R Can Show You the Worlds: Bridging Academic and Citizen Sciences - Boo, Wee Hin (Universitas Muhammadiyah Surakarta, 2025)
Field biologists and ecologists often handle large quantity of data, to provide comprehensive conclusion of ecosystem (biology + environment) interactions. However, this also raised an issue of the capacity to obtain such large numbers of data for the study. One such effort is to integrate publicly available data with public volunteerisms (citizen science initiatives) in understanding and protecting marine ecosystems. Furthermore, with additional advancement of computational power, machine learning, and available free resources to guide users in such efforts. Here presents three complementary research projects demonstrating data mining and citizen science approaches across international, national, and local scales. Furthermore, machine learnings were used to identify the patterns and help policy makers to understand the threats we are facing. International collaborations revealed significant change of Okinawan (Japan:1919 & 2018) land use due to coastal development, especially to convert to fishing ports and civilian recreational areas. This has raised the public awareness of anthropogenic developments across diverse coastal environments from ocean areas to sandy beaches. The national-scale project utilized volunteer data of Reef Check Malaysia from 513 sites to assess coral reef health, by narrowing on key indicator species such as hard corals, parrotfish, butterflyfish, parrotfish, and sea urchins. This citizen science generated targeted conservation guideline to protect the highly biodiverse ecosystem. Finally at the local level, the 20-year-old Merambong Shoal seahorse conservation project conducted population monitoring since 2005, address the vulnerable species in face of coastal developments. Machine learning clustering techniques revealed how geographical barriers influence seahorse distribution patterns in this critical habitat. The research emphasizes three key takeaway messages: valuable insights exist within publicly available datasets, data analyst is required in conservation, and coding skills are essential and can be learned easily. These findings demonstrate how integrating citizen science with academic research creates powerful frameworks for environmental and conservation guidelines and policies.