AI-Assisted Behaviour Tracking to Quantify Adaptation Windows in Species Following Natural Disasters

Abstract

The consequences of the natural disasters on wildlife populations are enormous, as they usually cause
alterations of the behavior, the places where such species live, and their existence. To preserve the
species, it is essential to understand how they react to such things by identifying the adaptation time
periods so that they could be preserved. The behavior-monitoring systems used in this paper are AI
based in measuring adaptation windows among natural species that are exposed to disasters. The
algorithms of machine learning, including deep learning models, were employed in this study to
classify numerous behavioral examples and predictive analytics to approximate the behavioral
adaptations that take place and when they would take place by examining post-disaster behavior in a
sample of species. The research study utilized over 5,000 hours of animal tracking data acquired in
different ecological regions that were severely affected by a natural calamity that occurred recently.
Within the framework of the statistical analysis, survival analysis, and regression model construction,
one of the measurable changes in the activity patterns and habitat preferences of the species in the first
three months after the disaster was observed. The rates of adaptation were diverse with herbivores
adapting more rapidly (mean = 28.5 days) than carnivores (mean = 48.7 days). These findings
underscore the relevance of species-specific conservation plans in order to endorse species-specific
recovery actions. The paper finds that the use of AI-assisted behavior monitoring is a powerful tool in
the quantification and understanding of the processes of wildlife adaptation to environmental stressors,
which should be applied in the future to improve post-disaster recovery interventions and in policy
making in wildlife management.