0.3231499 0.3148562 0.3067754 0.2989020 0.2912306 0.2837561
## [71] 0.2764735 0.2693778 0.2624641 0.2557280 0.2491647 0.2427698 0.2365391
## [78] 0.2304683 0.2245533 0.2187901 0.2131748 0.2077037 0.2023729 0.1971790
## [85] 0.1921184 0.1871876 0.1823834 0.1777025 0.1731418 0.1686981 0.1643684
## [92] 0.1601499 0.1560396 0.1520348 0.1481328 0.1443310 0.1406267 0.1370175
## [99] 0.1335009 0.1300746## [1] 92.37718## [1] 0.9673434## [1] 30.6217## [1] 0.4731487 13.0608517## [1] 2660.411## [1] 26.60412## [1] 27.60412
## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -15.60884159 13.74308689 0.30200051 -0.41611381 -9.72099927
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 0.05107192 -0.10752380 0.75720550 -9.92337525 -1.98612180## (Intercept) year_ct sin_td_rad siteDvir siteHar Amasa
## -1.86575223 0.30200122 -0.41610429 -35.29106415 0.05107478
## siteLahav siteLehavim siteMeitar siteMirsham
## -0.10751983 0.75720860 -35.49343545 -1.98612734## [1] 0.2062006 0.2112946 0.2165145 0.2218634 0.2273444 0.2329608 0.2387159
## [8] 0.2446132 0.2506562 0.2568485 0.2631938 0.2696959 0.2763585 0.2831858
## [15] 0.2901817 0.2973504 0.3046963 0.3122236 0.3199369 0.3278407 0.3359398
## [22] 0.3442390 0.3527432 0.3614575 0.3703871 0.3795373 0.3889135 0.3985213
## [29] 0.4083666 0.4184550 0.4287927 0.4393857 0.4502404 0.4613633 0.4727610
## [36] 0.4844403 0.4964081 0.5086715 0.5212379 0.5341148 0.5473097 0.5608307
## [43] 0.5746856 0.5888829 0.6034308 0.6183382 0.6336139 0.6492669 0.6653066
## [50] 0.6817426 0.6985846 0.7158427 0.7335271 0.7516484 0.7702174 0.7892451
## [57] 0.8087429 0.8287224 0.8491954 0.8701743 0.8916714 0.9136995 0.9362719
## [64] 0.9594019 0.9831033 1.0073902 1.0322772 1.0577789 1.0839107 1.1106880
## [71] 1.1381268 1.1662435 1.1950548 1.2245778 1.2548303 1.2858300 1.3175956
## [78] 1.3501460 1.3835005 1.4176790 1.4527018 1.4885899 1.5253645 1.5630477
## [85] 1.6016618 1.6412298 1.6817753 1.7233225 1.7658961 1.8095214 1.8542245
## [92] 1.9000319 1.9469709 1.9950696 2.0443565 2.0948610 2.1466132 2.1996439
## [99] 2.2539847 2.3096680## [1] 1020.107## [1] 10.20107## [1] 11.20107
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -14.76379810 13.87675561 -0.14841723 -0.39927299 -0.32015486
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -0.79522939 0.40930269 1.12953042 1.97056489 -0.09860667## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -1.1105960 3.3179648 -0.3217529 -2.3706068 0.1593137
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -0.8070914 -1.1022516 -0.1485468 -1.7699354 1.1851270## [1] 3.526405e-10 3.006890e-01
## Warning: glm.fit: fitted rates numerically 0 occurred## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -15.94540157 13.78748956 -0.04443269 0.20409935 2.50109902
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 1.89133568 3.30658511 2.77896378 -9.15701943 0.03772084## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -31.99689451 29.83896482 -0.04443280 0.20409925 2.50111822
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 1.89135476 3.30660493 2.77898337 -26.25815950 0.03772092
## Warning: glm.fit: fitted rates numerically 0 occurred## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -13.5890463 12.5621167 -0.1610875 -1.6624329 -11.0075650
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -0.2108969 0.1650222 -0.3427736 0.4594397 -11.0038973## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -34.0300817 33.0031558 -0.1610883 -1.6624397 -31.6652555
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -0.2108988 0.1650199 -0.3427768 0.4594354 -31.6412082## [1] 2.773366e-15 5.971669e-01
## Warning: glm.fit: fitted rates numerically 0 occurred## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -12.9694905 12.5002301 -0.1172293 0.6727212 0.2467421
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 0.9006157 0.3756373 0.1239605 1.4902069 -10.0815474## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -34.0677475 33.5984927 -0.1172305 0.6727187 0.2467410
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 0.9006205 0.3756369 0.1239606 1.4902069 -31.1638780
## Warning: glm.fit: fitted rates numerically 0 occurred## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -14.17505368 11.94790980 0.00412469 0.52734606 -9.27293690
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 2.66224692 1.77128672 -0.06080704 -9.01646338 -9.06083834## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -34.543926351 32.316787345 0.004124596 0.527345614 -30.016785218
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 2.662253454 1.771289221 -0.060809508 -29.760311918 -29.803774313## Warning: glm.fit: fitted rates numerically 0 occurred## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -13.51343635 11.65320800 0.07711059 1.92092987 2.67028539
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -8.98545269 3.40990316 1.43542922 -8.35716228 -8.84604330## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -34.55055160 32.69039054 0.07711744 1.92110268 2.67030268
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -30.02087486 3.40997492 1.43547703 -29.38222633 -29.87254372## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -2.09997307 3.50772911 -0.05143191 -0.58938946 1.53267517
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -1.89626110 -0.28967466 -0.48571467 -0.14901045 0.03804001## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -2.09994928 3.50770694 -0.05143300 -0.58939924 1.53263442
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -1.89626759 -0.28967817 -0.48572193 -0.14901898 0.03800344## [1] 0.1501667 5.0113110## [1] 3237.166## [1] 32.37165## [1] 33.37165## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -4.4595392 3.5917328 0.1934950 3.1739652 -11.4255596
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -0.8416089 2.5675920 3.1185990 4.7933308 2.4047970## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -4.4595422 3.5917351 0.1934950 3.1739715 -33.5063724
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -0.8416025 2.5675993 3.1186032 4.7933351 2.4048022## [1] 0.002765395 0.100375562## [1] 3529.7## [1] 35.29701## [1] 36.29701## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -1.79107320 2.59560051 0.01453756 0.47999242 0.46997224
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 0.57360737 0.96158393 0.32282073 0.99068065 0.68508944## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -1.79107286 2.59559857 0.01453838 0.47999454 0.46997713
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 0.57360973 0.96158533 0.32281947 0.99068714 0.68507610## [1] 0.1266684 1.6979397## [1] 1240.461## [1] 12.4046## [1] 13.4046## Warning in glm.nb(formula = Pycnonotus.xanthopygos ~ settlements + year_ct + :
## alternation limit reached## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -1.6449156 1.9807003 -0.1179208 1.1895879 -0.2191414
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 0.6489535 1.2004035 1.1630775 1.2593246 2.4113083## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -1.6449036 1.9806887 -0.1179209 1.1895865 -0.2191424
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 0.6489537 1.2004003 1.1630763 1.2593237 2.4112913## [1] 0.05284072 0.38297545## [1] 624.7733## [1] 6.247733## [1] 7.247733## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -3.9724453 1.8103955 -0.1390345 -2.4669111 3.4137131
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -1.9043463 1.7773560 -2.3646211 -13.6189360 2.0544543## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -3.9724313 1.8103890 -0.1390337 -2.4668935 3.4137032
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -1.9043429 1.7773578 -2.3646162 -35.7603308 2.0544496## [1] 0.05990911 0.36621389## [1] 511.2825## [1] 5.112825## [1] 6.112825## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -3.2927856 1.7980640 -0.1396831 1.2142572 1.2297556
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 2.8016678 2.7452205 0.1265903 1.8203068 3.0852608## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -3.2927874 1.7980643 -0.1396830 1.2142553 1.2297571
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 2.8016692 2.7452205 0.1265897 1.8203073 3.0852589## [1] 0.009166199 0.055345034## [1] 503.7948## [1] 5.037948## [1] 6.037948## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -0.99236961 -1.68347873 -0.04400099 1.90120228 1.31219156
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 0.85006123 0.34319109 0.52868215 1.15565273 1.28393220## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -0.99236961 -1.68347889 -0.04400106 1.90120201 1.31219205
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## 0.85006099 0.34319143 0.52868212 1.15565275 1.28393261## [1] 0.08315593 0.01544428## [1] 438.4255## [1] 4.384254## [1] 5.384254## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## 1.04469141 -2.93776365 -0.10129974 0.54394420 -0.46908933
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -0.25043287 0.17860903 -0.02802894 -1.10254931 -0.18544890## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## 1.04469107 -2.93776395 -0.10129986 0.54394448 -0.46908844
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -0.25043130 0.17861053 -0.02802755 -1.10254854 -0.18544866## [1] 1.30134374 0.06895049## [1] 1787.36## [1] 17.8736## [1] 18.8736## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -0.69745551 -3.03356413 -0.08288675 -0.66836457 -12.84389449
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -0.57490952 -1.57163622 -0.52021075 -13.16895203 -0.81746168## (Intercept) settlementsNear year_ct sin_td_rad siteDvir
## -0.69748361 -3.03354620 -0.08288128 -0.66822779 -18.62332362
## siteHar Amasa siteLahav siteLehavim siteMeitar siteMirsham
## -0.57479278 -1.57156237 -0.52007543 -18.94831464 -0.81731126## [1] 0.56880471 0.02738488## [1] 1977.076## [1] 19.77076## [1] 20.77076Session 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 2024-04-10
## 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)
## backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0)
## 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)
## broom 1.0.4 2023-03-11 [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)
## 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)
## 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)
## ggnewscale * 0.4.9 2023-05-25 [1] CRAN (R 4.2.3)
## ggplot2 * 3.5.0 2024-02-23 [1] CRAN (R 4.2.3)
## ggrepel * 0.9.3 2023-02-03 [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)
## 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)
## lubridate * 1.9.2 2023-02-10 [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)
## pbkrtest 0.5.2 2023-01-19 [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)
## solartime * 0.0.2 2021-04-22 [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)
## tidyr 1.3.0 2023-01-24 [1] CRAN (R 4.2.3)
## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.3)
## timechange 0.2.0 2023-01-11 [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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