An approach to incorporating inferred connectivity of adult movement into marine protected area design with limited data

Abstract

Marine protected areas (MPAs) are important conservation tools that can support the resilience of marine ecosystems. Many countries, including Canada, have committed to protecting at least 10% of their marine areas under the Convention on Biological Diversity's Aichi Target 11, which includes connectivity as a key aspect. Connectivity, the movement of individuals among habitats, can enhance population stability and resilience within and among MPAs. However, little is known about regional spatial patterns of marine ecological connectivity, particularly adult movement. We developed a method to assess and design MPA networks that maximize inferred connectivity within habitat types for adult movement when ecological data are limited. We used the Northern Shelf Bioregion in British Columbia, Canada, to explore two different approaches: (1) evaluating sites important for inferred regional connectivity (termed hotspots) and (2) assessing MPA network configurations based on their overlap with connectivity hotspots and interconnectedness between MPAs. To assess inferred connectivity via adult movement, we used two different threshold distances (15 and 50 km) to capture moderate home ranges, which are most appropriate to consider in MPA design. We applied graph theory to assess inferred connectivity within 16 habitat and depth categories (proxies for distinct ecological communities), and used novel multiplex network methodologies to perform an aggregated assessment of inferred connectivity. We evaluated inferred regional connectivity hotspots based on betweenness and eigenvector centrality metrics, finding that the existing MPA network overlapped a moderate proportion of these regional hotspots and identified key areas to be considered as candidate MPAs. Network density among existing MPAs was low within the individual habitat networks, as well as the multiplex. This work informs an ongoing MPA planning process, and approaches for incorporating connectivity into MPA design when data are limited, with lessons for other contexts.

Incorporating climate change adaptation into marine protected area planning

“…We found that 23% of studies increased connectivity as their adaptation strategy, tied with protecting future habitat and ~10% less than the protecting refugia (Figure 4). This in part may be due to the higher data requirements needed to accurately model connectivity (Friesen, Martone, Rubidge, Baggio, & Ban, 2019), whereas climate refugia can be categorized with only climate projections. Ensuring connectivity within an MPA network helps facilitate species persistence (McCook et al, 2009), and increases MPA benefits for the marine ecosystem (Carr et al, 2017; Olds et al, 2016).…”

The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

“…To test the capability of the AHN model in depicting the structural connectivity of animal habitats, we compared the topological properties of networks generated by the model using a given parameter space with those of empirical habitat networks characterizing habitat structural connectivity by Friesen et al (2019). First, we extracted the largest network component from each of the 62 empirical habitat networks, and kept 58 of them for benchmarking (the two largest were omitted due to computation limit and the two simplest were excluded).…”

“…Empirical data used is available from Friesen et al (2019). The code for implementing the model is available in the R package AnimalHabitatNetwork on the CRAN.…”

MPA network design based on graph theory and emergent properties of larval dispersal

“…In this context, graph theory (i.e. the mathematical study of the interaction of a system of connected elements) is a valuable approach for analysing MPA network performance, as it provides a simplified and quantitative view of the multiple factors involved in the exchange among system elements (Conklin et al 2018, Henry et al 2018, Friesen et al 2019, Kininmonth et al 2019.…”