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I'm a master's student working on an NSF-funded project investigating the impact of human visitation on island ecosystems. Our study involves three islands with varying levels of human visitation: high, medium, and low. The primary focus is on understanding the relationship between diet, influenced by visitor impact, and microbiome composition.

We've collected nearly 200 16S rRNA samples and a comprehensive dataset of physiological data (blood count, immune metrics, energy metrics, metabolome profiles, etc.) from multiple hosts on each island. Samples were collected annually (once every year for 3 consecutive years), but I'm unsure if the seasons were consistent across years.

My research questions are:

  1. Does diet, influenced by varying levels of human visitation, correlate with changes in microbiome composition?
  2. Which physiological factors (e.g., blood count, immune metrics) are most strongly associated with microbiome shifts?
  3. How does microbiome composition change from year to year on each island?
  4. Are there significant differences in microbiome composition between islands with varying visitor rates?

I need your help please

  • Recommended statistical methods for analyzing microbiome data and correlating it with physiological factors.

  • It's also worth mentioning that I haven't been exactly sure on which approach is generally more ideal. Would it be wiser to process all the data together y1vsy2vsy3 in qiime2 or is it better to process y1vsy2 and y2vsy3.

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  • $\begingroup$ Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. $\endgroup$
    – Community Bot
    Commented Aug 19 at 21:35
  • $\begingroup$ Okay, I have done that. thanks. $\endgroup$
    – saif s
    Commented Aug 20 at 2:49

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