*Result*: Managing the fuzzy boundaries and partitions of marine ecological systems using network theory.
Title:
Managing the fuzzy boundaries and partitions of marine ecological systems using network theory.
Authors:
Pastor Rollan A; School of Life and Environmental Sciences, Deakin University, Geelong, VIC, Australia. apro.pas@outlook.com., Berlow EL; Vibrant Data Labs, 2151 1/2 Stuart St, Berkeley, San Francisco, CA, 94705, USA., Williams R; Vibrant Data Labs, 2151 1/2 Stuart St, Berkeley, San Francisco, CA, 94705, USA., Treml EA; Australian Institute of Marine Science (AIMS) and UWA Oceans Institute, The University of Western Australia, MO96, 35 Stirling Highway, Crawley, 6009, WA, Australia.
Source:
Scientific reports [Sci Rep] 2025 Jul 16; Vol. 15 (1), pp. 25803. Date of Electronic Publication: 2025 Jul 16.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
MeSH Terms:
References:
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Spalding, M. D. et al. Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. 57, 573–583 (2007).
Jokiel, P. L., Rodgers, K. S., Walsh, W. J., Polhemus, D. A. & Wilhelm, T. A. Marine Resource management in the Hawaiian Archipelago: The traditional hawaiian system in relation to the western approach. J. Mar. Biol. 2011, 16 (2011).
Commonwealth of Australia 2006. A Guide to the Integrated Marine and Coastal Regionalisation of Australia Version 4.0. (2006).
Hale, J., Butcher, R., Collier, K. & Snelder, T. ANZECC/ARMCANZ Water Quality Guidelines Revision: Ecoregionalisation and Ecosystem Types in Australian and New Zealand Marine, Coastal and Inland Water Systems, Report Prepared for the Department of Sustainability, Environment, Water, Population and Commu. (2012).
Berline, L. O., Rammou, A. M., Doglioli, A., Molcard, A. & Petrenko, A. A connectivity-based eco-regionalization method of the mediterranean sea. PLoS One. https://doi.org/10.1371/journal.pone.0111978 (2014). (PMID: 10.1371/journal.pone.0111978253752124222956)
Treml, E. A. & Halpin, P. N. Marine population connectivity identifies ecological neighbors for conservation planning in the coral triangle. Conserv. Lett. 5, 441–449 (2012). (PMID: 10.1111/j.1755-263X.2012.00260.x)
Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009). (PMID: 2114104410.1146/annurev.marine.010908.163757)
Krueck, N. C. et al. Benefits of measurable population connectivity metrics for area-based marine management. Mar. Policy. 144, 105210 (2022). (PMID: 10.1016/j.marpol.2022.105210)
Botsford, L. W. et al. Connectivity and resilience of coral reef metapopulations in marine protected areas: matching empirical efforts to predictive needs. Coral Reefs. 28, 327–337 (2009). (PMID: 22833699340222910.1007/s00338-009-0466-z)
Kool, J. T., Moilanen, A. & Treml, E. A. Population connectivity: recent advances and new perspectives. Landsc. Ecol. 28, 165–185 (2013). (PMID: 10.1007/s10980-012-9819-z)
Aavik, T. & Helm, A. Restoration of plant species and genetic diversity depends on landscape-scale dispersal. Restor. Ecol. 26, S92–S102 (2018). (PMID: 10.1111/rec.12634)
Cristiani, J. et al. A biophysical model and network analysis of invertebrate community dispersal reveals regional patterns of seagrass habitat connectivity. 8, 1–19 (2021).
Urban, D. L., Minor, E. S., Treml, E. A. & Robert, S. S. Graph models of habitat mosaics. Ecol. Lett. 12, 260–273 (2009). (PMID: 1916143210.1111/j.1461-0248.2008.01271.x)
Urban, D. & Keitt, T. Landscape connectivity: A graph-theoretic perspective. Ecology. 82, 1205–1218 (2001). (PMID: 10.1890/0012-9658(2001)082[1205:LCAGTP]2.0.CO;2)
Galpern, P., Manseau, M. & Fall, A. Patch-based graphs of landscape connectivity: A guide to construction, analysis and application for conservation. Biol. Conserv. 144, 44–55 (2011). (PMID: 10.1016/j.biocon.2010.09.002)
Gauzens, B., Thébault, E., Lacroix, G. & Legendre, S. Trophic groups and modules: two levels of group detection in food webs. J. R. Soc. Interface. 12, (2015).
Young, J. G., Valdovinos, F. S. & Newman, M. E. J. Reconstruction of plant–pollinator networks from observational data. Nat. Commun. 12, 1–12 (2021). (PMID: 10.1038/s41467-021-24149-x)
Manel, S. et al. Long-distance benefits of marine reserves: myth or reality?? Trends Ecol. Evol. 34, 342–354 (2019). (PMID: 3077729510.1016/j.tree.2019.01.002)
Muenzel, D. et al. Integrating larval connectivity into the marine conservation decision-making process across spatial scales. Conserv. Biol. 37, 1–11 (2023). (PMID: 10.1111/cobi.14038)
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V. & Parisi, D. Defining and identifying communities in networks. PNAS. 101, 2658–2663 (2004). (PMID: 1498124036567710.1073/pnas.0400054101)
Newman, M. E. J. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E. 69, 1–16 (2004).
Schaeffer, S. E. Graph clustering. Comput. Sci. Rev. 1, 27–64 (2007). (PMID: 10.1016/j.cosrev.2007.05.001)
Ahn, Y. Y., Bagrow, J. P. & Lehmann, S. Link communities reveal multiscale complexity in networks. Nature. 466, 761–764 (2010). (PMID: 2056286010.1038/nature09182)
Weiss, R. S. & Jacobson, E. A method for the analysis of the structure of complex organizations. Am. Sociol. Assoc. 20, 661–668 (1955). (PMID: 10.2307/2088670)
Homans, G. C. The Human Groups (Harcourt, Brace & Co, 1950).
Zachary, W. W. An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977). (PMID: 10.1086/jar.33.4.3629752)
Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature. 402, 47–52 (1999). (PMID: 10.1038/35011540)
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N. & Barabasi, A. L. Hierarchical organization of modularity in metabolic networks. Sci. (1979). 297, 1551–1556 (2002).
Albert, R., Jeong, H. & Barabasi, A. L. Diameter of the world-wide web. Nature 401, 398–399 (1999). (PMID: 10.1038/43601)
Garlaschelli, D., Caldarelli, G. & Pietronero, L. Universal scaling relations in food webs. Nature. 423, 165–168 (2003). (PMID: 1273668410.1038/nature01604)
Barabási, A. L. & Pósfai, M. Network Science (Cambridge University Press, 2016).
Fortunato, S. & Hric, D. Community detection in networks: A user guide. Phys. Rep. 659, 1–44 (2016). (PMID: 10.1016/j.physrep.2016.09.002)
Gao, P., Kupfer, J. A., Guo, D. & Lei, T. L. Identifying functionally connected habitat compartments with a novel regionalization technique. Landsc. Ecol. 28, 1949–1959 (2013). (PMID: 10.1007/s10980-013-9938-1)
Luo, Y., Wu, J., Wang, X., Zhao, Y. & Feng, Z. Understanding ecological groups under landscape fragmentation based on network theory. Landsc. Urban Plan. 210, 104066 (2021). (PMID: 10.1016/j.landurbplan.2021.104066)
Blondel, V. D., Guillaume, J. L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008 (2008).
Yang, Z., Algesheimer, R. & Tessone, C. J. A comparative analysis of community detection algorithms on artificial networks. Sci. Rep. 6, (2016).
Rahiminejad, S., Maurya, M. R. & Subramaniam, S. Topological and functional comparison of community detection algorithms in biological networks. BMC Bioinform. 20, 1–25 (2019). (PMID: 10.1186/s12859-019-2746-0)
Reichardt, J. & Bornholdt, S. Statistical mechanics of community detection. Phys. Rev. E 74, (2006).
Pons, P. & Latapy, M. Computing communities in large networks using random walks. Download Springer Com. 3733, 284–293 (2005).
Rosvall, M., Axelsson, D. & Bergstrom, C. T. The map equation. Eur. Phys. J. Special Top. 178, 13–23 (2009). (PMID: 10.1140/epjst/e2010-01179-1)
Clauset, A., Newman, M. E. J. & Moore, C. Finding community structure in very large networks. Phys. Rev. E 1–6 (2004).
Newman, M. E. J. Fast algorithm for detecting community structure in networks. Phys. Rev. E. 69, 5 (2004).
Brandes, U. et al. On modularity clustering. IEEE Trans. Knowl. Data Eng. 20, 172–188 (2008). (PMID: 10.1109/TKDE.2007.190689)
Newman, M. E. J. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, (2006).
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to leiden: guaranteeing well-connected communities. Sci. Rep. 9, 1–12 (2019). (PMID: 10.1038/s41598-019-41695-z)
Rosvall, M. & Bergstrom, C. T. Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. U. S. A. 105, 1118–1123 (2008). (PMID: 18216267223410010.1073/pnas.0706851105)
Dugué, N. & Perez, A. Directed Louvain: Maximizing Modularity in Directed Networks [Research Report]. (2015). https://hal.archives-ouvertes.fr/hal-01231784 .
Selkoe, K. A. et al. The DNA of coral reef biodiversity: predicting and protecting genetic diversity of reef assemblages. Proc. R. Soc. B Biol. Sci. 283, (2016).
Hilliam, K., Floerl, O. & Treml, E. A. Science of the total environment priorities for improving predictions of vessel-mediated marine invasions. Sci. Total Environ. 921, 171162 (2024). (PMID: 3840173610.1016/j.scitotenv.2024.171162)
Hubert, L. & Arabie, P. Comparing partitions. J. Classif. 2, 193–218 (1985). (PMID: 10.1007/BF01908075)
Cram, J. A. et al. Cross-depth analysis of marine bacterial networks suggests downward propagation of Temporal changes. ISME J. 9, 2573–2586 (2015). (PMID: 25989373481762310.1038/ismej.2015.76)
Fortin, M. J. et al. Issues related to the detection of boundaries. Landsc. Ecol. 15, 453–466 (2000). (PMID: 10.1023/A:1008194205292)
Orman, K., Labatut, V. & Cherifi, H. An empirical study of the relation between community structure and transitivity. Stud. Comput. Intell. 424, 99–110 (2013). (PMID: 10.1007/978-3-642-30287-9_11)
Halpern, B. S. et al. Human impacts on the world ’ s ocean. Nat. Commun. 6, 1–7 (2015). (PMID: 10.1038/ncomms8615)
Spalding, M. D. et al. Building towards the marine conservation end-game: consolidating the role of MPAs in a future ocean. Aquat. Conserv. 26, 185–199 (2016). (PMID: 10.1002/aqc.2686)
Lubchenco, J. et al. Priorities for progress towards sustainable development goal 14 ‘life below water’. Nat. Ecol. Evol. 7, 1564–1569 (2023). (PMID: 3778383110.1038/s41559-023-02208-4)
Margules, C. R. & Pressey, R. L. Systematic conservation planning. Nature. 405, 243–253 (2000). (PMID: 1082128510.1038/35012251)
Spalding, M. D. et al. Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. 57, 573–583 (2007).
Jokiel, P. L., Rodgers, K. S., Walsh, W. J., Polhemus, D. A. & Wilhelm, T. A. Marine Resource management in the Hawaiian Archipelago: The traditional hawaiian system in relation to the western approach. J. Mar. Biol. 2011, 16 (2011).
Commonwealth of Australia 2006. A Guide to the Integrated Marine and Coastal Regionalisation of Australia Version 4.0. (2006).
Hale, J., Butcher, R., Collier, K. & Snelder, T. ANZECC/ARMCANZ Water Quality Guidelines Revision: Ecoregionalisation and Ecosystem Types in Australian and New Zealand Marine, Coastal and Inland Water Systems, Report Prepared for the Department of Sustainability, Environment, Water, Population and Commu. (2012).
Berline, L. O., Rammou, A. M., Doglioli, A., Molcard, A. & Petrenko, A. A connectivity-based eco-regionalization method of the mediterranean sea. PLoS One. https://doi.org/10.1371/journal.pone.0111978 (2014). (PMID: 10.1371/journal.pone.0111978253752124222956)
Treml, E. A. & Halpin, P. N. Marine population connectivity identifies ecological neighbors for conservation planning in the coral triangle. Conserv. Lett. 5, 441–449 (2012). (PMID: 10.1111/j.1755-263X.2012.00260.x)
Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009). (PMID: 2114104410.1146/annurev.marine.010908.163757)
Krueck, N. C. et al. Benefits of measurable population connectivity metrics for area-based marine management. Mar. Policy. 144, 105210 (2022). (PMID: 10.1016/j.marpol.2022.105210)
Botsford, L. W. et al. Connectivity and resilience of coral reef metapopulations in marine protected areas: matching empirical efforts to predictive needs. Coral Reefs. 28, 327–337 (2009). (PMID: 22833699340222910.1007/s00338-009-0466-z)
Kool, J. T., Moilanen, A. & Treml, E. A. Population connectivity: recent advances and new perspectives. Landsc. Ecol. 28, 165–185 (2013). (PMID: 10.1007/s10980-012-9819-z)
Aavik, T. & Helm, A. Restoration of plant species and genetic diversity depends on landscape-scale dispersal. Restor. Ecol. 26, S92–S102 (2018). (PMID: 10.1111/rec.12634)
Cristiani, J. et al. A biophysical model and network analysis of invertebrate community dispersal reveals regional patterns of seagrass habitat connectivity. 8, 1–19 (2021).
Urban, D. L., Minor, E. S., Treml, E. A. & Robert, S. S. Graph models of habitat mosaics. Ecol. Lett. 12, 260–273 (2009). (PMID: 1916143210.1111/j.1461-0248.2008.01271.x)
Urban, D. & Keitt, T. Landscape connectivity: A graph-theoretic perspective. Ecology. 82, 1205–1218 (2001). (PMID: 10.1890/0012-9658(2001)082[1205:LCAGTP]2.0.CO;2)
Galpern, P., Manseau, M. & Fall, A. Patch-based graphs of landscape connectivity: A guide to construction, analysis and application for conservation. Biol. Conserv. 144, 44–55 (2011). (PMID: 10.1016/j.biocon.2010.09.002)
Gauzens, B., Thébault, E., Lacroix, G. & Legendre, S. Trophic groups and modules: two levels of group detection in food webs. J. R. Soc. Interface. 12, (2015).
Young, J. G., Valdovinos, F. S. & Newman, M. E. J. Reconstruction of plant–pollinator networks from observational data. Nat. Commun. 12, 1–12 (2021). (PMID: 10.1038/s41467-021-24149-x)
Manel, S. et al. Long-distance benefits of marine reserves: myth or reality?? Trends Ecol. Evol. 34, 342–354 (2019). (PMID: 3077729510.1016/j.tree.2019.01.002)
Muenzel, D. et al. Integrating larval connectivity into the marine conservation decision-making process across spatial scales. Conserv. Biol. 37, 1–11 (2023). (PMID: 10.1111/cobi.14038)
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V. & Parisi, D. Defining and identifying communities in networks. PNAS. 101, 2658–2663 (2004). (PMID: 1498124036567710.1073/pnas.0400054101)
Newman, M. E. J. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E. 69, 1–16 (2004).
Schaeffer, S. E. Graph clustering. Comput. Sci. Rev. 1, 27–64 (2007). (PMID: 10.1016/j.cosrev.2007.05.001)
Ahn, Y. Y., Bagrow, J. P. & Lehmann, S. Link communities reveal multiscale complexity in networks. Nature. 466, 761–764 (2010). (PMID: 2056286010.1038/nature09182)
Weiss, R. S. & Jacobson, E. A method for the analysis of the structure of complex organizations. Am. Sociol. Assoc. 20, 661–668 (1955). (PMID: 10.2307/2088670)
Homans, G. C. The Human Groups (Harcourt, Brace & Co, 1950).
Zachary, W. W. An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977). (PMID: 10.1086/jar.33.4.3629752)
Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature. 402, 47–52 (1999). (PMID: 10.1038/35011540)
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N. & Barabasi, A. L. Hierarchical organization of modularity in metabolic networks. Sci. (1979). 297, 1551–1556 (2002).
Albert, R., Jeong, H. & Barabasi, A. L. Diameter of the world-wide web. Nature 401, 398–399 (1999). (PMID: 10.1038/43601)
Garlaschelli, D., Caldarelli, G. & Pietronero, L. Universal scaling relations in food webs. Nature. 423, 165–168 (2003). (PMID: 1273668410.1038/nature01604)
Barabási, A. L. & Pósfai, M. Network Science (Cambridge University Press, 2016).
Fortunato, S. & Hric, D. Community detection in networks: A user guide. Phys. Rep. 659, 1–44 (2016). (PMID: 10.1016/j.physrep.2016.09.002)
Gao, P., Kupfer, J. A., Guo, D. & Lei, T. L. Identifying functionally connected habitat compartments with a novel regionalization technique. Landsc. Ecol. 28, 1949–1959 (2013). (PMID: 10.1007/s10980-013-9938-1)
Luo, Y., Wu, J., Wang, X., Zhao, Y. & Feng, Z. Understanding ecological groups under landscape fragmentation based on network theory. Landsc. Urban Plan. 210, 104066 (2021). (PMID: 10.1016/j.landurbplan.2021.104066)
Blondel, V. D., Guillaume, J. L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008 (2008).
Yang, Z., Algesheimer, R. & Tessone, C. J. A comparative analysis of community detection algorithms on artificial networks. Sci. Rep. 6, (2016).
Rahiminejad, S., Maurya, M. R. & Subramaniam, S. Topological and functional comparison of community detection algorithms in biological networks. BMC Bioinform. 20, 1–25 (2019). (PMID: 10.1186/s12859-019-2746-0)
Reichardt, J. & Bornholdt, S. Statistical mechanics of community detection. Phys. Rev. E 74, (2006).
Pons, P. & Latapy, M. Computing communities in large networks using random walks. Download Springer Com. 3733, 284–293 (2005).
Rosvall, M., Axelsson, D. & Bergstrom, C. T. The map equation. Eur. Phys. J. Special Top. 178, 13–23 (2009). (PMID: 10.1140/epjst/e2010-01179-1)
Clauset, A., Newman, M. E. J. & Moore, C. Finding community structure in very large networks. Phys. Rev. E 1–6 (2004).
Newman, M. E. J. Fast algorithm for detecting community structure in networks. Phys. Rev. E. 69, 5 (2004).
Brandes, U. et al. On modularity clustering. IEEE Trans. Knowl. Data Eng. 20, 172–188 (2008). (PMID: 10.1109/TKDE.2007.190689)
Newman, M. E. J. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, (2006).
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to leiden: guaranteeing well-connected communities. Sci. Rep. 9, 1–12 (2019). (PMID: 10.1038/s41598-019-41695-z)
Rosvall, M. & Bergstrom, C. T. Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. U. S. A. 105, 1118–1123 (2008). (PMID: 18216267223410010.1073/pnas.0706851105)
Dugué, N. & Perez, A. Directed Louvain: Maximizing Modularity in Directed Networks [Research Report]. (2015). https://hal.archives-ouvertes.fr/hal-01231784 .
Selkoe, K. A. et al. The DNA of coral reef biodiversity: predicting and protecting genetic diversity of reef assemblages. Proc. R. Soc. B Biol. Sci. 283, (2016).
Hilliam, K., Floerl, O. & Treml, E. A. Science of the total environment priorities for improving predictions of vessel-mediated marine invasions. Sci. Total Environ. 921, 171162 (2024). (PMID: 3840173610.1016/j.scitotenv.2024.171162)
Hubert, L. & Arabie, P. Comparing partitions. J. Classif. 2, 193–218 (1985). (PMID: 10.1007/BF01908075)
Cram, J. A. et al. Cross-depth analysis of marine bacterial networks suggests downward propagation of Temporal changes. ISME J. 9, 2573–2586 (2015). (PMID: 25989373481762310.1038/ismej.2015.76)
Fortin, M. J. et al. Issues related to the detection of boundaries. Landsc. Ecol. 15, 453–466 (2000). (PMID: 10.1023/A:1008194205292)
Orman, K., Labatut, V. & Cherifi, H. An empirical study of the relation between community structure and transitivity. Stud. Comput. Intell. 424, 99–110 (2013). (PMID: 10.1007/978-3-642-30287-9_11)
Contributed Indexing:
Keywords: Clusters; Communities; Connectivity; Dispersal; Marine conservation; Network science
Entry Date(s):
Date Created: 20250716 Date Completed: 20250716 Latest Revision: 20250904
Update Code:
20260130
PubMed Central ID:
PMC12267638
DOI:
10.1038/s41598-025-09601-y
PMID:
40670463
Database:
MEDLINE
*Further Information*
*Management and monitoring of populations in complex habitat mosaics is challenging, requiring effective zonation and bio regionalization strategies. In recent years, marine systems have been partitioned in multiple ways, such as marine protected zones and fishery stocks to enhance conservation and resource management. Viewing these systems as complex ecological networks of connected areas, habitat patches, or sub-populations (nodes) connected by the movement of organisms (edges) helps improve management. Network theory identifies communities or clusters of tightly connected sub-populations, revealing ecologically meaningful structures. Applying network-based community detection algorithms can uncover these ecological units, enhancing marine seascape management. However, there is no consensus on the best methods for identifying ecologically meaningful communities. This study evaluates several community detection algorithms in ecology and demonstrates their effectiveness using two marine case studies: a larval dispersal network and a ship traffic network. We show where algorithms agree in detecting communities and highlight the importance of aligning the nature of the algorithm, connectivity data, and management goals. We also suggest that disagreements between algorithms may indicate areas where management boundaries should be flexible or fluid to better reflect the system's true nature. This study proposes an improved approach to partitioning for optimal conservation and management outcomes.
(© 2025. The Author(s).)*
*Declarations. Competing interests: The authors declare no competing interests.*