Selected Publications

Amphibians are widely recognized as sensitive indicators of environmental health, reacting to changes in water quality, air quality, and vegetation. Their vulnerability to ecological changes makes them excellent proxies for evaluating ecosystem conditions. This presentation describes the development of a machine learning algorithm that utilizes geospatial data from the US Geological Survey, species occurrence data from databases such as Map of Life, GBIF, AmphibiaWeb, and iNaturalist, and conservation statuses from the IUCN Red List. The algorithm predicts the health of environments based on the status of amphibian populations. This model not only advances our understanding of ecological dynamics but also enables proactive conservation efforts. By predicting potential habitat degradation and ecological damage before they become irreversible, this tool shifts the focus from mitigating environmental problems to preventing them, offering a transformative approach to conservation strategy