Olena Yarys PCA could be used after your data collection and the calculation of the covariance or correlation of your data collection. Then you should calculate and perform the eigenvalue on the covariance or correlation matrix of your variables. This result would show you the eigenvectors and eigenvalues of those components. Then sort the eigenvalues in descending order. The principal components corresponding to the highest eigenvalues capture the most variability in the data. Remember to choose the number of principal components based on the required threshold. Then Interpret the principal components and their respective loadings. The accumulation or sum of the loading of components should be higher than 50%. Then peform the biotope-species relationships into clusters or patterns in the form of the direction of the vectors, which would indicate which species contribute most to the separation (loadings). Then Validate the results using cross-validation or significance analysis of your results.

Principal Component Analysis (PCA) is a dimensionality reduction technique that is commonly used in ecology and environmental science to explore relationships between species and environmental variables, such as biotopes. When applying PCA to establish the relationship between species and biotopes, the interpretation involves understanding how the principal components and loadings relate to the underlying ecological patterns. Here's a step-by-step guide:

Remember that interpreting PCA results requires domain knowledge, and the identified patterns may be suggestive rather than conclusive. Additionally, the number of retained principal components should be based on both statistical criteria (explained variance) and ecological relevance.

Obs: This response was generated using gpt chat.

Yes :)

Olena Yarys If you are looking for patterns and relationships among those variables (species and biotopes), additional approaches like Canonical Correspondence Analysis (CCA) or regression models may be appropriate. Then you could validate and perform the sensibility analysis of your results.

Indeed, I agree with Anthony Bagherian Anthony Bagherian. Canonical Correspondence Analysis is a very good tool for visualizing links between species and habitat parameters (surface area, vegetation height, etc.). "This analysis is adapted to the vision of the ecological niche and the environmental gradients along which species niches are separated" (DRAY & CHESSEL, 2006).

Here's an example of the link between orthopterans and biotope parameters on dry grasslands (https://www.researchgate.net/publication/339103167_Suivi_des_orthopteres_des_pelouses_et_milieux_associes_des_Pelouses_de_la_Cote_Dijonnaise_Cote-d'Or_et_des_Pelouses_de_la_cote_de_Beaune; p. 17)