Birds_Arid_South_analysis_by_plot
Ron Chen
2024-05-19
## 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.Arid South
Factors are proximity to agriculture and time.
5 sites with 6 plots per site (3 for each proximity).
Total 5 campaigns, one of which is pilot in the year 2012. Two of the sites - Samar and Yahel, were sampled only in 2012 and then replaced with Zofar and Lotan.
Raw data Total abundance: 4987 Number of observations: 1512 Total richness: 83
Filtered data Total abundance: 2230 Number of observations: 793 Total richness: 35
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.
## [1] "RICHNESS WITH RARE SPECIES"
| richness | year_ct | site | agriculture | td_sc | cos_td_rad | sin_td_rad | h_from_sunrise | cos_hsun | sin_hsun | monitors_name | wind | precipitation | temperature | clouds | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Min. : 1.000 | Min. :0 | Ein Yahav:30 | Far :75 | Min. :-1.8356 | Min. :-0.09882 | Min. :-0.9951 | Length:150 | Min. :0.4281 | Min. :-0.2765 | Eyal Shochat : 30 | 0 : 0 | 0 : 0 | 0 : 0 | 0 : 0 | |
| 1st Qu.: 4.000 | 1st Qu.:2 | Lotan :24 | Near:75 | 1st Qu.:-0.5002 | 1st Qu.: 0.48251 | 1st Qu.:-0.8759 | Class :difftime | 1st Qu.:0.7840 | 1st Qu.: 0.1195 | Adi Domer : 0 | 1 : 0 | 3 : 0 | 1 : 0 | 1 : 0 | |
| Median : 5.000 | Median :4 | Paran :30 | NA | Median : 0.3440 | Median : 0.77352 | Median :-0.6338 | Mode :numeric | Median :0.9272 | Median : 0.3741 | Asaf Mayrose : 0 | 2 : 0 | NA’s:150 | 2 : 0 | 2 : 0 | |
| Mean : 5.287 | Mean :4 | Samar : 6 | NA | Mean : 0.1079 | Mean : 0.66846 | Mean :-0.6728 | NA | Mean :0.8678 | Mean : 0.3628 | Eliraz Dvir : 0 | 3 : 0 | NA | 3 : 0 | 3 : 0 | |
| 3rd Qu.: 7.000 | 3rd Qu.:6 | Yahel : 6 | NA | 3rd Qu.: 0.6063 | 3rd Qu.: 0.84294 | 3rd Qu.:-0.5380 | NA | 3rd Qu.:0.9852 | 3rd Qu.: 0.6206 | Eliraz Dvir and Yoav Barak: 0 | NA’s:150 | NA | NA’s:150 | NA’s:150 | |
| Max. :11.000 | Max. :8 | Yotvata :30 | NA | Max. : 0.9115 | Max. : 0.90882 | Max. :-0.4172 | NA | Max. :1.0000 | Max. : 0.9037 | (Other) : 0 | NA | NA | NA | NA | |
| NA | NA | Zofar :24 | NA | NA | NA | NA | NA | NA’s :60 | NA’s :60 | NA’s :120 | NA | NA | NA | NA |
no observation for all 4 weather variables. many NAs 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 | agriculture | td_sc | cos_td_rad | sin_td_rad | h_from_sunrise | cos_hsun | sin_hsun | monitors_name | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| richness | 1.0000000 | 0.1981351 | 0.2076365 | 0.3746224 | 0.0905232 | 0.1112419 | 0.0469072 | -0.1430392 | 0.1623966 | -0.1397273 | NA |
| year_ct | 0.1981351 | 1.0000000 | 0.0000000 | 0.0000000 | -0.5275969 | -0.4733929 | -0.6101868 | -0.5003522 | 0.4259879 | -0.5054987 | NA |
| site | 0.2076365 | 0.0000000 | 1.0000000 | 0.0000000 | 0.0205100 | 0.0106601 | 0.0292917 | -0.1193344 | 0.1023013 | -0.1261981 | NA |
| agriculture | 0.3746224 | 0.0000000 | 0.0000000 | 1.0000000 | 0.0109388 | 0.0094189 | 0.0134730 | -0.0963764 | 0.0663892 | -0.1022251 | NA |
| td_sc | 0.0905232 | -0.5275969 | 0.0205100 | 0.0109388 | 1.0000000 | 0.9922533 | 0.9665746 | -0.1444082 | 0.1044386 | -0.1539195 | NA |
| cos_td_rad | 0.1112419 | -0.4733929 | 0.0106601 | 0.0094189 | 0.9922533 | 1.0000000 | 0.9281118 | -0.1357885 | 0.0953119 | -0.1458357 | NA |
| sin_td_rad | 0.0469072 | -0.6101868 | 0.0292917 | 0.0134730 | 0.9665746 | 0.9281118 | 1.0000000 | -0.1486667 | 0.1157519 | -0.1554410 | NA |
| h_from_sunrise | -0.1430392 | -0.5003522 | -0.1193344 | -0.0963764 | -0.1444082 | -0.1357885 | -0.1486667 | 1.0000000 | -0.9217774 | 0.9961864 | NA |
| cos_hsun | 0.1623966 | 0.4259879 | 0.1023013 | 0.0663892 | 0.1044386 | 0.0953119 | 0.1157519 | -0.9217774 | 1.0000000 | -0.8861854 | NA |
| sin_hsun | -0.1397273 | -0.5054987 | -0.1261981 | -0.1022251 | -0.1539195 | -0.1458357 | -0.1554410 | 0.9961864 | -0.8861854 | 1.0000000 | NA |
| monitors_name | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
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 = 0.716371311042246"Overdispersion parameter is < 1. Poisson more appropriate. Compare Poisson and negative binomial.
## 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## df AIC
## m0.po 12 627.7586
## m0.nb 13 629.7610## [1] "poisson"
## [1] "neg bin"
negative binomial did not converge due to underdispersion.
mixed model converged.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: richness ~ agriculture * year_ct + cos_td_rad + sin_td_rad +
## (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 630.2 651.3 -308.1 616.2 143
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5254 -0.5179 -0.1258 0.5250 2.5241
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.01057 0.1028
## Number of obs: 150, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.62794 0.71027 0.884 0.37665
## agricultureNear 0.54938 0.13072 4.203 2.64e-05 ***
## year_ct 0.07214 0.02263 3.188 0.00143 **
## cos_td_rad 0.56636 0.47854 1.184 0.23661
## sin_td_rad -0.23758 0.62840 -0.378 0.70537
## agricultureNear:year_ct -0.05581 0.02535 -2.202 0.02767 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agrclN yer_ct cs_td_ sn_td_
## agricultrNr -0.105
## year_ct 0.207 0.565
## cos_td_rad -0.972 -0.010 -0.241
## sin_td_rad 0.964 0.007 0.391 -0.923
## agrcltrNr:_ 0.073 -0.834 -0.657 0.020 -0.021perform backward stepwise model selection of poisson glmer.
## Single term deletions
##
## Model:
## richness ~ agriculture * year_ct + cos_td_rad + sin_td_rad +
## (1 | site)
## npar AIC
## <none> 630.23
## cos_td_rad 1 629.68
## sin_td_rad 1 628.37
## agriculture:year_ct 1 633.08drop sine.
## Single term deletions
##
## Model:
## richness ~ agriculture + year_ct + cos_td_rad + (1 | site) +
## agriculture:year_ct
## npar AIC
## <none> 628.37
## cos_td_rad 1 631.32
## agriculture:year_ct 1 631.26final model includes year*agriculture, sampling time of year. Final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: richness ~ agriculture + year_ct + cos_td_rad + (1 | site) +
## agriculture:year_ct
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 628.4 646.4 -308.2 616.4 144
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5858 -0.5103 -0.1203 0.5165 2.5490
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.01041 0.102
## Number of obs: 150, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.88615 0.18745 4.727 2.28e-06 ***
## agricultureNear 0.54971 0.13075 4.204 2.62e-05 ***
## year_ct 0.07552 0.02082 3.628 0.000286 ***
## cos_td_rad 0.40007 0.18281 2.188 0.028634 *
## agricultureNear:year_ct -0.05601 0.02535 -2.209 0.027142 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agrclN yer_ct cs_td_
## agricultrNr -0.422
## year_ct -0.698 0.612
## cos_td_rad -0.808 -0.010 0.338
## agrcltrNr:_ 0.352 -0.834 -0.706 0.003## $site
## (Intercept)
## Ein Yahav -0.01340484
## Lotan -0.05564092
## Paran -0.02016408
## Samar -0.05572249
## Yahel -0.04520706
## Yotvata 0.16033758
## Zofar 0.03776431
##
## 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 | 628.369 |
| BIC | 646.433 |
| Pseudo-R² (fixed effects) | 0.203 |
| Pseudo-R² (total) | 0.247 |
| exp(Est.) | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 2.426 | 0.187 | 4.727 | 0.000 |
| agricultureNear | 1.733 | 0.131 | 4.204 | 0.000 |
| year_ct | 1.078 | 0.021 | 3.628 | 0.000 |
| cos_td_rad | 1.492 | 0.183 | 2.188 | 0.029 |
| agricultureNear:year_ct | 0.946 | 0.025 | -2.209 | 0.027 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| site | (Intercept) | 0.102 |
| Group | # groups | ICC |
|---|---|---|
| site | 7 | 0.010 |
## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.
## [1] 16.89209## [1] 0.1689211## [1] 1.168921## [1] 82.97453## [1] 0.8297452## [1] 1.829745## agriculture year_ct.trend SE df asymp.LCL asymp.UCL
## Far 0.0755 0.0208 Inf 0.0347 0.1163
## Near 0.0195 0.0182 Inf -0.0162 0.0552
##
## Confidence level used: 0.95## agriculture year_ct.trend SE df z.ratio p.value
## Far 0.0755 0.0208 Inf 3.628 0.0006
## Near 0.0195 0.0182 Inf 1.072 0.2837
##
## P value adjustment: fdr method for 2 tests## agriculture year_ct emmean SE df asymp.LCL asymp.UCL
## Far 4 1.46 0.0713 Inf 1.32 1.60
## Near 4 1.78 0.0647 Inf 1.65 1.91
##
## 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 4 -0.326 0.0725 Inf -4.493 <.0001
##
## Results are given on the log (not the response) scale.
statistically significant higher richness near agriculture.
Near plots have on average 1.6503966 more species than far plots, which is 38.4943533 percent higher.
Significant stronger relative increase in richness far from settlements:
On average, a near plot has 1.4713678 more species than a far plot, which is 25.9750404 percent higher.
The average rate of increase in richness far from settlements is FOUR FOLD the rate near settlements, 7.8447059 vs. 1.9701697, respectively.
geometric mean of abundance
Explore data. Exclude time of day because of high number of NAs.
## [1] "GEOMETRIC MEAN ABUNDANCE WITHOUT RARE SPECIES"## Warning: Removed 1 row containing non-finite outside the scale range (`stat_boxplot()`).
## Removed 1 row containing non-finite outside the scale range (`stat_boxplot()`).
## Removed 1 row containing non-finite outside the scale range (`stat_boxplot()`).
| gma | year_ct | site | agriculture | td_sc | cos_td_rad | sin_td_rad | |
|---|---|---|---|---|---|---|---|
| Min. :1.000 | Min. :0 | Ein Yahav:30 | Far :75 | Min. :-1.8356 | Min. :-0.09882 | Min. :-0.9951 | |
| 1st Qu.:1.587 | 1st Qu.:2 | Lotan :24 | Near:75 | 1st Qu.:-0.5002 | 1st Qu.: 0.48251 | 1st Qu.:-0.8759 | |
| Median :2.034 | Median :4 | Paran :30 | NA | Median : 0.3440 | Median : 0.77352 | Median :-0.6338 | |
| Mean :2.207 | Mean :4 | Samar : 6 | NA | Mean : 0.1079 | Mean : 0.66846 | Mean :-0.6728 | |
| 3rd Qu.:2.696 | 3rd Qu.:6 | Yahel : 6 | NA | 3rd Qu.: 0.6063 | 3rd Qu.: 0.84294 | 3rd Qu.:-0.5380 | |
| Max. :6.192 | Max. :8 | Yotvata :30 | NA | Max. : 0.9115 | Max. : 0.90882 | Max. :-0.4172 | |
| NA’s :1 | NA | Zofar :24 | NA | NA | NA | NA |
Exclude one plot with zero GMA (Yahel Far 2 2012) Fit glm, compare gamma, gaussian (poisson inappropriate because response is not discrete)
Gamma seems better than gaussian. Fit fixed and mixed models.
Mixed model converged.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( inverse )
## Formula: gma ~ agriculture * year_ct + cos_td_rad + sin_td_rad + (1 |
## site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 316.6 340.7 -150.3 300.6 141
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5304 -0.7568 -0.1935 0.4664 3.3741
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0004862 0.02205
## Residual 0.1063066 0.32605
## Number of obs: 149, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 0.055019 0.201160 0.274 0.78446
## agricultureNear -0.161841 0.040129 -4.033 5.51e-05 ***
## year_ct -0.012149 0.007618 -1.595 0.11076
## cos_td_rad 0.266141 0.132337 2.011 0.04432 *
## sin_td_rad -0.490219 0.185589 -2.641 0.00826 **
## agricultureNear:year_ct 0.014329 0.008355 1.715 0.08634 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agrclN yer_ct cs_td_ sn_td_
## agricultrNr -0.134
## year_ct 0.202 0.573
## cos_td_rad -0.967 -0.002 -0.248
## sin_td_rad 0.958 -0.004 0.399 -0.912
## agrcltrNr:_ 0.084 -0.810 -0.683 0.023 -0.023perform stepwise model selection of Gamma glmer.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00372887 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00257598 (tol = 0.002, component 1)## Single term deletions
##
## Model:
## gma ~ agriculture * year_ct + cos_td_rad + sin_td_rad + (1 |
## site)
## npar AIC
## <none> 316.63
## cos_td_rad 1 318.38
## sin_td_rad 1 321.30
## agriculture:year_ct 1 317.54drop agriculture:year.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00474736 (tol = 0.002, component 1)## Single term deletions
##
## Model:
## gma ~ agriculture + year_ct + cos_td_rad + sin_td_rad + (1 |
## site)
## npar AIC
## <none> 317.54
## agriculture 1 335.04
## year_ct 1 315.86
## cos_td_rad 1 319.13
## sin_td_rad 1 321.95drop cosine.
## Single term deletions
##
## Model:
## gma ~ agriculture + year_ct + sin_td_rad + (1 | site)
## npar AIC
## <none> 319.13
## agriculture 1 336.42
## year_ct 1 317.13
## sin_td_rad 1 321.03drop year.
## Single term deletions
##
## Model:
## gma ~ agriculture + sin_td_rad + (1 | site)
## npar AIC
## <none> 317.13
## agriculture 1 334.42
## sin_td_rad 1 321.15agriculture and sampling time of year remain. This is the final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( inverse )
## Formula: gma ~ agriculture + sin_td_rad + (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 317.1 332.1 -153.6 307.1 144
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5577 -0.7437 -0.2526 0.5694 3.7195
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0004287 0.0207
## Residual 0.1146477 0.3386
## Number of obs: 149, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 0.40903 0.04607 8.878 < 2e-16 ***
## agricultureNear -0.10768 0.02400 -4.486 7.24e-06 ***
## sin_td_rad -0.15651 0.06280 -2.492 0.0127 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agrclN
## agricultrNr -0.355
## sin_td_rad 0.874 -0.034## $site
## (Intercept)
## Ein Yahav 0.016757753
## Lotan 0.002440786
## Paran 0.006333531
## Samar -0.007165124
## Yahel -0.001758585
## Yotvata -0.011616275
## Zofar -0.004503318
##
## with conditional variances for "site"## $site
| Observations | 149 |
| Dependent variable | gma |
| Type | Mixed effects generalized linear model |
| Family | Gamma |
| Link | inverse |
| AIC | 317.13 |
| BIC | 332.15 |
| Pseudo-R² (fixed effects) | NA |
| Pseudo-R² (total) | NA |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 0.41 | 0.05 | 8.88 | 0.00 |
| agricultureNear | -0.11 | 0.02 | -4.49 | 0.00 |
| sin_td_rad | -0.16 | 0.06 | -2.49 | 0.01 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| site | (Intercept) | 0.02 |
| Residual | 0.34 |
| Group | # groups | ICC |
|---|---|---|
| site | 7 | 0.00 |
## 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] 26.46032## 1 2
## 1.943236 2.457422## [1] 26.18719## [1] 1.261872
## 2
## 26.46032Temporal trend in GMA is not significant.
Significant effect on GMA for agriculture proximity: higher near agriculture.
abundance
Explore data
## [1] "ABUNDANCE WITHOUT RARE SPECIES"
| abundance | year_ct | site | agriculture | td_sc | cos_td_rad | sin_td_rad | |
|---|---|---|---|---|---|---|---|
| Min. : 0.00 | Min. :0 | Ein Yahav:30 | Far :75 | Min. :-1.8356 | Min. :-0.09882 | Min. :-0.9951 | |
| 1st Qu.: 7.00 | 1st Qu.:2 | Lotan :24 | Near:75 | 1st Qu.:-0.5002 | 1st Qu.: 0.48251 | 1st Qu.:-0.8759 | |
| Median :11.00 | Median :4 | Paran :30 | NA | Median : 0.3440 | Median : 0.77352 | Median :-0.6338 | |
| Mean :14.48 | Mean :4 | Samar : 6 | NA | Mean : 0.1079 | Mean : 0.66846 | Mean :-0.6728 | |
| 3rd Qu.:19.00 | 3rd Qu.:6 | Yahel : 6 | NA | 3rd Qu.: 0.6063 | 3rd Qu.: 0.84294 | 3rd Qu.:-0.5380 | |
| Max. :47.00 | Max. :8 | Yotvata :30 | NA | Max. : 0.9115 | Max. : 0.90882 | Max. :-0.4172 | |
| NA | NA | Zofar :24 | NA | NA | NA | NA |
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(5.1103) ( log )
## Formula: abundance ~ agriculture * year_ct + cos_td_rad + sin_td_rad +
## (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 1009.6 1033.7 -496.8 993.6 142
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7064 -0.6575 -0.2270 0.3776 5.7327
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.02412 0.1553
## Number of obs: 150, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.66512 0.80703 2.063 0.039088 *
## agricultureNear 1.01253 0.15194 6.664 2.67e-11 ***
## year_ct 0.08997 0.02695 3.339 0.000841 ***
## cos_td_rad 0.46461 0.53763 0.864 0.387483
## sin_td_rad 0.16461 0.72690 0.226 0.820846
## agricultureNear:year_ct -0.08391 0.03058 -2.744 0.006070 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agrclN yer_ct cs_td_ sn_td_
## agricultrNr -0.096
## year_ct 0.215 0.537
## cos_td_rad -0.971 -0.008 -0.240
## sin_td_rad 0.962 0.017 0.400 -0.918
## agrcltrNr:_ 0.074 -0.825 -0.638 0.012 -0.020model selection of glmer.
## Single term deletions
##
## Model:
## abundance ~ agriculture * year_ct + cos_td_rad + sin_td_rad +
## (1 | site)
## npar AIC
## <none> 1009.6
## cos_td_rad 1 1008.4
## sin_td_rad 1 1007.7
## agriculture:year_ct 1 1015.2drop sine.
## Single term deletions
##
## Model:
## abundance ~ agriculture + year_ct + cos_td_rad + (1 | site) +
## agriculture:year_ct
## npar AIC
## <none> 1007.7
## cos_td_rad 1 1012.9
## agriculture:year_ct 1 1013.2final model includes agriculture*year, sampling time of year. The final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(5.1103) ( log )
## Formula: abundance ~ agriculture + year_ct + cos_td_rad + (1 | site) +
## agriculture:year_ct
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 1007.7 1028.8 -496.8 993.7 143
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7052 -0.6628 -0.2260 0.3689 5.7030
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.02404 0.155
## Number of obs: 150, groups: site, 7
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.48943 0.22138 6.728 1.72e-11 ***
## agricultureNear 1.01197 0.15194 6.660 2.73e-11 ***
## year_ct 0.08753 0.02469 3.544 0.000394 ***
## cos_td_rad 0.57638 0.21244 2.713 0.006663 **
## agricultureNear:year_ct -0.08378 0.03058 -2.740 0.006148 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agrclN yer_ct cs_td_
## agricultrNr -0.410
## year_ct -0.677 0.578
## cos_td_rad -0.809 0.018 0.351
## agrcltrNr:_ 0.338 -0.825 -0.687 -0.017## $site
## (Intercept)
## Ein Yahav -0.03298973
## Lotan -0.13119134
## Paran -0.05481234
## Samar -0.02620322
## Yahel -0.10433389
## Yotvata 0.22799096
## Zofar 0.12657362
##
## with conditional variances for "site"## $site
Interpretation of abundance model:
| Observations | 150 |
| Dependent variable | abundance |
| Type | Mixed effects generalized linear model |
| Family | Negative Binomial(5.1103) |
| Link | log |
| AIC | 1007.687 |
| BIC | 1028.761 |
| Pseudo-R² (fixed effects) | 0.617 |
| Pseudo-R² (total) | 0.716 |
| exp(Est.) | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 4.435 | 0.221 | 6.728 | 0.000 |
| agricultureNear | 2.751 | 0.152 | 6.660 | 0.000 |
| year_ct | 1.091 | 0.025 | 3.544 | 0.000 |
| cos_td_rad | 1.780 | 0.212 | 2.713 | 0.007 |
| agricultureNear:year_ct | 0.920 | 0.031 | -2.740 | 0.006 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| site | (Intercept) | 0.155 |
| Group | # groups | ICC |
|---|---|---|
| site | 7 | 0.054 |
## [1] 3.041974## [1] 0.03041955## [1] 1.03042## [1] 3.041974## [1] 1.014183## [1] 2.014183## agriculture year_ct.trend SE df asymp.LCL asymp.UCL
## Far 0.08753 0.0247 Inf 0.0391 0.1359
## Near 0.00375 0.0225 Inf -0.0404 0.0479
##
## Confidence level used: 0.95## agriculture year_ct.trend SE df z.ratio p.value
## Far 0.08753 0.0247 Inf 3.544 0.0008
## Near 0.00375 0.0225 Inf 0.166 0.8678
##
## P value adjustment: fdr method for 2 tests## agriculture year_ct emmean SE df asymp.LCL asymp.UCL
## Far 4 2.22 0.0911 Inf 2.05 2.40
## Near 4 2.90 0.0861 Inf 2.73 3.07
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95## [1] 0.3063063## contrast year_ct estimate SE df z.ratio p.value
## Far - Near 4 -0.677 0.086 Inf -7.869 <.0001
##
## Results are given on the log (not the response) scale.## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.
significantly higher abundance near agriculture compared to far from it.
Near plots have on average 8.9527241 more individuals than far plots, which is 96.7667539 percent higher.
significant increase in abundance far from agriculture (9.1471415%), no change near agriculture.
On average, a near plot has 8.3819296 more individuals than a far plot, which is 46.2977639 percent higher.
community analysis using package MVabund
Explore single species observations, including mean-variance plot. There is a strong mean-var relationship, indicating that employing GLMs is the proper way to analyze, rather than OLS (assumption of homogeneity is violated).
## Overlapping points were shifted along the y-axis to make them visible.##
## PIPING TO 2nd MVFACTOR## Only the variables Streptopelia.decaocto, Passer.domesticus, Pycnonotus.xanthopygos, Iduna.pallida, Spilopelia.senegalensis, Oenanthe.melanura, Cercotrichas.galactotes, Galerida.cristata, Prinia.gracilis, Streptopelia.turtur, Ammomanes.deserti, Argya.squamiceps were included in the plot
## (the variables with highest total abundance).
## [1] "abundance observations, aggregated into plots and species (i.e., several observations from the same species in the same plot are aggregated):"
## [1] "these are the actual observations, before aggregation into plots:"
## [1] "zoom in on high abundance observations:"
start model specification:
## nb po
## 255.0285 300.8678## [1] "POISSON"
## [1] "NEGATIVE BINOMIAL"
negative binomial model is better than poisson according to residuals and AIC comparison.
## nb po nb1
## 255.0285 300.8678 249.8495The addition of the explanatory variable ‘site’ is improving the AIC of the model. stepwise selection of model:
## Single term deletions
##
## Model:
## spp_no_rare ~ agriculture * year_ct + cos_td_rad + sin_td_rad +
## site
## Df AIC
## <none> 4997.0
## cos_td_rad 20 5015.2
## sin_td_rad 20 5004.7
## site 120 5100.6
## agriculture:year_ct 20 4995.4drop interaction of agriculture with year.
## Single term deletions
##
## Model:
## spp_no_rare ~ agriculture + year_ct + cos_td_rad + sin_td_rad +
## site
## Df AIC
## <none> 4995.4
## agriculture 20 5295.9
## year_ct 20 5012.7
## cos_td_rad 20 5013.7
## sin_td_rad 20 5003.2
## site 120 5090.1final model includes agriculture, year sampling time of year and site.
##
## Test statistics:
## wald value Pr(>wald)
## (Intercept) 7.119 0.001 ***
## agricultureNear 17.166 0.001 ***
## year_ct 7.599 0.001 ***
## cos_td_rad 6.435 0.016 *
## sin_td_rad 6.384 0.010 **
## siteLotan 6.371 0.002 **
## siteParan 6.713 0.001 ***
## siteSamar 3.369 0.294
## siteYahel 4.659 0.014 *
## siteYotvata 8.718 0.001 ***
## siteZofar 4.499 0.182
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Test statistic: 24.91, 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 ~ agriculture + year_ct + cos_td_rad + sin_td_rad + site
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 149
## agriculture 148 1 276.6 0.001 ***
## year_ct 147 1 56.8 0.001 ***
## cos_td_rad 146 1 57.4 0.001 ***
## sin_td_rad 145 1 40.4 0.019 *
## site 139 6 334.8 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Univariate Tests:
## Ammomanes.deserti Ammoperdix.heyi
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 19.513 0.001 12.276 0.005
## year_ct 0.182 0.999 0.298 0.999
## cos_td_rad 0.187 0.998 4.44 0.487
## sin_td_rad 3.651 0.678 0.689 0.984
## site 23.155 0.034 11.832 0.518
## Argya.squamiceps Cercotrichas.galactotes
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 0.04 0.987 21.531 0.001
## year_ct 0.016 0.999 0.023 0.999
## cos_td_rad 0.01 0.998 15.893 0.003
## sin_td_rad 0.502 0.984 5.291 0.428
## site 12.892 0.442 19.61 0.083
## Cinnyris.osea Columba.livia Corvus.ruficollis
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## agriculture 0.134 0.987 4.759 0.167 0.052
## year_ct 1.588 0.929 2.419 0.810 5.675
## cos_td_rad 0.21 0.998 0.587 0.995 3.459
## sin_td_rad 1.022 0.975 0.01 0.987 2.356
## site 33.277 0.002 5.498 0.815 11.447
## Galerida.cristata Iduna.pallida
## Pr(>Dev) Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 0.987 12.736 0.005 0.165 0.987
## year_ct 0.269 0.142 0.999 1.455 0.938
## cos_td_rad 0.606 2.143 0.818 11.499 0.011
## sin_td_rad 0.896 0.07 0.987 7.423 0.168
## site 0.518 15.992 0.239 10.481 0.518
## Merops.cyanophrys Oenanthe.melanura
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 2.058 0.598 35.967 0.001
## year_ct 0.093 0.999 15.114 0.004
## cos_td_rad 0.078 0.998 0.052 0.998
## sin_td_rad 1.36 0.967 0.322 0.984
## site 14.534 0.326 15.225 0.284
## Passer.domesticus Prinia.gracilis
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 25.097 0.001 18.803 0.001
## year_ct 4.157 0.491 2.826 0.754
## cos_td_rad 3.008 0.694 1.327 0.946
## sin_td_rad 0.722 0.984 4.95 0.459
## site 22.809 0.036 18.239 0.127
## Ptyonoprogne.fuligula Pycnonotus.xanthopygos
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 1.558 0.648 19.263 0.001
## year_ct 0.8 0.985 0.988 0.981
## cos_td_rad 2.309 0.818 3.767 0.582
## sin_td_rad 1.992 0.933 0.807 0.984
## site 2.915 0.863 29.907 0.005
## Scotocerca.inquieta Spilopelia.senegalensis
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 22.018 0.001 36.992 0.001
## year_ct 0.291 0.999 7.462 0.124
## cos_td_rad 0.031 0.998 4.066 0.536
## sin_td_rad 1.289 0.967 1.764 0.944
## site 18.62 0.115 24.766 0.024
## Streptopelia.decaocto Streptopelia.turtur
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 13.406 0.004 12.339 0.005
## year_ct 2.317 0.811 10.859 0.021
## cos_td_rad 3.615 0.587 0.636 0.995
## sin_td_rad 4.471 0.532 1.722 0.944
## site 13.148 0.442 20.585 0.066
## Vanellus.spinosus
## Dev Pr(>Dev)
## (Intercept)
## agriculture 17.848 0.001
## year_ct 0.119 0.999
## cos_td_rad 0.122 0.998
## sin_td_rad 0.032 0.987
## site 9.823 0.534
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.All factors (agriculture, year, time of year, site) have a statistically significant effect on community composition.
## 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.## [[1]]
##
## [[2]]
##
## [[3]]
## Loading required package: ggnewscale## 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\n\n"
## [1] "black arrow is median of synanthrope/invasive\ngreen arrow is median of batha specialists\n\n"
Significant effect of agriculture proximity: synanthrope / invasive species near agriculture, batha specialists far from agriculture. Significant temporal trend, however only few species are showing a statistically significant increase / decrease.
European turtle dove is decreasing, in accordance with what is seen in other units. House sparrow increasing, however not significant result. Blackstart increasing significant.
Oenanthe melanura
## 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) agricultureNear year_ct cos_td_rad sin_td_rad
## 0.33164590 -1.75497917 0.14019266 -0.73074031 1.31121497
## siteLotan siteParan siteSamar siteYahel siteYotvata
## 0.51393181 0.09207872 -12.94825508 -13.05852670 0.29497436
## siteZofar
## 0.89175592## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## 0.33147919 -1.75499772 0.14018578 -0.73062632 1.31101636
## siteLotan siteParan siteSamar siteYahel siteYotvata
## 0.51391105 0.09206032 -17.54419402 -17.65444915 0.29496277
## siteZofar
## 0.89176945## [1] "RESULTS FOR Oenanthe melanura"## [1] "P-values"## term p
## 1: (Intercept) NA
## 2: agriculture 0.001
## 3: year_ct 0.004
## 4: cos_td_rad 0.998
## 5: sin_td_rad 0.984
## 6: site 0.284
## [1] "change in Oenanthe melanura abundance over monitoring period: 206.941261376663 %"
## [1] "Oenanthe melanura abundance in high proximity is 478.343458356713 % higher than low proximity."Streptopelia turtur
## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -5.2086118 1.6492561 -0.1899857 4.1023818 -2.6326661
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -14.1614056 -1.4406736 -1.9726819 -1.9248743 -0.6590496
## siteZofar
## -0.9529573## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -5.2086100 1.6492586 -0.1899862 4.1023786 -2.6326651
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -30.7873477 -1.4406725 -1.9726820 -1.9248743 -0.6590477
## siteZofar
## -0.9529560## [1] "RESULTS FOR Streptopelia turtur"## [1] "P-values"## term p
## 1: (Intercept) NA
## 2: agriculture 0.005
## 3: year_ct 0.021
## 4: cos_td_rad 0.995
## 5: sin_td_rad 0.944
## 6: site 0.066
## [1] "change in Streptopelia turtur abundance over monitoring period: -78.126393186003 %"
## [1] "Streptopelia turtur abundance in high proximity is 420.312101038971 % higher than low proximity."## Warning: glm.fit: fitted rates numerically 0 occurred## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -13.09790713 11.95343497 0.06702196 0.36412234 0.68517386
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -10.67292519 -0.55628238 -10.43674329 -10.54350718 0.82389803
## siteZofar
## 0.10202225## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -34.14677451 33.00236642 0.06702561 0.36410162 0.68524755
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -31.72219461 -0.55628584 -31.48238291 -31.58915434 0.82390201
## siteZofar
## 0.10202529## [1] 2.151521e+16## [1] 4.20312## [1] 5.20312## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -11.5711744 2.8262403 -0.2103344 6.3302368 -6.0168666
## siteLotan siteParan siteSamar siteYahel siteYotvata
## 1.6335882 0.3035106 0.5804519 1.5474298 2.1027938
## siteZofar
## 1.0625223## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -11.5711604 2.8262395 -0.2103342 6.3302289 -6.0168565
## siteLotan siteParan siteSamar siteYahel siteYotvata
## 1.6335858 0.3035081 0.5804513 1.5474289 2.1027910
## siteZofar
## 1.0625203## [1] 1588.186## [1] 15.88186## [1] 16.88186## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -9.07285106 2.69853705 0.30958219 6.03111594 -3.77163756
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -1.83664597 -0.74618321 1.21992180 1.83355005 -0.03413473
## siteZofar
## 0.67564222## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -9.07250046 2.69846225 0.30959616 6.03091482 -3.77133523
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -1.83669309 -0.74623355 1.21992786 1.83356207 -0.03413064
## siteZofar
## 0.67558946## [1] 1385.687## [1] 13.85687## [1] 14.85687## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -13.4601951 2.3292424 0.1072389 6.5990326 -8.3926950
## siteLotan siteParan siteSamar siteYahel siteYotvata
## 0.2009255 -0.1304512 0.4839151 -12.0252175 1.1990900
## siteZofar
## -13.2303424## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -13.4601875 2.3292472 0.1072386 6.5990251 -8.3926886
## siteLotan siteParan siteSamar siteYahel siteYotvata
## 0.2009255 -0.1304512 0.4839137 -34.2533621 1.1990888
## siteZofar
## -35.4584860## [1] 927.0208## [1] 9.270209## [1] 10.27021## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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## control$trace > : iteration limit reached
## 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$maxit, trace =
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## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached## Warning in glm.nb(formula = Cercotrichas.galactotes ~ agriculture + year_ct + :
## alternation limit reached## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -14.68181876 1.55856195 -0.01129455 9.55210083 -7.93517191
## siteLotan siteParan siteSamar siteYahel siteYotvata
## 1.12496563 1.00124036 -0.48736423 0.53518837 1.63033560
## siteZofar
## 0.07535493## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -14.67769194 1.55822349 -0.01108243 9.54933226 -7.93092781
## siteLotan siteParan siteSamar siteYahel siteYotvata
## 1.12369964 1.00217681 -0.48677198 0.53527285 1.63030616
## siteZofar
## 0.07571518## [1] 375.0375## [1] 3.750374## [1] 4.750374## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -4.417556695 1.148894535 -0.008253081 1.641350611 -1.220068487
## siteLotan siteParan siteSamar siteYahel siteYotvata
## 1.028865127 1.771593958 0.639782420 1.008272211 1.095423092
## siteZofar
## 0.321167086## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -4.417556448 1.148894750 -0.008251932 1.641347183 -1.220064709
## siteLotan siteParan siteSamar siteYahel siteYotvata
## 1.028866423 1.771594008 0.639778222 1.008275122 1.095422907
## siteZofar
## 0.321165104## [1] 215.4704## [1] 2.154704## [1] 3.154704## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## 2.02383414 0.75212121 0.04737802 -1.34432923 2.29609020
## siteLotan siteParan siteSamar siteYahel siteYotvata
## 0.40417117 -0.59680132 0.29681599 -0.30760300 0.70057847
## siteZofar
## 0.50197202## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## 2.02383381 0.75212102 0.04737784 -1.34432813 2.29608943
## siteLotan siteParan siteSamar siteYahel siteYotvata
## 0.40417167 -0.59680128 0.29681542 -0.30760316 0.70057829
## siteZofar
## 0.50197143## [1] 112.1495## [1] 1.121495## [1] 2.121495## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## 2.98395825 0.54390316 0.06662386 -0.94361607 2.09552818
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -0.07187178 -0.01852355 -0.48310350 -0.94688060 0.29297207
## siteZofar
## -0.12964539## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## 2.98395751 0.54390281 0.06662385 -0.94361555 2.09552743
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -0.07187182 -0.01852339 -0.48310234 -0.94687947 0.29297213
## siteZofar
## -0.12964531## [1] 72.27172## [1] 0.7227171## [1] 1.722717## 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) agricultureNear year_ct cos_td_rad sin_td_rad
## -7.50472064 -1.48031913 0.02793128 5.75714533 -5.33626471
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -2.27551133 -0.36068092 -1.52279917 -1.25737494 -1.57024850
## siteZofar
## -0.32639079## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -7.50512708 -1.48035373 0.02793225 5.75743807 -5.33665039
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -2.27556585 -0.36076149 -1.52285091 -1.25739937 -1.57031216
## siteZofar
## -0.32645607## [1] 339.45## [1] 3.3945## [1] 4.3945## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -20.4985214 -2.3800557 0.1464316 16.0558411 -11.7363975
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -0.1210425 -0.8468276 -12.9840752 -12.6238788 -2.6108057
## siteZofar
## 0.1861919## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -20.4984948 -2.3800576 0.1464307 16.0558137 -11.7363814
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -0.1210344 -0.8468181 -35.2122128 -34.8520164 -2.6107954
## siteZofar
## 0.1862040## [1] 980.5526## [1] 9.805525## [1] 10.80553## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -4.15196006 -3.28462058 -0.03277352 2.30681570 -3.51249158
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -0.40950223 -0.22627447 -12.94729935 -12.67491375 -1.66467978
## siteZofar
## -12.95929318## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -4.15196670 -3.28465347 -0.03277362 2.30681979 -3.51249953
## siteLotan siteParan siteSamar siteYahel siteYotvata
## -0.40950218 -0.22627398 -34.58165256 -34.30926631 -1.66467964
## siteZofar
## -34.57817116## [1] 2569.973## [1] 25.69973## [1] 26.69973Session 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 2024-05-19
## 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)
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## 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
##
## ──────────────────────────────────────────────────────────────────────────────