How to plot a Community Comparions of longitudinal data (Spring, Summer), that also accounts for the different sampling sides ?

Dear Nadja Heitmann ,

One way to plot a community comparison of longitudinal data (Spring, Summer) while accounting for the different sampling sides is to use a non-metric multidimensional scaling (NMDS) plot. NMDS is a technique that can be used to visualize complex ecological data and it allows you to compare communities over time while accounting for the different sampling sides.

Here are the general steps you can follow to create an NMDS plot for your data:

Prepare your data: Organize your data in a matrix format, with rows representing samples and columns representing the different beetle species.

Use a statistical software (R, PRIMER, etc) to perform the NMDS analysis on your data. You should include the two different factors (Spring/Summer) and the different sampling sides as factors in the analysis.

Create the NMDS plot: The plot will show the similarity of the communities in two-dimensional space, and the position of the points on the plot will indicate the similarity of the beetle communities between spring and summer and different sampling sides.

You can use the software to add the groupings of samples (Spring, Summer) and different sampling sides as different colors or symbols on the plot.

It's important to note that the NMDS plot is based on a subjective interpretation of the data, and different runs of the analysis may result in slightly different plots. It's also important to validate the results obtained by this method by comparing them with other methods and by analyzing the data with different parameters.

Additionally, you can also use a permutational multivariate analysis of variance (PERMANOVA) to test if there is a significant difference between the two communities. This would allow you to also test if the difference between the communities is related to the different sampling sides.

Nadja Heitmann

Thanks for the answers,

but actually, I meant that the ordination should address that the data points are not independent of each other. A community sampled in spring is probably very similar to the same community sampled in summer. But I am not interested in the development of this particular community, but in how all communities change over time. So in a statistical Model, I could address repeated measurements or dependent data, and I was wondering, If an ordination could also do this, to focus more on general community changes, regardless of the repeated measurement.

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