Artificial Intelligence as a Useful Tool in Behavioural Studies

Dear Colleagues,

Animal monitoring is essential for understanding behaviour and social dynamics. Ethologists utilise focal animal sampling to collect detailed information on individual animals, which helps measure activity budgets and shape conservation strategies. This approach is particularly important for studying captive animals, as their ability to display natural behaviours is vital for their welfare and reintroduction success. The rapid advancement of subdisciplines within artificial intelligence, such as machine learning (ML), presents a unique opportunity for fields related to behavioural sciences. The growing availability of user-friendly ML software allows behavioural scientists to enhance the value and reproducibility of their research findings while also reducing their workload. The automated recording and quantification of behavioural traits create numerous possibilities for comparative studies, enabling robust quantitative conclusions. Consequently, there is an urgent need to integrate artificial intelligence into behavioural sciences for both in situ and ex situ studies of wild and captive populations. Furthermore, the application of AI techniques will facilitate quantitative assessments of animal welfare, potentially attracting researchers in the animal production field.

The aim of this Special Issue is to gather papers, including original research articles and review papers, short communications, perspectives, and technical notes, that provide insights into the application of artificial intelligence techniques in behavioural studies. By incorporating machine learning tools, this Special Issue will facilitate the precise quantification of the duration, speed, and direction of the behavioural traits being examined.

This Special Issue welcomes manuscripts that link together the following themes:

• Potentiality and perspectives of machine learning techniques for behavioural studies;
• Use of machine learnings techniques in behavioural science (with potential automatisation using machine learning techniques);
• Synergistic effects of the use of machine learnings techniques with more conventional behavioural studies techniques;

We look forward to receiving your original research articles and reviews.

Prof. Dr. Cino Pertoldi
Dr. Sussie Pagh
Guest Editors

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