Machine Learning Assessment of Protected Area Effectiveness for Vertebrate Conservation

Abstract

Introduction: The protected areas play a significant role in vertebrate conservation through habitat maintenance, increased biodiversity and preventing species decline. Nevertheless, the evaluation of the performance of the protected areas has been difficult because of differences in the ecology, habitat quality and environmental pressure. The new developments in machine learning present an avenue for biodiversity analysis. Materials and Methods: This research paper presents a framework based on machine learning for evaluating the effectiveness of protected areas by taking into account ecological, environmental, and biodiversity related variables. This methodology takes into account factors such as vertebrate presence, habitat condition, vegetation condition, climate, land use and anthropogenic impacts. Data obtained through this methodology have been analyzed through machine learning models such as SVM, RF, GB, ANN, and ML framework presented in this research paper. Results and Discussion: The experimental results proved that the ML models successfully recognized patterns of conservation effectiveness. The developed ML algorithm provided the best performance with 94.6% accuracy, 94.2% precision, 94.5% recall, 94.3% F1-score, and 0.97 AUC, which is higher compared to other algorithms such as SVM, RF, GB, and ANN. The better results prove the ability of the developed algorithm to recognize complex relations between the habitat, species, and environment. Conclusion: These results suggest that assessment using machine learning techniques is indeed a feasible method for assessing the effectiveness of protected areas and planning vertebrate conservation strategies. The suggested framework could help conservationists identify key ecological elements and implement monitoring and conservation measures based on scientific evidence.