AI’s Revolutionary Effects on Wildlife Conservation

D.Shobana, Vikramsai, Vishnupriya

Biodiversity Conservation; Artificial Intelligence; Systematic Conservation Planning; Conservation Strategies; Spatial Prioritization

Amid rising species extinction rates, we introduce a novel AI-based framework for spatial conservation prioritization. Leveraging reinforcement learning, it outperforms existing software by effectively balancing conservation costs and biodiversity benefits. This approach quantifies trade-offs and explores diverse metrics, consistently outshining simplistic methods. Empirical evidence demonstrates its efficacy in meeting conservation targets and generating interpretable prioritization maps. Integrating regular biodiversity monitoring enhances outcomes, offering a promising approach to optimize conservation efforts in a resource-constrained world.

[1] https://www.analyticsvidhya.com
[2] https://ieeexplore.ieee.org
[3] https://www.theguardian.com
[4] https://saiwa.ai/blog/ai-in-wildlife-conservation/
[5] https://medcraveonline.com/IJAWB/IJAWB-07-00192.pdf
[6] https://morungexpress.com/artificial-intelligence-ai-for-wildlife-conservation
[7] https://www.nature.com/articles/s41558-023-01769-3
[8] https://www.nature.com/articles/s41467-022-27980-y
[9] https://towardsdatascience.com/catching-poachers-with-machine-learning-118eec41d5b9
[10] https://www.analyticsvidhya.com/blog/2023/03/wildlife-conservation-through-ai/
[11] Artificial Intelligence and Conservation 2019 by Fei Fang. Book
[12] Our Planet Powered by AI: How We Use Artificial Intelligence to Create a Sustainable Future for Humanity 2023 by Mark Minevich. Book