Representativity and connectivity are core design principles when establishing MPA networks to ensure that the protection is equitably distributed through space. Genomic approaches, such as reduced representation genome sequencing (e.g., restriction site–associated DNA sequencing, RAD-seq; Davey and Blaxter, 2010), pooled sequencing (PoolSeq; Schlötterer et al., 2014), and low-coverage whole-genome resequencing (lcWGS; Lou et al., 2021), have considerably advanced our understanding of genetic diversity and population connectivity in non-model species over the last decade. Characterizing genome-wide variation through genomic sequencing has revealed cryptic intraspecific population structure where panmixia has otherwise been assumed, informing both representativity and connectivity for conservation network design (e.g., Barney et al., 2017; Van Wyngaarden et al., 2017).

MPA networks that consider connectivity and gene flow in their design will be better positioned to conserve diversity and adaptive variation within species (von der Heyden 2009), while also protecting genetically distinct populations (Andrello et al., 2022). Explicit consideration of genetically unique or diverse populations can help maintain diversity, and may allow for potential genetic rescue or the re-establishment of populations following extirpation (Xuereb et al., 2019), thus increasing resilience via the conservation of biocomplexity (Mendez et al., 2014). Movement of individuals between protected areas can occur through active migration or passive dispersal (e.g., Roberts et al., 2021), in some cases resulting in gene flow, as well as contributing to productivity and recruitment to adjacent fished stocks (e.g., Huserbråten et al., 2013). Genomic tools can provide empirical evidence of connectivity, which is difficult to quantify with conventional monitoring approaches for many taxa (Balbar et al., 2020). For example, genetic studies assessing kinship in Australasian snapper (Chrysophrys auratus) revealed that reproductive productivity within an MPA disproportionately contributed to recruitment in surrounding areas (Le Port et al., 2017). In other cases, genetic methods have revealed evidence of selection for limited dispersal, which correspondingly drove fine-scale genetic structure associated with the MPA protective measures (Sanford et al., 2006; Baskett and Barnett, 2015).

Often, fine-scale population differentiation identified in marine species is driven by adaptation to gradients in physical and chemical ocean conditions (e.g., Stanley et al., 2018). Recent genomic studies have revealed molecular underpinnings of local adaptation to the environment (e.g., temperature, salinity, and oceanography). Adaptive traits may be underlain by genetic architectures ranging from many small-effect loci (Bay R. A. et al., 2017) to single large-effect genes (Barson et al., 2015; Prince et al., 2017). The genetic architecture underlying local adaptation is predicted to affect how species respond to both current conditions and future change, making quantification of adaptive variation essential information for building resiliency into conservation planning (Bay R. A. et al., 2017; Lowen et al., 2019; Oomen et al., 2020; Layton and Bradbury 2022). Genomic data can also reveal other types of variation in the genome that play key roles in adaptation and population structure and persistence; this variation could inform conservation design and be used for monitoring. For example, Catanach et al. (2019) demonstrated that structural variants in the Australasian snapper genome outnumbered the SNP associated variation based on total bases affected. Structural variation such as large chromosomal inversions (Kess et al., 2019) and copy number variants (CNVs) (Kess et al., 2021; Layton et al., 2021) have also been identified in aquatic systems underlying divergent ecotypes and genomic signals of climate adaptation. By identifying and incorporating information on adaptive variation into conservation planning, specific areas can be prioritized to help protect biodiversity and promote long-term persistence of populations under climate change (Xuereb et al., 2021).

Recent genomic studies have also revealed the magnitudes of population decline using measures of genetic diversity such as effective population size (N _e), and have uncovered genome-wide differences between declining and stable populations (Hollenbeck et al., 2016; Lehnert et al., 2019). These types of analyses hold promise to identify species and populations of conservation concern for MPA design. Similarly, metrics of genetic diversity and connectivity, including N _e and heterozygosity, can be incorporated into conservation planning through network objectives (e.g., Gajdzik et al., 2021) or planning tools such as Marxan. In coastal Africa, Phair et al. (2021) demonstrated that conservation planning based on habitat models alone risked missing important genetic variation in the coastal seagrass (Zostera capensis) leading to design configurations that did not include evolutionarily unique populations and were thus less resilient to environmental change. These results emphasize the value of collecting genetic information a priori. Incorporating population-level stratification into the MPA network design can promote the conservation of genetic diversity and resiliency following the stewardship aspects of the BES.

While the theoretical applicability of omics tools in the MPA design and monitoring are robust, few real-world examples exist. One example of genomic application to MPA research focuses on the Gilbert Bay MPA in Labrador, Canada. With the collapse and subsequent moratorium of the Atlantic cod (Gadus morhua) came an increased pressure on nearshore cod stocks. A phenotypically and genetically distinct population of inshore cod reside within the Gilbert Bay MPA, which was established to protect this population (DFO 2010). Though the MPA protected the core habitat, genomic stock identification revealed that these genetically divergent cod were being exploited in adjacent fisheries (Sinclair-Waters et al., 2018a,b). In this case, genomic data corroborated prior tagging data (Morris et al., 2014) but also revealed the adaptive mechanism (i.e., chromosomal inversions) of divergence between the Gilbert Bay and northern cod (Sinclair-Waters et al., 2018a). In the long-term, the ability to genetically assign catch in fisheries outside the MPA will provide an important and robust basis for MPA management. Another study conducted on the California sea cucumber (Parastichopus californicus) used genomic data to identify priority areas for MPAs (Xuereb et al., 2021). The study found that focal areas for conservation differed among genomic metrics (i.e., diversity vs adaptive variation), highlighting the need to match the genetic metrics with the conservation objectives when evaluating design decisions. In each example, the application of genomic approaches revealed novel information about the conservation priorities that were directly applicable to design and monitoring.

Spatial conservation tools like MPAs help to regulate anthropogenic impacts on populations, and these impacts can be monitored using molecular omics approaches. Short-term human impacts, including overfishing, aquaculture escape events, and pollution, have produced measurable differences in adaptive variation among populations (Wringe et al., 2018; Therkildsen et al., 2019; Phair et al., 2020), demonstrating that omics have utility in quantifying anthropogenic impacts within and outside of MPAs. Transcriptomic methods (i.e., RNA-sequencing) can identify differences in gene expression among individuals, including variation in the transcript level and splicing, and can be used to identify mechanisms of rapid adaptation (Jacobs and Elmer, 2021). For example, Baratti et al. (2022) demonstrated higher concentrations of contaminants in marbled crab (Pachygrapsus marmoratus) from a polluted port compared to those in an adjacent MPA, and RNA-sequencing supported corresponding differences in the expression of stress-related genes supporting the conservation efficacy of the MPA. Epigenetic changes, such as DNA methylation, can provide another heritable means by which organisms may alter their phenotype and rapidly respond to environmental or anthropogenic stressors without changes to their underlying DNA sequence (Anastasiadi et al., 2021). As technologies evolve to accurately characterize genomic variation associated with phenotype, greater capacity to quantify and mitigate impacts of stressors, predict population responses, and ultimately conserve genetic biodiversity will improve.

A growing area of omics application and research is eDNA metabarcoding, which has revolutionized biomonitoring in aquatic ecosystems through detection of invasive and/or rare species (Weltz et al., 2017; Matejusova et al., 2021) and the characterization of community-level biodiversity (Bani et al., 2020; Djurhuus et al., 2020). This method involves the collection of extracellular DNA from aquatic samples and is often more taxonomically and monetarily efficient than traditional monitoring approaches (Fediajevaite et al., 2021; He et al., 2022). eDNA collection is minimally invasive and can be implemented in various environments and conditions, and thus represents a useful tool for MPA monitoring (Sawaya et al., 2019; Gold et al., 2021). Globally, MPAs cover millions of square kilometers, and scalable, cost-effective tools such as metabarcoding represent a tractable efficient solution for monitoring at large spatial extents and in remote areas. Traditional survey-based methods (e.g., trawling) are time-intensive and often limited by depth or topography, whereas eDNA sampling, while not without its own biases, is relatively simple, cost-effective, can be scaled across large MPAs (Gold et al., 2021), and provides a standardized biomonitoring platform deployable across ecosystem types (e.g., coastal to remote deep ocean).

New eDNA studies are developing PCR-free methods that employ direct sequencing of genomic DNA through shotgun metagenomic sequencing or “genome skimming” (Cowart et al., 2018), which can provide a more accurate measure of species abundance and prevent amplification biases (Peel et al., 2019). In addition, it is now common practice to sequence DNA directly in the field, using long-read Nanopore sequencing which can generate full-length gene or genome sequences to improve taxonomic resolution (Baloğlu et al., 2021). For reliable identifications, comprehensive and well-curated reference databases are critical; while such databases are publicly available and growing, many remain incomplete and can be difficult to curate (Weigand et al., 2019). In light of this, new bioinformatic approaches make use of reference-free identification algorithms, employing density-based clustering to detect both known and unknown species (Baloğlu et al., 2021). Exploration of the use of automated eDNA collection and processing systems, either mounted statically in key areas, or onboard automated vehicles is underway, providing a platform for real-time, continuous monitoring (e.g., Hansen et al., 2020). The use of marine invertebrates as natural eDNA samplers has also gained recent attention (e.g., sponges, Mariani et al., 2019). Though more work remains to test the applicability of eDNA as a surrogate for long-term monitoring approaches (Antich et al., 2021; Gold et al., 2021; He et al., 2022), it shows promise to be an adaptable technology for biomonitoring, which can inform management, detect species distribution shifts under climate change, and be a core part of the larger effort to sustainably manage marine activities under a BES (Curry and Ausubel 2021).

Interest in the blue economy and research involving genomics in MPAs has expanded markedly since 2018 (Figure 1). The proliferation of marine omics studies and the move to open-access data repositories have the potential to contribute to the planning and monitoring of MPAs and be a key tool in fisheries management for the blue economy. Advances in genome assembly approaches (e.g., chromosome level assemblies using chromatin capture, optical mapping, and long-read sequencing) add to a growing number of high quality reference genomes, through larger initiatives such as the Vertebrate Genomes Project. As the number of resources for species grows, so does the ability to integrate datasets into larger-scale single-species studies (Jeffery et al., 2018), or multi-species research (Stanley et al., 2018; Gajdzik et al., 2021). We recommend the regular incorporation of genomic and other omics data for species of interest (i.e., at the very least, those that are conservation priorities for the network) in the design and ongoing monitoring of marine conservation areas, to understand population structure, connectivity, and species diversity (see Table 1 for a list of applications and recommendations).

Multi-species genomic approaches can provide information for understanding interactions between various levels of the ecosystem within an MPA network (Andrello et al., 2022). For example, comparative genomic approaches can help prioritize species (or populations) needing protection by identifying those that are most vulnerable to threats (Zoonomia Consortium 2020) or those with the greatest likelihood of recovery (Beichman et al., 2019). In contrast to the current genomic approaches relying on single reference genomes, a pangenomics approach which focuses not only on sequence diversity but also on the structural diversity of genomes would improve the quantification of adaptive diversity and predictions of future changes (Brockhurst et al., 2019).

The non-invasive nature of eDNA metabarcoding sampling makes it an ideal alternative to direct, in some cases destructive, monitoring techniques, and a strong candidate for real-time autonomous sampling (e.g., Hansen et al., 2020). Similarly, metagenomic sequencing of microorganisms provides a tool for monitoring ecological quality (Fruehe et al., 2021) and ecosystem services within MPAs (Curry and Ausubel 2021). For example, eDNA of benthic microbial community composition coupled with machine learning approaches has shown promise for improving indices of environmental impacts (Fruehe et al., 2021). While metabarcoding is now routinely applied by researchers to answer ecological questions, its uptake by managers and commercial industries is lagging behind (Curry and Ausubel 2021). Yet eDNA has great potential to complement existing biodiversity sampling approaches such as trawling and electrofishing (e.g., Afzali et al., 2021; Gold et al., 2021; He et al., 2022), and eventually, potentially replace these traditional methods in sensitive habitats often associated with protection by MPAs.

To contribute to real-time monitoring for the blue economy, it has even been proposed that ships could be designed to filter water along their transport routes, potentially covering large geographic areas and making the data readily available to resource managers and the public (Curry and Ausubel 2021). The “real-time” inference drawn from eDNA research can be further enhanced using environmental RNA (eRNA), which offers additional power to these eDNA approaches due to the high turnover rate of RNA compared to DNA (Yates et al., 2021). eRNA can enhance spatio-temporal resolution compared to traditional eDNA methods, as it primarily reflects physiologically active organisms that are in close proximity to sampling locations, and it can provide novel monitoring approaches, such as the ability to assess the health status of organisms and communities within a more focal area (Cristescu 2019; Yates et al., 2021).