Birds_Negev_Highlands_analysis_by_plot
Ron Chen
2023-05-01
## 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.Negev Highlands
Factors are proximity to settlements, habitat (slope vs. wadi but only far from settlements, near has only slope) and time. Total 5 campaigns, one of which is pilot and will be included only for temporal analysis (separate model without habitat for temporal analysis). 5 sites with 9 plots per site (3 for each habitat X proximity combination).
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"
## Overlapping points were shifted along the y-axis to make them visible.##
## PIPING TO 2nd MVFACTOR## Only the variables Passer.domesticus, Galerida.cristata, Passer.hispaniolensis, Calandrella.brachydactyla, Streptopelia.decaocto, Columba.livia, Ammomanes.deserti, Pycnonotus.xanthopygos, Spilopelia.senegalensis, Corvus.cornix, Curruca.curruca, Alectoris.chukar were included in the plot
## (the variables with highest total abundance).
| richness | year_ct | site | settlements | habitat | 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. :0.000 | Sde Boker:42 | Far :134 | Slope:120 | Min. :-1.60673 | Min. :0.004304 | Min. :-1.0000 | Length:210 | Min. :0.2054 | Min. :-0.2965 | Eyal Shochat: 41 | 0 : 0 | 0 : 0 | 0 : 0 | 0 : 0 | |
| 1st Qu.: 4.000 | 1st Qu.:2.000 | Yeruham :42 | Near: 76 | Wadi : 60 | 1st Qu.:-1.22033 | 1st Qu.:0.177649 | 1st Qu.:-0.9841 | Class :difftime | 1st Qu.:0.7812 | 1st Qu.: 0.1758 | Other : 4 | 1 : 0 | 3 : 0 | 1 : 0 | 1 : 0 | |
| Median : 5.000 | Median :4.000 | Bislach :36 | NA | NA’s : 30 | Median :-0.76238 | Median :0.375715 | Median :-0.9267 | Mode :numeric | Median :0.9157 | Median : 0.4019 | Adi Domer : 0 | 2 : 0 | NA’s:210 | 2 : 0 | 2 : 0 | |
| Mean : 5.995 | Mean :4.286 | Ezuz :36 | NA | NA | Mean :-0.47336 | Mean :0.440753 | Mean :-0.8039 | NA | Mean :0.8356 | Mean : 0.4129 | Asaf Mayrose: 0 | 3 : 0 | NA | 3 : 0 | 3 : 0 | |
| 3rd Qu.: 7.000 | 3rd Qu.:6.000 | Merhav Am:36 | NA | NA | 3rd Qu.: 0.07243 | 3rd Qu.:0.690173 | 3rd Qu.:-0.7236 | NA | 3rd Qu.:0.9813 | 3rd Qu.: 0.6242 | Eliraz Dvir : 0 | NA’s:210 | NA | NA’s:210 | NA’s:210 | |
| Max. :16.000 | Max. :8.000 | Ashalim : 6 | NA | NA | Max. : 2.97281 | Max. :0.999407 | Max. : 0.4787 | NA | Max. :1.0000 | Max. : 0.9787 | (Other) : 0 | NA | NA | NA | NA | |
| NA | NA | (Other) :12 | NA | NA | NA | NA | NA | NA | NA’s :75 | NA’s :75 | NA’s :165 | NA | NA | NA | NA |
no observation for all 4 weather variables. many NAs for sampling time of day variables.exclude from model. some very high observations for C. brachydactyla and P. hispaniolensis. consider removing in abundance and gma analysis.
| richness | year_ct | site | settlements | habitat | td_sc | cos_td_rad | sin_td_rad | h_from_sunrise | cos_hsun | sin_hsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| richness | 1.0000000 | 0.1174052 | -0.0518983 | 0.4198064 | 0.0673781 | 0.1018583 | 0.1138871 | 0.0850412 | 0.0381659 | 0.0625278 | 0.0776173 |
| year_ct | 0.1174052 | 1.0000000 | -0.0195456 | -0.0866957 | 0.0000000 | -0.2769423 | -0.2743588 | -0.2470754 | -0.2130095 | 0.1769441 | -0.2089485 |
| site | -0.0518983 | -0.0195456 | 1.0000000 | -0.0050223 | 0.0000000 | 0.0012529 | 0.0254386 | -0.0200397 | -0.0591920 | 0.0649672 | -0.0532192 |
| settlements | 0.4198064 | -0.0866957 | -0.0050223 | 1.0000000 | -0.5062633 | -0.0578505 | -0.0886498 | -0.0259035 | 0.1001375 | 0.0051981 | 0.1351982 |
| habitat | 0.0673781 | 0.0000000 | 0.0000000 | -0.5062633 | 1.0000000 | 0.0215880 | 0.0428524 | -0.0015303 | 0.0339170 | -0.0318150 | 0.0277413 |
| td_sc | 0.1018583 | -0.2769423 | 0.0012529 | -0.0578505 | 0.0215880 | 1.0000000 | 0.9677863 | 0.9679062 | 0.0805063 | -0.0915903 | 0.0697040 |
| cos_td_rad | 0.1138871 | -0.2743588 | 0.0254386 | -0.0886498 | 0.0428524 | 0.9677863 | 1.0000000 | 0.8777655 | 0.0850144 | -0.0994639 | 0.0727109 |
| sin_td_rad | 0.0850412 | -0.2470754 | -0.0200397 | -0.0259035 | -0.0015303 | 0.9679062 | 0.8777655 | 1.0000000 | 0.0797859 | -0.0800387 | 0.0732132 |
| h_from_sunrise | 0.0381659 | -0.2130095 | -0.0591920 | 0.1001375 | 0.0339170 | 0.0805063 | 0.0850144 | 0.0797859 | 1.0000000 | -0.9348973 | 0.9913101 |
| cos_hsun | 0.0625278 | 0.1769441 | 0.0649672 | 0.0051981 | -0.0318150 | -0.0915903 | -0.0994639 | -0.0800387 | -0.9348973 | 1.0000000 | -0.8826232 |
| sin_hsun | 0.0776173 | -0.2089485 | -0.0532192 | 0.1351982 | 0.0277413 | 0.0697040 | 0.0727109 | 0.0732132 | 0.9913101 | -0.8826232 | 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 = 0.998947985653261"Overdispersion parameter is approximately 1. 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 12 811.0396
## m0.nb 13 813.0416## [1] "poisson"
## [1] "neg bin"
negative binomial did not converge, probably because there is no overdispersion and poisson is better. remove outliers
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00312836 (tol = 0.002, component 1)go with mixed model.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: richness ~ settlements * year_ct + habitat * year_ct + cos_td_rad +
## sin_td_rad + (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 811.3 840.0 -396.6 793.3 171
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8377 -0.6935 -0.0927 0.5519 3.2457
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.000318 0.01783
## Number of obs: 180, groups: site, 5
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.087557 0.336553 3.231 0.00123 **
## settlementsNear 0.262826 0.195240 1.346 0.17825
## year_ct -0.005147 0.029158 -0.177 0.85988
## habitatWadi 0.244747 0.198249 1.235 0.21700
## cos_td_rad 0.480798 0.229260 2.097 0.03598 *
## sin_td_rad -0.204783 0.270718 -0.756 0.44938
## settlementsNear:year_ct 0.067014 0.035075 1.911 0.05606 .
## year_ct:habitatWadi 0.030698 0.036264 0.847 0.39727
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct hbttWd cs_td_ sn_td_ sttN:_
## settlmntsNr -0.501
## year_ct -0.340 0.691
## habitatWadi -0.348 0.606 0.685
## cos_td_rad -0.874 0.166 -0.022 -0.001
## sin_td_rad 0.856 -0.145 0.133 0.007 -0.891
## sttlmntsN:_ 0.430 -0.913 -0.778 -0.566 -0.111 0.097
## yr_ct:hbttW 0.319 -0.556 -0.756 -0.912 0.002 -0.007 0.626
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00312836 (tol = 0.002, component 1)perform stepwise model selection of poisson mixed model.
## boundary (singular) fit: see help('isSingular')## Single term deletions
##
## Model:
## richness ~ settlements * year_ct + habitat * year_ct + cos_td_rad +
## sin_td_rad + (1 | site)
## npar AIC
## <none> 811.26
## cos_td_rad 1 813.31
## sin_td_rad 1 809.83
## settlements:year_ct 1 812.92
## year_ct:habitat 1 809.98remove sine of time of year.
## Single term deletions
##
## Model:
## richness ~ settlements * year_ct + habitat * year_ct + cos_td_rad +
## (1 | site)
## npar AIC
## <none> 809.83
## cos_td_rad 1 816.87
## settlements:year_ct 1 811.84
## year_ct:habitat 1 808.54drop year X habitat. define model without pilot observations in which habitat is NA, to continue with model selection (otherwise throws error). Attempt also model with pilot and without habitat variable.
## Single term deletions
##
## Model:
## richness ~ settlements * year_ct + habitat + cos_td_rad + (1 |
## site)
## npar AIC
## <none> 808.54
## habitat 1 831.12
## cos_td_rad 1 815.63
## settlements:year_ct 1 810.13## Single term deletions
##
## Model:
## richness ~ settlements * year_ct + cos_td_rad + (1 | site)
## npar AIC
## <none> 962.12
## cos_td_rad 1 970.42
## settlements:year_ct 1 960.12interaction of settlements and year drops in both models.
## Single term deletions
##
## Model:
## richness ~ settlements + year_ct + habitat + cos_td_rad + (1 |
## site)
## npar AIC
## <none> 810.13
## settlements 1 868.25
## year_ct 1 815.30
## habitat 1 832.86
## cos_td_rad 1 817.98## Single term deletions
##
## Model:
## richness ~ settlements + year_ct + cos_td_rad + (1 | site)
## npar AIC
## <none> 960.12
## settlements 1 1013.00
## year_ct 1 968.81
## cos_td_rad 1 968.45In both models settlements, year and cosine of time of year remain. Habitat remains in the model without the pilot data. Final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: richness ~ settlements + year_ct + habitat + cos_td_rad + (1 |
## site)
## Data: P.anal_no_pilot
##
## AIC BIC logLik deviance df.resid
## 810.1 829.3 -399.1 798.1 174
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7883 -0.6744 -0.1446 0.5094 3.8026
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.00147 0.03834
## Number of obs: 180, groups: site, 5
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.09404 0.12372 8.843 < 2e-16 ***
## settlementsNear 0.59607 0.07858 7.585 3.32e-14 ***
## year_ct 0.03879 0.01446 2.682 0.00732 **
## habitatWadi 0.39964 0.08120 4.922 8.58e-07 ***
## cos_td_rad 0.33492 0.10539 3.178 0.00148 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct hbttWd
## settlmntsNr -0.466
## year_ct -0.759 0.043
## habitatWadi -0.405 0.623 0.011
## cos_td_rad -0.642 0.080 0.381 0.008## $site
## (Intercept)
## Bislach -0.007261337
## Ezuz 0.023177430
## Merhav Am 0.005715354
## Sde Boker -0.032234389
## Yeruham 0.011278869
##
## with conditional variances for "site"## $site
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: richness ~ settlements + year_ct + cos_td_rad + (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 960.1 976.9 -475.1 950.1 205
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7447 -0.7054 -0.1618 0.6443 3.8688
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.00175 0.04183
## Number of obs: 210, groups: site, 8
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.31182 0.08932 14.687 < 2e-16 ***
## settlementsNear 0.42720 0.05711 7.480 7.42e-14 ***
## year_ct 0.03617 0.01101 3.287 0.00101 **
## cos_td_rad 0.33036 0.10143 3.257 0.00113 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct
## settlmntsNr -0.407
## year_ct -0.713 0.105
## cos_td_rad -0.701 0.109 0.286## $site
## (Intercept)
## Ashalim 0.0023300013
## Bislach -0.0096039922
## Ezuz 0.0257454807
## Mashabei Sadeh 0.0072947751
## Merhav Am 0.0057852499
## Mitzpe Ramon 0.0001246131
## Sde Boker -0.0426170356
## Yeruham 0.0119704829
##
## with conditional variances for "site"## $site
Coefficients are quite similar in both models (model without pilot campaign and with habitat variable, vs. model with pilot data and without habitat variable). Prefer model with habitat variable, throw out pilot.
## Registered S3 methods overwritten by 'broom':
## method from
## tidy.glht jtools
## tidy.summary.glht jtools| Observations | 180 |
| Dependent variable | richness |
| Type | Mixed effects generalized linear model |
| Family | poisson |
| Link | log |
| AIC | 810.132 |
| BIC | 829.290 |
| Pseudo-R² (fixed effects) | 0.309 |
| Pseudo-R² (total) | 0.315 |
| exp(Est.) | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 2.986 | 0.124 | 8.843 | 0.000 |
| settlementsNear | 1.815 | 0.079 | 7.585 | 0.000 |
| year_ct | 1.040 | 0.014 | 2.682 | 0.007 |
| habitatWadi | 1.491 | 0.081 | 4.922 | 0.000 |
| cos_td_rad | 1.398 | 0.105 | 3.178 | 0.001 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| site | (Intercept) | 0.038 |
| Group | # groups | ICC |
|---|---|---|
| site | 5 | 0.001 |
## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.
## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.
## 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 higher richness near settlements and in the wadi compared to slope. significant increase 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 | habitat | td_sc | cos_td_rad | sin_td_rad | |
|---|---|---|---|---|---|---|---|---|
| Min. :1.000 | Min. :0.000 | Sde Boker:42 | Far :134 | Slope:120 | Min. :-1.60673 | Min. :0.004304 | Min. :-1.0000 | |
| 1st Qu.:1.529 | 1st Qu.:2.000 | Yeruham :42 | Near: 76 | Wadi : 60 | 1st Qu.:-1.22033 | 1st Qu.:0.177649 | 1st Qu.:-0.9841 | |
| Median :2.000 | Median :4.000 | Bislach :36 | NA | NA’s : 30 | Median :-0.76238 | Median :0.375715 | Median :-0.9267 | |
| Mean :2.223 | Mean :4.286 | Ezuz :36 | NA | NA | Mean :-0.47336 | Mean :0.440753 | Mean :-0.8039 | |
| 3rd Qu.:2.640 | 3rd Qu.:6.000 | Merhav Am:36 | NA | NA | 3rd Qu.: 0.07243 | 3rd Qu.:0.690173 | 3rd Qu.:-0.7236 | |
| Max. :6.258 | Max. :8.000 | Ashalim : 6 | NA | NA | Max. : 2.97281 | Max. :0.999407 | Max. : 0.4787 | |
| NA | NA | (Other) :12 | NA | NA | NA | NA | NA |
Fit glm, compare gamma, gaussian (poisson inappropriate because response is not discrete)
Gamma seems better than gaussian. Remove rows 121, 133 and 33. Fit fixed and mixed models.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0102318 (tol = 0.002, component 1)Mixed model did not converge, use glm:
##
## Call:
## glm(formula = gma ~ settlements * year_ct + habitat * year_ct +
## cos_td_rad + sin_td_rad + site, family = Gamma, data = P.anal)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70693 -0.26208 -0.02656 0.17541 0.76620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6332093 0.1133520 5.586 9.39e-08 ***
## settlementsNear -0.1916040 0.0648771 -2.953 0.00360 **
## year_ct -0.0003538 0.0098713 -0.036 0.97146
## habitatWadi -0.1572311 0.0702246 -2.239 0.02649 *
## cos_td_rad -0.1007308 0.0832628 -1.210 0.22809
## sin_td_rad 0.0677847 0.0822054 0.825 0.41080
## siteEzuz -0.1129750 0.0340624 -3.317 0.00112 **
## siteMerhav Am -0.0179475 0.0378792 -0.474 0.63626
## siteSde Boker 0.0154967 0.0379448 0.408 0.68351
## siteYeruham 0.0356482 0.0389215 0.916 0.36106
## settlementsNear:year_ct 0.0145032 0.0119441 1.214 0.22638
## year_ct:habitatWadi 0.0324498 0.0134723 2.409 0.01711 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.1020244)
##
## Null deviance: 24.208 on 176 degrees of freedom
## Residual deviance: 16.777 on 165 degrees of freedom
## (30 observations deleted due to missingness)
## AIC: 365.46
##
## Number of Fisher Scoring iterations: 5perform stepwise model selection of Gaussian model.
## Start: AIC=365.46
## gma ~ settlements * year_ct + habitat * year_ct + cos_td_rad +
## sin_td_rad + site
##
## Df Deviance AIC
## - sin_td_rad 1 16.851 364.18
## - settlements:year_ct 1 16.928 364.94
## - cos_td_rad 1 16.932 364.97
## <none> 16.777 365.46
## - year_ct:habitat 1 17.373 369.30
## - site 4 19.281 382.00
##
## Step: AIC=364.24
## gma ~ settlements + year_ct + habitat + cos_td_rad + site + settlements:year_ct +
## year_ct:habitat
##
## Df Deviance AIC
## - cos_td_rad 1 16.951 363.23
## - settlements:year_ct 1 16.982 363.53
## <none> 16.851 364.24
## - year_ct:habitat 1 17.450 368.12
## - site 4 19.351 380.79
##
## Step: AIC=363.31
## gma ~ settlements + year_ct + habitat + site + settlements:year_ct +
## year_ct:habitat
##
## Df Deviance AIC
## - settlements:year_ct 1 17.084 362.63
## <none> 16.951 363.31
## - year_ct:habitat 1 17.562 367.37
## - site 4 19.352 379.12
##
## Step: AIC=362.72
## gma ~ settlements + year_ct + habitat + site + year_ct:habitat
##
## Df Deviance AIC
## <none> 17.084 362.72
## - year_ct:habitat 1 17.575 365.57
## - site 4 19.532 378.92
## - settlements 1 19.069 380.35Settlements, year, habitat, year X habitat and site remain. This is the final model:
##
## Call:
## glm(formula = gma ~ settlements + year_ct + habitat + site +
## year_ct:habitat, family = Gamma, data = P.anal_no_pilot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.81409 -0.27678 -0.05694 0.17422 1.04883
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.430465 0.043909 9.803 < 2e-16 ***
## settlementsNear -0.092062 0.028266 -3.257 0.00136 **
## year_ct 0.011044 0.006127 1.803 0.07322 .
## habitatWadi -0.080421 0.061639 -1.305 0.19374
## siteEzuz -0.084487 0.033626 -2.513 0.01291 *
## siteMerhav Am -0.001550 0.037091 -0.042 0.96672
## siteSde Boker 0.056872 0.039657 1.434 0.15337
## siteYeruham 0.064972 0.040023 1.623 0.10636
## year_ct:habitatWadi 0.018978 0.011769 1.612 0.10870
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.1244957)
##
## Null deviance: 26.615 on 179 degrees of freedom
## Residual deviance: 19.963 on 171 degrees of freedom
## AIC: 399.1
##
## Number of Fisher Scoring iterations: 5
habitat and habitat X year are not significant, year is almost significant. compare model without habitat variable.
## Single term deletions
##
## Model:
## gma ~ settlements + year_ct + (1 | site)
## npar AIC
## <none> 436.07
## settlements 1 472.50
## year_ct 1 445.17## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( inverse )
## Formula: gma ~ settlements + year_ct + (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 436.1 452.7 -213.0 426.1 202
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6711 -0.6637 -0.0455 0.5364 3.6409
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.002339 0.04836
## Residual 0.111967 0.33461
## Number of obs: 207, groups: site, 8
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 0.495065 0.038716 12.787 < 2e-16 ***
## settlementsNear -0.130597 0.019932 -6.552 5.68e-11 ***
## year_ct 0.014193 0.004249 3.341 0.000836 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN
## settlmntsNr -0.312
## year_ct -0.226 0.087year comes out significant when excluding habitat variable and including pilot, and the coefficients are similar. use mixed model without habitat to display temporal and settlement effects.
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( inverse )
## Formula: gma ~ settlements + year_ct + (1 | site)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 436.1 452.7 -213.0 426.1 202
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6711 -0.6637 -0.0455 0.5364 3.6409
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.002339 0.04836
## Residual 0.111967 0.33461
## Number of obs: 207, groups: site, 8
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 0.495065 0.038716 12.787 < 2e-16 ***
## settlementsNear -0.130597 0.019932 -6.552 5.68e-11 ***
## year_ct 0.014193 0.004249 3.341 0.000836 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN
## settlmntsNr -0.312
## year_ct -0.226 0.087## 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.
Very significant temporal decrease in gma as well as lower gma far from settlements.
abundance
Explore data
## [1] "ABUNDANCE WITHOUT RARE SPECIES"
| abundance | year_ct | site | settlements | habitat | td_sc | cos_td_rad | sin_td_rad | |
|---|---|---|---|---|---|---|---|---|
| Min. : 1.0 | Min. :0.000 | Sde Boker:42 | Far :134 | Slope:120 | Min. :-1.60673 | Min. :0.004304 | Min. :-1.0000 | |
| 1st Qu.: 7.0 | 1st Qu.:2.000 | Yeruham :42 | Near: 76 | Wadi : 60 | 1st Qu.:-1.22033 | 1st Qu.:0.177649 | 1st Qu.:-0.9841 | |
| Median : 14.0 | Median :4.000 | Bislach :36 | NA | NA’s : 30 | Median :-0.76238 | Median :0.375715 | Median :-0.9267 | |
| Mean : 19.9 | Mean :4.286 | Ezuz :36 | NA | NA | Mean :-0.47336 | Mean :0.440753 | Mean :-0.8039 | |
| 3rd Qu.: 26.0 | 3rd Qu.:6.000 | Merhav Am:36 | NA | NA | 3rd Qu.: 0.07243 | 3rd Qu.:0.690173 | 3rd Qu.:-0.7236 | |
| Max. :189.0 | Max. :8.000 | Ashalim : 6 | NA | NA | Max. : 2.97281 | Max. :0.999407 | Max. : 0.4787 | |
| NA | NA | (Other) :12 | NA | NA | NA | NA | NA |
4 outliers with total abundance >70. Examine:
## unit subunit site year year_ct settlements agriculture habitat
## 1: Negev Highlands <NA> Ezuz 2014 2 Far <NA> Wadi
## 2: Negev Highlands <NA> Bislach 2018 6 Far <NA> Slope
## 3: Negev Highlands <NA> Ezuz 2018 6 Near <NA> Slope
## 4: Negev Highlands <NA> Bislach 2020 8 Near <NA> Slope
## dunes land_use point_name date datetime
## 1: <NA> <NA> Ezuz Far Wadi 6 2014-04-14 00:00:00 <NA>
## 2: <NA> <NA> Bislach Far Slope 1 2018-03-30 00:00:00 2018-03-30 08:59:00
## 3: <NA> <NA> Ezuz Near Slope 2 2018-04-06 00:00:00 2018-04-06 09:13:00
## 4: <NA> <NA> Bislach Near Slope 6 2020-04-06 03:00:00 2020-04-06 11:06:40
## td_sc td_rad cos_td_rad sin_td_rad timediff_Jun21 monitors_name wind
## 1: -0.767146 -1.187780 0.3737197 -0.9275417 -69.000 days <NA> <NA>
## 2: -1.339587 -1.445993 0.1244793 -0.9922222 -84.000 days <NA> <NA>
## 3: -1.072448 -1.325494 0.2428497 -0.9700639 -77.000 days <NA> <NA>
## 4: -1.029515 -1.306128 0.2615893 -0.9651793 -75.875 days Eyal Shochat <NA>
## precipitation temperature clouds h_from_sunrise cos_hsun sin_hsun pilot
## 1: <NA> <NA> <NA> NA hours NA NA FALSE
## 2: <NA> <NA> <NA> 2.35 hours 0.8166416 0.5771452 FALSE
## 3: <NA> <NA> <NA> 2.70 hours 0.7604060 0.6494480 FALSE
## 4: <NA> <NA> <NA> 1.64 hours 0.9092361 0.4162808 FALSE
## richness abundance gma
## 1: 5 87 5.378269
## 2: 5 189 4.919019
## 3: 8 89 4.314174
## 4: 8 110 2.306143Exclude points with abundance>100.
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(2.1123) ( log )
## Formula: abundance ~ settlements * year_ct + habitat * year_ct + cos_td_rad +
## sin_td_rad + (1 | site)
## Data: P.anal_no_pilot
##
## AIC BIC logLik deviance df.resid
## 1396.1 1428.0 -688.1 1376.1 170
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2857 -0.7267 -0.2570 0.3103 13.4944
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.06924 0.2631
## Number of obs: 180, groups: site, 5
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.96294 0.62563 4.736 2.18e-06 ***
## settlementsNear 0.42891 0.33469 1.282 0.200
## year_ct -0.01201 0.04997 -0.240 0.810
## habitatWadi 0.40277 0.33848 1.190 0.234
## cos_td_rad -0.24369 0.45523 -0.535 0.592
## sin_td_rad 0.28716 0.49505 0.580 0.562
## settlementsNear:year_ct 0.07502 0.06108 1.228 0.219
## year_ct:habitatWadi -0.05994 0.06186 -0.969 0.333
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct hbttWd cs_td_ sn_td_ sttN:_
## settlmntsNr -0.394
## year_ct -0.320 0.669
## habitatWadi -0.254 0.575 0.676
## cos_td_rad -0.880 0.101 0.023 -0.045
## sin_td_rad 0.850 -0.075 0.141 0.079 -0.885
## sttlmntsN:_ 0.359 -0.914 -0.736 -0.532 -0.089 0.056
## yr_ct:hbttW 0.258 -0.534 -0.741 -0.911 0.014 -0.056 0.583model selection of mixed model.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00387969 (tol = 0.002, component 1)## Single term deletions
##
## Model:
## abundance ~ settlements * year_ct + habitat * year_ct + cos_td_rad +
## sin_td_rad + (1 | site)
## npar AIC
## <none> 1396.1
## cos_td_rad 1 1394.4
## sin_td_rad 1 1394.5
## settlements:year_ct 1 1395.6
## year_ct:habitat 1 1395.0drop interaction habitat X year.
## Single term deletions
##
## Model:
## abundance ~ settlements * year_ct + habitat + cos_td_rad + sin_td_rad +
## (1 | site)
## npar AIC
## <none> 1395.0
## habitat 1 1393.6
## cos_td_rad 1 1393.3
## sin_td_rad 1 1393.3
## settlements:year_ct 1 1397.9drop sine of time of year.
## Single term deletions
##
## Model:
## abundance ~ settlements * year_ct + habitat + cos_td_rad + (1 |
## site)
## npar AIC
## <none> 1393.3
## habitat 1 1391.8
## cos_td_rad 1 1391.3
## settlements:year_ct 1 1395.9drop cosine of time of year.
## Single term deletions
##
## Model:
## abundance ~ settlements * year_ct + habitat + (1 | site)
## npar AIC
## <none> 1391.3
## habitat 1 1389.8
## settlements:year_ct 1 1394.0Settlement, year, settlement X year remain. The final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(2.097) ( log )
## Formula: abundance ~ settlements * year_ct + habitat + (1 | site)
## Data: P.anal_no_pilot
##
## AIC BIC logLik deviance df.resid
## 1391.3 1413.7 -688.7 1377.3 173
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2825 -0.7062 -0.2602 0.2931 13.9972
##
## Random effects:
## Groups Name Variance Std.Dev.
## site (Intercept) 0.06869 0.2621
## Number of obs: 180, groups: site, 5
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.81742 0.21948 12.837 <2e-16 ***
## settlementsNear 0.27514 0.28359 0.970 0.3320
## year_ct -0.04903 0.03067 -1.599 0.1099
## habitatWadi 0.09773 0.13960 0.700 0.4839
## settlementsNear:year_ct 0.10622 0.04948 2.147 0.0318 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) sttlmN yer_ct hbttWd
## settlmntsNr -0.551
## year_ct -0.715 0.547
## habitatWadi -0.367 0.291 0.056
## sttlmntsN:_ 0.441 -0.878 -0.614 -0.040## $site
## (Intercept)
## Bislach 0.29802232
## Ezuz 0.18942049
## Merhav Am 0.04368878
## Sde Boker -0.35239474
## Yeruham -0.17589115
##
## with conditional variances for "site"## $site
Interpretation of abundance model:
| Observations | 180 |
| Dependent variable | abundance |
| Type | Mixed effects generalized linear model |
| Family | Negative Binomial(2.097) |
| Link | log |
| AIC | 1391.339 |
| BIC | 1413.690 |
| Pseudo-R² (fixed effects) | 0.546 |
| Pseudo-R² (total) | 0.807 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 2.817 | 0.219 | 12.837 | 0.000 |
| settlementsNear | 0.275 | 0.284 | 0.970 | 0.332 |
| year_ct | -0.049 | 0.031 | -1.599 | 0.110 |
| habitatWadi | 0.098 | 0.140 | 0.700 | 0.484 |
| settlementsNear:year_ct | 0.106 | 0.049 | 2.147 | 0.032 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| site | (Intercept) | 0.262 |
| Group | # groups | ICC |
|---|---|---|
| site | 5 | 0.111 |
## 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 in wadi compared to slope. significant interaction between settlement proximity and year: higher abundance near settlements and increasing with time; lower abundance far from settlements and decreasing with time. No interaction of year with habitat (both wadi and slope are decreasing). This was meant to examine climate change, assuming wadi may decrease faster
community analysis using package MVabund
## nb po
## 198.9584 296.4435## [1] "POISSON"
## [1] "NEGATIVE BINOMIAL"
negative binomial model is better than poisson according to residuals and AIC comparison.
## nb po nb1
## 198.9584 296.4435 197.4497The addition of the explanatory variable ‘site’ is improving the AIC of the model. stepwise selection of model:
## Single term deletions
##
## Model:
## spp_no_rare ~ settlements * year_ct + habitat * year_ct + cos_td_rad +
## sin_td_rad + site
## Df AIC
## <none> 7700.5
## cos_td_rad 39 7708.3
## sin_td_rad 39 7689.4
## site 156 7759.4
## settlements:year_ct 39 7676.4
## year_ct:habitat 39 7675.1drop interaction of habitat with year.
## Single term deletions
##
## Model:
## spp_no_rare ~ settlements * year_ct + habitat + cos_td_rad +
## sin_td_rad + site
## Df AIC
## <none> 7675.1
## habitat 39 7775.8
## cos_td_rad 39 7685.4
## sin_td_rad 39 7664.4
## site 156 7736.2
## settlements:year_ct 39 7642.1drop interaction of settlements with year.
## Single term deletions
##
## Model:
## spp_no_rare ~ settlements + year_ct + habitat + cos_td_rad +
## sin_td_rad + site
## Df AIC
## <none> 7642.1
## settlements 39 8058.1
## year_ct 39 7702.8
## habitat 39 7738.4
## cos_td_rad 39 7658.7
## sin_td_rad 39 7636.4
## site 156 7699.7drop sinus of time of year.
## Single term deletions
##
## Model:
## spp_no_rare ~ settlements + year_ct + habitat + cos_td_rad +
## site
## Df AIC
## <none> 7636.4
## settlements 39 8058.7
## year_ct 39 7714.1
## habitat 39 7731.1
## cos_td_rad 39 7737.6
## site 156 7711.9final model includes settlements, year, habitat, sampling time of year and site.
##
## Test statistics:
## wald value Pr(>wald)
## (Intercept) 12.861 0.011 *
## settlementsNear 16.894 0.012 *
## year_ct 10.736 0.012 *
## habitatWadi 11.160 0.010 **
## cos_td_rad 11.730 0.010 **
## siteEzuz 7.993 0.012 *
## siteMerhav Am 6.561 0.075 .
## siteSde Boker 7.684 0.013 *
## siteYeruham 10.249 0.011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Test statistic: 30.62, p-value: 0.012
## Arguments:
## Test statistics calculated assuming response assumed to be uncorrelated
## P-value calculated using 999 resampling iterations via pit.trap resampling (to account for correlation in testing).## Analysis of Deviance Table
##
## Model: spp_no_rare ~ settlements + year_ct + habitat + cos_td_rad + site
##
## Multivariate test:
## Res.Df Df.diff Dev Pr(>Dev)
## (Intercept) 179
## settlements 178 1 437.9 0.001 ***
## year_ct 177 1 153.3 0.001 ***
## habitat 176 1 156.2 0.001 ***
## cos_td_rad 175 1 229.3 0.001 ***
## site 171 4 394.5 0.001 ***
## ---
## 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 25.066 0.002 1.939 0.928
## year_ct 17.001 0.009 3.26 0.910
## habitat 2.583 0.975 7.393 0.355
## cos_td_rad 0.034 1.000 1.479 0.993
## site 5.56 0.982 4.68 0.982
## Ammomanes.deserti Ammoperdix.heyi
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 72.645 0.001 1.785 0.934
## year_ct 0.001 1.000 0.968 0.999
## habitat 7.15 0.382 1.317 0.989
## cos_td_rad 16.791 0.008 0.641 1.000
## site 10.819 0.740 6.94 0.982
## Anthus.campestris Apus.pallidus Argya.squamiceps
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 1.017 0.978 1.932 0.928 2.074
## year_ct 18.314 0.006 2.542 0.960 0.494
## habitat 0.198 1.000 0.14 1.000 1.402
## cos_td_rad 5.274 0.503 11.261 0.075 3.334
## site 11.07 0.713 7.803 0.976 3.194
## Burhinus.oedicnemus Calandrella.brachydactyla
## Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 0.920 4.775 0.615 2.996
## year_ct 1.000 0.003 1.000 4.466
## habitat 0.989 0 1.000 2.41
## cos_td_rad 0.850 0.071 1.000 43.79
## site 0.982 2.508 0.982 24.241
## Cercotrichas.galactotes Chloris.chloris
## Pr(>Dev) Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 0.835 0.429 0.993 0.013 1.000
## year_ct 0.745 0.982 0.999 0.391 1.000
## habitat 0.975 4.157 0.834 8.734 0.227
## cos_td_rad 0.001 8.494 0.189 0.061 1.000
## site 0.013 8.673 0.939 6.185 0.982
## Cinnyris.osea Columba.livia Corvus.cornix
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 8.599 0.149 11.995 0.024 43.436
## year_ct 0.1 1.000 2.429 0.961 2.032
## habitat 14.283 0.021 0.989 0.996 9.322
## cos_td_rad 9.594 0.132 1.549 0.993 0.029
## site 5.555 0.982 7.587 0.976 9.386
## Corvus.ruficollis Curruca.curruca
## Pr(>Dev) Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 0.001 1.546 0.939 9.682 0.079
## year_ct 0.984 2.221 0.973 7.173 0.337
## habitat 0.186 0.267 1.000 4.226 0.825
## cos_td_rad 1.000 0.198 1.000 19.845 0.003
## site 0.887 6.049 0.982 8.731 0.939
## Emberiza.caesia Falco.tinnunculus
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 6.78 0.334 0.942 0.978
## year_ct 23.49 0.003 1.132 0.999
## habitat 1.04 0.996 1.68 0.988
## cos_td_rad 8.796 0.178 0.176 1.000
## site 5.245 0.982 5.671 0.982
## Galerida.cristata Hirundo.rustica Iduna.pallida
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 4.027 0.696 0.086 1.000 0.743
## year_ct 1.291 0.999 0.581 1.000 0.27
## habitat 0.113 1.000 9.084 0.203 0.929
## cos_td_rad 0.846 1.000 0.166 1.000 10.247
## site 26.79 0.005 8.422 0.939 17.256
## Lanius.excubitor Oenanthe.isabellina
## Pr(>Dev) Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 0.980 7.882 0.199 2.612 0.851
## year_ct 1.000 5.333 0.623 0.161 1.000
## habitat 0.996 5.152 0.664 2.592 0.975
## cos_td_rad 0.097 5.659 0.454 18.829 0.004
## site 0.117 12.503 0.526 13.169 0.458
## Oenanthe.lugens Oenanthe.melanura
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 4.482 0.619 0.037 1.000
## year_ct 1.177 0.999 11.719 0.062
## habitat 0.244 1.000 2.607 0.975
## cos_td_rad 8.944 0.172 0.332 1.000
## site 13.385 0.445 13.697 0.411
## Onychognathus.tristramii Passer.domesticus
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 0.023 1.000 40.498 0.001
## year_ct 0.029 1.000 0.445 1.000
## habitat 0.042 1.000 2.275 0.975
## cos_td_rad 2.007 0.975 7.875 0.224
## site 16.317 0.160 21.282 0.034
## Passer.hispaniolensis Prinia.gracilis
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 1.197 0.970 5.385 0.510
## year_ct 0.19 1.000 0.831 1.000
## habitat 6.505 0.481 14.901 0.018
## cos_td_rad 1.588 0.993 0.778 1.000
## site 6.942 0.982 10.422 0.786
## Pterocles.orientalis Ptyonoprogne.fuligula
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 5.913 0.476 0.326 0.997
## year_ct 2.634 0.960 3.571 0.887
## habitat 0.213 1.000 1.548 0.989
## cos_td_rad 7.544 0.224 4.519 0.642
## site 13.269 0.451 15.971 0.177
## Pycnonotus.xanthopygos Rhodospiza.obsoleta
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 37.902 0.001 0.846 0.980
## year_ct 4.919 0.684 0.675 1.000
## habitat 7.501 0.355 2.672 0.975
## cos_td_rad 3.18 0.868 1.222 0.995
## site 4.735 0.982 6.614 0.982
## Scotocerca.inquieta Spilopelia.senegalensis
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 27.175 0.001 70.028 0.001
## year_ct 8.743 0.182 0.149 1.000
## habitat 1.574 0.989 5.32 0.660
## cos_td_rad 9.006 0.172 0.26 1.000
## site 6.276 0.982 12.849 0.502
## Streptopelia.decaocto Streptopelia.turtur
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## settlements 9.087 0.109 0.028 1.000
## year_ct 13.97 0.023 0.726 1.000
## habitat 21.3 0.001 2.8 0.967
## cos_td_rad 2.814 0.899 7.758 0.224
## site 9.447 0.885 12.84 0.502
## Sylvia.atricapilla Upupa.epops Vanellus.spinosus
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## settlements 5.909 0.476 0.092 1.000 15.94
## year_ct 7.501 0.295 0.628 1.000 0.762
## habitat 1.022 0.996 0.548 0.996 0
## cos_td_rad 3.897 0.744 0.404 1.000 0.017
## site 7.051 0.978 0.314 0.994 5.042
##
## Pr(>Dev)
## (Intercept)
## settlements 0.003
## year_ct 1.000
## habitat 1.000
## cos_td_rad 1.000
## site 0.982
## Arguments:
## Test statistics calculated assuming uncorrelated response (for faster computation)
## P-value calculated using 999 iterations via PIT-trap resampling.All factors (settlements, year, time of year and 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
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Significant negative temporal effect on house sparrow and positive effect on common myna. Significant effect of settlement proximity: synanthrope / invasive species near settlements, endangered and / or batha specialists far from settlements.
