Not sure how you sampled through time, but you could, potentially, use a GLM, and test for differences through time, across treatments, and as an interaction of time and treatment. You can do this pretty easily in SPSS and R. That would help shape an understanding of how changes through time affected each community as well as just the treatment. When you talk about community composition, what metric are you using for that? Is it like one index value or is it a consideration of richness, diversity, evenness all as separate indices? If all you did was sample at the end of the 55 days, you can't really map changes through time, then just comparing the treatments through ANOVA should probably work. Dissimilarity indices are useful too, and applying them in concert with a GLM might help you better understand what drove some of the community changes.

Hope that helps.

HI

You can use dissimilarity index. They are useful to summmarize abundance and diversity data. There's plenty of dissimilarity index, i use Morisita-Horn in my Msc Thesis.

Check this paper for further informations : Wolda, H. (1981). Similarity indices, sample size and diversity. Oecologia 50, 296–302. doi:10.1007/BF00344966.

Serveral indexes are implemented in R with package Vegan.

Best regards

Hi

I tjhink you can use alpha and beta diversity indexes that are in past sofware.

 Also you can compare two Shannon indexes by t  Student. 

For abundance if you have replicates could employ ANOVA

Best Wishes

Jose

Hello

I agree with Jose that you can use alpha and beta diversity indexes that are in "past software".

Best regards

Lorena

How can I best analyze the change of an insect community composition and diversity over time? - ResearchGate. Available from: https://www.researchgate.net/post/How_can_I_best_analyze_the_change_of_an_insect_community_composition_and_diversity_over_time [accessed Dec 9, 2015].

Not sure how you sampled through time, but you could, potentially, use a GLM, and test for differences through time, across treatments, and as an interaction of time and treatment. You can do this pretty easily in SPSS and R. That would help shape an understanding of how changes through time affected each community as well as just the treatment. When you talk about community composition, what metric are you using for that? Is it like one index value or is it a consideration of richness, diversity, evenness all as separate indices? If all you did was sample at the end of the 55 days, you can't really map changes through time, then just comparing the treatments through ANOVA should probably work. Dissimilarity indices are useful too, and applying them in concert with a GLM might help you better understand what drove some of the community changes.

Hope that helps.

You could use rarefaction curves with EstimateS to see if sampling overtime is adding species http://viceroy.eeb.uconn.edu/estimates/

Hello!

I would use the Bray-Curtis similarity index and NMDS to visualize if there is any trend in changes in community composition across your sampling period. After that I would test for significant differences using ANOSIM.

Hope it helps...Good luck!

If you have sampled repeatedly in time, and have replicates for each treatment, in addition to the (dis)similarity index analyses you could do Principle Response Curves analyses (with CANOCO). 

Regards,

Saskia