| Abstrakt: |
The application of computational techniques to the study of zoology has greatly improved our knowledge of animal behaviour and species conservation. This study offers a thorough analysis of the ways that computational methods, such as data mining, simulation modelling, machine learning and geographic information systems (GIS) are revolutionising research in both applied and pure zoology. This review illustrates how these techniques can improve species distribution modelling, habitat suitability analysis, wildlife monitoring and behavioural investigations by looking at current case studies. More accurate forecasts have been produced by machine learning approaches, especially in the areas of species distribution and habitat suitability, which has improved the knowledge base for conservation initiatives. Through the extraction of significant patterns from large, complicated datasets, data mining has made it possible to get new insights into the vocalisations, social relationships and movement of animals. Because GIS provides precise habitat maps, landscape connectivity assessments, and spatial models of species distributions, it has proven to be an invaluable tool in ecological research. Important projections regarding the long-term effects of conservation initiatives have been made possible via simulation modelling, including individualbased models and population viability studies. The study does, however, also address the drawbacks of these computational methods, including data quality, model complexity, resource requirements and integrating disparate data sources. Notwithstanding these obstacles, zoological research seems to have a bright future because computational techniques provide effective instruments for addressing urgent ecological problems. In order to fully realise the potential of computational approaches in promoting species conservation and behavioural studies, this review emphasises the significance of ongoing innovation and multidisciplinary collaboration. [ABSTRACT FROM AUTHOR] |
