Predictive Modeling of Tiger Population Trends in Indian Reserves

The goal of this study is to develop predictive models to forecast future tiger population trends in protected reserves across India, with a focus on providing actionable conservation strategies. Utilizing a combination of time series analysis and regression techniques, specifically the ARIMA model, the research aims to assess the impact of environmental factors such as habitat quality, prey availability, and human disturbances on tiger populations. The methodology involves collecting historical tiger population data from wildlife agencies, followed by data cleaning and exploratory analysis to identify key trends. The ARIMA model will predict population trends, while regression models will evaluate the effects of environmental variables. The models' accuracy will be assessed using performance metrics such as RMSE and MAE, with cross-validation to ensure generalization. This comprehensive approach aims to provide wildlife managers with data-driven recommendations for targeted interventions, such as improving habitat connectivity or controlling human encroachment. The study's novel integration of multiple environmental factors into a holistic predictive model distinguishes it from previous research, offering a robust tool for tiger conservation efforts.