Machine Learning Forecasting of Amphibian Diversity Across Elevational Gradients

Abstract

Introduction: Amphibians are among the most ecologically sensitive animals and form an essential indicator of ecosystem status because of their specific ecological requirements regarding climatic, hydrological and habitat characteristics. But changes in the environment, habitat fragmentation, climatic fluctuations and elevation changes have greatly affected the pattern and the diversity of amphibian species. Existing methods of biodiversity evaluation rely mostly on field studies and visual observation; however, this approach is costly, limited in terms of space and not always applicable to mountain regions. Thus, development of the predictive approaches to analysis of ecological characteristics and forecasting amphibian diversity patterns according to elevation is crucial for the conservation planning. Materials and Methods: This paper describes a theoretical machine learning approach to forecasting amphibian diversity at different elevational gradients through the use of ecological observations, environmental characteristics and species distribution properties. The elevation-based characteristics such as temperature variations, precipitation, humidity, vegetation characteristics, habitat characteristics and landscape characteristics are used as predictor variables in the suggested approach. The methodology consists of data acquisition, data pre-processing, feature selection, modeling using machine learning approaches, diversity forecast, and ecological interpretation. Machine learning techniques including Random Forest, Support Vector Machine, Gradient Boosting, and Artificial Neural Network have been suggested for establishing non-linear connections between environmental parameters and amphibian species richness. Feature importance analysis is used in order to reveal major factors that affect the diversity distribution of amphibians in relation to elevation gradient. Results: The suggested methodology shows the capabilities of machine learning techniques in terms of identification of non-linear ecological relations and prediction of amphibian diversity changes in various altitude areas. Elevation, quality of the habitat, and changes in the climate are considered important factors that determine the diversity and presence of amphibians. This forecasting framework is considered an organized way of comprehending the distribution of species and the decision-making process in ecology. Conclusion: The machine learning-based biodiversity forecasting framework constitutes an effective method of exploring the relationship between the environmental factors and amphibian diversity at elevational gradients. The developed framework may be useful for conservationists and wildlife managers in identifying fragile areas and predicting biodiversity changes. Future research can improve the efficiency of the presented framework by incorporating real-time ecological monitoring, remote sensing, GIS, and deep learning.