Deep Learning Assessment of Environmental Stress and Amphibian Disease Susceptibility

Abstract

Amphibians have been identified as ecological indicators owing to their sensitivity to changes in the environment and their need for both aquatic and terrestrial habitats. However, the rising levels of environmental stress resulting from degradation of habitats, climatic variations, pollution, and ecosystem disturbances have greatly affected the health of amphibians and increased their risk to infections. Environmental conditions such as temperature, moisture content, water quality, and habitat conditions affect the survival of pathogens, immune response of amphibians, and the incidences of diseases among amphibians. Traditional methods of ecological monitoring involve manual observation and assessment that are not only time consuming but also unable to detect complex interplay between different environmental factors. This study aims at developing an assessment approach using deep learning technology to analyze the effect of environmental stress conditions on disease susceptibility of amphibians. The deep learning algorithm evaluates ecological factors, habitat characteristics, and disease occurrences to detect possible risk conditions for amphibians. The results of the evaluation show that environmental degradation, pollution increase, and climatic variations are key determinants that affect the susceptibility of amphibians to diseases. The methodology helps to detect ecological risks at an early stage and gives useful information on the monitoring of health conditions in wildlife and habitat conservation. This study emphasizes the possibility of using ecological evaluation with assistance of deep learning for amphibian conservation, biodiversity protection, and sustainable environmental management