Here my focus is to measure proximity (of group members) of free-roaming animals while resting. I collected data using instantaneous scans and now want to see the effects of seasons, human habitat, group size, sexes, and dominance-ranks on proximity. Interestingly, I have observed different clusters in same groups (for example, XYZ rest together and ABS rest together). What would be best way to analyze the data? (unfortunately, I don't have data on relatedness of the individuals).
PERMANOVA. Proximity is your dependent variable. You could also do an ordination
You can use FAMD (Factor analysis of mixed data) to visualize and analyze the data, as your data contains both categorical and numerical observations. FAMD is an ordination method similar to PCA, and will be helpful in this scenario. The result will tell you how much each of the predictors are contributing towards the variance in the data.
If you only want to investigate which of the predictors are significant, you can consider the suggestion made by Philip D DeWitt
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