Birds_Maquis_analysis_by_plot
Ron Chen
2023-04-30
## 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 species (not resident nor breeding) were set to zero. Abreed_and_non_breed contains the non-breeders as well.
## - White stork observations removed altogether - currently (2023) species is breeding in Israel very scarcely (<10 pairs) and only in the Golan heights.
## Very large stork numbers observed near plots are migrants.Mediterranean Maquis
Unit is divided into 3 subunits: Judea, Carmel and Galillea. Factors are proximity to settlements and time. Sampling started in spring 2012 (pilot year), but during T0 sampling was performed only in winter of 2014, and the next spring sampling was done in T1 (2015). Therefore 2012 will not be considered as pilot here but as T0. Total 5 campaigns, 3subunits per campaign, 5 sites per subunit, with 6 plots per site (total of 450 plot-campaign combinations).
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 | subunit | 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. :0.0 | Nir Etzion : 31 | Far :224 | Judean Highlands:149 | Min. :-1.9884 | Min. :-0.1671 | Min. :-1.0000 | Length:444 | Min. :-0.3289 | Min. :-0.1253 | Sassi Haham : 53 | 0 : 33 | 0 :119 | 0 : 4 | 0 : 32 | |
| 1st Qu.: 6.000 | 1st Qu.:3.0 | Ein Yaakov : 30 | Near:220 | Carmel :150 | 1st Qu.:-0.2710 | 1st Qu.: 0.5702 | 1st Qu.:-0.8215 | Class :difftime | 1st Qu.: 0.6855 | 1st Qu.: 0.2563 | Eyal Shochat: 47 | 1 : 42 | 3 : 2 | 1 : 38 | 1 : 0 | |
| Median : 8.000 | Median :5.0 | Givat Yearim : 30 | NA | Galilee :145 | Median : 0.4922 | Median : 0.8140 | Median :-0.5808 | Mode :numeric | Median : 0.8738 | Median : 0.4863 | Eran Banker : 30 | 2 : 12 | NA’s:323 | 2 : 37 | 2 : 5 | |
| Mean : 8.511 | Mean :4.8 | Givat Yeshayahu: 30 | NA | NA | Mean : 0.3209 | Mean : 0.7098 | Mean :-0.5864 | NA | Mean : 0.7997 | Mean : 0.4836 | Asaf Mayrose: 18 | 3 : 2 | NA | 3 : 18 | 3 : 13 | |
| 3rd Qu.:11.000 | 3rd Qu.:7.0 | Goren : 30 | NA | NA | 3rd Qu.: 1.1028 | 3rd Qu.: 0.9413 | 3rd Qu.:-0.3375 | NA | 3rd Qu.: 0.9666 | 3rd Qu.: 0.7281 | Ohad Sharir : 18 | NA’s:355 | NA | NA’s:347 | NA’s:394 | |
| Max. :22.000 | Max. :9.0 | Kerem Maharal : 30 | NA | NA | Max. : 1.7134 | Max. : 0.9976 | Max. :-0.0688 | NA | Max. : 1.0000 | Max. : 0.9998 | (Other) : 58 | NA | NA | NA | NA | |
| NA | NA | (Other) :263 | NA | NA | NA | NA | NA | NA | NA’s :89 | NA’s :89 | NA’s :220 | NA | NA | NA | NA |
no observation for all 4 weather variables. many NAs for sampling time of day variables.exclude from model. An extreme observation of richness>20 in Judean highlands near settlements.
## unit subunit site year year_ct settlements
## 1: Mediterranean Maquis Judean Highlands Givat Yearim 2017 5 Near
## agriculture habitat dunes land_use point_name date
## 1: <NA> <NA> <NA> <NA> Givat Yearim Near 2 2017-05-04
## datetime td_sc td_rad cos_td_rad sin_td_rad
## 1: 2017-05-04 07:58:00 -0.003890625 -0.8434961 0.6648554 -0.7469721
## timediff_Jun21 monitors_name wind precipitation temperature clouds
## 1: -49 days Eran Banker <NA> <NA> <NA> <NA>
## h_from_sunrise cos_hsun sin_hsun pilot richness abundance gma
## 1: 2.02 hours 0.8633956 0.5045276 FALSE 22 96 3.185435richness outlier (22): consider removing Givat Yearim Near 2 in 2017 if hinders the analysis.
| richness | year_ct | site | settlements | subunit | td_sc | cos_td_rad | sin_td_rad | h_from_sunrise | cos_hsun | sin_hsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| richness | 1.0000000 | -0.0129449 | 0.0960807 | 0.4424038 | -0.1850164 | -0.0001437 | 0.0193023 | -0.0203458 | -0.0305757 | 0.0706456 | -0.0040407 |
| year_ct | -0.0129449 | 1.0000000 | -0.0460962 | -0.0041782 | 0.0046005 | -0.6240340 | -0.5599811 | -0.6642968 | 0.0244310 | -0.0070326 | 0.0341395 |
| site | 0.0960807 | -0.0460962 | 1.0000000 | 0.0038640 | -0.1472070 | 0.0103683 | 0.0017250 | 0.0296647 | -0.0105111 | -0.0092740 | -0.0240278 |
| settlements | 0.4424038 | -0.0041782 | 0.0038640 | 1.0000000 | -0.0167082 | 0.0029695 | 0.0016285 | 0.0037459 | 0.0341209 | -0.0071595 | 0.0507160 |
| subunit | -0.1850164 | 0.0046005 | -0.1472070 | -0.0167082 | 1.0000000 | -0.0056370 | -0.0129685 | -0.0136922 | 0.1193296 | -0.1166460 | 0.1174210 |
| td_sc | -0.0001437 | -0.6240340 | 0.0103683 | 0.0029695 | -0.0056370 | 1.0000000 | 0.9788766 | 0.9693667 | -0.1975082 | 0.1811635 | -0.2043768 |
| cos_td_rad | 0.0193023 | -0.5599811 | 0.0017250 | 0.0016285 | -0.0129685 | 0.9788766 | 1.0000000 | 0.9008724 | -0.1878186 | 0.1700185 | -0.1970275 |
| sin_td_rad | -0.0203458 | -0.6642968 | 0.0296647 | 0.0037459 | -0.0136922 | 0.9693667 | 0.9008724 | 1.0000000 | -0.2005880 | 0.1868900 | -0.2040813 |
| h_from_sunrise | -0.0305757 | 0.0244310 | -0.0105111 | 0.0341209 | 0.1193296 | -0.1975082 | -0.1878186 | -0.2005880 | 1.0000000 | -0.9598144 | 0.9818950 |
| cos_hsun | 0.0706456 | -0.0070326 | -0.0092740 | -0.0071595 | -0.1166460 | 0.1811635 | 0.1700185 | 0.1868900 | -0.9598144 | 1.0000000 | -0.8930078 |
| sin_hsun | -0.0040407 | 0.0341395 | -0.0240278 | 0.0507160 | 0.1174210 | -0.2043768 | -0.1970275 | -0.2040813 | 0.9818950 | -0.8930078 | 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 = 0.950744418370608"Overdispersion parameter is < 1. Choose Poisson.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00219227 (tol = 0.002, component 1)mixed model failed to converge. check vif.
## GVIF Df GVIF^(1/(2*Df))
## settlements 3.358182 1 1.832534
## year_ct 4.602842 1 2.145423
## subunit 3.067975 2 1.323467
## cos_td_rad 5.682920 1 2.383888
## sin_td_rad 7.439447 1 2.727535
## settlements:year_ct 4.694147 1 2.166598
## year_ct:subunit 6.818978 2 1.615957go with mixed model, attempt model selection yet.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: richness ~ settlements * year_ct + subunit * year_ct + cos_td_rad +
## sin_td_rad + (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 2156.6 2201.7 -1067.3 2134.6 433
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6883 -0.6077 -0.0944 0.6069 3.8694
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.009445 0.09719
## Number of obs: 444, groups: site, 16
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.426120 0.212472 6.712 1.92e-11 ***
## settlementsNear 0.396739 0.060455 6.563 5.29e-11 ***
## year_ct 0.008678 0.011209 0.774 0.4388
## subunitCarmel 0.120959 0.094587 1.279 0.2010
## subunitGalilee -0.069713 0.098736 -0.706 0.4802
## cos_td_rad 0.365554 0.146705 2.492 0.0127 *
## sin_td_rad -0.411212 0.174004 -2.363 0.0181 *
## settlementsNear:year_ct -0.009369 0.010548 -0.888 0.3744
## year_ct:subunitCarmel -0.004854 0.012633 -0.384 0.7008
## year_ct:subunitGalilee -0.025797 0.014216 -1.815 0.0696 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct sbntCr sbntGl cs_td_ sn_td_ sttN:_ yr_c:C
## settlmntsNr -0.179
## year_ct -0.228 0.466
## subunitCrml -0.345 -0.002 0.324
## subunitGall -0.380 0.008 0.304 0.515
## cos_td_rad -0.917 0.009 0.042 0.111 0.149
## sin_td_rad 0.877 -0.011 0.101 -0.158 -0.212 -0.857
## sttlmntsN:_ 0.151 -0.838 -0.554 0.001 -0.008 -0.009 0.011
## yr_ct:sbntC 0.322 0.001 -0.498 -0.643 -0.360 -0.158 0.247 0.002
## yr_ct:sbntG 0.352 -0.008 -0.440 -0.330 -0.673 -0.202 0.294 0.014 0.516
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00219227 (tol = 0.002, component 1)perform stepwise model selection of poisson mixed model.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00276967 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00331218 (tol = 0.002, component 1)## Single term deletions
##
## Model:
## richness ~ settlements * year_ct + subunit * year_ct + cos_td_rad +
## sin_td_rad + (1 | site)
## npar AIC
## <none> 2156.6
## cos_td_rad 1 2160.9
## sin_td_rad 1 2160.2
## settlements:year_ct 1 2155.4
## year_ct:subunit 2 2156.3remove settlements X year.
## Single term deletions
##
## Model:
## richness ~ settlements + subunit * year_ct + cos_td_rad + sin_td_rad +
## (1 | site)
## npar AIC
## <none> 2155.4
## settlements 1 2268.7
## cos_td_rad 1 2159.7
## sin_td_rad 1 2158.9
## subunit:year_ct 2 2155.1drop year X subunit.
## Single term deletions
##
## Model:
## richness ~ settlements + subunit + year_ct + cos_td_rad + sin_td_rad +
## (1 | site)
## npar AIC
## <none> 2155.1
## settlements 1 2268.6
## subunit 2 2162.2
## year_ct 1 2153.3
## cos_td_rad 1 2158.1
## sin_td_rad 1 2157.1drop year.
## Single term deletions
##
## Model:
## richness ~ settlements + subunit + cos_td_rad + sin_td_rad +
## (1 | site)
## npar AIC
## <none> 2153.3
## settlements 1 2266.8
## subunit 2 2160.3
## cos_td_rad 1 2156.1
## sin_td_rad 1 2155.3Settlements, subunit and time of year remain. Final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: richness ~ settlements + subunit + cos_td_rad + sin_td_rad +
## (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 2153.3 2182.0 -1069.6 2139.3 437
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7024 -0.6170 -0.1147 0.6142 3.9324
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.01029 0.1014
## Number of obs: 444, groups: site, 16
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.57413 0.19309 8.152 3.57e-16 ***
## settlementsNear 0.35218 0.03299 10.675 < 2e-16 ***
## subunitCarmel 0.09863 0.07477 1.319 0.1871
## subunitGalilee -0.18993 0.07519 -2.526 0.0115 *
## cos_td_rad 0.30988 0.14275 2.171 0.0299 *
## sin_td_rad -0.29645 0.14822 -2.000 0.0455 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN sbntCr sbntGl cs_td_
## settlmntsNr -0.106
## subunitCrml -0.208 0.002
## subunitGall -0.212 0.009 0.504
## cos_td_rad -0.937 0.004 0.016 0.016
## sin_td_rad 0.927 -0.006 -0.007 -0.018 -0.902## $site
## (Intercept)
## Abirim -0.081765292
## Aderet 0.056668537
## Beit Oren -0.039180686
## Ein Yaakov -0.022412507
## Givat Yearim -0.078324707
## Givat Yeshayahu 0.103756948
## Goren -0.012415542
## Iftach 0.192314597
## Kerem Maharal 0.111713024
## Kfar Shamai -0.078372070
## Margaliot 0.009402784
## Nehusha -0.030575156
## Nir Etzion -0.135938432
## Ofer 0.082736130
## Ramat Raziel -0.046440076
## Yagur -0.014426673
##
## with conditional variances for "site"## $site
attempt to add year, see if significant:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula:
## richness ~ year_ct + settlements + subunit + cos_td_rad + sin_td_rad +
## (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 2155.1 2187.8 -1069.5 2139.1 436
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7081 -0.6057 -0.1045 0.6211 3.9155
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.0101 0.1005
## Number of obs: 444, groups: site, 16
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.564548 0.193930 8.068 7.17e-16 ***
## year_ct -0.003309 0.007103 -0.466 0.6413
## settlementsNear 0.352159 0.032993 10.674 < 2e-16 ***
## subunitCarmel 0.098329 0.074261 1.324 0.1855
## subunitGalilee -0.189970 0.074713 -2.543 0.0110 *
## cos_td_rad 0.317522 0.143565 2.212 0.0270 *
## sin_td_rad -0.330836 0.165319 -2.001 0.0454 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) yer_ct sttlmN sbntCr sbntGl cs_td_
## year_ct 0.105
## settlmntsNr -0.105 0.002
## subunitCrml -0.205 0.008 0.002
## subunitGall -0.210 0.001 0.009 0.503
## cos_td_rad -0.938 -0.112 0.004 0.015 0.016
## sin_td_rad 0.873 0.445 -0.005 -0.003 -0.016 -0.853year not significant, rightfully dropped. center time of year variables, highly correlated with intercept.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: richness ~ settlements + subunit + cos_td_rad_c + sin_td_rad_c +
## (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 2153.3 2182.0 -1069.6 2139.3 437
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7024 -0.6170 -0.1147 0.6142 3.9323
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.01029 0.1014
## Number of obs: 444, groups: site, 16
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.96790 0.05667 34.724 <2e-16 ***
## settlementsNear 0.35219 0.03299 10.675 <2e-16 ***
## subunitCarmel 0.09864 0.07476 1.319 0.1870
## subunitGalilee -0.18992 0.07518 -2.526 0.0115 *
## cos_td_rad_c 0.30988 0.14275 2.171 0.0299 *
## sin_td_rad_c -0.29645 0.14822 -2.000 0.0455 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN sbntCr sbntGl cs_t__
## settlmntsNr -0.344
## subunitCrml -0.669 0.002
## subunitGall -0.667 0.009 0.504
## cos_td_rd_c -0.020 0.004 0.016 0.016
## sin_td_rd_c 0.014 -0.006 -0.007 -0.018 -0.902## Registered S3 methods overwritten by 'broom':
## method from
## tidy.glht jtools
## tidy.summary.glht jtools| Observations | 444 |
| Dependent variable | richness |
| Type | Mixed effects generalized linear model |
| Family | poisson |
| Link | log |
| AIC | 2153.298 |
| BIC | 2181.969 |
| Pseudo-R² (fixed effects) | 0.276 |
| Pseudo-R² (total) | 0.337 |
| exp(Est.) | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 7.156 | 0.057 | 34.724 | 0.000 |
| settlementsNear | 1.422 | 0.033 | 10.675 | 0.000 |
| subunitCarmel | 1.104 | 0.075 | 1.319 | 0.187 |
| subunitGalilee | 0.827 | 0.075 | -2.526 | 0.012 |
| cos_td_rad_c | 1.363 | 0.143 | 2.171 | 0.030 |
| sin_td_rad_c | 0.743 | 0.148 | -2.000 | 0.045 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| site | (Intercept) | 0.101 |
| Group | # groups | ICC |
|---|---|---|
| site | 16 | 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.
## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.
statistically significant lower richness in Galilee and far from settlements. No significant change in richness over time.
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 | subunit | td_sc | cos_td_rad | sin_td_rad | |
|---|---|---|---|---|---|---|---|---|
| Min. : 1.000 | Min. :0.0 | Nir Etzion : 31 | Far :224 | Judean Highlands:149 | Min. :-1.9884 | Min. :-0.1671 | Min. :-1.0000 | |
| 1st Qu.: 2.000 | 1st Qu.:3.0 | Ein Yaakov : 30 | Near:220 | Carmel :150 | 1st Qu.:-0.2710 | 1st Qu.: 0.5702 | 1st Qu.:-0.8215 | |
| Median : 2.531 | Median :5.0 | Givat Yearim : 30 | NA | Galilee :145 | Median : 0.4922 | Median : 0.8140 | Median :-0.5808 | |
| Mean : 2.776 | Mean :4.8 | Givat Yeshayahu: 30 | NA | NA | Mean : 0.3209 | Mean : 0.7098 | Mean :-0.5864 | |
| 3rd Qu.: 3.195 | 3rd Qu.:7.0 | Goren : 30 | NA | NA | 3rd Qu.: 1.1028 | 3rd Qu.: 0.9413 | 3rd Qu.:-0.3375 | |
| Max. :10.301 | Max. :9.0 | Kerem Maharal : 30 | NA | NA | Max. : 1.7134 | Max. : 0.9976 | Max. :-0.0688 | |
| NA | NA | (Other) :263 | NA | NA | NA | NA | NA |
Fit glm, compare gamma, gaussian (poisson inappropriate because response is not discrete)
Gamma seems better than gaussian. Remove rows 213, 376, 408. Fit fixed and mixed models.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0899589 (tol = 0.002, component 1)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?Mixed model did not converge, use glm:
##
## Call:
## glm(formula = gma ~ settlements * year_ct + subunit * year_ct +
## cos_td_rad + sin_td_rad, family = Gamma, data = P.anal)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.87772 -0.27931 -0.06567 0.16264 1.24089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.673142 0.079096 8.510 2.9e-16 ***
## settlementsNear -0.034145 0.023075 -1.480 0.139672
## year_ct -0.001933 0.004132 -0.468 0.640065
## subunitCarmel -0.039019 0.027515 -1.418 0.156885
## subunitGalilee -0.006441 0.030167 -0.214 0.831037
## cos_td_rad -0.202066 0.056826 -3.556 0.000418 ***
## sin_td_rad 0.242919 0.062810 3.868 0.000127 ***
## settlementsNear:year_ct 0.003621 0.004024 0.900 0.368696
## year_ct:subunitCarmel 0.006057 0.004857 1.247 0.213067
## year_ct:subunitGalilee 0.002864 0.005285 0.542 0.588095
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.1299043)
##
## Null deviance: 51.879 on 440 degrees of freedom
## Residual deviance: 49.261 on 431 degrees of freedom
## AIC: 1148.2
##
## Number of Fisher Scoring iterations: 5perform stepwise model selection of Gamma model.
## Start: AIC=1148.24
## gma ~ settlements * year_ct + subunit * year_ct + cos_td_rad +
## sin_td_rad
##
## Df Deviance AIC
## - year_ct:subunit 2 49.463 1145.8
## - settlements:year_ct 1 49.366 1147.0
## <none> 49.261 1148.2
## - cos_td_rad 1 51.015 1159.7
## - sin_td_rad 1 51.211 1161.2
##
## Step: AIC=1146.08
## gma ~ settlements + year_ct + subunit + cos_td_rad + sin_td_rad +
## settlements:year_ct
##
## Df Deviance AIC
## - subunit 2 49.633 1143.4
## - settlements:year_ct 1 49.567 1144.9
## <none> 49.463 1146.1
## - cos_td_rad 1 51.130 1156.9
## - sin_td_rad 1 51.322 1158.4
##
## Step: AIC=1143.62
## gma ~ settlements + year_ct + cos_td_rad + sin_td_rad + settlements:year_ct
##
## Df Deviance AIC
## - settlements:year_ct 1 49.740 1142.5
## <none> 49.633 1143.6
## - cos_td_rad 1 51.282 1154.4
## - sin_td_rad 1 51.487 1156.0
##
## Step: AIC=1142.59
## gma ~ settlements + year_ct + cos_td_rad + sin_td_rad
##
## Df Deviance AIC
## - year_ct 1 49.880 1141.7
## - settlements 1 49.977 1142.4
## <none> 49.740 1142.6
## - cos_td_rad 1 51.383 1153.4
## - sin_td_rad 1 51.586 1155.0
##
## Step: AIC=1141.85
## gma ~ settlements + cos_td_rad + sin_td_rad
##
## Df Deviance AIC
## - settlements 1 50.116 1141.7
## <none> 49.880 1141.8
## - cos_td_rad 1 51.448 1152.1
## - sin_td_rad 1 51.631 1153.5
##
## Step: AIC=1141.98
## gma ~ cos_td_rad + sin_td_radOnly time of year remains. This is the final model:
##
## Call:
## glm(formula = gma ~ cos_td_rad + sin_td_rad, family = Gamma,
## data = P.anal)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.90336 -0.27930 -0.06894 0.16177 1.23098
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.62188 0.07242 8.587 < 2e-16 ***
## cos_td_rad -0.18869 0.05612 -3.362 0.000841 ***
## sin_td_rad 0.20456 0.05681 3.601 0.000354 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.1293445)
##
## Null deviance: 51.879 on 440 degrees of freedom
## Residual deviance: 50.116 on 438 degrees of freedom
## AIC: 1142
##
## Number of Fisher Scoring iterations: 5
Not a great fit. None of the factors of interest (year, subunit, settlements) are significant.
| Observations | 441 |
| Dependent variable | gma |
| Type | Generalized linear model |
| Family | Gamma |
| Link | inverse |
| χ²(2) | 1.76 |
| Pseudo-R² (Cragg-Uhler) | 0.04 |
| Pseudo-R² (McFadden) | 0.01 |
| AIC | 1141.98 |
| BIC | 1158.33 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 0.62 | 0.07 | 8.59 | 0.00 |
| cos_td_rad | -0.19 | 0.06 | -3.36 | 0.00 |
| sin_td_rad | 0.20 | 0.06 | 3.60 | 0.00 |
| Standard errors: MLE |
abundance
Explore data
## [1] "ABUNDANCE WITHOUT RARE SPECIES"
| abundance | year_ct | site | settlements | subunit | td_sc | cos_td_rad | sin_td_rad | |
|---|---|---|---|---|---|---|---|---|
| Min. : 2.00 | Min. :0.0 | Nir Etzion : 31 | Far :224 | Judean Highlands:149 | Min. :-1.9884 | Min. :-0.1671 | Min. :-1.0000 | |
| 1st Qu.: 16.00 | 1st Qu.:3.0 | Ein Yaakov : 30 | Near:220 | Carmel :150 | 1st Qu.:-0.2710 | 1st Qu.: 0.5702 | 1st Qu.:-0.8215 | |
| Median : 25.50 | Median :5.0 | Givat Yearim : 30 | NA | Galilee :145 | Median : 0.4922 | Median : 0.8140 | Median :-0.5808 | |
| Mean : 33.23 | Mean :4.8 | Givat Yeshayahu: 30 | NA | NA | Mean : 0.3209 | Mean : 0.7098 | Mean :-0.5864 | |
| 3rd Qu.: 38.00 | 3rd Qu.:7.0 | Goren : 30 | NA | NA | 3rd Qu.: 1.1028 | 3rd Qu.: 0.9413 | 3rd Qu.:-0.3375 | |
| Max. :345.00 | Max. :9.0 | Kerem Maharal : 30 | NA | NA | Max. : 1.7134 | Max. : 0.9976 | Max. :-0.0688 | |
| NA | NA | (Other) :263 | NA | NA | NA | NA | NA |
Some outliers with total abundance >100. Examine:
## subunit point_name datetime monitors_name
## 1: Judean Highlands Givat Yearim Near 1 2017-05-04 08:33:00 Eran Banker
## 2: Judean Highlands Givat Yeshayahu Far 2 2017-04-20 10:20:00 Eran Banker
## 3: Judean Highlands Givat Yeshayahu Far 3 2017-04-20 10:55:00 Eran Banker
## 4: Judean Highlands Givat Yeshayahu Near 1 2017-04-20 06:25:00 Eran Banker
## 5: Judean Highlands Givat Yeshayahu Near 2 2017-04-20 06:50:00 Eran Banker
## 6: Judean Highlands Givat Yeshayahu Near 3 2017-04-20 07:15:00 Eran Banker
## 7: Judean Highlands Ramat Raziel Near 3 2017-05-16 07:15:00 Eran Banker
## 8: Judean Highlands Aderet Near 2 2019-05-26 06:52:00 Eran Banker
## 9: Carmel Beit Oren Near 1 2019-04-19 07:49:00 Other
## 10: Carmel Beit Oren Near 3 2019-04-19 07:35:00 Other
## 11: Carmel Kerem Maharal Far 2 2019-05-07 08:01:00 Sassi Haham
## 12: Carmel Kerem Maharal Near 2 2019-05-07 08:34:00 Sassi Haham
## 13: Carmel Kerem Maharal Near 3 2021-04-17 07:41:00 Eliraz Dvir
## 14: Carmel Nir Etzion Far 11 2021-04-23 07:24:00 Eliraz Dvir
## richness gma abundance
## 1: 13 4.343684 112
## 2: 14 5.504450 194
## 3: 9 5.768372 118
## 4: 16 7.879133 177
## 5: 15 6.681847 139
## 6: 16 9.098580 221
## 7: 16 3.761509 105
## 8: 15 5.408017 106
## 9: 6 5.682233 130
## 10: 10 7.338970 254
## 11: 7 3.351723 103
## 12: 11 4.404296 134
## 13: 11 10.301194 345
## 14: 11 2.664203 122Exclude 5 plots with high abundance (>150) to improve model fit.
PHI>1, hence choose negative binomial. Fit fixed and mixed models. Choose mixed model if possible, otherwise choose a model with fixed-effects only.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00382071 (tol = 0.002, component 1)mixed model converged. Perform stepwise model selection of mixed model.
## Single term deletions
##
## Model:
## abundance ~ settlements * year_ct + subunit * year_ct + cos_td_rad +
## sin_td_rad + (1 | site)
## npar AIC
## <none> 3766.9
## cos_td_rad 1 3799.4
## sin_td_rad 1 3806.0
## settlements:year_ct 1 3765.5
## year_ct:subunit 2 3773.0drop settlements X year because \(\Delta AIC<2\)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00338774 (tol = 0.002, component 1)## Single term deletions
##
## Model:
## abundance ~ settlements + subunit * year_ct + cos_td_rad + sin_td_rad +
## (1 | site)
## npar AIC
## <none> 3765.5
## settlements 1 3832.2
## cos_td_rad 1 3798.1
## sin_td_rad 1 3804.7
## subunit:year_ct 2 3771.5subunit, settlement, year, subunit X year and time of year remain. The final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(3.1798) ( log )
## Formula:
## abundance ~ settlements + subunit * year_ct + cos_td_rad + sin_td_rad +
## (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 3765.5 3810.5 -1871.7 3743.5 433
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5234 -0.7191 -0.2314 0.3733 7.0049
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.02306 0.1519
## Number of obs: 444, groups: site, 16
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.950424 0.370957 2.562 0.0104 *
## settlementsNear 0.467853 0.056287 8.312 < 2e-16 ***
## subunitCarmel 0.157585 0.167980 0.938 0.3482
## subunitGalilee 0.056810 0.168782 0.337 0.7364
## year_ct 0.007430 0.019263 0.386 0.6997
## cos_td_rad 1.542164 0.254486 6.060 1.36e-09 ***
## sin_td_rad -1.954247 0.303854 -6.432 1.26e-10 ***
## subunitCarmel:year_ct -0.003339 0.024747 -0.135 0.8927
## subunitGalilee:year_ct -0.065088 0.025284 -2.574 0.0100 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN sbntCr sbntGl yer_ct cs_td_ sn_td_ sbnC:_
## settlmntsNr -0.125
## subunitCrml -0.412 0.027
## subunitGall -0.438 0.044 0.571
## year_ct -0.228 0.033 0.467 0.453
## cos_td_rad -0.924 0.038 0.164 0.184 0.058
## sin_td_rad 0.881 -0.036 -0.210 -0.248 0.108 -0.853
## sbntCrml:y_ 0.379 -0.027 -0.712 -0.456 -0.651 -0.178 0.262
## sbntGll:yr_ 0.411 -0.033 -0.442 -0.722 -0.641 -0.218 0.293 0.619## $site
## (Intercept)
## Abirim -0.102757905
## Aderet 0.154367270
## Beit Oren 0.076805586
## Ein Yaakov 0.054580098
## Givat Yearim -0.194333521
## Givat Yeshayahu 0.228820719
## Goren -0.058941715
## Iftach 0.112072542
## Kerem Maharal 0.195872158
## Kfar Shamai -0.009153433
## Margaliot 0.006079901
## Nehusha -0.065768490
## Nir Etzion -0.124436590
## Ofer -0.059810496
## Ramat Raziel -0.121861390
## Yagur -0.087359448
##
## with conditional variances for "site"## $site
Not a great fit, high residuals. center time of year variables, highly correlated with intercept.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(3.1798) ( log )
## Formula: abundance ~ settlements + subunit * year_ct + cos_td_rad_c +
## sin_td_rad_c + (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 3765.5 3810.5 -1871.7 3743.5 433
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5234 -0.7191 -0.2314 0.3733 7.0049
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.02306 0.1519
## Number of obs: 444, groups: site, 16
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.190985 0.128752 24.784 < 2e-16 ***
## settlementsNear 0.467853 0.056287 8.312 < 2e-16 ***
## subunitCarmel 0.157587 0.167980 0.938 0.348
## subunitGalilee 0.056815 0.168782 0.337 0.736
## year_ct 0.007431 0.019263 0.386 0.700
## cos_td_rad_c 1.542160 0.254480 6.060 1.36e-09 ***
## sin_td_rad_c -1.954244 0.303845 -6.432 1.26e-10 ***
## subunitCarmel:year_ct -0.003339 0.024747 -0.135 0.893
## subunitGalilee:year_ct -0.065089 0.025284 -2.574 0.010 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN sbntCr sbntGl yer_ct cs_t__ sn_t__ sbnC:_
## settlmntsNr -0.257
## subunitCrml -0.667 0.027
## subunitGall -0.661 0.044 0.571
## year_ct -0.725 0.033 0.467 0.453
## cos_td_rd_c -0.079 0.038 0.164 0.184 0.058
## sin_td_rd_c -0.042 -0.036 -0.210 -0.248 0.108 -0.853
## sbntCrml:y_ 0.479 -0.027 -0.712 -0.456 -0.651 -0.178 0.262
## sbntGll:yr_ 0.474 -0.033 -0.442 -0.722 -0.641 -0.218 0.293 0.619only one level of the interaction term subunit X year is significant, and this too is marginal. subunit and year terms are not significant. \(\Delta AIC\) was 2.1. remove interaction term to simplify model.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(3.115) ( log )
## Formula: abundance ~ settlements + subunit + year_ct + cos_td_rad_c +
## sin_td_rad_c + (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 3771.4 3808.3 -1876.7 3753.4 435
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5374 -0.7305 -0.2353 0.3665 7.6820
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.02485 0.1577
## Number of obs: 444, groups: site, 16
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.29178 0.11263 29.225 < 2e-16 ***
## settlementsNear 0.46421 0.05677 8.176 2.93e-16 ***
## subunitCarmel 0.14116 0.12125 1.164 0.2443
## subunitGalilee -0.24899 0.12007 -2.074 0.0381 *
## year_ct -0.01258 0.01372 -0.917 0.3593
## cos_td_rad_c 1.42120 0.25068 5.669 1.43e-08 ***
## sin_td_rad_c -1.77544 0.29378 -6.043 1.51e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN sbntCr sbntGl yer_ct cs_t__
## settlmntsNr -0.276
## subunitCrml -0.546 0.011
## subunitGall -0.551 0.028 0.506
## year_ct -0.589 0.014 0.006 -0.004
## cos_td_rd_c 0.051 0.032 0.048 0.040 -0.155
## sin_td_rd_c -0.263 -0.027 -0.026 -0.056 0.508 -0.845## Single term deletions
##
## Model:
## abundance ~ settlements + subunit + year_ct + cos_td_rad_c +
## sin_td_rad_c + (1 | site)
## npar AIC
## <none> 3771.4
## settlements 1 3835.9
## subunit 2 3775.7
## year_ct 1 3770.2
## cos_td_rad_c 1 3799.6
## sin_td_rad_c 1 3805.7drop year.
## Single term deletions
##
## Model:
## abundance ~ settlements + subunit + cos_td_rad_c + sin_td_rad_c +
## (1 | site)
## npar AIC
## <none> 3770.2
## settlements 1 3834.9
## subunit 2 3774.5
## cos_td_rad_c 1 3797.6
## sin_td_rad_c 1 3808.5final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(3.1104) ( log )
## Formula: abundance ~ settlements + subunit + cos_td_rad_c + sin_td_rad_c +
## (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 3770.2 3803.0 -1877.1 3754.2 436
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5344 -0.7403 -0.2430 0.3884 7.5406
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.02529 0.159
## Number of obs: 444, groups: site, 16
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.23126 0.09149 35.316 < 2e-16 ***
## settlementsNear 0.46494 0.05681 8.185 2.73e-16 ***
## subunitCarmel 0.14175 0.12198 1.162 0.2452
## subunitGalilee -0.24955 0.12079 -2.066 0.0388 *
## cos_td_rad_c 1.38684 0.24778 5.597 2.18e-08 ***
## sin_td_rad_c -1.63989 0.25301 -6.482 9.08e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN sbntCr sbntGl cs_t__
## settlmntsNr -0.331
## subunitCrml -0.672 0.011
## subunitGall -0.685 0.028 0.506
## cos_td_rd_c -0.050 0.034 0.049 0.041
## sin_td_rd_c 0.053 -0.040 -0.034 -0.063 -0.900## $site
## (Intercept)
## Abirim -0.15062404
## Aderet 0.15414583
## Beit Oren 0.07560413
## Ein Yaakov 0.06292471
## Givat Yearim -0.18382923
## Givat Yeshayahu 0.24855515
## Goren -0.04946051
## Iftach 0.10818494
## Kerem Maharal 0.19785111
## Kfar Shamai -0.02325790
## Margaliot 0.05424765
## Nehusha -0.09644283
## Nir Etzion -0.14968633
## Ofer -0.04582119
## Ramat Raziel -0.12115394
## Yagur -0.07683652
##
## with conditional variances for "site"## $site
Interpretation of abundance model:
| Observations | 444 |
| Dependent variable | abundance |
| Type | Mixed effects generalized linear model |
| Family | Negative Binomial(3.1104) |
| Link | log |
| AIC | 3770.248 |
| BIC | 3803.015 |
| Pseudo-R² (fixed effects) | 0.674 |
| Pseudo-R² (total) | 0.820 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 3.231 | 0.091 | 35.316 | 0.000 |
| settlementsNear | 0.465 | 0.057 | 8.185 | 0.000 |
| subunitCarmel | 0.142 | 0.122 | 1.162 | 0.245 |
| subunitGalilee | -0.250 | 0.121 | -2.066 | 0.039 |
| cos_td_rad_c | 1.387 | 0.248 | 5.597 | 0.000 |
| sin_td_rad_c | -1.640 | 0.253 | -6.482 | 0.000 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| site | (Intercept) | 0.159 |
| Group | # groups | ICC |
|---|---|---|
| site | 16 | 0.065 |
## 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.
significantly lower abundance in Galilee subunit and far from settlements. No significant temporal trend.
community analysis using package MVabund
## Overlapping points were shifted along the y-axis to make them visible.##
## PIPING TO 2nd MVFACTOR## Only the variables Curruca.melanocephala, Turdus.merula, Streptopelia.decaocto, Passer.domesticus, Pycnonotus.xanthopygos, Columba.livia, Parus.major, Apus.apus, Garrulus.glandarius, Cinnyris.osea, Spilopelia.senegalensis, Merops.apiaster were included in the plot
## (the variables with highest total abundance).
## Overlapping points were shifted along the y-axis to make them visible.##
## PIPING TO 2nd MVFACTOR## Only the variables Curruca.melanocephala, Turdus.merula, Streptopelia.decaocto, Passer.domesticus, Pycnonotus.xanthopygos, Columba.livia, Parus.major, Apus.apus, Garrulus.glandarius, Cinnyris.osea, Spilopelia.senegalensis, Merops.apiaster were included in the plot
## (the variables with highest total abundance).
There are few observations with counts of >60. Examine these:
## point_name datetime SciName monitors_name
## 1: Kerem Maharal Far 2 2019-05-07 08:01:00 Merops apiaster Sassi Haham
## 2: Kerem Maharal Near 2 2019-05-07 08:34:00 Merops apiaster Sassi Haham
## 3: Kerem Maharal Near 3 2021-04-17 07:41:00 Chloris chloris Eliraz Dvir
## 4: Kerem Maharal Near 3 2021-04-17 07:41:00 Garrulus glandarius Eliraz Dvir
## 5: Beit Oren Near 3 2019-04-19 07:35:00 Curruca curruca Other
## 6: Beit Oren Near 1 2019-04-19 07:49:00 Curruca curruca Other
## notes rad_0_20 rad_20_100 rad_100_250 rad_over_250
## 1: <NA> 0 30 40 0
## 2: <NA> 0 30 40 0
## 3: 0 80 0 0
## 4: 0 0 150 0
## 5: <NA> 12 30 100 0
## 6: <NA> 14 35 50 0G. glandarius observation seems highly irregular. Remove altogether, might be a mistake. C. curruca observations are due to migration waves, this species is breeding mainly in the far North. Convert to presence observation (count=1).
start model specification:
## nb po
## 806.7111 1215.6466## [1] "POISSON"
## [1] "NEGATIVE BINOMIAL"
negative binomial model is better than poisson according to residuals and AIC comparison.
## nb po nb1
## 806.7111 1215.6466 800.2988The addition of the explanatory variable ‘site’ is improving the AIC of the model. Prefer to exclude site, for simplification. stepwise selection of model:
## Single term deletions
##
## Model:
## spp_no_rare ~ settlements * year_ct + subunit * year_ct + cos_td_rad +
## sin_td_rad
## Df AIC
## <none> 25815
## cos_td_rad 32 25930
## sin_td_rad 32 25970
## settlements:year_ct 32 25793
## year_ct:subunit 64 25849drop settlements X year.
## Single term deletions
##
## Model:
## spp_no_rare ~ settlements + subunit * year_ct + cos_td_rad +
## sin_td_rad
## Df AIC
## <none> 25793
## settlements 32 26629
## cos_td_rad 32 25907
## sin_td_rad 32 25950
## subunit:year_ct 64 25839final model includes settlements, year, subunit, subunit X year, sampling time of year.
##
## Test statistics:
## wald value Pr(>wald)
## (Intercept) 15.681 0.001 ***
## settlementsNear 29.720 0.001 ***
## subunitCarmel 12.075 0.001 ***
## subunitGalilee 8.457 0.010 **
## year_ct 7.356 0.022 *
## cos_td_rad 13.144 0.001 ***
## sin_td_rad 14.543 0.001 ***
## subunitCarmel:year_ct 8.126 0.002 **
## subunitGalilee:year_ct 8.569 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Test statistic: 42.28, 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 + subunit * year_ct + cos_td_rad + sin_td_rad
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 443
## settlements 442 1 798.7 0.01 **
## subunit 440 2 472.0 0.01 **
## year_ct 439 1 183.1 0.01 **
## cos_td_rad 438 1 67.0 0.01 **
## sin_td_rad 437 1 192.9 0.01 **
## subunit:year_ct 435 2 174.0 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 79.707 0.01 38.366 0.01
## subunit 39.138 0.01 10.607 0.11
## year_ct 29.844 0.01 9.298 0.13
## cos_td_rad 3.002 0.98 2.276 0.99
## sin_td_rad 5.64 0.40 0.596 1.00
## subunit:year_ct 8.217 0.42 2.436 1.00
## Apus.apus Bubulcus.ibis Carduelis.carduelis
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 2.336 0.83 0.848 0.89 8.13
## subunit 8.999 0.24 11.606 0.09 21.019
## year_ct 12.567 0.04 5.949 0.44 6.527
## cos_td_rad 0.182 1.00 5.318 0.62 0.389
## sin_td_rad 20.69 0.01 0.59 1.00 1.61
## subunit:year_ct 5.421 0.86 0.513 1.00 12.307
## Cecropis.daurica Chloris.chloris
## Pr(>Dev) Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 0.16 33.414 0.01 30.55 0.01
## subunit 0.01 1.066 0.94 0.613 0.94
## year_ct 0.33 1.257 0.98 5.072 0.54
## cos_td_rad 1.00 0.127 1.00 0.072 1.00
## sin_td_rad 0.98 3.044 0.90 0.112 1.00
## subunit:year_ct 0.14 0.41 1.00 20.167 0.02
## Cinnyris.osea Circaetus.gallicus
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 41.948 0.01 1.493 0.84
## subunit 17.867 0.01 12.886 0.05
## year_ct 3.939 0.67 2.786 0.89
## cos_td_rad 1.651 1.00 0.159 1.00
## sin_td_rad 2.01 0.96 2.586 0.92
## subunit:year_ct 12.677 0.14 0.613 1.00
## Clamator.glandarius Columba.livia
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 0.518 0.91 6.276 0.26
## subunit 12.124 0.09 19.317 0.01
## year_ct 0.336 1.00 20.916 0.01
## cos_td_rad 0.853 1.00 0.01 1.00
## sin_td_rad 0.216 1.00 11.564 0.05
## subunit:year_ct 3.244 0.99 2.764 1.00
## Corvus.cornix Corvus.monedula Curruca.curruca
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 22.932 0.01 0.428 0.91 1.46
## subunit 47.477 0.01 22.714 0.01 5.615
## year_ct 0.695 1.00 0.301 1.00 18.262
## cos_td_rad 1.013 1.00 0.184 1.00 19.292
## sin_td_rad 0.157 1.00 0.053 1.00 9.842
## subunit:year_ct 7.109 0.63 5.316 0.88 0.076
## Curruca.melanocephala Dendrocopos.syriacus
## Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 0.84 21.24 0.01 9.285
## subunit 0.60 41.067 0.01 3.648
## year_ct 0.02 0.747 1.00 0.002
## cos_td_rad 0.01 0.3 1.00 1.417
## sin_td_rad 0.10 0.429 1.00 3.35
## subunit:year_ct 1.00 9.877 0.23 0.232
## Falco.tinnunculus Garrulus.glandarius
## Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 0.12 1.849 0.84 3.502
## subunit 0.86 9.122 0.24 24.625
## year_ct 1.00 0.543 1.00 4.924
## cos_td_rad 1.00 0.078 1.00 1.156
## sin_td_rad 0.88 0.641 1.00 1.081
## subunit:year_ct 1.00 5.248 0.90 5.242
## Hirundo.rustica Iduna.pallida
## Pr(>Dev) Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 0.56 2.62 0.78 0.097 0.91
## subunit 0.01 3.962 0.86 1.537 0.94
## year_ct 0.54 0.561 1.00 7.746 0.26
## cos_td_rad 1.00 0.052 1.00 0.555 1.00
## sin_td_rad 0.99 0.115 1.00 18.574 0.01
## subunit:year_ct 0.90 20.45 0.02 1.56 1.00
## Merops.apiaster Parus.major Passer.domesticus
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 0.328 0.91 3.908 0.56 166.539
## subunit 7.149 0.44 25.606 0.01 21.305
## year_ct 4.191 0.62 0.176 1.00 1.781
## cos_td_rad 0.02 1.00 3.376 0.95 0.485
## sin_td_rad 35.395 0.01 7.701 0.19 2.962
## subunit:year_ct 3.013 0.99 6.652 0.72 2.283
## Prinia.gracilis Psittacula.krameri
## Pr(>Dev) Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 0.01 48.787 0.01 15.594 0.01
## subunit 0.01 1.864 0.94 14.393 0.04
## year_ct 0.98 16.036 0.02 1.082 0.98
## cos_td_rad 1.00 0.633 1.00 0.793 1.00
## sin_td_rad 0.90 1.004 0.99 1.724 0.98
## subunit:year_ct 1.00 13.996 0.07 3.863 0.97
## Pycnonotus.xanthopygos Spilopelia.senegalensis
## Dev Pr(>Dev) Dev
## (Intercept)
## settlements 45.339 0.01 157.013
## subunit 25.29 0.01 27.046
## year_ct 2.87 0.87 2.224
## cos_td_rad 11.394 0.01 0.333
## sin_td_rad 0.499 1.00 0.603
## subunit:year_ct 3.75 0.97 4.692
## Streptopelia.decaocto Streptopelia.turtur
## Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 0.01 1.955 0.84 1.175
## subunit 0.01 12.168 0.09 6.566
## year_ct 0.93 0.068 1.00 18.089
## cos_td_rad 1.00 6.672 0.35 3.045
## sin_td_rad 1.00 14.008 0.02 1.847
## subunit:year_ct 0.90 1.683 1.00 1.887
## Troglodytes.troglodytes Turdus.merula
## Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 0.84 26.472 0.01 3.926
## subunit 0.47 4.411 0.77 2.142
## year_ct 0.02 0.576 1.00 3.137
## cos_td_rad 0.98 0.48 1.00 0.347
## sin_td_rad 0.97 10.786 0.05 33.19
## subunit:year_ct 1.00 5.003 0.90 1.539
## Vanellus.spinosus
## Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 0.56 20.629 0.01
## subunit 0.94 9.058 0.24
## year_ct 0.85 0.554 1.00
## cos_td_rad 1.00 1.337 1.00
## sin_td_rad 0.01 0.243 1.00
## subunit:year_ct 1.00 1.766 1.00
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 99 iterations via PIT-trap resampling.Factors settlements, year, subunit, subunit X year, time of year and site have a statistically significant effect on community composition. settlements X year nearly significant.
## 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
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Significant negative temporal effect on graceful prinia, European turtle dove and barn swallow; positive effect on common myna. Significant effect of settlement proximity: most of the synanthrope / invasive species are near settlements.
