Birds_Batha_analysis_by_plot
Ron Chen
2024-02-25
## 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.Herbaceous and Dwarf-Shrub Vegetation
Factors are proximity to agriculture (plantations and orchards) and time. Total 4 campaigns. 2 regions: herbaceous batha (grassland) and dwarf-shrub batha (low shrubland). 5 sites per region (10 sites total) with 6 plots per site (60 plots total). Raw data Total abundance: 14138 Number of observations: 3561 Total richness: 122
Filtered data Total abundance: 6571 Number of observations: Cannot calculate because observations were summed by plot and year. Total richness: 56
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 | habitat | 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. :2.0 | Basalt :120 | Batot Taninim :24 | Far :120 | Min. :-2.17579 | Min. :-0.2498 | Min. :-0.9995 | Length:240 | Min. :-0.1175 | Min. :-0.1771 | Other : 53 | 0 : 0 | 0 : 13 | 0 : 0 | 0 : 6 | |
| 1st Qu.: 6.000 | 1st Qu.:3.5 | Limestone:120 | Gamla :24 | Near:120 | 1st Qu.:-0.76251 | 1st Qu.: 0.3757 | 1st Qu.:-0.9267 | Class :difftime | 1st Qu.: 0.7043 | 1st Qu.: 0.2034 | Eyal Shochat: 7 | 1 : 0 | 3 : 0 | 1 : 0 | 1 : 1 | |
| Median : 8.000 | Median :5.0 | NA | Hatzor :24 | NA | Median :-0.51689 | Median : 0.4758 | Median :-0.8792 | Mode :numeric | Median : 0.8880 | Median : 0.4598 | Adi Domer : 0 | 2 : 0 | NA’s:227 | 2 : 6 | 2 : 4 | |
| Mean : 7.987 | Mean :5.0 | NA | Karei Deshe :24 | NA | Mean : 0.09807 | Mean : 0.5626 | Mean :-0.6066 | NA | Mean : 0.8063 | Mean : 0.4517 | Asaf Mayrose: 0 | 3 : 1 | NA | 3 : 0 | 3 : 0 | |
| 3rd Qu.:10.000 | 3rd Qu.:6.5 | NA | Natur :24 | NA | 3rd Qu.: 1.11539 | 3rd Qu.: 0.9048 | 3rd Qu.:-0.3233 | NA | 3rd Qu.: 0.9791 | 3rd Qu.: 0.7099 | Eliraz Dvir : 0 | NA’s:239 | NA | NA’s:234 | NA’s:229 | |
| Max. :16.000 | Max. :8.0 | NA | Ramat Hashofet:24 | NA | Max. : 2.28504 | Max. : 0.9905 | Max. : 0.1882 | NA | Max. : 1.0000 | Max. : 0.9998 | (Other) : 0 | NA | NA | NA | NA | |
| NA | NA | NA | (Other) :96 | NA | NA | NA | NA | NA | NA’s :60 | NA’s :60 | NA’s :180 | 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[1:12]], : the standard deviation is zero| richness | year_ct | habitat | site | agriculture | td_sc | cos_td_rad | sin_td_rad | h_from_sunrise | cos_hsun | sin_hsun | monitors_name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| richness | 1.0000000 | 0.2085540 | 0.1464875 | 0.0465554 | 0.4125841 | -0.2185634 | -0.1680955 | -0.2477434 | -0.1349472 | 0.1528232 | -0.1213437 | 0.1142529 |
| year_ct | 0.2085540 | 1.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | -0.7463144 | -0.6558516 | -0.7587452 | -0.0861080 | 0.0846278 | -0.0719069 | NA |
| habitat | 0.1464875 | 0.0000000 | 1.0000000 | -0.0348155 | 0.0000000 | -0.0427597 | -0.0632391 | -0.0406794 | 0.1656632 | -0.1716399 | 0.1548456 | 0.2595871 |
| site | 0.0465554 | 0.0000000 | -0.0348155 | 1.0000000 | 0.0000000 | -0.0384624 | -0.0368278 | -0.0572513 | -0.0170782 | -0.0178346 | -0.0435386 | 0.4247701 |
| agriculture | 0.4125841 | 0.0000000 | 0.0000000 | 0.0000000 | 1.0000000 | -0.0030733 | -0.0053199 | -0.0014405 | -0.0292304 | 0.0494225 | -0.0202046 | 0.0519174 |
| td_sc | -0.2185634 | -0.7463144 | -0.0427597 | -0.0384624 | -0.0030733 | 1.0000000 | 0.9542304 | 0.9752788 | -0.1904036 | 0.1801172 | -0.1861300 | -0.5897244 |
| cos_td_rad | -0.1680955 | -0.6558516 | -0.0632391 | -0.0368278 | -0.0053199 | 0.9542304 | 1.0000000 | 0.8674071 | -0.1918124 | 0.1793379 | -0.1892847 | -0.5202428 |
| sin_td_rad | -0.2477434 | -0.7587452 | -0.0406794 | -0.0572513 | -0.0014405 | 0.9752788 | 0.8674071 | 1.0000000 | -0.1677980 | 0.1708620 | -0.1544942 | -0.7612499 |
| h_from_sunrise | -0.1349472 | -0.0861080 | 0.1656632 | -0.0170782 | -0.0292304 | -0.1904036 | -0.1918124 | -0.1677980 | 1.0000000 | -0.9486862 | 0.9809251 | -0.0373316 |
| cos_hsun | 0.1528232 | 0.0846278 | -0.1716399 | -0.0178346 | 0.0494225 | 0.1801172 | 0.1793379 | 0.1708620 | -0.9486862 | 1.0000000 | -0.8726227 | 0.0445516 |
| sin_hsun | -0.1213437 | -0.0719069 | 0.1548456 | -0.0435386 | -0.0202046 | -0.1861300 | -0.1892847 | -0.1544942 | 0.9809251 | -0.8726227 | 1.0000000 | -0.0380718 |
| monitors_name | 0.1142529 | NA | 0.2595871 | 0.4247701 | 0.0519174 | -0.5897244 | -0.5202428 | -0.7612499 | -0.0373316 | 0.0445516 | -0.0380718 | 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 = 0.96607529001649"Overdispersion parameter is <1 (underdispersion) and therefore Poisson is preferable to negative binomial.
| Observations | 180 (60 missing obs. deleted) |
| Dependent variable | richness |
| Type | Generalized linear model |
| Family | poisson |
| Link | log |
| χ²(7) | 56.48 |
| Pseudo-R² (Cragg-Uhler) | 0.27 |
| Pseudo-R² (McFadden) | 0.06 |
| AIC | 887.71 |
| BIC | 913.25 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 0.82 | 0.56 | 1.47 | 0.14 |
| agricultureNear | 0.17 | 0.20 | 0.85 | 0.39 |
| year_ct | -0.01 | 0.02 | -0.24 | 0.81 |
| cos_td_rad | 0.42 | 0.22 | 1.92 | 0.06 |
| sin_td_rad | -0.83 | 0.40 | -2.09 | 0.04 |
| cos_hsun | 0.29 | 0.25 | 1.19 | 0.24 |
| sin_hsun | 0.06 | 0.17 | 0.34 | 0.73 |
| agricultureNear:year_ct | 0.03 | 0.03 | 0.94 | 0.34 |
| Standard errors: MLE |
cosine and sine of hours from sunrise - remove them because of many NAs.
## 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 | habitat)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 1163.0 1187.4 -574.5 1149.0 233
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.21425 -0.66921 -0.02546 0.60489 2.84633
##
## Random effects:
## Groups Name Variance Std.Dev.
## habitat (Intercept) 0.002033 0.04509
## Number of obs: 240, groups: habitat, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.60725 0.16363 9.823 < 2e-16 ***
## agricultureNear 0.14033 0.11870 1.182 0.23712
## year_ct -0.01153 0.01972 -0.585 0.55878
## cos_td_rad 0.25117 0.14167 1.773 0.07624 .
## sin_td_rad -0.34303 0.11862 -2.892 0.00383 **
## agricultureNear:year_ct 0.03545 0.02121 1.672 0.09459 .
## ---
## 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 -0.410
## year_ct -0.420 0.580
## cos_td_rad -0.814 -0.002 -0.024
## sin_td_rad 0.583 0.019 0.393 -0.760
## agrcltrNr:_ 0.378 -0.921 -0.638 0.006 -0.023Mixed model with site as random variable did not converge. Mixed model with habitat as random variable converged. perform stepwise model selection of poisson mixed model.
## Single term deletions
##
## Model:
## richness ~ agriculture * year_ct + cos_td_rad + sin_td_rad +
## (1 | habitat)
## npar AIC
## <none> 1163.0
## cos_td_rad 1 1164.2
## sin_td_rad 1 1169.5
## agriculture:year_ct 1 1163.8drop cosine.
## Single term deletions
##
## Model:
## richness ~ agriculture * year_ct + sin_td_rad + (1 | habitat)
## npar AIC
## <none> 1164.2
## sin_td_rad 1 1167.8
## agriculture:year_ct 1 1165.0drop agriculture : year.
## Single term deletions
##
## Model:
## richness ~ agriculture + year_ct + sin_td_rad + (1 | habitat)
## npar AIC
## <none> 1165.0
## agriculture 1 1212.3
## year_ct 1 1163.4
## sin_td_rad 1 1168.5drop year.
## Single term deletions
##
## Model:
## richness ~ agriculture + sin_td_rad + (1 | habitat)
## npar AIC
## <none> 1163.4
## agriculture 1 1210.7
## sin_td_rad 1 1179.0agriculture and time of year remain. Final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: richness ~ agriculture + sin_td_rad + (1 | habitat)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 1163.4 1177.3 -577.7 1155.4 236
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.37551 -0.68774 -0.03242 0.58979 2.80348
##
## Random effects:
## Groups Name Variance Std.Dev.
## habitat (Intercept) 0.001729 0.04158
## Number of obs: 240, groups: habitat, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.76583 0.05724 30.848 < 2e-16 ***
## agricultureNear 0.32276 0.04627 6.975 3.05e-12 ***
## sin_td_rad -0.21746 0.05272 -4.125 3.71e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agrclN
## agricultrNr -0.468
## sin_td_rad 0.597 0.002## $habitat
## (Intercept)
## Basalt -0.03262836
## Limestone 0.03303290
##
## with conditional variances for "habitat"## $habitat
## Registered S3 methods overwritten by 'broom':
## method from
## tidy.glht jtools
## tidy.summary.glht jtools| Observations | 240 |
| Dependent variable | richness |
| Type | Mixed effects generalized linear model |
| Family | poisson |
| Link | log |
| AIC | 1163.401 |
| BIC | 1177.323 |
| Pseudo-R² (fixed effects) | 0.232 |
| Pseudo-R² (total) | 0.243 |
| exp(Est.) | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 5.846 | 0.057 | 30.848 | 0.000 |
| agricultureNear | 1.381 | 0.046 | 6.975 | 0.000 |
| sin_td_rad | 0.805 | 0.053 | -4.125 | 0.000 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| habitat | (Intercept) | 0.042 |
| Group | # groups | ICC |
|---|---|---|
| habitat | 2 | 0.002 |
## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.
There is a statistically significant effect for proximity to agriculture.
Near plots have on average 2.5410982 more species than far plots, which is 38.0931463 percent higher.
No statistically significant effect for the other factors tested.
geometric mean of abundance
Explore data. Exclude time of day because of high number of NAs.
## [1] "GEOMETRIC MEAN ABUNDANCE WITHOUT RARE SPECIES"
| gma | year_ct | habitat | site | agriculture | td_sc | cos_td_rad | sin_td_rad | |
|---|---|---|---|---|---|---|---|---|
| Min. :1.080 | Min. :2.0 | Basalt :120 | Batot Taninim :24 | Far :120 | Min. :-2.17579 | Min. :-0.2498 | Min. :-0.9995 | |
| 1st Qu.:2.000 | 1st Qu.:3.5 | Limestone:120 | Gamla :24 | Near:120 | 1st Qu.:-0.76251 | 1st Qu.: 0.3757 | 1st Qu.:-0.9267 | |
| Median :2.472 | Median :5.0 | NA | Hatzor :24 | NA | Median :-0.51689 | Median : 0.4758 | Median :-0.8792 | |
| Mean :2.688 | Mean :5.0 | NA | Karei Deshe :24 | NA | Mean : 0.09807 | Mean : 0.5626 | Mean :-0.6066 | |
| 3rd Qu.:3.018 | 3rd Qu.:6.5 | NA | Natur :24 | NA | 3rd Qu.: 1.11539 | 3rd Qu.: 0.9048 | 3rd Qu.:-0.3233 | |
| Max. :8.367 | Max. :8.0 | NA | Ramat Hashofet:24 | NA | Max. : 2.28504 | Max. : 0.9905 | Max. : 0.1882 | |
| NA | NA | NA | (Other) :96 | NA | NA | NA | NA |
Fit glm, compare gamma, gaussian (poisson inappropriate because response is not discrete)
Gamma seems better than gaussian. exclude four outlier points: 27, 49, 69, 199 Fit fixed and mixed models.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00592212 (tol = 0.002, component 1)Mixed model with site as random variable did not converge. Mixed model with habitat as random variable converged:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( inverse )
## Formula: gma ~ agriculture * year_ct + cos_td_rad + sin_td_rad + (1 |
## habitat)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 524.5 552.2 -254.3 508.5 228
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8427 -0.6774 -0.1787 0.5592 3.9652
##
## Random effects:
## Groups Name Variance Std.Dev.
## habitat (Intercept) 0.0004997 0.02235
## Residual 0.0851398 0.29179
## Number of obs: 236, groups: habitat, 2
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 0.451637 0.062194 7.262 3.82e-13 ***
## agricultureNear -0.051086 0.038296 -1.334 0.182
## year_ct 0.024064 0.005983 4.022 5.76e-05 ***
## cos_td_rad -0.044375 0.044470 -0.998 0.318
## sin_td_rad 0.246805 0.035537 6.945 3.78e-12 ***
## agricultureNear:year_ct 0.010977 0.007077 1.551 0.121
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agrclN yer_ct cs_td_ sn_td_
## agricultrNr -0.316
## year_ct -0.417 0.542
## cos_td_rad -0.713 -0.010 0.113
## sin_td_rad 0.532 -0.026 0.285 -0.724
## agrcltrNr:_ 0.285 -0.933 -0.567 0.017 0.022
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00592212 (tol = 0.002, component 1)perform stepwise model selection of Gamma model.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00262225 (tol = 0.002, component 1)## Single term deletions
##
## Model:
## gma ~ agriculture * year_ct + cos_td_rad + sin_td_rad + (1 |
## habitat)
## npar AIC
## <none> 524.51
## cos_td_rad 1 523.52
## sin_td_rad 1 568.33
## agriculture:year_ct 1 524.90drop cosine.
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00202193 (tol = 0.002, component 1)## Single term deletions
##
## Model:
## gma ~ agriculture * year_ct + sin_td_rad + (1 | habitat)
## npar AIC
## <none> 523.52
## sin_td_rad 1 592.67
## agriculture:year_ct 1 523.97drop agriculture : year.
## Single term deletions
##
## Model:
## gma ~ agriculture + year_ct + sin_td_rad + (1 | habitat)
## npar AIC
## <none> 523.97
## agriculture 1 522.08
## year_ct 1 557.83
## sin_td_rad 1 591.93drop distance from agriculture
## Single term deletions
##
## Model:
## gma ~ year_ct + sin_td_rad + (1 | habitat)
## npar AIC
## <none> 522.08
## year_ct 1 555.92
## sin_td_rad 1 590.04year 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 ~ year_ct + sin_td_rad + (1 | habitat)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 522.1 539.4 -256.0 512.1 231
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7570 -0.6749 -0.1413 0.5314 4.0072
##
## Random effects:
## Groups Name Variance Std.Dev.
## habitat (Intercept) 0.0005235 0.02288
## Residual 0.0867701 0.29457
## Number of obs: 236, groups: habitat, 2
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 0.380889 0.039494 9.644 < 2e-16 ***
## year_ct 0.030245 0.004948 6.113 9.81e-10 ***
## sin_td_rad 0.220057 0.024748 8.892 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) yer_ct
## year_ct -0.321
## sin_td_rad 0.007 0.697## $habitat
## (Intercept)
## Basalt -0.03517951
## Limestone 0.02929934
##
## with conditional variances for "habitat"## $habitat
| Observations | 236 |
| Dependent variable | gma |
| Type | Mixed effects generalized linear model |
| Family | Gamma |
| Link | inverse |
| AIC | 522.080 |
| BIC | 539.399 |
| Pseudo-R² (fixed effects) | NA |
| Pseudo-R² (total) | NA |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 0.381 | 0.039 | 9.644 | 0.000 |
| year_ct | 0.030 | 0.005 | 6.113 | 0.000 |
| sin_td_rad | 0.220 | 0.025 | 8.892 | 0.000 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| habitat | (Intercept) | 0.023 |
| Residual | 0.295 |
| Group | # groups | ICC |
|---|---|---|
| habitat | 2 | 0.006 |
## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.
## Confidence intervals for merMod models is an experimental feature. The
## intervals reflect only the variance of the fixed effects, not the random
## effects.
## 1 2 3 4 5 6 7 8
## 3.258350 3.239005 3.219887 3.200994 3.182322 3.163866 3.145622 3.127588
## 9 10 11 12 13 14 15 16
## 3.109760 3.092134 3.074706 3.057474 3.040434 3.023582 3.006917 2.990434
## 17 18 19 20 21 22 23 24
## 2.974131 2.958005 2.942052 2.926271 2.910658 2.895211 2.879927 2.864804
## 25 26 27 28 29 30 31 32
## 2.849838 2.835028 2.820371 2.805865 2.791508 2.777296 2.763229 2.749303
## 33 34 35 36 37 38 39 40
## 2.735517 2.721869 2.708356 2.694976 2.681728 2.668610 2.655620 2.642755
## 41 42 43 44 45 46 47 48
## 2.630014 2.617396 2.604898 2.592519 2.580257 2.568110 2.556078 2.544157
## 49 50 51 52 53 54 55 56
## 2.532347 2.520647 2.509054 2.497567 2.486185 2.474906 2.463729 2.452652
## 57 58 59 60 61 62 63 64
## 2.441675 2.430795 2.420012 2.409324 2.398730 2.388229 2.377820 2.367501
## 65 66 67 68 69 70 71 72
## 2.357271 2.347129 2.337074 2.327104 2.317220 2.307419 2.297700 2.288063
## 73 74 75 76 77 78 79 80
## 2.278507 2.269030 2.259632 2.250311 2.241066 2.231898 2.222804 2.213784
## 81 82 83 84 85 86 87 88
## 2.204837 2.195961 2.187157 2.178424 2.169759 2.161164 2.152636 2.144175
## 89 90 91 92 93 94 95 96
## 2.135781 2.127452 2.119188 2.110987 2.102850 2.094776 2.086763 2.078811
## 97 98 99 100
## 2.070920 2.063088 2.055315 2.047601## [1] -0.3686258## [1] 0.3715836significant temporal decrease in gma. No effect found for distance from agriculture.
GMA is decreasing on average in 0.2017916 individuals per species per year, which is an average rate of decrease of 7.4503926% per year.
abundance
Explore data
## [1] "ABUNDANCE WITHOUT RARE SPECIES"
| abundance | year_ct | habitat | site | agriculture | td_sc | cos_td_rad | sin_td_rad | |
|---|---|---|---|---|---|---|---|---|
| Min. : 3.00 | Min. :2.0 | Basalt :120 | Batot Taninim :24 | Far :120 | Min. :-2.17579 | Min. :-0.2498 | Min. :-0.9995 | |
| 1st Qu.:15.00 | 1st Qu.:3.5 | Limestone:120 | Gamla :24 | Near:120 | 1st Qu.:-0.76251 | 1st Qu.: 0.3757 | 1st Qu.:-0.9267 | |
| Median :23.00 | Median :5.0 | NA | Hatzor :24 | NA | Median :-0.51689 | Median : 0.4758 | Median :-0.8792 | |
| Mean :25.79 | Mean :5.0 | NA | Karei Deshe :24 | NA | Mean : 0.09807 | Mean : 0.5626 | Mean :-0.6066 | |
| 3rd Qu.:34.25 | 3rd Qu.:6.5 | NA | Natur :24 | NA | 3rd Qu.: 1.11539 | 3rd Qu.: 0.9048 | 3rd Qu.:-0.3233 | |
| Max. :82.00 | Max. :8.0 | NA | Ramat Hashofet:24 | NA | Max. : 2.28504 | Max. : 0.9905 | Max. : 0.1882 | |
| NA | NA | NA | (Other) :96 | 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(5.7253) ( log )
## Formula: abundance ~ agriculture * year_ct + cos_td_rad + sin_td_rad +
## (1 | habitat)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 1839.3 1867.1 -911.6 1823.3 232
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8916 -0.7190 -0.1302 0.5664 3.7818
##
## Random effects:
## Groups Name Variance Std.Dev.
## habitat (Intercept) 0.004466 0.06683
## Number of obs: 240, groups: habitat, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.97519 0.20458 14.543 < 2e-16 ***
## agricultureNear 0.17089 0.14986 1.140 0.254
## year_ct -0.10707 0.02483 -4.312 1.62e-05 ***
## cos_td_rad 0.09897 0.17093 0.579 0.563
## sin_td_rad -0.92831 0.14250 -6.515 7.29e-11 ***
## agricultureNear:year_ct 0.02297 0.02724 0.843 0.399
## ---
## 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 -0.377
## year_ct -0.484 0.519
## cos_td_rad -0.818 0.006 0.084
## sin_td_rad 0.523 0.007 0.384 -0.698
## agrcltrNr:_ 0.341 -0.916 -0.562 0.000 -0.007Perform stepwise model selection of mixed model.
## Single term deletions
##
## Model:
## abundance ~ agriculture * year_ct + cos_td_rad + sin_td_rad +
## (1 | habitat)
## npar AIC
## <none> 1839.2
## cos_td_rad 1 1837.6
## sin_td_rad 1 1879.0
## agriculture:year_ct 1 1838.0drop cosine.
## Single term deletions
##
## Model:
## abundance ~ agriculture * year_ct + sin_td_rad + (1 | habitat)
## npar AIC
## <none> 1837.6
## sin_td_rad 1 1908.4
## agriculture:year_ct 1 1836.3drop agriculture : year.
## Single term deletions
##
## Model:
## abundance ~ agriculture + year_ct + sin_td_rad + (1 | habitat)
## npar AIC
## <none> 1836.3
## agriculture 1 1856.8
## year_ct 1 1856.3
## sin_td_rad 1 1906.7agriculture, year and sampling time of year remain. The final model:
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Negative Binomial(5.695) ( log )
## Formula: abundance ~ agriculture + year_ct + sin_td_rad + (1 | habitat)
## Data: P.anal
##
## AIC BIC logLik deviance df.resid
## 1836.3 1857.2 -912.1 1824.3 234
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9074 -0.7302 -0.1421 0.5526 3.5859
##
## Random effects:
## Groups Name Variance Std.Dev.
## habitat (Intercept) 0.004545 0.06742
## Number of obs: 240, groups: habitat, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.01337 0.09474 31.806 < 2e-16 ***
## agricultureNear 0.28618 0.06025 4.750 2.03e-06 ***
## year_ct -0.09639 0.02041 -4.722 2.34e-06 ***
## sin_td_rad -0.86941 0.10217 -8.510 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) agrclN yer_ct
## agricultrNr -0.317
## year_ct -0.583 0.000
## sin_td_rad -0.135 0.016 0.743## $habitat
## (Intercept)
## Basalt 0.05708559
## Limestone -0.05690504
##
## with conditional variances for "habitat"## $habitat
Interpretation of abundance model:
| Observations | 240 |
| Dependent variable | abundance |
| Type | Mixed effects generalized linear model |
| Family | Negative Binomial(5.695) |
| Link | log |
| AIC | 1836.295 |
| BIC | 1857.179 |
| Pseudo-R² (fixed effects) | 0.691 |
| Pseudo-R² (total) | 0.724 |
| exp(Est.) | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 20.356 | 0.095 | 31.806 | 0.000 |
| agricultureNear | 1.331 | 0.060 | 4.750 | 0.000 |
| year_ct | 0.908 | 0.020 | -4.722 | 0.000 |
| sin_td_rad | 0.419 | 0.102 | -8.510 | 0.000 |
| Group | Parameter | Std. Dev. |
|---|---|---|
| habitat | (Intercept) | 0.067 |
| Group | # groups | ICC |
|---|---|---|
| habitat | 2 | 0.020 |
## 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.
Significant higher abundance near agriculture, and overall decrease in total abundance
Near plots have on average 7.0579097 more individuals than far plots, which is 33.133568 percent higher.
Over the entire monitoring period, total abundance decreases 0.4391762%, which is 0.0918915% per year. On average, total abundance decreased 2.0820073 individuals per year.
community analysis using package MVabund
## Overlapping points were shifted along the y-axis to make them visible.##
## PIPING TO 2nd MVFACTOR## Only the variables Galerida.cristata, Streptopelia.decaocto, Curruca.melanocephala, Carduelis.carduelis, Pycnonotus.xanthopygos, Emberiza.calandra, Prinia.gracilis, Alectoris.chukar, Chloris.chloris, Passer.domesticus, Corvus.cornix, Parus.major were included in the plot
## (the variables with highest total abundance).
start model specification:
## nb po
## 470.2985 690.1617## [1] "POISSON"
## [1] "NEGATIVE BINOMIAL"
negative binomial model is better than poisson according to residuals and AIC comparison.
## nb po nb1 nb2
## 470.2985 690.1617 437.8273 457.9745The addition of the explanatory variable ‘site’ is improving the AIC of the model the most. stepwise selection of model:
## Single term deletions
##
## Model:
## spp_no_rare ~ agriculture * year_ct + cos_td_rad + sin_td_rad +
## site
## Df AIC
## <none> 13135
## cos_td_rad 30 13153
## sin_td_rad 30 13231
## site 270 14109
## agriculture:year_ct 30 13105drop agriculture:year
## Single term deletions
##
## Model:
## spp_no_rare ~ agriculture + year_ct + cos_td_rad + sin_td_rad +
## site
## Df AIC
## <none> 13105
## agriculture 30 13303
## year_ct 30 13110
## cos_td_rad 30 13127
## sin_td_rad 30 13204
## site 270 14073final model includes agriculture, year, sampling time of year and site.
##
## Test statistics:
## wald value Pr(>wald)
## (Intercept) 10.039 0.01 **
## agricultureNear 16.925 0.01 **
## year_ct 8.703 0.01 **
## cos_td_rad 8.965 0.01 **
## sin_td_rad 12.566 0.01 **
## siteGamla 10.016 0.01 **
## siteHatzor 10.668 0.01 **
## siteKarei Deshe 8.590 0.01 **
## siteNatur 10.007 0.01 **
## siteRamat Hashofet 9.789 0.01 **
## siteSde Eliezer 12.247 0.01 **
## siteShaal 9.891 0.01 **
## siteYavneel 10.574 0.01 **
## siteYiron 11.389 0.01 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Test statistic: 40.02, p-value: 0.01
## Arguments:
## Test statistics calculated assuming response assumed to be uncorrelated
## P-value calculated using 99 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) 239
## agriculture 238 1 147.9 0.001 ***
## year_ct 237 1 73.5 0.001 ***
## cos_td_rad 236 1 125.5 0.001 ***
## sin_td_rad 235 1 148.3 0.001 ***
## site 226 9 1508.0 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Univariate Tests:
## Alectoris.chukar Burhinus.oedicnemus
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 0.467 0.994 0.708 0.993
## year_ct 1.128 0.998 0.143 1.000
## cos_td_rad 0.224 0.989 0.699 0.989
## sin_td_rad 3.683 0.664 0.704 0.980
## site 50.77 0.001 22.157 0.055
## Cinnyris.osea Falco.tinnunculus Galerida.cristata
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## agriculture 4.29 0.527 0.451 0.994 16.671
## year_ct 0.29 1.000 1.158 0.998 2.965
## cos_td_rad 6.775 0.197 0.087 0.989 0.171
## sin_td_rad 6.23 0.253 5.597 0.339 16.318
## site 74.209 0.001 32.451 0.008 98.456
## Hirundo.rustica Passer.domesticus
## Pr(>Dev) Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 0.001 0.768 0.991 2.103 0.919
## year_ct 0.829 0.423 1.000 2.846 0.842
## cos_td_rad 0.989 7.003 0.185 0.452 0.989
## sin_td_rad 0.005 0.234 0.992 11.605 0.026
## site 0.001 18.257 0.101 61.938 0.001
## Prinia.gracilis Pycnonotus.xanthopygos
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 7.265 0.142 30.215 0.001
## year_ct 0.548 1.000 1.157 0.998
## cos_td_rad 0.797 0.989 0.021 0.989
## sin_td_rad 4.38 0.537 7.306 0.164
## site 68.361 0.001 56.799 0.001
## Streptopelia.decaocto Streptopelia.turtur
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 10.294 0.032 1.876 0.938
## year_ct 0.697 1.000 0.203 1.000
## cos_td_rad 1.35 0.972 7.086 0.185
## sin_td_rad 9.246 0.074 9.263 0.074
## site 42.477 0.001 26.85 0.029
## Vanellus.spinosus Carduelis.carduelis
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 0.932 0.989 0.802 0.991
## year_ct 0.21 1.000 0.644 1.000
## cos_td_rad 2.184 0.926 0.436 0.989
## sin_td_rad 2.078 0.846 0.028 0.992
## site 31.887 0.009 51.057 0.001
## Cecropis.daurica Cettia.cetti Chloris.chloris
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## agriculture 1.137 0.989 2.871 0.812 35.448
## year_ct 2.66 0.871 3.083 0.816 0.182
## cos_td_rad 1.586 0.972 14.062 0.008 1.331
## sin_td_rad 0.947 0.980 2.469 0.842 3.212
## site 16.71 0.101 59.697 0.001 47.492
## Cisticola.juncidis Clamator.glandarius
## Pr(>Dev) Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 0.001 0.009 1.000 0.011 1.000
## year_ct 1.000 6.504 0.276 0.854 1.000
## cos_td_rad 0.972 1.613 0.972 1.387 0.972
## sin_td_rad 0.752 0.229 0.992 0.94 0.980
## site 0.001 32.302 0.009 29.807 0.017
## Corvus.cornix Curruca.communis
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 3.995 0.589 0.005 1.000
## year_ct 0.21 1.000 6.235 0.282
## cos_td_rad 5.43 0.360 18.584 0.002
## sin_td_rad 0.673 0.980 22.056 0.002
## site 36.927 0.002 39.874 0.001
## Curruca.melanocephala Dendrocopos.syriacus
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 2.748 0.823 0.311 0.994
## year_ct 0.082 1.000 0.099 1.000
## cos_td_rad 19.653 0.001 0.732 0.989
## sin_td_rad 8.936 0.074 0.001 0.992
## site 162.311 0.001 53.462 0.001
## Emberiza.calandra Falco.naumanni
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 1.887 0.938 0.306 0.994
## year_ct 7.956 0.143 2.252 0.917
## cos_td_rad 3.526 0.676 0.726 0.989
## sin_td_rad 8.345 0.101 0.184 0.992
## site 61.528 0.001 57.733 0.001
## Garrulus.glandarius Lanius.senator
## Dev Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 5.943 0.264 0.147 0.994
## year_ct 1.034 0.998 16.679 0.005
## cos_td_rad 3.434 0.676 6.29 0.242
## sin_td_rad 1.344 0.957 0.745 0.980
## site 60.46 0.001 23.892 0.037
## Linaria.cannabina Parus.major Psittacula.krameri
## Dev Pr(>Dev) Dev Pr(>Dev) Dev
## (Intercept)
## agriculture 1.433 0.967 5.211 0.368 2.416
## year_ct 0.021 1.000 6.291 0.282 1.202
## cos_td_rad 3.979 0.601 4.484 0.539 1.721
## sin_td_rad 2.361 0.842 9.573 0.069 2.75
## site 27.576 0.026 68.967 0.001 40.069
## Turdus.merula
## Pr(>Dev) Dev Pr(>Dev)
## (Intercept)
## agriculture 0.875 7.219 0.146
## year_ct 0.998 5.73 0.326
## cos_td_rad 0.972 9.661 0.057
## sin_td_rad 0.811 6.885 0.201
## site 0.001 53.565 0.001
## 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 and site) have a statistically significant effect on community composition.
Below plots for crested lark, house sparrow and corn bunting
## [1] "Galerida cristata"## [1] "manyglm coefficients"## (Intercept) agricultureNear year_ct cos_td_rad
## 0.1520779 -0.7592007 -0.0731277 0.7692822
## sin_td_rad siteGamla siteHatzor siteKarei Deshe
## -1.8545331 0.9115167 -1.0991985 0.3222154
## siteNatur siteRamat Hashofet siteSde Eliezer siteShaal
## 1.1731718 -0.6210582 -1.0092375 0.7693465
## siteYavneel siteYiron
## -0.9695270 -1.4923494## [1] "species-specific glm"## (Intercept) agricultureNear year_ct cos_td_rad
## 0.15208103 -0.75920009 -0.07312734 0.76927779
## sin_td_rad siteGamla siteHatzor siteKarei Deshe
## -1.85452759 0.91151810 -1.09919768 0.32221281
## siteNatur siteRamat Hashofet siteSde Eliezer siteShaal
## 1.17317199 -0.62105611 -1.00923659 0.76934814
## siteYavneel siteYiron
## -0.96952666 -1.49234746##
## Call:
## glm.nb(formula = Galerida.cristata ~ agriculture + year_ct +
## cos_td_rad + sin_td_rad + site, data = d.Galerida.cristata,
## init.theta = 1.133538392, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1334 -1.1160 -0.5629 0.2753 3.5101
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.15208 0.55151 0.276 0.782738
## agricultureNear -0.75920 0.15490 -4.901 9.53e-07 ***
## year_ct -0.07313 0.05248 -1.393 0.163503
## cos_td_rad 0.76928 0.51678 1.489 0.136593
## sin_td_rad -1.85453 0.41019 -4.521 6.15e-06 ***
## siteGamla 0.91152 0.33676 2.707 0.006796 **
## siteHatzor -1.09920 0.37848 -2.904 0.003682 **
## siteKarei Deshe 0.32221 0.32645 0.987 0.323635
## siteNatur 1.17317 0.32149 3.649 0.000263 ***
## siteRamat Hashofet -0.62106 0.34736 -1.788 0.073785 .
## siteSde Eliezer -1.00924 0.36962 -2.730 0.006325 **
## siteShaal 0.76935 0.32366 2.377 0.017454 *
## siteYavneel -0.96953 0.36135 -2.683 0.007295 **
## siteYiron -1.49235 0.39481 -3.780 0.000157 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.1335) family taken to be 1)
##
## Null deviance: 476.85 on 239 degrees of freedom
## Residual deviance: 263.52 on 226 degrees of freedom
## AIC: 1011.2
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 1.134
## Std. Err.: 0.195
##
## 2 x log-likelihood: -981.203
## [1] 113.6566## [1] 1.136567## [1] 2.136567
## [1] "Passer domesticus"## [1] "manyglm coefficients"## (Intercept) agricultureNear year_ct cos_td_rad
## -19.88088676 1.28793838 -0.46850611 7.31989885
## sin_td_rad siteGamla siteHatzor siteKarei Deshe
## -5.08502546 15.02813251 12.43437133 13.44651904
## siteNatur siteRamat Hashofet siteSde Eliezer siteShaal
## 14.47406153 -0.07960262 -0.77593558 12.80483035
## siteYavneel siteYiron
## 15.14996176 -0.57012558## [1] "species-specific glm"## (Intercept) agricultureNear year_ct cos_td_rad sin_td_rad
## -4.8526707 1.2879144 -0.4685084 7.3197411 -5.0850012
## siteHatzor siteKarei Deshe siteNatur siteShaal siteYavneel
## -2.5936490 -1.5815319 -0.5540002 -2.2232172 0.1218321
##
## Call:
## glm.nb(formula = Passer.domesticus ~ agriculture + year_ct +
## cos_td_rad + sin_td_rad + site, data = d.Passer.domesticus,
## init.theta = 0.2085806794, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.43320 -0.82266 -0.51143 -0.07757 1.75834
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.8527 2.3740 -2.044 0.040944 *
## agricultureNear 1.2879 0.4444 2.898 0.003753 **
## year_ct -0.4685 0.1590 -2.946 0.003217 **
## cos_td_rad 7.3197 2.1975 3.331 0.000866 ***
## sin_td_rad -5.0850 1.3665 -3.721 0.000198 ***
## siteHatzor -2.5936 0.8020 -3.234 0.001220 **
## siteKarei Deshe -1.5815 0.8115 -1.949 0.051295 .
## siteNatur -0.5540 0.6893 -0.804 0.421590
## siteShaal -2.2232 0.7715 -2.882 0.003955 **
## siteYavneel 0.1218 0.7798 0.156 0.875851
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.2086) family taken to be 1)
##
## Null deviance: 122.858 on 143 degrees of freedom
## Residual deviance: 84.891 on 134 degrees of freedom
## AIC: 388.79
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.2086
## Std. Err.: 0.0448
##
## 2 x log-likelihood: -366.7920
## [1] "Emberiza calandra"## [1] "manyglm coefficients"## (Intercept) agricultureNear year_ct cos_td_rad
## -0.64160316 -0.79613036 -0.05601435 -1.19122249
## sin_td_rad siteGamla siteHatzor siteKarei Deshe
## -1.42714754 1.96787355 -13.54797201 0.28693655
## siteNatur siteRamat Hashofet siteSde Eliezer siteShaal
## 1.51812736 1.57155269 -1.22080035 2.18924457
## siteYavneel siteYiron
## 1.53390365 -1.53045976## [1] "species-specific glm"## (Intercept) agricultureNear year_ct cos_td_rad
## -0.64165224 -0.79611605 -0.05601625 -1.19120333
## sin_td_rad siteGamla siteKarei Deshe siteNatur
## -1.42719124 1.96789227 0.28694631 1.51814160
## siteRamat Hashofet siteSde Eliezer siteShaal siteYavneel
## 1.57155713 -1.22078848 2.18926918 1.53392503
## siteYiron
## -1.53044741##
## Call:
## glm.nb(formula = Emberiza.calandra ~ agriculture + year_ct +
## cos_td_rad + sin_td_rad + site, data = d.Emberiza.calandra,
## init.theta = 0.4078431376, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5653 -0.8307 -0.5076 -0.1497 3.2630
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.64165 1.02463 -0.626 0.531167
## agricultureNear -0.79612 0.27892 -2.854 0.004313 **
## year_ct -0.05602 0.09810 -0.571 0.568008
## cos_td_rad -1.19120 0.93009 -1.281 0.200286
## sin_td_rad -1.42719 0.74917 -1.905 0.056776 .
## siteGamla 1.96789 0.63517 3.098 0.001947 **
## siteKarei Deshe 0.28695 0.63672 0.451 0.652230
## siteNatur 1.51814 0.61320 2.476 0.013295 *
## siteRamat Hashofet 1.57156 0.57950 2.712 0.006690 **
## siteSde Eliezer -1.22079 0.82869 -1.473 0.140711
## siteShaal 2.18927 0.59596 3.674 0.000239 ***
## siteYavneel 1.53393 0.58187 2.636 0.008384 **
## siteYiron -1.53045 0.86496 -1.769 0.076831 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.4078) family taken to be 1)
##
## Null deviance: 242.54 on 215 degrees of freedom
## Residual deviance: 157.61 on 203 degrees of freedom
## AIC: 594.84
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.4078
## Std. Err.: 0.0772
##
## 2 x log-likelihood: -566.8360
## [1] 28.54466## [1] 0.2854466Corn bunting (Emberiza calandra) has nearly insignificant temporal trend in manyglm anova, and far from significant temporal trend in individual model. keep p-value because relies on many points. 2014 was sampled in July, other campaigns in april - may, hence sinus has important effect.
## [1] "Lanius senator"## [1] "manyglm coefficients"## (Intercept) agricultureNear year_ct cos_td_rad
## -1.732078113 -0.112497226 0.002825420 0.006786666
## sin_td_rad siteGamla siteHatzor siteKarei Deshe
## 1.602269524 1.518830855 1.148920146 1.081453255
## siteNatur siteRamat Hashofet siteSde Eliezer siteShaal
## 1.987113048 -0.122718744 1.601819660 2.041445368
## siteYavneel siteYiron
## 2.534845404 2.154151552## [1] "species-specific glm"## (Intercept) agricultureNear year_ct cos_td_rad
## -1.732071160 -0.112496433 0.002824343 0.006777268
## sin_td_rad siteGamla siteHatzor siteKarei Deshe
## 1.602261618 1.518836606 1.148926678 1.081460293
## siteNatur siteRamat Hashofet siteSde Eliezer siteShaal
## 1.987116798 -0.122714158 1.601816442 2.041447242
## siteYavneel siteYiron
## 2.534834920 2.154142240
##
## Call:
## glm.nb(formula = Lanius.senator ~ agriculture + year_ct + cos_td_rad +
## sin_td_rad + site, data = d.Lanius.senator, init.theta = 0.7472594152,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5488 -0.7310 -0.5720 -0.2506 2.5133
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.732071 1.401592 -1.236 0.21654
## agricultureNear -0.112496 0.272426 -0.413 0.67965
## year_ct 0.002824 0.108156 0.026 0.97917
## cos_td_rad 0.006777 1.316477 0.005 0.99589
## sin_td_rad 1.602262 0.865913 1.850 0.06426 .
## siteGamla 1.518837 0.895596 1.696 0.08991 .
## siteHatzor 1.148927 0.913299 1.258 0.20839
## siteKarei Deshe 1.081460 0.908517 1.190 0.23391
## siteNatur 1.987117 0.866011 2.295 0.02176 *
## siteRamat Hashofet -0.122714 1.108572 -0.111 0.91186
## siteSde Eliezer 1.601816 0.882291 1.816 0.06944 .
## siteShaal 2.041447 0.862228 2.368 0.01790 *
## siteYavneel 2.534835 0.839345 3.020 0.00253 **
## siteYiron 2.154142 0.854070 2.522 0.01166 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(0.7473) family taken to be 1)
##
## Null deviance: 204.82 on 239 degrees of freedom
## Residual deviance: 148.80 on 226 degrees of freedom
## AIC: 390.3
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 0.747
## Std. Err.: 0.254
##
## 2 x log-likelihood: -360.297## [1] 0.01709045## [1] 1.709037## [1] 1.01709Lanius senator woodchat shrike has far from significant temporal trend in individual glm and only 104 individuals observed. replace p-value with p-value from individual glm.
## [1] "Chloris chloris"## [1] "manyglm coefficients"## (Intercept) agricultureNear year_ct cos_td_rad
## -2.3945343 1.9172073 -0.1019446 -0.2077219
## sin_td_rad siteGamla siteHatzor siteKarei Deshe
## -0.7496393 1.5170050 0.8399366 1.7734053
## siteNatur siteRamat Hashofet siteSde Eliezer siteShaal
## 2.5193582 -0.7838948 1.5256463 -0.1240388
## siteYavneel siteYiron
## 0.9373908 0.9332871## [1] "species-specific glm"## (Intercept) agricultureNear year_ct cos_td_rad
## -2.3945308 1.9172052 -0.1019447 -0.2077230
## sin_td_rad siteGamla siteHatzor siteKarei Deshe
## -0.7496384 1.5170057 0.8399366 1.7734041
## siteNatur siteRamat Hashofet siteSde Eliezer siteShaal
## 2.5193580 -0.7838968 1.5256419 -0.1240383
## siteYavneel siteYiron
## 0.9373914 0.9332859## [1] 580.1922## [1] 5.801921## [1] 6.801921## [1] "Pycnonotus xanthopygos"## [1] "manyglm coefficients"## (Intercept) agricultureNear year_ct cos_td_rad
## -2.6125907 1.4974694 -0.1433114 1.3604260
## sin_td_rad siteGamla siteHatzor siteKarei Deshe
## -1.4928996 0.9183052 1.8802649 0.5712497
## siteNatur siteRamat Hashofet siteSde Eliezer siteShaal
## 0.4151581 0.7974485 1.9490966 -0.9886388
## siteYavneel siteYiron
## 1.0852433 0.8450630## [1] "species-specific glm"## (Intercept) agricultureNear year_ct cos_td_rad
## -2.3945308 1.9172052 -0.1019447 -0.2077230
## sin_td_rad siteGamla siteHatzor siteKarei Deshe
## -0.7496384 1.5170057 0.8399366 1.7734041
## siteNatur siteRamat Hashofet siteSde Eliezer siteShaal
## 2.5193580 -0.7838968 1.5256419 -0.1240383
## siteYavneel siteYiron
## 0.9373914 0.9332859## [1] 347.0346## [1] 0.1904754 0.8514907## [1] 3.470345## [1] 4.470345## [1] "Streptopelia decaocto"## [1] "manyglm coefficients"## (Intercept) agricultureNear year_ct cos_td_rad
## -0.7398394 0.5984852 -0.1401676 1.6736556
## sin_td_rad siteGamla siteHatzor siteKarei Deshe
## -1.7011389 -0.8245745 -0.1817819 -0.1460711
## siteNatur siteRamat Hashofet siteSde Eliezer siteShaal
## -0.1888911 0.5555522 -0.1772067 -1.8308680
## siteYavneel siteYiron
## 0.3919578 -0.0914818## [1] "species-specific glm"## (Intercept) agricultureNear year_ct cos_td_rad
## -0.73984092 0.59848610 -0.14016778 1.67365609
## sin_td_rad siteGamla siteHatzor siteKarei Deshe
## -1.70114021 -0.82457377 -0.18178159 -0.14607044
## siteNatur siteRamat Hashofet siteSde Eliezer siteShaal
## -0.18889045 0.55555366 -0.17720583 -1.83086739
## siteYavneel siteYiron
## 0.39195803 -0.09148064## [1] 81.93624## [1] 1.703636 3.099531## [1] 0.8193622## [1] 1.819362## 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]]
## 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]]
##
## [[2]]
##
## [[3]]
## [1] "black arrow is median of synanthrope/invasive\ngreen arrow is median of batha specialists\n\n"
Significant effect of plantation agriculture proximity: synanthrope / invasive species near agriculture. Most species showing negative coefs, consistent with overall decrease in gma and total abundance.
Significant negative temporal effect on corn bunting and lesser whitethroat (but see species-specific plots above). Positive significant trend for woodchat shrike, but the magnitude is very small (also see above).
Worth noting that the following have consistent results across several units:
crested lark, negative, in Sfar and Batha. house sparrow, negative, in Sfar, Batha and Maquis. red-rumped swallow, positive, in sfar, batha and maquis. palestine sunbird, negative, in sfar, batha and maquis. goldfinch, negative, in maquis and batha.