Marine Threat: Overfishing
Marine Threat: Overfishing
Consultants: Hayley Nuetzel & Anthony Rogers
Academic & Industry Insight
Jarred Callura (Biotech Industry), J Carlos Garza (NOAA), Devon Pearse (NOAA)
Kim Silkoe (UCSB Marine Science Institute), Nina Therkildsen (Cornell University)
Demian Willette (Loyola Marymount University)
Pictured Above: Fishing has become one of the most significant drivers of declines in ocean wildlife populations. Today, third of the world’s assessed fisheries are unsustainable.
There is broad consensus of an immediate global threat from overfishing the ocean. Roughly 90 percent of fish stocks are fully or over-exploited, with more than 1/3 fished at unsustainable levels (FAO 2018). Overfishing threatens marine ecological integrity but also puts at risk whole economies and vital sources of protein for communities worldwide. Fisheries provide an estimated 15 percent or more of worldwide dietary protein (Bene et. al. 2015), as well as millions of vital livelihoods. Global fisheries represent the most valuable food commodities traded internationally, and are a particularly important source of export earnings in developing nations (Smith et al., 2010). In 2016, fishery exports were worth an estimated $148 billion (FAO 2016).
Compounding threats from climate change are worsening fears of massive perturbations in the availability of wild-caught fish. In the United States, climate‐related impacts to marine ecosystems threaten the biological, social, and economic resilience of the United States’ $208 billion fishing industry and the 1.6 million jobs it supports (NMFS, 2017). These include range shifts of mobile species, timing shifts of migratory species, fishery closures due to increased frequency and severity of harmful algal blooms, and potentially pervasive effects (such as disrupted food chains) from increased acidification (Pinsky and Mantua, 2014).
A fishery generally refers to the wild harvesting of fish or shellfish resources for commercial or recreational purposes. Fisheries are typically defined by the targeted fish stocks, where a stock represents both a biologically and a jurisdictionally defined population. Fisheries have historically been managed using a “single-species” framework to achieve the highest amount of sustainable catch within the target stock. This management typically entails regular assessments of stock status (underexploited, fully exploited, or overexploited) used to set catch limits (NOAA Fisheries 2017). However, the expansive ocean environment makes assessing stocks extremely challenging and fisheries management is chronically data limited.
Unfortunately, in addition to managed and reported fisheries, illegal, unreported, and unregulated fishing threatens to undermine the sustainability of fishing even in the most conservation-oriented countries. Experts estimate the global economy loses $10-23 billion in revenue due to illegal, unreported, and unregulated fishing (Agnew et al 2009). In some cases, fish are deliberately mislabeled to disguise catch of forbidden species such as shark fin. In other cases, a high value catch such as bluefin tuna will be labeled as skipjack tuna, a lower value catch.
Explosive growth in the aquaculture sector has created new pressure on global forage fish stocks. In particular, the global demand for smaller fast-growing forage fish (that are pelletized into fish food) has skyrocketed in recent decades. Forage fish species such as sardine, capelin, herring and anchovy represent critical food chain linkages that support large commercial fish, marine mammals and marine birds. The top harvested forage fish species is the Peruvian anchovy followed by Atlantic herring. It is estimated that forage fisheries are valued at $5.6 billion and these forage species support the fisheries of larger species, valued at $11.3 billion. Today, the global aquaculture industry continues to grow at an annual rate of 5.8 percent and forage fisheries are looking deeper into the ocean for fish to convert into feed (Sylvia Earle, pers. comm.).
PROGRESS TO DATE
The combined global importance of fish as vital to a bio-abundant marine ecosystem and as a vital source of protein for human consumption compels immediate attention to improving the conservation and sustainability of global fisheries. However, transformative innovations in the control and management of fisheries have been few and far between. Synergistic climate change threats to marine ecosystems compound the issues and the need for innovation. To date, efforts have focused on spatial closures in the form of marine protected area designations and temporal restrictions on fishing effort. Advances in telecommunications and other electronic technologies present potentially powerful new tools that, when coupled with new fishery management paradigms, could prove to be transformative. Modern genomic technologies offer additional new approaches to enhance the sustainability of fisheries management.
A recent study used genetics to correct a pervasive misidentification of sardine populations, enabling improved management of a highly valuable fishery. Since the 1900s, the most commonly landed sardine species in the Philippines was identified as the Indian oil sardine (Sardinella longiceps). However, a study that combined morphological and molecular data to discovered that sardines caught at sites in the Philippines were Bali sardinella (Sardinella lemuru) (Willette & Santos 2013). These data informed an update of sardine management plans to enable accurate stock monitoring at a local and international level (Willette & Santos 2013). However, as of 2017, there were only 22 published, scaffold-level genome sequences for finfish (Yue and Wang, 2017). Broadening the base of genomic knowledge of important fisheries would enable new ecological and management insights.
As noted above, illegal, unreported, and unregulated fishing threatens the sustainability of fisheries worldwide, and modern genomics offers cost-effective new tools to transform the monitoring of fish products from the sea to the shelf. When coupled with modern electronic and computing capabilities, genetic and genomic resources provide powerful tools, with high reproducibility and reliability, for tracing and identifying marine products. Genomic information can be combined and compared with reference materials (need reference) to determine authenticity, and to verify labelling information such as higher value eco-certifications. Further improvements in the ability to identify fish species and trace those species to their origins, can give seafood buyers, sellers, and consumers verification of the geographic and biological origins of seafood. In general, species and their origin may be identified by external traits until the fish is processed. Genomic tools offer new ways to monitor processed fish products.
Genomic insights that inform stock delineations have the potential to transform the management of certain fisheries. The best-known example of how genetic insights have improved management are in Pacific salmon stocks. For decades, scientists have used genetics to examine salmon population genetics and stock delineations. Genetic data are used to map stock structure, population structure, standing diversity, effective population size, and the demographic history of natural populations (Ward 2000). Using genetic markers, salmon caught at sea can be assigned to their “stock of origin,” or natal river basin. When combined with fishery landings data, these insights can reveal how fishing pressure is distributed across the known stocks (Clemento et al., 2014). These tools are becoming increasingly powerful as technologies advance (Wilmot et al., 1996; Ramstad et al., 2004; Palsbøll, Bérubé & Allendorf 2006; Hess et al., 2011).
Genetic-based population data can also reveal particularly vulnerable stocks and provide evidence critical to justifying immediate mitigation action, such as closures or endangered species designations of “distinct population segments.” A recent example of genetic insight applied to management concerns two species of anadromous river herring in New England, alewife (Alosa pseudoharengus) and blueback herring (Alosa aestivalis). These are important migratory fishes in coastal freshwater and marine food webs, but have experienced dramatic declines in the abundance of spawning adults. Twenty-five years of restoration efforts (principally restoring access to spawning streams) in Southern New England yielded few consistent signs of recovery. This raised concerns that bycatch of anadromous herring in the Atlantic herring fishery has been negating these restoration efforts.
In order to investigate this threat, researchers sampled several thousand fish in the offshore fishery and used genetic identification markers (microsatellites) to calculate how bycatch was partitioned among previously identified regional genetic stocks. The genetics identified the majority of bycatch in the Atlantic herring fishery as belonging to severely depleted genetic stocks (alewife of the southern New England stock—70 percent of sampled alewife bycatch; blueback herring of the mid-Atlantic stock—78 percent of sampled blueback herring bycatch).
The southern New England and mid-Atlantic genetic stocks overlap in the waters surrounding Long Island Sound, indicating that bycatch taken from this area is negatively impacting recovery efforts. These genetic insights compelled the State of Connecticut to request closures of the Atlantic herring fishery on the southern New England fishing grounds in order to reduce or eliminate bycatch. In September 2018, the New England Fisheries Management Council implemented a closure of the Atlantic herring fishery over 12 nautical miles off the New England coast from Canada to the eastern end of Long Island Sound. The closure closely resembles what river herring conservationists had advocated based on the genetic studies.
With plunging sequencing costs, genomic data has become increasing applicable to commercially important fish species as a means to guide conservation and management practices. These insights can increase the resolution of population structure at finer spatial scales and identify adaptive responses to a changing and impacted environment.
The text below describes opportunities (eDNA, close kin mark and recapture, population analysis, traceability and outlier loci) to apply genomics as a tool to advance stock assessments in a manner that could transform fishery management and conservation.
eDNA holds promise for vastly improving the tracking of temporal and spatial changes in species distributions. eDNA can inform model predictions for fishery stock distributions, improving the ability to accurately map the spatial extent of a fishery. This is of particular interest in the context of changing ocean conditions and climate change. A number of studies have demonstrated the utility of using eDNA metabarcoding to measure fish diversity (e.g., Thomsen et al. 2012; Miya et al. 2015; Evans et al. 2016). In various studies, eDNA sampling was comparable or superior to traditional and costly techniques like trawling, line fishing, and diver observations for characterizing fish diversity (Thomsen et al. 2012; Shaw et al. 2016, Thomsen et al. 2016, Port et al. 2016).
eDNA sampling has also been proposed as a cost-effective means to check existing species ranges both year-to-year and for those species that have intra-annual migrations such as inshore-offshore movements. These movements are correlated with water temperature, and thus eDNA could be used as an early warning indicator of when such movements occur each year.
The U.S. National Oceanic and Atmospheric Administration (NOAA) and Norwegian Institute of Marine Research (IMR) have created a joint working group for the application of eDNA to fisheries stock assessments. As noted, stock assessments are a quantitative metric. However, methods that correlate eDNA data to abundance metrics are underdeveloped. The working group is thus interested in furthering eDNA as a quantitative metric, with the ultimate goal of making stock assessments more cost effective. To do this, these agencies have begun building time series datasets. They will collect water samples and preserve them alongside “standard” trawls to create an index of relative abundance. This partnership represents a notable investment by two key government agencies that would benefit from such improvements.
Close-Kin Mark and Recapture
A recently optimized and insightful application of genetic data within fisheries management is the close-kin mark-recapture (CKMR) method. CKMR method uses non-invasive and inexpensive tissue samples to reconstruct pedigrees and estimate stock abundance independent of fishery-derived data. The basic premise of mark and recapture is that individuals can be distinctly marked or “tagged,” and those marks will then be recognized if the individual is recaptured in a subsequent sample. CMKR uses DNA markers to reveal information about relatedness, and since most fish are highly fecund, the volume of “tagged” individuals represented by their offspring is huge – orders of magnitude more than would be possible in conventional mark and recapture programs. The recapture of marked individuals can provide estimates of species abundance and survival rate. The average time taken to “recapture” a parent after sampling its offspring can indicate species abundance and adult survival rate (Bravington, 2016 Grewe & Davies 2016). Bravington (2016) developed an estimated abundance of adult Southern bluefin tuna based on the detection of 45 parent-offspring pairs in 13,000 samples, constituting the first large scale application of CKMR. The CKMR procedure could be applied to commercially important stocks worldwide to estimate abundances instead of using often unreliable or biased fishery catch or effort data to derive estimates.
Genetic analyses, including close-kin mark-recapture (CKMR), are also revolutionizing our ability to estimate demographic connectivity of marine fishes over the small spatial scales relevant to the design and ecological benefits of marine protected areas (MPAs). When CKMR is applied to parent-offspring relationships, estimates of larval dispersal – the distance between a sedentary adult and its settled progeny – are obtained. CKMR also reveals the variety of dispersal trajectories from a single parent when genetic markers can identify full-sibling relationships.
In a recent study (Baetscher DS, et. al., in press), researchers sampled kelp rockfish (Sebastes atrovirens) along approximately 25 kilometers of coastline in Monterey Bay and applied CKMR to identify dispersal events. Large sample sizes and intensive sampling are critical for increasing the likelihood of detecting parent-offspring matches in such systems. The researchers genotyped more than 6,000 kelp rockfish and identified eight parent-offspring pairs, which included two juvenile fish that were born inside MPAs and dispersed to areas outside MPAs. Four fish born in MPAs dispersed to other nearby MPAs. Additionally, the research identified 25 full-sibling pairs, which occurred throughout the sampling area and included all possible combinations of inferred dispersal trajectories. This study provides the first direct observation of larval dispersal events in a current-dominated ecosystem and direct evidence that larvae produced within MPAs are exported both to neighboring MPAs and proximate areas where commercial and recreational harvest is allowed.
Further CKMR work on high-value and highly migratory commercial fisheries could revolutionize our understanding of population dynamics. Also, CKMR work on the ecological function of MPAs fulfills a long-standing data need concerning the benefits of the protection strategy to the sustainability of fisheries.
Many instances of fish mislabeling have been detected via a technique known as DNA barcoding, but these have mostly taken the form of after-the-fact randomized checks in retail locations. It is possible, and indeed desirable, to integrate genetic traceability into an enforcement or supply chain framework. Recent technological advances, such as the DNA Barcode Scanner by Conservation X Labs, promise to shrink DNA barcoding into portable and cheap packages that can produce real-time results that could conceivably be adopted at various checkpoints along seafood supply chains.
Even without such checkpoints, genetic markers with enough power to resolve the geographical origin of the traded seafood on their own, combined with existing traceability or enforcement programs, could radically transform supply chain transparency and facilitate penalization of entities fishing illegally. For example, by targeting genetic markers with higher levels of differentiation – which are suggestive of ongoing selection and, perhaps, local adaptation – fine-scale population structure can be identified that would otherwise not be captured by neutral markers. This can then be utilized within the existing frameworks such as FishPopTrace – a collaborative project involving 15 research groups from the EU, Norway and Russia – to expand the program and include more commercially important species. This would require investment in infrastructure to robustly sample individuals throughout a species’ known geographic range to capture enough genetic variation to create a comprehensive marker panel. These panels could then be widely shared with entities already engaged in seafood authentication schemes to help identify offending fishers and enforce regulations.
DNA barcoding is less well developed in shellfish because species identification often requires the development and application of mitochondrial and nuclear molecular markers, as well as SNP panels, depending on taxon and these are largely undeveloped for shellfish. These small panels of informative SNPs usually perform better than microsatellite markers when allocating individuals to geographic origin. However, work to date has demonstrated the identification of Mediterranean mussel, common blue mussel, Baltic mussel, and Chilean mussel with high accuracy using a panel of 49 SNPs (Larraín et al. in preparation) and the separation of Chilean and Mediterranean mussels with a subpanel of 19 SNPs (Araneda and Larraín, S1). In Chilean mussel, it is possible to differentiate populations from three different environments, two of which were affected by the red tide in 2016. (Thomsen PF, Kielgast J, Iversen LL, Moller PR, Rasmussen M, Willerslev E.)
Genomic tools are providing remarkable insights into the evolutionary pressures of fishing on heavily exploited species and effective management should ideally account for these pressures (Laugen et al., 2012). While the techniques used to assess evolutionary consequences are still developing, experimental investigations have identified significant genetic changes associated with notable phenotypic changes, often in traits that will affect long term productivity, such as size-at-age (Swain, Sinclair & Hanson 2007; Conover & Munch 2002). Thus, high quality, annotated genome data for many commercially important species will allow for a more reactive, eco-evolutionary approach to stock management, as well as long-term predictions of stock sustainability that could transform fisheries management.
“Outlier loci” are loci with elevated levels of differentiation, and therefore may be associated with selective pressures. Genome scans for outlier loci amongst kokanee salmon found these loci to be particularly effective in revealing differentiation at the ecotype-level (Russello et al., 2012). This finding suggests outlier loci would be far more powerful than current methods for identifying differentiation amongst recently diverged populations, potentially producing more accurate stock delineations for fisheries management. An analysis of Atlantic cod revealed dynamic temporal and spatial patterns of selection over the last century (Therkildsen et al., 2013). These signatures of selection correlate with environmental variation and life history changes at certain loci, suggesting potentially adaptive responses to fishing pressure and expanding our understanding of fisheries-induced evolution (Therkildsen et al., 2013). Advanced investigations of local adaptation or evolutionary responses to selective forces are limited to species (such as Atlantic cod, rainbow trout, and salmon) with substantial genomic data resources.
RISKS & CHALLENGES
The innovations outlined above will rely upon underlying genetic databases for species of concern. The availability of real-time data for any of these use cases is contingent upon sequencing with a high enough level of coverage to identify unique species or from a sample size large enough to identify genetic variation within a species.
Unfortunately, to date, genetic data for many species of concern is inadequate, with sequencing conducted at a level of coverage too low or from a sample size too small to provide these important insights.
The promise of eDNA is subject to significant technical challenges stemming from the validation and indexing of data from eDNA surveys. Several factors currently confound the ability to interpret eDNA data beyond simple presence/absence questions. This is inherently an empirical question that will likely be ameliorated by increased eDNA studies.
Emerging genomic technologies can only live up to the promise of more sensitive and precise management of fisheries if the management entities have the capacity and willingness to utilize new data. The management of fisheries is notoriously data-limited and fisheries management agencies are often under-funded, usually with a conflicting mandate to both protect the resource and the economic use of the fishery. Limited resources for monitoring and enforcement restricts the ability for management entities to incorporate new data or perspectives on management. Disparate management priorities in regional fisheries management entities and a lack of willingness to innovate within fisheries can compound these challenges.
University of California, Santa Cruz
National Oceanic and Atmospheric Administration Southwest Fisheries Science Center, Fisheries Ecology Division
University of Washington
Agnew, David J., et al. “Estimating the worldwide extent of illegal fishing.” PloS one 4.2 (2009): e4570.
Araneda, Cristián, et al. “Adaptive genetic variation distinguishes Chilean blue mussels (Mytilus chilensis) from different marine environments.” Ecology and evolution 6.11 (2016): 3632-3644.
Baetscher DS, Anderson EC, Gilbert-Horvath EA, Malone DP, Saarman ET, Carr MH, Garza JC (in press) Dispersal of a nearshore marine fish connects marine reserves and adjacent fished areas along an open coast.
Béné, Christophe & Barange, Manuel & Subasinghe, Rohana & Hemre, G.-I & Williams, Meryl. (2015). Bene et al. 2015 Feeding 9 billion fish on the menu Food Security.
Bernatchez, Louis, et al. (2017) “Harnessing the power of genomics to secure the future of seafood.” Trends in Ecology & Evolution 32.9: 665-680.
Bravington, M.V., Grewe, P.M., & Davies, C.R. 2016. Absolute abundance of southern bluefin tuna estimated by close-kin mark-recapture. Nature Communications 7. doi: 10.1038/ncomms13162.
Clemento, A.J., Crandall, E.D., Garza, J.C., et al. 2014. Evaluation of a single nucleotide polymorphism baseline for genetic stock identification of Chinook salmon (Oncorhynchus tshawytscha) in the California Current large marine ecosystem. Fishery Bulletin 112: 112-130. doi: 10.7755/FB.112.2-3.2.
Conover, D.O. & Munch, S.B. 2002. Sustaining fisheries yields over evolutionary time scales. Science 297: 94-96. doi: 10.1126/science.1074085.
Evans, Nathan T., et al. “Quantification of mesocosm fish and amphibian species diversity via environmental DNA metabarcoding.” Molecular ecology resources 16.1 (2016): 29-41.
Food and Agriculture Organization of the United Nations. Fisheries Department. The state of world fisheries and aquaculture. Food and Agriculture Organization of the United Nations, 2016
Hess, J.E., Matala, A.P., & Narum, S.R. 2011. Comparison of SNPs and microsatellites for fine-scale application of genetic stock identification of Chinook salmon in the Columbia River Basin. Molecular Ecology Resources 11: 137-149. doi: 10.1111/j.1755-0998.2010.02958.x.
Kijewski, T. et al. (2011) Distribution of Mytilus taxa in European coastal areas as inferred from molecular markers. J. Sea Res. 65, 224–234
Laugen, Ane T., et al. “Evolutionary impact assessment: accounting for evolutionary consequences of fishing in an ecosystem approach to fisheries management.” Fish and Fisheries 15.1 (2014): 65-96.
Miya, Masaki, et al. “MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species.” Royal Society open science 2.7 (2015): 150088.
National Marine Fisheries Service (2018) Fisheries of the United States, 2017. U.S. Department of Commerce, NOAA Current Fishery Statistics No. 2017 Available at: https://www.fisheries.noaa.gov/feature-story/ fisheries-united-states-2017
Ogden, R. and Linacre, A. (2015) Wildlife forensic science: a review of genetic geographic origin assignment. Forensic Sci. Int. Genet. 18, 152–159
Palsbøll, P.J., Bérubé, M. & Allendorf, F.W. 2006. Identification of management units using population genetic data. Trends in Ecology and Evolution 22. doi: 10.1016/j.tree.2006.09.03.
Pikitch, E., Boersma, P.D., Boyd, I.L., Conover, D.O., Cury, P., Essington, T., Heppell, S.S., Houde, E.D., Mangel, M., Pauly, D., Plagányi, É., Sainsbury, K., and Steneck, R.S. 2012. Little Fish, Big Impact: Managing a Crucial Link in Ocean Food Webs. Lenfest Ocean Program. Washington, DC. 108 pp.
Pinsky, Malin L., and Nathan J. Mantua. “Emerging adaptation approaches for climate-ready fisheries management.” Oceanography 27.4 (2014): 146-159.
Ramstad, K.M., Woody, C.A., Sage, G.K., et al. 2004. Founding events influence genetic population structure of sockeye salmon (Oncorhynchus nerka) in Lake Clark, Alaska. Molecular Ecology 13: 277-290. doi: 10.1046/j.1365-294X.2003.02062.x.
Reid, K., E.P. Palkovacs, D.J. Hasselman, D. Baetscher, J. Kibele, B. Gahagan, P. Bentzen, M.C. McBride, J.C. Garza. 2018. Comprehensive evaluation of genetic population structure for anadromous river herring with single nucleotide polymorphism data. Fisheries Research 206: 247-258. https://doi.org/10.1016/j.fishres.2018.04.014.
Russello, M.A., Kirk, S.L., Frazer, K.K., et al. 2012. Detection of outlier loci and their utility for fisheries management. Evolutionary Applications 5: 39-52. doi: 10.1111/j.1752-4571.2011.00206.x.
Smith, Courtland L., and Patricia M. Clay. “Measuring subjective and objective well-being: analyses from five marine commercial fisheries.” Human Organization (2010): 158-168.
Smith, Martin D., et al. “Sustainability and global seafood.” Science 327.5967 (2010): 784-786.
Swain, D.P., Sinclair, A.F. & Hanson, J.M. 2007. Evolutionary response to size-selective mortality in an exploited fish population. Proceedings of the Royal Society 274: 1015-1022. doi: 10.1098/rspb.2006.0275.
Therkildsen, N.O., Hemmer-Hansen, J., Als, T.D., et al. 2013. Microevolution in time and space: SNP analysis of historical DNA reveals dynamic signatures of selection in Atlantic cod. Molecular Ecology 22: 2424-2440. doi: 10.1111/mec.12260.
Thomsen, P. F., Kielgast, J., Iversen, L. L., Møller, P. R., Rasmussen, M., & Willerslev, E. (2012). Detection of a diverse marine fish fauna using environmental DNA from seawater samples. PLoS one, 7(8), e41732.
Valentini, Alice, et al. “Next‐generation monitoring of aquatic biodiversity using environmental DNA metabarcoding.” Molecular Ecology 25.4 (2016): 929-942.
Ward, R.D. 2000. Genetics in fisheries management. Hydrobiologia 420: 191-200.
Willette, D. A., and M. D. Santos. “Correcting widespread misidentifications of the highly abundant and commercially important sardine species Sardinella lemuru, Bleeker, 1853 in the Philippines.” Journal of Applied Ichthyology 29.4 (2013): 881-885.
Wilmot, R.L., Kondzela, C.M., Guthrie, C.M., et al. 1996. Genetic stock identification of chum salmon harvested incidentally in the 1994 and 1995 Bering Sea trawl fishery. Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, Juneau, AK. NPAFC Doc. 244. 22 pp.
Wilson, Jono R., et al. “Adaptive comanagement to achieve climate‐ready fisheries.” Conservation Letters (2018): e12452.
Yue, G. H., and L. Wang. “Current status of genome sequencing and its applications in aquaculture.” Aquaculture468 (2017): 337-347.
Zbawicka, M. et al. (2012) Identification and validation of novel SNP markers in European populations of marine Mytilus mussels. Mar. Biol. 159, 1347–1362.
Back to Top