Birds_Negev_Highlands_analysis_by

## 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.

Negev Highlands

Factors are proximity to settlements, habitat (slope vs. wadi but only far from settlements, near has only slope) and time.

5 sites with 9 plots per site (3 for each habitat X proximity combination).

Total 5 campaigns, one of which is pilot in the year 2012. No sampling was performed in the Wadi habitat during the pilot.

Raw data Total abundance: 7597 Number of observations: 2111 Total richness: 108

Filtered data Total abundance: 3735 Number of observations: 1095 Total richness: 48

Model gma, abundance and richness

Richness done with rare species. Abundance and mean abundance done without rare species. Full models include cosine and sine of the time difference from June 21st (in radians).

richness

Explore data.

## [1] "RICHNESS WITH RARE SPECIES"

richness	year_ct	site	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:150	Min. :-1.60666	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.: 3.000	1st Qu.:2.000	Yeruham :42	Near: 76	Wadi : 60	1st Qu.:-1.22035	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	Median :-0.76251	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 : 5.214	Mean :4.286	Ezuz :36	NA	NA	Mean :-0.47356	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.: 0.07211	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. :15.000	Max. :8.000	Ashalim : 6	NA	NA	Max. : 2.97181	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.

## Warning in cor(P.anal[, lapply(X = .SD, FUN = as.numeric), .SDcols =
## IVs[1:12]], : the standard deviation is zero

	richness	year_ct	site	settlements	habitat	td_sc	cos_td_rad	sin_td_rad	h_from_sunrise	cos_hsun	sin_hsun	monitors_name
richness	1.0000000	0.0103577	-0.0002292	0.4194268	0.0539383	0.2521594	0.2664592	0.2251954	0.0857425	0.0187119	0.1234390	0.1890127
year_ct	0.0103577	1.0000000	-0.0195456	-0.0866957	0.1666667	-0.2769423	-0.2743588	-0.2470754	-0.2130095	0.1769441	-0.2089485	NA
site	-0.0002292	-0.0195456	1.0000000	-0.0050223	-0.0078182	0.0012529	0.0254386	-0.0200397	-0.0591920	0.0649672	-0.0532192	-0.2739469
settlements	0.4194268	-0.0866957	-0.0050223	1.0000000	-0.4763042	-0.0578505	-0.0886498	-0.0259035	0.1001375	0.0051981	0.1351982	0.1104315
habitat	0.0539383	0.1666667	-0.0078182	-0.4763042	1.0000000	0.0393762	0.0463499	0.0369900	0.0339170	-0.0318150	0.0277413	0.1104315
td_sc	0.2521594	-0.2769423	0.0012529	-0.0578505	0.0393762	1.0000000	0.9677863	0.9679062	0.0805063	-0.0915903	0.0697040	0.0466138
cos_td_rad	0.2664592	-0.2743588	0.0254386	-0.0886498	0.0463499	0.9677863	1.0000000	0.8777655	0.0850144	-0.0994639	0.0727109	0.0666735
sin_td_rad	0.2251954	-0.2470754	-0.0200397	-0.0259035	0.0369900	0.9679062	0.8777655	1.0000000	0.0797859	-0.0800387	0.0732132	-0.0344973
h_from_sunrise	0.0857425	-0.2130095	-0.0591920	0.1001375	0.0339170	0.0805063	0.0850144	0.0797859	1.0000000	-0.9348973	0.9913101	0.0672842
cos_hsun	0.0187119	0.1769441	0.0649672	0.0051981	-0.0318150	-0.0915903	-0.0994639	-0.0800387	-0.9348973	1.0000000	-0.8826232	0.0115382
sin_hsun	0.1234390	-0.2089485	-0.0532192	0.1351982	0.0277413	0.0697040	0.0727109	0.0732132	0.9913101	-0.8826232	1.0000000	0.0810296
monitors_name	0.1890127	NA	-0.2739469	0.1104315	0.1104315	0.0466138	0.0666735	-0.0344973	0.0672842	0.0115382	0.0810296	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 = 0.917237513515559"

Overdispersion parameter is < 1. Poisson more appropriate. Compare Poisson and negative binomial.

## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached

## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached

##       df      AIC
## m0.po 15 898.0002
## m0.nb 16 900.0029

## [1] "poisson"

## [1] "neg bin"

negative binomial did not converge due to underdispersion.

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00255536 (tol = 0.002, component 1)

mixed model did not converge. go with glm.

## 
## Call:
## glm(formula = richness ~ settlements * year_ct + habitat * year_ct + 
##     cos_td_rad + sin_td_rad + site, family = poisson, data = P.anal)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -2.24407  -0.67956  -0.09001   0.43547   2.90185  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              0.824194   0.403615   2.042   0.0411 *  
## settlementsNear          0.522044   0.132989   3.925 8.66e-05 ***
## year_ct                 -0.008411   0.024016  -0.350   0.7262    
## habitatWadi              0.340126   0.177333   1.918   0.0551 .  
## cos_td_rad               0.589455   0.255940   2.303   0.0213 *  
## sin_td_rad              -0.100095   0.257286  -0.389   0.6972    
## siteBislach              0.082076   0.229625   0.357   0.7208    
## siteEzuz                 0.195652   0.227413   0.860   0.3896    
## siteMashabei Sadeh       0.143101   0.267942   0.534   0.5933    
## siteMerhav Am            0.185915   0.227452   0.817   0.4137    
## siteMitzpe Ramon         0.057565   0.272391   0.211   0.8326    
## siteSde Boker           -0.035671   0.226967  -0.157   0.8751    
## siteYeruham              0.201637   0.218949   0.921   0.3571    
## settlementsNear:year_ct  0.031812   0.027164   1.171   0.2416    
## year_ct:habitatWadi      0.015152   0.034139   0.444   0.6572    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 262.91  on 209  degrees of freedom
## Residual deviance: 154.64  on 195  degrees of freedom
## AIC: 898
## 
## Number of Fisher Scoring iterations: 4

perform stepwise model selection of poisson glm.

## Start:  AIC=898
## richness ~ settlements * year_ct + habitat * year_ct + cos_td_rad + 
##     sin_td_rad + site
## 
##                       Df Deviance    AIC
## - site                 7   162.28 891.63
## - sin_td_rad           1   154.80 896.15
## - year_ct:habitat      1   154.84 896.20
## - settlements:year_ct  1   156.02 897.37
## <none>                     154.64 898.00
## - cos_td_rad           1   160.11 901.47
## 
## Step:  AIC=891.63
## richness ~ settlements + year_ct + habitat + cos_td_rad + sin_td_rad + 
##     settlements:year_ct + year_ct:habitat
## 
##                       Df Deviance    AIC
## - year_ct:habitat      1   162.34 889.70
## - settlements:year_ct  1   163.53 890.88
## - sin_td_rad           1   164.27 891.62
## <none>                     162.28 891.63
## - cos_td_rad           1   176.30 903.66
## 
## Step:  AIC=889.7
## richness ~ settlements + year_ct + habitat + cos_td_rad + sin_td_rad + 
##     settlements:year_ct
## 
##                       Df Deviance    AIC
## - settlements:year_ct  1   163.70 889.06
## <none>                     162.34 889.70
## - sin_td_rad           1   164.41 889.77
## - cos_td_rad           1   176.41 901.76
## - habitat              1   187.12 912.47
## 
## Step:  AIC=889.06
## richness ~ settlements + year_ct + habitat + cos_td_rad + sin_td_rad
## 
##               Df Deviance    AIC
## - sin_td_rad   1   165.69 889.05
## <none>             163.70 889.06
## - year_ct      1   165.83 889.19
## - cos_td_rad   1   177.94 901.30
## - habitat      1   187.40 910.76
## - settlements  1   242.07 965.42
## 
## Step:  AIC=889.05
## richness ~ settlements + year_ct + habitat + cos_td_rad
## 
##               Df Deviance    AIC
## <none>             165.69 889.05
## - year_ct      1   168.04 889.40
## - habitat      1   188.85 910.21
## - cos_td_rad   1   191.61 912.97
## - settlements  1   242.24 963.59

keep removing terms because \(\Delta AIC<2\). remove year.

## Single term deletions
## 
## Model:
## richness ~ settlements + habitat + cos_td_rad
##             Df Deviance    AIC
## <none>           168.04 889.40
## settlements  1   243.91 963.27
## habitat      1   193.59 912.94
## cos_td_rad   1   191.62 910.98

final model includes settlements, habitat, sampling time of year. Final model:

## 
## Call:
## glm(formula = richness ~ settlements + habitat + cos_td_rad, 
##     family = poisson, data = P.anal)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1269  -0.6569  -0.0935   0.4146   3.1465  
## 
## Coefficients:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)      1.03876    0.07902  13.146  < 2e-16 ***
## settlementsNear  0.64592    0.07608   8.490  < 2e-16 ***
## habitatWadi      0.41693    0.08281   5.035 4.78e-07 ***
## cos_td_rad       0.48810    0.09977   4.892 9.98e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 262.91  on 209  degrees of freedom
## Residual deviance: 168.05  on 206  degrees of freedom
## AIC: 889.4
## 
## Number of Fisher Scoring iterations: 4

Observations	210
Dependent variable	richness
Type	Generalized linear model
Family	poisson
Link	log

χ²(3)	94.868
Pseudo-R² (Cragg-Uhler)	0.367
Pseudo-R² (McFadden)	0.097
AIC	889.402
BIC	902.790

	exp(Est.)	2.5%	97.5%	z val.	p
(Intercept)	2.826	2.420	3.299	13.146	0.000
settlementsNear	1.908	1.643	2.215	8.490	0.000
habitatWadi	1.517	1.290	1.785	5.035	0.000
cos_td_rad	1.629	1.340	1.981	4.892	0.000
Standard errors: MLE

## [1] 90.77338

## [1] 51.72905

statistically significant higher richness near settlements and in the wadi compared to slope. No significant temporal trend.

Near plots have on average 3.1806487 more species than far plots, which is 90.773377 percent higher. Wadi plots have on average 1.8125573 more species than far plots, which is 51.7290533 percent higher.

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:150	Min. :-1.60666	Min. :0.004304	Min. :-1.0000
1st Qu.:1.644	1st Qu.:2.000	Yeruham :42	Near: 76	Wadi : 60	1st Qu.:-1.22035	1st Qu.:0.177649	1st Qu.:-0.9841
Median :2.142	Median :4.000	Bislach :36	NA	NA	Median :-0.76251	Median :0.375715	Median :-0.9267
Mean :2.395	Mean :4.286	Ezuz :36	NA	NA	Mean :-0.47356	Mean :0.440753	Mean :-0.8039
3rd Qu.:2.884	3rd Qu.:6.000	Merhav Am:36	NA	NA	3rd Qu.: 0.07211	3rd Qu.:0.690173	3rd Qu.:-0.7236
Max. :9.898	Max. :8.000	Ashalim : 6	NA	NA	Max. : 2.97181	Max. :0.999407	Max. : 0.4787
NA	NA	(Other) :12	NA	NA	NA	NA	NA

Exclude outlier plot with gma of nearly 10, from Ezuz Near Slope 11 2016-03-23. Richness of 4 and abundance of 61 (40 of which are house sparrow). Fit glm, compare gamma, gaussian (poisson inappropriate because response is not discrete)

Gamma seems better than gaussian. Fit fixed and mixed models.

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.066625 (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  
## -0.73450  -0.30596  -0.06747   0.17704   1.35814  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.894883   0.147782   6.055 7.12e-09 ***
## settlementsNear         -0.225280   0.046072  -4.890 2.11e-06 ***
## year_ct                 -0.009126   0.008079  -1.130  0.26001    
## habitatWadi             -0.120965   0.067159  -1.801  0.07323 .  
## cos_td_rad              -0.229967   0.094345  -2.438  0.01569 *  
## sin_td_rad               0.165542   0.091706   1.805  0.07260 .  
## siteBislach             -0.130512   0.087961  -1.484  0.13950    
## siteEzuz                -0.231613   0.085237  -2.717  0.00718 ** 
## siteMashabei Sadeh      -0.102513   0.098209  -1.044  0.29787    
## siteMerhav Am           -0.112233   0.087904  -1.277  0.20321    
## siteMitzpe Ramon        -0.159833   0.092136  -1.735  0.08437 .  
## siteSde Boker           -0.135849   0.085501  -1.589  0.11372    
## siteYeruham             -0.104415   0.084732  -1.232  0.21933    
## settlementsNear:year_ct  0.024836   0.009358   2.654  0.00862 ** 
## year_ct:habitatWadi      0.026204   0.013000   2.016  0.04520 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1451561)
## 
##     Null deviance: 34.538  on 208  degrees of freedom
## Residual deviance: 25.286  on 194  degrees of freedom
## AIC: 512.1
## 
## Number of Fisher Scoring iterations: 5

perform stepwise model selection of Gamma model.

## Start:  AIC=512.1
## gma ~ settlements * year_ct + habitat * year_ct + cos_td_rad + 
##     sin_td_rad + site
## 
##                       Df Deviance    AIC
## <none>                     25.286 512.10
## - sin_td_rad           1   25.817 513.76
## - year_ct:habitat      1   25.877 514.18
## - cos_td_rad           1   26.213 516.49
## - site                 7   28.051 517.15
## - settlements:year_ct  1   26.316 517.20

Keep dropping terms because \(\Delta AIC<2\). drop sine.

## Single term deletions
## 
## Model:
## gma ~ settlements * year_ct + habitat * year_ct + cos_td_rad + 
##     site
##                     Df Deviance    AIC
## <none>                   25.817 514.53
## cos_td_rad           1   26.259 515.50
## site                 7   28.257 516.88
## settlements:year_ct  1   26.832 519.33
## year_ct:habitat      1   26.395 516.41

drop cosine.

## Single term deletions
## 
## Model:
## gma ~ settlements * year_ct + habitat * year_ct + site
##                     Df Deviance    AIC
## <none>                   26.259 516.16
## site                 7   28.468 517.10
## settlements:year_ct  1   27.298 521.19
## year_ct:habitat      1   26.902 518.51

drop site.

## Single term deletions
## 
## Model:
## gma ~ settlements * year_ct + habitat * year_ct
##                     Df Deviance    AIC
## <none>                   28.468 519.40
## settlements:year_ct  1   29.545 524.43
## year_ct:habitat      1   29.349 523.15

Settlements * year and habitat * year remain. This is the final model:

## 
## Call:
## glm(formula = gma ~ settlements * year_ct + habitat * year_ct, 
##     family = Gamma, data = P.anal)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.79797  -0.27674  -0.07058   0.20624   1.41435  
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              0.524412   0.040233  13.035  < 2e-16 ***
## settlementsNear         -0.222319   0.047258  -4.704  4.7e-06 ***
## year_ct                 -0.010785   0.007711  -1.399  0.16344    
## habitatWadi             -0.152894   0.067549  -2.263  0.02467 *  
## settlementsNear:year_ct  0.025386   0.009598   2.645  0.00881 ** 
## year_ct:habitatWadi      0.031540   0.013190   2.391  0.01770 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1533962)
## 
##     Null deviance: 34.538  on 208  degrees of freedom
## Residual deviance: 28.468  on 203  degrees of freedom
## AIC: 519.4
## 
## Number of Fisher Scoring iterations: 5

Fit also a model without interaction to understand whether there is a general trend in GMA:

## 
## Call:
## glm(formula = gma ~ settlements + habitat + year_ct, family = Gamma, 
##     data = P.anal)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.74325  -0.30483  -0.06355   0.19569   1.18267  
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.448039   0.026822  16.704  < 2e-16 ***
## settlementsNear -0.122927   0.026977  -4.557 8.92e-06 ***
## habitatWadi     -0.016436   0.032469  -0.506   0.6133    
## year_ct          0.007960   0.004186   1.901   0.0586 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 0.1524776)
## 
##     Null deviance: 34.538  on 208  degrees of freedom
## Residual deviance: 29.806  on 205  degrees of freedom
## AIC: 525.23
## 
## Number of Fisher Scoring iterations: 5

Overall temporal trend in GMA is not significant. So is difference in GMA between wadi and slope (but significant for settlement proximity).

Observations	209
Dependent variable	gma
Type	Generalized linear model
Family	Gamma
Link	inverse

χ²(5)	6.07
Pseudo-R² (Cragg-Uhler)	0.19
Pseudo-R² (McFadden)	0.08
AIC	519.40
BIC	542.80

	Est.	S.E.	t val.	p
(Intercept)	0.52	0.04	13.03	0.00
settlementsNear	-0.22	0.05	-4.70	0.00
year_ct	-0.01	0.01	-1.40	0.16
habitatWadi	-0.15	0.07	-2.26	0.02
settlementsNear:year_ct	0.03	0.01	2.64	0.01
year_ct:habitatWadi	0.03	0.01	2.39	0.02
Standard errors: MLE

### test for overall trend:

##  settlements year_ct habitat emmean     SE  df lower.CL upper.CL
##  Far            4.29 Slope    0.478 0.0218 203    0.435    0.521
##  Near           4.29 Slope    0.365 0.0169 203    0.331    0.398
##  Far            4.29 Wadi     0.460 0.0239 203    0.413    0.508
##  Near           4.29 Wadi     0.347 0.0365 203    0.275    0.419
## 
## Results are given on the inverse (not the response) scale. 
## Confidence level used: 0.95

##  contrast             estimate     SE  df t.ratio p.value
##  slope_far_minus_near   0.1135 0.0276 203   4.118  0.0001
##  far_slope_minus_wadi   0.0177 0.0323 203   0.547  0.5849
## 
## Note: contrasts are still on the inverse scale

##   settlements  year_ct habitat c.1 c.2
## 1         Far 4.287081   Slope   1   1
## 2        Near 4.287081   Slope  -1   0
## 3         Far 4.287081    Wadi   0  -1
## 4        Near 4.287081    Wadi   0   0

test for difference of each trend from zero. Trends for distance from settlements:

##  settlements year_ct.trend      SE  df t.ratio p.value
##  Far               0.00498 0.00659 203   0.756  0.4506
##  Near              0.03037 0.00873 203   3.480  0.0012
## 
## Results are averaged over the levels of: habitat 
## P value adjustment: fdr method for 2 tests

test for difference of each trend from zero. Trends for habitat type:

##  habitat year_ct.trend     SE  df t.ratio p.value
##  Slope         0.00191 0.0048 203   0.398  0.6914
##  Wadi          0.03345 0.0117 203   2.852  0.0096
## 
## Results are averaged over the levels of: settlements 
## P value adjustment: fdr method for 2 tests

Overall temporal trend in GMA is not significant.

So is difference in GMA between wadi and slope (but significant for settlement proximity: higher near settlements).

Significant opposing temporal trends for settlement proximity and habitat type:

GMA decreasing near settlements and increasing far from settlements (no statistically significant difference between the two in the last campaign).

GMA is decreasing in Wadi habitat and increasing in slope habitat (no statistically significant difference between the two in the last campaign).

On average, GMA is significantly higher in a near plot compared to a far plot by 0.650794 (31.119285 percent) individuals per species (tested in slope habitat only; P<0.001). No significant difference between slope and wadi habitats (tested only 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. : 1.00	Min. :0.000	Sde Boker:42	Far :134	Slope:150	Min. :-1.60666	Min. :0.004304	Min. :-1.0000
1st Qu.: 5.00	1st Qu.:2.000	Yeruham :42	Near: 76	Wadi : 60	1st Qu.:-1.22035	1st Qu.:0.177649	1st Qu.:-0.9841
Median : 9.00	Median :4.000	Bislach :36	NA	NA	Median :-0.76251	Median :0.375715	Median :-0.9267
Mean :14.19	Mean :4.286	Ezuz :36	NA	NA	Mean :-0.47356	Mean :0.440753	Mean :-0.8039
3rd Qu.:19.00	3rd Qu.:6.000	Merhav Am:36	NA	NA	3rd Qu.: 0.07211	3rd Qu.:0.690173	3rd Qu.:-0.7236
Max. :66.00	Max. :8.000	Ashalim : 6	NA	NA	Max. : 2.97181	Max. :0.999407	Max. : 0.4787
NA	NA	(Other) :12	NA	NA	NA	NA	NA

remove outlier of extremely high abundance (56, next highest far from settlements is ~25) far from settlements.

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| = 0.00251329 (tol = 0.002, component 1)

Mixed model failed to converge. Use fixed-effects model.

## 
## Call:
## glm.nb(formula = abundance ~ settlements * year_ct + habitat * 
##     year_ct + cos_td_rad + sin_td_rad + site, data = P.anal, 
##     init.theta = 3.519359983, link = log)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1898  -0.8713  -0.1829   0.4280   4.4996  
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              1.264552   0.561030   2.254   0.0242 *  
## settlementsNear          1.250375   0.174803   7.153 8.49e-13 ***
## year_ct                  0.005588   0.029862   0.187   0.8516    
## habitatWadi              0.521220   0.245480   2.123   0.0337 *  
## cos_td_rad               0.659199   0.362502   1.818   0.0690 .  
## sin_td_rad              -0.194256   0.371532  -0.523   0.6011    
## siteBislach              0.179728   0.303827   0.592   0.5542    
## siteEzuz                 0.661778   0.299005   2.213   0.0269 *  
## siteMashabei Sadeh       0.383491   0.352463   1.088   0.2766    
## siteMerhav Am            0.318582   0.302141   1.054   0.2917    
## siteMitzpe Ramon         0.433684   0.351461   1.234   0.2172    
## siteSde Boker           -0.078285   0.297580  -0.263   0.7925    
## siteYeruham              0.188354   0.288533   0.653   0.5139    
## settlementsNear:year_ct -0.021634   0.035563  -0.608   0.5430    
## year_ct:habitatWadi     -0.050389   0.046476  -1.084   0.2783    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Negative Binomial(3.5194) family taken to be 1)
## 
##     Null deviance: 419.11  on 209  degrees of freedom
## Residual deviance: 216.26  on 195  degrees of freedom
## AIC: 1417.3
## 
## Number of Fisher Scoring iterations: 1
## 
## 
##               Theta:  3.519 
##           Std. Err.:  0.446 
## 
##  2 x log-likelihood:  -1385.299

model selection of glm.

## Start:  AIC=1415.3
## abundance ~ settlements * year_ct + habitat * year_ct + cos_td_rad + 
##     sin_td_rad + site
## 
##                       Df Deviance    AIC
## - sin_td_rad           1   216.51 1413.5
## - settlements:year_ct  1   216.62 1413.7
## - year_ct:habitat      1   217.47 1414.5
## <none>                     216.26 1415.3
## - cos_td_rad           1   219.35 1416.4
## - site                 7   247.04 1432.1
## 
## Step:  AIC=1413.55
## abundance ~ settlements + year_ct + habitat + cos_td_rad + site + 
##     settlements:year_ct + year_ct:habitat
## 
##                       Df Deviance    AIC
## - settlements:year_ct  1   216.53 1411.9
## - year_ct:habitat      1   217.34 1412.7
## <none>                     216.18 1413.5
## - cos_td_rad           1   224.15 1419.5
## - site                 7   247.74 1431.1
## 
## Step:  AIC=1411.9
## abundance ~ settlements + year_ct + habitat + cos_td_rad + site + 
##     year_ct:habitat
## 
##                   Df Deviance    AIC
## - year_ct:habitat  1   216.99 1410.7
## <none>                 216.17 1411.9
## - cos_td_rad       1   224.14 1417.9
## - site             7   247.94 1429.7
## - settlements      1   343.84 1537.6
## 
## Step:  AIC=1410.72
## abundance ~ settlements + year_ct + habitat + cos_td_rad + site
## 
##               Df Deviance    AIC
## - year_ct      1   216.57 1409.4
## <none>             215.93 1410.7
## - habitat      1   222.52 1415.3
## - cos_td_rad   1   224.62 1417.4
## - site         7   248.23 1429.0
## - settlements  1   343.29 1536.1
## 
## Step:  AIC=1409.36
## abundance ~ settlements + habitat + cos_td_rad + site
## 
##               Df Deviance    AIC
## <none>             216.05 1409.4
## - habitat      1   222.39 1413.7
## - cos_td_rad   1   228.24 1419.6
## - site         7   248.27 1427.6
## - settlements  1   343.07 1534.4

final model includes settlements, habitat, sampling time of year and site. The final model:

## 
## Call:
## glm.nb(formula = abundance ~ settlements + habitat + cos_td_rad + 
##     site, data = P.anal, init.theta = 3.468806504, link = log)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1667  -0.8387  -0.1934   0.4415   4.4653  
## 
## Coefficients:
##                    Estimate Std. Error z value Pr(>|z|)    
## (Intercept)          1.5280     0.2723   5.612    2e-08 ***
## settlementsNear      1.1599     0.1012  11.459  < 2e-16 ***
## habitatWadi          0.2821     0.1128   2.502 0.012350 *  
## cos_td_rad           0.5659     0.1606   3.525 0.000424 ***
## siteBislach          0.1206     0.2773   0.435 0.663774    
## siteEzuz             0.6197     0.2771   2.236 0.025332 *  
## siteMashabei Sadeh   0.4036     0.3535   1.142 0.253482    
## siteMerhav Am        0.2736     0.2787   0.982 0.326330    
## siteMitzpe Ramon     0.4514     0.3525   1.281 0.200337    
## siteSde Boker       -0.1342     0.2754  -0.487 0.626069    
## siteYeruham          0.1528     0.2740   0.558 0.576939    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Negative Binomial(3.4688) family taken to be 1)
## 
##     Null deviance: 414.53  on 209  degrees of freedom
## Residual deviance: 216.05  on 199  degrees of freedom
## AIC: 1411.4
## 
## Number of Fisher Scoring iterations: 1
## 
## 
##               Theta:  3.469 
##           Std. Err.:  0.437 
## 
##  2 x log-likelihood:  -1387.360

Interpretation of abundance model:

## Error in glm.control(...) : 
##   unused argument (family = list("Negative Binomial(3.4688)", "log", function (mu) 
## log(mu), function (eta) 
## pmax(exp(eta), .Machine$double.eps), function (mu) 
## mu + mu^2/.Theta, function (y, mu, wt) 
## 2 * wt * (y * log(pmax(1, y)/mu) - (y + .Theta) * log((y + .Theta)/(mu + .Theta))), function (y, n, mu, wt, dev) 
## {
##     term <- (y + .Theta) * log(mu + .Theta) - y * log(mu) + lgamma(y + 1) - .Theta * log(.Theta) + lgamma(.Theta) - lgamma(.Theta + y)
##     2 * sum(term * wt)
## }, function (eta) 
## pmax(exp(eta), .Machine$double.eps), expression({
##     if (any(y < 0)) stop("negative values not allowed for the negative binomial family")
##     n <- rep(1, nobs)
##     mustart <- y + (y == 0)/6
## }), function (mu) 
## all(mu > 0), function (eta) 
## TRUE, function (object, nsim) 
## {
##     ftd <- fitted(object)
##     rnegbin(nsim * length(ftd), ftd, .Theta)
## }))

Observations	210
Dependent variable	abundance
Type	Generalized linear model
Family	Negative Binomial(3.4688)
Link	log

χ²(NA)	NA
Pseudo-R² (Cragg-Uhler)	NA
Pseudo-R² (McFadden)	NA
AIC	1411.360
BIC	1451.526

	exp(Est.)	2.5%	97.5%	z val.	p
(Intercept)	4.609	2.703	7.859	5.612	0.000
settlementsNear	3.190	2.616	3.890	11.459	0.000
habitatWadi	1.326	1.063	1.654	2.502	0.012
cos_td_rad	1.761	1.286	2.413	3.525	0.000
siteBislach	1.128	0.655	1.943	0.435	0.664
siteEzuz	1.858	1.080	3.199	2.236	0.025
siteMashabei Sadeh	1.497	0.749	2.993	1.142	0.253
siteMerhav Am	1.315	0.761	2.270	0.982	0.326
siteMitzpe Ramon	1.571	0.787	3.134	1.281	0.200
siteSde Boker	0.874	0.510	1.500	-0.487	0.626
siteYeruham	1.165	0.681	1.993	0.558	0.577
Standard errors: MLE

## [1] 218.9735

## [1] 218.9735

## [1] 5.914524 7.842395

## [1] 32.59555

## [1] 1.927872

significantly higher abundance in wadi compared to slope, and near settlements compared to far from them.

No temporal trend was detected in abundance.

Near plots have on average 12.9512394 more individuals than far plots, which is 218.9734927 percent higher.

Wadi habitat plots have on average 1.9278717 more individuals than slope habitat plots, which is 32.5955515 percent higher.

community analysis using package MVabund

Explore single species observations, including mean-variance plot. There is a strong mean-var relationship, indicating that employing GLMs is the proper way to analyze, rather than OLS (assumption of homogeneity is violated).

## Overlapping points were shifted along the y-axis to make them visible.

## 
##  PIPING TO 2nd MVFACTOR

## Only the variables Passer.domesticus, Galerida.cristata, Streptopelia.decaocto, Columba.livia, Ammomanes.deserti, Pycnonotus.xanthopygos, Spilopelia.senegalensis, Corvus.cornix, Alectoris.chukar, Scotocerca.inquieta, Onychognathus.tristramii, Prinia.gracilis 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 Passer.domesticus, Galerida.cristata, Streptopelia.decaocto, Columba.livia, Ammomanes.deserti, Pycnonotus.xanthopygos, Spilopelia.senegalensis, Corvus.cornix, Alectoris.chukar, Scotocerca.inquieta, Onychognathus.tristramii, Prinia.gracilis were included in the plot 
## (the variables with highest total abundance).

## [1] "abundance observations, aggregated into plots and species (i.e., several observations from the same species in the same plot are aggregated):"

## [1] "zoom in on high abundance observations:"

start model specification:

##       nb       po 
## 371.2266 495.4784

## [1] "POISSON"

## [1] "NEGATIVE BINOMIAL"

negative binomial model is better than poisson according to residuals and AIC comparison.

##       nb       po      nb1 
## 371.2266 495.4784 368.1321

The addition of the explanatory variable ‘site’ is improving the AIC of the model very slightly. Choose to exclude. stepwise selection of model:

## Single term deletions
## 
## Model:
## spp_no_rare ~ settlements * year_ct + habitat * year_ct + cos_td_rad + 
##     sin_td_rad
##                     Df    AIC
## <none>                 5939.6
## cos_td_rad          16 5979.9
## sin_td_rad          16 5938.1
## settlements:year_ct 16 5932.7
## year_ct:habitat     16 5930.5

drop interaction of habitat with year.

## Single term deletions
## 
## Model:
## spp_no_rare ~ settlements * year_ct + habitat + cos_td_rad + 
##     sin_td_rad
##                     Df    AIC
## <none>                 5930.5
## habitat             16 5997.4
## cos_td_rad          16 5970.4
## sin_td_rad          16 5927.7
## settlements:year_ct 16 5914.5

drop interaction of settlements with year.

## Single term deletions
## 
## Model:
## spp_no_rare ~ settlements + year_ct + habitat + cos_td_rad + 
##     sin_td_rad
##             Df    AIC
## <none>         5914.5
## settlements 16 6313.3
## year_ct     16 5926.4
## habitat     16 5979.3
## cos_td_rad  16 5956.7
## sin_td_rad  16 5912.7

drop sine.

## Single term deletions
## 
## Model:
## spp_no_rare ~ settlements + year_ct + habitat + cos_td_rad
##             Df    AIC
## <none>         5912.7
## settlements 16 6320.2
## year_ct     16 5924.2
## habitat     16 5977.7
## cos_td_rad  16 5978.9

final model includes settlements, habitat, year and sampling time of year.

## 
## Test statistics:
##                 wald value Pr(>wald)    
## (Intercept)         13.249     0.001 ***
## settlementsNear     17.115     0.001 ***
## year_ct              6.516     0.002 ** 
## habitatWadi          8.999     0.001 ***
## cos_td_rad          10.206     0.001 ***
## --- 
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
## 
## Test statistic:  23.91, p-value: 0.001 
## Arguments:
##  Test statistics calculated assuming response assumed to be uncorrelated 
##  P-value calculated using 999 resampling iterations via pit.trap resampling (to account for correlation in testing).

## Analysis of Deviance Table
## 
## Model: spp_no_rare ~ settlements + year_ct + habitat + cos_td_rad
## 
## Multivariate test:
##             Res.Df Df.diff   Dev Pr(>Dev)    
## (Intercept)    209                           
## settlements    208       1 432.3    0.001 ***
## year_ct        207       1  31.2    0.053 .  
## habitat        206       1  97.6    0.001 ***
## cos_td_rad     205       1  98.2    0.001 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Univariate Tests:
##             Alectoris.chukar          Ammomanes.deserti          Cinnyris.osea
##                          Dev Pr(>Dev)               Dev Pr(>Dev)           Dev
## (Intercept)                                                                   
## settlements            1.997    0.314            86.976    0.001        15.715
## year_ct                6.051    0.332             2.238    0.871         1.346
## habitat                3.362    0.593             4.712    0.420         9.209
## cos_td_rad             2.698    0.460            21.787    0.003         3.748
##                      Columba.livia          Galerida.cristata         
##             Pr(>Dev)           Dev Pr(>Dev)               Dev Pr(>Dev)
## (Intercept)                                                           
## settlements    0.001        13.997    0.001             6.487    0.039
## year_ct        0.932         0.513    0.964             1.723    0.927
## habitat        0.126          2.13    0.775             0.761    0.812
## cos_td_rad     0.357          0.53    0.733             3.298    0.399
##             Lanius.excubitor          Onychognathus.tristramii         
##                          Dev Pr(>Dev)                      Dev Pr(>Dev)
## (Intercept)                                                            
## settlements            7.372    0.039                    0.159    0.659
## year_ct                1.651    0.927                    0.464    0.964
## habitat                7.628    0.192                    0.347    0.812
## cos_td_rad             8.337    0.061                    2.279    0.505
##             Passer.domesticus          Prinia.gracilis         
##                           Dev Pr(>Dev)             Dev Pr(>Dev)
## (Intercept)                                                    
## settlements            58.153    0.001           4.863    0.059
## year_ct                 0.804    0.959             0.2    0.964
## habitat                 1.188    0.812          17.471    0.012
## cos_td_rad             11.005    0.042           1.363    0.684
##             Ptyonoprogne.fuligula          Pycnonotus.xanthopygos         
##                               Dev Pr(>Dev)                    Dev Pr(>Dev)
## (Intercept)                                                               
## settlements                 1.064    0.417                 57.819    0.001
## year_ct                     3.912    0.621                  4.034    0.621
## habitat                     2.069    0.775                 11.684    0.065
## cos_td_rad                  4.869    0.272                  4.505    0.288
##             Scotocerca.inquieta          Spilopelia.senegalensis         
##                             Dev Pr(>Dev)                     Dev Pr(>Dev)
## (Intercept)                                                              
## settlements              29.413    0.001                  80.296    0.001
## year_ct                   1.148    0.939                       0    0.990
## habitat                   4.083    0.486                   6.235    0.290
## cos_td_rad               15.106    0.014                    0.15    0.733
##             Streptopelia.decaocto          Corvus.cornix         
##                               Dev Pr(>Dev)           Dev Pr(>Dev)
## (Intercept)                                                      
## settlements                10.425    0.004        50.478    0.001
## year_ct                     5.282    0.414          1.64    0.927
## habitat                    24.799    0.002         1.802    0.775
## cos_td_rad                  5.623    0.188         1.424    0.684
##             Oenanthe.lugens         
##                         Dev Pr(>Dev)
## (Intercept)                         
## settlements           7.058    0.039
## year_ct               0.192    0.964
## habitat               0.106    0.812
## cos_td_rad           11.504    0.034
## Arguments:
##  Test statistics calculated assuming uncorrelated response (for faster computation) 
## P-value calculated using 999 iterations via PIT-trap resampling.

All factors (settlements, habitat, time of year) have a statistically significant effect on community composition.

## Loading required package: ggnewscale

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## [1] "black arrow is median of synanthrope/invasive\ngreen arrow is median of batha specialists\n\n"

## [1] "black arrow is median of synanthrope/invasive\ngreen arrow is median of batha specialists\n\n"

## [1] "black arrow is median of synanthrope/invasive\ngreen arrow is median of batha specialists\n\n"

Significant effect of settlement proximity: synanthrope / invasive species near settlements, batha specialists far from settlements. Significant effect of habitat type, where synanthropes tend to be in the Wadi and non-synanthropes in both habitats. Marginally significant temporal trend, and indeed no single species is showing a statistically significant increase / decrease.

## [1] "Spilopelia senegalensis"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##    -14.66209669     15.10517559     -0.01413157     12.12040410     -0.21033914

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##    -29.12232138     29.56540068     -0.01413166     26.58062924     -0.21033928

## [1] 1.930951e-13 1.336170e+00

## [1] 6.919749e+14

## [1] "Pycnonotus xanthopygos"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -3.79063710      3.95804227     -0.08643604      2.14607475      0.95933928

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -3.79063521      3.95804114     -0.08643594      2.14607491      0.95933584

## [1] 0.02379584 1.24582328

## [1] 5135.467

## [1] 51.35467

## [1] 52.35467

## [1] 101

## [1] 15

## [1] 0.02379584 0.20348365

## [1] 755.1228

## [1] 7.551228

## [1] 8.551228

## [1] 13

## [1] 103

## [1] "Passer domesticus"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -0.09028468      3.30820426      0.04725519      0.06445169     -1.94004981

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -0.09019369      3.30814789      0.04724988      0.06440417     -1.94012606

## [1] 88

## [1] 933

## [1]  0.4757746 13.0050380

## [1] 2633.445

## [1] 26.33445

## [1] 27.33445

## [1] "Corvus cornix"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -2.27848226      2.92215989     -0.09070328      0.89667859     -0.73174230

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -2.27848380      2.92216004     -0.09070286      0.89667814     -0.73174240

## [1] 0.05030111 0.93466403

## [1] 1758.138

## [1] 17.58138

## [1] 18.58138

## [1] 11

## [1] 77

## [1] "Cinnyris osea"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -3.93299699      2.75167788     -0.06780283      1.97750478      1.12084780

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -3.93299691      2.75167796     -0.06780283      1.97750460      1.12084772

## [1] 0.02400332 0.37610562

## [1] 1466.89

## [1] 14.6689

## [1] 15.6689

## [1] 13

## [1] 31

## [1] "Streptopelia.decaocto"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##      -1.1238560       1.7429427      -0.1003957       1.5955825       0.9329801

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##      -1.1238563       1.7429428      -0.1003957       1.5955826       0.9329805

## [1] 0.31889 1.82218

## [1] 471.4134

## [1] 4.714133

## [1] 5.714133

## [1] 122

## [1] 154

## [1] 0.318890 1.572511

## [1] 393.1201

## [1] 3.931202

## [1] 4.931202

## [1] 94

## [1] 182

## [1] "Columba livia"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -1.04544676      1.39680987      0.06328264     -0.76706064      0.56514552

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -1.04544557      1.39681240      0.06328198     -0.76705864      0.56514573

## [1] 0.5914683 2.3908887

## [1] 304.2294

## [1] 3.042294

## [1] 4.042294

## [1] 64

## [1] 174

## [1] "Galerida cristata"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##      0.42753840     -0.25224131      0.04216481      0.14649496      0.39446296

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##       0.4275385      -0.2522413       0.0421648       0.1464949       0.3944629

## [1] 2.186070 1.698702

## [1] 28.69065

## [1] 0.2869061

## [1] 1.286906

## [1] 320

## [1] 128

## [1] "Lanius excubitor"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -3.08596446     -0.70771505     -0.08986236      1.32879416      2.27477141

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##      -2.7849977      -0.7026412      -0.1206938       1.2610606       2.1093275

## [1] 0.09023377 0.04469057

## [1] 101.9078

## [1] 1.019079

## [1] 2.019079

## [1] 34

## [1] 4

## [1] "Oenanthe lugens"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -2.91999460     -1.39982922      0.06409002     -0.28173367      2.48153319

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##      -2.9199916      -1.3998277       0.0640897      -0.2817330       2.4815291

## [1] 0.21191300 0.05226611

## [1] 305.4501

## [1] 3.054501

## [1] 4.054501

## [1] 32

## [1] 5

## [1] "Ammomanes deserti"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -0.46155222     -2.46472804      0.09842393     -0.40766761      1.25228982

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -0.46155150     -2.46472779      0.09842386     -0.40766746      1.25228875

## [1] 1.6689846 0.1419171

## [1] 1076.028

## [1] 10.76028

## [1] 11.76028

## [1] 210

## [1] 11

## [1] "Scotocerca inquieta"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##    -2.274361302    -2.591469875    -0.004472523     0.677113555     2.396278023

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##    -2.274360191    -2.591469956    -0.004472617     0.677113386     2.396276892

## [1] 0.29014870 0.02173499

## [1] 1234.938

## [1] 12.34938

## [1] 13.34938

## [1] "Prinia gracilis"

## [1] "manyglm coefficients"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -4.17577649      3.07746345      0.04512158      2.69455105      0.79217110

## [1] "species-specific glm"

##     (Intercept) settlementsNear         year_ct     habitatWadi      cos_td_rad 
##     -3.74481003      3.09423219     -0.01612094      2.62647104      0.67035825

## [1] 0.02944298 0.40704610

## [1] 1282.49

## [1] 12.82489

## [1] 13.82489

## [1] 27

## [1] 43

## [1] 0.02944298 0.64981493

## [1] 2107.029

## [1] 21.07028

## [1] 22.07028

Birds_Negev_Highlands_analysis_by_plot

Ron Chen

2024-02-11