I have an experiment to look at habitat preferences of beetles and the effect of competition. In the first test, a beetle is on his own in an arena and I measured the % of time he spends in each of THREE types of habitat (open, bush and underground). I repeat this experiment with several beetles. In the second test, beetles are placed together with a beetle of a competing species in the arena and they are again observed for their % time in each habitat.

I want to test if the amount of time spent in each habitat is affected by the presence of the competitor.

But how can I compare the % time in each of the three habitats when the competitor is absent (test 1) and present (test 2)? Which test can answer this question? Do I need to test the means?

My expectation is that in the presence of the competitor the use of Habitat 2 would increase.
Apologies, I dont know how to import data.

data: Habitat 1=

  • $\begingroup$ Even in test 1, I'm not sure a chi-square test of association would be helpful. This is because time is not decidedly countable. For example, if you observe a beetle for 12 hours, if you count where it is each hour you will get a different result than if you count each minute than if you count each second. $\endgroup$ – Sal Mangiafico Jan 15 at 9:28
  • $\begingroup$ time was standardised - so every test ran for 2 hours and location in every second was recorded .. does that make it appropriate? $\endgroup$ – Birdonawire Jan 16 at 10:06
  • $\begingroup$ for me, I wouldn't treat time as something countable... In your case, I think you might find the hypothesis test with a chi-square analysis isn't very useful simply because you have 7200 (?) observations per subject. With that many observations, even small differences many be statistically significant. So, I wouldn't put much weight in a small p-value without thinking critically about the difference in percents or time. $\endgroup$ – Sal Mangiafico Jan 17 at 15:02

Using a $\chi^2$ test you would always check 33 %. A linear model would be able to capture a more complex situation. With it you could set up hypotheses to test the % time in each habitat and whether is changes with number of competitors present.

  • $\begingroup$ Wouldn't it be problematic for the linear model that the percents in a scenario always add up to 100%? That it, as the percent in one habitat increases, the percent in another must decrease. So the observations of the dependent variable are not independent? $\endgroup$ – Sal Mangiafico Jan 15 at 9:30
  • $\begingroup$ @SalMangiafico you are right, I overlooked that. Maybe if the variable was not transformed and kept in raw values (time) it would work. Afterwards the raw results could be transformed into percentages. $\endgroup$ – user2974951 Jan 15 at 9:50
  • $\begingroup$ I think there is a type of model that is used for data that sum to a whole. Unfortunately I don't remember the name and can't find anything online about it. ... Whether as a percent or as a time, I would think that focusing on just one habitat would be approachable with a linear model, beta regression, or something similar. $\endgroup$ – Sal Mangiafico Jan 17 at 15:14
  • $\begingroup$ It might be "compositional data" $\endgroup$ – Sal Mangiafico Jan 17 at 22:21

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