Adaptive artificial intelligence agents for dynamic conservation planning under climate uncertainty: A multi-agent reinforcement learning approach

Abstract

The rapidly changing climate is a predicament in conservation science because the reduction of biodiversity is one of the most complicated issues to plan. Conservation models based on a fixed structure are incapable of responding to sudden ecological alterations, which are provoked by the changes in the rainfall patterns, temperatures, and phenological weakening. This research is concerned with the gaping chasm between traditional strategies of protected area management and real-time, data-driven adaptive management. The simulation-based modeling analysis was developed to apply to a multi-agent reinforcement learning model of whether adaptive agents based on artificial intelligence can enhance the result of dynamic conservation planning when facing high levels of climate uncertainty. The framework combines species distribution modeling with Qlearning agents updating habitat protection priorities with a sequence of environmental state variables varying. Findings indicate that relative to the other rule-based planning systems, adaptive AI agents realized much higher rates of species persistence, lower land-use conflict scores, and more accurate prioritization in all contexts. The high-uncertainty level had the agents lessening the time of biodiversity informatics to 47 percent and increasing conservation return on investment by 31 percent. These findings are discussed with respect to developing multi-agent system applications in ecological decision support, and are shown to be limited by factors such as computational scalability and data dependency. The research adds a modelable and theoretically-based modeling architecture, linking reinforcing learning, quantification of climate uncertainty and planning biodiversity conservation. The paper recommends the incorporation of adaptive AI agents within conservation policy frameworks, as the climate crisis deepens.