There is data about the location and characteristics of plots of Oilseed Rape on several farms, as well as the relative abundance of different pollinator species counted at each plot. The variables included are:
Farm
latitude
longitude
pct_flower
temp
variety
type
species
group
relative_abundance
1. Investigate the effect of temperature and flower coverage on the relative abundance of pollinators (overall and for honeybees specifically - in the group variable).
2. The difference in pollinator relative abundance (overall and for honeybees specifically) among farms, varieties, and types.
I have researched papers and many google searches to try and understand which test I need to complete for these. Am i correct in thinking temperature, flower coverage, and relative abundance are all continuous data and i look at these against honeybees. Or do I need to extract honeybees vs each of the other variables in question?
I have looked at Kruskal Wallis tests, or ANOVA, and also Poisson regression, though I am confused on if I am correct.
Any help would be appreciated!
Have you asked your advisor or other people in the department for help? You should be able to find someone at your university to help with these sorts of questions.
From UCLA, though:
"The [linked] table shows general guidelines for choosing a statistical analysis. We emphasize that these are general guidelines and should not be construed as hard and fast rules. Usually your data could be analyzed in multiple ways, each of which could yield legitimate answers."
https://stats.idre.ucla.edu/other/mult-pkg/whatstat/
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