As part of the MARMAPS project, our spatial prioritization tool focuses on identifying optimal locations for the designation of new marine protected areas. It employs an innovative three-dimensional spatial analysis that moves beyond traditional 2D methodology to consider the full depth of marine ecosystems.
Using the prior3D R package (Doxa et al., 2025), we identify priority areas across bathymetric zones by starting from the deepest waters and progressively moving toward the surface. The analysis runs iteratively across different protection targets (10%, 20%, 30%, etc.) to generate maps that show how frequently each area is selected for protection.
To enhance ecological connectivity, we incorporate ocean current data, using the priorCON R package (Adam et al., 2024), where we apply Google’s PageRank algorithm (Brin & Page, 1998) to assess how each planning unit contributes to overall ecosystem connectivity.
The final prioritization integrates biodiversity features, connectivity values and socio-economic costs (e.g. areas with high fishing activity or those with high proximity to ports, where introducing conservation zones might be more challenging due to economic use) using the prioritizr R package (Hanson et al., 2016).
The existing marine protected areas of the Black Sea region are automatically included in all scenarios, ensuring continuity and strengthening of the current network.
This approach not only enhances the protection of important species but also supports the preservation of ecological processes that are essential for healthy marine ecosystems. At the same time, the framework takes into account the impacts of human presence and activities in the area, aiming for a balanced and effective conservation strategy.
Complete description of methodology, input datasets and results is available here:
Dalla, D., Tsavdaridou, A. I., & Mazaris, A. (2025). MARMAPS – eFolio material. Zenodo.
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References
Adam, C., Doxa, A., Nagkoulis, N., Papazekou, M., Mazaris, A. D., & Katsanevakis, S. (2024). priorCON: Graph Community Detection Methods into Systematic Conservation Planning (p. 0.1.5) [Dataset].
Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1–7), 107–117.
Doxa, A., Adam, C., Nagkoulis, N., Mazaris, A. D., & Katsanevakis, S. (2025). prior3D: An R package for three-dimensional conservation prioritization. Ecological Modelling, 499, 110919.
Hanson, J. O., Schuster, R., Morrell, N., Strimas-Mackey, M., Edwards, B. P. M., Watts, M. E., Arcese, P., Bennett, J. R., & Possingham, H. P. (2016). prioritizr: Systematic Conservation Prioritization in R (p. 8.0.6) [Dataset].