question: logically, how could the likelihood developed for a combined population (~0.05) be significantly different that for each half of the same population (~0.9 and ~0.75) ?

ref: graphics: https://github.com/cordphelps/brm ; first README figure, data mentioned for cluster one (red), first seasonal timeframe, week 23-25)

I have two (experimental and control) insect populations that yield ‘trapped insects’ over the course of time. I segment each of these populations into 3 clusters across 3 *seasonal* timeframes (9 separate sub-populations per transect)

I propose that the population drivers allow me to use the Oceanic Tool Complexity model described in McElreath (and https://bookdown.org/connect/#/apps/1850/access section 10.2) to assess the likelihood that the independent variable influences the number of trapped insects.

For the first seasonal population (weeks 23-25), I find that the likelihood that the dependent variable influences the number of trapped spiders in the third cluster (blue) is ~.75 and ~.05 for the first cluster (red).

I then partition this population by time of day (data was collected twice each day). I now find the half day (‘am’ and ‘pm’) likelihood for the first and third clusters is inverted compared to the analysis of the full day population. This seems weird to me. What could explain this?

‘brms’ package (version 2.6.0)

R version 3.3.3 (2017-03-06)

Platform: x86_64-apple-darwin13.4.0 (64-bit)

Running under: OS X El Capitan 10.11.6