I would like to know what environmental variables allows to explain the presence of several species (binomial glm). I used a stepAIC procedure to select the best model.

But when I do a summary(model), some variables are not significant (according to pvalues).

How can I interpret these results ? Can one says that all the variables are usefull to explain the presence of the species but that only some of them are significant ?

Must I interpret coefficient estimates of unsignificant variables ?

Thank you very much for your help !

Here are the results of my summary(model) :

> summary(LiDAR_selection10[[1]])

Call:

glm(formula = dfSURVEY_presence_absence[, i] ~ Chm10 + Wetness10 +

Light.Bode10 + Chm10:Wetness10 + Chm10:Light.Bode10, family = binomial)

Deviance Residuals:

Min 1Q Median 3Q Max

-1.9818 -1.0054 0.5533 0.9494 1.7402

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) 27.243650 16.103729 1.692 0.0907 .

Chm10 -0.983108 0.498498 -1.972 0.0486 *

Wetness10 -1.159092 0.926263 -1.251 0.2108

Light.Bode10 -0.181315 0.096564 -1.878 0.0604 .

Chm10:Wetness10 0.042711 0.029435 1.451 0.1468

Chm10:Light.Bode10 0.006562 0.003038 2.160 0.0308 *

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 84.635 on 62 degrees of freedom

Residual deviance: 72.251 on 57 degrees of freedom

AIC: 84.251