Association of IUCN-threatened Indian mangroves: A novel data-driven rule filtering approach for restoration strategy

Abstract

Restoring biodiversity is crucial for ecological sustainability. This study introduces a novel data-driven rule-filtering framework that adopts domain knowledge of taxonomic distinctness and proposes a new metric, total taxonomic distinctness, to prioritize species selection for targeted restoration efforts. We extract and validate association rules to identify frequently co-occurring species and rank them based on total taxonomic distinctness. This structured approach ensures the selection of ecologically significant species that enhance biodiversity and ecosystem resilience. We apply this three-stage framework to Indian mangrove ecosystems, focusing on four IUCN Red List species: Heritiera fomes, Sonneratia griffithii, Ceriops decandra, and Phoenix paludosa. Our results indicate that taxonomically distinct species tend to co-occur more frequently, enhancing ecosystem resilience. Statistical validation using multiple hypothesis testing ensures the robustness of our findings. To assess the framework’s broader applicability, we extend our analysis to species presence-absence data from sacred groves in the laterite regions of eastern India. The results reinforce our previous findings, demonstrating frequent association patterns among taxonomically distinct species. This study provides actionable insights for ecological restoration, guiding species selection and co-planting strategies. The framework is adaptable across ecosystems, offering a scalable approach to biodiversity conservation.