Plot model average

Hello everyone here,

I'm analyzing my data research on wildlife using of seasonal waterholes.

I did the model average (of the 2 models below) using #model.avg function
(package MuMIn).

glmmadmb(BT~water+tree+offset(log(trap))+(1|obs), family="nbinom",
data=ndata4)

glmmadmb(BT~water+tree+road+offset(log(trap))+(1|obs), family="nbinom",
data=ndata4)

Model-averaged coefficients:
(full average)
            Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept) -3.33821    0.16062     0.16128  20.698   <2e-16 ***
water       -0.42935    0.18091     0.18166   2.364   0.0181 *
tree         0.28022    0.11639     0.11684   2.398   0.0165 *
road         0.05068    0.09926     0.09950   0.509   0.6105

(conditional average)
            Estimate Std. Error Adjusted SE z value Pr(>|z|)
(Intercept)  -3.3382     0.1606      0.1613  20.698   <2e-16 ***
water        -0.4294     0.1809      0.1817   2.364   0.0181 *
tree          0.2802     0.1164      0.1168   2.398   0.0165 *
road          0.1315     0.1222      0.1227   1.071   0.2841
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

Relative variable importance:
                     offset(log(trap)) tree water road
Importance:          1.00              1.00 1.00  0.39
N containing models:    2                 2    2     1

How can I plot the beta coefficient of model average above?

Thanks,

Ratana