Hi everyone,

I'm running behavioral trials on Pachycondyla ants as a side project alongside my main research (which focuses on ant venom biochemistry), but I wanted to look at when/how the venom actually gets used in different contexts. Problem is I have zero background in ecology or ethology, so I'd love some guidance from people with an ecology/ethology background cause I want to do this properly.

I'm running behavioral trials with Pachycondyla ants confronted with 6 different opponent ant species, across 3 different experimental setups. Each trial is filmed for 1.5–2 minutes (duration varies slightly between videos), so I know I need to normalize for time somehow. And each individual (from both side) is only ever used once so no ant gets reused across trials. For each video, I count occurrences of 5 behaviors:

• Antennal contact (antennae touch any part of the opponent's body
• Mandible opening (mandibles open, with the two individuals about one body length apart)
• Biting
• Gaster flexing (abdomen curved as if about to sting)
• Stinging

 What statistical approach would you recommend for count data like this? What kind of plots actually work well for this kind of data? not looking to just dump a boxplot of raw counts if there's something better... I've already gone through some papers on ant behavior / interspecific aggression, but if you have specific methodological references you think are worth checking that use a similar design, I'd love pointers.

Not trying to overthink this, just want something defensible since it'll probably end up as a supplementary part of a bigger paper!

Thanks in advance :)

Hello, I am having trouble finding the marginals option using the scatr module. I have downloaded it and followed the instructions that my professor provided but it seems that the Marginal option does not pop up after I input my x and y axis.

Anyone know how to access that option?

Hi I am a master’s student and I have a rather basic understanding of linear mixed effects models. I am helping with statistics in a study which has 2x2x10 mixed design (2 between and 10 within). I am confused as to how am I supposed to start modelling. Do I do it according to the hypothesis so I all of our IVs in the model and only then check whether the random slopes/ intercepts are needed, or do specify the random model and only then look at the fixed one and add the predictors one by one and see how the model is. Can someone please help me understand?

I've got some relatively unbalanced panel data and I'm trying to understand how Stata is calculating coefficients.

Let's say I want to look at the relationship between supervisor status and job satisfaction, and I have 10,000 observations over 5 years of annual surveys. I want to run a fixed effects model to focus on within-person effects only and control for time-invariant individual differences.

So, I run the model: xtset id year xtreg c.jobsatisfaction i.year i.supervisor, fe

But let's also say that 50% of observations in my dataset are from people who only appear once, and of the remaining half only 20% of those individuals ever switch supervisor status at all. Only 10% of the original sample seems like it provides any information toward the within-person estimates, but if I drop the other 90% of cases before running the model then the results are different.

Can anyone help me understand what information is using from the other 9,000 observations in the full sample so I can decide which sample is more appropriate to use? For the record, I have also run a mixed effects model but I am trying to better understand what's happening in the data.