model.beta <- betareg(pctrans ~ tree, data=BT.data, link="logit")
modelnull.beta <- betareg(pctrans ~ tree, data=BT.data, link="logit")
lrtest(model1.beta, modelnull.beta)

Call:
betareg(formula = pctrans ~ tree, data = BT.data, link = "logit")

Standardized weighted residuals 2:
    Min      1Q  Median      3Q     Max
-2.7716 -0.5800  0.0472  0.5351  3.5109

Coefficients (mean model with logit link):
             Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.111504   0.069191 -16.064  < 2e-16 ***
treeBC3F3   -0.050940   0.095889  -0.531  0.59525
treeD54     -0.279927   0.096470  -2.902  0.00371 **
treeD58     -0.034000   0.095716  -0.355  0.72242
treeEllis1  -0.006764   0.095175  -0.071  0.94334
treeQing     0.785992   0.094003   8.361  < 2e-16 ***

Phi coefficients (precision model with identity link):
      Estimate Std. Error z value Pr(>|z|)
(phi)   3.5549     0.1352   26.29   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Type of estimator: ML (maximum likelihood)
Log-likelihood: 529.8 on 7 Df
Pseudo R-squared: 0.1105
Number of iterations: 20 (BFGS) + 2 (Fisher scoring)

Likelihood ratio test

Model 1: pctrans ~ tree
Model 2: pctrans ~ 1
  #Df LogLik Df  Chisq Pr(>Chisq)
1   7 529.82
2   2 460.70 -5 138.25  < 2.2e-16 ***

beta.glm <- glm(pctrans ~ tree, data=BT.data, family=quasibinomial)

Call:
glm(formula = pctrans ~ tree, family = quasibinomial, data = BT.data)

Deviance Residuals:
     Min        1Q    Median        3Q       Max
-0.94474  -0.38492  -0.08785   0.22725   1.80291

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.22601    0.07643 -16.042  < 2e-16 ***
treeBC3F3    0.06826    0.10660   0.640  0.52205
treeD54     -0.33864    0.11312  -2.994  0.00281 **
treeD58     -0.19878    0.11062  -1.797  0.07260 .
treeEllis1  -0.07763    0.10808  -0.718  0.47276
treeQing     0.88596    0.09978   8.879  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for quasibinomial family taken to be 0.2069603)

    Null deviance: 307.54  on 1240  degrees of freedom
Residual deviance: 267.59  on 1235  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 4

Analysis of Deviance Table
Model: quasibinomial, link: logit
Response: pctrans
Terms added sequentially (first to last)

     Df Deviance Resid. Df Resid. Dev  Pr(>Chi)
NULL                  1240     307.54
tree  5   39.951      1235     267.59 < 2.2e-16 ***