Hi, I would suggest a multiple linear regression model, since all your variables are numerical. You can set modern pollen as the dependent variable and the others as explanatory.

Dear

I think the proposed topics for analysis are not useful.

Stamatios Ntanos Thank you very much. I have found an article and they are using constrained and partial canonical ordination techniques (RDA and partial RDA). What do you think about these methods?

Link of the paper: Article Modern pollen assemblages from human-influenced vegetation i...

Meshal Harbi Odah Thank you. Then can you please suggest any appropriate method for the analysis?

Hi Sazal, you should look at your data first. Basically, if you are just searching the correlation between the 2 factors then both Pearson and Spearman correlation analysis would be fine. Please remember that using Pearson Correlation assumes that your data meets the normal distribution however Spearman has no assumption on the normality on your data. In addition, please note that the Principal component analysis (PCA), Factor analysis (FA) and cluster analysis are usually used to describe the variability among observed, correlated variables to convert a set of observations of possibly correlated variables into sets of values which are linearly uncorrelated. So, all these analyses are preformed based on the Correlation or Covariance matrix of the data.