Biophysical modelling and graph theory identify key connectivity hubs in the Mediterranean marine reserve network

Abstract

Connectivity plays a key role in the effectiveness of MPA networks ensuring metapopulation resilience through gene flow and recruitment effect. Yet, despite its recognized importance for proper MPA network functioning, connectivity is not often assessed and is very seldomly used in marine spatial planning. Here, we combined biophysical modelling with graph theory to identify Mediterranean marine reserves that support connectivity between different ecoregions through stepping-stone processes, thus preventing network fragmentation, and those that have an important role as propagule source areas contributing to the recruitment and rescue effects. We identified 19 reserves that play a key role towards the functioning of the network, serving either as stepping-stones or as propagule sources, yet with distinct patterns between ecological groups with contrasting propagule duration (PD). The Côte D’Azur marine reserves are important both as stepping-stones and propagule sources for several ecological groups. Also, key is the Capo Rizzuto and Plemmirio marine reserves due to their role as stepping stones between different marine ecoregions, particularly for species with longer PD (Pisces, Crustacea and Echinodermata). These results provide stakeholders and managers with crucial information for the implementation and management of an efficient marine reserve network in the Mediterranean.

Coastal oceanographic connectivity estimates at the global scale

“…Overall, the example shows that oceanographic connectivity influences population differentiation of the kelp species, explaining ~65 % of genetic data variability. The second example 19 maps oceanographic connectivity of fish populations along a network of Mediterranean MPAs. The code estimates fish connectivity (using a typical pelagic larvae duration of 32 days 7,15 ) between spatial polygon data representing the shape and distribution of MPAs (Figure 2b; Supplement 2).…”

“…Such an approach considers biological populations as graph vertices with their edges represented by oceanographic connectivity estimates (e.g., probability of connectivity). Specific graph algorithms can compute multigenerational stepping-stone connectivity 12 or identify critical connectivity components with higher centrality that can bridge populations across large water masses 19 . Despite the significant advancements made through biophysical modeling in understanding the role of coastal oceanographic connectivity on marine biodiversity patterns 2,3,14,20 , and its implications for marine conservation and management 7,15,19 , research is hindered by the lack of a globally ready-to-use connectivity database.…”

“…Specific graph algorithms can compute multigenerational stepping-stone connectivity 12 or identify critical connectivity components with higher centrality that can bridge populations across large water masses 19 . Despite the significant advancements made through biophysical modeling in understanding the role of coastal oceanographic connectivity on marine biodiversity patterns 2,3,14,20 , and its implications for marine conservation and management 7,15,19 , research is hindered by the lack of a globally ready-to-use connectivity database. This limitation is impaired by the technical and computational requirements associated with the development of such complex modeling tools.…”

Quantifying the effects of fragmentation of connectivity networks of deep‐sea vulnerable marine ecosystems

“…Some of those approaches have been applied to marine ecosystems (e.g., Abecasis et al., 2023; David et al., 2022; Ospina‐Alvarez et al., 2020; Thomas et al., 2014; Treml et al., 2008), but their application to the deep‐sea benthos over spatial scales of hundreds of kilometres remains challenging, requiring trade‐offs between the information content of available metrics and their data requirements (Calabrese & Fagan, 2004). Habitat patches of VMEs in the deep sea can neither be precisely localized nor well characterized.…”

“…Indices that are responsive to the extent of the areas of patches, within viable dispersal distances of others, best capture patch connectivity (Bender et al, 2003;Fahrig, 2013;Fortin & Dale, 2005;Rutledge, 2003;Taylor et al, 1993). Some of those approaches have been applied to marine ecosystems (e.g., Abecasis et al, 2023;David et al, 2022; Ospina-Alvarez TA B L E 1 Extent of suitable habitat and network characteristics for each of seven groups of deep-sea benthic invertebrate taxa forming vulnerable marine ecosystems. , 2020;Thomas et al, 2014;Treml et al, 2008), but their application to the deep-sea benthos over spatial scales of hundreds of kilometres remains challenging, requiring trade-offs between the information content of available metrics and their data requirements (Calabrese & Fagan, 2004).…”