AI Models for Wildlife Population Dynamics: Machine Learning vs. Deep Learning

AI-driven solutions have been involved in the development of ecosystem population models and have shown unprecedented growth in applying these capabilities to the field of conservation sciences. This research article does a systematic comparative analysis of species distribution modeling, population prediction, and wildlife monitoring using machine learning (ML) and deep learning (DL) methods. ML techniques such as Random Forests and Support Vector Machines are the main tools of ML, as they give rise to a high degree of interpretability and computational efficiency, especially within modest data contexts. On the other hand, deep learning techniques, e.g., Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are more useful in image-based population counting and temporal pattern analysis, although they require large data and computational resources. This paper tries to evaluate model performance in terms of the main metrics like prediction accuracy, F1 scores, and computational efficiency, so by doing this, we will be able to see the trade-offs of the two methods. Further, the concerns about data quality, model validation, and spatial distribution within the conservation frameworks are tackled. We cope with such challenges by introducing new mechanisms like multi-modal data fusion, edge computing, and federated learning. The main message that can be drawn from the data is that hybrid AI models, uniform data frameworks, and mixed disciplinary methods are the most successful ways to conserve wildlife. In addition, it can benefit scientists and practitioners in the verification of AIs appropriated for ecological challenges by offering new points as well. The cure-all for this would be to come up with more practical conservation strategies.