## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.2.17
## Current Matrix version is 1.2.18
## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package
library(BAT)
library(readr)
library(FD)
library(alphahull)
library(hypervolume)
library(car)
library(MASS)
library(lme4)
library(here)
library(dplyr) # to manage data
library(magrittr) # to use the pipe operator %>%
library(MuMIn)
library(bbmle)
library(performance)
Results2 <- read.csv2(here("results","RESULTS.CSV"), header=TRUE, row.names = 1, stringsAsFactors = T,sep = ",", dec = ".")
# Loading the excel file with the formulas
model.formulas <- read.csv2(here("data","model.formulas.csv"), header=TRUE, stringsAsFactors = T, dec = ".")
### Overdispersion function
overdisp_fun <- function(model) {
rdf <- df.residual(model)
rp <- residuals(model,type="pearson")
Pearson.chisq <- sum(rp^2)
prat <- Pearson.chisq/rdf
pval <- pchisq(Pearson.chisq, df=rdf, lower.tail=FALSE)
c(chisq=Pearson.chisq,ratio=prat,rdf=rdf,p=pval)
}
## Making a function to extract all the necessary outputs out of each model
model.output <- function(model) {
print(model)
print(summary(model))
print(performance::r2(model))
print(overdisp_fun(model))
}
TAlphaAll.glmm.1 = glmmTMB(TAlphaAll ~ (1 | ForestID), data= Results2,family = "poisson")
model.output(TAlphaAll.glmm.1)
## Formula: TAlphaAll ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 113.82930 115.91834 -54.91465 19
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.2564
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## 2.481
## Family: poisson ( log )
## Formula: TAlphaAll ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 113.8 115.9 -54.9 109.8 19
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.06576 0.2564
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.4814 0.1356 18.29 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # R2 for Mixed Models
##
## Conditional R2: 0.450
## Marginal R2: 0.000
## chisq ratio rdf p
## 10.1261416 0.5329548 19.0000000 0.9497652
## Formula: TAlphaNat ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 95.91131 98.00035 -45.95565 19
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 9.93e-06
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## 2.203
## Family: poisson ( log )
## Formula: TAlphaNat ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 95.9 98.0 -46.0 91.9 19
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 9.861e-11 9.93e-06
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.20250 0.07255 30.36 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.000
## chisq ratio rdf p
## 6.7368420 0.3545706 19.0000000 0.9954900
## Formula: TAlphaNInd ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 85.90528 87.99432 -40.95264 19
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.7897
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## 0.8873
## Family: poisson ( log )
## Formula: TAlphaNInd ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 85.9 88.0 -41.0 81.9 19
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.6236 0.7897
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.8873 0.3857 2.3 0.0214 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.644
## Marginal R2: 0.000
## chisq ratio rdf p
## 18.0549702 0.9502616 19.0000000 0.5187692
## Formula: TAlphaNInd ~ Dist_trail_beginning_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 85.55084 88.68440 -39.77542 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.7681
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_beginning_std
## 1.0243 -0.3052
## Family: poisson ( log )
## Formula: TAlphaNInd ~ Dist_trail_beginning_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 85.6 88.7 -39.8 79.6 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.5899 0.7681
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.0243 0.3866 2.650 0.00806 **
## Dist_trail_beginning_std -0.3052 0.2162 -1.412 0.15806
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.653
## Marginal R2: 0.060
## chisq ratio rdf p
## 15.8344488 0.8796916 18.0000000 0.6041000
## Formula: TAlphaNInd ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 86.72401 89.85758 -40.36200 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.7797
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## 0.9374 -0.1872
## Family: poisson ( log )
## Formula: TAlphaNInd ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 86.7 89.9 -40.4 80.7 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.6079 0.7797
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.9374 0.3839 2.442 0.0146 *
## Dist_trail_std -0.1872 0.1866 -1.004 0.3156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.649
## Marginal R2: 0.030
## chisq ratio rdf p
## 16.6030729 0.9223929 18.0000000 0.5505350
## Formula: TAlphaNInd ~ Dist_edge_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 87.09460 90.22817 -40.54730 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.7282
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std
## 1.0070 -0.2665
## Family: poisson ( log )
## Formula: TAlphaNInd ~ Dist_edge_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 87.1 90.2 -40.5 81.1 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.5302 0.7282
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.0070 0.3835 2.626 0.00863 **
## Dist_edge_std -0.2665 0.3060 -0.871 0.38375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 2.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.625
## Marginal R2: 0.047
## chisq ratio rdf p
## 17.2869103 0.9603839 18.0000000 0.5034692
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Formula: FAlphaAll ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 68.95103 72.08460 -31.47552 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.04381
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0377
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## 0.2074
## Family: Gamma ( inverse )
## Formula: FAlphaAll ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 69.0 72.1 -31.5 63.0 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.00192 0.04381
## Number of obs: 21, groups: ForestID, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0377
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.20744 0.02177 9.529 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # R2 for Mixed Models
##
## Conditional R2: 0.048
## Marginal R2: 0.000
## chisq ratio rdf p
## 0.60177879 0.03343215 18.00000000 1.00000000
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Formula: FAlphaAll ~ Dist_trail_beginning_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 69.42266 73.60075 -30.71133 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.04412
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0343
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_beginning_std
## 0.2002 0.0147
## Family: Gamma ( inverse )
## Formula: FAlphaAll ~ Dist_trail_beginning_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 69.4 73.6 -30.7 61.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.001946 0.04412
## Number of obs: 21, groups: ForestID, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0343
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.20020 0.02233 8.965 <2e-16 ***
## Dist_trail_beginning_std 0.01470 0.01200 1.225 0.221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # R2 for Mixed Models
##
## Conditional R2: 0.057
## Marginal R2: 0.004
## chisq ratio rdf p
## 0.52889840 0.03111167 17.00000000 1.00000000
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Warning in nlminb(start = par, objective = fn, gradient = gr, control =
## control$optCtrl): NA/NaN function evaluation
## Formula: FAlphaNat ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 7.86818 11.00175 -0.93409 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.03963
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0108
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## 0.4686
## Family: Gamma ( inverse )
## Formula: FAlphaNat ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 7.9 11.0 -0.9 1.9 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.00157 0.03963
## Number of obs: 21, groups: ForestID, 5
##
## Dispersion estimate for Gamma family (sigma^2): 0.0108
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4686 0.0217 21.59 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # R2 for Mixed Models
##
## Conditional R2: 0.127
## Marginal R2: 0.000
## chisq ratio rdf p
## 0.18188683 0.01010482 18.00000000 1.00000000
## Formula: FAlphaNInd ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 45.92022 48.00927 -20.96011 19
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 2.72e-05
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## -0.2706
## Family: poisson ( log )
## Formula: FAlphaNInd ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 45.9 48.0 -21.0 41.9 19
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 7.396e-10 2.72e-05
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2706 0.2498 -1.083 0.279
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.8 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.000
## chisq ratio rdf p
## 8.0661820 0.4245359 19.0000000 0.9859925
## Formula:
## abund.all ~ Dist_edge_std + Dist_trail_beginning_std + Dist_trail_std +
## (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 312.2955 317.5181 -151.1478 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.3339
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std
## 5.0043 -0.3643
## Dist_trail_beginning_std Dist_trail_std
## 0.1158 0.1432
## Family: poisson ( log )
## Formula:
## abund.all ~ Dist_edge_std + Dist_trail_beginning_std + Dist_trail_std +
## (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 312.3 317.5 -151.1 302.3 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.1115 0.3339
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.00429 0.15344 32.61 < 2e-16 ***
## Dist_edge_std -0.36428 0.09194 -3.96 7.43e-05 ***
## Dist_trail_beginning_std 0.11582 0.04337 2.67 0.00757 **
## Dist_trail_std 0.14315 0.04481 3.19 0.00140 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # R2 for Mixed Models
##
## Conditional R2: 0.962
## Marginal R2: 0.364
## chisq ratio rdf p
## 1.329744e+02 8.310902e+00 1.600000e+01 1.695091e-20
## Formula:
## abund.nat ~ Dist_edge_std + Dist_trail_beginning_std + Dist_trail_std +
## (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 293.7646 298.9872 -141.8823 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.3492
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std
## 4.9394 -0.4281
## Dist_trail_beginning_std Dist_trail_std
## 0.1264 0.1357
## Family: poisson ( log )
## Formula:
## abund.nat ~ Dist_edge_std + Dist_trail_beginning_std + Dist_trail_std +
## (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 293.8 299.0 -141.9 283.8 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.1219 0.3492
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.93944 0.16055 30.765 < 2e-16 ***
## Dist_edge_std -0.42809 0.09715 -4.406 1.05e-05 ***
## Dist_trail_beginning_std 0.12639 0.04587 2.755 0.00587 **
## Dist_trail_std 0.13571 0.04661 2.911 0.00360 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # R2 for Mixed Models
##
## Conditional R2: 0.964
## Marginal R2: 0.408
## chisq ratio rdf p
## 1.169212e+02 7.307573e+00 1.600000e+01 2.141864e-17
## Formula: abund.nind ~ Dist_edge_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 221.3960 224.5296 -107.6980 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 1.015
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std
## 1.9888 0.4281
## Family: poisson ( log )
## Formula: abund.nind ~ Dist_edge_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 221.4 224.5 -107.7 215.4 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.03 1.015
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.9888 0.4658 4.269 1.96e-05 ***
## Dist_edge_std 0.4281 0.1017 4.212 2.53e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # R2 for Mixed Models
##
## Conditional R2: 0.916
## Marginal R2: 0.090
## chisq ratio rdf p
## 1.240372e+02 6.890958e+00 1.800000e+01 7.230178e-18
## Formula: abund.nind ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## 222.1632 225.2967 -108.0816 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.9499
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## 2.1198 0.2152
## Family: poisson ( log )
## Formula: abund.nind ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## 222.2 225.3 -108.1 216.2 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.9023 0.9499
## Number of obs: 21, groups: ForestID, 5
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.11983 0.43479 4.876 1.09e-06 ***
## Dist_trail_std 0.21521 0.05228 4.116 3.85e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # R2 for Mixed Models
##
## Conditional R2: 0.900
## Marginal R2: 0.037
## chisq ratio rdf p
## 1.224116e+02 6.800647e+00 1.800000e+01 1.469360e-17
## Formula: prop.Talpha ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -44.45760 -41.32404 25.22880 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.7403
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 4.98
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## 1.708
## Family: beta ( logit )
## Formula: prop.Talpha ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -44.5 -41.3 25.2 -50.5 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.5481 0.7403
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 4.98
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.7080 0.4264 4.006 6.18e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 5.5 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 1.367
## Marginal R2: 0.000
## chisq ratio rdf p
## 2.2023134 0.1223507 18.0000000 0.9999976
## Formula: prop.Talpha ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -45.26116 -41.08307 26.63058 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.7349
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 5.74
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## 1.5663 0.4633
## Family: beta ( logit )
## Formula: prop.Talpha ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -45.3 -41.1 26.6 -53.3 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.54 0.7349
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 5.74
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.5663 0.4205 3.725 0.000196 ***
## Dist_trail_std 0.4633 0.3251 1.425 0.154114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 5.5 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 1.219
## Marginal R2: 0.307
## chisq ratio rdf p
## 2.3882907 0.1404877 17.0000000 0.9999869
## Formula: prop.Falpha ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -50.54869 -47.41512 28.27434 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.142
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 77.1
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## -0.1659
## Family: beta ( logit )
## Formula: prop.Falpha ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -50.5 -47.4 28.3 -56.5 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.02016 0.142
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 77.1
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.16590 0.09412 -1.762 0.078 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.8 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.897
## Marginal R2: 0.000
## chisq ratio rdf p
## 0.22976531 0.01276474 18.00000000 1.00000000
## Formula: prop.Falpha ~ Dist_trail_beginning_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -49.65403 -45.47594 28.82702 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.1767
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 88.7
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_beginning_std
## -0.21851 0.07238
## Family: beta ( logit )
## Formula: prop.Falpha ~ Dist_trail_beginning_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -49.7 -45.5 28.8 -57.7 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.03122 0.1767
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 88.7
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.21851 0.10782 -2.027 0.0427 *
## Dist_trail_beginning_std 0.07238 0.06581 1.100 0.2715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.8 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.945
## Marginal R2: 0.092
## chisq ratio rdf p
## 0.19503099 0.01147241 17.00000000 1.00000000
## Formula: prop.abund ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -78.42321 -75.28964 42.21160 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.5683
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 9.46
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## 2.659
## Family: beta ( logit )
## Formula: prop.abund ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -78.4 -75.3 42.2 -84.4 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.323 0.5683
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 9.46
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.6587 0.3817 6.966 3.26e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # R2 for Mixed Models
##
## Conditional R2: 1.405
## Marginal R2: 0.000
## chisq ratio rdf p
## 1.01738616 0.05652145 18.00000000 1.00000000
## Formula: all.tax.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -33.88890 -29.71081 20.94445 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.9278
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 3
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## -0.8334 -1.2546
## Family: beta ( logit )
## Formula: all.tax.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -33.9 -29.7 20.9 -41.9 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.8608 0.9278
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.8334 0.4983 -1.673 0.09443 .
## Dist_trail_std -1.2546 0.4024 -3.118 0.00182 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.870
## Marginal R2: 0.528
## chisq ratio rdf p
## 2.7779510 0.1634089 17.0000000 0.9999601
## Formula:
## all.tax.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -35.16903 -29.94641 22.58451 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.914
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.46
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -1.250 1.132 -1.704
## Family: beta ( logit )
## Formula:
## all.tax.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -35.2 -29.9 22.6 -45.2 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.8354 0.914
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.46
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2502 0.5557 -2.250 0.024453 *
## Dist_edge_std 1.1322 0.6595 1.717 0.086022 .
## Dist_trail_std -1.7044 0.4517 -3.773 0.000161 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.899
## Marginal R2: 0.619
## chisq ratio rdf p
## 2.8891280 0.1805705 16.0000000 0.9998683
## Formula: all.tax.brich ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -79.19751 -75.01942 43.59875 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 1.258
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 15
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## -2.424 -1.450
## Family: beta ( logit )
## Formula: all.tax.brich ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -79.2 -75.0 43.6 -87.2 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.581 1.258
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 15
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.4243 0.6253 -3.877 0.000106 ***
## Dist_trail_std -1.4497 0.3959 -3.662 0.000250 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.901
## Marginal R2: 0.477
## chisq ratio rdf p
## 0.69342191 0.04078952 17.00000000 1.00000000
## Formula:
## all.tax.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -79.78142 -74.55881 44.89071 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 1.077
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 14.7
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -2.7132 0.8819 -1.6443
## Family: beta ( logit )
## Formula:
## all.tax.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -79.8 -74.6 44.9 -89.8 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.16 1.077
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 14.7
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.7132 0.5796 -4.681 2.86e-06 ***
## Dist_edge_std 0.8819 0.5185 1.701 0.089 .
## Dist_trail_std -1.6443 0.3823 -4.301 1.70e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.884
## Marginal R2: 0.527
## chisq ratio rdf p
## 0.81349415 0.05084338 16.00000000 0.99999999
## Formula: all.tax.brepl ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -47.39823 -43.22014 27.69911 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.4016
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.26
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## -1.2313 -0.6954
## Family: beta ( logit )
## Formula: all.tax.brepl ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -47.4 -43.2 27.7 -55.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.1613 0.4016
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.26
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2313 0.3787 -3.251 0.00115 **
## Dist_trail_std -0.6954 0.3399 -2.046 0.04074 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.470
## Marginal R2: 0.337
## chisq ratio rdf p
## 3.0185237 0.1775602 17.0000000 0.9999274
## Formula:
## all.tax.brepl ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -48.16975 -42.94714 29.08488 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.0001152
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.35
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -1.4674 0.6402 -0.9475
## Family: beta ( logit )
## Formula:
## all.tax.brepl ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -48.2 -42.9 29.1 -58.2 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.327e-08 0.0001152
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.35
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.4674 0.3437 -4.269 1.96e-05 ***
## Dist_edge_std 0.6402 0.3526 1.816 0.06945 .
## Dist_trail_std -0.9475 0.3522 -2.690 0.00714 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.475
## chisq ratio rdf p
## 3.8469197 0.2404325 16.0000000 0.9991423
## Formula: nat.tax.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -37.43930 -33.26121 22.71965 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.9177
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.69
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## -1.038 -1.265
## Family: beta ( logit )
## Formula: nat.tax.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -37.4 -33.3 22.7 -45.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.8423 0.9177
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.69
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0376 0.4926 -2.106 0.03518 *
## Dist_trail_std -1.2646 0.3991 -3.168 0.00153 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.860
## Marginal R2: 0.530
## chisq ratio rdf p
## 2.2357030 0.1315119 17.0000000 0.9999920
## Formula:
## nat.tax.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -38.73086 -33.50825 24.36543 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.8598
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 4.14
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -1.431 1.073 -1.662
## Family: beta ( logit )
## Formula:
## nat.tax.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -38.7 -33.5 24.4 -48.7 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.7393 0.8598
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 4.14
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.4310 0.5293 -2.704 0.006859 **
## Dist_edge_std 1.0727 0.6115 1.754 0.079376 .
## Dist_trail_std -1.6624 0.4307 -3.859 0.000114 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.883
## Marginal R2: 0.621
## chisq ratio rdf p
## 2.4201718 0.1512607 16.0000000 0.9999608
## Formula: nat.tax.brich ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -135.83009 -132.69652 70.91504 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 3.094e-05
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.08
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## -2.636
## Family: beta ( logit )
## Formula: nat.tax.brich ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -135.8 -132.7 70.9 -141.8 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 9.572e-10 3.094e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.08
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.6358 0.4236 -6.223 4.88e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.1 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.000
## chisq ratio rdf p
## 3.698676 0.205482 18.000000 0.999866
## Formula: nat.tax.brepl ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -47.98269 -43.80460 27.99135 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.4623
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.32
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## -1.2587 -0.7198
## Family: beta ( logit )
## Formula: nat.tax.brepl ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -48.0 -43.8 28.0 -56.0 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.2137 0.4623
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.32
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2587 0.3933 -3.200 0.00137 **
## Dist_trail_std -0.7198 0.3466 -2.077 0.03781 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.506
## Marginal R2: 0.340
## chisq ratio rdf p
## 3.3206201 0.1953306 17.0000000 0.9998569
## Formula:
## nat.tax.brepl ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -48.84569 -43.62308 29.42285 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.3497
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.56
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -1.5571 0.7591 -1.0586
## Family: beta ( logit )
## Formula:
## nat.tax.brepl ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -48.8 -43.6 29.4 -58.8 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.1223 0.3497
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.56
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5571 0.4806 -3.240 0.0012 **
## Dist_edge_std 0.7591 0.5949 1.276 0.2020
## Dist_trail_std -1.0586 0.5402 -1.960 0.0500 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.3 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.581
## Marginal R2: 0.497
## chisq ratio rdf p
## 3.6036548 0.2252284 16.0000000 0.9994348
## Formula:
## nind.tax.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -153.39008 -148.16747 81.69504 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 4.331e-05
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.396
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -0.03587 0.90713 -0.97860
## Family: beta ( logit )
## Formula:
## nind.tax.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -153.4 -148.2 81.7 -163.4 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.876e-09 4.331e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.396
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.03587 0.38041 -0.094 0.9249
## Dist_edge_std 0.90713 0.44137 2.055 0.0399 *
## Dist_trail_std -0.97860 0.40175 -2.436 0.0149 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 1.176
## chisq ratio rdf p
## 11.164000 0.697750 16.000000 0.799251
## Formula: nind.tax.btotal ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -150.52152 -147.38796 78.26076 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 6.489e-05
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.287
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## 0.1433
## Family: beta ( logit )
## Formula: nind.tax.btotal ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -150.5 -147.4 78.3 -156.5 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 4.211e-09 6.489e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.287
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1433 0.3052 0.47 0.639
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: -0.000
## chisq ratio rdf p
## 16.4515482 0.9139749 18.0000000 0.5610653
## Formula: nind.tax.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -151.22891 -147.05082 79.61446 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 8.646e-05
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.327
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## 0.3882 -0.5428
## Family: beta ( logit )
## Formula: nind.tax.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -151.2 -147.1 79.6 -159.2 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 7.475e-09 8.646e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.327
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3882 0.3348 1.160 0.246
## Dist_trail_std -0.5428 0.3386 -1.603 0.109
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 1.740
## chisq ratio rdf p
## 14.0916376 0.8289199 17.0000000 0.6606050
## Formula:
## nind.tax.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -155.7636 -150.5410 82.8818 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 5.162e-05
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.479
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -0.6909 1.4233 -1.0907
## Family: beta ( logit )
## Formula:
## nind.tax.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -155.8 -150.5 82.9 -165.8 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 2.665e-09 5.162e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.479
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6909 0.3859 -1.790 0.07342 .
## Dist_edge_std 1.4233 0.4788 2.973 0.00295 **
## Dist_trail_std -1.0907 0.4112 -2.652 0.00799 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.8 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.908
## chisq ratio rdf p
## 8.6087198 0.5380450 16.0000000 0.9286488
## Formula: nind.tax.brepl ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -155.99305 -152.85948 80.99652 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.0001298
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 1.08
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## -1.862
## Family: beta ( logit )
## Formula: nind.tax.brepl ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -156.0 -152.9 81.0 -162.0 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.685e-08 0.0001298
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 1.08
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.8618 0.4048 -4.6 4.23e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.000
## chisq ratio rdf p
## 12.0360372 0.6686687 18.0000000 0.8453714
## Formula: nind.tax.brepl ~ Dist_edge_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -155.59071 -151.41263 81.79536 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.0001051
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 1.16
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std
## -1.6300 -0.3985
## Family: beta ( logit )
## Formula: nind.tax.brepl ~ Dist_edge_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -155.6 -151.4 81.8 -163.6 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.105e-08 0.0001051
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 1.16
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6300 0.4351 -3.746 0.00018 ***
## Dist_edge_std -0.3985 0.3195 -1.247 0.21226
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.072
## chisq ratio rdf p
## 9.5742292 0.5631900 17.0000000 0.9205096
## Formula: all.func.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -33.44919 -29.27110 20.72459 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.7879
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.66
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## -0.8169 -1.1691
## Family: beta ( logit )
## Formula: all.func.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -33.4 -29.3 20.7 -41.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.6208 0.7879
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.66
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.8169 0.4538 -1.800 0.07186 .
## Dist_trail_std -1.1691 0.4214 -2.775 0.00553 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.832
## Marginal R2: 0.542
## chisq ratio rdf p
## 3.0771443 0.1810085 17.0000000 0.9999166
## Formula:
## all.func.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -34.91468 -29.69207 22.45734 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.7072
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.03
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -1.204 1.058 -1.596
## Family: beta ( logit )
## Formula:
## all.func.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -34.9 -29.7 22.5 -44.9 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.5002 0.7072
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.03
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.2038 0.4923 -2.445 0.01448 *
## Dist_edge_std 1.0580 0.6150 1.720 0.08536 .
## Dist_trail_std -1.5955 0.4718 -3.382 0.00072 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.865
## Marginal R2: 0.660
## chisq ratio rdf p
## 3.1770939 0.1985684 16.0000000 0.9997516
## Formula: all.func.brich ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -53.78730 -49.60921 30.89365 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 5.226e-05
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 3
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## -1.464 -0.647
## Family: beta ( logit )
## Formula: all.func.brich ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -53.8 -49.6 30.9 -61.8 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 2.731e-09 5.226e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.4636 0.2954 -4.955 7.22e-07 ***
## Dist_trail_std -0.6470 0.2886 -2.242 0.025 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.326
## chisq ratio rdf p
## 3.0800536 0.1811796 17.0000000 0.9999161
## Formula:
## all.func.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -54.39010 -49.16749 32.19505 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 3.542e-05
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.48
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -1.7317 0.5463 -0.9422
## Family: beta ( logit )
## Formula:
## all.func.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -54.4 -49.2 32.2 -64.4 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.254e-09 3.542e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.48
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7317 0.3294 -5.257 1.46e-07 ***
## Dist_edge_std 0.5463 0.3226 1.694 0.09035 .
## Dist_trail_std -0.9422 0.3389 -2.780 0.00543 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.453
## chisq ratio rdf p
## 3.1695803 0.1980988 16.0000000 0.9997555
## Formula: all.func.brepl ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -55.25373 -51.07564 31.62687 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 1.034
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 7.85
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## -1.745 -1.335
## Family: beta ( logit )
## Formula: all.func.brepl ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -55.3 -51.1 31.6 -63.3 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.069 1.034
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 7.85
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7452 0.5325 -3.277 0.001048 **
## Dist_trail_std -1.3352 0.3762 -3.549 0.000387 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.872
## Marginal R2: 0.510
## chisq ratio rdf p
## 1.15079724 0.06769396 17.00000000 0.99999995
## Formula:
## all.func.brepl ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -55.08074 -49.85812 32.54037 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.8205
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 7.36
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -1.9694 0.7494 -1.5033
## Family: beta ( logit )
## Formula:
## all.func.brepl ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -55.1 -49.9 32.5 -65.1 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.6732 0.8205
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 7.36
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.9694 0.4866 -4.047 5.18e-05 ***
## Dist_edge_std 0.7494 0.5172 1.449 0.147
## Dist_trail_std -1.5033 0.3766 -3.992 6.55e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.842
## Marginal R2: 0.573
## chisq ratio rdf p
## 1.25871265 0.07866954 16.00000000 0.99999965
## Formula: nat.func.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -32.44195 -28.26386 20.22098 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.7555
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.48
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## -0.7555 -1.1455
## Family: beta ( logit )
## Formula: nat.func.btotal ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -32.4 -28.3 20.2 -40.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.5708 0.7555
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.48
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7555 0.4443 -1.701 0.08903 .
## Dist_trail_std -1.1455 0.4259 -2.690 0.00715 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.826
## Marginal R2: 0.546
## chisq ratio rdf p
## 3.2690116 0.1922948 17.0000000 0.9998719
## Formula:
## nat.func.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -33.29778 -28.07517 21.64889 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.6878
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.74
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -1.0996 0.9476 -1.5235
## Family: beta ( logit )
## Formula:
## nat.func.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -33.3 -28.1 21.6 -43.3 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 0.4731 0.6878
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.74
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0996 0.4853 -2.266 0.02345 *
## Dist_edge_std 0.9476 0.6052 1.566 0.11739
## Dist_trail_std -1.5235 0.4747 -3.209 0.00133 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.4 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.853
## Marginal R2: 0.645
## chisq ratio rdf p
## 3.4625313 0.2164082 16.0000000 0.9995634
## Formula: nat.func.brich ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -56.61263 -52.43454 32.30631 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 2.701e-05
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.75
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## -1.5156 -0.5725
## Family: beta ( logit )
## Formula: nat.func.brich ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -56.6 -52.4 32.3 -64.6 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 7.296e-10 2.701e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 2.75
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5156 0.3081 -4.919 8.72e-07 ***
## Dist_trail_std -0.5725 0.2789 -2.053 0.0401 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.260
## chisq ratio rdf p
## 3.9103731 0.2300219 17.0000000 0.9995565
## Formula:
## nat.func.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -57.11194 -51.88933 33.55597 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 2.999e-05
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.18
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -1.7868 0.5497 -0.8650
## Family: beta ( logit )
## Formula:
## nat.func.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -57.1 -51.9 33.6 -67.1 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 8.997e-10 2.999e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.18
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.7868 0.3435 -5.202 1.98e-07 ***
## Dist_edge_std 0.5497 0.3319 1.656 0.09767 .
## Dist_trail_std -0.8650 0.3327 -2.600 0.00933 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.393
## chisq ratio rdf p
## 4.0314491 0.2519656 16.0000000 0.9988482
## Formula: nat.func.brepl ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -52.86565 -48.68756 30.43283 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 1.054
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 6.22
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_trail_std
## -1.599 -1.308
## Family: beta ( logit )
## Formula: nat.func.brepl ~ Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -52.9 -48.7 30.4 -60.9 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.112 1.054
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 6.22
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.5986 0.5436 -2.941 0.003276 **
## Dist_trail_std -1.3075 0.3824 -3.419 0.000628 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: mu of 0.2 is too close to zero, estimate of random effect variances may be unreliable.
## # R2 for Mixed Models
##
## Conditional R2: 0.866
## Marginal R2: 0.490
## chisq ratio rdf p
## 1.64497455 0.09676321 17.00000000 0.99999923
## Formula:
## nind.func.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -136.37470 -131.15209 73.18735 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 3.541e-05
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.468
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -0.2244 1.1157 -1.0829
## Family: beta ( logit )
## Formula:
## nind.func.btotal ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -136.4 -131.2 73.2 -146.4 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.254e-09 3.541e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.468
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2244 0.3775 -0.594 0.55231
## Dist_edge_std 1.1157 0.4471 2.496 0.01258 *
## Dist_trail_std -1.0829 0.4067 -2.663 0.00775 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.1 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 1.058
## chisq ratio rdf p
## 11.2042651 0.7002666 16.0000000 0.7967050
## Formula:
## nind.func.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -133.27324 -128.05063 71.63662 16
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 3.693e-05
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.496
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std Dist_trail_std
## -0.3288 1.2141 -1.1193
## Family: beta ( logit )
## Formula:
## nind.func.brich ~ Dist_edge_std + Dist_trail_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -133.3 -128.1 71.6 -143.3 16
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.364e-09 3.693e-05
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 0.496
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3288 0.3773 -0.872 0.38343
## Dist_edge_std 1.2141 0.4522 2.685 0.00726 **
## Dist_trail_std -1.1193 0.4092 -2.735 0.00624 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 1.0 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 1.022
## chisq ratio rdf p
## 9.9632102 0.6227006 16.0000000 0.8685425
## Formula: nind.func.brepl ~ (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -172.68248 -169.54892 89.34124 18
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.0001045
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.76
##
## Fixed Effects:
##
## Conditional model:
## (Intercept)
## -3
## Family: beta ( logit )
## Formula: nind.func.brepl ~ (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -172.7 -169.5 89.3 -178.7 18
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.093e-08 0.0001045
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 3.76
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.9995 0.4635 -6.471 9.71e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.0 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.000
## chisq ratio rdf p
## 6.3657915 0.3536551 18.0000000 0.9944736
## Formula: nind.func.brepl ~ Dist_edge_std + (1 | ForestID)
## Data: Results2
## AIC BIC logLik df.resid
## -172.36359 -168.18550 90.18179 17
## Random-effects (co)variances:
##
## Conditional model:
## Groups Name Std.Dev.
## ForestID (Intercept) 0.0001402
##
## Number of obs: 21 / Conditional model: ForestID, 5
##
## Overdispersion parameter for beta family (): 4.07
##
## Fixed Effects:
##
## Conditional model:
## (Intercept) Dist_edge_std
## -2.7956 -0.3791
## Family: beta ( logit )
## Formula: nind.func.brepl ~ Dist_edge_std + (1 | ForestID)
## Data: Results2
##
## AIC BIC logLik deviance df.resid
## -172.4 -168.2 90.2 -180.4 17
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## ForestID (Intercept) 1.965e-08 0.0001402
## Number of obs: 21, groups: ForestID, 5
##
## Overdispersion parameter for beta family (): 4.07
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.7956 0.4708 -5.939 2.87e-09 ***
## Dist_edge_std -0.3791 0.2983 -1.271 0.204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: Can't compute random effect variances. Some variance components equal zero.
## Solution: Respecify random structure!
## Warning: mu of 0.0 is too close to zero, estimate of random effect variances may be unreliable.
## Random effect variances not available. Returned R2 does not account for random effects.
## # R2 for Mixed Models
##
## Conditional R2: NA
## Marginal R2: 0.053
## chisq ratio rdf p
## 5.1487467 0.3028675 17.0000000 0.9973187