Abstract: The global biodiversity crisis demands targeted conservation strategies that maximize impact despite limited
resources. Surrogate species approaches, particularly using umbrella, keystone, and flagship species, offer
practical targets for conservation planning that may indirectly benefit ecosystems. However, selecting target
species is often hindered by conceptual ambiguities and inconsistent methodologies. To address these challenges, we present an integrative framework that systematically identifies effective surrogate species through Multi-Criteria Decision Analysis (MCDA) combined with big data. Our framework quantifies each species' conservation potential using three indices: an Umbrella index, a Keystone index, and a Flagship index. The Umbrella index assesses habitat overlap using Area of Habitat (AOH) data, the Keystone index is calculated through a network analysis of predator-prey relationships, and the Flagship index analyzes public interest via Google Trends and Baidu Index. These indices are integrated into a composite Effectiveness index using the Multi-Attribute Utility Theory (MAUT) model, with sensitivity analysis to evaluate the robustness of species rankings. We applied this framework to Three-River-Source National Park in the Qinghai-Tibetan Plateau. Our results identified the snow leopard (Panthera uncia) as the most effective surrogate species among mammals, ranking first in both the Flagship and Keystone indices, and tenth in the Umbrella index, leading to its top position in the composite Effectiveness index. This data-driven, transparent approach enhances objectivity in surrogate species selection, promising more strategic and impactful biodiversity conservation efforts worldwide.