Connectivity modelling informs metapopulation structure and conservation priorities for a reef‐building species

Abstract

Aim:In coastal marine systems, biogenic reef-building species have great importance for conservation as they provide habitat for a wide range of species, promoting biodiversity, ecosystem functioning and services. Biogenic reef persistence and recovery from perturbations depend on recolonization by new recruits. Characterizing larval dispersal among distant reefs is key to understanding how connectivity shapes metapopulation structure and determines network coherence; all of which are of critical importance for effective conservation.Location: Northeast Atlantic coast and western English Channel, France. Methods:We used a biophysical transport model to simulate larval dispersal of the reef-building polychaete Sabellaria alveolata. We combined dispersal modelling and network analysis into a framework aiming to identify key reef areas and critical dispersal pathways, whose presence in the network is vital to its overall coherence. We evaluated changes in dispersal pathways constrained by different connectivity thresholds, i.e., minimum dispersal rate for the presence of a connection. We tested scenarios of sequential loss of reefs: randomly, by habitat quality (a score for reef status and occupancy in an area) or by betweenness centrality metric (BC; quantifying the proportion of shortest paths connecting all areas that are passing through any given area). Results:We found that the network of S. alveolata reefs forms two main regional clusters, the Atlantic coast and the English Channel, which are connected only through weak sporadic dispersal events. Within each cluster, the network is characterized by relatively high connectivity among neighbouring areas with reefs, maintained even under higher connectivity thresholds. Simulating scenarios of sequential loss of reefs further identified high centrality reefs, those with highest BC values, key to network coherence. Main conclusions:Effective conservation of this important reef habitat requires a network of protected areas designed to sustain a combination of locally important source reefs, and those that act as stepping stones connecting distant reefs.

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

“…Two additional metrics were computed to characterize network structure, namely, number of components (clusters) and the relative size of the largest component. The first represents the number of disconnected subgraphs, while the second represents the ratio between the number of reserves existing in the largest component (subgraph) and the number of reserves in the entire graph (Urban & Keitt, 2001;David et al, 2022). Lastly, to examine the potential effect of losing one or more reserves (e.g., through perturbation) in network structure we performed an analysis of sequential reserve (node) deletion (Urban & Keitt, 2001;David et al, 2022), which allowed testing the importance of individual reserves to the overall coherence of the network.…”

“…The first represents the number of disconnected subgraphs, while the second represents the ratio between the number of reserves existing in the largest component (subgraph) and the number of reserves in the entire graph (Urban & Keitt, 2001;David et al, 2022). Lastly, to examine the potential effect of losing one or more reserves (e.g., through perturbation) in network structure we performed an analysis of sequential reserve (node) deletion (Urban & Keitt, 2001;David et al, 2022), which allowed testing the importance of individual reserves to the overall coherence of the network. To this end, reserves (and all associated oceanographic connections) were removed iteratively from the network (69 iterations; considering the total number of reserves) under 3 scenarios: (1) from the highest to the lowest betweenness centrality, from the highest to the lowest out-strength centrality and randomly with 999 permutations, with no replacement.…”

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

“…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).…”

“…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.…”

“…Conversely, dispersal vectors can be modelled with Lagrangian particle tracking (LPT) methods, in which virtual particles are released into the flow fields extracted from numerical ocean models (Cowen & Sponaugle, 2009; Lange & van Sebille, 2017). That approach has successfully identified connectivity pathways in the deep sea, including their sometimes complex curvilinearities (e.g., David et al., 2022; Wang et al., 2020, 2022; Wang, Kenchington, Wang, et al., 2021; Xu et al., 2018; Zeng et al., 2019). It can also be tailored to specific spawning seasons and larval depth distributions (Kenchington, Wang, et al., 2019; Wang et al., 2020).…”