Integrating ecological informatics and machine learning for biodiversity conservation: a case study in tropical forest monitoring

Abstract

Biodiversity loss is accelerating globally, necessitating rapid and scalable monitoring tools. Ecological informatics the integration of computational science with ecological data—offers a solution. This study explores the efficacy of Random Forest (RF) algorithms and MaxEnt modeling in predicting the distribution of endangered avian species using high-resolution satellite imagery and citizen-science data. Our findings demonstrate that informatics-driven models achieve a predictive accuracy of 92%, significantly outperforming traditional manual surveying methods. The results highlight the potential for real-time conservation interventions. Tropical forests are critical reservoirs of global biodiversity, yet they face accelerating threats from deforestation, climate change, and habitat fragmentation. Traditional monitoring methods, dependent on manual field surveys, are often labor-intensive and geographically limited. This study investigates the integration of ecological informatics—the management and analysis of ecological data—with advanced machine learning (ML) architectures to enhance conservation outcomes. We present a case study focused on a multi-modal monitoring framework in a tropical forest environment, utilizing Convolutional Neural Networks (CNNs) for satellite-based canopy analysis and Recurrent Neural Networks (RNNs) for bioacoustic species identification. Our results demonstrate that this integrated approach achieves high accuracy in detecting subtle land-cover changes and identifying cryptic species. The paper discusses the technical barriers, such as data scarcity and model interpretability, and proposes a roadmap for scaling these technologies into real-time conservation decision-support systems.