I am currently analysing my thesis data which is looking at counts of different bat species across a number of sites, using the predictors: canopy gap fraction, canopy height, clutter index and woodland connectivity.

When I run hurdle models on individual species I see slightly different significant predictors (I expected this since different species utilise different landscape features).

However, when I run a GLM with quasi-poisson family on all of the bats lumped together regardless of species (data is not zero-inflated but there is overdispersion, quasipoisson fits the data better than negative binomial model), none of the predictors are significant.

glm(formula = Total_Bats ~ Tree_Height + Clutter_Index + Canopy_Cover + 
Connectivity, family = quasipoisson, data = speciesbats)

Deviance Residuals: 
Min       1Q   Median       3Q      Max  
-9.3579  -4.6243  -0.9103   2.7429  17.1388  

               Estimate Std. Error t value Pr(>|t|)    
(Intercept)    3.768092   0.212199  17.757   <2e-16 ***
Tree_Height    0.041352   0.022548   1.834   0.0685 .  
Clutter_Index  0.214933   0.543463   0.395   0.6930    
Canopy_Cover  -0.007780   0.008568  -0.908   0.3652    
Connectivity  -0.129322   0.077727  -1.664   0.0981 .  
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for quasipoisson family taken to be 28.46795)

Null deviance: 4883.0  on 167  degrees of freedom
Residual deviance: 4574.2  on 163  degrees of freedom

Number of Fisher Scoring iterations: 5

Is it reasonable to make the interpretation that since different predictors are of different importance to different species, that these effects are hidden when all bats are put together? This doesn't seem right as I would still have expected woodland connectivity to have been a positive predictor for all bat species. Or is it an indication of a problem with my model?

  • 1
    $\begingroup$ At the very least you'll want a (factor) variable for the species, as the counts can be expected to vary significantly by species regardless of whether any of the independent variables are significant or not. That alone could be confounding your results. $\endgroup$ – jbowman Aug 7 '17 at 22:36

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