Effective fisheries management instrumental in improving fish stock status

Marine fish stocks are an important part of the world food system and are particularly important for many of the poorest people of the world. Most existing analyses suggest overfishing is increasing, and there is widespread concern that fish stocks are decreasing throughout most of the world. We assembled trends in abundance and harvest rate of stocks that are scientifically assessed, constituting half of the reported globalmarine fish catch. For these stocks, on average, abundance is increasing and is at proposed target levels. Compared with regions that are intensively managed, regions with less-developed fisheries management have, on average, 3-fold greater harvest rates and half the abundance as assessed stocks. Available evidence suggests that the regions without assessments of abundance have little fisheries management, and stocks are in poor shape. Increased application of area-appropriate fisheries science recommendations and management tools are still needed for sustaining fisheries in places where they are lacking.Fil: Hilborn, Ray. University of Washington; Estados UnidosFil: Amoroso, Ricardo Oscar. University of Washington; Estados UnidosFil: Anderson, Christopher M.. University of Washington; Estados UnidosFil: Baum, Julia K.. University of Victoria; CanadáFil: Branch, Trevor A.. University of Washington; Estados UnidosFil: Costello, Christopher. University of California at Santa Barbara; Estados UnidosFil: de Moor, Carryn L.. University of Cape Town; SudáfricaFil: Faraj, Abdelmalek. Einstitut National de Recherche Halieutique; MarruecosFil: Hively, Daniel. University of Washington; Estados UnidosFil: Jensen, Olaf P.. Rutgers University; Estados UnidosFil: Kurota, Hiroyuki. Japan Fisheries Research and Education Agency; JapónFil: Little, L. Richard. Csiro Oceans and Atmosphere; AustraliaFil: Mace, Pamela. Ministry for Primary Industries; Nueva ZelandaFil: McClanahan, Tim. Wildlife Conservation Society; Estados UnidosFil: Melnychuk, Michael C.. University of Washington; Estados UnidosFil: Minto, Cóilín. Galway-Mayo Institute of Technology; IrlandaFil: Osio, Giacomo Chato. Joint Research Centre (JRC); Italia. DG Maritime Affairs and Fisheries, European Commission; BélgicaFil: Pons, Maite. University of Washington; Estados UnidosFil: Parma, Ana María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; ArgentinaFil: Segurado, Susana. Sustainable Fisheries Partnership; Estados UnidosFil: Szuwalski, Cody S.. University of California at Santa Barbara; Estados UnidosFil: Wilson, Jono R.. University of California at Santa Barbara; Estados Unidos. The Nature Conservancy; Estados UnidosFil: Ye, Yimin. Food and Agriculture Organization of the United Nations; Itali

High fishery catches through trophic cascades in China

Indiscriminate and intense fishing has occurred in many marine ecosystems around the world. Although this practice may have negative effects on biodiversity and populations of individual species, it may also increase total fishery productivity by removing predatory fish. We examine the potential for this phenomenon to explain the high reported wild catches in the East China Sea-one of the most productive ecosystems in the world that has also had its catch reporting accuracy and fishery management questioned. We show that reported catches can be approximated using an ecosystem model that allows for trophic cascades (i.e., the depletion of predators and consequent increases in production of their prey). This would be the world's largest known example of marine ecosystem "engineering" and suggests that trade-offs between conservation and food production exist. We project that fishing practices could be modified to increase total catches, revenue, and biomass in the East China Sea, but single-species management would decrease both catches and revenue by reversing the trophic cascades. Our results suggest that implementing single-species management in currently lightly managed and highly exploited multispecies fisheries (which account for a large fraction of global fish catch) may result in decreases in global catch. Efforts to reform management in these fisheries will need to consider system wide impacts of changes in management, rather than focusing only on individual species

Fig 1 -

Left. Artic Circle view of the northern hemisphere showing the nine sea Ice Regions considered in analysis. Dashed circle shows Arctic Circle delineation. Right. Top Row: Snow crab total exploitable biomass index by Stock Region (AK = Alaska, NL = Newfoundland & Labrador, sGSL = Southern Gulf of St. Lawrence). Second Row: Maximum sea ice extent index by snow crab Stock Region. Third Row: Cod biomass index by snow crab Stock Region. Fourth Row: Annual Arctic Oscillation and North Atlantic Oscillation Indices. Bottom Row: Annual Pacific Decadal and Southern Oscillation Indices. Map source file “The Blue Marble” modified from and credited to NASA Earth Observations (https://neo.gsfc.nasa.gov/view.php?datasetId=BlueMarbleNG-TB). Snow crab distribution is related to seasonal ice cover. Globally, areas either perpetually or ephemerally covered in sea ice do not support large stock biomasses. In cases where exceptions occur, such as in the southernmost (ice-free) extent of the Atlantic Canadian stock range (Nova Scotia), direct cold water inputs from adjacent ice-covered ecosystems occur (Petrie and Drinkwater, 1993). In recent decades, increased ice free periods in Arctic Regions have enabled habitat shifts for snow crab. Numerous recent observations of increasing abundances or first occurrences in Arctic waters (i.e. north of 66.56°N) such as the Chukchi [2], Beaufort [3], Barents [4]), Kara [5], and East Siberian and Laptev Seas [6] collectively confirm that the distribution of snow crab is now near-circumpolar and that the species should no longer be characterized as sub-Arctic.</p

Future Habitat Model (FHM) outputs by Ice Region (Total Arctic [TotArc], Arctic Ocean [ArcOcn], Canadian Archipelago [CanArch], Huddon Bay [Hudson], East Greenland [EGreen], Barents and Kara Seas [BarKara], Baffin Bay and Newfoundland & Labrador [BaffNL], Southern Gulf of St. Lawrence [sGSL], Bering Sea [Bering], and Seas of Okhotsk and Japan [OkhJpn]).

Predictor terms are calendar month, Arctic Oscillation (AO) and carbon dioxide (CO2). edf refers to effective degrees of freedom, Ref.df refers to reference degrees of freedom, and dev.expl. refers to deviance explained.</p

Pearson cross-correlations with total exploitable biomass (tonnes) versus sea ice and climate system indices (North Atlantic Oscillation, NAO; Southern Oscillation, SO; Pacific Decadal Oscillaion, PDO) as well as cod biomass indices (tonnes) by Stock Region (Alaska, AK; Newfoundland and Labrador, NL; southern Gulf of St. Lawrence, sGSL).

Pearson cross-correlations with total exploitable biomass (tonnes) versus sea ice and climate system indices (North Atlantic Oscillation, NAO; Southern Oscillation, SO; Pacific Decadal Oscillaion, PDO) as well as cod biomass indices (tonnes) by Stock Region (Alaska, AK; Newfoundland and Labrador, NL; southern Gulf of St. Lawrence, sGSL).</p

Future habitat model (FHM) of predicted versus observed values of ice extent by Ice Region and year.

Future habitat model (FHM) of predicted versus observed values of ice extent by Ice Region and year.</p