Clustering analysis of EOF to identify seasons and essential
habitats
To identify distinct essential habitats and to relate these with
ecological season, a clustering analysis was performed on the loading
factors and the EOF maps.
Through EOF or PCA, individuals (here time steps) and variables (here
locations) are projected into two distinct spaces: the space of the
individuals (time steps) and the space of the variables (locations).
Often in standard PCA, clustering is realized in the space of the
individuals only, but the same can be done in the space of the
variables. While the first clustering allows to regroup individuals that
have the same variable values, the second clustering allows to
differentiate individuals.
This way, the clustering will regroup locations that have similar
temporal patterns and time steps that have similar spatial patterns.
Clusters of locations will be interpreted as distinct essential habitats
and clusters of time steps will be interpreted as ecological seasons.
We performed clustering based on a Hierarchical Clustering on Principal
Components (HCPC) through the package FactoMineR (LĂȘ et al., 2008).