# Thread: Flight times of insects

1. ## Re: Flight times of insects

Yes that makes sense. But as I said, sample time is a random source of varaition over and above the higher levels in your model (region, year and month?). Even if you sampled on exactly the same date and time in 2009 as you did in 2010 you will still find differences. Model sample time within year or even within month if you want to make inferences about specific months. Don't get to caught up in the different sample dates or the slighly unbalanced nature of this design.

2. ## Re: Flight times of insects

So I added a column to my data with the number of traps at each site (some of the old sites had 3 traps and newer ones only have 2) to get an average sirex per trap - this way I'm not comparing 30 sirex from 3 traps to 30 sirex from 1 or 2. This converted my data into continuous responses so I performed a glm.

> fit<-glm(Average~Geo.Loc+Year, family=gaussian, data=total)
> summary(fit)

Call:
glm(formula = Average ~ Geo.Loc + Year, family = gaussian, data = total)

Deviance Residuals:
Min 1Q Median 3Q Max
-2.3714 -1.3835 -0.7539 0.6006 7.6006

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -29.59235 261.91135 -0.113 0.910132
Geo.LocOZ -0.97200 0.32126 -3.026 0.002739 **
Geo.LocSAR -1.57775 0.41916 -3.764 0.000208 ***
Year 0.01589 0.13024 0.122 0.903014
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 4.150306)

Null deviance: 1109.4 on 255 degrees of freedom
Residual deviance: 1045.9 on 252 degrees of freedom
AIC: 1096.8

Number of Fisher Scoring iterations: 2

This gives me significance between regions but not years - which is what we expected. However, my data is highly overdispersed but I have a lot of 0s and 1s so I guess that's not so surprising. Does this also seem like a good way to look at it? These patterns make sense and I'm gonna do the same with collection dates within each year.