Bio

Julián’s (he\his) research seeks to understand and strengthen the relationships between marine ecosystems, the communities that depend on them, and the policies that shape their management. Positioned at the intersection of ecology, spatial analysis, and social science, his work produces actionable insights to support the conservation and sustainable governance of marine systems. He approaches the ocean as a socioecological system—an integrated network where natural and human components co-evolve—and applies a transdisciplinary lens to address urgent global challenges, including biodiversity loss, fisheries collapse, and climate change. At Stanford University, he has led research on nature-based solutions, developing spatially explicit fishery models that assess how coral reef and mangrove restoration can enhance ecological resilience and improve fisheries outcomes for coastal communities across the Caribbean and U.S.

Stanford Advisors

Contact

  • Academic
    jolaya@stanford.edu
    University - Scholar Department: Biology Position: Postdoctoral Scholar

Additional Info

Links

Research Interests

  • Data Sciences
  • Research Methods
  • Science Education

All Publications

  • Networks of interactions between Marine Protected Areas and their effects on the conservation of the South American sea lion and the Southern right whale in the Western South Atlantic Ocean. Journal of environmental management Oliveira do Nascimento, L., Zapelini, C., Olaya-Restrepo, J., Cinti, A., Chalar, G., Machado, R., Schiavetti, A. 2025; 392: 126679

    Abstract

    Marine protected areas (MPAs) are key for biodiversity conservation and natural resource management, contributing to ecosystem services. However, fixed MPA boundaries present challenges for species with large geographic ranges, such as marine mammals. It is essential to evaluate the role of international MPA networks in protecting species like the South American sea lion (Otaria flavescens) and the Southern right whale (Eubalaena australis), whose distribution spans the southern coasts of Brazil, Uruguay, and Argentina. MPA networks can benefit from manager interactions through information exchange, knowledge sharing, and joint management strategies, addressing socio-environmental issues more effectively. In this study, we used graph theory and complex network analysis to investigate the structure of interactions among 27 MPAs based on interviews with managers across the three countries. Our findings reveal that interactions are limited to within-country networks, with no transboundary cooperation for the conservation of O. flavescens and E. australis. The networks showed low density, with geographic and hierarchical proximity influencing interaction likelihood. Management networks were generally broader than species-specific biological networks. Although our study is limited by its reliance on self-reported data and the absence of direct geospatial validation, the findings underscore critical governance gaps and emphasize the urgent need for enhanced international collaboration in the conservation of marine megafauna in the South Atlantic. Strengthening both national and transnational networks of MPAs is essential to ensure the effective protection of migratory marine species.

    View details for DOI 10.1016/j.jenvman.2025.126679

    View details for PubMedID 40712516

  • AI-based coral species discrimination: A case study of the <i>Siderastrea</i> Atlantic Complex PLOS ONE Barbeitos, M., Perez, F., Olaya-Restrepo, J., Winter, A., Florindo, J., Laureano, E. 2024; 19 (12): e0312494

    Abstract

    Species delimitation in hard corals remains controversial even after 250+ years of taxonomy. Confusing taxonomy in Scleractinia is not the result of sloppy work: clear boundaries are hard to draw because most diagnostic characters are quantitative and subjected to considerable morphological plasticity. In this study, we argue that taxonomists may actually be able to visually discriminate among morphospecies, but fail to translate their visual perception into accurate species descriptions. In this article, we introduce automated quantification of morphological traits using computer vision (Completed Local Binary Patterns-CLBP) and test its efficiency on the problematic genus Siderastrea. An artificial neural network employing fuzzy logic (Θ-FAM), intrinsically formulated to deal with soft and subtle decision boundaries, was used to factor a priori species identification uncertainty into the supervised classification procedure. Machine learning statistics demonstrate that automated species identification using CLBP and Θ-FAM outperformed the combination of traditional morphometric characters and Θ-FAM, and was also superior to CLBP+LDA (Linear Discriminant Analysis). These results suggest that human discrimination ability can be emulated by the association of computer vision and artificial intelligence, a potentially valuable tool to overcome taxonomic impediment to end users working on hard corals.

    View details for DOI 10.1371/journal.pone.0312494

    View details for Web of Science ID 001376267500020

    View details for PubMedID 39661592

    View details for PubMedCentralID PMC11634003

  • Environmental, social, and management aspects in a hotspot: Interaction networks between marine protected areas OCEAN & COASTAL MANAGEMENT Zapelini, C., de Souza, D., de Santana, R., Olaya-Restrepo, J., Schiavetti, A. 2024; 251

    View details for DOI 10.1016/j.ocecoaman.2024.107068

    View details for Web of Science ID 001197491600001

https://orcid.org/0000-0003-1150-6974