Classifying animal migration patterns using small-shuffle surrogate and PCA evaluating short-term trends

What is it about?

This paper presents an analytical method for surveying the characteristics of wildlife migration, which can be challenging due to the varying distances and directions of animal travel and the diverse characteristics of different species. To gain a comprehensive understanding of migratory behavior, it is necessary to integrate migration interrelationships and correlated migratory modes. To achieve this, we propose the SSS-PCA method, which combines the previously proposed small-shuffle surrogate (SSS) method and principal component analysis (PCA). The proposed method can reveal the migration patterns of individuals and the influence of individuals on the whole herd.

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